diff --git a/.github/actions/cache-cargo-build/action.yml b/.github/actions/cache-cargo-build/action.yml index 36c6c790b84..c3b8ce22c68 100644 --- a/.github/actions/cache-cargo-build/action.yml +++ b/.github/actions/cache-cargo-build/action.yml @@ -4,17 +4,16 @@ description: >- so only the first job on a given Cargo.lock compiles the bridge from scratch. litellm builds through maturin, which compiles litellm-rust/crates/python-bridge - in release mode before it can produce a wheel. `uv sync` therefore pays a full - build in every job that installs the workspace: measured at 2m40s per unit shard - on 2026-08-21, more than the whole unit tier spends running tests. Nothing caught - it, because the uv cache holds wheels uv downloads rather than wheels it builds, - and a path dependency whose source moves every commit could never hit that cache - anyway. Cargo rebuilds only what changed when its target directory survives, so a - warm job pays for the bridge crate alone. + in the dev profile for editable installs. `uv sync` therefore pays a full build + in every job that installs the workspace. Nothing caught it, because the uv cache + holds wheels uv downloads rather than wheels it builds, and a path dependency + whose source moves every commit could never hit that cache anyway. Cargo rebuilds + only what changed when its target directory survives, so a warm job pays for the + bridge crate alone. - The key namespace is separate from test-rust.yml's. Both cache the same directory, - but that workflow fills it with debug and clippy artifacts, which a release build - cannot reuse, and a shared key would let whichever ran first deny the other a save. + The key namespace is separate from test-rust.yml's check and release caches. They + cache the same directory for different workloads, and a shared key would let + whichever ran first deny the others a save. runs: using: composite @@ -26,6 +25,6 @@ runs: ~/.cargo/registry ~/.cargo/git litellm-rust/target - key: ${{ runner.os }}-cargo-release-${{ hashFiles('litellm-rust/Cargo.lock') }} + key: ${{ runner.os }}-maturin-dev-${{ hashFiles('litellm-rust/Cargo.lock') }} restore-keys: | - ${{ runner.os }}-cargo-release- + ${{ runner.os }}-maturin-dev- diff --git a/.github/scripts/close_duplicate_issues.py b/.github/scripts/close_duplicate_issues.py deleted file mode 100755 index ec522af4f88..00000000000 --- a/.github/scripts/close_duplicate_issues.py +++ /dev/null @@ -1,230 +0,0 @@ -#!/usr/bin/env python3 -""" -Detect and close duplicate GitHub issues using title similarity. - -Modes: - --scan Compare all open issues against each other (batch) - --issue-number N Check a single issue against older open issues - -Requires the `gh` CLI to be authenticated. -""" - -import argparse -import difflib -import json -import re -import subprocess -import sys - - -def normalize_title(title: str) -> str: - """Strip common prefixes, lowercase, and collapse whitespace.""" - title = re.sub( - r"^\[?(bug|feature request|enhancement|question|docs)[:\]]?\s*", - "", - title, - flags=re.IGNORECASE, - ) - return " ".join(title.lower().split()) - - -def gh(*args: str) -> str: - """Run a gh CLI command and return stdout.""" - result = subprocess.run( - ["gh", *args], - capture_output=True, - text=True, - check=True, - ) - return result.stdout - - -def fetch_open_issues(repo: str | None) -> list[dict]: - """Fetch all open issues (excluding PRs) via gh api --paginate.""" - if repo: - endpoint = ( - f"repos/{repo}/issues?state=open&per_page=100&sort=created&direction=asc" - ) - else: - endpoint = "repos/{owner}/{repo}/issues?state=open&per_page=100&sort=created&direction=asc" - cmd = ["api", "--paginate", endpoint] - - raw = gh(*cmd) - # gh --paginate concatenates JSON arrays, so we may get multiple arrays - issues = [] - for line in raw.strip().splitlines(): - line = line.strip() - if not line: - continue - parsed = json.loads(line) - if isinstance(parsed, list): - issues.extend(parsed) - else: - issues.append(parsed) - - # Filter out pull requests (they also appear in the issues endpoint) - return [i for i in issues if "pull_request" not in i] - - -def close_as_duplicate( - issue_number: int, duplicate_of: int, repo: str | None, dry_run: bool -) -> None: - """Close an issue as duplicate of another, adding a comment and label.""" - repo_args = ["--repo", repo] if repo else [] - - if dry_run: - print( - f" [DRY RUN] Would close #{issue_number} as duplicate of #{duplicate_of}" - ) - return - - # Add comment - comment_body = ( - f"Closing as duplicate of #{duplicate_of}.\n\n" - "If you believe this is not a duplicate, please reopen and add context " - "explaining how this differs." - ) - gh("issue", "comment", str(issue_number), "--body", comment_body, *repo_args) - - # Add label - gh("issue", "edit", str(issue_number), "--add-label", "duplicate", *repo_args) - - # Close with not_planned reason - gh( - "api", - f"repos/{repo or '{owner}/{repo}'}/issues/{issue_number}", - "-X", - "PATCH", - "-f", - "state=closed", - "-f", - "state_reason=not_planned", - ) - - print(f" Closed #{issue_number} as duplicate of #{duplicate_of}") - - -def find_duplicate( - issue: dict, candidates: list[dict], threshold: float -) -> dict | None: - """Return the first candidate whose normalized title is above threshold.""" - norm = normalize_title(issue["title"]) - for candidate in candidates: - if candidate["number"] == issue["number"]: - continue - cand_norm = normalize_title(candidate["title"]) - ratio = difflib.SequenceMatcher(None, norm, cand_norm).ratio() - if ratio >= threshold: - return candidate - return None - - -def scan_all( - issues: list[dict], threshold: float, repo: str | None, dry_run: bool -) -> int: - """Compare every issue against all older issues. Returns count of duplicates found.""" - # Sort oldest first - issues.sort(key=lambda i: i["number"]) - closed_count = 0 - - for idx, issue in enumerate(issues): - older = issues[:idx] - if not older: - continue - dup = find_duplicate(issue, older, threshold) - if dup: - ratio = difflib.SequenceMatcher( - None, - normalize_title(issue["title"]), - normalize_title(dup["title"]), - ).ratio() - print( - f"#{issue['number']}: \"{issue['title']}\"\n" - f" -> duplicate of #{dup['number']}: \"{dup['title']}\" " - f"({ratio:.0%} similar)" - ) - close_as_duplicate(issue["number"], dup["number"], repo, dry_run) - closed_count += 1 - - return closed_count - - -def check_single( - issue_number: int, - issues: list[dict], - threshold: float, - repo: str | None, - dry_run: bool, -) -> bool: - """Check a single issue against all older open issues. Returns True if duplicate found.""" - target = None - for i in issues: - if i["number"] == issue_number: - target = i - break - - if target is None: - print(f"Issue #{issue_number} not found among open issues.") - return False - - older = [i for i in issues if i["number"] < issue_number] - dup = find_duplicate(target, older, threshold) - if dup: - ratio = difflib.SequenceMatcher( - None, - normalize_title(target["title"]), - normalize_title(dup["title"]), - ).ratio() - print( - f"#{target['number']}: \"{target['title']}\"\n" - f" -> duplicate of #{dup['number']}: \"{dup['title']}\" " - f"({ratio:.0%} similar)" - ) - close_as_duplicate(issue_number, dup["number"], repo, dry_run) - return True - - print(f"#{issue_number}: no duplicate found above threshold {threshold}") - return False - - -def main() -> None: - parser = argparse.ArgumentParser( - description="Detect and close duplicate GitHub issues" - ) - mode = parser.add_mutually_exclusive_group(required=True) - mode.add_argument("--scan", action="store_true", help="Scan all open issues") - mode.add_argument("--issue-number", type=int, help="Check a single issue number") - parser.add_argument( - "--threshold", type=float, default=0.85, help="Similarity threshold (0-1)" - ) - parser.add_argument( - "--close", - action="store_true", - help="Actually close duplicates (default is dry-run)", - ) - parser.add_argument( - "--repo", type=str, help="Repository (owner/repo). Auto-detected if omitted." - ) - args = parser.parse_args() - - dry_run = not args.close - - if dry_run: - print("=== DRY RUN MODE (pass --close to actually close issues) ===\n") - - print("Fetching open issues...") - issues = fetch_open_issues(args.repo) - print(f"Found {len(issues)} open issues.\n") - - if args.scan: - count = scan_all(issues, args.threshold, args.repo, dry_run) - print(f"\nTotal duplicates {'found' if dry_run else 'closed'}: {count}") - else: - found = check_single( - args.issue_number, issues, args.threshold, args.repo, dry_run - ) - sys.exit(0 if found else 0) # Always exit 0; finding no dup is not an error - - -if __name__ == "__main__": - main() diff --git a/.github/workflows/auto-close-duplicates.yml b/.github/workflows/auto-close-duplicates.yml new file mode 100644 index 00000000000..d8256917805 --- /dev/null +++ b/.github/workflows/auto-close-duplicates.yml @@ -0,0 +1,69 @@ +name: Auto-close duplicate issues + +on: + schedule: + - cron: "0 9 * * *" + workflow_dispatch: + inputs: + dry_run: + description: Log which issues would close without closing anything + type: boolean + default: true + grace_period_days: + description: Days a duplicate notice must go unanswered before the close + type: number + default: 3 + pull_request: + paths: + - .github/workflows/auto-close-duplicates.yml + - scripts/auto-close-duplicates.ts + - scripts/auto-close-duplicates.test.ts + +permissions: {} + +jobs: + test: + if: github.event_name == 'pull_request' + runs-on: ubuntu-latest + timeout-minutes: 5 + permissions: + contents: read + steps: + - name: Checkout repository + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false + + - name: Setup Bun + uses: oven-sh/setup-bun@0c5077e51419868618aeaa5fe8019c62421857d6 # v2.2.0 + with: + bun-version: "1.4.0" + + - name: Test the sweep + run: bun test scripts/auto-close-duplicates.test.ts + + sweep: + if: github.event_name != 'pull_request' && github.repository == 'BerriAI/litellm' + runs-on: ubuntu-latest + timeout-minutes: 10 + permissions: + contents: read + issues: write + steps: + - name: Checkout repository + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false + + - name: Setup Bun + uses: oven-sh/setup-bun@0c5077e51419868618aeaa5fe8019c62421857d6 # v2.2.0 + with: + # Exact version, never latest: the next step holds an issues: write token + bun-version: "1.4.0" + + - name: Close unanswered duplicates, reopen ones the reporter answered + run: bun run scripts/auto-close-duplicates.ts + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + DRY_RUN: ${{ inputs.dry_run == true }} + GRACE_PERIOD_DAYS: ${{ inputs.grace_period_days }} diff --git a/.github/workflows/check_duplicate_issues.yml b/.github/workflows/check_duplicate_issues.yml index 78198b2c7bb..41ec43a1d9b 100644 --- a/.github/workflows/check_duplicate_issues.yml +++ b/.github/workflows/check_duplicate_issues.yml @@ -1,12 +1,19 @@ name: Check Duplicate Issues +# Flagging only. "Auto-close duplicate issues" closes a flagged issue 3 days later, +# and only when its title is identical to an older open issue and nobody replied. +# The HTML marker below is the handshake between the two, so keep it in the template. + on: issues: types: [opened, edited] +permissions: {} + jobs: check-duplicate: runs-on: ubuntu-latest + timeout-minutes: 5 permissions: issues: write contents: read @@ -19,35 +26,12 @@ jobs: threshold: 0.6 reaction: eyes comment: | - **⚠️ Potential duplicate detected** + + **Potential duplicate detected** - This issue appears similar to existing issue(s): + This looks similar to: {{#issues}} - - [#{{number}}]({{html_url}}) - {{title}} ({{accuracy}}% similar) + - #{{number}} - {{title}} {{/issues}} - Please review the linked issue(s) to see if they address your concern. If this is not a duplicate, please provide additional context to help us understand the difference. - - - name: Checkout close script - if: github.event.action == 'opened' - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 - with: - sparse-checkout: .github/scripts - persist-credentials: false - - - name: Set up Python - if: github.event.action == 'opened' - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 - with: - python-version: "3.12" - - - name: Auto-close if high-confidence duplicate - if: github.event.action == 'opened' - env: - GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} - run: | - python3 .github/scripts/close_duplicate_issues.py \ - --issue-number ${{ github.event.issue.number }} \ - --repo ${{ github.repository }} \ - --threshold 0.85 \ - --close + If this is a duplicate, add a thumbs-up reaction to the existing issue and follow along there. When the title is identical to an older open issue, this issue closes automatically in 3 days unless someone responds. If it is not a duplicate, comment here or add a thumbs-down reaction to this comment and it stays open. diff --git a/.github/workflows/codspeed.yml b/.github/workflows/codspeed.yml index a69e50b5753..7e013b7bb0b 100644 --- a/.github/workflows/codspeed.yml +++ b/.github/workflows/codspeed.yml @@ -12,6 +12,7 @@ on: - "uv.lock" - ".github/workflows/codspeed.yml" - ".github/actions/setup-uv-with-retries/**" + - ".github/actions/cache-cargo-build/**" pull_request: branches: - main @@ -23,6 +24,7 @@ on: - "uv.lock" - ".github/workflows/codspeed.yml" - ".github/actions/setup-uv-with-retries/**" + - ".github/actions/cache-cargo-build/**" # Allow CodSpeed to trigger backtest performance analysis # in order to generate initial data workflow_dispatch: @@ -55,6 +57,26 @@ jobs: with: version: "0.10.9" + - name: Cache the Rust build + uses: ./.github/actions/cache-cargo-build + + # Build the wheel and resolve every dependency outside the CodSpeed + # runner: the same maturin build took 42 minutes inside `codspeed run` + # versus under 3 minutes as a plain step (LIT-6183) + - name: Build environment + run: > + env PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 + uv run --frozen --no-default-groups + --with pytest==8.3.5 + --with pytest-codspeed==4.3.0 + --with "mcp>=1.26.0,<2.0" + --with "a2a-sdk>=1.1.0,<2.0" + pytest + -p pytest_codspeed.plugin + tests/benchmarks/ + --codspeed + --collect-only -q + - name: Run benchmarks uses: CodSpeedHQ/action@1c8ae4843586d3ba879736b7f6b7b0c990757fab # v4.12.1 with: diff --git a/.github/workflows/image-scan.yml b/.github/workflows/image-scan.yml index bb04563c1a8..206bb809e0c 100644 --- a/.github/workflows/image-scan.yml +++ b/.github/workflows/image-scan.yml @@ -80,7 +80,7 @@ jobs: LITELLM_IMAGE: litellm-image-scan:${{ github.sha }} run: | python -m pip install "pytest==9.0.3" - python -m pytest tests/proxy_migration_tests/test_offline_image_migration.py -v + python -m pytest tests/proxy_migration_tests/test_offline_image_migration.py tests/proxy_migration_tests/test_image_bedrock_realtime_extra.py -v # Scans the whole shipped artifact: OS/apk plus every language package # baked into the image, including ones no lockfile declares (e.g. prisma's @@ -124,7 +124,7 @@ jobs: LITELLM_IMAGE: litellm-runtime-scan:${{ github.sha }} run: | python -m pip install "pytest==9.0.3" - python -m pytest tests/proxy_migration_tests/test_offline_image_migration.py -v + python -m pytest tests/proxy_migration_tests/test_offline_image_migration.py tests/proxy_migration_tests/test_image_bedrock_realtime_extra.py -v migrations-image: name: migrations-image @@ -185,7 +185,7 @@ jobs: LITELLM_COMPONENT_PORT: "4000" run: | python -m pip install "pytest==9.0.3" - python -m pytest tests/proxy_migration_tests/test_component_image_serves_offline.py -v + python -m pytest tests/proxy_migration_tests/test_component_image_serves_offline.py tests/proxy_migration_tests/test_image_bedrock_realtime_extra.py -v ui-image: name: ui-image diff --git a/.github/workflows/test-redis-compat.yml b/.github/workflows/test-redis-compat.yml new file mode 100644 index 00000000000..f29755a74b1 --- /dev/null +++ b/.github/workflows/test-redis-compat.yml @@ -0,0 +1,77 @@ +name: "Unit Tests: Redis Client Version Compatibility" + +on: + pull_request: + branches: + - main + - litellm_internal_staging + - litellm_oss_staging + - "litellm_**" + paths: + - "litellm/_redis.py" + - "litellm/_redis_credential_provider.py" + - "tests/test_litellm/test_redis.py" + - "tests/test_litellm/caching/test_redis_connection_pool.py" + - ".github/workflows/test-redis-compat.yml" + - "pyproject.toml" + - "uv.lock" + +permissions: + contents: read + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + redis-compat: + name: "redis-py ${{ matrix.redis-version }}" + runs-on: ubuntu-latest + timeout-minutes: 15 + + strategy: + fail-fast: false + matrix: + # 5.3.1 is the version pinned in uv.lock (redisvl caps it below 6); the + # newer legs prove the inspect.signature introspection in litellm/_redis.py + # keeps extracting kwargs on the redis-py releases people actually run now. + # Only the exact release 6.0.0 is skipped: rq (pulled by the proxy extra) + # specifies `redis != 6`, which excludes 6.0.0 alone, so 6.4.0 stands in + # for the 6.x line. + redis-version: ["5.3.1", "6.4.0", "7.4.1", "8.0.1"] + + steps: + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" + + - name: Set up uv + uses: ./.github/actions/setup-uv-with-retries + with: + version: "0.10.9" + + - name: Install dependencies + run: | + .github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router + + - name: Pin redis-py to the matrix version + env: + REDIS_VERSION: ${{ matrix.redis-version }} + run: | + uv pip install "redis==${REDIS_VERSION:?}" + uv run --no-sync python -c "import redis; assert redis.__version__ == '${REDIS_VERSION:?}', redis.__version__; print('redis-py', redis.__version__)" + + - name: Run redis unit tests + run: | + uv run --no-sync pytest \ + tests/test_litellm/test_redis.py \ + tests/test_litellm/caching/test_redis_connection_pool.py \ + --tb=short -vv \ + --reruns 2 \ + --reruns-delay 1 \ + --durations=20 diff --git a/.github/workflows/test-unit.yml b/.github/workflows/test-unit.yml index ed9d8800202..c2dff805772 100644 --- a/.github/workflows/test-unit.yml +++ b/.github/workflows/test-unit.yml @@ -103,6 +103,7 @@ jobs: tests/test_litellm/completion_extras tests/test_litellm/compression tests/test_litellm/containers + tests/test_litellm/endpoints tests/test_litellm/experimental_mcp_client tests/test_litellm/models tests/test_litellm/repositories diff --git a/CLAUDE.md b/CLAUDE.md index 930825aeb89..d9e9e8f1586 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -23,6 +23,8 @@ When adding new features, add meaningful tests. Don't add tests that don't check Same thing for bug fixes. The tests should make it so that this specific bug can never happen again without failing tests (i.e., regression) +Never test structure of code only function of it + `tests/test_litellm/` mirrors `litellm/` in a parallel path (see `tests/test_litellm/readme.md`). Name tests `test_.py`, but always match the existing test file in the directory you touch — many provider dirs use longer descriptive names (e.g. `test_anthropic_chat_transformation.py`) to avoid ambiguity across sibling folders. For bug fixes, extend the existing mapped test file rather than creating a new one. Only create a new test file for a new feature (provider, endpoint, or transformation module) that has no mapped test yet, following that directory's naming convention (or `test_.py` if you're the first test there). One focused regression test beats many shallow ones End-to-end tests belong in `tests/e2e/` and must follow the harness conventions documented in that directory's `CLAUDE.md` diff --git a/Dockerfile b/Dockerfile index 700b0d6525e..0a92aa9a68c 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,10 +1,10 @@ # syntax=docker/dockerfile:1.7 # Base image for building -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d # Runtime image -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a # Pinned by digest like the other base images; bump explicitly on Node upgrades. ARG UI_BUILD_IMAGE=node:24.19-alpine3.24@sha256:d32cdf619f63fe0471182d08996dd516c6275bb5fd31ae06e55a570bd9e1ad43 @@ -40,8 +40,8 @@ COPY --from=uvbin /uvx /usr/local/bin/uvx RUN apk add --no-cache \ bash \ gcc \ - python3 \ - python3-dev \ + python-3.13 \ + python-3.13-dev \ rust \ openssl \ openssl-dev \ @@ -51,6 +51,7 @@ RUN apk add --no-cache \ ENV UV_PROJECT_ENVIRONMENT=/app/.venv \ UV_LINK_MODE=copy \ + UV_PYTHON_DOWNLOADS=0 \ PATH="/app/.venv/bin:${PATH}" # Copy dependency metadata first for layer caching @@ -65,7 +66,8 @@ RUN uv sync --frozen --no-install-project --no-install-workspace --no-default-gr --extra extra_proxy \ --extra semantic-router \ --extra saml \ - --python python3 + --extra bedrock-realtime \ + --python python3.13 # Copy full source tree COPY . . @@ -86,7 +88,8 @@ RUN uv sync --frozen --no-default-groups --no-editable \ --extra extra_proxy \ --extra semantic-router \ --extra saml \ - --python python3 + --extra bedrock-realtime \ + --python python3.13 RUN HOME=/opt/prisma XDG_CACHE_HOME=/opt/prisma/.cache PRISMA_BINARY_CACHE_DIR=/opt/prisma/binaries \ npm_config_cache=/root/.npm \ @@ -100,8 +103,14 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime USER root +# The base image only configures Chainguard's authenticated apk repo, which +# requires an enterprise subscription. Add the public Wolfi repo so `apk add` +# also works for anyone installing extra packages into a running container. +# https://github.com/BerriAI/litellm/issues/33518 +RUN echo "https://packages.wolfi.dev/os" >> /etc/apk/repositories + # node (without npm) is required by the prisma CLI at runtime -RUN apk add --no-cache bash openssl tzdata nodejs python3 libsndfile +RUN apk add --no-cache bash openssl tzdata nodejs python-3.13 libsndfile WORKDIR /app ENV PATH="/app/.venv/bin:${PATH}" \ diff --git a/README.md b/README.md index 68aaa09ec98..92757fcbbc1 100644 --- a/README.md +++ b/README.md @@ -354,6 +354,8 @@ curl -X POST 'http://0.0.0.0:4000/v1/chat/completions' \ | [Petals (`petals`)](https://docs.litellm.ai/docs/providers/petals) | ✅ | ✅ | ✅ | | | | | | | | | [Pinstripes (`pinstripes`)](https://docs.litellm.ai/docs/providers/pinstripes) | ✅ | ✅ | ✅ | | | | | | | | | [Predibase (`predibase`)](https://docs.litellm.ai/docs/providers/predibase) | ✅ | ✅ | ✅ | | | | | | | | +| [Qwen AI Platform (`qwen_ai_platform`)](https://docs.litellm.ai/docs/providers/qwencloud) | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | ✅ | +| [QwenCloud (`qwencloud`)](https://docs.litellm.ai/docs/providers/qwencloud) | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | ✅ | | [Recraft (`recraft`)](https://docs.litellm.ai/docs/providers/recraft) | | | | | ✅ | | | | | | | [Replicate (`replicate`)](https://docs.litellm.ai/docs/providers/replicate) | ✅ | ✅ | ✅ | | | | | | | | | [Sagemaker Chat (`sagemaker_chat`)](https://docs.litellm.ai/docs/providers/aws_sagemaker) | ✅ | ✅ | ✅ | | | | | | | | diff --git a/backend/Dockerfile b/backend/Dockerfile index 4ca40944606..622fedcd70d 100644 --- a/backend/Dockerfile +++ b/backend/Dockerfile @@ -1,5 +1,5 @@ -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a FROM $UV_IMAGE AS uvbin @@ -16,7 +16,7 @@ COPY --from=uvbin /uv /uvx /usr/local/bin/ # instead of nodeenv downloading one whose dynamic deps may not be in Wolfi # (e.g. Node 26.2.0 needs libatomic). Retry for transient apk.cgr.dev flakes. RUN for i in 1 2 3; do \ - apk add --no-cache bash gcc python3 python3-dev openssl openssl-dev libsndfile nodejs npm && break; \ + apk add --no-cache bash gcc python-3.13 python-3.13-dev openssl openssl-dev libsndfile nodejs npm && break; \ [ $i = 3 ] && { echo "apk add failed after 3 retries" >&2; exit 1; }; \ sleep 5; \ done @@ -46,7 +46,8 @@ RUN --mount=type=cache,target=/root/.cache/uv \ --extra proxy-runtime \ --extra extra_proxy \ --extra semantic-router \ - --python python3 + --extra saml \ + --python python3.13 # Stage 2 — copy source and install the project + workspace members. COPY . . @@ -57,7 +58,8 @@ RUN --mount=type=cache,target=/root/.cache/uv \ --extra proxy-runtime \ --extra extra_proxy \ --extra semantic-router \ - --python python3 + --extra saml \ + --python python3.13 RUN HOME=/opt/prisma XDG_CACHE_HOME=/opt/prisma/.cache PRISMA_BINARY_CACHE_DIR=/opt/prisma/binaries \ npm_config_cache=/root/.npm \ @@ -71,7 +73,7 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime USER root RUN for i in 1 2 3; do \ - apk add --no-cache bash openssl tzdata python3 libsndfile libatomic && break; \ + apk add --no-cache bash openssl tzdata python-3.13 libsndfile libatomic && break; \ [ $i = 3 ] && { echo "apk add failed after 3 retries" >&2; exit 1; }; \ sleep 5; \ done diff --git a/basedpyright-code-budget.json b/basedpyright-code-budget.json index 962d1266fd7..da788bf1ce3 100644 --- a/basedpyright-code-budget.json +++ b/basedpyright-code-budget.json @@ -1,9 +1,9 @@ { "reportAny": { - "limit": 17271 + "limit": 14076 }, "reportArgumentType": { - "limit": 2539 + "limit": 2216 }, "reportAssignmentType": { "limit": 319 @@ -18,13 +18,13 @@ "limit": 40 }, "reportDeprecated": { - "limit": 212 + "limit": 211 }, "reportDuplicateImport": { "limit": 19 }, "reportExplicitAny": { - "limit": 5486 + "limit": 4128 }, "reportFunctionMemberAccess": { "limit": 7 @@ -42,7 +42,7 @@ "limit": 12 }, "reportIndexIssue": { - "limit": 35 + "limit": 25 }, "reportInvalidTypeForm": { "limit": 34 @@ -54,10 +54,10 @@ "limit": 0 }, "reportMissingParameterType": { - "limit": 5658 + "limit": 5601 }, "reportMissingTypeArgument": { - "limit": 15425 + "limit": 15306 }, "reportMissingTypeStubs": { "limit": 40 @@ -72,7 +72,7 @@ "limit": 0 }, "reportOptionalMemberAccess": { - "limit": 1055 + "limit": 0 }, "reportOptionalOperand": { "limit": 0 @@ -90,40 +90,40 @@ "limit": 8 }, "reportReturnType": { - "limit": 213 + "limit": 181 }, "reportTypedDictNotRequiredAccess": { - "limit": 25 + "limit": 24 }, "reportUndefinedVariable": { "limit": 0 }, "reportUnknownArgumentType": { - "limit": 44526 + "limit": 44364 }, "reportUnknownLambdaType": { "limit": 109 }, "reportUnknownMemberType": { - "limit": 38721 + "limit": 38350 }, "reportUnknownParameterType": { - "limit": 19778 + "limit": 19626 }, "reportUnknownVariableType": { - "limit": 30290 + "limit": 29890 }, "reportUnnecessaryCast": { - "limit": 117 + "limit": 111 }, "reportUnnecessaryComparison": { - "limit": 697 + "limit": 692 }, "reportUnnecessaryContains": { "limit": 5 }, "reportUnnecessaryIsInstance": { - "limit": 829 + "limit": 826 }, "reportUntypedBaseClass": { "limit": 0 @@ -141,6 +141,6 @@ "limit": 543 }, "reportUnusedVariable": { - "limit": 139 + "limit": 137 } } diff --git a/codecov.yaml b/codecov.yaml index bc0b3604329..4d93c18f3ac 100644 --- a/codecov.yaml +++ b/codecov.yaml @@ -25,6 +25,8 @@ flag_management: carryforward: false - name: proxy-db-schema-migration carryforward: false + - name: circleci + carryforward: false component_management: individual_components: diff --git a/docker/Dockerfile.database b/docker/Dockerfile.database index f0d6d02fccf..e9ad2849bb2 100644 --- a/docker/Dockerfile.database +++ b/docker/Dockerfile.database @@ -1,10 +1,10 @@ # syntax=docker/dockerfile:1.7 # Base image for building -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d # Runtime image -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a # Pinned by digest like the other base images; bump explicitly on Node upgrades. ARG UI_BUILD_IMAGE=node:24.19-alpine3.24@sha256:d32cdf619f63fe0471182d08996dd516c6275bb5fd31ae06e55a570bd9e1ad43 @@ -39,8 +39,8 @@ COPY --from=uvbin /uvx /usr/local/bin/uvx RUN apk add --no-cache \ bash \ gcc \ - python3 \ - python3-dev \ + python-3.13 \ + python-3.13-dev \ openssl \ openssl-dev \ nodejs \ @@ -49,6 +49,7 @@ RUN apk add --no-cache \ ENV UV_PROJECT_ENVIRONMENT=/app/.venv \ UV_LINK_MODE=copy \ + UV_PYTHON_DOWNLOADS=0 \ PATH="/app/.venv/bin:${PATH}" # Copy dependency metadata first for layer caching @@ -63,7 +64,8 @@ RUN uv sync --frozen --no-install-project --no-install-workspace --no-default-gr --extra extra_proxy \ --extra semantic-router \ --extra saml \ - --python python3 + --extra bedrock-realtime \ + --python python3.13 # Copy full source tree COPY . . @@ -84,7 +86,8 @@ RUN uv sync --frozen --no-default-groups --no-editable \ --extra extra_proxy \ --extra semantic-router \ --extra saml \ - --python python3 + --extra bedrock-realtime \ + --python python3.13 RUN HOME=/opt/prisma XDG_CACHE_HOME=/opt/prisma/.cache PRISMA_BINARY_CACHE_DIR=/opt/prisma/binaries \ npm_config_cache=/root/.npm \ @@ -98,7 +101,7 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime USER root # node (without npm) is required by the prisma CLI at runtime -RUN apk add --no-cache bash openssl tzdata nodejs python3 libsndfile +RUN apk add --no-cache bash openssl tzdata nodejs python-3.13 libsndfile WORKDIR /app ENV PATH="/app/.venv/bin:${PATH}" \ diff --git a/docker/Dockerfile.non_root b/docker/Dockerfile.non_root index 4a5df6ecd69..edf20e8bbff 100644 --- a/docker/Dockerfile.non_root +++ b/docker/Dockerfile.non_root @@ -1,8 +1,8 @@ # syntax=docker/dockerfile:1.7 # Base images -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d ARG PROXY_EXTRAS_SOURCE=published ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a # Pinned by digest like the other base images; bump explicitly on Node upgrades. @@ -37,8 +37,8 @@ COPY --from=uvbin /uvx /usr/local/bin/uvx RUN for i in 1 2 3; do \ apk add --no-cache \ - python3 \ - python3-dev \ + python-3.13 \ + python-3.13-dev \ gcc \ rust \ bash \ @@ -52,6 +52,7 @@ RUN for i in 1 2 3; do \ ENV UV_PROJECT_ENVIRONMENT=/app/.venv \ UV_LINK_MODE=copy \ + UV_PYTHON_DOWNLOADS=0 \ PATH="/app/.venv/bin:${PATH}" \ LITELLM_NON_ROOT=true \ XDG_CACHE_HOME=/app/.cache @@ -69,7 +70,8 @@ RUN --mount=type=cache,target=/app/.cache/uv,id=litellm-uv-cache \ --extra extra_proxy \ --extra semantic-router \ --extra saml \ - --python python3 + --extra bedrock-realtime \ + --python python3.13 # Copy full source tree COPY . . @@ -96,7 +98,8 @@ RUN --mount=type=cache,target=/app/.cache/uv,id=litellm-uv-cache \ --extra extra_proxy \ --extra semantic-router \ --extra saml \ - --python python3 \ + --extra bedrock-realtime \ + --python python3.13 \ --no-sources-package litellm-proxy-extras; \ else \ uv sync --frozen --no-default-groups --no-editable \ @@ -105,7 +108,8 @@ RUN --mount=type=cache,target=/app/.cache/uv,id=litellm-uv-cache \ --extra extra_proxy \ --extra semantic-router \ --extra saml \ - --python python3; \ + --extra bedrock-realtime \ + --python python3.13; \ fi RUN HOME=/opt/prisma XDG_CACHE_HOME=/opt/prisma/.cache PRISMA_BINARY_CACHE_DIR=/opt/prisma/binaries \ @@ -124,7 +128,7 @@ RUN for i in 1 2 3; do \ apk upgrade --no-cache && break || sleep 5; \ done && \ for i in 1 2 3; do \ - apk add --no-cache python3 bash openssl tzdata libsndfile nodejs && break || sleep 5; \ + apk add --no-cache python-3.13 bash openssl tzdata libsndfile nodejs && break || sleep 5; \ done # Copy only what runtime needs. The application is installed inside the venv; diff --git a/enterprise/litellm_enterprise/proxy/audit_logging_endpoints.py b/enterprise/litellm_enterprise/proxy/audit_logging_endpoints.py index 8f4e999bb9d..b6f8bf2dc5b 100644 --- a/enterprise/litellm_enterprise/proxy/audit_logging_endpoints.py +++ b/enterprise/litellm_enterprise/proxy/audit_logging_endpoints.py @@ -7,7 +7,7 @@ GET - /audit/{id} - Get audit log by id GET - /audit - Get all audit logs """ -from typing import TYPE_CHECKING, Final, Optional +from typing import TYPE_CHECKING, Final #### AUDIT LOGGING #### from fastapi import APIRouter, Depends, HTTPException, Query @@ -58,33 +58,33 @@ async def get_audit_logs( page: int = Query(1, ge=1), page_size: int = Query(10, ge=1, le=100), # Filter parameters - changed_by: Optional[str] = Query( + changed_by: str | None = Query( None, description="Filter by user or system that performed the action" ), - changed_by_api_key: Optional[str] = Query( + changed_by_api_key: str | None = Query( None, description="Filter by API key hash that performed the action" ), - action: Optional[str] = Query( + action: str | None = Query( None, description="Filter by action type (create, update, delete)" ), - table_name: Optional[str] = Query( + table_name: str | None = Query( None, description="Filter by table name that was modified" ), - object_id: Optional[str] = Query( + object_id: str | None = Query( None, description="Filter by ID of the object that was modified" ), - start_date: Optional[str] = Query(None, description="Filter logs after this date"), - end_date: Optional[str] = Query(None, description="Filter logs before this date"), - object_team_id: Optional[str] = Query( + start_date: str | None = Query(None, description="Filter logs after this date"), + end_date: str | None = Query(None, description="Filter logs before this date"), + object_team_id: str | None = Query( None, description="Filter by team_id present in before_value or updated_values JSON (PostgreSQL only)", ), - object_key_hash: Optional[str] = Query( + object_key_hash: str | None = Query( None, description="Filter by token (key hash) present in before_value or updated_values JSON (PostgreSQL only)", ), # Sorting parameters - sort_by: Optional[str] = Query( + sort_by: str | None = Query( None, description="Column to sort by (e.g. 'updated_at', 'action', 'table_name')", ), diff --git a/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py b/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py index aee3295d1da..354a6ed2fd0 100644 --- a/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py +++ b/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py @@ -2,9 +2,10 @@ Polls LiteLLM_ManagedObjectTable to check if the batch job is complete, and if the cost has been tracked. """ +from dataclasses import replace as dataclasses_replace from datetime import datetime, timedelta, timezone from types import MappingProxyType -from typing import TYPE_CHECKING, Any, Dict, Final, List, Optional, Tuple +from typing import TYPE_CHECKING, Final, List, Literal, Optional, Tuple, cast from litellm._logging import verbose_proxy_logger from litellm._uuid import uuid @@ -86,7 +87,7 @@ class CheckBatchCost: return self.batch_processed_support_confirmed = True - async def _get_user_info(self, batch_id: str, user_id: Optional[str]) -> Dict[str, Any]: + async def _get_user_info(self, batch_id: str, user_id: Optional[str]) -> dict[str, str | None]: """ Look up user email and key alias by user_id for enriching the S3 callback metadata. Returns a dict with user_api_key_user_email and user_api_key_alias (both may be None). @@ -96,8 +97,10 @@ class CheckBatchCost: if not user_id: return {} try: - user_row = await self.prisma_client.db.litellm_usertable.find_unique( - where={"user_id": user_id} + user_row: prisma_models.LiteLLM_UserTable | None = ( + await self.prisma_client.db.litellm_usertable.find_unique( + where={"user_id": user_id} + ) ) if user_row is None: return {} @@ -114,8 +117,10 @@ class CheckBatchCost: if not api_key: return None try: - key_row = await self.prisma_client.db.litellm_verificationtoken.find_unique( - where={"token": api_key} + key_row: prisma_models.LiteLLM_VerificationToken | None = ( + await self.prisma_client.db.litellm_verificationtoken.find_unique( + where={"token": api_key} + ) ) return getattr(key_row, "key_alias", None) if key_row is not None else None except Exception as e: @@ -127,8 +132,10 @@ class CheckBatchCost: if not team_id: return None try: - team_row = await self.prisma_client.db.litellm_teamtable.find_unique( - where={"team_id": team_id} + team_row: prisma_models.LiteLLM_TeamTable | None = ( + await self.prisma_client.db.litellm_teamtable.find_unique( + where={"team_id": team_id} + ) ) return getattr(team_row, "team_alias", None) if team_row is not None else None except Exception as e: @@ -137,7 +144,7 @@ class CheckBatchCost: async def _build_creator_attribution_metadata( self, job: "LiteLLM_ManagedObjectTable", batch_id: str - ) -> Dict[str, Any]: + ) -> dict[str, object]: """ Rebuild the spend-tracking metadata for the key, team, and tags that created the batch so the batch-cost spend log is attributed the same way a non-batch request @@ -151,7 +158,7 @@ class CheckBatchCost: team_id = getattr(job, "team_id", None) request_tags = getattr(job, "request_tags", None) - metadata: Dict[str, Any] = { + metadata: dict[str, object] = { "user_api_key_user_id": job.created_by, "user_api_key": api_key, "user_api_key_team_id": team_id, @@ -626,6 +633,7 @@ class CheckBatchCost: later poll. """ from litellm.batches.batch_utils import ( + count_error_file_failed_requests, _get_file_content_as_dictionary, calculate_batch_cost_and_usage, ) @@ -761,16 +769,33 @@ class CheckBatchCost: model_id=model_id, deployment_model=litellm_model_name, ) - batch_cost, batch_usage, batch_models = ( - await calculate_batch_cost_and_usage( - file_content_dictionary=file_content_as_dict, - custom_llm_provider=llm_provider, # type: ignore - model_name=model_name, - model_info=deployment_model_info, + batch_file_provider: Final = cast( + Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic"], llm_provider + ) + output_file_result: Final = await calculate_batch_cost_and_usage( + file_content_dictionary=file_content_as_dict, + custom_llm_provider=batch_file_provider, + model_name=model_name, + model_info=deployment_model_info, + ) + error_file_failed_requests: Final = await count_error_file_failed_requests( + response, + custom_llm_provider=batch_file_provider, + litellm_params={ + **credentials, + "_litellm_internal_model_credentials": MappingProxyType(dict(credentials)), + }, + ) + batch_result: Final = ( + output_file_result + if not error_file_failed_requests + else dataclasses_replace( + output_file_result, + failed_requests=output_file_result.failed_requests + error_file_failed_requests, ) ) logging_obj = LiteLLMLogging( - model=batch_models[0], + model=batch_result.models[0], messages=[{"role": "user", "content": ""}], stream=False, call_type="aretrieve_batch", @@ -802,9 +827,11 @@ class CheckBatchCost: try: await logging_obj.async_success_handler( result=response, - batch_cost=batch_cost, - batch_usage=batch_usage, - batch_models=batch_models, + batch_cost=batch_result.cost, + batch_usage=batch_result.usage, + batch_models=batch_result.models, + batch_successful_requests=batch_result.successful_requests, + batch_failed_requests=batch_result.failed_requests, ) except Exception: await self._release_job_claim(job) diff --git a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py index cf2cee9b6ef..5cfcf6129f0 100644 --- a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py +++ b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py @@ -36,6 +36,7 @@ from litellm.llms.base_llm.managed_resources.isolation import ( build_list_page, build_owner_filter, can_access_resource, + resolve_resource_owner_id, ) from litellm.proxy._types import ( CallTypes, @@ -181,6 +182,10 @@ class _ManagedObjectTableActions(Protocol): async def update_many(self, where: Mapping[str, object], data: Mapping[str, object]) -> int: ... +class _SchedulerWithJobLookup(Protocol): + def get_job(self, job_id: str) -> object: ... + + class _CursorPageArgs(TypedDict, total=False): cursor: Mapping[str, str] skip: int @@ -222,7 +227,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): file_object=file_object, model_mappings=model_mappings, flat_model_file_ids=list(model_mappings.values()), - created_by=user_api_key_dict.user_id, + created_by=resolve_resource_owner_id(user_api_key_dict), team_id=user_api_key_dict.team_id, updated_by=user_api_key_dict.user_id, ) @@ -238,7 +243,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): "unified_file_id": file_id, "model_mappings": json.dumps(model_mappings), "flat_model_file_ids": list(model_mappings.values()), - "created_by": user_api_key_dict.user_id, + "created_by": resolve_resource_owner_id(user_api_key_dict), "team_id": user_api_key_dict.team_id, "updated_by": user_api_key_dict.user_id, } @@ -342,7 +347,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): "file_object": file_object.model_dump_json(), "model_object_id": model_object_id, "file_purpose": file_purpose, - "created_by": user_api_key_dict.user_id, + "created_by": resolve_resource_owner_id(user_api_key_dict), "team_id": user_api_key_dict.team_id, "updated_by": user_api_key_dict.user_id, "status": file_object.status, @@ -852,7 +857,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): file_ids.append(file_id) return file_ids - def get_file_ids_from_responses_input(self, input: Union[str, List[Dict[str, Any]]]) -> List[str]: + def get_file_ids_from_responses_input(self, input: Union[str, List[Dict[str, object]]]) -> List[str]: """ Gets file ids from responses API input. @@ -877,7 +882,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): # Check for direct input_file type if item.get("type") == "input_file": file_id = item.get("file_id") - if file_id: + if isinstance(file_id, str) and file_id: file_ids.append(file_id) # Check for input_file in content array @@ -886,7 +891,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): for content_item in content: if isinstance(content_item, dict) and content_item.get("type") == "input_file": file_id = content_item.get("file_id") - if file_id: + if isinstance(file_id, str) and file_id: file_ids.append(file_id) return file_ids @@ -1226,7 +1231,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): # Handle both output_file_id and error_file_id for file_attr in ["output_file_id", "error_file_id"]: - file_id_value = getattr(response, file_attr, None) + file_id_value: str | None = getattr(response, file_attr, None) if file_id_value and model_id: decoded_output_file_id = _is_base64_encoded_unified_file_id(file_id_value) if decoded_output_file_id and "llm_output_file_id," in decoded_output_file_id: @@ -1495,7 +1500,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): import litellm.proxy.proxy_server as proxy_server_module # Check if the scheduler has the batch cost checking job registered - scheduler = getattr(proxy_server_module, "scheduler", None) + scheduler: Final[_SchedulerWithJobLookup | None] = getattr(proxy_server_module, "scheduler", None) if scheduler is None: return False @@ -1541,7 +1546,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ) MAX_MATCHES_TO_RETURN = 10 - batches = await self.prisma_client.db.litellm_managedobjecttable.find_many( + batches = await _managed_object_table(self.prisma_client).find_many( where={ "file_purpose": "batch", "batch_processed": False, @@ -1551,11 +1556,14 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): order={"created_at": "desc"}, ) - referencing_batches = [] + referencing_batches: Final[list[dict[str, object]]] = [] for batch in batches: try: # Parse the batch file_object to check for file references - batch_data = json.loads(batch.file_object) if isinstance(batch.file_object, str) else batch.file_object + decoded_file_object = _decode_json_blob(batch.file_object) + batch_data: Mapping[str, object] = ( + decoded_file_object if isinstance(decoded_file_object, Mapping) else {} + ) # Extract file IDs from batch # Batches typically reference the unified file ID in input_file_id diff --git a/enterprise/pyproject.toml b/enterprise/pyproject.toml index 3653aba67ef..8360c0a077d 100644 --- a/enterprise/pyproject.toml +++ b/enterprise/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm-enterprise" -version = "0.1.61" +version = "0.1.63" description = "Package for LiteLLM Enterprise features" readme = "README.md" requires-python = ">=3.9" @@ -26,7 +26,7 @@ required-version = ">=0.10.9" module-root = "" [tool.commitizen] -version = "0.1.61" +version = "0.1.63" version_files = [ "pyproject.toml:^version", "../pyproject.toml:litellm-enterprise==", diff --git a/gateway/Dockerfile b/gateway/Dockerfile index 4a2e32e186e..308d70a6b26 100644 --- a/gateway/Dockerfile +++ b/gateway/Dockerfile @@ -1,5 +1,5 @@ -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a FROM $UV_IMAGE AS uvbin @@ -16,7 +16,7 @@ COPY --from=uvbin /uv /uvx /usr/local/bin/ # instead of nodeenv downloading one whose dynamic deps may not be in Wolfi # (e.g. Node 26.2.0 needs libatomic). Retry for transient apk.cgr.dev flakes. RUN for i in 1 2 3; do \ - apk add --no-cache bash gcc python3 python3-dev openssl openssl-dev libsndfile nodejs npm && break; \ + apk add --no-cache bash gcc python-3.13 python-3.13-dev openssl openssl-dev libsndfile nodejs npm && break; \ [ $i = 3 ] && { echo "apk add failed after 3 retries" >&2; exit 1; }; \ sleep 5; \ done @@ -47,7 +47,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \ --extra extra_proxy \ --extra semantic-router \ --extra bedrock-realtime \ - --python python3 + --python python3.13 # Stage 2 — copy source and install the project + workspace members. COPY . . @@ -59,7 +59,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \ --extra extra_proxy \ --extra semantic-router \ --extra bedrock-realtime \ - --python python3 + --python python3.13 RUN HOME=/opt/prisma XDG_CACHE_HOME=/opt/prisma/.cache PRISMA_BINARY_CACHE_DIR=/opt/prisma/binaries \ npm_config_cache=/root/.npm \ @@ -73,7 +73,7 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime USER root RUN for i in 1 2 3; do \ - apk add --no-cache bash openssl tzdata python3 libsndfile libatomic && break; \ + apk add --no-cache bash openssl tzdata python-3.13 libsndfile libatomic && break; \ [ $i = 3 ] && { echo "apk add failed after 3 retries" >&2; exit 1; }; \ sleep 5; \ done diff --git a/gateway/routes/allowlist.py b/gateway/routes/allowlist.py index 05baf98bbb5..92b73867e67 100644 --- a/gateway/routes/allowlist.py +++ b/gateway/routes/allowlist.py @@ -86,6 +86,7 @@ GATEWAY_PATH_PREFIXES: tuple[str, ...] = ( "/comprehendmedical", "/cohere/", "/gemini/", + "/gigachat/", "/google/", "/vertex_ai/", "/vertex-ai/", diff --git a/helm/litellm-helm/README.md b/helm/litellm-helm/README.md index b242373de5d..bf4089404db 100644 --- a/helm/litellm-helm/README.md +++ b/helm/litellm-helm/README.md @@ -26,7 +26,7 @@ If `db.useStackgresOperator` is used (not yet implemented): | `replicaCount` | The number of LiteLLM Proxy pods to be deployed | `1` | | `masterkeySecretName` | The name of the Kubernetes Secret that contains the Master API Key for LiteLLM. If not specified, use the generated secret name. | N/A | | `masterkeySecretKey` | The key within the Kubernetes Secret that contains the Master API Key for LiteLLM. If not specified, use `masterkey` as the key. | N/A | -| `masterkey` | The Master API Key for LiteLLM. If not specified, a random key in the `sk-...` format is generated. | N/A | +| `masterkey` | The Master API Key for LiteLLM. If not specified, a random key in the `sk-...` format is generated on first install and reused on upgrades. | N/A | | `environmentSecrets` | An optional array of Secret object names. The keys and values in these secrets will be presented to the LiteLLM proxy pod as environment variables. See below for an example Secret object. | `[]` | | `environmentConfigMaps` | An optional array of ConfigMap object names. The keys and values in these configmaps will be presented to the LiteLLM proxy pod as environment variables. See below for an example Secret object. | `[]` | | `image.repository` | LiteLLM Proxy image repository | `ghcr.io/berriai/litellm` | @@ -212,6 +212,8 @@ service, the **Proxy Endpoint** should be set to `http://-litellm:4000` The **Proxy Key** is the value specified for `masterkey` or, if a `masterkey` was not provided to the helm command line, the `masterkey` is a randomly generated string in the `sk-...` format stored in the `-litellm-masterkey` Kubernetes Secret. +The key is generated once on the first install; later `helm upgrade` runs reuse the +value already in that Secret, so upgrading never rotates the master key. ```bash kubectl -n litellm get secret -litellm-masterkey -o jsonpath="{.data.masterkey}" diff --git a/helm/litellm-helm/templates/secret-masterkey.yaml b/helm/litellm-helm/templates/secret-masterkey.yaml index 7c8560cc2cc..60ab4e74c6b 100644 --- a/helm/litellm-helm/templates/secret-masterkey.yaml +++ b/helm/litellm-helm/templates/secret-masterkey.yaml @@ -1,9 +1,11 @@ {{- if not .Values.masterkeySecretName }} -{{ $masterkey := (.Values.masterkey | default (printf "sk-%s" (randAlphaNum 18))) }} +{{- $secretName := printf "%s-masterkey" (include "litellm.fullname" .) }} +{{- $existing := lookup "v1" "Secret" .Release.Namespace $secretName }} +{{- $masterkey := .Values.masterkey | default (dig "data" "masterkey" "" $existing | b64dec) | default (printf "sk-%s" (randAlphaNum 18)) }} apiVersion: v1 kind: Secret metadata: - name: {{ include "litellm.fullname" . }}-masterkey + name: {{ $secretName }} data: masterkey: {{ $masterkey | b64enc }} type: Opaque diff --git a/helm/litellm-helm/tests/masterkey-secret_tests.yaml b/helm/litellm-helm/tests/masterkey-secret_tests.yaml index bbbade9d802..296f26755b8 100644 --- a/helm/litellm-helm/tests/masterkey-secret_tests.yaml +++ b/helm/litellm-helm/tests/masterkey-secret_tests.yaml @@ -15,6 +15,53 @@ tests: # Note: The masterkey is generated as "sk-<18-random-chars>" in plain text, # but stored as base64 encoded in Kubernetes secret (requirement). # "sk-" base64 encodes to "c2st", so we check for "^c2st" pattern. + - it: should reuse the master key already stored in the cluster instead of generating a new one on upgrade + template: secret-masterkey.yaml + set: + masterkeySecretName: "" + kubernetesProvider: + scheme: + "v1/Secret": + gvr: + version: "v1" + resource: "secrets" + namespaced: true + objects: + - kind: Secret + apiVersion: v1 + metadata: + name: RELEASE-NAME-litellm-masterkey + namespace: NAMESPACE + data: + masterkey: c2stZXhpc3Rpbmcta2V5 + asserts: + - equal: + path: data.masterkey + value: c2stZXhpc3Rpbmcta2V5 + - it: should let an explicit masterkey value override the one already stored in the cluster + template: secret-masterkey.yaml + set: + masterkeySecretName: "" + masterkey: sk-explicit + kubernetesProvider: + scheme: + "v1/Secret": + gvr: + version: "v1" + resource: "secrets" + namespaced: true + objects: + - kind: Secret + apiVersion: v1 + metadata: + name: RELEASE-NAME-litellm-masterkey + namespace: NAMESPACE + data: + masterkey: c2stZXhpc3Rpbmcta2V5 + asserts: + - equal: + path: data.masterkey + value: c2stZXhwbGljaXQ= - it: should not create a secret if masterkeySecretName is set template: secret-masterkey.yaml set: diff --git a/helm/litellm/templates/backend/deployment.yaml b/helm/litellm/templates/backend/deployment.yaml index 5c0431fc0bd..0db2f0b3d43 100644 --- a/helm/litellm/templates/backend/deployment.yaml +++ b/helm/litellm/templates/backend/deployment.yaml @@ -7,6 +7,10 @@ metadata: {{- include "litellm.commonLabels" . | nindent 4 }} app.kubernetes.io/component: backend spec: + {{- with .Values.backend.strategy }} + strategy: + {{- toYaml . | nindent 4 }} + {{- end }} selector: matchLabels: {{- include "litellm.backend.selectorLabels" . | nindent 6 }} diff --git a/helm/litellm/templates/gateway/deployment.yaml b/helm/litellm/templates/gateway/deployment.yaml index d5363d0096e..5030ba2c9dc 100644 --- a/helm/litellm/templates/gateway/deployment.yaml +++ b/helm/litellm/templates/gateway/deployment.yaml @@ -7,6 +7,10 @@ metadata: {{- include "litellm.commonLabels" . | nindent 4 }} app.kubernetes.io/component: gateway spec: + {{- with .Values.gateway.strategy }} + strategy: + {{- toYaml . | nindent 4 }} + {{- end }} selector: matchLabels: {{- include "litellm.gateway.selectorLabels" . | nindent 6 }} diff --git a/helm/litellm/templates/ingress.yaml b/helm/litellm/templates/ingress.yaml index ab609354d7b..f77ef537b02 100644 --- a/helm/litellm/templates/ingress.yaml +++ b/helm/litellm/templates/ingress.yaml @@ -5,6 +5,41 @@ {{- $gatewayPort := .Values.gateway.service.port -}} {{- $backendPort := .Values.backend.service.port -}} {{- $uiPort := .Values.ui.service.port -}} +{{/* + Backends addressable from ingress.extraPaths, keyed by the `service` field. +*/}} +{{- $extraPathBackends := dict + "gateway" (dict "name" $gatewayName "port" $gatewayPort) + "backend" (dict "name" $backendName "port" $backendPort) + "ui" (dict "name" $uiName "port" $uiPort) +-}} +{{/* + UI paths (Next.js static export). + + /ui/* is where the SPA serves its login + dashboard routes (e.g. /ui/login). + Without it, /ui/* falls into the catch-all → backend → 404. + + The App Router (output: "export", basePath: "") emits the RSC/flight payload + for every route as a ROOT-level .txt (/index.txt, /teams.txt, + /__next._tree.txt, ...). The client router fetches these on every soft + navigation / prefetch as .txt?_rsc= (the query string is + irrelevant to path matching). They are not under /ui, /_next, or + /litellm-asset-prefix, so without /*.txt they fall to the backend catch-all + → 404 → client-side navigation never settles and the login flow spins in an + infinite redirect loop (/ ⇄ /ui/login). ui/nginx.conf already serves *.txt + from the export; the rule only routes the request to it. Needs an ingress + controller whose ImplementationSpecific path is a wildcard pattern + (AWS ALB: `*` = 0+ chars); this chart targets the AWS Load Balancer + Controller. +*/}} +{{- $uiPaths := list + (dict "path" "/" "pathType" "Exact") + (dict "path" "/favicon.ico" "pathType" "Exact") + (dict "path" "/litellm-asset-prefix" "pathType" "Prefix") + (dict "path" "/_next" "pathType" "Prefix") + (dict "path" "/ui" "pathType" "Prefix") + (dict "path" "/*.txt" "pathType" "ImplementationSpecific") +-}} {{/* Gateway data-plane prefixes — must mirror gateway/routes/allowlist.py. Versioned paths are listed explicitly to avoid routing management routes @@ -39,6 +74,21 @@ routes at startup -> 404. So /test is rendered as a standalone Exact path and /test/* falls through to the backend catch-all. */}} +{{/* + Every "|" this template renders on its own. An + ingress.extraPaths entry that repeats one of these is rejected: duplicates + in a single rule are resolved by position or by controller-specific tie + breaking, so the operator entry could take over a built-in route (an entry + at "/" Prefix would swallow the whole backend management API) instead of + adding to it. +*/}} +{{- $builtinPathKeys := list "/test|Exact" "/|Prefix" -}} +{{- range $uiPaths }} +{{- $builtinPathKeys = append $builtinPathKeys (printf "%s|%s" .path .pathType) }} +{{- end }} +{{- range $gatewayPrefixes }} +{{- $builtinPathKeys = append $builtinPathKeys (printf "%s|Prefix" .) }} +{{- end }} apiVersion: networking.k8s.io/v1 kind: Ingress metadata: @@ -64,65 +114,15 @@ spec: http: paths: # --- UI (Next.js static export) --- - - path: / - pathType: Exact - backend: - service: - name: {{ $uiName }} - port: - number: {{ $uiPort }} - - path: /favicon.ico - pathType: Exact - backend: - service: - name: {{ $uiName }} - port: - number: {{ $uiPort }} - - path: /litellm-asset-prefix - pathType: Prefix - backend: - service: - name: {{ $uiName }} - port: - number: {{ $uiPort }} - - path: /_next - pathType: Prefix - backend: - service: - name: {{ $uiName }} - port: - number: {{ $uiPort }} - # /ui/* is where the Next.js SPA serves its login + dashboard - # routes (e.g. /ui/login). Without this, /ui/* falls into the - # catch-all → backend → 404. - - path: /ui - pathType: Prefix - backend: - service: - name: {{ $uiName }} - port: - number: {{ $uiPort }} - # Next.js App Router (output: "export", basePath: "") emits the - # RSC/flight payload for every route as a ROOT-level .txt - # (/index.txt, /teams.txt, /__next._tree.txt, ...). The client - # router fetches these on every soft navigation / prefetch as - # .txt?_rsc= (the query string is irrelevant to path - # matching). They are not under /ui, /_next, or - # /litellm-asset-prefix, so without this rule they fall to the - # backend catch-all → 404 → client-side navigation never settles - # and the login flow spins in an infinite redirect loop - # (/ ⇄ /ui/login). ui/nginx.conf already serves *.txt from the - # export; this rule only routes the request to it. Needs an - # ingress controller whose ImplementationSpecific path is a - # wildcard pattern (AWS ALB: `*` = 0+ chars); this chart targets - # the AWS Load Balancer Controller. - - path: /*.txt - pathType: ImplementationSpecific + {{- range $uiPaths }} + - path: {{ .path }} + pathType: {{ .pathType }} backend: service: name: {{ $uiName }} port: number: {{ $uiPort }} + {{- end }} # --- Gateway data plane --- # Exact /test only (see the $gatewayPrefixes comment above); # /test/* MCP management endpoints fall to the backend catch-all. @@ -142,6 +142,46 @@ spec: port: number: {{ $gatewayPort }} {{- end }} + {{- /* + --- Operator-supplied extra paths (ingress.extraPaths) --- + Rendered after every built-in path so an entry can never take + precedence over a default, and before the backend catch-all. + Position only decides the match on controllers that honour manifest + order: the AWS Load Balancer Controller this chart targets sorts + Exact paths first and Prefix paths longest-first, but keeps + ImplementationSpecific paths in manifest order, which is what the + /*.txt rule above already depends on. + */}} + {{- range $idx, $extra := .Values.ingress.extraPaths }} + {{- if not (kindIs "map" $extra) }} + {{- fail (printf "ingress.extraPaths[%d]: each entry must be a mapping with a 'path' key" $idx) }} + {{- end }} + {{- if not $extra.path }} + {{- fail (printf "ingress.extraPaths[%d]: 'path' is required" $idx) }} + {{- end }} + {{- $service := $extra.service | default "gateway" }} + {{- $target := get $extraPathBackends $service }} + {{- if not $target }} + {{- fail (printf "ingress.extraPaths[%d] (path %s): unknown service %q, expected one of backend, gateway, ui" $idx $extra.path $service) }} + {{- end }} + {{- $pathType := $extra.pathType | default "Prefix" }} + {{- if not (has $pathType (list "Prefix" "Exact" "ImplementationSpecific")) }} + {{- fail (printf "ingress.extraPaths[%d] (path %s): unknown pathType %q, expected one of Exact, ImplementationSpecific, Prefix" $idx $extra.path $pathType) }} + {{- end }} + {{- if eq $extra.path "/" }} + {{- fail (printf "ingress.extraPaths[%d]: path / is already routed in both directions, Exact to ui and Prefix to backend, so no pathType leaves a request for an entry here to capture" $idx) }} + {{- end }} + {{- if has (printf "%s|%s" $extra.path $pathType) $builtinPathKeys }} + {{- fail (printf "ingress.extraPaths[%d]: path %s with pathType %s is already routed by this chart, and a duplicate would take it over rather than add to it" $idx $extra.path $pathType) }} + {{- end }} + - path: {{ $extra.path | quote }} + pathType: {{ $pathType }} + backend: + service: + name: {{ $target.name }} + port: + number: {{ $target.port }} + {{- end }} # --- Catch-all → backend (management API: /key/*, /user/*, /team/*, ...) --- - path: / pathType: Prefix diff --git a/helm/litellm/templates/migrations-job.yaml b/helm/litellm/templates/migrations-job.yaml index 9cd8397f794..8d33081e72f 100644 --- a/helm/litellm/templates/migrations-job.yaml +++ b/helm/litellm/templates/migrations-job.yaml @@ -7,6 +7,8 @@ # # Running this pre-upgrade closes the window where new application pods would # otherwise serve traffic against the previous release's unmigrated schema. +# Argo CD users can swap the Helm hook for a PreSync hook through +# `migrationJob.hooks`, which re-runs the Job on every sync. apiVersion: batch/v1 kind: Job metadata: @@ -14,10 +16,18 @@ metadata: labels: {{- include "litellm.commonLabels" . | nindent 4 }} app.kubernetes.io/component: migrations + {{- if or .Values.migrationJob.hooks.helm.enabled .Values.migrationJob.hooks.argocd.enabled }} annotations: + {{- if .Values.migrationJob.hooks.helm.enabled }} helm.sh/hook: pre-install,pre-upgrade helm.sh/hook-delete-policy: before-hook-creation - helm.sh/hook-weight: "0" + helm.sh/hook-weight: {{ .Values.migrationJob.hooks.helm.weight | default "0" | quote }} + {{- end }} + {{- if .Values.migrationJob.hooks.argocd.enabled }} + argocd.argoproj.io/hook: PreSync + argocd.argoproj.io/hook-delete-policy: BeforeHookCreation + {{- end }} + {{- end }} spec: backoffLimit: {{ .Values.migrationJob.backoffLimit }} ttlSecondsAfterFinished: {{ .Values.migrationJob.ttlSecondsAfterFinished }} diff --git a/helm/litellm/templates/ui/deployment.yaml b/helm/litellm/templates/ui/deployment.yaml index 91d6de39ea6..b992b347bad 100644 --- a/helm/litellm/templates/ui/deployment.yaml +++ b/helm/litellm/templates/ui/deployment.yaml @@ -7,6 +7,10 @@ metadata: {{- include "litellm.commonLabels" . | nindent 4 }} app.kubernetes.io/component: ui spec: + {{- with .Values.ui.strategy }} + strategy: + {{- toYaml . | nindent 4 }} + {{- end }} selector: matchLabels: {{- include "litellm.ui.selectorLabels" . | nindent 6 }} diff --git a/helm/litellm/tests/ingress_extra_paths_tests.yaml b/helm/litellm/tests/ingress_extra_paths_tests.yaml new file mode 100644 index 00000000000..fc7d5943278 --- /dev/null +++ b/helm/litellm/tests/ingress_extra_paths_tests.yaml @@ -0,0 +1,317 @@ +suite: test ingress.extraPaths +templates: + - ingress.yaml +values: + - ./values/required.yaml +tests: + - it: renders nothing extra between the built-in gateway prefixes and the backend catch-all when unset + set: + ingress.enabled: true + asserts: + - equal: + path: spec.rules[0].http.paths[-1] + value: + path: / + pathType: Prefix + backend: + service: + name: RELEASE-NAME-litellm-backend + port: + number: 4001 + - equal: + path: spec.rules[0].http.paths[-2] + value: + path: /metrics + pathType: Prefix + backend: + service: + name: RELEASE-NAME-litellm-gateway + port: + number: 4000 + + - it: routes an extra path to the gateway by default, immediately before the backend catch-all + set: + ingress.enabled: true + ingress.extraPaths: + - path: /watsonx + asserts: + - equal: + path: spec.rules[0].http.paths[-2] + value: + path: /watsonx + pathType: Prefix + backend: + service: + name: RELEASE-NAME-litellm-gateway + port: + number: 4000 + - equal: + path: spec.rules[0].http.paths[-1] + value: + path: / + pathType: Prefix + backend: + service: + name: RELEASE-NAME-litellm-backend + port: + number: 4001 + + - it: keeps every built-in path when extra paths are supplied + set: + ingress.enabled: true + ingress.extraPaths: + - path: /watsonx + asserts: + - contains: + path: spec.rules[0].http.paths + content: + path: / + pathType: Exact + backend: + service: + name: RELEASE-NAME-litellm-ui + port: + number: 3000 + - contains: + path: spec.rules[0].http.paths + content: + path: /ui + pathType: Prefix + backend: + service: + name: RELEASE-NAME-litellm-ui + port: + number: 3000 + - contains: + path: spec.rules[0].http.paths + content: + path: /test + pathType: Exact + backend: + service: + name: RELEASE-NAME-litellm-gateway + port: + number: 4000 + - contains: + path: spec.rules[0].http.paths + content: + path: /v1/chat + pathType: Prefix + backend: + service: + name: RELEASE-NAME-litellm-gateway + port: + number: 4000 + - contains: + path: spec.rules[0].http.paths + content: + path: /vertex_ai + pathType: Prefix + backend: + service: + name: RELEASE-NAME-litellm-gateway + port: + number: 4000 + + - it: renders every entry in order and honours the service and pathType selectors + set: + ingress.enabled: true + ingress.extraPaths: + - path: /watsonx + service: gateway + - path: /my-passthrough + pathType: Exact + service: backend + - path: /brand.txt + pathType: ImplementationSpecific + service: ui + asserts: + - equal: + path: spec.rules[0].http.paths[-4] + value: + path: /watsonx + pathType: Prefix + backend: + service: + name: RELEASE-NAME-litellm-gateway + port: + number: 4000 + - equal: + path: spec.rules[0].http.paths[-3] + value: + path: /my-passthrough + pathType: Exact + backend: + service: + name: RELEASE-NAME-litellm-backend + port: + number: 4001 + - equal: + path: spec.rules[0].http.paths[-2] + value: + path: /brand.txt + pathType: ImplementationSpecific + backend: + service: + name: RELEASE-NAME-litellm-ui + port: + number: 3000 + + - it: addresses the component services by their configured ports + set: + ingress.enabled: true + gateway.service.port: 8000 + backend.service.port: 8001 + ui.service.port: 8080 + ingress.extraPaths: + - path: /watsonx + - path: /my-passthrough + service: backend + - path: /brand.txt + service: ui + asserts: + - equal: + path: spec.rules[0].http.paths[-4].backend.service.port.number + value: 8000 + - equal: + path: spec.rules[0].http.paths[-3].backend.service.port.number + value: 8001 + - equal: + path: spec.rules[0].http.paths[-2].backend.service.port.number + value: 8080 + + - it: rejects an entry naming a service the chart does not deploy + set: + ingress.enabled: true + ingress.extraPaths: + - path: /watsonx + service: proxy + asserts: + - failedTemplate: + errorMessage: 'ingress.extraPaths[0] (path /watsonx): unknown service "proxy", expected one of backend, gateway, ui' + + - it: rejects an entry whose pathType is not a kubernetes pathType + set: + ingress.enabled: true + ingress.extraPaths: + - path: /watsonx + pathType: prefix + asserts: + - failedTemplate: + errorMessage: 'ingress.extraPaths[0] (path /watsonx): unknown pathType "prefix", expected one of Exact, ImplementationSpecific, Prefix' + + - it: rejects an entry with no path + set: + ingress.enabled: true + ingress.extraPaths: + - service: gateway + asserts: + - failedTemplate: + errorMessage: "ingress.extraPaths[0]: 'path' is required" + + + - it: rejects a root entry that would take over the backend catch-all + set: + ingress.enabled: true + ingress.extraPaths: + - path: / + service: gateway + asserts: + - failedTemplate: + errorMessage: "ingress.extraPaths[0]: path / is already routed in both directions, Exact to ui and Prefix to backend, so no pathType leaves a request for an entry here to capture" + + - it: rejects a root entry that would take over the UI root + set: + ingress.enabled: true + ingress.extraPaths: + - path: / + pathType: Exact + service: gateway + asserts: + - failedTemplate: + errorMessage: "ingress.extraPaths[0]: path / is already routed in both directions, Exact to ui and Prefix to backend, so no pathType leaves a request for an entry here to capture" + + # A root ImplementationSpecific entry duplicates no built-in pair, so the + # duplicate check alone would admit it. It is still dead: the built-in + # Exact / sorts ahead of it on the AWS Load Balancer Controller and claims + # the only request its pattern matches, so it renders and never routes. + - it: rejects a root entry that would render but never match + set: + ingress.enabled: true + ingress.extraPaths: + - path: / + pathType: ImplementationSpecific + service: gateway + asserts: + - failedTemplate: + errorMessage: "ingress.extraPaths[0]: path / is already routed in both directions, Exact to ui and Prefix to backend, so no pathType leaves a request for an entry here to capture" + + - it: rejects an entry that would take over a UI prefix + set: + ingress.enabled: true + ingress.extraPaths: + - path: /ui + service: gateway + asserts: + - failedTemplate: + errorMessage: "ingress.extraPaths[0]: path /ui with pathType Prefix is already routed by this chart, and a duplicate would take it over rather than add to it" + + - it: rejects an entry that would take over the UI RSC payload rule + set: + ingress.enabled: true + ingress.extraPaths: + - path: /*.txt + pathType: ImplementationSpecific + service: backend + asserts: + - failedTemplate: + errorMessage: "ingress.extraPaths[0]: path /*.txt with pathType ImplementationSpecific is already routed by this chart, and a duplicate would take it over rather than add to it" + + - it: rejects an entry that would take over a gateway data-plane prefix + set: + ingress.enabled: true + ingress.extraPaths: + - path: /v1/chat + service: backend + asserts: + - failedTemplate: + errorMessage: "ingress.extraPaths[0]: path /v1/chat with pathType Prefix is already routed by this chart, and a duplicate would take it over rather than add to it" + + - it: rejects an entry that would take over the exact /test route + set: + ingress.enabled: true + ingress.extraPaths: + - path: /test + pathType: Exact + service: backend + asserts: + - failedTemplate: + errorMessage: "ingress.extraPaths[0]: path /test with pathType Exact is already routed by this chart, and a duplicate would take it over rather than add to it" + + - it: allows a built-in path under a different pathType, which is a distinct rule + set: + ingress.enabled: true + ingress.extraPaths: + - path: /ui + pathType: Exact + service: ui + asserts: + - equal: + path: spec.rules[0].http.paths[-2] + value: + path: /ui + pathType: Exact + backend: + service: + name: RELEASE-NAME-litellm-ui + port: + number: 3000 + + - it: rejects a bare string entry instead of failing on template internals + set: + ingress.enabled: true + ingress.extraPaths: + - /watsonx + asserts: + - failedTemplate: + errorMessage: "ingress.extraPaths[0]: each entry must be a mapping with a 'path' key" diff --git a/helm/litellm/tests/migration_job_hooks_tests.yaml b/helm/litellm/tests/migration_job_hooks_tests.yaml new file mode 100644 index 00000000000..650d2700429 --- /dev/null +++ b/helm/litellm/tests/migration_job_hooks_tests.yaml @@ -0,0 +1,63 @@ +suite: test migrations Job hook annotations +templates: + - migrations-job.yaml +values: + - ./values/required.yaml +tests: + - it: runs as a Helm pre-install / pre-upgrade hook by default + asserts: + - equal: + path: metadata.annotations["helm.sh/hook"] + value: pre-install,pre-upgrade + - equal: + path: metadata.annotations["helm.sh/hook-delete-policy"] + value: before-hook-creation + - equal: + path: metadata.annotations["helm.sh/hook-weight"] + value: "0" + - notExists: + path: metadata.annotations["argocd.argoproj.io/hook"] + + - it: adds the Argo CD PreSync hook when asked + set: + migrationJob.hooks.argocd.enabled: true + asserts: + - equal: + path: metadata.annotations["argocd.argoproj.io/hook"] + value: PreSync + - equal: + path: metadata.annotations["argocd.argoproj.io/hook-delete-policy"] + value: BeforeHookCreation + + - it: drops the Helm hook so Argo CD owns the Job + set: + migrationJob.hooks.argocd.enabled: true + migrationJob.hooks.helm.enabled: false + asserts: + - equal: + path: metadata.annotations["argocd.argoproj.io/hook"] + value: PreSync + - notExists: + path: metadata.annotations["helm.sh/hook"] + - notExists: + path: metadata.annotations["helm.sh/hook-delete-policy"] + - notExists: + path: metadata.annotations["helm.sh/hook-weight"] + + - it: renders an ordinary Job when both hooks are disabled + set: + migrationJob.hooks.helm.enabled: false + asserts: + - notExists: + path: metadata.annotations + - equal: + path: kind + value: Job + + - it: honours a custom Helm hook weight + set: + migrationJob.hooks.helm.weight: "-5" + asserts: + - equal: + path: metadata.annotations["helm.sh/hook-weight"] + value: "-5" diff --git a/helm/litellm/tests/rollout_strategy_tests.yaml b/helm/litellm/tests/rollout_strategy_tests.yaml new file mode 100644 index 00000000000..b12e2073c7c --- /dev/null +++ b/helm/litellm/tests/rollout_strategy_tests.yaml @@ -0,0 +1,66 @@ +suite: test rolling update strategy on the component deployments +templates: + - gateway/deployment.yaml + - gateway/configmap.yaml + - backend/deployment.yaml + - ui/deployment.yaml +values: + - ./values/required.yaml +tests: + - it: leaves the strategy to Kubernetes defaults when unset + asserts: + - notExists: + path: spec.strategy + + - it: renders the configured strategy on each deployment + set: + gateway.strategy: + type: RollingUpdate + rollingUpdate: + maxUnavailable: 0 + maxSurge: 1 + backend.strategy: + type: RollingUpdate + rollingUpdate: + maxUnavailable: "25%" + maxSurge: 2 + ui.strategy: + type: Recreate + asserts: + - equal: + path: spec.strategy + value: + type: RollingUpdate + rollingUpdate: + maxUnavailable: 0 + maxSurge: 1 + template: gateway/deployment.yaml + - equal: + path: spec.strategy + value: + type: RollingUpdate + rollingUpdate: + maxUnavailable: 25% + maxSurge: 2 + template: backend/deployment.yaml + - equal: + path: spec.strategy + value: + type: Recreate + template: ui/deployment.yaml + + - it: keeps a component on the cluster default when only another one sets a strategy + set: + gateway.strategy: + type: Recreate + asserts: + - equal: + path: spec.strategy.type + value: Recreate + template: gateway/deployment.yaml + - notExists: + path: spec.strategy + template: backend/deployment.yaml + - notExists: + path: spec.strategy + template: ui/deployment.yaml diff --git a/helm/litellm/values.yaml b/helm/litellm/values.yaml index 06ba72d84b3..378c3b7a618 100644 --- a/helm/litellm/values.yaml +++ b/helm/litellm/values.yaml @@ -13,6 +13,27 @@ ingress: annotations: {} host: "" # optional; if set, becomes the rule's host tls: [] + # Extra HTTP paths appended to the ingress rule. Additive: every built-in + # UI / gateway / backend path is still rendered, these entries are placed + # after them and before the backend catch-all, and an entry that repeats a + # path the chart already routes is rejected at render time rather than + # silently taking it over. + # + # The chart's built-in gateway prefix list is a snapshot of the data-plane + # surface at release time. Use extraPaths for passthrough routes it does not + # cover: a provider prefix added upstream after this chart version, or a + # custom general_settings.pass_through_endpoints route. + # + # path required; the HTTP path to route + # service which component serves it: gateway (default), backend, or ui + # pathType Prefix (default), Exact, or ImplementationSpecific + # + # The target component only answers paths its own route allowlist keeps, so + # a path here still has to be one that component serves. + extraPaths: [] + # - path: /watsonx + # pathType: Prefix + # service: gateway # Per-component ServiceAccounts for gateway, backend, and ui. # @@ -54,6 +75,22 @@ serviceAccounts: # generate` — the migration engine doesn't need the generated client. migrationJob: enabled: true + # Which controller is responsible for running the Job. + # + # `helm.enabled` renders the Helm pre-install / pre-upgrade hook, so the Job + # runs whenever `helm upgrade` sees a change to apply. `argocd.enabled` + # renders an Argo CD PreSync hook instead, which runs the Job on every sync + # even when the rendered manifests are unchanged: the way to re-run + # migrations on demand from a GitOps pipeline. Turning the Helm hook off + # while the Argo CD hook is on leaves the Job out of Helm's own upgrade + # path, which is what Argo CD users want since Argo, not Helm, applies the + # manifests. + hooks: + helm: + enabled: true + weight: "0" + argocd: + enabled: false backoffLimit: 4 ttlSecondsAfterFinished: 120 # Wall-clock budget for the whole Job, shared across every `backoffLimit` @@ -236,6 +273,15 @@ gateway: initialDelaySeconds: 5 periodSeconds: 10 timeoutSeconds: 10 + # Rolling update tuning for the gateway Deployment. Empty by default, so + # Kubernetes applies its own RollingUpdate defaults (25% maxSurge / + # 25% maxUnavailable). Example, for a surge-only rollout behind a load + # balancer that must never lose capacity: + # type: RollingUpdate + # rollingUpdate: + # maxUnavailable: 0 + # maxSurge: 1 + strategy: {} # Optional startupProbe. Empty by default, so existing installs are unchanged # and liveness/readiness apply from container start. Set it to gate # liveness/readiness until a slow cold start finishes — a high failureThreshold @@ -348,6 +394,8 @@ backend: initialDelaySeconds: 5 periodSeconds: 10 timeoutSeconds: 10 + # Same shape as gateway.strategy. + strategy: {} # Optional startupProbe; same shape as gateway.startupProbe. Empty by default. startupProbe: {} hpa: @@ -412,6 +460,8 @@ ui: httpGet: { path: /, port: http } initialDelaySeconds: 2 periodSeconds: 10 + # Same shape as gateway.strategy. + strategy: {} # Optional startupProbe; same shape as gateway.startupProbe. Empty by default. startupProbe: {} hpa: diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260804162853_add_budget_window_spend_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260804162853_add_budget_window_spend_table/migration.sql new file mode 100644 index 00000000000..c3018006adb --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260804162853_add_budget_window_spend_table/migration.sql @@ -0,0 +1,12 @@ +CREATE TABLE IF NOT EXISTS "LiteLLM_BudgetWindowSpend" ( + "entity_type" TEXT NOT NULL, + "entity_id" TEXT NOT NULL, + "window_duration" TEXT NOT NULL, + "window_start" TIMESTAMP(3) NOT NULL, + "spend" DOUBLE PRECISION NOT NULL DEFAULT 0.0, + "created_at" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, + "updated_at" TIMESTAMP(3) NOT NULL, + + CONSTRAINT "LiteLLM_BudgetWindowSpend_pkey" PRIMARY KEY ("entity_type","entity_id","window_duration") +); + diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260829000000_add_model_access_group_budget_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260829000000_add_model_access_group_budget_table/migration.sql new file mode 100644 index 00000000000..62398da7f04 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260829000000_add_model_access_group_budget_table/migration.sql @@ -0,0 +1,20 @@ +-- CreateTable +CREATE TABLE IF NOT EXISTS "LiteLLM_ModelAccessGroupBudgetTable" ( + "access_group_name" TEXT NOT NULL, + "spend" DOUBLE PRECISION NOT NULL DEFAULT 0.0, + "budget_id" TEXT, + "created_at" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, + "created_by" TEXT, + "updated_at" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, + "updated_by" TEXT, + + CONSTRAINT "LiteLLM_ModelAccessGroupBudgetTable_pkey" PRIMARY KEY ("access_group_name") +); + +-- AddForeignKey +DO $$ +BEGIN + IF NOT EXISTS (SELECT 1 FROM pg_constraint WHERE conname = 'LiteLLM_ModelAccessGroupBudgetTable_budget_id_fkey') THEN + ALTER TABLE "LiteLLM_ModelAccessGroupBudgetTable" ADD CONSTRAINT "LiteLLM_ModelAccessGroupBudgetTable_budget_id_fkey" FOREIGN KEY ("budget_id") REFERENCES "LiteLLM_BudgetTable"("budget_id") ON DELETE SET NULL ON UPDATE CASCADE; + END IF; +END $$; diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260831000000_shadow_eval_typed_targets/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260831000000_shadow_eval_typed_targets/migration.sql new file mode 100644 index 00000000000..b7dbe931dd2 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260831000000_shadow_eval_typed_targets/migration.sql @@ -0,0 +1,21 @@ +DO $$ +BEGIN + IF EXISTS ( + SELECT 1 FROM information_schema.columns + WHERE table_name = 'LiteLLM_ShadowEvalJob' AND column_name = 'api_key_id' + ) THEN + ALTER TABLE "LiteLLM_ShadowEvalJob" RENAME COLUMN "api_key_id" TO "target_id"; + END IF; +END $$; + +ALTER TABLE "LiteLLM_ShadowEvalJob" ADD COLUMN IF NOT EXISTS "target_type" TEXT NOT NULL DEFAULT 'key'; + +DROP INDEX IF EXISTS "LiteLLM_ShadowEvalJob_one_active_per_key_direction"; + +CREATE UNIQUE INDEX IF NOT EXISTS "LiteLLM_ShadowEvalJob_one_active_per_target_direction" + ON "LiteLLM_ShadowEvalJob"("target_type", "target_id", "direction") WHERE "stopped_at" IS NULL; + +DROP INDEX IF EXISTS "LiteLLM_ShadowEvalJob_api_key_id_idx"; + +CREATE INDEX IF NOT EXISTS "LiteLLM_ShadowEvalJob_target_type_target_id_idx" + ON "LiteLLM_ShadowEvalJob"("target_type", "target_id"); diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260901000000_shadow_eval_multi_router/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260901000000_shadow_eval_multi_router/migration.sql new file mode 100644 index 00000000000..90b21205310 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260901000000_shadow_eval_multi_router/migration.sql @@ -0,0 +1,3 @@ +ALTER TABLE "LiteLLM_ShadowEvalJob" ADD COLUMN IF NOT EXISTS "router_names" TEXT[] NOT NULL DEFAULT ARRAY[]::TEXT[]; + +ALTER TABLE "LiteLLM_ShadowEvalAttempt" ADD COLUMN IF NOT EXISTS "router_name" TEXT; diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma index 2bb850139a2..7604ceadf7a 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma +++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma @@ -29,6 +29,7 @@ model LiteLLM_BudgetTable { keys LiteLLM_VerificationToken[] // multiple keys can have the same budget end_users LiteLLM_EndUserTable[] // multiple end-users can have the same budget tags LiteLLM_TagTable[] // multiple tags can have the same budget + model_access_groups LiteLLM_ModelAccessGroupBudgetTable[] // multiple model access groups can have the same budget team_membership LiteLLM_TeamMembership[] // budgets of Users within a Team organization_membership LiteLLM_OrganizationMembership[] // budgets of Users within a Organization } @@ -585,6 +586,20 @@ model LiteLLM_EndUserTable { blocked Boolean @default(false) } +// Budget and shared spend for a model access group. The groups themselves are not rows anywhere: +// they are free-text strings in LiteLLM_ProxyModelTable.model_info.access_groups, so a row here +// exists only once someone gives that group a budget. +model LiteLLM_ModelAccessGroupBudgetTable { + access_group_name String @id + spend Float @default(0.0) + budget_id String? + litellm_budget_table LiteLLM_BudgetTable? @relation(fields: [budget_id], references: [budget_id]) + created_at DateTime @default(now()) @map("created_at") + created_by String? + updated_at DateTime @default(now()) @updatedAt + updated_by String? +} + // Track tags with budgets and spend model LiteLLM_TagTable { tag_name String @id @@ -649,6 +664,18 @@ model LiteLLM_SpendLogs { @@index([session_id]) } +model LiteLLM_BudgetWindowSpend { + entity_type String + entity_id String + window_duration String + window_start DateTime + spend Float @default(0.0) + created_at DateTime @default(now()) + updated_at DateTime @updatedAt + + @@id([entity_type, entity_id, window_duration]) +} + // View spend, model, api_key per request model LiteLLM_ErrorLogs { request_id String @id @default(uuid()) @@ -1502,14 +1529,16 @@ model LiteLLM_AutoRouterSession { model LiteLLM_ShadowEvalJob { id String @id @default(cuid()) group_id String // legs of one job share this; the API's job id - api_key_id String // hashed virtual key whose traffic this leg shadows - router_name String // the auto-router under evaluation, in either direction + target_type String @default("key") // key | team | user + target_id String // hashed virtual key, team_id, or user_id whose traffic this leg shadows + router_name String // first (often only) auto-router under evaluation; router_names is the full set + router_names String[] @default([]) // all routers this job runs as shadow arms; empty on legacy rows, whose set is (router_name) direction String @default("forward") // forward | reverse baseline_model String? // reverse only: the fixed model the router is judged against judge_model String shadow_percentage Float max_turns Int // sample-count ceiling: the whole budget on pre-max_budget jobs, the error-loop valve otherwise - max_budget Float? // per-key USD cap on the eval's own shadow + judge spend; null on jobs from before spend budgets + max_budget Float? // per-target USD cap on the eval's own shadow + judge spend; null on jobs from before spend budgets created_at DateTime @default(now()) created_by String? ends_at DateTime @@ -1517,7 +1546,7 @@ model LiteLLM_ShadowEvalJob { stopped_by String? // operator who stopped it early; null when it ended on its own @@index([group_id]) - @@index([api_key_id]) + @@index([target_type, target_id]) @@index([created_at]) } @@ -1527,6 +1556,7 @@ model LiteLLM_ShadowEvalAttempt { job_id String request_id String // the judged real request outcome String // real | shadow | tie | error + router_name String? // the arm this verdict scores; NULL on legacy rows, meaning the job's own router tier String? // router's tier for the prompt, when classified real_model String? shadow_model String? diff --git a/litellm-proxy-extras/litellm_proxy_extras/utils.py b/litellm-proxy-extras/litellm_proxy_extras/utils.py index b2dc0a52c8f..b8032dd0d28 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/utils.py +++ b/litellm-proxy-extras/litellm_proxy_extras/utils.py @@ -512,6 +512,13 @@ class ProxyExtrasDBManager: try: import psycopg except ImportError: + logger.warning( + "psycopg is not installed; skipping the LiteLLM_SpendLogs " + "partition check. If this table is partitioned (see " + "db_scripts/partition_spend_logs.sql), schema reconciliation " + "will try to rewrite its primary key and fail. Install the " + "litellm[extra_proxy] extra, which now includes psycopg." + ) return False cleaned_url = ProxyExtrasDBManager._strip_prisma_query_params(database_url) diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml index 0ef5cd1e856..0944f99ad54 100644 --- a/litellm-proxy-extras/pyproject.toml +++ b/litellm-proxy-extras/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm-proxy-extras" -version = "0.4.90" +version = "0.4.92" description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package." readme = "README.md" requires-python = ">=3.9" @@ -26,7 +26,7 @@ required-version = ">=0.10.9" module-root = "" [tool.commitizen] -version = "0.4.90" +version = "0.4.92" version_files = [ "pyproject.toml:^version", "../pyproject.toml:litellm-proxy-extras==", diff --git a/litellm-rust/Cargo.toml b/litellm-rust/Cargo.toml index 481ea3f8f66..c17a0605fc7 100644 --- a/litellm-rust/Cargo.toml +++ b/litellm-rust/Cargo.toml @@ -30,3 +30,12 @@ tokio = { version = "1", features = ["rt-multi-thread", "macros", "time", "net"] tokio-tungstenite = { version = "0.24", default-features = false, features = ["connect", "rustls-tls-native-roots"] } futures-util = { version = "0.3", default-features = false, features = ["sink", "std"] } base64 = "0.22" + +[profile.release] +opt-level = 3 +lto = "thin" +codegen-units = 1 +panic = "unwind" +debug = false +incremental = false +strip = "symbols" diff --git a/litellm-rust/crates/python-bridge/Cargo.toml b/litellm-rust/crates/python-bridge/Cargo.toml index 0c4a753f762..d461a483ae0 100644 --- a/litellm-rust/crates/python-bridge/Cargo.toml +++ b/litellm-rust/crates/python-bridge/Cargo.toml @@ -10,7 +10,8 @@ name = "_native" crate-type = ["cdylib"] [features] -default = ["extension-module"] +default = ["abi3"] +abi3 = ["pyo3/abi3-py310"] extension-module = ["pyo3/extension-module"] [dependencies] diff --git a/litellm/__init__.py b/litellm/__init__.py index c83e72a78b4..4eeececdb7e 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -7,6 +7,9 @@ warnings.filterwarnings("ignore", message=".*conflict with protected namespace.* # Suppress Pydantic 2.11+ deprecation warning about accessing model_fields on instances # This warning can accumulate during streaming and cause memory leaks warnings.filterwarnings("ignore", message=".*Accessing the.*attribute on the instance is deprecated.*") +# ReadOnly on TypedDict fields is repo-wide static discipline (LIT012); pydantic warns it +# cannot enforce it at runtime, which floods proxy boot once such a type is schema-walked +warnings.filterwarnings("ignore", message=".*`ReadOnly` qualifier.*") ### INIT VARIABLES ######################### import threading import os @@ -656,6 +659,8 @@ aiml_models: Set = set() deepgram_models: Set = set() elevenlabs_models: Set = set() dashscope_models: Set = set() +qwencloud_models: Set = set() +qwen_ai_platform_models: Set = set() moonshot_models: Set = set() publicai_models: Set = set() darkbloom_models: Set = set() @@ -906,6 +911,10 @@ def _populate_provider_model_sets(model_cost_map: Dict) -> None: heroku_models.add(key) elif value.get("litellm_provider") == "dashscope": dashscope_models.add(key) + elif value.get("litellm_provider") == "qwencloud": + qwencloud_models.add(key) + elif value.get("litellm_provider") == "qwen_ai_platform": + qwen_ai_platform_models.add(key) elif value.get("litellm_provider") == "modelscope": modelscope_models.add(key) elif value.get("litellm_provider") == "moonshot": @@ -1069,6 +1078,8 @@ model_list = list( | deepgram_models | elevenlabs_models | dashscope_models + | qwencloud_models + | qwen_ai_platform_models | moonshot_models | publicai_models | darkbloom_models @@ -1175,6 +1186,8 @@ def _build_models_by_provider() -> dict: "elevenlabs": elevenlabs_models, "heroku": heroku_models, "dashscope": dashscope_models, + "qwencloud": qwencloud_models, + "qwen_ai_platform": qwen_ai_platform_models, "modelscope": modelscope_models, "moonshot": moonshot_models, "publicai": publicai_models, @@ -2011,6 +2024,24 @@ if TYPE_CHECKING: from .llms.dashscope.rerank.transformation import ( DashScopeRerankConfig as DashScopeRerankConfig, ) + from .llms.dashscope.qwencloud import ( + QwenCloudChatConfig as QwenCloudChatConfig, + ) + from .llms.dashscope.qwencloud import ( + QwenCloudEmbeddingConfig as QwenCloudEmbeddingConfig, + ) + from .llms.dashscope.qwencloud import ( + QwenCloudRerankConfig as QwenCloudRerankConfig, + ) + from .llms.dashscope.qwen_ai_platform import ( + QwenAIPlatformChatConfig as QwenAIPlatformChatConfig, + ) + from .llms.dashscope.qwen_ai_platform import ( + QwenAIPlatformEmbeddingConfig as QwenAIPlatformEmbeddingConfig, + ) + from .llms.dashscope.qwen_ai_platform import ( + QwenAIPlatformRerankConfig as QwenAIPlatformRerankConfig, + ) from .llms.modelscope.chat.transformation import ( ModelScopeChatConfig as ModelScopeChatConfig, ) diff --git a/litellm/_lazy_imports.py b/litellm/_lazy_imports.py index d7e00a81b38..553aeb6680d 100644 --- a/litellm/_lazy_imports.py +++ b/litellm/_lazy_imports.py @@ -17,7 +17,7 @@ until they're actually needed. import importlib import sys -from collections.abc import Callable +from collections.abc import Callable, Mapping from types import ModuleType from typing import TYPE_CHECKING, Any, Final, cast @@ -57,10 +57,11 @@ from ._lazy_imports_registry import ( ) if TYPE_CHECKING: + import httpx from tiktoken import Encoding -def get_litellm_globals() -> dict: +def get_litellm_globals() -> dict[str, object]: """ Get the globals dictionary of the litellm module. @@ -70,7 +71,7 @@ def get_litellm_globals() -> dict: return sys.modules["litellm"].__dict__ -def _get_utils_globals() -> dict: +def _get_utils_globals() -> dict[str, object]: """ Get the globals dictionary of the utils module. @@ -80,6 +81,11 @@ def _get_utils_globals() -> dict: return sys.modules["litellm.utils"].__dict__ +def _get_module_level_client_timeout(litellm_globals: Mapping[str, Any]) -> "float | httpx.Timeout | None": + """Read the configured `litellm.request_timeout` used for the module level http clients.""" + return litellm_globals.get("request_timeout") + + # These are special lazy loaders for things that are used internally # They're separate from the main lazy import system because they have specific use cases @@ -435,8 +441,8 @@ def _lazy_import_http_handlers(name: str) -> object: from litellm.llms.custom_httpx.http_handler import get_async_httpx_client # Get timeout from module config (if set) - timeout = _globals.get("request_timeout") - params: Final = {"timeout": timeout, "client_alias": "module level aclient"} + async_timeout: Final = _get_module_level_client_timeout(_globals) + params: Final = {"timeout": async_timeout, "client_alias": "module level aclient"} # Create the client instance provider_id: Final = cast(Any, "litellm_module_level_client") @@ -453,8 +459,8 @@ def _lazy_import_http_handlers(name: str) -> object: # Create a sync HTTP client from litellm.llms.custom_httpx.http_handler import HTTPHandler - timeout = _globals.get("request_timeout") - sync_client: Final = HTTPHandler(timeout=timeout) + sync_timeout: Final = _get_module_level_client_timeout(_globals) + sync_client: Final = HTTPHandler(timeout=sync_timeout) # Cache it _globals["module_level_client"] = sync_client diff --git a/litellm/_lazy_imports_registry.py b/litellm/_lazy_imports_registry.py index 1c833256598..e9199e1ec80 100644 --- a/litellm/_lazy_imports_registry.py +++ b/litellm/_lazy_imports_registry.py @@ -310,6 +310,8 @@ LLM_CONFIG_NAMES: Final = ( "GigaChatConfig", "GigaChatEmbeddingConfig", "DashScopeChatConfig", + "QwenCloudChatConfig", + "QwenAIPlatformChatConfig", "ModelScopeChatConfig", "MoonshotChatConfig", "DockerModelRunnerChatConfig", @@ -1172,6 +1174,14 @@ _LLM_CONFIGS_IMPORT_MAP: Final = { ".llms.dashscope.chat.transformation", "DashScopeChatConfig", ), + "QwenCloudChatConfig": ( + ".llms.dashscope.qwencloud", + "QwenCloudChatConfig", + ), + "QwenAIPlatformChatConfig": ( + ".llms.dashscope.qwen_ai_platform", + "QwenAIPlatformChatConfig", + ), "GDCGeminiConfig": ( ".llms.gdc.chat.transformation", "GDCGeminiConfig", diff --git a/litellm/_logging.py b/litellm/_logging.py index fbb35b72be2..9435562f890 100644 --- a/litellm/_logging.py +++ b/litellm/_logging.py @@ -264,13 +264,17 @@ def _plain_log_format(stdout: TextIO | None, stderr: TextIO | None) -> str: class LevelRoutingStreamHandler(logging.StreamHandler): - """Writes records below WARNING to stdout and WARNING and above to stderr. + """Writes records below WARNING and invalid-key warnings to stdout, others to stderr. Collectors that derive severity from the stream report every stderr line as an error. + Invalid-key warnings route to stdout so LITELLM_LOG=ERROR can suppress them. """ def emit(self, record: logging.LogRecord) -> None: - preferred: Final = sys.stdout if record.levelno < logging.WARNING else sys.stderr + is_stdout_record: Final = record.levelno < logging.WARNING or ( + record.levelno == logging.WARNING and record.name == verbose_proxy_stdout_logger.name + ) + preferred: Final = sys.stdout if is_stdout_record else sys.stderr if preferred is None or getattr(preferred, "closed", False): self.stream = sys.stderr # rebind-ok: fall back to the pre-fix stream rather than raising per record else: @@ -508,6 +512,9 @@ else: handler.setFormatter(formatter) verbose_proxy_logger = logging.getLogger("LiteLLM Proxy") +# Malformed virtual key rejections log through this child; LevelRoutingStreamHandler +# writes its WARNING records to stdout. It has no handler or level of its own. +verbose_proxy_stdout_logger: Final = verbose_proxy_logger.getChild("stdout") verbose_router_logger = logging.getLogger("LiteLLM Router") verbose_logger = logging.getLogger("LiteLLM") @@ -520,6 +527,7 @@ verbose_logger.addHandler(handler) # handlers (JSON mode, uvicorn log config, a host app's root handler). verbose_router_logger.addFilter(_stdout_truncation_filter) verbose_proxy_logger.addFilter(_stdout_truncation_filter) +verbose_proxy_stdout_logger.addFilter(_stdout_truncation_filter) verbose_logger.addFilter(_stdout_truncation_filter) @@ -683,6 +691,7 @@ def _turn_on_json(): - Adds a JSON formatter to all loggers """ handler: Final = LevelRoutingStreamHandler() + handler.setLevel(numeric_level) handler.setFormatter(JsonFormatter()) _initialize_loggers_with_handler(handler) # Set up exception handlers @@ -700,12 +709,14 @@ def _disable_debugging(): verbose_logger.disabled = True verbose_router_logger.disabled = True verbose_proxy_logger.disabled = True + verbose_proxy_stdout_logger.disabled = True def _enable_debugging(): verbose_logger.disabled = False verbose_router_logger.disabled = False verbose_proxy_logger.disabled = False + verbose_proxy_stdout_logger.disabled = False def print_verbose(print_statement): diff --git a/litellm/_redis.py b/litellm/_redis.py index 9381357931e..3e68d50cf16 100644 --- a/litellm/_redis.py +++ b/litellm/_redis.py @@ -13,6 +13,7 @@ import json # s/o [@Frank Colson](https://www.linkedin.com/in/frank-colson-422b9b183/) for this redis implementation import os from collections.abc import Callable, Mapping +from types import MappingProxyType from typing import Final from urllib.parse import urlsplit, urlunsplit @@ -38,9 +39,25 @@ from ._logging import verbose_logger AZURE_REDIS_SCOPE: Final = "https://redis.azure.com/.default" -def _get_redis_kwargs(): - arg_spec: Final = inspect.getfullargspec(redis.Redis) +def _unwrapped_init_args(cls: type) -> frozenset[str]: + """Every parameter on a single class's own ``__init__``, decorator-unwrapped. + Unlike ``_init_arg_names`` below, this does not walk the MRO: ``redis.Redis`` + and ``redis.RedisCluster`` (sync and async) each declare every real + constructor parameter directly on their own ``__init__``, so MRO-walking is + unnecessary — and it actively breaks the several tests here that mock the + class with ``patch(..., autospec=True)``, since ``inspect.getmro`` needs a + real ``__mro__`` that an autospec'd stand-in for a class does not provide. + + Still unwraps first: redis-py >= 7.4 decorates these ``__init__``s with + ``@deprecated_args`` too, which the same class of bug as ``_init_arg_names`` + would otherwise silently empty this allowlist through (see its docstring). + """ + spec: Final = inspect.getfullargspec(inspect.unwrap(cls.__init__)) + return frozenset(spec.args + spec.kwonlyargs) + + +def _get_redis_kwargs(): # Only allow primitive arguments exclude_args: Final = { "self", @@ -60,7 +77,7 @@ def _get_redis_kwargs(): "azure_client_secret", } - available_args: Final = {x for x in arg_spec.args if x not in exclude_args} | include_args + available_args: Final = {x for x in _unwrapped_init_args(redis.Redis) if x not in exclude_args} | include_args return available_args @@ -120,15 +137,23 @@ def _get_redis_url_kwargs(client: type | None = None) -> tuple[str, ...]: return tuple(x for x in _init_arg_names(connection_cls) if x not in exclude_args) + include_args -def _get_redis_cluster_kwargs(client=None): +def _get_redis_cluster_kwargs(client: type | None = None): + """Config kwargs the target cluster client's constructor actually accepts. + + Defaults to the sync ``redis.RedisCluster``, but the async cluster client + (``redis.asyncio.cluster.RedisCluster``) declares connection settings such as + ``decode_responses`` on its own constructor, where the sync class takes them + through ``**kwargs`` and so never names them in its signature. Introspecting + only the sync class regardless of which client is actually built silently + drops those for every async cluster caller. + """ if client is None: - client = redis.Redis.from_url - arg_spec: Final = inspect.getfullargspec(redis.RedisCluster) + client = redis.RedisCluster # Only allow primitive arguments exclude_args: Final = {"self", "connection_pool", "retry", "host", "port", "startup_nodes"} - available_args = {x for x in arg_spec.args if x not in exclude_args} + available_args = {x for x in _unwrapped_init_args(client) if x not in exclude_args} available_args |= { "password", "username", @@ -161,6 +186,79 @@ def _get_redis_env_kwarg_mapping(): return {f"{PREFIX}{x.upper()}": x for x in _get_redis_kwargs() if x not in exclude_from_environment} +def _str_to_bool(value: str) -> bool: + return value.lower() in ("true", "1", "yes") + + +def _coerce_redis_kwargs_types( + redis_kwargs: Mapping[str, object], + client: type | tuple[type, ...] = redis.Redis, +) -> dict[str, object]: # mutable-ok: a caller mutates the returned kwargs before constructing its client + """Coerces string values to the numeric/boolean type ``client``'s constructor + declares for that parameter. ``client`` may be a tuple of client classes; a + parameter's type is taken from the first signature that declares it, which + lets cluster callers coerce cluster-only kwargs such as + ``cluster_error_retry_attempts`` alongside the shared connection kwargs. + + Environment variables are always strings, and Helm ``--set`` stringifies values + too, so a config value like ``health_check_interval`` or ``socket_timeout`` + can arrive as ``"30"``/``"5.5"`` rather than a real number. redis-py's own + connection-health-check arithmetic (``loop.time() + self.health_check_interval``) + then raises ``TypeError`` on every Redis operation instead of connecting. + + ``max_connections``, ``socket_timeout``, and ``socket_connect_timeout`` use an + explicit target type rather than the parameter's own signature default: redis-py + 8.x changed the timeout defaults from ``None`` to int ``5``, so inferring the + type from the default would make a fractional ``"5.5"`` fail ``int()`` and get + silently dropped on 8.x while working on older versions. ``socket_keepalive`` + is explicit too: its signature default is ``None``, which carries no type to + infer from, and leaving it a string makes ``"false"`` truthy. + """ + signatures: Final = tuple(inspect.signature(c) for c in (client if isinstance(client, tuple) else (client,))) + explicit_param_types: Final = MappingProxyType( + { + "max_connections": int, + "socket_timeout": float, + "socket_connect_timeout": float, + "socket_keepalive": bool, + } + ) + result: Final = dict(redis_kwargs) # mutable-ok: per-key try/except coercion below needs to drop individual keys + for key, value in redis_kwargs.items(): + if not isinstance(value, str): + continue + param = next((sig.parameters[key] for sig in signatures if key in sig.parameters), None) + if param is None: + continue + explicit_type = explicit_param_types.get(key) + if explicit_type is bool: + result[key] = _str_to_bool(value) + continue + if explicit_type is not None: + try: + result[key] = explicit_type(value) + except (ValueError, TypeError): + del result[key] + continue + default: object = param.default # pyright: ignore[reportAny] # inspect.Parameter.default is stubbed as Any + if default is inspect.Parameter.empty: + continue + # bool must be checked before int, since bool subclasses int + if isinstance(default, bool): + result[key] = _str_to_bool(value) + elif isinstance(default, int): + try: + result[key] = int(value) + except (ValueError, TypeError): + del result[key] + elif isinstance(default, float): + try: + result[key] = float(value) + except (ValueError, TypeError): + del result[key] + return result + + def _redis_kwargs_from_environment(): mapping: Final = _get_redis_env_kwarg_mapping() @@ -505,7 +603,12 @@ def _get_redis_client_logic(**env_overrides): raise ValueError("Either 'host' or 'url' must be specified for redis.") # litellm.print_verbose(f"redis_kwargs: {redis_kwargs}") - return redis_kwargs + coercion_client: Final = ( + (redis.Redis, redis.RedisCluster, async_redis.RedisCluster) + if redis_kwargs.get("startup_nodes") + else redis.Redis + ) + return _coerce_redis_kwargs_types(redis_kwargs, client=coercion_client) def init_redis_cluster(redis_kwargs) -> redis.RedisCluster: @@ -657,7 +760,9 @@ def get_redis_client(**env_overrides): if "sentinel_nodes" in redis_kwargs and "service_name" in redis_kwargs: return _init_redis_sentinel(redis_kwargs) - return redis.Redis(**redis_kwargs) + return redis.Redis( # pyright: ignore[reportCallIssue] # object-valued kwargs match no overload statically + **redis_kwargs, # pyright: ignore[reportArgumentType] # allow-listed and coerced against this signature + ) def get_redis_async_client( @@ -669,7 +774,7 @@ def get_redis_async_client( if "startup_nodes" in redis_kwargs: from redis.cluster import ClusterNode - args = _get_redis_cluster_kwargs() + args = _get_redis_cluster_kwargs(async_redis.RedisCluster) cluster_kwargs: Final = {} for arg in redis_kwargs: if arg in args: diff --git a/litellm/batches/batch_utils.py b/litellm/batches/batch_utils.py index 2bc61aed771..3831f57a10d 100644 --- a/litellm/batches/batch_utils.py +++ b/litellm/batches/batch_utils.py @@ -1,6 +1,8 @@ import json from collections.abc import Iterable, Iterator, Mapping from dataclasses import dataclass +from dataclasses import replace as dataclasses_replace +from enum import Enum from typing import Any, Final, Literal import litellm @@ -12,12 +14,23 @@ from litellm.types.utils import CallTypes, ModelInfo, Usage from litellm.utils import token_counter +@dataclass(frozen=True, slots=True) +class BatchCostUsageResult: + """Aggregate cost, usage, and per-line pass/fail counts for a completed batch.""" + + cost: float + usage: Usage + models: list[str] + successful_requests: int + failed_requests: int + + async def calculate_batch_cost_and_usage( file_content_dictionary: list[dict], custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic"], model_name: str | None = None, model_info: ModelInfo | None = None, -) -> tuple[float, Usage, list[str]]: +) -> BatchCostUsageResult: """ Calculate the cost and usage of a batch. @@ -32,8 +45,7 @@ async def calculate_batch_cost_and_usage( and model_name and getattr(litellm, "disable_vertex_batch_output_transformation", False) ): - batch_cost, batch_usage = calculate_vertex_ai_batch_cost_and_usage(file_content_dictionary, model_name) - return batch_cost, batch_usage, [model_name] + return calculate_vertex_ai_batch_cost_and_usage(file_content_dictionary, model_name) return _aggregate_batch_cost_usage_models( entries=file_content_dictionary, @@ -49,7 +61,7 @@ async def _handle_completed_batch( model_name: str | None = None, litellm_params: dict | None = None, model_info: ModelInfo | None = None, -) -> tuple[float, Usage, list[str]]: +) -> BatchCostUsageResult: """Fetch a completed batch's output file and aggregate its cost, usage, and models in a single pass over the JSONL lines, so the parsed file content is never materialized in memory. @@ -72,27 +84,49 @@ async def _handle_completed_batch( # The generic retrieval helper keeps raising for callers that explicitly ask # for a missing output file. if batch.output_file_id is None: - return 0.0, Usage(prompt_tokens=0, completion_tokens=0, total_tokens=0), [] + return BatchCostUsageResult( + cost=0.0, + usage=Usage(prompt_tokens=0, completion_tokens=0, total_tokens=0), + models=[], # mutable-ok: no output file means no model was ever priced; BatchCostUsageResult.models requires list[str] + successful_requests=0, + failed_requests=await count_error_file_failed_requests( + batch, custom_llm_provider=custom_llm_provider, litellm_params=litellm_params + ), + ) file_content = await _fetch_batch_output_file_content(batch, custom_llm_provider, litellm_params=litellm_params) - - if ( - custom_llm_provider == "vertex_ai" - and model_name - and getattr(litellm, "disable_vertex_batch_output_transformation", False) - ): - batch_cost, batch_usage = calculate_vertex_ai_batch_cost_and_usage( - _get_file_content_as_dictionary(file_content), model_name - ) - return batch_cost, batch_usage, [model_name] - - return _aggregate_batch_cost_usage_models( - entries=_iter_batch_output_entries(file_content), - custom_llm_provider=custom_llm_provider, - model_name=model_name, - model_info=model_info, + error_file_failed_requests: Final = await count_error_file_failed_requests( + batch, custom_llm_provider=custom_llm_provider, litellm_params=litellm_params ) + output_file_result: Final = ( + calculate_vertex_ai_batch_cost_and_usage(_get_file_content_as_dictionary(file_content), model_name) + if ( + custom_llm_provider == "vertex_ai" + and model_name + and getattr(litellm, "disable_vertex_batch_output_transformation", False) + ) + else _aggregate_batch_cost_usage_models( + entries=_iter_batch_output_entries(file_content), + custom_llm_provider=custom_llm_provider, + model_name=model_name, + model_info=model_info, + ) + ) + + if not error_file_failed_requests: + return output_file_result + return dataclasses_replace( + output_file_result, failed_requests=output_file_result.failed_requests + error_file_failed_requests + ) + + +class _LineOutcome(Enum): + """A batch output line that yielded no billable stats.""" + + PROVIDER_FAILED = "provider_failed" + UNCOSTABLE = "uncostable" + @dataclass(frozen=True, slots=True) class _BatchOutputLineStats: @@ -102,19 +136,27 @@ class _BatchOutputLineStats: total_tokens: int cache_read_tokens: int cache_creation_tokens: int + reasoning_tokens: int model: str | None -def _iter_successful_output_line_stats( +def _classify_output_line_stats( entries: Iterable[dict], custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic", "bedrock"], model_name: str | None, model_info: ModelInfo | None, -) -> Iterator[_BatchOutputLineStats]: +) -> Iterator[_BatchOutputLineStats | _LineOutcome]: + """Classify every output line in a single pass, so counting failures never needs + a second read of a potentially huge output file. A line the provider reported as + failed yields ``PROVIDER_FAILED``; a successful line litellm could not price + yields ``UNCOSTABLE`` and still counts as a successful request billed at $0, so + the counts stay reconcilable with the provider's own ``request_counts``.""" for entry in entries: + if not _batch_response_was_successful(entry, custom_llm_provider): + yield _LineOutcome.PROVIDER_FAILED + continue stats = _safe_output_line_stats(entry, custom_llm_provider, model_name, model_info) - if stats is not None: - yield stats + yield stats if stats is not None else _LineOutcome.UNCOSTABLE def _safe_output_line_stats( @@ -123,13 +165,11 @@ def _safe_output_line_stats( model_name: str | None, model_info: ModelInfo | None, ) -> _BatchOutputLineStats | None: - """Return the stats for one batch output line, or None for a line that is - unsuccessful or cannot be costed, so a single bad line never aborts the - whole batch's cost accounting.""" + """Return the stats for one provider-successful batch output line, or None when + it cannot be costed, so a single bad line never aborts the whole batch's cost + accounting.""" custom_id: Final = entry.get("custom_id") if isinstance(entry, dict) else None try: - if not _batch_response_was_successful(entry, custom_llm_provider): - return None return _compute_output_line_stats(entry, custom_llm_provider, model_name, model_info) except Exception as e: # noqa: BLE001 # any single line's costing failure must not abort the whole batch verbose_logger.warning( @@ -152,6 +192,7 @@ def _compute_output_line_stats( prompt_details: Final = parse_prompt_tokens_details(usage) raw_model: Final = response_body.get("model") response_model: Final = raw_model if isinstance(raw_model, str) and raw_model else None + completion_details: Final = usage.completion_tokens_details return _BatchOutputLineStats( cost=_output_line_cost( response_body=response_body, @@ -166,6 +207,7 @@ def _compute_output_line_stats( total_tokens=usage.total_tokens, cache_read_tokens=prompt_details["cache_hit_tokens"], cache_creation_tokens=prompt_details["cache_creation_tokens"], + reasoning_tokens=(completion_details.reasoning_tokens if completion_details else None) or 0, model=response_model, ) @@ -203,10 +245,14 @@ def _aggregate_batch_cost_usage_models( custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic", "bedrock"], model_name: str | None = None, model_info: ModelInfo | None = None, -) -> tuple[float, Usage, list[str]]: - """Aggregate cost, usage, and models from batch output entries in a single - pass, holding one small stats record per line instead of the parsed file.""" - line_stats: Final = tuple(_iter_successful_output_line_stats(entries, custom_llm_provider, model_name, model_info)) +) -> BatchCostUsageResult: + """Aggregate cost, usage, models, and pass/fail counts from batch output + entries in a single pass, holding one small stats record per line instead + of the parsed file.""" + all_results: Final = tuple(_classify_output_line_stats(entries, custom_llm_provider, model_name, model_info)) + line_stats: Final = tuple(result for result in all_results if isinstance(result, _BatchOutputLineStats)) + failed_requests: Final = sum(1 for result in all_results if result is _LineOutcome.PROVIDER_FAILED) + successful_requests: Final = len(all_results) - failed_requests cache_token_params: Final = { key: tokens @@ -220,18 +266,32 @@ def _aggregate_batch_cost_usage_models( total_tokens=sum(stats.total_tokens for stats in line_stats), prompt_tokens=sum(stats.prompt_tokens for stats in line_stats), completion_tokens=sum(stats.completion_tokens for stats in line_stats), + reasoning_tokens=sum(stats.reasoning_tokens for stats in line_stats), **cache_token_params, ) batch_models: Final = [model_name] if model_name else [stats.model for stats in line_stats if stats.model] total_cost: Final = sum((stats.cost for stats in line_stats), 0.0) - verbose_logger.debug("batch output aggregate: cost=%s usage=%s models=%s", total_cost, batch_usage, batch_models) - return total_cost, batch_usage, batch_models + verbose_logger.debug( + "batch output aggregate: cost=%s usage=%s models=%s successful=%d failed=%d", + total_cost, + batch_usage, + batch_models, + successful_requests, + failed_requests, + ) + return BatchCostUsageResult( + cost=total_cost, + usage=batch_usage, + models=batch_models, + successful_requests=successful_requests, + failed_requests=failed_requests, + ) def calculate_vertex_ai_batch_cost_and_usage( vertex_ai_batch_responses: list[dict], model_name: str | None = None, -) -> tuple[float, Usage]: +) -> BatchCostUsageResult: """ Calculate both cost and usage from raw Vertex AI batch responses. @@ -242,6 +302,10 @@ def calculate_vertex_ai_batch_cost_and_usage( {"request": ..., "response": {"candidates": [...], "usageMetadata": {...}}} usageMetadata contains promptTokenCount, candidatesTokenCount, totalTokenCount. + + A row with no ``response`` is counted as failed - the same signal already + used to skip it from cost/usage aggregation, since Vertex batch prediction + output doesn't establish a distinct error shape in this (non-default) path. """ from litellm.cost_calculator import batch_cost_calculator @@ -249,12 +313,16 @@ def calculate_vertex_ai_batch_cost_and_usage( total_tokens = 0 prompt_tokens = 0 completion_tokens = 0 + successful_requests = 0 # rebind-ok: loop accumulator, matches total_cost/total_tokens above + failed_requests = 0 # rebind-ok: loop accumulator, matches total_cost/total_tokens above actual_model_name: Final = model_name or "gemini-2.0-flash-001" for response in vertex_ai_batch_responses: response_body = response.get("response") if response_body is None: + failed_requests += 1 continue + successful_requests += 1 usage_metadata = response_body.get("usageMetadata", {}) _prompt = usage_metadata.get("promptTokenCount", 0) or 0 @@ -282,17 +350,25 @@ def calculate_vertex_ai_batch_cost_and_usage( total_tokens += _total verbose_logger.info( - "vertex_ai batch cost: cost=%s, prompt=%d, completion=%d, total=%d", + "vertex_ai batch cost: cost=%s, prompt=%d, completion=%d, total=%d, successful=%d, failed=%d", total_cost, prompt_tokens, completion_tokens, total_tokens, + successful_requests, + failed_requests, ) - return total_cost, Usage( - total_tokens=total_tokens, - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, + return BatchCostUsageResult( + cost=total_cost, + usage=Usage( + total_tokens=total_tokens, + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + ), + models=[actual_model_name], + successful_requests=successful_requests, + failed_requests=failed_requests, ) @@ -322,6 +398,36 @@ def _provider_output_file_id(output_file_id: str) -> str: return extracted +async def _fetch_batch_managed_file_content( + file_id: str, + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic"] = "openai", + litellm_params: dict | None = None, +) -> bytes: + """ + Fetch a batch's output or error file and return its raw JSONL bytes. + + Args: + file_id: The provider or unified (litellm-managed) file id to fetch + custom_llm_provider: The LLM provider + litellm_params: Optional litellm parameters containing credentials (api_key, api_base, etc.) + Required for Azure and other providers that need authentication + """ + from litellm.files.main import afile_content + + # Build kwargs for afile_content with credentials from litellm_params + file_content_kwargs: Final = { + "file_id": _provider_output_file_id(file_id), + "custom_llm_provider": custom_llm_provider, + } + + # Extract and add credentials for file access + credentials: Final = _extract_file_access_credentials(litellm_params) + file_content_kwargs.update(credentials) + + _file_content: Final = await afile_content(**file_content_kwargs) + return _file_content.content + + async def _fetch_batch_output_file_content( batch: Batch, custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic"] = "openai", @@ -336,25 +442,36 @@ async def _fetch_batch_output_file_content( litellm_params: Optional litellm parameters containing credentials (api_key, api_base, etc.) Required for Azure and other providers that need authentication """ - from litellm.files.main import afile_content - if batch.output_file_id is None: raise ValueError("Output file id is None cannot retrieve file content") - file_id: Final = _provider_output_file_id(batch.output_file_id) + return await _fetch_batch_managed_file_content( + batch.output_file_id, custom_llm_provider=custom_llm_provider, litellm_params=litellm_params + ) - # Build kwargs for afile_content with credentials from litellm_params - file_content_kwargs: Final = { - "file_id": file_id, - "custom_llm_provider": custom_llm_provider, - } - # Extract and add credentials for file access - credentials: Final = _extract_file_access_credentials(litellm_params) - file_content_kwargs.update(credentials) +async def count_error_file_failed_requests( + batch: Batch, + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic"], + litellm_params: dict | None, +) -> int: + """Count failed requests reported only in the batch's separate error file. - _file_content: Final = await afile_content(**file_content_kwargs) - return _file_content.content + OpenAI-shaped batch providers write successful lines to ``output_file_id`` + and per-request failures (e.g. a rejected param) to a distinct + ``error_file_id`` - they never appear in the output file at all, so + counting failures from the output file alone silently undercounts them. + """ + if batch.error_file_id is None: + return 0 + try: + error_file_content = await _fetch_batch_managed_file_content( + batch.error_file_id, custom_llm_provider=custom_llm_provider, litellm_params=litellm_params + ) + except Exception as e: # noqa: BLE001 # a failed/missing error file must not abort cost tracking for the batch + verbose_logger.debug("Failed to fetch batch error file %s: %s", batch.error_file_id, e) + return 0 + return sum(1 for _ in _iter_batch_input_lines(error_file_content)) def _extract_file_access_credentials(litellm_params: dict | None) -> dict: diff --git a/litellm/caching/caching.py b/litellm/caching/caching.py index cefe6aae9ed..754815fce47 100644 --- a/litellm/caching/caching.py +++ b/litellm/caching/caching.py @@ -12,6 +12,7 @@ import hashlib import json import time import traceback +from collections.abc import Mapping from enum import Enum from typing import Any, Final @@ -506,7 +507,7 @@ class Cache: def _get_cache_logic( self, - cached_result: Any | None, + cached_result: object | None, max_age: float | None, ): """ @@ -538,8 +539,8 @@ class Cache: return cached_result @staticmethod - def _get_safe_cache_lookup_kwargs(kwargs: dict[str, Any]) -> dict[str, Any]: - cache_lookup_kwargs: Final[dict[str, Any]] = {} + def _get_safe_cache_lookup_kwargs(kwargs: Mapping[str, object]) -> dict[str, object]: + cache_lookup_kwargs: Final[dict[str, object]] = {} for prompt_kwarg in ("messages", "input"): if prompt_kwarg in kwargs: cache_lookup_kwargs[prompt_kwarg] = kwargs[prompt_kwarg] @@ -552,7 +553,7 @@ class Cache: @staticmethod def _update_metadata_from_cache_lookup_kwargs( - original_kwargs: dict[str, Any], cache_lookup_kwargs: dict[str, Any] + original_kwargs: Mapping[str, object], cache_lookup_kwargs: Mapping[str, object] ) -> None: original_metadata: Final = original_kwargs.get("metadata") cache_lookup_metadata: Final = cache_lookup_kwargs.get("metadata") diff --git a/litellm/caching/qdrant_semantic_cache.py b/litellm/caching/qdrant_semantic_cache.py index 4898700c403..c5876e993d3 100644 --- a/litellm/caching/qdrant_semantic_cache.py +++ b/litellm/caching/qdrant_semantic_cache.py @@ -12,7 +12,7 @@ import ast import asyncio import json import os -from typing import TYPE_CHECKING, Any, Final, cast +from typing import TYPE_CHECKING, Any, Final, Protocol, cast import litellm from litellm._logging import print_verbose @@ -39,6 +39,12 @@ if TYPE_CHECKING: from litellm.router import Router +class _QdrantCollectionDetailsResponse(Protocol): + """The qdrant `/collections/{name}` response, whose body is kept as an opaque JSON object.""" + + def json(self) -> dict[str, object]: ... + + class QdrantSemanticCache(BaseCache): CACHE_KEY_FIELD_NAME = "litellm_cache_key" embedding_max_input_tokens: int | None = None @@ -115,15 +121,15 @@ class QdrantSemanticCache(BaseCache): raise ValueError(f"Error from qdrant checking if /collections exist {collection_exists.text}") if collection_exists.json()["result"]["exists"]: - collection_details = self.sync_client.get( + collection_details: _QdrantCollectionDetailsResponse = self.sync_client.get( url=f"{self.qdrant_api_base}/collections/{self.collection_name}", headers=self.headers, ) - self.collection_info = collection_details.json() + self.collection_info: dict[str, object] = collection_details.json() print_verbose(f"Collection already exists.\nCollection details:{self.collection_info}") self._ensure_cache_key_payload_index() else: - quantization_params: dict[str, Any] + quantization_params: dict[str, dict[str, object]] if quantization_config is None or quantization_config == "binary": quantization_params = { "binary": { @@ -214,7 +220,7 @@ class QdrantSemanticCache(BaseCache): resolve_embedding_max_input_tokens(self.embedding_max_input_tokens, self.embedding_model, router), ) - def _get_embedding(self, prompt: str, metadata: dict[str, Any] | None = None) -> EmbeddingResponse: + def _get_embedding(self, prompt: str, metadata: dict[str, object] | None = None) -> EmbeddingResponse: """Embed via the proxy Router when it serves the model, else direct.""" try: from litellm.proxy.proxy_server import llm_model_list, llm_router @@ -241,7 +247,7 @@ class QdrantSemanticCache(BaseCache): num_retries=0, ) - async def _get_async_embedding(self, prompt: str, metadata: dict[str, Any] | None = None) -> EmbeddingResponse: + async def _get_async_embedding(self, prompt: str, metadata: dict[str, object] | None = None) -> EmbeddingResponse: try: from litellm.proxy.proxy_server import llm_model_list, llm_router except ImportError: diff --git a/litellm/caching/redis_cache.py b/litellm/caching/redis_cache.py index f1c80eaacbe..2b04a075114 100644 --- a/litellm/caching/redis_cache.py +++ b/litellm/caching/redis_cache.py @@ -18,7 +18,7 @@ import time from collections.abc import Awaitable, Callable, Sequence from contextvars import ContextVar from datetime import timedelta -from typing import TYPE_CHECKING, Any, Final, TypeVar, cast +from typing import TYPE_CHECKING, Any, Final, Protocol, TypeVar, cast import litellm from litellm._logging import print_verbose, verbose_logger @@ -58,6 +58,26 @@ else: Span = Any +class _AsyncRedisCommands(Protocol): + """Async redis commands this cache issues. + + redis-py's type stubs omit these methods on RedisCluster, so the union returned by + init_async_client() is untyped at every call site without this protocol. + """ + + def ping(self) -> Awaitable[bool]: ... + + def delete(self, *names: str) -> Awaitable[int]: ... + + def ttl(self, name: str) -> Awaitable[int]: ... + + def rpush(self, name: str, *values: str | bytes | float) -> Awaitable[int]: ... + + def lpop(self, name: str, count: int | None = None) -> Awaitable[object]: ... + + def pipeline(self, transaction: bool = True) -> "Pipeline[bytes]": ... + + def _get_call_stack_info(num_frames: int = 2) -> str: """ Get the function names from the previous 1-2 functions in the call stack. @@ -429,6 +449,9 @@ class RedisCache(BaseCache): self.redis_async_client = redis_async_client return redis_async_client + def _async_commands(self) -> _AsyncRedisCommands: + return self.init_async_client() + def check_and_fix_namespace(self, key: str) -> str: """ Make sure each key starts with the given namespace @@ -1055,19 +1078,17 @@ class RedisCache(BaseCache): await self.async_set_cache_pipeline(self.redis_batch_writing_buffer) self.redis_batch_writing_buffer = [] - def _get_cache_logic(self, cached_response: Any): + def _get_cache_logic(self, cached_response: bytes | str | None): """ Common 'get_cache_logic' across sync + async redis client implementations """ if cached_response is None: - return cached_response - # cached_response is in `b{} convert it to ModelResponse - cached_response = cached_response.decode("utf-8") # Convert bytes to string + return None + decoded: Final = cached_response.decode("utf-8") if isinstance(cached_response, bytes) else cached_response try: - cached_response = json.loads(cached_response) # Convert string to dictionary + return json.loads(decoded) except Exception: - cached_response = ast.literal_eval(cached_response) - return cached_response + return ast.literal_eval(decoded) def get_cache(self, key, parent_otel_span: Span | None = None, **kwargs): try: @@ -1314,8 +1335,7 @@ class RedisCache(BaseCache): raise e async def ping(self) -> bool: - # typed as Any, redis python lib has incomplete type stubs for RedisCluster and does not include `ping` - _redis_client: Final[Any] = self.init_async_client() + _redis_client: Final = self._async_commands() start_time: Final = time.time() print_verbose("Pinging Async Redis Cache") try: @@ -1349,8 +1369,7 @@ class RedisCache(BaseCache): @_redis_circuit_breaker_guard async def delete_cache_keys(self, keys): - # typed as Any, redis python lib has incomplete type stubs for RedisCluster and does not include `delete` - _redis_client: Final[Any] = self.init_async_client() + _redis_client: Final = self._async_commands() keys = [self.check_and_fix_namespace(key=key) for key in keys] # keys is a list, unpack it so it gets passed as individual elements to delete await _redis_client.delete(*keys) @@ -1415,8 +1434,7 @@ class RedisCache(BaseCache): @_redis_circuit_breaker_guard async def async_delete_cache(self, key: str): - # typed as Any, redis python lib has incomplete type stubs for RedisCluster and does not include `delete` - _redis_client: Final[Any] = self.init_async_client() + _redis_client: Final = self._async_commands() key = self.check_and_fix_namespace(key=key) # keys is str return await _redis_client.delete(key) @@ -1523,8 +1541,7 @@ class RedisCache(BaseCache): Redis ref: https://redis.io/docs/latest/commands/ttl/ """ try: - # typed as Any, redis python lib has incomplete type stubs for RedisCluster and does not include `ttl` - _redis_client: Final[Any] = self.init_async_client() + _redis_client: Final = self._async_commands() key = self.check_and_fix_namespace(key=key) ttl: Final = await _redis_client.ttl(key) if ttl <= -1: # -1 means the key does not exist, -2 key does not exist @@ -1554,7 +1571,7 @@ class RedisCache(BaseCache): Returns: int: The length of the list after the push operation """ - _redis_client: Final[Any] = self.init_async_client() + _redis_client: Final = self._async_commands() key = self.check_and_fix_namespace(key=key) start_time: Final = time.time() try: @@ -1621,7 +1638,7 @@ class RedisCache(BaseCache): if len(rpush_list) == 0: return [] - _redis_client: Final[Any] = self.init_async_client() + _redis_client: Final = self._async_commands() start_time: Final = time.time() try: @@ -1678,7 +1695,7 @@ class RedisCache(BaseCache): parent_otel_span: Span | None = None, **kwargs, ) -> Any | list[Any]: - _redis_client: Final[Any] = self.init_async_client() + _redis_client: Final = self._async_commands() key = self.check_and_fix_namespace(key=key) start_time: Final = time.time() print_verbose(f"LPOP from Redis list: key: {key}, count: {count}") @@ -1810,7 +1827,7 @@ class RedisCache(BaseCache): if len(lpop_list) == 0: return [] - _redis_client: Final[Any] = self.init_async_client() + _redis_client: Final = self._async_commands() start_time: Final = time.time() try: diff --git a/litellm/caching/valkey_semantic_cache.py b/litellm/caching/valkey_semantic_cache.py index c66f6873383..58b76d98d6d 100644 --- a/litellm/caching/valkey_semantic_cache.py +++ b/litellm/caching/valkey_semantic_cache.py @@ -17,6 +17,7 @@ RedisSemanticCache since those are backend agnostic. import asyncio import hashlib import os +from collections.abc import Mapping, Sequence from dataclasses import dataclass from typing import Any, Final @@ -64,7 +65,7 @@ class ValkeySemanticCache(RedisSemanticCache): async_client: AsyncRedis | None = None, embedding_max_input_tokens: int | None = None, embedding_timeout: float | None = None, - **kwargs: Any, + **kwargs: object, ): if similarity_threshold is None: raise ValueError("similarity_threshold must be provided, passed None") @@ -87,11 +88,13 @@ class ValkeySemanticCache(RedisSemanticCache): self.key_prefix = f"{self.index_name}:" self._index_dim: int | None = None - resolved_url = None - if sync_client is None or async_client is None: - resolved_url = redis_url or self._build_valkey_url(host, port, password, ssl) - self.sync_client = sync_client if sync_client is not None else Redis.from_url(resolved_url) - self.async_client = async_client if async_client is not None else AsyncRedis.from_url(resolved_url) + if sync_client is not None and async_client is not None: + self.sync_client = sync_client + self.async_client = async_client + else: + resolved_url: Final = redis_url or self._build_valkey_url(host, port, password, ssl) + self.sync_client = sync_client if sync_client is not None else Redis.from_url(resolved_url) + self.async_client = async_client if async_client is not None else AsyncRedis.from_url(resolved_url) print_verbose(f"Valkey semantic-cache initializing index - {self.index_name}") @@ -118,7 +121,7 @@ class ValkeySemanticCache(RedisSemanticCache): return hashlib.sha256(str(key).encode("utf-8")).hexdigest() @staticmethod - def _embedding_to_bytes(embedding: list[float]) -> bytes: + def _embedding_to_bytes(embedding: Sequence[float]) -> bytes: return pack_vector(embedding) def _index_schema(self, dim: int) -> tuple[TagField, VectorField]: @@ -192,7 +195,9 @@ class ValkeySemanticCache(RedisSemanticCache): def _doc_key(self, key: str) -> str: return f"{self.key_prefix}{self._scope_tag(key)}:{uuid.uuid4()}" - def _doc_mapping(self, key: str, prompt: str, value_str: str, embedding: list[float]) -> dict: + def _doc_mapping( + self, key: str, prompt: str, value_str: str, embedding: Sequence[float] + ) -> Mapping[str | bytes, str | bytes]: return { self.CACHE_KEY_FIELD_NAME: self._scope_tag(key), self.PROMPT_FIELD_NAME: prompt, @@ -208,30 +213,49 @@ class ValkeySemanticCache(RedisSemanticCache): ) return Query(query_string).return_fields(self.RESPONSE_FIELD_NAME, self.DISTANCE_FIELD_NAME).dialect(2) + async def _async_search(self, key: str, embedding: Sequence[float]) -> object: + """Run the KNN query on the async client, stopping the untyped search surface here.""" + return await self.async_client.ft(self.index_name).search( + self._knn_query(key), + query_params={"vec": self._embedding_to_bytes(embedding)}, # pyright: ignore[reportArgumentType] # redis stubs omit bytes; KNN vectors are raw bytes at runtime + ) + @classmethod - def _first_hit(cls, search_result: Any) -> _ValkeyCacheHit | None: - docs: Final = getattr(search_result, "docs", []) + def _first_hit(cls, search_result: object) -> _ValkeyCacheHit | None: + docs: Final[Sequence[object]] = getattr(search_result, "docs", []) if not docs: return None doc: Final = docs[0] + response_field: Final[object] = getattr(doc, cls.RESPONSE_FIELD_NAME) + distance_field: Final[str | bytes | float] = getattr(doc, cls.DISTANCE_FIELD_NAME) return _ValkeyCacheHit( - response=str(getattr(doc, cls.RESPONSE_FIELD_NAME)), - distance=float(getattr(doc, cls.DISTANCE_FIELD_NAME)), + response=str(response_field), + distance=float(distance_field), ) - def _resolve_hit(self, hit: _ValkeyCacheHit | None, key: str, **kwargs: Any) -> Any: + @staticmethod + def _record_similarity(kwargs: dict[str, Any], similarity: float) -> None: + """Stamp the semantic-similarity score onto the request metadata carried in ``kwargs``.""" + kwargs.setdefault("metadata", {})["semantic-similarity"] = similarity + + @staticmethod + def _embedding_metadata(kwargs: dict[str, Any]) -> dict[str, Any] | None: + """The request metadata forwarded to the embedding call.""" + return kwargs.get("metadata") + + def _resolve_hit(self, hit: _ValkeyCacheHit | None, key: str, **kwargs: object) -> object: if hit is None: - kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 + self._record_similarity(kwargs, 0.0) return None similarity: Final = 1 - hit.distance - kwargs.setdefault("metadata", {})["semantic-similarity"] = similarity + self._record_similarity(kwargs, similarity) if similarity < self.similarity_threshold: return None return self._get_cache_logic(cached_response=hit.response) - def set_cache(self, key: str, value: Any, **kwargs: Any) -> None: + def set_cache(self, key: str, value: object, **kwargs: object) -> None: print_verbose(f"Valkey semantic-cache set_cache, kwargs: {kwargs}") try: prompt: Final = self._get_prompt_from_kwargs(**kwargs) @@ -250,12 +274,12 @@ class ValkeySemanticCache(RedisSemanticCache): except Exception as e: print_verbose(f"Error in Valkey semantic-cache set_cache: {e}") - def get_cache(self, key: str, **kwargs: Any) -> Any: + def get_cache(self, key: str, **kwargs: object) -> object: print_verbose(f"Valkey semantic-cache get_cache, kwargs: {kwargs}") try: prompt: Final = self._get_prompt_from_kwargs(**kwargs) if prompt is None: - kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 + self._record_similarity(kwargs, 0.0) return None embedding: Final = self._get_embedding(prompt) @@ -263,14 +287,14 @@ class ValkeySemanticCache(RedisSemanticCache): search_result: Final = self.sync_client.ft(self.index_name).search( self._knn_query(key), - query_params={"vec": self._embedding_to_bytes(embedding)}, + query_params={"vec": self._embedding_to_bytes(embedding)}, # pyright: ignore[reportArgumentType] # redis stubs omit bytes; KNN vectors are raw bytes at runtime ) return self._resolve_hit(self._first_hit(search_result), key, **kwargs) except Exception as e: print_verbose(f"Error in Valkey semantic-cache get_cache: {e}") - kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 + self._record_similarity(kwargs, 0.0) - async def async_set_cache(self, key: str, value: Any, **kwargs: Any) -> None: + async def async_set_cache(self, key: str, value: object, **kwargs: object) -> None: print_verbose(f"Async Valkey semantic-cache set_cache, kwargs: {kwargs}") try: prompt: Final = self._get_prompt_from_kwargs(**kwargs) @@ -278,7 +302,7 @@ class ValkeySemanticCache(RedisSemanticCache): print_verbose("No prompt provided for semantic caching") return - embedding: Final = await self._get_async_embedding(prompt, metadata=kwargs.get("metadata")) + embedding: Final = await self._get_async_embedding(prompt, metadata=self._embedding_metadata(kwargs)) await self._ensure_index_async(len(embedding)) doc_key: Final = self._doc_key(key) @@ -289,31 +313,28 @@ class ValkeySemanticCache(RedisSemanticCache): except Exception as e: print_verbose(f"Error in async Valkey semantic-cache set_cache: {e}") - async def async_get_cache(self, key: str, **kwargs: Any) -> Any: + async def async_get_cache(self, key: str, **kwargs: object) -> object: print_verbose(f"Async Valkey semantic-cache get_cache, kwargs: {kwargs}") try: prompt: Final = self._get_prompt_from_kwargs(**kwargs) if prompt is None: - kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 + self._record_similarity(kwargs, 0.0) return None - embedding: Final = await self._get_async_embedding(prompt, metadata=kwargs.get("metadata")) + embedding: Final = await self._get_async_embedding(prompt, metadata=self._embedding_metadata(kwargs)) await self._ensure_index_async(len(embedding)) - search_result: Final = await self.async_client.ft(self.index_name).search( - self._knn_query(key), - query_params={"vec": self._embedding_to_bytes(embedding)}, - ) + search_result: Final[object] = await self._async_search(key, embedding) return self._resolve_hit(self._first_hit(search_result), key, **kwargs) except Exception as e: print_verbose(f"Error in async Valkey semantic-cache get_cache: {e}") - kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 + self._record_similarity(kwargs, 0.0) - async def async_set_cache_pipeline(self, cache_list: list[tuple[str, Any]], **kwargs: Any) -> None: + async def async_set_cache_pipeline(self, cache_list: list[tuple[str, object]], **kwargs: object) -> None: try: await asyncio.gather(*[self.async_set_cache(key, value, **kwargs) for key, value in cache_list]) except Exception as e: print_verbose(f"Error in Valkey semantic-cache async_set_cache_pipeline: {e}") - async def _index_info(self) -> dict: + async def _index_info(self) -> Mapping[str, object]: return await self.async_client.ft(self.index_name).info() diff --git a/litellm/completion_extras/litellm_responses_transformation/handler.py b/litellm/completion_extras/litellm_responses_transformation/handler.py index 727c39c16ec..f494d6610a1 100644 --- a/litellm/completion_extras/litellm_responses_transformation/handler.py +++ b/litellm/completion_extras/litellm_responses_transformation/handler.py @@ -45,14 +45,14 @@ class ResponsesToCompletionBridgeHandler: return bool(stream) @staticmethod - def _is_preformatted_cached_chat_stream(result: Any) -> bool: + def _is_preformatted_cached_chat_stream(result: object) -> bool: from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper return isinstance(result, CustomStreamWrapper) and result.custom_llm_provider == "cached_response" @staticmethod def _coerce_response_object( - response_obj: Any, + response_obj: object, hidden_params: dict | None, ) -> "ResponsesAPIResponse": if isinstance(response_obj, ResponsesAPIResponse): @@ -78,8 +78,8 @@ class ResponsesToCompletionBridgeHandler: for _ in stream_iter: pass - completed: Final = getattr(stream_iter, "completed_response", None) - response_obj: Final = getattr(completed, "response", None) if completed else None + completed: Final[object] = getattr(stream_iter, "completed_response", None) + response_obj: Final[object] = getattr(completed, "response", None) if completed else None if response_obj is None: raise ValueError("Stream ended without a completed response") @@ -93,8 +93,8 @@ class ResponsesToCompletionBridgeHandler: async for _ in stream_iter: pass - completed: Final = getattr(stream_iter, "completed_response", None) - response_obj: Final = getattr(completed, "response", None) if completed else None + completed: Final[object] = getattr(stream_iter, "completed_response", None) + response_obj: Final[object] = getattr(completed, "response", None) if completed else None if response_obj is None: raise ValueError("Stream ended without a completed response") @@ -157,7 +157,7 @@ class ResponsesToCompletionBridgeHandler: def completion( self, *args, **kwargs ) -> Union[ - Coroutine[Any, Any, Union["ModelResponse", "CustomStreamWrapper"]], + Coroutine[None, None, Union["ModelResponse", "CustomStreamWrapper"]], "ModelResponse", "CustomStreamWrapper", ]: diff --git a/litellm/completion_extras/litellm_responses_transformation/transformation.py b/litellm/completion_extras/litellm_responses_transformation/transformation.py index 85fb0bc8dc6..7368de1e968 100644 --- a/litellm/completion_extras/litellm_responses_transformation/transformation.py +++ b/litellm/completion_extras/litellm_responses_transformation/transformation.py @@ -212,7 +212,8 @@ def _tool_call_dict_from_output_item(item: Mapping[str, Any], index: int) -> _Ch LiteLLMCompletionResponsesConfig, ) - is_custom: Final = item.get("type") == "custom_tool_call" + item_type: Final[object] = item.get("type") + is_custom: Final = item_type == "custom_tool_call" arguments: Final = (item.get("input") if is_custom else item.get("arguments")) or "" name: Final = item.get("name") or ("custom_tool" if is_custom else "") function_chunk: Final = ChatCompletionToolCallFunctionChunk(name=name, arguments=arguments) @@ -222,7 +223,7 @@ def _tool_call_dict_from_output_item(item: Mapping[str, Any], index: int) -> _Ch function=function_chunk, index=index, ) - raw_provider_fields: Final = item.get("provider_specific_fields") + raw_provider_fields: Final[object] = item.get("provider_specific_fields") if isinstance(raw_provider_fields, dict): provider_specific_fields = raw_provider_fields elif raw_provider_fields and hasattr(raw_provider_fields, "__dict__"): @@ -507,7 +508,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): def _merge_responses_api_request_into_request_data( self, - request_data: dict[str, Any], + request_data: dict[str, object], responses_api_request: "ResponsesAPIOptionalRequestParams", instructions: str | None, ) -> None: diff --git a/litellm/constants.py b/litellm/constants.py index fc88086805f..1bd977dd9a9 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -13,6 +13,12 @@ DEFAULT_BATCH_SIZE: Final = int(os.getenv("DEFAULT_BATCH_SIZE", 512)) DEFAULT_FLUSH_INTERVAL_SECONDS: Final = int(os.getenv("DEFAULT_FLUSH_INTERVAL_SECONDS", 5)) DEFAULT_S3_FLUSH_INTERVAL_SECONDS: Final = int(os.getenv("DEFAULT_S3_FLUSH_INTERVAL_SECONDS", 10)) DEFAULT_S3_BATCH_SIZE: Final = int(os.getenv("DEFAULT_S3_BATCH_SIZE", 512)) +# https://docs.aws.amazon.com/AmazonS3/latest/userguide/object-keys.html +MAX_S3_OBJECT_KEY_BYTES: Final = 1024 +S3_BOUNDED_OBJECT_KEY_HEAD_BYTES: Final = 64 +S3_PREFIX_DIGEST_CHARS: Final = 16 +# s3 allows 2048 bytes of combined metadata headers, which Content-Disposition counts against +MAX_S3_OBJECT_DOWNLOAD_FILENAME_BYTES: Final = 1024 DEFAULT_SQS_FLUSH_INTERVAL_SECONDS: Final = int(os.getenv("DEFAULT_SQS_FLUSH_INTERVAL_SECONDS", 10)) DEFAULT_NUM_WORKERS_LITELLM_PROXY: Final = int(os.getenv("DEFAULT_NUM_WORKERS_LITELLM_PROXY", 1)) DYNAMIC_RATE_LIMIT_ERROR_THRESHOLD_PER_MINUTE = int(os.getenv("DYNAMIC_RATE_LIMIT_ERROR_THRESHOLD_PER_MINUTE", 1)) @@ -35,6 +41,7 @@ DEFAULT_COOLDOWN_TIME_SECONDS: Final = int(os.getenv("DEFAULT_COOLDOWN_TIME_SECO DEFAULT_REPLICATE_POLLING_RETRIES: Final = int(os.getenv("DEFAULT_REPLICATE_POLLING_RETRIES", 5)) DEFAULT_REPLICATE_POLLING_DELAY_SECONDS: Final = int(os.getenv("DEFAULT_REPLICATE_POLLING_DELAY_SECONDS", 1)) DEFAULT_IMAGE_TOKEN_COUNT: Final = int(os.getenv("DEFAULT_IMAGE_TOKEN_COUNT", 250)) +HF_CONFIG_FETCH_TIMEOUT_SECONDS: Final = 10.0 # Maximum wall-clock seconds a streaming response is allowed to run. # Streams exceeding this duration are terminated with a Timeout error. @@ -129,6 +136,7 @@ MCP_CLIENT_TIMEOUT: Final = float(os.getenv("LITELLM_MCP_CLIENT_TIMEOUT", "60.0" MCP_TOOL_LISTING_TIMEOUT: Final = float(os.getenv("LITELLM_MCP_TOOL_LISTING_TIMEOUT", "30.0")) MCP_METADATA_TIMEOUT: Final = float(os.getenv("LITELLM_MCP_METADATA_TIMEOUT", "10.0")) MCP_HEALTH_CHECK_TIMEOUT: Final = float(os.getenv("LITELLM_MCP_HEALTH_CHECK_TIMEOUT", "10.0")) +MCP_TOOL_LISTING_MAX_PAGES: Final = 1000 # Allowlist of commands permitted for MCP stdio transport. # Prevents arbitrary command execution via /mcp-rest/test/* endpoints or server creation. @@ -288,6 +296,7 @@ REDIS_DAILY_ORG_SPEND_UPDATE_BUFFER_KEY: Final = "litellm_daily_org_spend_update REDIS_DAILY_END_USER_SPEND_UPDATE_BUFFER_KEY: Final = "litellm_daily_end_user_spend_update_buffer" REDIS_DAILY_AGENT_SPEND_UPDATE_BUFFER_KEY: Final = "litellm_daily_agent_spend_update_buffer" REDIS_DAILY_TAG_SPEND_UPDATE_BUFFER_KEY: Final = "litellm_daily_tag_spend_update_buffer" +REDIS_WINDOW_SPEND_UPDATE_BUFFER_KEY: Final = "litellm_window_spend_update_buffer" MAX_REDIS_BUFFER_DEQUEUE_COUNT: Final = int(os.getenv("MAX_REDIS_BUFFER_DEQUEUE_COUNT", 100)) # Bounds asyncio.Queue() instances (log queues, spend update queues, etc.) to prevent unbounded memory growth LITELLM_ASYNCIO_QUEUE_MAXSIZE: Final = int(os.getenv("LITELLM_ASYNCIO_QUEUE_MAXSIZE", 1000)) @@ -482,6 +491,22 @@ FIREWORKS_AI_80_B: Final = int(os.getenv("FIREWORKS_AI_80_B", 80)) #### Logging callback constants #### REDACTED_BY_LITELM_STRING: Final = "REDACTED_BY_LITELM" MAX_LANGFUSE_INITIALIZED_CLIENTS: Final = int(os.getenv("MAX_LANGFUSE_INITIALIZED_CLIENTS", 50)) +# Backpressure + lifetime bounds for the /v1/messages streaming relay (see +# BaseAnthropicMessagesStreamingIterator.async_sse_wrapper). The relay queue is +# bounded so a slow client throttles the upstream pump instead of letting it +# buffer the whole response in memory; the detached-drain cap bounds how many +# post-disconnect drains may run concurrently so client behavior can't create +# unbounded worker state. +ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE: Final = int( + os.getenv("ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE", "1024") +) +# Setting this to 0 disables detached draining entirely: every post-disconnect +# pump bills whatever partial output it has already collected and aborts the +# upstream stream immediately, instead of continuing to drain for the real +# terminal usage. +ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS: Final = int( + os.getenv("ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS", "100") +) LOGGING_WORKER_CONCURRENCY: Final = int(os.getenv("LOGGING_WORKER_CONCURRENCY", 100)) # Must be above 0 LOGGING_WORKER_MAX_QUEUE_SIZE: Final = int(os.getenv("LOGGING_WORKER_MAX_QUEUE_SIZE", 50_000)) LOGGING_WORKER_MAX_TIME_PER_COROUTINE: Final = float(os.getenv("LOGGING_WORKER_MAX_TIME_PER_COROUTINE", 20.0)) @@ -612,6 +637,8 @@ LITELLM_CHAT_PROVIDERS: Final = [ "nscale", "nebius", "dashscope", + "qwencloud", + "qwen_ai_platform", "modelscope", "moonshot", "publicai", @@ -781,6 +808,7 @@ openai_compatible_endpoints: Final[list] = [ "inference.api.nscale.com/v1", "api.studio.nebius.ai/v1", "https://dashscope-intl.aliyuncs.com/compatible-mode/v1", + "https://dashscope.aliyuncs.com/compatible-mode/v1", "https://api-inference.modelscope.cn/v1", "https://api.moonshot.ai/v1", "https://api.publicai.co/v1", @@ -804,6 +832,7 @@ openai_compatible_endpoints: Final[list] = [ "https://api.meta.ai/v1", "https://api.cognition.ai/v1", "https://api.scx.ai/v1", + "https://gigachat.devices.sberbank.ru/api/v1", ] @@ -853,6 +882,8 @@ openai_compatible_providers: Final[list] = [ "nscale", "nebius", "dashscope", + "qwencloud", + "qwen_ai_platform", "modelscope", "moonshot", "v0", @@ -883,6 +914,8 @@ openai_text_completion_compatible_providers: Final[list] = [ # providers that s "featherless_ai", "nebius", "dashscope", + "qwencloud", + "qwen_ai_platform", "modelscope", "moonshot", "publicai", @@ -1090,7 +1123,7 @@ nebius_models: Final[set] = set( ] ) -dashscope_models: Final[set] = set( +dashscope_models: Final[frozenset] = frozenset( [ "qwen-turbo", "qwen-plus", @@ -1105,6 +1138,10 @@ dashscope_models: Final[set] = set( ] ) +qwencloud_models: Final[frozenset] = frozenset(dashscope_models) + +qwen_ai_platform_models: Final[frozenset] = frozenset(dashscope_models) + nebius_embedding_models: Final[set] = set( [ "BAAI/bge-en-icl", @@ -1221,6 +1258,7 @@ BEDROCK_CONVERSE_MODELS: Final = [ "openai.gpt-oss-120b-1:0", "anthropic.claude-haiku-4-5-20251001-v1:0", "anthropic.claude-sonnet-4-5-20250929-v1:0", + "anthropic.claude-fable-5-1", "anthropic.claude-fable-5", "anthropic.claude-sonnet-5", "anthropic.claude-opus-5", @@ -1408,6 +1446,12 @@ DEFAULT_SOFT_BUDGET: Final = float( ) # by default all litellm proxy keys have a soft budget of 50.0 # makes it clear this is a rate limit error for a litellm virtual key RATE_LIMIT_ERROR_MESSAGE_FOR_VIRTUAL_KEY: Final = "LiteLLM Virtual Key user_api_key_hash" +# Prefix of the 401 raised when a submitted virtual key is not shaped like one. +INVALID_VIRTUAL_KEY_ERROR_MESSAGE: Final = "LiteLLM Virtual Key expected" +# Attribute stamped on that 401 at its raise site so log routing recognises it by +# provenance. Message text is caller-influenceable on other 401s, so it must not +# be used to classify. +INVALID_VIRTUAL_KEY_ERROR_MARKER: Final = "_litellm_invalid_virtual_key_error" # Python garbage collection threshold configuration # Format: "gen0,gen1,gen2" e.g., "1000,50,50" @@ -1546,6 +1590,7 @@ KEY_ROTATION_JOB_NAME: Final = "litellm_key_rotation_job" EXPIRED_UI_SESSION_KEY_CLEANUP_JOB_NAME: Final = "litellm_expired_ui_session_key_cleanup_job" WEEKLY_SPEND_REPORT_JOB_ID: Final = "weekly_spend_report_job" MONTHLY_SPEND_REPORT_JOB_ID: Final = "monthly_spend_report_job" +USER_SPEND_ALERTS_JOB_ID: Final = "user_spend_alerts_job" PROMETHEUS_FALLBACK_STATS_JOB_ID: Final = "prometheus_fallback_stats_job" SLACK_DAILY_REPORT_LOCK_ID: Final = "slack_daily_report" SLACK_MODEL_DEPRECATION_LOCK_ID: Final = "slack_model_deprecation_warning" @@ -1681,6 +1726,7 @@ DEFAULT_MCP_NAMESPACE_CSV_MAX_TOKENS: Final = 16 # Ceilings on the cached auth registries; larger tables fall back to per-row lookups # instead of holding an unbounded id set in every worker. TAG_REGISTRY_MAX_SIZE: Final = 5000 +MODEL_ACCESS_GROUP_REGISTRY_MAX_SIZE: Final = 5000 END_USER_RESTRICTED_REGISTRY_MAX_SIZE: Final = 5000 # How long a failed registry load is remembered as "unusable", so a degraded Postgres # is not re-scanned on every request on top of the per-id lookups it falls back to. @@ -1725,6 +1771,7 @@ SENTRY_DENYLIST: Final = [ "jwt_token", "private_key", "SLACK_WEBHOOK_URL", + "ALERTING_WEBHOOK_URL", "webhook_url", "LANGFUSE_SECRET_KEY", # Email Configuration diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index 3adc1c25dfd..b83e9b395a8 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -641,12 +641,12 @@ def cost_per_token( return xai_cost_per_token(model=model, usage=usage_block) elif custom_llm_provider == "lemonade": return lemonade_cost_per_token(model=model, usage=usage_block) - elif custom_llm_provider == "dashscope": + elif custom_llm_provider in ("dashscope", "qwencloud", "qwen_ai_platform"): from litellm.llms.dashscope.cost_calculator import ( cost_per_token as dashscope_cost_per_token, ) - return dashscope_cost_per_token(model=model, usage=usage_block) + return dashscope_cost_per_token(model=model, usage=usage_block, custom_llm_provider=custom_llm_provider) elif custom_llm_provider == "azure_ai": return azure_ai_cost_per_token( model=model, @@ -1910,12 +1910,15 @@ def ocr_cost( if credits is not None and cost_per_credit is not None: return cost_per_credit * credits, 0.0 - ocr_cost_per_page: float | None = None - if model_info is not None: - ocr_cost_per_page = model_info.get("ocr_cost_per_page") + ocr_cost_per_page: Final = model_info.get("ocr_cost_per_page") if model_info is not None else None + annotation_cost_per_page: Final = model_info.get("annotation_cost_per_page") if model_info is not None else None + annotation_rate: Final = annotation_cost_per_page if annotation_cost_per_page is not None else ocr_cost_per_page pages_processed: Final = response.usage_info.pages_processed - if pages_processed is None: + annotation_pages: Final = response.usage_info.pages_processed_annotation or 0 + has_billable_annotation_pages: Final = annotation_rate is not None and annotation_pages > 0 + + if pages_processed is None and not has_billable_annotation_pages: if cost_per_credit is not None or ocr_cost_per_page is None: # Surface missing usage data instead of silently under-reporting # cost. The previous behavior raised ValueError; we now return 0.0 @@ -1931,7 +1934,7 @@ def ocr_cost( return 0.0, 0.0 raise ValueError("OCR response pages_processed is None") - if ocr_cost_per_page is None: + if ocr_cost_per_page is None and not has_billable_annotation_pages: # No per-page pricing configured. Either the model is on credit-based # pricing (and credits weren't returned, so the credit branch above did # not match) or the model has no OCR pricing entry at all. Surface a @@ -1947,8 +1950,9 @@ def ocr_cost( ) return 0.0, 0.0 - total_ocr_processing_cost: Final[float] = ocr_cost_per_page * pages_processed - return total_ocr_processing_cost, 0.0 + ocr_pages_cost: Final = (ocr_cost_per_page or 0.0) * (pages_processed or 0) + annotation_pages_cost: Final = (annotation_rate or 0.0) * annotation_pages + return ocr_pages_cost + annotation_pages_cost, 0.0 def vector_store_search_cost( @@ -2268,6 +2272,10 @@ def batch_cost_calculator( return total_prompt_cost, total_completion_cost +def _attribute_value(obj: object, name: str) -> object: + return getattr(obj, name) + + def _summable_prompt_token_fields(prompt_tokens_details: BaseModel) -> list[str]: field_names: Final = list(type(prompt_tokens_details).model_fields) if getattr(prompt_tokens_details, "cache_write_tokens", None) is None: @@ -2293,7 +2301,7 @@ class BaseTokenUsageProcessor: for usage in usage_objects: # Handle direct attributes by checking what exists in the model for attr in dir(usage): - if not attr.startswith("_") and not callable(getattr(usage, attr)): + if not attr.startswith("_") and not callable(_attribute_value(usage, attr)): current_val = getattr(combined, attr, 0) new_val = getattr(usage, attr, 0) if ( @@ -2313,7 +2321,7 @@ class BaseTokenUsageProcessor: if ( hasattr(usage.prompt_tokens_details, attr) and not attr.startswith("_") - and not callable(getattr(usage.prompt_tokens_details, attr)) + and not callable(_attribute_value(usage.prompt_tokens_details, attr)) ): current_val = getattr(combined.prompt_tokens_details, attr, 0) or 0 new_val = getattr(usage.prompt_tokens_details, attr, 0) or 0 @@ -2332,7 +2340,9 @@ class BaseTokenUsageProcessor: # Check what keys exist in the model's completion_tokens_details # Access model_fields on the class, not the instance, to avoid Pydantic 2.11+ deprecation warnings for attr in type(usage.completion_tokens_details).model_fields: - if not attr.startswith("_") and not callable(getattr(usage.completion_tokens_details, attr)): + if not attr.startswith("_") and not callable( + _attribute_value(usage.completion_tokens_details, attr) + ): current_val = getattr(combined.completion_tokens_details, attr, 0) or 0 new_val = getattr(usage.completion_tokens_details, attr, 0) or 0 if isinstance(new_val, (int, float)): diff --git a/litellm/endpoints/speech/speech_to_completion_bridge/handler.py b/litellm/endpoints/speech/speech_to_completion_bridge/handler.py index 9e949db625a..6c33621ec89 100644 --- a/litellm/endpoints/speech/speech_to_completion_bridge/handler.py +++ b/litellm/endpoints/speech/speech_to_completion_bridge/handler.py @@ -115,9 +115,11 @@ class SpeechToCompletionBridgeHandler: **request_data, ) + requested_response_format: Final = optional_params.get("response_format") if isinstance(result, ModelResponse): return self.transformation_handler.transform_response( model_response=result, + response_format=requested_response_format if isinstance(requested_response_format, str) else None, ) else: raise Exception(f"Unmapped response type. Got type: {type(result)}") diff --git a/litellm/endpoints/speech/speech_to_completion_bridge/transformation.py b/litellm/endpoints/speech/speech_to_completion_bridge/transformation.py index fb66edbf272..2ed140c0208 100644 --- a/litellm/endpoints/speech/speech_to_completion_bridge/transformation.py +++ b/litellm/endpoints/speech/speech_to_completion_bridge/transformation.py @@ -1,10 +1,14 @@ +from collections.abc import Mapping +from types import MappingProxyType from typing import TYPE_CHECKING, Final, cast +from typing_extensions import NotRequired, ReadOnly, TypedDict + from litellm.constants import OPENAI_CHAT_COMPLETION_PARAMS if TYPE_CHECKING: from litellm import Logging as LiteLLMLoggingObj - from litellm.types.llms.openai import HttpxBinaryResponseContent + from litellm.types.llms.openai import ChatCompletionUserMessage, HttpxBinaryResponseContent from litellm.types.utils import ModelResponse @@ -16,7 +20,64 @@ def _completion_response_cost(model_response: "ModelResponse") -> float | None: return response_cost if isinstance(response_cost, float) else None +GEMINI_TTS_CHAT_AUDIO_FORMAT: Final = "pcm16" +GEMINI_TTS_RAW_RESPONSE_FORMAT: Final = "pcm" +GEMINI_TTS_SUPPORTED_RESPONSE_FORMATS: Final = frozenset({"wav", GEMINI_TTS_RAW_RESPONSE_FORMAT}) + + +class ChatAudioParam(TypedDict): + voice: ReadOnly[str] + format: ReadOnly[NotRequired[str]] + + class SpeechToCompletionBridgeTransformationHandler: + def _validate_response_format( + self, model: str, custom_llm_provider: str, optional_params: Mapping[str, object] + ) -> None: + if not self._is_gemini_tts_model(model): + return + response_format: Final = optional_params.get("response_format") + if not isinstance(response_format, str) or response_format in GEMINI_TTS_SUPPORTED_RESPONSE_FORMATS: + return + from litellm.exceptions import BadRequestError + + supported: Final = ", ".join(sorted(GEMINI_TTS_SUPPORTED_RESPONSE_FORMATS)) + raise BadRequestError( + message=( + f"Gemini TTS only produces raw PCM16 audio, so response_format='{response_format}'" + f" is not supported. Supported response formats: {supported}." + ), + model=model, + llm_provider=custom_llm_provider, + ) + + def _chat_completion_params(self, optional_params: Mapping[str, object]) -> Mapping[str, object]: + return MappingProxyType( + { + param: value + for param, value in optional_params.items() + if param in OPENAI_CHAT_COMPLETION_PARAMS and param != "response_format" + } + ) + + def _chat_audio_format(self, model: str, optional_params: Mapping[str, object]) -> str | None: + if self._is_gemini_tts_model(model): + return GEMINI_TTS_CHAT_AUDIO_FORMAT + response_format: Final = optional_params.get("response_format") + return response_format if isinstance(response_format, str) else None + + def _chat_audio_param( + self, model: str, voice: str | Mapping[str, object] | None, optional_params: Mapping[str, object] + ) -> ChatAudioParam | None: + if not isinstance(voice, str): + return None + audio_format: Final = self._chat_audio_format(model, optional_params) + if audio_format is None: + voice_only: Final[ChatAudioParam] = {"voice": voice} + return voice_only + audio: Final[ChatAudioParam] = {"voice": voice, "format": audio_format} + return audio + def transform_request( self, model: str, @@ -28,36 +89,20 @@ class SpeechToCompletionBridgeTransformationHandler: litellm_logging_obj: "LiteLLMLoggingObj", custom_llm_provider: str, ) -> dict: - passed_optional_params: Final = {} - for op in optional_params: - if op in OPENAI_CHAT_COMPLETION_PARAMS: - passed_optional_params[op] = optional_params[op] - - if voice is not None: - if isinstance(voice, str): - passed_optional_params["audio"] = {"voice": voice} - if "response_format" in optional_params: - passed_optional_params["audio"]["format"] = optional_params["response_format"] - - return_kwargs = { + self._validate_response_format(model, custom_llm_provider, optional_params) + user_message: Final[ChatCompletionUserMessage] = {"role": "user", "content": input} + return_kwargs: Final = { "model": model, - "messages": [ - { - "role": "user", - "content": input, - } - ], + "messages": [user_message], "modalities": ["audio"], - **passed_optional_params, + **self._chat_completion_params(optional_params), + "audio": self._chat_audio_param(model, voice, optional_params), **litellm_params, "headers": headers, "litellm_logging_obj": litellm_logging_obj, "custom_llm_provider": custom_llm_provider, } - - # filter out None values - return_kwargs = {k: v for k, v in return_kwargs.items() if v is not None} - return return_kwargs + return {k: v for k, v in return_kwargs.items() if v is not None} def _convert_pcm16_to_wav(self, pcm_data: bytes, sample_rate: int = 24000, channels: int = 1) -> bytes: """ @@ -103,7 +148,14 @@ class SpeechToCompletionBridgeTransformationHandler: """Check if the model is a Gemini TTS model that returns PCM16 data.""" return "gemini" in model.lower() and ("tts" in model.lower() or "preview-tts" in model.lower()) - def transform_response(self, model_response: "ModelResponse") -> "HttpxBinaryResponseContent": + def _gemini_tts_response_body(self, decoded_audio: bytes, response_format: str | None) -> tuple[bytes, str]: + if response_format == GEMINI_TTS_RAW_RESPONSE_FORMAT: + return decoded_audio, "audio/pcm" + return self._convert_pcm16_to_wav(decoded_audio), "audio/wav" + + def transform_response( + self, model_response: "ModelResponse", response_format: str | None + ) -> "HttpxBinaryResponseContent": import base64 import httpx @@ -114,23 +166,17 @@ class SpeechToCompletionBridgeTransformationHandler: audio_part: Final = cast(Choices, model_response.choices[0]).message.audio if audio_part is None: raise ValueError("No audio part found in the response") - audio_content: Final = audio_part.data + decoded_audio: Final = base64.b64decode(audio_part.data) - # Decode base64 to get binary content - binary_data = base64.b64decode(audio_content) - - # Check if this is a Gemini TTS model that returns raw PCM16 data model: Final = getattr(model_response, "model", "") - headers: Final = {} - if self._is_gemini_tts_model(model): - # Convert PCM16 to WAV format for proper audio file playback - binary_data = self._convert_pcm16_to_wav(binary_data) - headers["Content-Type"] = "audio/wav" - else: - headers["Content-Type"] = "audio/mpeg" - - # Create an httpx.Response object - response: Final = httpx.Response(status_code=200, content=binary_data, headers=headers) + content, content_type = ( + self._gemini_tts_response_body(decoded_audio, response_format) + if self._is_gemini_tts_model(model) + else (decoded_audio, "audio/mpeg") + ) + response: Final = httpx.Response( + status_code=200, content=content, headers=MappingProxyType({"Content-Type": content_type}) + ) binary_response: Final = HttpxBinaryResponseContent(response) binary_response.set_response_cost(_completion_response_cost(model_response)) return binary_response diff --git a/litellm/experimental_mcp_client/client.py b/litellm/experimental_mcp_client/client.py index f0a1bff8fdc..ea81e323da4 100644 --- a/litellm/experimental_mcp_client/client.py +++ b/litellm/experimental_mcp_client/client.py @@ -7,6 +7,7 @@ import base64 import os from collections.abc import Awaitable, Callable, Generator from datetime import timedelta +from functools import partial from importlib import metadata from typing import Any, Final, TypeVar @@ -47,7 +48,8 @@ from mcp.types import Tool as MCPTool from pydantic import AnyUrl from litellm._logging import verbose_logger -from litellm.constants import MCP_CLIENT_TIMEOUT, MCP_NPM_CACHE_DIR +from litellm.constants import MCP_CLIENT_TIMEOUT, MCP_NPM_CACHE_DIR, MCP_TOOL_LISTING_TIMEOUT +from litellm.experimental_mcp_client.tools import list_tools_with_pagination from litellm.llms.custom_httpx.http_handler import get_ssl_configuration from litellm.types.llms.custom_http import VerifyTypes from litellm.types.mcp import ( @@ -603,17 +605,19 @@ class MCPClient: """ verbose_logger.debug("MCP client listing tools from %s", self.server_url or "stdio") - async def _list_tools_operation(session: ClientSession): - return await session.list_tools() - try: - result: Final = await self.run_with_session(_list_tools_operation, quiet_on_error=raise_on_error) - tool_count: Final = len(result.tools) - tool_names: Final = [tool.name for tool in result.tools] + # A per-server timeout above the global default extends the whole-walk deadline + listing_deadline: Final = max(self.timeout, MCP_TOOL_LISTING_TIMEOUT) + tools: Final = await self.run_with_session( + partial(list_tools_with_pagination, listing_deadline=listing_deadline), + quiet_on_error=raise_on_error, + ) + tool_count: Final = len(tools) + tool_names: Final = tuple(tool.name for tool in tools) verbose_logger.info( "MCP client listed %s tools from %s: %s", tool_count, self.server_url or "stdio", tool_names ) - return result.tools + return tools except asyncio.CancelledError: verbose_logger.warning("MCP client list_tools was cancelled") raise diff --git a/litellm/experimental_mcp_client/tools.py b/litellm/experimental_mcp_client/tools.py index 30d50e2a74b..51d2139ef3b 100644 --- a/litellm/experimental_mcp_client/tools.py +++ b/litellm/experimental_mcp_client/tools.py @@ -1,14 +1,22 @@ import json from typing import Final, Literal +import anyio from mcp import ClientSession from mcp.types import CallToolRequestParams as MCPCallToolRequestParams from mcp.types import CallToolResult as MCPCallToolResult +from mcp.types import PaginatedRequestParams from mcp.types import Tool as MCPTool from openai.types.chat import ChatCompletionToolParam from openai.types.responses.function_tool_param import FunctionToolParam from openai.types.shared_params.function_definition import FunctionDefinition +from litellm._logging import verbose_logger +from litellm.constants import ( + MCP_CLIENT_TIMEOUT, + MCP_TOOL_LISTING_MAX_PAGES, + MCP_TOOL_LISTING_TIMEOUT, +) from litellm.types.llms.anthropic import AnthropicMessagesTool from litellm.types.utils import ChatCompletionMessageToolCall @@ -90,6 +98,64 @@ def transform_mcp_tool_to_anthropic_tool(mcp_tool: MCPTool) -> AnthropicMessages ) +async def list_tools_with_pagination( + session: ClientSession, listing_deadline: float | None = None +) -> list[MCPTool]: # mutable-ok: list return contract + """Collect tools from every tools/list page by following nextCursor. + + Stops and returns the tools collected so far when the upstream repeats a + cursor, the page cap is reached, or the whole-walk deadline expires, so a + buggy or slow upstream yields a partial catalog instead of an error. + listing_deadline overrides the default whole-walk deadline; callers with a + per-server timeout above the global default pass it through here. + """ + tools: Final[list[MCPTool]] = [] # mutable-ok: accumulates each page's tools + seen_cursors: Final[set[str]] = set() # mutable-ok: guards against cursor loops + cursor: str | None = None # rebind-ok: advances to each page's nextCursor + # The per-request session read timeout restarts on every page, so a multi-page + # walk needs its own overall deadline. max() keeps the pre-pagination guarantee + # that a single page slower than the listing timeout but within the client + # timeout still succeeds. + effective_deadline: Final = ( + listing_deadline if listing_deadline is not None else max(MCP_CLIENT_TIMEOUT, MCP_TOOL_LISTING_TIMEOUT) + ) + + with anyio.move_on_after(effective_deadline): + for _ in range(MCP_TOOL_LISTING_MAX_PAGES): + result = ( + await session.list_tools() + if cursor is None + else await session.list_tools(params=PaginatedRequestParams(cursor=cursor)) + ) + tools.extend(result.tools) + + next_cursor = getattr(result, "nextCursor", None) + if not isinstance(next_cursor, str) or not next_cursor: + return tools + if next_cursor in seen_cursors: + verbose_logger.warning( + "MCP server repeated a tools/list cursor while listing tools; returning %s tools collected so far", + len(tools), + ) + return tools + seen_cursors.add(next_cursor) + cursor = next_cursor + + verbose_logger.warning( + "MCP server tools/list pagination exceeded the maximum of %s pages; returning %s tools collected so far", + MCP_TOOL_LISTING_MAX_PAGES, + len(tools), + ) + return tools + + verbose_logger.warning( + "MCP server tools/list pagination exceeded the %s second listing deadline; returning %s tools collected so far", + effective_deadline, + len(tools), + ) + return tools + + async def load_mcp_tools( session: ClientSession, format: Literal["mcp", "openai"] = "mcp" ) -> list[MCPTool] | list[ChatCompletionToolParam]: @@ -103,10 +169,12 @@ async def load_mcp_tools( If format is set to "openai", the tools are converted to OpenAI API compatible tools. """ - tools: Final = await session.list_tools() + tools: Final = await list_tools_with_pagination(session) if format == "openai": - return [transform_mcp_tool_to_openai_tool(mcp_tool=tool) for tool in tools.tools] - return tools.tools + return [ # mutable-ok: public API returns a list + transform_mcp_tool_to_openai_tool(mcp_tool=tool) for tool in tools + ] + return tools ######################################################## diff --git a/litellm/google_genai/adapters/transformation.py b/litellm/google_genai/adapters/transformation.py index 7c86ceafd7f..6a698bb6018 100644 --- a/litellm/google_genai/adapters/transformation.py +++ b/litellm/google_genai/adapters/transformation.py @@ -1,8 +1,9 @@ import json -from collections.abc import AsyncIterator, Iterator, Sequence -from typing import Any, Final, TypedDict, cast +from collections.abc import AsyncIterator, Callable, Iterator, Mapping, Sequence +from types import MappingProxyType +from typing import Any, Final, TypeAlias, cast -from typing_extensions import ReadOnly +from typing_extensions import ReadOnly, TypedDict from litellm import verbose_logger from litellm.litellm_core_utils.json_validation_rule import normalize_tool_schema @@ -11,7 +12,6 @@ from litellm.types.llms.openai import ( ChatCompletionAssistantMessage, ChatCompletionAssistantToolCall, ChatCompletionImageObject, - ChatCompletionRequest, ChatCompletionSystemMessage, ChatCompletionTextObject, ChatCompletionToolCallFunctionChunk, @@ -23,35 +23,63 @@ from litellm.types.llms.openai import ( from litellm.types.router import GenericLiteLLMParams from litellm.types.utils import ( AdapterCompletionStreamWrapper, + ChatCompletionDeltaCustomToolCall, + ChatCompletionDeltaToolCall, + ChatCompletionMessageCustomToolCall, + ChatCompletionMessageToolCall, Choices, + Delta, + Function, + Message, ModelResponse, ModelResponseStream, StreamingChoices, - Usage, ) - -class _GenAITextPart(TypedDict, total=False): - text: ReadOnly[str] +_JsonDict: TypeAlias = dict[str, object] +_JsonDictList: TypeAlias = list[_JsonDict] -class _GenAISystemInstruction(TypedDict, total=False): - parts: ReadOnly[list[_GenAITextPart]] +class _ToolCallAccumulator(TypedDict): + name: ReadOnly[str] + arguments: ReadOnly[str] + + +class _GenAIFunctionCall(TypedDict): + name: ReadOnly[str] + args: ReadOnly[Mapping[str, object]] class _GenAIPart(TypedDict, total=False): text: ReadOnly[str] - functionCall: ReadOnly[dict[str, object]] + functionCall: ReadOnly[_GenAIFunctionCall] + + +class _GenAIFunctionResponse(TypedDict, total=False): + name: ReadOnly[str] + response: ReadOnly[object] + + +class _GenAIRequestFunctionCall(TypedDict, total=False): + name: ReadOnly[str] + args: ReadOnly[Mapping[str, object]] + + +class _GenAIContentPart(TypedDict, total=False): + text: ReadOnly[str] + inline_data: ReadOnly[Mapping[str, str]] + functionResponse: ReadOnly[_GenAIFunctionResponse] + functionCall: ReadOnly[_GenAIRequestFunctionCall] class _GenAIFunctionDeclaration(TypedDict, total=False): name: ReadOnly[str] description: ReadOnly[str] - parametersJsonSchema: ReadOnly[dict[str, object]] + parametersJsonSchema: ReadOnly[object] class _GenAITool(TypedDict, total=False): - functionDeclarations: ReadOnly[list[_GenAIFunctionDeclaration]] + functionDeclarations: ReadOnly[Sequence[_GenAIFunctionDeclaration]] class _GenAIFunctionCallingConfig(TypedDict, total=False): @@ -62,9 +90,11 @@ class _GenAIToolConfig(TypedDict, total=False): functionCallingConfig: ReadOnly[_GenAIFunctionCallingConfig] -def _decode_tool_call_arguments(raw_arguments: str) -> object: - """Decode a tool call's JSON-encoded arguments into the value Google GenAI expects.""" - return json.loads(raw_arguments) +class _GenAISystemInstruction(TypedDict, total=False): + parts: ReadOnly[Sequence[Mapping[str, str]]] + + +_EMPTY_STR_MAPPING: Final[Mapping[str, str]] = MappingProxyType({}) class GoogleGenAIStreamWrapper(AdapterCompletionStreamWrapper): @@ -74,12 +104,11 @@ class GoogleGenAIStreamWrapper(AdapterCompletionStreamWrapper): """ sent_first_chunk: bool = False - # State tracking for accumulating partial tool calls - accumulated_tool_calls: dict[int, dict[str, str]] + _parse_accumulated_args: Callable[[str], Mapping[str, object]] = staticmethod(json.loads) def __init__(self, completion_stream: object): self.sent_first_chunk = False - self.accumulated_tool_calls = {} + self.accumulated_tool_calls = dict[int, _ToolCallAccumulator]() self._returned_response = False super().__init__(completion_stream) @@ -124,7 +153,7 @@ class GoogleGenAIStreamWrapper(AdapterCompletionStreamWrapper): # After the stream is exhausted, check for any remaining accumulated tool calls if self.accumulated_tool_calls: try: - parts: Final[list[_GenAIPart]] = [] + parts: Final = list[_GenAIPart]() for ( tool_call_index, tool_call_data, @@ -132,7 +161,9 @@ class GoogleGenAIStreamWrapper(AdapterCompletionStreamWrapper): try: # For tool calls with no arguments, accumulated_args will be "", which is not valid JSON. # We default to an empty JSON object in this case. - parsed_args = _decode_tool_call_arguments(tool_call_data["arguments"] or "{}") + parsed_args: Mapping[str, object] = self._parse_accumulated_args( + tool_call_data["arguments"] or "{}" + ) function_call_part: _GenAIPart = { "functionCall": { "name": tool_call_data["name"] or "undefined_tool_name", @@ -149,7 +180,7 @@ class GoogleGenAIStreamWrapper(AdapterCompletionStreamWrapper): tool_call_data["arguments"], ) if parts: - final_chunk: Final[dict[str, object]] = { + final_chunk: Final = { "candidates": [ { "content": {"parts": parts, "role": "model"}, @@ -211,14 +242,16 @@ class GoogleGenAIStreamWrapper(AdapterCompletionStreamWrapper): class GoogleGenAIAdapter: """Adapter for transforming Google GenAI generate_content requests to/from litellm.completion format""" + _parse_tool_call_args: Callable[[str], Mapping[str, object]] = staticmethod(json.loads) + def __init__(self) -> None: pass def translate_generate_content_to_completion( self, model: str, - contents: list[dict[str, Any]] | dict[str, Any], - config: dict[str, Any] | None = None, + contents: _JsonDictList | _JsonDict, + config: Mapping[str, object] | None = None, litellm_params: GenericLiteLLMParams | None = None, **kwargs, ) -> dict[str, Any]: @@ -250,7 +283,7 @@ class GoogleGenAIAdapter: messages: Final = self._transform_contents_to_messages(contents_list, system_instruction=system_instruction) # Create base request as dict (which is compatible with ChatCompletionRequest) - completion_request: Final[ChatCompletionRequest] = { + completion_request: Final[_JsonDict] = { "model": model, "messages": messages, } @@ -312,9 +345,9 @@ class GoogleGenAIAdapter: def _add_generic_litellm_params_to_request( self, - completion_request_dict: dict[str, object], + completion_request_dict: _JsonDict, litellm_params: GenericLiteLLMParams | None = None, - ) -> dict[str, object]: + ) -> _JsonDict: """Add generic litellm params to request. e.g add api_base, api_key, api_version, etc. Args: @@ -326,7 +359,7 @@ class GoogleGenAIAdapter: """ allowed_fields: Final = GenericLiteLLMParams.model_fields.keys() if litellm_params: - litellm_dict: Final = litellm_params.model_dump(exclude_none=True) + litellm_dict: Final[_JsonDict] = litellm_params.model_dump(exclude_none=True) for key, value in litellm_dict.items(): if key in allowed_fields: completion_request_dict[key] = value @@ -346,12 +379,12 @@ class GoogleGenAIAdapter: tools: Sequence[_GenAITool], ) -> list[ChatCompletionToolParam]: """Transform Google GenAI tools to OpenAI tools format""" - openai_tools: Final[list[dict[str, object]]] = [] + openai_tools: Final = list[_JsonDict]() for tool in tools: if "functionDeclarations" in tool: for func_decl in tool["functionDeclarations"]: - function_chunk: dict[str, object] = { + function_chunk: _JsonDict = { "name": func_decl.get("name", ""), } @@ -360,7 +393,7 @@ class GoogleGenAIAdapter: if "parametersJsonSchema" in func_decl: function_chunk["parameters"] = func_decl["parametersJsonSchema"] - openai_tool: dict[str, object] = {"type": "function", "function": function_chunk} + openai_tool: _JsonDict = {"type": "function", "function": function_chunk} openai_tools.append(openai_tool) # normalize the tool schemas @@ -391,13 +424,13 @@ class GoogleGenAIAdapter: # Handle system instruction if system_instruction: - system_parts: Final = system_instruction.get("parts", []) + system_parts: Final[Sequence[Mapping[str, str]]] = system_instruction.get("parts", []) if system_parts and "text" in system_parts[0]: messages.append(ChatCompletionSystemMessage(role="system", content=system_parts[0]["text"])) for content in contents: role = content.get("role", "user") - parts = content.get("parts", []) + parts: Sequence[_GenAIContentPart | str | None] = content.get("parts", []) if role == "user": # Handle user messages with potential function responses @@ -500,7 +533,7 @@ class GoogleGenAIAdapter: def translate_completion_to_generate_content( self, response: ModelResponse, - ) -> dict[str, object]: + ) -> _JsonDict: """ Transform litellm completion response to Google GenAI generate_content format @@ -523,13 +556,13 @@ class GoogleGenAIAdapter: parts = self._transform_openai_message_to_google_genai_parts(choice.message) else: # Fallback for generic choice objects - message_content = getattr(choice, "message", {}).get("content", "") or getattr(choice, "delta", {}).get( - "content", "" - ) + message_content: str = getattr(choice, "message", _EMPTY_STR_MAPPING).get("content", "") or getattr( + choice, "delta", _EMPTY_STR_MAPPING + ).get("content", "") parts = [{"text": message_content}] if message_content else [] # Create Google GenAI format response - generate_content_response: Final[dict[str, object]] = { + generate_content_response: Final[_JsonDict] = { "candidates": [ { "content": {"parts": parts, "role": "model"}, @@ -563,7 +596,7 @@ class GoogleGenAIAdapter: self, response: ModelResponse | ModelResponseStream, wrapper: GoogleGenAIStreamWrapper, - ) -> dict[str, object] | None: + ) -> Mapping[str, object] | None: """ Transform streaming litellm completion chunk to Google GenAI generate_content format @@ -590,7 +623,7 @@ class GoogleGenAIAdapter: finish_reason: str | None = getattr(choice, "finish_reason", None) else: # Fallback for generic choice objects - message_content: Final = getattr(choice, "delta", {}).get("content", "") + message_content: Final[str] = getattr(choice, "delta", _EMPTY_STR_MAPPING).get("content", "") parts = [{"text": message_content}] if message_content else [] finish_reason = getattr(choice, "finish_reason", None) @@ -599,7 +632,7 @@ class GoogleGenAIAdapter: return None # Create Google GenAI streaming format response - streaming_chunk: Final[dict[str, object]] = { + streaming_chunk: Final[_JsonDict] = { "candidates": [ { "content": {"parts": parts, "role": "model"}, @@ -635,10 +668,10 @@ class GoogleGenAIAdapter: def _transform_openai_message_to_google_genai_parts( self, - message: Any, - ) -> list[_GenAIPart]: + message: Message, + ) -> Sequence[_GenAIPart]: """Transform OpenAI message to Google GenAI parts format""" - parts: Final[list[_GenAIPart]] = [] + parts: Final = list[_GenAIPart]() # Add text content if present if hasattr(message, "content") and message.content: @@ -646,20 +679,22 @@ class GoogleGenAIAdapter: # Add tool calls if present if hasattr(message, "tool_calls") and message.tool_calls: - for tool_call in message.tool_calls: - if hasattr(tool_call, "function") and tool_call.function: + tool_calls: Final[Sequence[ChatCompletionMessageToolCall | ChatCompletionMessageCustomToolCall]] = ( + message.tool_calls + ) + for tool_call in tool_calls: + function: Function | None = getattr(tool_call, "function", None) + if function: try: - args = ( - _decode_tool_call_arguments(tool_call.function.arguments) - if tool_call.function.arguments - else {} + args: Mapping[str, object] = ( + self._parse_tool_call_args(function.arguments) if function.arguments else {} ) except json.JSONDecodeError: args = {} function_call_part: _GenAIPart = { "functionCall": { - "name": tool_call.function.name or "undefined_tool_name", + "name": function.name or "undefined_tool_name", "args": args, } } @@ -668,24 +703,26 @@ class GoogleGenAIAdapter: return parts if parts else [{"text": ""}] def _transform_openai_delta_to_google_genai_parts_with_accumulation( - self, delta: Any, wrapper: GoogleGenAIStreamWrapper - ) -> list[_GenAIPart]: + self, delta: Delta, wrapper: GoogleGenAIStreamWrapper + ) -> Sequence[_GenAIPart]: """Transforms OpenAI delta to Google GenAI parts, accumulating streaming tool calls.""" # 1. Initialize wrapper state if it doesn't exist if not hasattr(wrapper, "accumulated_tool_calls"): wrapper.accumulated_tool_calls = {} - parts: Final[list[_GenAIPart]] = [] + parts: Final = list[_GenAIPart]() if hasattr(delta, "content") and delta.content: parts.append({"text": delta.content}) # 2. Ensure tool_calls is iterable - tool_calls: Final = delta.tool_calls or [] + tool_calls: Final[Sequence[ChatCompletionDeltaToolCall | ChatCompletionDeltaCustomToolCall]] = ( + delta.tool_calls or [] + ) for tool_call in tool_calls: - if not hasattr(tool_call, "function"): + if not hasattr(tool_call, "function") or isinstance(tool_call, ChatCompletionDeltaCustomToolCall): continue # 3. Use `index` as the primary key for accumulation @@ -701,19 +738,20 @@ class GoogleGenAIAdapter: } # Accumulate name and arguments - function_name = getattr(tool_call.function, "name", None) - args_chunk = getattr(tool_call.function, "arguments", None) + delta_function: Function | None = getattr(tool_call, "function", None) + function_name: str | None = getattr(delta_function, "name", None) + args_chunk: str | None = getattr(delta_function, "arguments", None) # Optimization: Skip chunks that have no new data if not function_name and not args_chunk: verbose_logger.debug("Skipping empty tool call chunk for index: %s", tool_call_index) continue - if function_name: - wrapper.accumulated_tool_calls[tool_call_index]["name"] = function_name - - if args_chunk: - wrapper.accumulated_tool_calls[tool_call_index]["arguments"] += args_chunk + previous_data: _ToolCallAccumulator = wrapper.accumulated_tool_calls[tool_call_index] + wrapper.accumulated_tool_calls[tool_call_index] = _ToolCallAccumulator( + name=function_name or previous_data["name"], + arguments=previous_data["arguments"] + (args_chunk or ""), + ) # Attempt to parse and emit a complete tool call accumulated_data = wrapper.accumulated_tool_calls[tool_call_index] @@ -723,7 +761,7 @@ class GoogleGenAIAdapter: # 5. Attempt to parse arguments even if name hasn't arrived. try: # Attempt to parse the accumulated arguments string - parsed_args = _decode_tool_call_arguments(accumulated_args) + parsed_args: Mapping[str, object] = self._parse_tool_call_args(accumulated_args) # If parsing succeeds, but we don't have a name yet, wait. # The part will be created by a later chunk that brings the name. @@ -757,7 +795,7 @@ class GoogleGenAIAdapter: return mapping.get(finish_reason, "STOP") - def _map_usage(self, usage: Usage | None) -> dict[str, int]: + def _map_usage(self, usage: object) -> Mapping[str, int]: """Map OpenAI usage to Google GenAI usage format""" return { "promptTokenCount": getattr(usage, "prompt_tokens", 0) or 0, diff --git a/litellm/google_genai/main.py b/litellm/google_genai/main.py index b5815bd3f7c..c1822e4720d 100644 --- a/litellm/google_genai/main.py +++ b/litellm/google_genai/main.py @@ -52,10 +52,10 @@ class GenerateContentSetupResult(BaseModel): model_config: ClassVar[ConfigDict] = ConfigDict(arbitrary_types_allowed=True) model: str - request_body: dict[str, Any] + request_body: dict[str, object] custom_llm_provider: str generate_content_provider_config: BaseGoogleGenAIGenerateContentConfig | None - generate_content_config_dict: dict[str, Any] + generate_content_config_dict: dict[str, object] native_request_fields: dict[str, object] litellm_params: GenericLiteLLMParams litellm_logging_obj: LiteLLMLoggingObj @@ -68,7 +68,7 @@ class GenerateContentHelper: @staticmethod def mock_generate_content_response( mock_response: str = "This is a mock response from Google GenAI generate_content.", - ) -> dict[str, Any]: + ) -> dict[str, object]: """Mock response for generate_content for testing purposes""" return { "text": mock_response, @@ -239,9 +239,9 @@ async def agenerate_content( tools: ToolConfigDict | None = None, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, + extra_body: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, @@ -307,9 +307,9 @@ def generate_content( tools: ToolConfigDict | None = None, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, + extra_body: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, @@ -397,9 +397,9 @@ async def agenerate_content_stream( tools: ToolConfigDict | None = None, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, + extra_body: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, @@ -492,9 +492,9 @@ def generate_content_stream( tools: ToolConfigDict | None = None, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, + extra_body: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, diff --git a/litellm/images/main.py b/litellm/images/main.py index 1688087c2da..6a94e7c8df2 100644 --- a/litellm/images/main.py +++ b/litellm/images/main.py @@ -3,7 +3,7 @@ import contextvars import importlib from collections.abc import Coroutine from functools import partial -from typing import TYPE_CHECKING, Any, Final, Literal, Optional, cast, overload +from typing import TYPE_CHECKING, Final, Literal, Optional, cast, overload if TYPE_CHECKING: from litellm.images.utils import ImageEditRequestUtils @@ -151,7 +151,7 @@ def image_generation( *, aimg_generation: Literal[True], **kwargs, -) -> Coroutine[Any, Any, ImageResponse]: +) -> Coroutine[object, object, ImageResponse]: ... @@ -197,7 +197,7 @@ def image_generation( api_version: str | None = None, custom_llm_provider=None, **kwargs, -) -> ImageResponse | Coroutine[Any, Any, ImageResponse]: +) -> ImageResponse | Coroutine[object, object, ImageResponse]: """ Maps the https://api.openai.com/v1/images/generations endpoint. @@ -386,6 +386,8 @@ def image_generation( litellm.LlmProviders.VERTEX_AI, litellm.LlmProviders.OPENROUTER, litellm.LlmProviders.DASHSCOPE, + litellm.LlmProviders.QWENCLOUD, + litellm.LlmProviders.QWEN_AI_PLATFORM, ): if image_generation_config is None: raise ValueError(f"image generation config is not supported for {custom_llm_provider}") @@ -723,14 +725,14 @@ def image_edit( user: str | None = None, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, + extra_body: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, **kwargs, -) -> ImageResponse | Coroutine[Any, Any, ImageResponse]: +) -> ImageResponse | Coroutine[object, object, ImageResponse]: """ Maps the image edit functionality, similar to OpenAI's images/edits endpoint. """ @@ -769,7 +771,7 @@ def image_edit( images: Final = image if isinstance(image, list) else ([image] if image is not None else []) headers_from_kwargs: Final = kwargs.get("headers") - merged_extra_headers: Final[dict[str, Any]] = {} + merged_extra_headers: Final[dict[str, object]] = {} if isinstance(headers_from_kwargs, dict): merged_extra_headers.update(headers_from_kwargs) if isinstance(extra_headers, dict): @@ -974,9 +976,9 @@ async def aimage_edit( user: str | None = None, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, + extra_body: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, @@ -1044,7 +1046,7 @@ async def aimage_edit( ) -def __getattr__(name: str) -> Any: +def __getattr__(name: str) -> type["ImageEditRequestUtils"]: """Lazy import handler for images.main module""" if name == "ImageEditRequestUtils": # Lazy load ImageEditRequestUtils to avoid heavy import from images.utils at module load time diff --git a/litellm/integrations/SlackAlerting/slack_alerting.py b/litellm/integrations/SlackAlerting/slack_alerting.py index d7d06387d85..748ef938cea 100644 --- a/litellm/integrations/SlackAlerting/slack_alerting.py +++ b/litellm/integrations/SlackAlerting/slack_alerting.py @@ -68,6 +68,7 @@ from .utils import process_slack_alerting_variables if TYPE_CHECKING: from litellm.proxy.db.db_transaction_queue.pod_lock_manager import PodLockManager + from litellm.proxy.utils import PrismaClient from litellm.router import Router as _Router Router = _Router @@ -545,7 +546,6 @@ class SlackAlerting(CustomBatchLogger): # Get the appropriate budget alert type handler budget_alert_class: Final = get_budget_alert_type(type) _id: Final = budget_alert_class.get_id(user_info) - user_info_json: Final = user_info.model_dump(exclude_none=True) user_info_str: Final = self._get_user_info_str(user_info) event_message = budget_alert_class.get_event_message() @@ -575,7 +575,22 @@ class SlackAlerting(CustomBatchLogger): webhook_event = WebhookEvent( event=event, event_message=event_message, - **user_info_json, + spend=user_info.spend, + max_budget=user_info.max_budget, + soft_budget=user_info.soft_budget, + token=user_info.token, + customer_id=user_info.customer_id, + user_id=user_info.user_id, + team_id=user_info.team_id, + team_alias=user_info.team_alias, + organization_id=user_info.organization_id, + user_email=user_info.user_email, + key_alias=user_info.key_alias, + projected_exceeded_date=user_info.projected_exceeded_date, + projected_spend=user_info.projected_spend, + event_group=user_info.event_group, + alert_emails=user_info.alert_emails, + max_budget_alert_emails=user_info.max_budget_alert_emails, ) await self.send_alert( message=event_message + "\n\n" + user_info_str, @@ -657,7 +672,7 @@ class SlackAlerting(CustomBatchLogger): """ Create a standard message for a budget alert """ - _all_fields_as_dict: Final = user_info.model_dump(exclude_none=True) + _all_fields_as_dict: Final[dict[str, object]] = user_info.model_dump(exclude_none=True) _all_fields_as_dict.pop("token") msg = "" for k, v in _all_fields_as_dict.items(): @@ -1006,7 +1021,7 @@ class SlackAlerting(CustomBatchLogger): except Exception: pass - async def model_added_alert(self, model_name: str, litellm_model_name: str, passed_model_info: Any): + async def model_added_alert(self, model_name: str, litellm_model_name: str, passed_model_info: object): base_model_from_user: Final = getattr(passed_model_info, "base_model", None) model_info = {} base_model = "" @@ -1485,9 +1500,9 @@ Model Info: elif self.default_webhook_url is not None: _digest_webhook = self.default_webhook_url else: - _digest_webhook = os.getenv("SLACK_WEBHOOK_URL", None) + _digest_webhook = os.getenv("SLACK_WEBHOOK_URL") or os.getenv("ALERTING_WEBHOOK_URL") if _digest_webhook is None: - raise ValueError("Missing SLACK_WEBHOOK_URL from environment") + raise ValueError("Missing SLACK_WEBHOOK_URL / ALERTING_WEBHOOK_URL from environment") digest_key: Final = f"{alert_type_name_str}:{request_model or ''}:{api_base or ''}" @@ -1516,10 +1531,10 @@ Model Info: elif self.default_webhook_url is not None: slack_webhook_url = self.default_webhook_url else: - slack_webhook_url = os.getenv("SLACK_WEBHOOK_URL", None) + slack_webhook_url = os.getenv("SLACK_WEBHOOK_URL") or os.getenv("ALERTING_WEBHOOK_URL") if slack_webhook_url is None: - raise ValueError("Missing SLACK_WEBHOOK_URL from environment") + raise ValueError("Missing SLACK_WEBHOOK_URL / ALERTING_WEBHOOK_URL from environment") payload: Final = {"text": formatted_message} headers: Final = {"Content-type": "application/json"} @@ -1930,6 +1945,69 @@ Model Info: except Exception as e: verbose_proxy_logger.exception("Error sending weekly spend report %s", e) + async def send_user_spend_alerts(self, prisma_client: "PrismaClient | None" = None) -> None: + """Check per-user daily/monthly spend thresholds and spend anomalies, alerting once per user per period.""" + if self.alerting is None or "slack" not in self.alerting: + return + + thresholds_enabled: Final = AlertType.user_spend_thresholds in self.alert_types + anomalies_enabled: Final = AlertType.user_spend_anomalies in self.alert_types + if not thresholds_enabled and not anomalies_enabled: + return + + if prisma_client is None: + from litellm.proxy.proxy_server import prisma_client as global_prisma_client + + prisma_client = global_prisma_client # rebind-ok: fall back to the proxy's global client + if prisma_client is None: + return + + from litellm.integrations.SlackAlerting.user_spend_alerts import ( + evaluate_user_spend, + fetch_user_spend_rows, + ) + + try: + today: Final = datetime.datetime.now(datetime.timezone.utc).date() + rows: Final = await fetch_user_spend_rows( + prisma_client=prisma_client, + today=today, + baseline_days=self.alerting_args.spend_anomaly_baseline_days, + ) + all_events: Final = tuple( + event + for row in rows + for event in evaluate_user_spend( + row=row, + args=self.alerting_args, + today=today, + thresholds_enabled=thresholds_enabled, + anomalies_enabled=anomalies_enabled, + ) + ) + cached_flags: Final = await asyncio.gather( + *(self.internal_usage_cache.async_get_cache(key=event.cache_key) for event in all_events) + ) + new_events: Final = tuple(event for event, cached in zip(all_events, cached_flags) if not cached) + for alert_type in (AlertType.user_spend_thresholds, AlertType.user_spend_anomalies): + typed_events = tuple(event for event in new_events if event.alert_type == alert_type) + if not typed_events: + continue + await self.send_alert( + message="\n\n".join(event.message for event in typed_events), + level="High", + alert_type=alert_type, + alerting_metadata={}, # mutable-ok: send_alert takes a dict payload + ) + for event in typed_events: + await self.internal_usage_cache.async_set_cache( + key=event.cache_key, + value="SENT", + ttl=event.cache_ttl, + ) + except Exception as e: # noqa: BLE001 # background job must not crash the scheduler + verbose_proxy_logger.exception("Error sending user spend alerts: %s", e) + async def send_fallback_stats_from_prometheus(self): """ Helper to send fallback statistics from prometheus server -> to slack @@ -1973,7 +2051,7 @@ Model Info: try: message = f"`{event_name}`\n" - key_event_dict: Final = key_event.model_dump() + key_event_dict: Final[dict[str, object]] = key_event.model_dump() # Add Created by information first message += "*Action Done by:*\n" diff --git a/litellm/integrations/SlackAlerting/user_spend_alerts.py b/litellm/integrations/SlackAlerting/user_spend_alerts.py new file mode 100644 index 00000000000..38794735c1b --- /dev/null +++ b/litellm/integrations/SlackAlerting/user_spend_alerts.py @@ -0,0 +1,139 @@ +"""Per-user daily/monthly spend threshold alerts and spend anomaly detection.""" + +import datetime +from dataclasses import dataclass +from typing import TYPE_CHECKING, Final, Literal + +from pydantic import TypeAdapter + +from litellm.constants import HOURS_IN_A_DAY +from litellm.types.integrations.slack_alerting import AlertType, SlackAlertingArgs + +if TYPE_CHECKING: + from litellm.proxy.utils import PrismaClient + +DAY_SECONDS: Final = HOURS_IN_A_DAY * 60 * 60 +MONTHLY_ALERT_TTL_SECONDS: Final = 32 * DAY_SECONDS + +USER_SPEND_QUERY: Final = """ +SELECT + user_id, + COALESCE(SUM(spend) FILTER (WHERE date = $1), 0)::float AS daily_spend, + COALESCE(SUM(spend) FILTER (WHERE date >= $2), 0)::float AS monthly_spend, + COALESCE(SUM(spend) FILTER (WHERE date >= $3 AND date < $1), 0)::float AS baseline_spend +FROM "LiteLLM_DailyUserSpend" +WHERE date >= LEAST($2, $3) AND user_id IS NOT NULL +GROUP BY user_id +HAVING COALESCE(SUM(spend) FILTER (WHERE date >= $2), 0) > 0 +""" + + +@dataclass(frozen=True, slots=True) +class UserSpendRow: + user_id: str + daily_spend: float + monthly_spend: float + baseline_spend: float + + +@dataclass(frozen=True, slots=True) +class UserSpendAlertEvent: + kind: Literal["daily_threshold", "monthly_threshold", "anomaly"] + alert_type: AlertType + message: str + cache_key: str + cache_ttl: int + + +USER_SPEND_ROWS_ADAPTER: Final = TypeAdapter(tuple[UserSpendRow, ...]) + + +async def fetch_user_spend_rows( + prisma_client: "PrismaClient", + today: datetime.date, + baseline_days: int, +) -> tuple[UserSpendRow, ...]: + today_str: Final = today.strftime("%Y-%m-%d") + month_start_str: Final = today.replace(day=1).strftime("%Y-%m-%d") + baseline_start_str: Final = (today - datetime.timedelta(days=max(baseline_days, 1))).strftime("%Y-%m-%d") + raw: Final = await prisma_client.db.query_raw(USER_SPEND_QUERY, today_str, month_start_str, baseline_start_str) + return USER_SPEND_ROWS_ADAPTER.validate_python(raw) + + +def _daily_threshold_event(row: UserSpendRow, args: SlackAlertingArgs, today_str: str) -> UserSpendAlertEvent | None: + threshold: Final = args.daily_spend_per_user_threshold + if threshold is None or row.daily_spend < threshold: + return None + return UserSpendAlertEvent( + kind="daily_threshold", + alert_type=AlertType.user_spend_thresholds, + message=( + f"User Daily Spend Threshold Crossed:\n" + f"User: `{row.user_id}`\n" + f"Spend Today: `${row.daily_spend:.2f}`\n" + f"Daily Threshold: `${threshold:.2f}`" + ), + cache_key=f"user_spend_alert_daily_{row.user_id}_{today_str}", + cache_ttl=DAY_SECONDS, + ) + + +def _monthly_threshold_event(row: UserSpendRow, args: SlackAlertingArgs, month_str: str) -> UserSpendAlertEvent | None: + threshold: Final = args.monthly_spend_per_user_threshold + if threshold is None or row.monthly_spend < threshold: + return None + return UserSpendAlertEvent( + kind="monthly_threshold", + alert_type=AlertType.user_spend_thresholds, + message=( + f"User Monthly Spend Threshold Crossed:\n" + f"User: `{row.user_id}`\n" + f"Spend This Month: `${row.monthly_spend:.2f}`\n" + f"Monthly Threshold: `${threshold:.2f}`" + ), + cache_key=f"user_spend_alert_monthly_{row.user_id}_{month_str}", + cache_ttl=MONTHLY_ALERT_TTL_SECONDS, + ) + + +def _anomaly_event(row: UserSpendRow, args: SlackAlertingArgs, today_str: str) -> UserSpendAlertEvent | None: + if row.daily_spend < args.spend_anomaly_min_spend: + return None + baseline_daily_avg: Final = row.baseline_spend / args.spend_anomaly_baseline_days + if row.baseline_spend > 0 and row.daily_spend <= args.spend_anomaly_multiplier * baseline_daily_avg: + return None + return UserSpendAlertEvent( + kind="anomaly", + alert_type=AlertType.user_spend_anomalies, + message=( + f"User Spend Anomaly Detected:\n" + f"User: `{row.user_id}`\n" + f"Spend Today: `${row.daily_spend:.2f}`\n" + f"Daily Average (last {args.spend_anomaly_baseline_days} days): `${baseline_daily_avg:.2f}`\n" + f"Trigger: spend above `{args.spend_anomaly_multiplier}x` the daily average " + f"(minimum `${args.spend_anomaly_min_spend:.2f}`)" + ), + cache_key=f"user_spend_alert_anomaly_{row.user_id}_{today_str}", + cache_ttl=DAY_SECONDS, + ) + + +def evaluate_user_spend( + row: UserSpendRow, + args: SlackAlertingArgs, + today: datetime.date, + thresholds_enabled: bool, + anomalies_enabled: bool, +) -> tuple[UserSpendAlertEvent, ...]: + today_str: Final = today.strftime("%Y-%m-%d") + month_str: Final = today.strftime("%Y-%m") + threshold_events: Final = ( + ( + _daily_threshold_event(row=row, args=args, today_str=today_str), + _monthly_threshold_event(row=row, args=args, month_str=month_str), + ) + if thresholds_enabled + else () + ) + anomaly_events: Final = (_anomaly_event(row=row, args=args, today_str=today_str),) if anomalies_enabled else () + return tuple(event for event in (*threshold_events, *anomaly_events) if event is not None) diff --git a/litellm/integrations/arize/arize_phoenix_prompt_manager.py b/litellm/integrations/arize/arize_phoenix_prompt_manager.py index 71f4902bbe5..0c9e868c146 100644 --- a/litellm/integrations/arize/arize_phoenix_prompt_manager.py +++ b/litellm/integrations/arize/arize_phoenix_prompt_manager.py @@ -3,10 +3,12 @@ Arize Phoenix prompt manager that integrates with LiteLLM's prompt management sy Fetches prompt versions from Arize Phoenix and provides workspace-based access control. """ -from typing import Any, Final +from collections.abc import Mapping, Sequence +from typing import Any, Final, cast from jinja2 import DictLoader, select_autoescape from jinja2.sandbox import ImmutableSandboxedEnvironment +from typing_extensions import ReadOnly, TypedDict from litellm.integrations.custom_prompt_management import CustomPromptManagement from litellm.integrations.prompt_management_base import ( @@ -20,6 +22,31 @@ from litellm.types.utils import StandardCallbackDynamicParams from .arize_phoenix_client import ArizePhoenixClient +class ArizePhoenixContentPart(TypedDict, total=False): + type: ReadOnly[str] + text: ReadOnly[str] + + +class ArizePhoenixTemplateMessage(TypedDict, total=False): + role: ReadOnly[str] + content: ReadOnly[Sequence[ArizePhoenixContentPart]] + + +class ArizePhoenixTemplateBody(TypedDict, total=False): + messages: ReadOnly[Sequence[ArizePhoenixTemplateMessage]] + + +class ArizePhoenixPromptMetadata(TypedDict): + model_name: ReadOnly[str | None] + model_provider: ReadOnly[str | None] + description: ReadOnly[str] + template_type: ReadOnly[str | None] + template_format: ReadOnly[str] + invocation_parameters: ReadOnly[Mapping[str, Mapping[str, object]]] + temperature: ReadOnly[float | None] + max_tokens: ReadOnly[int | None] + + class ArizePhoenixPromptTemplate: """ Represents a prompt template loaded from Arize Phoenix. @@ -28,10 +55,10 @@ class ArizePhoenixPromptTemplate: def __init__( self, template_id: str, - messages: list[dict[str, Any]], - metadata: dict[str, Any], + messages: Sequence[ArizePhoenixTemplateMessage], + metadata: ArizePhoenixPromptMetadata, model: str | None = None, - ): + ) -> None: self.template_id = template_id self.messages = messages self.metadata = metadata @@ -43,7 +70,7 @@ class ArizePhoenixPromptTemplate: self.description = metadata.get("description", "") self.template_format = metadata.get("template_format", "MUSTACHE") - def __repr__(self): + def __repr__(self) -> str: return f"ArizePhoenixPromptTemplate(id='{self.template_id}', model='{self.model}')" @@ -109,7 +136,7 @@ class ArizePhoenixTemplateManager: def _parse_prompt_data(self, data: dict[str, Any], prompt_version_id: str) -> ArizePhoenixPromptTemplate: """Parse Arize Phoenix prompt data and extract messages and metadata.""" - template_data: Final = data.get("template", {}) + template_data: Final[ArizePhoenixTemplateBody] = data.get("template", {}) messages: Final = template_data.get("messages", []) # Extract invocation parameters @@ -129,7 +156,7 @@ class ArizePhoenixTemplateManager: break # Build metadata dictionary - metadata: Final = { + metadata: Final[ArizePhoenixPromptMetadata] = { "model_name": data.get("model_name"), "model_provider": data.get("model_provider"), "description": data.get("description", ""), @@ -146,7 +173,9 @@ class ArizePhoenixTemplateManager: metadata=metadata, ) - def render_template(self, template_id: str, variables: dict[str, Any] | None = None) -> list[AllMessageValues]: + def render_template( + self, template_id: str, variables: Mapping[str, object] | None = None + ) -> list[AllMessageValues]: """Render a template with the given variables and return formatted messages.""" if template_id not in self.prompts: raise ValueError(f"Template '{template_id}' not found") @@ -174,7 +203,9 @@ class ArizePhoenixTemplateManager: # Combine rendered content final_content = " ".join(rendered_content_parts) - rendered_messages.append({"role": role, "content": final_content}) + rendered_messages.append( + cast("AllMessageValues", {"role": role, "content": final_content}) # cast-ok: Phoenix roles are OpenAI + ) return rendered_messages @@ -243,8 +274,8 @@ class ArizePhoenixPromptManager(CustomPromptManagement): def get_prompt_template( self, prompt_id: str, - prompt_variables: dict[str, Any] | None = None, - ) -> tuple[list[AllMessageValues], dict[str, Any]]: + prompt_variables: Mapping[str, object] | None = None, + ) -> tuple[list[AllMessageValues], dict[str, object]]: """ Get a prompt template and render it with variables. @@ -263,7 +294,7 @@ class ArizePhoenixPromptManager(CustomPromptManagement): rendered_messages: Final = self.prompt_manager.render_template(prompt_id, prompt_variables or {}) # Extract metadata - metadata: Final = { + metadata: Final[dict[str, object]] = { "model": template.model, "temperature": template.temperature, "max_tokens": template.max_tokens, @@ -271,7 +302,7 @@ class ArizePhoenixPromptManager(CustomPromptManagement): # Add additional invocation parameters invocation_params: Final = template.invocation_parameters - provider_params = {} + provider_params: Mapping[str, object] = {} if "openai" in invocation_params: provider_params = invocation_params["openai"] @@ -289,12 +320,12 @@ class ArizePhoenixPromptManager(CustomPromptManagement): self, user_id: str | None, messages: list[AllMessageValues], - function_call: dict[str, Any] | str | None = None, - litellm_params: dict[str, Any] | None = None, + function_call: dict[str, object] | str | None = None, + litellm_params: dict[str, object] | None = None, prompt_id: str | None = None, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: dict[str, object] | None = None, **kwargs, - ) -> tuple[list[AllMessageValues], dict[str, Any] | None]: + ) -> tuple[list[AllMessageValues], dict[str, object] | None]: """ Pre-call hook that processes the prompt template before making the LLM call. """ @@ -335,9 +366,9 @@ class ArizePhoenixPromptManager(CustomPromptManagement): except Exception as e: # Log error but don't fail the call - import litellm + from litellm._logging import verbose_proxy_logger - litellm._logging.verbose_proxy_logger.error("Error in Arize Phoenix prompt pre_call_hook: %s", e) + verbose_proxy_logger.error("Error in Arize Phoenix prompt pre_call_hook: %s", e) return messages, litellm_params def get_available_prompts(self) -> list[str]: @@ -393,7 +424,8 @@ class ArizePhoenixPromptManager(CustomPromptManagement): rendered_messages, prompt_metadata = self.get_prompt_template(prompt_id, prompt_variables) # Extract model from metadata (if specified) - template_model: Final = prompt_metadata.get("model") + raw_template_model: Final = prompt_metadata.get("model") + template_model: Final = raw_template_model if isinstance(raw_template_model, str) else None # Extract optional parameters from metadata optional_params: Final = {} diff --git a/litellm/integrations/bitbucket/bitbucket_client.py b/litellm/integrations/bitbucket/bitbucket_client.py index 9c964d8c10c..e7256da0237 100644 --- a/litellm/integrations/bitbucket/bitbucket_client.py +++ b/litellm/integrations/bitbucket/bitbucket_client.py @@ -163,15 +163,11 @@ class BitBucketClient: response.raise_for_status() data: Final[BitBucketSrcListing] = response.json() - files: Final[list[str]] = [] - - for item in data.get("values", []): - if item.get("type") == "commit_file": - file_path = item.get("path", "") - if file_path.endswith(file_extension): - files.append(file_path) - - return files + return [ + file_path + for item in data.get("values", []) + if item.get("type") == "commit_file" and (file_path := item.get("path", "")).endswith(file_extension) + ] except Exception as e: # Check if it's an HTTP error diff --git a/litellm/integrations/bitbucket/bitbucket_prompt_manager.py b/litellm/integrations/bitbucket/bitbucket_prompt_manager.py index 6a03e3ee93c..ff34bd91e31 100644 --- a/litellm/integrations/bitbucket/bitbucket_prompt_manager.py +++ b/litellm/integrations/bitbucket/bitbucket_prompt_manager.py @@ -3,6 +3,7 @@ BitBucket prompt manager that integrates with LiteLLM's prompt management system Fetches .prompt files from BitBucket repositories and provides team-based access control. """ +from collections.abc import Mapping from typing import TYPE_CHECKING, Any, Final from jinja2 import DictLoader, select_autoescape @@ -65,7 +66,7 @@ class BitBucketTemplateManager: def __init__( self, - bitbucket_config: dict[str, Any], + bitbucket_config: Mapping[str, object], prompt_id: str | None = None, ): self.bitbucket_config = bitbucket_config @@ -123,7 +124,7 @@ class BitBucketTemplateManager: template_content = content # Parse YAML frontmatter - metadata: dict[str, Any] = {} + metadata: dict[str, object] = {} if frontmatter_str: try: import yaml @@ -141,9 +142,9 @@ class BitBucketTemplateManager: metadata=metadata, ) - def _parse_yaml_basic(self, yaml_str: str) -> dict[str, Any]: + def _parse_yaml_basic(self, yaml_str: str) -> dict[str, object]: """Basic YAML parser for simple cases when PyYAML is not available.""" - result: Final[dict[str, Any]] = {} + result: Final[dict[str, object]] = {} for line in yaml_str.split("\n"): line = line.strip() if ":" in line and not line.startswith("#"): @@ -162,7 +163,7 @@ class BitBucketTemplateManager: result[key] = value.strip("\"'") return result - def render_template(self, template_id: str, variables: dict[str, Any] | None = None) -> str: + def render_template(self, template_id: str, variables: Mapping[str, object] | None = None) -> str: """Render a template with the given variables.""" if template_id not in self.prompts: raise ValueError(f"Template '{template_id}' not found") @@ -209,7 +210,7 @@ class BitBucketPromptManager(CustomPromptManagement): def __init__( self, - bitbucket_config: dict[str, Any], + bitbucket_config: Mapping[str, object], prompt_id: str | None = None, ): self.bitbucket_config = bitbucket_config @@ -234,7 +235,7 @@ class BitBucketPromptManager(CustomPromptManagement): def get_prompt_template( self, prompt_id: str, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, ) -> tuple[str, dict[str, Any]]: """ Get a prompt template and render it with variables. @@ -267,12 +268,12 @@ class BitBucketPromptManager(CustomPromptManagement): self, user_id: str | None, messages: list[AllMessageValues], - function_call: dict[str, Any] | str | None = None, - litellm_params: dict[str, Any] | None = None, + function_call: Mapping[str, object] | str | None = None, + litellm_params: dict[str, object] | None = None, prompt_id: str | None = None, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, **kwargs, - ) -> tuple[list[AllMessageValues], dict[str, Any] | None]: + ) -> tuple[list[AllMessageValues], dict[str, object] | None]: """ Pre-call hook that processes the prompt template before making the LLM call. """ @@ -316,9 +317,9 @@ class BitBucketPromptManager(CustomPromptManagement): except Exception as e: # Log error but don't fail the call - import litellm + from litellm._logging import verbose_proxy_logger - litellm._logging.verbose_proxy_logger.error("Error in BitBucket prompt pre_call_hook: %s", e) + verbose_proxy_logger.error("Error in BitBucket prompt pre_call_hook: %s", e) return messages, litellm_params def _parse_prompt_to_messages(self, prompt_content: str) -> list[AllMessageValues]: @@ -384,14 +385,14 @@ class BitBucketPromptManager(CustomPromptManagement): def post_call_hook( self, user_id: str | None, - response: Any, + response: object, input_messages: list[AllMessageValues], - function_call: dict[str, Any] | str | None = None, - litellm_params: dict[str, Any] | None = None, + function_call: Mapping[str, object] | str | None = None, + litellm_params: Mapping[str, object] | None = None, prompt_id: str | None = None, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, **kwargs, - ) -> Any: + ) -> object: """ Post-call hook for any post-processing after the LLM call. """ diff --git a/litellm/integrations/cloudzero/transform.py b/litellm/integrations/cloudzero/transform.py index f0d4d67fc22..ffc8fe1c1f5 100644 --- a/litellm/integrations/cloudzero/transform.py +++ b/litellm/integrations/cloudzero/transform.py @@ -19,14 +19,29 @@ """Transform LiteLLM data to CloudZero AnyCost CBF format.""" from datetime import datetime -from typing import Any, Final +from typing import Final, SupportsFloat, SupportsIndex, SupportsInt import polars as pl +from typing_extensions import Buffer from ...types.integrations.cloudzero import CBFRecord from .cz_resource_names import CZEntityType, CZRNGenerator +def _as_int(value: object) -> int: + """The integer form of a spend table cell, computed the way :func:`int` computes it.""" + if isinstance(value, (str, Buffer, SupportsInt, SupportsIndex)): + return int(value) + raise TypeError(f"int() argument must be a string or a number, not {type(value).__name__!r}") + + +def _as_float(value: object) -> float: + """The floating point form of a spend table cell, computed the way :func:`float` computes it.""" + if isinstance(value, (str, Buffer, SupportsFloat, SupportsIndex)): + return float(value) + raise TypeError(f"float() argument must be a string or a number, not {type(value).__name__!r}") + + class CBFTransformer: """Transform LiteLLM usage data to CloudZero Billing Format (CBF).""" @@ -82,15 +97,15 @@ class CBFTransformer: return pl.DataFrame(cbf_data) - def _create_cbf_record(self, row: dict[str, Any]) -> CBFRecord: + def _create_cbf_record(self, row: dict[str, object]) -> CBFRecord: """Create a single CBF record from LiteLLM daily spend row.""" # Parse date (daily spend tables use date strings like '2025-04-19') usage_date: Final = self._parse_date(row.get("date")) # Calculate total tokens - prompt_tokens: Final = int(row.get("prompt_tokens", 0)) - completion_tokens: Final = int(row.get("completion_tokens", 0)) + prompt_tokens: Final = _as_int(row.get("prompt_tokens", 0)) + completion_tokens: Final = _as_int(row.get("completion_tokens", 0)) total_tokens: Final = prompt_tokens + completion_tokens # Create CloudZero Resource Name (CZRN) as resource_id @@ -154,7 +169,7 @@ class CBFTransformer: "time/usage_start": ( usage_date.isoformat() if usage_date else None ), # Required: ISO-formatted UTC datetime - "cost/cost": float(row.get("spend", 0.0)), # Required: billed cost + "cost/cost": _as_float(row.get("spend", 0.0)), # Required: billed cost "resource/id": resource_id, # CZRN (CloudZero Resource Name) # Usage metrics for token consumption "usage/amount": total_tokens, # Numeric value of tokens consumed @@ -187,7 +202,7 @@ class CBFTransformer: return CBFRecord(cbf_record) - def _parse_date(self, date_str) -> datetime | None: + def _parse_date(self, date_str: object) -> datetime | None: """Parse date string from daily spend tables (e.g., '2025-04-19').""" if date_str is None: return None diff --git a/litellm/integrations/compression_interception/handler.py b/litellm/integrations/compression_interception/handler.py index 321c7896d63..1be7a01ba3a 100644 --- a/litellm/integrations/compression_interception/handler.py +++ b/litellm/integrations/compression_interception/handler.py @@ -7,7 +7,10 @@ litellm_content_retrieve tool calls server-side via the typed agentic loop plan. import time import uuid -from typing import TYPE_CHECKING, Any, ClassVar, Final, cast +from collections.abc import Mapping, Sequence +from typing import Any, ClassVar, Final, Protocol, cast + +from typing_extensions import ReadOnly, TypedDict from litellm._logging import verbose_logger from litellm.compression import compress @@ -22,13 +25,23 @@ from litellm.types.integrations.custom_logger import ( ) from litellm.types.utils import CallTypes -if TYPE_CHECKING: - from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj - LITELLM_CONTENT_RETRIEVE_TOOL_NAME: Final = "litellm_content_retrieve" _CACHE_TTL_SECONDS: Final = 15 * 60 +class _AgenticLoopParams(TypedDict, total=False): + """The ``agentic_loop_params`` entry the agentic loop driver records on the logging object.""" + + model: ReadOnly[str] + + +class _AgenticLoopLoggingObj(Protocol): + """Logging object view exposing the untyped call details this handler reads.""" + + @property + def model_call_details(self) -> Mapping[str, _AgenticLoopParams]: ... + + def _compression_savings_from_counts( original_tokens: object, compressed_tokens: object ) -> CompressionSavingsMetadata | None: @@ -83,7 +96,7 @@ class CompressionInterceptionLogger(CustomLogger): compression_trigger: int = 200_000, compression_target: int | None = None, embedding_model: str | None = None, - embedding_model_params: dict[str, Any] | None = None, + embedding_model_params: dict[str, object] | None = None, ): super().__init__() self.enabled = enabled @@ -106,7 +119,7 @@ class CompressionInterceptionLogger(CustomLogger): @staticmethod def initialize_from_proxy_config( litellm_settings: dict[str, Any], - callback_specific_params: dict[str, Any], + callback_specific_params: Mapping[str, object], ) -> "CompressionInterceptionLogger": compression_params: CompressionInterceptionConfig = {} if "compression_interception_params" in litellm_settings: @@ -120,7 +133,9 @@ class CompressionInterceptionLogger(CustomLogger): ) return CompressionInterceptionLogger.from_config_yaml(compression_params) - async def async_pre_call_deployment_hook(self, kwargs: dict[str, Any], call_type: CallTypes | None) -> dict | None: + async def async_pre_call_deployment_hook( + self, kwargs: dict[str, Any], call_type: CallTypes | None + ) -> dict[str, object] | None: if not self.enabled: return None if call_type is not None and call_type != CallTypes.anthropic_messages: @@ -150,7 +165,7 @@ class CompressionInterceptionLogger(CustomLogger): cache: Final = cast(dict[str, str], compressed.get("cache", {})) skip_reason: Final = cast(str | None, compressed.get("compression_skipped_reason")) - compressed_tools: Final = cast(list[dict[str, Any]], compressed.get("tools", [])) + compressed_tools: Final = cast(list[dict[str, object]], compressed.get("tools", [])) # Only mutate kwargs when compression actually produced a result. # If compression was a no-op (below trigger, invalid tool sequence, etc.), @@ -161,7 +176,7 @@ class CompressionInterceptionLogger(CustomLogger): kwargs["messages"] = compressed["messages"] if compressed_tools: kwargs["tools"] = self._merge_tools( - existing_tools=cast(list[dict[str, Any]] | None, kwargs.get("tools")), + existing_tools=cast(list[dict[str, object]] | None, kwargs.get("tools")), compressed_tools=compressed_tools, ) call_id = cast(str | None, kwargs.get("litellm_call_id")) @@ -194,14 +209,14 @@ class CompressionInterceptionLogger(CustomLogger): async def async_should_run_agentic_loop( self, - response: Any, + response: object, model: str, - messages: list[dict], - tools: list[dict] | None, + messages: Sequence[Mapping[str, object]], + tools: Sequence[Mapping[str, object]] | None, stream: bool, custom_llm_provider: str, - kwargs: dict, - ) -> tuple[bool, dict]: + kwargs: Mapping[str, object], + ) -> tuple[bool, dict[str, object]]: if not self.enabled: return False, {} if not self._has_retrieval_tool(tools): @@ -219,19 +234,19 @@ class CompressionInterceptionLogger(CustomLogger): async def async_build_agentic_loop_plan( self, - tools: dict, + tools: Mapping[str, object], model: str, - messages: list[dict], - response: Any, - anthropic_messages_provider_config: Any, - anthropic_messages_optional_request_params: dict, - logging_obj: "LiteLLMLoggingObj | None", + messages: list[dict[str, object]], + response: object, + anthropic_messages_provider_config: object, + anthropic_messages_optional_request_params: Mapping[str, object], + logging_obj: _AgenticLoopLoggingObj | None, stream: bool, - kwargs: dict, + kwargs: Mapping[str, object], ) -> AgenticLoopPlan: self._prune_expired_cache() - tool_calls: Final = cast(list[dict[str, Any]], tools.get("tool_calls", [])) - thinking_blocks: Final = cast(list[dict[str, Any]], tools.get("thinking_blocks", [])) + tool_calls: Final = cast(list[dict[str, object]], tools.get("tool_calls", [])) + thinking_blocks: Final = cast(list[dict[str, object]], tools.get("thinking_blocks", [])) call_id: Final = self._resolve_call_id(logging_obj=logging_obj, kwargs=kwargs) cache: Final = self._get_cache(call_id=call_id) @@ -274,7 +289,7 @@ class CompressionInterceptionLogger(CustomLogger): full_model_name = model if logging_obj is not None: agentic_params: Final = logging_obj.model_call_details.get("agentic_loop_params", {}) - full_model_name = cast(str, agentic_params.get("model", model)) + full_model_name = agentic_params.get("model", model) request_patch: Final = AgenticLoopRequestPatch( model=full_model_name, @@ -309,15 +324,15 @@ class CompressionInterceptionLogger(CustomLogger): return {} return cache_entry[0] - def _resolve_call_id(self, logging_obj: Any, kwargs: dict[str, Any]) -> str | None: + def _resolve_call_id(self, logging_obj: _AgenticLoopLoggingObj | None, kwargs: Mapping[str, object]) -> str | None: if logging_obj is not None: logging_call_id: Final = getattr(logging_obj, "litellm_call_id", None) if isinstance(logging_call_id, str) and logging_call_id: return logging_call_id kwargs_call_id: Final = kwargs.get("litellm_call_id") - return cast(str | None, kwargs_call_id if isinstance(kwargs_call_id, str) else None) + return kwargs_call_id if isinstance(kwargs_call_id, str) else None - def _resolve_retrieval_content(self, tool_call: dict[str, Any], cache: dict[str, str]) -> str: + def _resolve_retrieval_content(self, tool_call: Mapping[str, object], cache: Mapping[str, str]) -> str: raw_input: Final = tool_call.get("input", {}) key = "" if isinstance(raw_input, dict): @@ -328,7 +343,9 @@ class CompressionInterceptionLogger(CustomLogger): return cache[key] return f"[compressed content key '{key}' not found]" - def _extract_retrieval_tool_calls(self, response: Any) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]: + def _extract_retrieval_tool_calls( + self, response: object + ) -> tuple[list[dict[str, object]], list[dict[str, object]]]: if isinstance(response, dict): content = response.get("content", []) else: @@ -337,8 +354,8 @@ class CompressionInterceptionLogger(CustomLogger): if not isinstance(content, list): return [], [] - tool_calls: Final[list[dict[str, Any]]] = [] - thinking_blocks: Final[list[dict[str, Any]]] = [] + tool_calls: Final[list[dict[str, object]]] = [] + thinking_blocks: Final[list[dict[str, object]]] = [] for block in content: if isinstance(block, dict): @@ -385,13 +402,13 @@ class CompressionInterceptionLogger(CustomLogger): return tool_calls, thinking_blocks - def _prepare_followup_kwargs(self, kwargs: dict[str, Any]) -> dict[str, Any]: + def _prepare_followup_kwargs(self, kwargs: Mapping[str, object]) -> dict[str, object]: internal_keys: Final = {"litellm_logging_obj"} return { k: v for k, v in kwargs.items() if not k.startswith("_compression_interception") and k not in internal_keys } - def _has_retrieval_tool(self, tools: Any) -> bool: + def _has_retrieval_tool(self, tools: object) -> bool: if not isinstance(tools, list): return False for tool in tools: @@ -407,9 +424,9 @@ class CompressionInterceptionLogger(CustomLogger): def _merge_tools( self, - existing_tools: list[dict[str, Any]] | None, - compressed_tools: list[dict[str, Any]], - ) -> list[dict[str, Any]]: + existing_tools: Sequence[Mapping[str, object]] | None, + compressed_tools: Sequence[Mapping[str, object]], + ) -> list[Mapping[str, object]]: merged: Final = list(existing_tools or []) if self._has_retrieval_tool(merged): return merged diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py index 8dc6881d23e..372c9bf6b91 100644 --- a/litellm/integrations/custom_guardrail.py +++ b/litellm/integrations/custom_guardrail.py @@ -1,7 +1,9 @@ import contextvars +import copy import hashlib import os import secrets +from collections.abc import Mapping from datetime import datetime from typing import TYPE_CHECKING, Any, ClassVar, Final, Literal, Optional, get_args @@ -38,6 +40,7 @@ except ImportError: if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation dc: Final = DualCache() @@ -227,13 +230,13 @@ class CustomGuardrail(CustomLogger): ) super().__init__(**kwargs) - def render_violation_message(self, default: str, context: dict[str, Any] | None = None) -> str: + def render_violation_message(self, default: str, context: Mapping[str, object] | None = None) -> str: """Return a custom violation message if template is configured.""" if not self.violation_message_template: return default - format_context: Final[dict[str, Any]] = {"default_message": default} + format_context: Final[dict[str, object]] = {"default_message": default} if context: format_context.update(context) try: @@ -661,7 +664,7 @@ class CustomGuardrail(CustomLogger): value: Final = self._get_admin_metadata(data).get("opted_out_global_guardrails") return value if isinstance(value, list) else [] - def _is_valid_response_type(self, result: Any) -> bool: + def _is_valid_response_type(self, result: object) -> bool: """ Check if result is a valid LLMResponseTypes instance. @@ -722,7 +725,7 @@ class CustomGuardrail(CustomLogger): return None return f"{_PRE_CALL_EXECUTED_TOKEN}:{name}" - def mark_pre_call_hook_ran(self, data: dict[str, Any]) -> None: + def mark_pre_call_hook_ran(self, data: dict[str, object]) -> None: """ Record that this guardrail's ``async_pre_call_hook`` already ran for this request, so the deployment-level hook does not run it a second time. @@ -747,7 +750,7 @@ class CustomGuardrail(CustomLogger): return data["metadata"] = {PRE_CALL_EXECUTED_GUARDRAILS_KEY: [marker]} - def _pre_call_hook_already_ran(self, data: dict[str, Any]) -> bool: + def _pre_call_hook_already_ran(self, data: dict[str, object]) -> bool: marker: Final = self._pre_call_marker() if marker is None: return False @@ -851,6 +854,69 @@ class CustomGuardrail(CustomLogger): return result + async def async_logging_hook( + self, + kwargs: dict, # mutable-ok: CustomLogger.async_logging_hook contract + result: object, + call_type: str, + ) -> tuple[dict, object]: # mutable-ok: CustomLogger.async_logging_hook contract + """logging_only: run apply_guardrail on copies of the logged request/response and record the verdict.""" + from litellm.llms import get_guardrail_translation_mapping + + if not self.uses_apply_guardrail_interface() or self.use_native_lifecycle_hooks: + return kwargs, result + try: + translation: Final = get_guardrail_translation_mapping(CallTypes(call_type))() + except ValueError: + verbose_logger.debug( + "Guardrail %s: no guardrail translation for call_type=%s, skipping logging_only scan", + self.guardrail_name, + call_type, + ) + return kwargs, result + litellm_params: Final = kwargs.get("litellm_params") or {} + scratch_metadata: Final = { + key: value + for key, value in (litellm_params.get("metadata") or {}).items() + if key != "standard_logging_guardrail_information" + } + try: + await self._scan_logged_call(kwargs, result, translation, scratch_metadata) + except Exception as e: + verbose_logger.warning("Guardrail %s: logging_only scan raised: %s", self.guardrail_name, e) + recorded: Final = scratch_metadata.get("standard_logging_guardrail_information") + standard_logging_object: Final = kwargs.get("standard_logging_object") + if not recorded or not isinstance(standard_logging_object, dict): + return kwargs, result + entries: Final = recorded if isinstance(recorded, list) else [recorded] + existing: Final = standard_logging_object.get("guardrail_information") or [] + return { + **kwargs, + "standard_logging_object": {**standard_logging_object, "guardrail_information": [*existing, *entries]}, + }, result + + async def _scan_logged_call( + self, + kwargs: dict, # mutable-ok: CustomLogger.async_logging_hook contract + result: object, + translation: "BaseTranslation", + scratch_metadata: dict, # mutable-ok: apply_guardrail records its verdict into request metadata + ) -> None: + optional_params: Final = kwargs.get("optional_params") or {} + scratch_input: Final = copy.deepcopy(kwargs.get("messages") or kwargs.get("input")) + scratch_request: Final = { + "model": kwargs.get("model"), + "messages": scratch_input, + "input": scratch_input, + "tools": copy.deepcopy(optional_params.get("tools")), + "litellm_call_id": kwargs.get("litellm_call_id"), + "metadata": scratch_metadata, + } + await translation.process_input_messages(data=scratch_request, guardrail_to_apply=self) + await translation.process_output_response( + response=copy.deepcopy(result), guardrail_to_apply=self, request_data=scratch_request + ) + def supports_scan_only_tool_results(self) -> bool: """Whether this guardrail can scan tool-result content. @@ -1170,7 +1236,7 @@ class CustomGuardrail(CustomLogger): This gets logged on downsteam Langfuse, DataDog, etc. """ # Convert None to empty dict to satisfy type requirements - guardrail_response: dict[str, Any] | str = {} if response is None else response + guardrail_response: dict[str, object] | str = {} if response is None else response # For apply_guardrail functions in custom_code_guardrail scenario, # simplify the logged response to "allow", "deny", or "mask" diff --git a/litellm/integrations/custom_logger.py b/litellm/integrations/custom_logger.py index 41caf732db0..8f03e08f02d 100644 --- a/litellm/integrations/custom_logger.py +++ b/litellm/integrations/custom_logger.py @@ -2,7 +2,7 @@ # On success, logs events to Promptlayer import re import traceback -from collections.abc import AsyncGenerator, Mapping +from collections.abc import AsyncGenerator, Mapping, Sequence from typing import TYPE_CHECKING, Any, ClassVar, Final, Optional from pydantic import BaseModel @@ -31,6 +31,9 @@ if TYPE_CHECKING: from litellm.caching.caching import DualCache from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.llms.base_llm.anthropic_messages.transformation import ( + BaseAnthropicMessagesConfig, + ) from litellm.proxy._types import UserAPIKeyAuth from litellm.types.mcp import ( MCPPostCallResponseObject, @@ -39,7 +42,7 @@ if TYPE_CHECKING: ) from litellm.types.router import PreRoutingHookResponse - Span = _Span | Any + Span = _Span else: Span = Any LiteLLMLoggingObj = Any @@ -123,11 +126,11 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac return [] callbacks: Final = AllCallbacks() - callback_info: Final = getattr(callbacks, lookup_name, None) + callback_info: Final[object] = getattr(callbacks, lookup_name, None) if callback_info is None: return [] - params: Final = getattr(callback_info, "litellm_callback_params", None) + params: Final[Sequence[str] | None] = getattr(callback_info, "litellm_callback_params", None) if not params: return [] @@ -268,7 +271,9 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac ) -> list[dict]: return healthy_deployments - async def async_pre_call_deployment_hook(self, kwargs: dict[str, Any], call_type: CallTypes | None) -> dict | None: + async def async_pre_call_deployment_hook( + self, kwargs: dict[str, object], call_type: CallTypes | None + ) -> dict | None: """ Allow modifying the request just before it's sent to the deployment. @@ -344,9 +349,9 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac async def async_post_call_streaming_deployment_hook( self, request_data: dict, - response_chunk: Any, + response_chunk: object, call_type: CallTypes | None, - ) -> Any | None: + ) -> object | None: """ Allow modifying streaming chunks just before they're returned to the user. @@ -378,7 +383,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac """ def translate_completion_output_params_streaming( - self, completion_stream: Any + self, completion_stream: object ) -> AdapterCompletionStreamWrapper | None: """ Translates the streaming chunk, from the OpenAI format to the custom format. @@ -418,9 +423,9 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac self, data: dict, user_api_key_dict: UserAPIKeyAuth, - response: Any, + response: object, request_headers: dict[str, str] | None = None, - litellm_call_info: dict[str, Any] | None = None, + litellm_call_info: dict[str, object] | None = None, ) -> dict[str, str] | None: """ Called after an LLM API call (success or failure) to allow injecting custom HTTP response headers. @@ -471,11 +476,11 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac ) -> Any: pass - async def async_logging_hook(self, kwargs: dict, result: Any, call_type: str) -> tuple[dict, Any]: + async def async_logging_hook(self, kwargs: dict, result: object, call_type: str) -> tuple[dict, object]: """For masking logged request/response. Return a modified version of the request/result.""" return kwargs, result - def logging_hook(self, kwargs: dict, result: Any, call_type: str) -> tuple[dict, Any]: + def logging_hook(self, kwargs: dict, result: object, call_type: str) -> tuple[dict, object]: """For masking logged request/response. Return a modified version of the request/result.""" return kwargs, result @@ -581,7 +586,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac async def async_should_run_agentic_loop( self, - response: Any, + response: object, model: str, messages: list[dict], tools: list[dict] | None, @@ -642,8 +647,8 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac tools: dict, model: str, messages: list[dict], - response: Any, - anthropic_messages_provider_config: Any, + response: object, + anthropic_messages_provider_config: "BaseAnthropicMessagesConfig | None", anthropic_messages_optional_request_params: dict, logging_obj: "LiteLLMLoggingObj", stream: bool, @@ -711,8 +716,8 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac tools: dict, model: str, messages: list[dict], - response: Any, - anthropic_messages_provider_config: Any, + response: object, + anthropic_messages_provider_config: "BaseAnthropicMessagesConfig | None", anthropic_messages_optional_request_params: dict, logging_obj: "LiteLLMLoggingObj", stream: bool, @@ -728,7 +733,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac async def async_post_agentic_loop_response_hook( self, - response: Any, + response: object, plan: AgenticLoopPlan, kwargs: dict, ) -> Any: @@ -767,7 +772,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac async def async_should_run_chat_completion_agentic_loop( self, - response: Any, + response: object, model: str, messages: list[dict], tools: list[dict] | None, @@ -785,12 +790,12 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac tools: dict, model: str, messages: list[dict], - response: Any, + response: object, optional_params: dict, logging_obj: "LiteLLMLoggingObj", stream: bool, kwargs: dict, - ) -> Any: + ) -> object: """ Hook to execute chat completion agentic loop based on context from should_run hook. """ @@ -800,7 +805,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac tools: dict, model: str, messages: list[dict], - response: Any, + response: object, optional_params: dict, logging_obj: "LiteLLMLoggingObj", stream: bool, @@ -851,7 +856,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac - Converting to string and then truncating the logged content catches this 2. We want to avoid modifying the original `messages`, `response`, and `error_str` in the logging payload since these are in kwargs and could be returned to the user """ - field_value: Final = standard_logging_object.get(field_name) + field_value: Final[object] = standard_logging_object.get(field_name) if field_value: str_value: Final = str(field_value) if len(str_value) > max_length: @@ -1005,8 +1010,8 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac • Keep untyped or text content. • Recursively redact inline base64 blobs in *any* string field, at any depth. """ - raw_messages: Final[Any] = payload.get("messages", []) - messages: Final[list[Any]] = raw_messages if isinstance(raw_messages, list) else [] + raw_messages: Final[object] = payload.get("messages", []) + messages: Final[list[object]] = raw_messages if isinstance(raw_messages, list) else [] verbose_logger.debug("[CustomLogger] Stripping base64 from %s messages", len(messages)) if messages: @@ -1037,8 +1042,8 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac • Keep untyped or text content. • Recursively redact inline base64 blobs in *any* string field, at any depth. """ - raw_messages: Final[Any] = payload.get("messages", []) - messages: Final[list[Any]] = raw_messages if isinstance(raw_messages, list) else [] + raw_messages: Final[object] = payload.get("messages", []) + messages: Final[list[object]] = raw_messages if isinstance(raw_messages, list) else [] verbose_logger.debug("[CustomLogger] Stripping base64 from %s messages", len(messages)) if messages: @@ -1056,10 +1061,10 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac def _redact_base64( self, - value: Any, + value: object, depth: int = 0, max_depth: int = DEFAULT_MAX_RECURSE_DEPTH_SENSITIVE_DATA_MASKER, - ) -> Any: + ) -> object: """Recursively redact inline base64 from any nested structure with a max recursion depth limit.""" if depth > max_depth: verbose_logger.warning("[CustomLogger] Max recursion depth %s reached while redacting base64", max_depth) @@ -1079,7 +1084,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac return value - def _should_keep_content(self, content: Any) -> bool: + def _should_keep_content(self, content: object) -> bool: """Return True if this content item should be retained.""" if not isinstance(content, dict): return True @@ -1090,16 +1095,16 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac def _process_messages( self, - messages: list[Any], + messages: list[object], max_depth: int = DEFAULT_MAX_RECURSE_DEPTH_SENSITIVE_DATA_MASKER, - ) -> list[dict[str, Any]]: - filtered_messages: Final[list[dict[str, Any]]] = [] + ) -> list[dict[str, object]]: + filtered_messages: Final[list[dict[str, object]]] = [] for msg in messages: if not isinstance(msg, dict): continue - contents: Any = msg.get("content") + contents: object = msg.get("content") if isinstance(contents, list): - cleaned: list[Any] = [] + cleaned: list[object] = [] for c in contents: if self._should_keep_content(content=c): cleaned.append(self._redact_base64(value=c, max_depth=max_depth)) diff --git a/litellm/integrations/datadog/datadog.py b/litellm/integrations/datadog/datadog.py index 04f1c6dff15..866076a3c49 100644 --- a/litellm/integrations/datadog/datadog.py +++ b/litellm/integrations/datadog/datadog.py @@ -20,10 +20,11 @@ import time import traceback from collections.abc import Sequence from datetime import datetime as datetimeObj -from typing import Any, Final +from typing import TYPE_CHECKING, Any, Final import httpx from httpx import Response +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -62,6 +63,18 @@ from litellm.types.utils import StandardLoggingPayload from ..additional_logging_utils import AdditionalLoggingUtils +if TYPE_CHECKING: + from fastapi import HTTPException + + from litellm.proxy._types import UserAPIKeyAuth + + +class _DatadogLoggingKwargs(TypedDict, total=False): + """The subset of logging ``kwargs`` that the Datadog payload builder reads.""" + + standard_logging_object: ReadOnly[StandardLoggingPayload | None] + + # max number of logs DD API can accept @@ -87,6 +100,11 @@ def _resolve_dd_batch_size() -> int: return max(1, min(value, DD_MAX_BATCH_SIZE)) +def _span_attribute(span: object, name: str) -> object: + """Read an optional attribute off whatever span object the active tracer hands back.""" + return getattr(span, name, None) + + class DataDogLogger( CustomBatchLogger, AdditionalLoggingUtils, @@ -271,9 +289,9 @@ class DataDogLogger( self, request_data: dict, original_exception: Exception, - user_api_key_dict: Any, + user_api_key_dict: "UserAPIKeyAuth", traceback_str: str | None = None, - ) -> Any | None: + ) -> "HTTPException | None": """ Log proxy-level failures (e.g. 401 auth, DB connection errors) to Datadog. @@ -297,7 +315,7 @@ class DataDogLogger( status_code = int(_code) # Use project-standard sanitized user context when running in proxy - user_context: dict[str, Any] = {} + user_context: dict[str, object] = {} try: from litellm.proxy.litellm_pre_call_utils import ( LiteLLMProxyRequestSetup, @@ -553,8 +571,8 @@ class DataDogLogger( def create_datadog_logging_payload( self, - kwargs: dict | Any, - response_obj: Any, + kwargs: _DatadogLoggingKwargs, + response_obj: object, start_time: datetime.datetime, end_time: datetime.datetime, ) -> DatadogPayload: @@ -562,8 +580,8 @@ class DataDogLogger( Helper function to create a datadog payload for logging Args: - kwargs (Union[dict, Any]): request kwargs - response_obj (Any): llm api response + kwargs: request kwargs, read for its standard logging object + response_obj: llm api response start_time (datetime.datetime): start time of request end_time (datetime.datetime): end time of request @@ -625,7 +643,7 @@ class DataDogLogger( self, payload: ServiceLoggerPayload, error: str | None = "", - parent_otel_span: Any | None = None, + parent_otel_span: object = None, start_time: datetimeObj | float | None = None, end_time: float | datetimeObj | None = None, event_metadata: dict | None = None, @@ -659,7 +677,7 @@ class DataDogLogger( self, payload: ServiceLoggerPayload, error: str | None = "", - parent_otel_span: Any | None = None, + parent_otel_span: object = None, start_time: datetimeObj | float | None = None, end_time: float | datetimeObj | None = None, event_metadata: dict | None = None, @@ -696,7 +714,7 @@ class DataDogLogger( def _create_v0_logging_payload( self, - kwargs: dict | Any, + kwargs: dict, response_obj: Any, start_time: datetime.datetime, end_time: datetime.datetime, @@ -810,11 +828,11 @@ class DataDogLogger( if current_span is None: return None - trace_id: Final = getattr(current_span, "trace_id", None) + trace_id: Final = _span_attribute(current_span, "trace_id") if trace_id is None: return None - span_id: Final = getattr(current_span, "span_id", None) + span_id: Final = _span_attribute(current_span, "span_id") trace_context: Final[dict[str, str]] = {"trace_id": str(trace_id)} if span_id is not None: trace_context["span_id"] = str(span_id) diff --git a/litellm/integrations/datadog/datadog_llm_obs.py b/litellm/integrations/datadog/datadog_llm_obs.py index 704f0323e95..5e116b7301a 100644 --- a/litellm/integrations/datadog/datadog_llm_obs.py +++ b/litellm/integrations/datadog/datadog_llm_obs.py @@ -9,7 +9,9 @@ API Reference: https://docs.datadoghq.com/llm_observability/setup/api/?tab=examp import asyncio import json import os +from collections.abc import Mapping, Sequence from datetime import datetime +from types import MappingProxyType from typing import Any, Final, Literal import httpx @@ -29,12 +31,16 @@ from litellm.integrations.datadog.datadog_mock_client import ( ) from litellm.litellm_core_utils.dd_tracing import tracer from litellm.litellm_core_utils.prompt_templates.common_utils import ( + convert_content_list_to_str, handle_any_messages_to_chat_completion_str_messages_conversion, ) +from litellm.litellm_core_utils.safe_json_dumps import safe_dumps +from litellm.litellm_core_utils.safe_json_loads import safe_json_loads from litellm.llms.custom_httpx.http_handler import ( get_async_httpx_client, httpxSpecialProvider, ) +from litellm.proxy.spend_tracking.savings import extract_cache_creation_tokens, extract_cache_read_tokens from litellm.types.integrations.datadog_llm_obs import * from litellm.types.utils import ( CallTypes, @@ -43,6 +49,189 @@ from litellm.types.utils import ( StandardLoggingPayloadErrorInformation, ) +_EMPTY_MAPPING: Final[Mapping[str, Any]] = MappingProxyType({}) +_EMPTY_MESSAGE: Final[Message] = {"role": "", "content": ""} +_MAX_PARSED_TOOL_ARGUMENT_CHARS: Final = 256 * 1024 + + +def _mapping_field(source: Mapping[str, Any], key: str) -> Mapping[str, Any]: + """The value at `key` when it is a mapping, else an empty one.""" + value: Final = source.get(key) + return value if isinstance(value, dict) else _EMPTY_MAPPING + + +def _content_blocks(message: Mapping[str, Any]) -> tuple[Mapping[str, Any], ...]: + content: Final = message.get("content") + if not isinstance(content, list): + return () + return tuple(block for block in content if isinstance(block, dict)) + + +def _to_dd_arguments(raw_arguments: object) -> dict[str, Any] | str: + """ + Arguments as the object LLM Obs types them as, or the raw string when they are not one. + + Strings past the size bound ship unparsed: decoding multiplies memory on hostile compact + JSON, and the raw string is what the intake receives either way. + """ + if not isinstance(raw_arguments, str): + return raw_arguments if isinstance(raw_arguments, dict) else str(raw_arguments) + if len(raw_arguments) > _MAX_PARSED_TOOL_ARGUMENT_CHARS: + return raw_arguments + parsed: Final = safe_json_loads(raw_arguments) + return parsed if isinstance(parsed, dict) else raw_arguments + + +def _to_dd_tool_calls(message: Mapping[str, Any]) -> tuple[ToolCall, ...]: + """ + The tool calls a message carries, in LLM Obs' ToolCall schema, from either dialect. + + OpenAI puts them in `tool_calls` with the callee nested under `function` and `arguments` + serialized; Anthropic puts them in `content` as `tool_use` blocks with `input` already an + object. LLM Obs reads `name` / `arguments` / `tool_id` either way. + """ + raw_tool_calls: Final = message.get("tool_calls") + openai_calls: Final = tuple( + ToolCall( + name=function.get("name", ""), + arguments=_to_dd_arguments(function.get("arguments", "")), + tool_id=tool_call.get("id", ""), + type=tool_call.get("type", "function"), + ) + for tool_call in (raw_tool_calls if isinstance(raw_tool_calls, list) else ()) + if isinstance(tool_call, dict) + for function in [_mapping_field(tool_call, "function")] + ) + anthropic_calls: Final = tuple( + ToolCall( + name=block.get("name", ""), + arguments=_to_dd_arguments(block.get("input") or {}), + tool_id=block.get("id", ""), + type="tool_use", + ) + for block in _content_blocks(message) + if block.get("type") == "tool_use" + ) + return openai_calls + anthropic_calls + + +def _to_dd_tool_results(message: Mapping[str, Any], tool_call_names: Mapping[str, str]) -> tuple[ToolResult, ...]: + """ + The tool results a message carries, linked back to the call each answers. + + OpenAI models a result as a whole `role: "tool"` message keyed by `tool_call_id`; + Anthropic nests `tool_result` blocks inside a user message, keyed by `tool_use_id`. + """ + + def to_result(tool_id: str, result: object) -> ToolResult: + return ToolResult( + name=tool_call_names.get(tool_id, ""), + result=result if isinstance(result, str) else safe_dumps(result), + tool_id=tool_id, + type="function", + ) + + if message.get("role") == "tool": + return (to_result(str(message.get("tool_call_id", "")), message.get("content") or ""),) + return tuple( + to_result(str(block.get("tool_use_id", "")), block.get("content") or "") + for block in _content_blocks(message) + if block.get("type") == "tool_result" + ) + + +def _tool_call_names_by_id(messages: Sequence[object]) -> Mapping[str, str]: + """Ids to tool names for result linking; reads names structurally and parses nothing.""" + openai_pairs: Final = tuple( + (tool_call.get("id"), function.get("name", "")) + for message in messages + if isinstance(message, dict) and isinstance(message.get("tool_calls"), list) + for tool_call in message["tool_calls"] + if isinstance(tool_call, dict) + for function in [_mapping_field(tool_call, "function")] + ) + anthropic_pairs: Final = tuple( + (block.get("id"), block.get("name", "")) + for message in messages + if isinstance(message, dict) + for block in _content_blocks(message) + if block.get("type") == "tool_use" + ) + return MappingProxyType({str(tool_id): str(name) for tool_id, name in openai_pairs + anthropic_pairs if tool_id}) + + +def _to_dd_message(message: object, tool_call_names: Mapping[str, str]) -> Message: + """ + Map one chat message onto LLM Obs' Message schema, adding fields and never destroying content. + + Content collapses to its text only when it has text; a content list with none (tool blocks, + images) rides along unchanged so nothing the caller logged is lost. Tool calls and results + move into the fields the LLM Obs Tools panel reads, from both the OpenAI and Anthropic shapes. + """ + if not isinstance(message, dict): + converted: Final = handle_any_messages_to_chat_completion_str_messages_conversion(message) + return converted[0] if converted else _EMPTY_MESSAGE + + text: Final = convert_content_list_to_str(message) # pyright: ignore[reportArgumentType] # caller-supplied dict + original_content: Final = message.get("content") + content: Final = ( + text if text or not isinstance(original_content, list) or not original_content else original_content + ) + reasoning: Final = message.get("reasoning_content") + tool_calls: Final = _to_dd_tool_calls(message) + tool_results: Final = _to_dd_tool_results(message, tool_call_names) + dd_message: Final[Message] = { + "role": message.get("role", ""), + "content": content, + **({"reasoning_content": reasoning} if reasoning is not None else {}), + **({"tool_calls": tool_calls} if tool_calls else {}), + **({"tool_results": tool_results} if tool_results else {}), + } + return dd_message + + +def _to_dd_messages(messages: object) -> tuple[Message, ...]: + """Map a whole conversation, resolving each tool result against the calls that precede it.""" + if messages is None: + return () + if not isinstance(messages, list): + return tuple(handle_any_messages_to_chat_completion_str_messages_conversion(messages)) + tool_call_names: Final = _tool_call_names_by_id(messages) + return tuple(_to_dd_message(message, tool_call_names) for message in messages) + + +def _to_dd_tool_definition(entry: Mapping[str, Any]) -> ToolDefinition | None: + function: Final = entry.get("function") + declared: Final[Mapping[str, Any]] = function if isinstance(function, dict) else entry + name: Final = declared.get("name") + if not name: + return None + schema: Final = declared.get("parameters") or declared.get("input_schema") + description: Final = declared.get("description", "") + if not isinstance(schema, dict): + return ToolDefinition(name=name, description=description) + return ToolDefinition(name=name, description=description, schema=schema) + + +def _to_dd_tool_definitions(model_parameters: object) -> tuple[ToolDefinition, ...]: + """ + Map the request's declared tools onto LLM Obs' ToolDefinition schema. + + Handles the wrapped chat-completions shape and the bare shape the Anthropic and + Responses surfaces use, since both reach this logger through `model_parameters`. + """ + if not isinstance(model_parameters, dict): + return () + raw_tools: Final = model_parameters.get("tools") or model_parameters.get("functions") + if not isinstance(raw_tools, list): + return () + return tuple( + definition + for entry in raw_tools + if isinstance(entry, dict) + if (definition := _to_dd_tool_definition(entry)) is not None + ) + class DataDogLLMObsLogger(CustomBatchLogger): def __init__(self, **kwargs): @@ -221,12 +410,9 @@ class DataDogLLMObsLogger(CustomBatchLogger): if standard_logging_payload is None: raise Exception("DataDogLLMObs: standard_logging_object is not set") - messages = standard_logging_payload["messages"] - messages = self._ensure_string_content(messages=messages) - metadata: Final = kwargs.get("litellm_params", {}).get("metadata", {}) - input_meta: Final = InputMeta(messages=handle_any_messages_to_chat_completion_str_messages_conversion(messages)) + input_meta: Final = InputMeta(messages=_to_dd_messages(standard_logging_payload["messages"])) output_meta: Final = OutputMeta( messages=self._get_response_messages( standard_logging_payload=standard_logging_payload, @@ -240,22 +426,20 @@ class DataDogLLMObsLogger(CustomBatchLogger): if isinstance(metadata, dict): metadata_parent_id = metadata.get("parent_id") - meta: Final = Meta( - kind=self._get_datadog_span_kind(standard_logging_payload.get("call_type"), metadata_parent_id), - input=input_meta, - output=output_meta, - metadata=self._get_dd_llm_obs_payload_metadata(standard_logging_payload), - error=error_info, - ) + tool_definitions: Final = _to_dd_tool_definitions(standard_logging_payload.get("model_parameters")) + span_kind: Final = self._get_datadog_span_kind(standard_logging_payload.get("call_type"), metadata_parent_id) + payload_metadata: Final = self._get_dd_llm_obs_payload_metadata(standard_logging_payload) - # Calculate metrics (you may need to adjust these based on available data) - metrics: Final = LLMMetrics( - input_tokens=float(standard_logging_payload.get("prompt_tokens", 0)), - output_tokens=float(standard_logging_payload.get("completion_tokens", 0)), - total_tokens=float(standard_logging_payload.get("total_tokens", 0)), - total_cost=float(standard_logging_payload.get("response_cost", 0)), - time_to_first_token=self._get_time_to_first_token_seconds(standard_logging_payload), - ) + meta: Final[Meta] = { + "kind": span_kind, + "input": input_meta, + "output": output_meta, + "metadata": payload_metadata, + "error": error_info, + **({"tool_definitions": tool_definitions} if tool_definitions else {}), + } + + metrics: Final = self._assemble_metrics(standard_logging_payload) payload: Final[LLMObsPayload] = LLMObsPayload( parent_id=metadata_parent_id if metadata_parent_id else "undefined", @@ -313,6 +497,45 @@ class DataDogLLMObsLogger(CustomBatchLogger): ) return error_info + def _assemble_metrics(self, standard_logging_payload: StandardLoggingPayload) -> LLMMetrics: + """ + Build the span metrics, including the prompt-cache counts LLM Obs charts cache savings from. + + Cache counts resolve through the same owners the savings dashboard uses, so every provider + spelling is covered, and `non_cached_input_tokens` subtracts BOTH cache categories because + litellm's normalized prompt count includes both (the invariant the cost calculator's custom + pricing helper documents). A zero residual on a fully cached request is real data and is + emitted; a zero read or write count is absence and is not. + """ + prompt_tokens: Final = float(standard_logging_payload.get("prompt_tokens", 0)) + completion_tokens: Final = float(standard_logging_payload.get("completion_tokens", 0)) + total_tokens: Final = float(standard_logging_payload.get("total_tokens", 0)) + total_cost: Final = float(standard_logging_payload.get("response_cost", 0)) + time_to_first_token: Final = self._get_time_to_first_token_seconds(standard_logging_payload) + + raw_usage: Final = (standard_logging_payload.get("metadata") or {}).get("usage_object") + usage_object: Final = raw_usage if isinstance(raw_usage, dict) else None + cache_read: Final = float(extract_cache_read_tokens(usage_object)) + cache_write: Final = float(extract_cache_creation_tokens(usage_object)) + + metrics: Final[LLMMetrics] = { + "input_tokens": prompt_tokens, + "output_tokens": completion_tokens, + "total_tokens": total_tokens, + "total_cost": total_cost, + "time_to_first_token": time_to_first_token, + **( + { + **({"cache_read_input_tokens": cache_read} if cache_read else {}), + **({"cache_write_input_tokens": cache_write} if cache_write else {}), + "non_cached_input_tokens": max(prompt_tokens - cache_read - cache_write, 0.0), + } + if cache_read or cache_write + else {} + ), + } + return metrics + def _get_time_to_first_token_seconds(self, standard_logging_payload: StandardLoggingPayload) -> float: """ Get the time to first token in seconds @@ -334,7 +557,7 @@ class DataDogLLMObsLogger(CustomBatchLogger): def _get_response_messages( self, standard_logging_payload: StandardLoggingPayload, call_type: str | None - ) -> list[Any]: + ) -> tuple[Message, ...]: """ Get the messages from the response object @@ -343,7 +566,7 @@ class DataDogLLMObsLogger(CustomBatchLogger): response_obj = standard_logging_payload.get("response") if response_obj is None: - return [] + return () # edge case: handle response_obj is a string representation of a dict if isinstance(response_obj, str): @@ -356,7 +579,7 @@ class DataDogLLMObsLogger(CustomBatchLogger): # fallback to json parsing response_obj = json.loads(str(response_obj)) except json.JSONDecodeError: - return [] + return () if call_type in [ CallTypes.completion.value, @@ -374,12 +597,12 @@ class DataDogLLMObsLogger(CustomBatchLogger): if isinstance(response_obj, dict) and "choices" in response_obj: choices: Final = response_obj["choices"] if choices and len(choices) > 0 and "message" in choices[0]: - return [choices[0]["message"]] - return [] + return _to_dd_messages([choices[0]["message"]]) + return () except (KeyError, IndexError, TypeError): # In case of any error accessing the response structure, return empty list - return [] - return [] + return () + return () def _get_datadog_span_kind( self, call_type: str | None, parent_id: str | None = None @@ -484,22 +707,11 @@ class DataDogLLMObsLogger(CustomBatchLogger): # Default fallback for unknown or passthrough operations return "llm" - def _ensure_string_content(self, messages: str | list[Any] | dict[Any, Any] | None) -> list[Any]: - if messages is None: - return [] - if isinstance(messages, str): - return [messages] - elif isinstance(messages, list): - return [message for message in messages] - elif isinstance(messages, dict): - return [str(messages.get("content", ""))] - return [] - - def _get_dd_llm_obs_payload_metadata(self, standard_logging_payload: StandardLoggingPayload) -> dict[str, Any]: + def _get_dd_llm_obs_payload_metadata(self, standard_logging_payload: StandardLoggingPayload) -> dict[str, object]: """ Fields to track in DD LLM Observability metadata from litellm standard logging payload """ - _metadata: Final[dict[str, Any]] = { + _metadata: Final[dict[str, object]] = { "model_name": standard_logging_payload.get("model", "unknown"), "model_provider": standard_logging_payload.get("custom_llm_provider", "unknown"), "id": standard_logging_payload.get("id", "unknown"), @@ -523,10 +735,6 @@ class DataDogLLMObsLogger(CustomBatchLogger): spend_metrics: Final = self._get_spend_metrics(standard_logging_payload) _metadata.update({"spend_metrics": dict(spend_metrics)}) - ## extract tool calls and add to metadata - tool_call_metadata: Final = self._extract_tool_call_metadata(standard_logging_payload) - _metadata.update(tool_call_metadata) - _standard_logging_metadata: Final[dict] = dict(standard_logging_payload.get("metadata", {})) or {} _metadata.update(_standard_logging_metadata) return _metadata @@ -646,107 +854,3 @@ class DataDogLLMObsLogger(CustomBatchLogger): verbose_logger.debug("Original value: %s", user_api_key_budget_reset_at) return spend_metrics - - def _process_input_messages_preserving_tool_calls(self, messages: list[Any]) -> list[dict[str, Any]]: - """ - Process input messages while preserving tool_calls and tool message types. - - This bypasses the lossy string conversion when tool calls are present, - allowing complex nested tool_calls objects to be preserved for Datadog. - """ - processed: Final = [] - for msg in messages: - if isinstance(msg, dict): - # Preserve messages with tool_calls or tool role as-is - if "tool_calls" in msg or msg.get("role") == "tool": - processed.append(msg) - else: - # For regular messages, still apply string conversion - converted = handle_any_messages_to_chat_completion_str_messages_conversion([msg]) - processed.extend(converted) - else: - # For non-dict messages, apply string conversion - converted = handle_any_messages_to_chat_completion_str_messages_conversion([msg]) - processed.extend(converted) - return processed - - @staticmethod - def _tool_calls_kv_pair(tool_calls: list[dict[str, Any]]) -> dict[str, Any]: - """ - Extract tool call information into key-value pairs for Datadog metadata. - - Similar to OpenTelemetry's implementation but adapted for Datadog's format. - """ - kv_pairs: Final[dict[str, Any]] = {} - for idx, tool_call in enumerate(tool_calls): - try: - # Extract tool call ID - tool_id = tool_call.get("id") - if tool_id: - kv_pairs[f"tool_calls.{idx}.id"] = tool_id - - # Extract tool call type - tool_type = tool_call.get("type") - if tool_type: - kv_pairs[f"tool_calls.{idx}.type"] = tool_type - - # Extract function information - function = tool_call.get("function") - if function: - function_name = function.get("name") - if function_name: - kv_pairs[f"tool_calls.{idx}.function.name"] = function_name - - function_arguments = function.get("arguments") - if function_arguments: - # Store arguments as JSON string for Datadog - if isinstance(function_arguments, str): - kv_pairs[f"tool_calls.{idx}.function.arguments"] = function_arguments - else: - import json - - kv_pairs[f"tool_calls.{idx}.function.arguments"] = json.dumps(function_arguments) - except (KeyError, TypeError, ValueError) as e: - verbose_logger.debug("DataDogLLMObs: Error processing tool call %s: %s", idx, e) - continue - - return kv_pairs - - def _extract_tool_call_metadata(self, standard_logging_payload: StandardLoggingPayload) -> dict[str, Any]: - """ - Extract tool call information from both input messages and response for Datadog metadata. - """ - tool_call_metadata: Final[dict[str, Any]] = {} - - try: - # Extract tool calls from input messages - messages: Final = standard_logging_payload.get("messages", []) - if messages and isinstance(messages, list): - for message in messages: - if isinstance(message, dict) and "tool_calls" in message: - tool_calls = message.get("tool_calls") - if tool_calls: - input_tool_calls_kv = self._tool_calls_kv_pair(tool_calls) - # Prefix with "input_" to distinguish from response tool calls - for key, value in input_tool_calls_kv.items(): - tool_call_metadata[f"input_{key}"] = value - - # Extract tool calls from response - response_obj: Final = standard_logging_payload.get("response") - if response_obj and isinstance(response_obj, dict): - choices: Final = response_obj.get("choices", []) - for choice in choices: - if isinstance(choice, dict): - message = choice.get("message") - if message and isinstance(message, dict): - tool_calls = message.get("tool_calls") - if tool_calls: - response_tool_calls_kv = self._tool_calls_kv_pair(tool_calls) - # Prefix with "output_" to distinguish from input tool calls - for key, value in response_tool_calls_kv.items(): - tool_call_metadata[f"output_{key}"] = value - - except Exception as e: - verbose_logger.debug("DataDogLLMObs: Error extracting tool call metadata: %s", e) - - return tool_call_metadata diff --git a/litellm/integrations/dotprompt/prompt_manager.py b/litellm/integrations/dotprompt/prompt_manager.py index fd0b17ba746..9c82ff7c5ba 100644 --- a/litellm/integrations/dotprompt/prompt_manager.py +++ b/litellm/integrations/dotprompt/prompt_manager.py @@ -3,12 +3,21 @@ Based on Google's GenAI Kit dotprompt implementation: https://google.github.io/d """ import re +from collections.abc import Mapping from pathlib import Path from typing import Any, Final import yaml from jinja2 import DictLoader, select_autoescape from jinja2.sandbox import ImmutableSandboxedEnvironment +from typing_extensions import NotRequired, ReadOnly, TypedDict + + +class _PromptFileJson(TypedDict): + """JSON form of a .prompt file: rendered template text plus its frontmatter.""" + + content: ReadOnly[NotRequired[str]] + metadata: ReadOnly[NotRequired[dict[str, object]]] def strip_version_suffix(prompt_id: str) -> str | None: @@ -167,7 +176,7 @@ class PromptManager: template_id=prompt_id, ) - def _parse_frontmatter(self, content: str) -> tuple[dict[str, Any], str]: + def _parse_frontmatter(self, content: str) -> tuple[dict[str, object], str]: """Parse YAML frontmatter from prompt content.""" # Match YAML frontmatter between --- delimiters frontmatter_pattern: Final = r"^---\s*\n(.*?)\n---\s*\n(.*)$" @@ -178,7 +187,7 @@ class PromptManager: template_content = match.group(2) try: - frontmatter = yaml.safe_load(frontmatter_yaml) or {} + frontmatter: dict[str, object] = yaml.safe_load(frontmatter_yaml) or {} except yaml.YAMLError as e: raise ValueError(f"Invalid YAML frontmatter: {e}") else: @@ -191,7 +200,7 @@ class PromptManager: def render( self, prompt_id: str, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, version: int | None = None, ) -> str: """ @@ -231,7 +240,7 @@ class PromptManager: except Exception as e: raise ValueError(f"Error rendering template '{prompt_id}': {e}") - def _validate_input(self, variables: dict[str, Any], schema: dict[str, Any]) -> None: + def _validate_input(self, variables: Mapping[str, object], schema: Mapping[str, str]) -> None: """Basic validation of input variables against schema.""" for field_name, field_type in schema.items(): if field_name in variables: @@ -291,7 +300,7 @@ class PromptManager: """Get a list of all available prompt IDs.""" return list(self.prompts.keys()) - def get_prompt_metadata(self, prompt_id: str) -> dict[str, Any] | None: + def get_prompt_metadata(self, prompt_id: str) -> dict[str, object] | None: """Get metadata for a specific prompt.""" template: Final = self.prompts.get(prompt_id) return template.metadata if template else None @@ -302,12 +311,12 @@ class PromptManager: if self.prompt_directory: self._load_prompts() - def add_prompt(self, prompt_id: str, content: str, metadata: dict[str, Any] | None = None) -> None: + def add_prompt(self, prompt_id: str, content: str, metadata: dict[str, object] | None = None) -> None: """Add a prompt template programmatically.""" template: Final = PromptTemplate(content=content, metadata=metadata or {}, template_id=prompt_id) self.prompts[prompt_id] = template - def prompt_file_to_json(self, file_path: str | Path) -> dict[str, Any]: + def prompt_file_to_json(self, file_path: str | Path) -> _PromptFileJson: """Convert a .prompt file to JSON format. Args: @@ -324,7 +333,7 @@ class PromptManager: return {"content": template_content.strip(), "metadata": frontmatter} - def json_to_prompt_file(self, prompt_data: dict[str, Any]) -> str: + def json_to_prompt_file(self, prompt_data: _PromptFileJson) -> str: """Convert JSON prompt data to .prompt file format. Args: diff --git a/litellm/integrations/galileo.py b/litellm/integrations/galileo.py index 23727801a6f..b27618993a3 100644 --- a/litellm/integrations/galileo.py +++ b/litellm/integrations/galileo.py @@ -6,10 +6,11 @@ import re import uuid from collections.abc import Mapping, Sequence from datetime import datetime, timezone, tzinfo -from typing import Any, Final, TypedDict, cast +from typing import Any, Final, Protocol, cast import httpx from pydantic import BaseModel, Field +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -35,6 +36,34 @@ GALILEO_CLOUD_API_BASE_URL: Final = "https://api.galileo.ai" GALILEO_MAX_IN_MEMORY_RECORDS: Final = 1000 +class _GalileoLoginBody(TypedDict): + """Decoded body of the Galileo login response.""" + + access_token: ReadOnly[str] + + +class _GalileoLoginResponse(Protocol): + """The login call's HTTP response, read for the access token it carries.""" + + def json(self) -> _GalileoLoginBody: ... + + +class _JsonResponse(Protocol): + """An HTTP response read only for whatever JSON body it decodes to.""" + + def json(self) -> object: ... + + +def _login_access_token(response: _GalileoLoginResponse) -> str: + """Read the bearer token out of a Galileo login response body.""" + return response.json()["access_token"] + + +def _decoded_body(response: _JsonResponse) -> object: + """Decode a response body without asserting anything about its shape.""" + return response.json() + + class GalileoStandardLoggingFields(TypedDict, total=False): call_type: str model: str @@ -156,7 +185,7 @@ class GalileoObserve(CustomLogger): }, ) galileo_login_response.raise_for_status() - access_token: Final = galileo_login_response.json()["access_token"] + access_token: Final = _login_access_token(galileo_login_response) self.headers = { "accept": "application/json", "Content-Type": "application/json", @@ -421,7 +450,7 @@ class GalileoObserve(CustomLogger): try: verbose_logger.debug( "Galileo Logger HTTP error response json: %s", - response.json(), + _decoded_body(response), ) except Exception: pass diff --git a/litellm/integrations/gitlab/gitlab_client.py b/litellm/integrations/gitlab/gitlab_client.py index 0690ccc8c15..813a2ef2821 100644 --- a/litellm/integrations/gitlab/gitlab_client.py +++ b/litellm/integrations/gitlab/gitlab_client.py @@ -4,12 +4,80 @@ Now supports selecting a tag via `config["tag"]`; falls back to branch ("main"). """ import base64 -from typing import Any, Final +from collections.abc import Mapping, Sequence +from typing import Any, Final, Protocol, TypedDict from urllib.parse import quote +from typing_extensions import ReadOnly + from litellm.llms.custom_httpx.http_handler import HTTPHandler +class GitLabFilePayload(TypedDict, total=False): + """A repository-files API entry.""" + + content: ReadOnly[str] + encoding: ReadOnly[str] + + +class GitLabTreeEntry(TypedDict, total=False): + """A repository-tree API entry.""" + + path: ReadOnly[str] + type: ReadOnly[str] + + +class GitLabBranch(TypedDict, total=False): + """A repository-branches API entry.""" + + name: ReadOnly[str] + type: ReadOnly[str] + + +class GitLabFileMetadata(TypedDict): + """The response headers a raw file request exposes as metadata.""" + + content_type: ReadOnly[str | None] + content_length: ReadOnly[str | None] + last_modified: ReadOnly[str | None] + + +class _FileJsonResponse(Protocol): + def json(self) -> GitLabFilePayload: ... + + +class _TreeJsonResponse(Protocol): + def json(self) -> Sequence[GitLabTreeEntry] | None: ... + + +class _ProjectJsonResponse(Protocol): + def json(self) -> Mapping[str, object]: ... + + +class _BranchesJsonResponse(Protocol): + def json(self) -> Sequence[GitLabBranch] | None: ... + + +def _file_payload(resp: _FileJsonResponse) -> GitLabFilePayload: + """The JSON body of a repository-files response.""" + return resp.json() + + +def _tree_entries(resp: _TreeJsonResponse) -> Sequence[GitLabTreeEntry]: + """The entries of a repository-tree response.""" + return resp.json() or [] + + +def _project_info(resp: _ProjectJsonResponse) -> Mapping[str, object]: + """The JSON body of a project response.""" + return resp.json() + + +def _branch_entries(resp: _BranchesJsonResponse) -> Sequence[GitLabBranch] | None: + """The JSON body of a repository-branches response.""" + return resp.json() + + class GitLabClient: """ Client for interacting with the GitLab API to fetch files. @@ -42,12 +110,12 @@ class GitLabClient: self.project: str | int = project self.access_token: str = str(access_token) - self.auth_method = config.get("auth_method", "token") # 'token' or 'oauth' + self.auth_method: str = config.get("auth_method", "token") # 'token' or 'oauth' self.branch = config.get("branch", None) if not self.branch: self.branch = "main" self.tag = config.get("tag") - self.base_url = config.get("base_url", "https://gitlab.com/api/v4") + self.base_url: str = config.get("base_url", "https://gitlab.com/api/v4") if not all([self.project, self.access_token]): raise ValueError("project and access_token are required") @@ -159,7 +227,7 @@ class GitLabClient: if resp.status_code == 404: return None resp.raise_for_status() - data: Final = resp.json() + data: Final = _file_payload(resp) content: Final = data.get("content") encoding: Final = data.get("encoding", "") if content and encoding == "base64": @@ -208,7 +276,7 @@ class GitLabClient: return [] resp.raise_for_status() - data: Final = resp.json() or [] + data: Final = _tree_entries(resp) files: Final[list[str]] = [] for item in data: if item.get("type") == "blob": @@ -229,13 +297,13 @@ class GitLabClient: raise Exception("Authentication failed. Check your GitLab token and auth_method.") raise Exception(f"Failed to list files in '{directory_path}': {e}") - def get_repository_info(self) -> dict[str, Any]: + def get_repository_info(self) -> Mapping[str, object]: """Get information about the project/repository.""" url: Final = f"{self.base_url}/projects/{self._project_enc}" try: resp: Final = self.http_handler.get(url, headers=self.headers) resp.raise_for_status() - return resp.json() + return _project_info(resp) except Exception as e: raise Exception(f"Failed to get repository info: {e}") @@ -247,18 +315,18 @@ class GitLabClient: except Exception: return False - def get_branches(self) -> list[dict[str, Any]]: + def get_branches(self) -> list[GitLabBranch]: """Get list of branches in the repository.""" url: Final = f"{self.base_url}/projects/{self._project_enc}/repository/branches" try: resp: Final = self.http_handler.get(url, headers=self.headers) resp.raise_for_status() - data: Final = resp.json() + data: Final = _branch_entries(resp) return data if isinstance(data, list) else [] except Exception as e: raise Exception(f"Failed to get branches: {e}") - def get_file_metadata(self, file_path: str, *, ref: str | None = None) -> dict[str, Any] | None: + def get_file_metadata(self, file_path: str, *, ref: str | None = None) -> GitLabFileMetadata | None: """ Get minimal metadata about a file via RAW endpoint headers at a given ref. diff --git a/litellm/integrations/gitlab/gitlab_prompt_manager.py b/litellm/integrations/gitlab/gitlab_prompt_manager.py index c41d9dd240f..d4602176650 100644 --- a/litellm/integrations/gitlab/gitlab_prompt_manager.py +++ b/litellm/integrations/gitlab/gitlab_prompt_manager.py @@ -2,10 +2,12 @@ GitLab prompt manager with configurable prompts folder. """ -from typing import TYPE_CHECKING, Any, Final +from collections.abc import Mapping +from typing import TYPE_CHECKING, Any, Final, TypeVar from jinja2 import DictLoader, select_autoescape from jinja2.sandbox import ImmutableSandboxedEnvironment +from typing_extensions import ReadOnly, TypedDict from litellm.integrations.custom_prompt_management import CustomPromptManagement @@ -24,6 +26,19 @@ from litellm.types.utils import StandardCallbackDynamicParams GITLAB_PREFIX: Final = "gitlab::" +_ResponseT = TypeVar("_ResponseT") + + +class GitLabCachedPrompt(TypedDict): + id: ReadOnly[str] + path: ReadOnly[str] + content: ReadOnly[str] + metadata: ReadOnly[Mapping[str, object]] + model: ReadOnly[str | None] + temperature: ReadOnly[float | None] + max_tokens: ReadOnly[int | None] + optional_params: ReadOnly[Mapping[str, object]] + def encode_prompt_id(raw_id: str) -> str: """Convert GitLab path IDs like 'invoice/extract' → 'gitlab::invoice::extract'""" @@ -206,7 +221,7 @@ class GitLabTemplateManager: result[key] = value.strip("\"'") return result - def render_template(self, template_id: str, variables: dict[str, Any] | None = None) -> str: + def render_template(self, template_id: str, variables: Mapping[str, object] | None = None) -> str: if template_id not in self.prompts: raise ValueError(f"Template '{template_id}' not found") template: Final = self.prompts[template_id] @@ -313,7 +328,7 @@ class GitLabPromptManager(CustomPromptManagement): def get_prompt_template( self, prompt_id: str, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, *, ref: str | None = None, ) -> tuple[str, dict[str, Any]]: @@ -338,13 +353,13 @@ class GitLabPromptManager(CustomPromptManagement): self, user_id: str | None, messages: list[AllMessageValues], - function_call: dict[str, Any] | str | None = None, - litellm_params: dict[str, Any] | None = None, + function_call: Mapping[str, object] | str | None = None, + litellm_params: dict[str, object] | None = None, prompt_id: str | None = None, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, prompt_version: str | None = None, **kwargs, - ) -> tuple[list[AllMessageValues], dict[str, Any] | None]: + ) -> tuple[list[AllMessageValues], dict[str, object] | None]: if not prompt_id: return messages, litellm_params try: @@ -377,9 +392,9 @@ class GitLabPromptManager(CustomPromptManagement): return final_messages, litellm_params except Exception as e: - import litellm + from litellm._logging import verbose_proxy_logger - litellm._logging.verbose_proxy_logger.error("Error in GitLab prompt pre_call_hook: %s", e) + verbose_proxy_logger.error("Error in GitLab prompt pre_call_hook: %s", e) return messages, litellm_params def _parse_prompt_to_messages(self, prompt_content: str) -> list[AllMessageValues]: @@ -435,14 +450,14 @@ class GitLabPromptManager(CustomPromptManagement): def post_call_hook( self, user_id: str | None, - response: Any, + response: _ResponseT, input_messages: list[AllMessageValues], - function_call: dict[str, Any] | str | None = None, - litellm_params: dict[str, Any] | None = None, + function_call: Mapping[str, object] | str | None = None, + litellm_params: Mapping[str, object] | None = None, prompt_id: str | None = None, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, **kwargs, - ) -> Any: + ) -> _ResponseT: return response def get_available_prompts(self) -> list[str]: @@ -498,7 +513,7 @@ class GitLabPromptManager(CustomPromptManagement): messages: Final = self._parse_prompt_to_messages(rendered_prompt) template_model: Final = prompt_metadata.get("model") - optional_params: Final[dict[str, Any]] = {} + optional_params: Final[dict[str, object]] = {} for param in [ "temperature", "max_tokens", @@ -658,14 +673,14 @@ class GitLabPromptCache: self.template_manager: GitLabTemplateManager = self.prompt_manager.prompt_manager # In-memory stores - self._by_file: dict[str, dict[str, Any]] = {} - self._by_id: dict[str, dict[str, Any]] = {} + self._by_file: dict[str, GitLabCachedPrompt] = {} + self._by_id: dict[str, GitLabCachedPrompt] = {} # ------------------------- # Public API # ------------------------- - def load_all(self, *, recursive: bool = True) -> dict[str, dict[str, Any]]: + def load_all(self, *, recursive: bool = True) -> dict[str, GitLabCachedPrompt]: """ Scan GitLab for all .prompt files under prompts_path, load and parse each, and return the mapping of repo file path -> JSON-like dict. @@ -695,7 +710,7 @@ class GitLabPromptCache: return self._by_id - def reload(self, *, recursive: bool = True) -> dict[str, dict[str, Any]]: + def reload(self, *, recursive: bool = True) -> dict[str, GitLabCachedPrompt]: """Clear the cache and re-load from GitLab.""" self._by_file.clear() self._by_id.clear() @@ -709,11 +724,11 @@ class GitLabPromptCache: """Return the template IDs (relative to prompts_path, without extension) currently cached.""" return list(self._by_id.keys()) - def get_by_file(self, file_path: str) -> dict[str, Any] | None: + def get_by_file(self, file_path: str) -> GitLabCachedPrompt | None: """Get a cached prompt JSON by repo file path.""" return self._by_file.get(file_path) - def get_by_id(self, prompt_id: str) -> dict[str, Any] | None: + def get_by_id(self, prompt_id: str) -> GitLabCachedPrompt | None: """Get a cached prompt JSON by prompt ID (relative to prompts_path).""" if prompt_id in self._by_id: return self._by_id[prompt_id] @@ -728,7 +743,7 @@ class GitLabPromptCache: # Internals # ------------------------- - def _template_to_json(self, prompt_id: str, tmpl: GitLabPromptTemplate) -> dict[str, Any]: + def _template_to_json(self, prompt_id: str, tmpl: GitLabPromptTemplate) -> GitLabCachedPrompt: """ Normalize a GitLabPromptTemplate into a JSON-like dict that is easy to serialize. """ diff --git a/litellm/integrations/langfuse/langfuse.py b/litellm/integrations/langfuse/langfuse.py index 296c2b5714e..9576eabaa34 100644 --- a/litellm/integrations/langfuse/langfuse.py +++ b/litellm/integrations/langfuse/langfuse.py @@ -89,7 +89,7 @@ def _extract_cache_read_input_tokens(usage_obj) -> int: # Check prompt_tokens_details.cached_tokens (used by Gemini and other providers) if hasattr(usage_obj, "prompt_tokens_details"): - prompt_tokens_details: Final = getattr(usage_obj, "prompt_tokens_details", None) + prompt_tokens_details: Final[object] = getattr(usage_obj, "prompt_tokens_details", None) if prompt_tokens_details is not None and hasattr(prompt_tokens_details, "cached_tokens"): cached_tokens: Final = getattr(prompt_tokens_details, "cached_tokens", None) if cached_tokens is not None and isinstance(cached_tokens, (int, float)) and cached_tokens > 0: @@ -623,9 +623,16 @@ class LangFuseLogger: ) # Apply custom masking function if provided - if masking_function is not None and callable(masking_function): - input = self._apply_masking_function(input, masking_function) - output = self._apply_masking_function(output, masking_function) + masked_input: Final[object] = ( + self._apply_masking_function(input, masking_function) + if masking_function is not None and callable(masking_function) + else input + ) + masked_output: Final[object] = ( + self._apply_masking_function(output, masking_function) + if masking_function is not None and callable(masking_function) + else output + ) clean_metadata = redact_user_api_key_info(metadata=clean_metadata) @@ -651,15 +658,15 @@ class LangFuseLogger: # Special keys that are found in the function arguments and not the metadata if "input" in update_trace_keys: - trace_params["input"] = input if not mask_input else "redacted-by-litellm" + trace_params["input"] = masked_input if not mask_input else "redacted-by-litellm" if "output" in update_trace_keys: - trace_params["output"] = output if not mask_output else "redacted-by-litellm" + trace_params["output"] = masked_output if not mask_output else "redacted-by-litellm" else: # don't overwrite an existing trace trace_params = { "id": trace_id, "name": trace_name, "session_id": session_id, - "input": input if not mask_input else "redacted-by-litellm", + "input": masked_input if not mask_input else "redacted-by-litellm", "version": clean_metadata.pop( "trace_version", clean_metadata.get("version", None) ), # If provided just version, it will applied to the trace as well, if applied a trace version it will take precedence @@ -669,9 +676,9 @@ class LangFuseLogger: trace_params[key.replace("trace_", "")] = clean_metadata.pop(key, None) if level == "ERROR": - trace_params["status_message"] = output + trace_params["status_message"] = masked_output else: - trace_params["output"] = output if not mask_output else "redacted-by-litellm" + trace_params["output"] = masked_output if not mask_output else "redacted-by-litellm" if debug is True or (isinstance(debug, str) and debug.lower() == "true"): debug_metadata: Final = { @@ -708,7 +715,7 @@ class LangFuseLogger: ("aws_region_name", aws_region_name, bool(aws_region_name)), ("cache_hit", kwargs.get("cache_hit") or False, self._supports_tags() and "cache_hit" in kwargs), ) - enrichments: Final[Mapping[str, Any]] = { + enrichments: Final[Mapping[str, object]] = { key: value for key, value, include in candidate_enrichments if include } @@ -802,8 +809,8 @@ class LangFuseLogger: "end_time": end_time, "model": model_name, "model_parameters": optional_params, - "input": input if not mask_input else "redacted-by-litellm", - "output": output if not mask_output else "redacted-by-litellm", + "input": masked_input if not mask_input else "redacted-by-litellm", + "output": masked_output if not mask_output else "redacted-by-litellm", "usage": usage, "usage_details": usage_details, "metadata": { @@ -825,8 +832,8 @@ class LangFuseLogger: prompt_management_metadata=prompt_management_metadata, langfuse_client=self.Langfuse, ) - if output is not None and isinstance(output, str) and level == "ERROR": - generation_params["status_message"] = output + if masked_output is not None and isinstance(masked_output, str) and level == "ERROR": + generation_params["status_message"] = masked_output if self._supports_completion_start_time(): generation_params["completion_start_time"] = kwargs.get("completion_start_time", None) @@ -935,7 +942,7 @@ class LangFuseLogger: return Version(self.langfuse_sdk_version) >= Version("2.7.3") @staticmethod - def _apply_masking_function(data: Any, masking_function: Callable[[Any], Any]) -> Any: + def _apply_masking_function(data: object, masking_function: Callable[[object], object]) -> object: """ Apply a masking function to data, handling different data types. @@ -1049,7 +1056,7 @@ def _add_prompt_to_generation_params( generation_params: dict, clean_metadata: dict, prompt_management_metadata: StandardLoggingPromptManagementMetadata | None, - langfuse_client: Any, + langfuse_client: object, ) -> dict: from langfuse import Langfuse from langfuse.model import ( diff --git a/litellm/integrations/opik/opik.py b/litellm/integrations/opik/opik.py index fae93f03d1e..ce47d7fe27a 100644 --- a/litellm/integrations/opik/opik.py +++ b/litellm/integrations/opik/opik.py @@ -4,9 +4,12 @@ Opik Logger that logs LLM events to an Opik server import asyncio import traceback +from collections.abc import Mapping from datetime import datetime from typing import Any, Final +from typing_extensions import ReadOnly, TypedDict, Unpack + from litellm._logging import verbose_logger from litellm.integrations.custom_batch_logger import CustomBatchLogger from litellm.llms.custom_httpx.http_handler import ( @@ -23,7 +26,7 @@ except Exception: opik_client = None -def _should_skip_event(kwargs: dict[str, Any]) -> bool: +def _should_skip_event(kwargs: Mapping[str, object]) -> bool: """Check if event should be skipped due to missing standard_logging_object.""" if kwargs.get("standard_logging_object") is None: verbose_logger.debug("OpikLogger skipping event; no standard_logging_object found") @@ -31,12 +34,24 @@ def _should_skip_event(kwargs: dict[str, Any]) -> bool: return False +class _OpikLoggerKwargs(TypedDict, total=False): + """Constructor options accepted by ``OpikLogger``.""" + + project_name: ReadOnly[str | None] + url: ReadOnly[str | None] + api_key: ReadOnly[str | None] + workspace: ReadOnly[str | None] + batch_size: ReadOnly[int | None] + flush_interval: ReadOnly[int | None] + max_queue_size: ReadOnly[int | None] + + class OpikLogger(CustomBatchLogger): """ Opik Logger for logging events to an Opik Server """ - def __init__(self, **kwargs: Any) -> None: + def __init__(self, **kwargs: Unpack[_OpikLoggerKwargs]) -> None: self.async_httpx_client = get_async_httpx_client(llm_provider=httpxSpecialProvider.LoggingCallback) self.sync_httpx_client = _get_httpx_client() @@ -95,7 +110,7 @@ class OpikLogger(CustomBatchLogger): async def async_log_success_event( self, - kwargs: dict[str, Any], + kwargs: dict[str, object], response_obj: Any, start_time: datetime, end_time: datetime, @@ -163,7 +178,7 @@ class OpikLogger(CustomBatchLogger): except Exception as e: verbose_logger.exception("OpikLogger failed to log success event - %s\n%s", e, traceback.format_exc()) - def _sync_send(self, url: str, headers: dict[str, str], batch: dict[str, Any]) -> None: + def _sync_send(self, url: str, headers: dict[str, str], batch: dict[str, object]) -> None: try: response: Final = self.sync_httpx_client.post( url=url, @@ -178,7 +193,7 @@ class OpikLogger(CustomBatchLogger): def log_success_event( self, - kwargs: dict[str, Any], + kwargs: dict[str, object], response_obj: Any, start_time: datetime, end_time: datetime, @@ -247,7 +262,7 @@ class OpikLogger(CustomBatchLogger): except Exception as e: verbose_logger.exception("OpikLogger failed to log success event - %s\n%s", e, traceback.format_exc()) - async def _submit_batch(self, url: str, headers: dict[str, str], batch: dict[str, Any]) -> None: + async def _submit_batch(self, url: str, headers: dict[str, str], batch: dict[str, object]) -> None: try: response: Final = await self.async_httpx_client.post( url=url, diff --git a/litellm/integrations/opik/opik_payload_builder/extractors.py b/litellm/integrations/opik/opik_payload_builder/extractors.py index 92a7eca7f3e..4dd3d40fae3 100644 --- a/litellm/integrations/opik/opik_payload_builder/extractors.py +++ b/litellm/integrations/opik/opik_payload_builder/extractors.py @@ -1,6 +1,7 @@ """Data extraction functions for Opik payload building.""" import json +from collections.abc import Mapping from typing import Any, Final from litellm import _logging @@ -35,8 +36,8 @@ def normalize_provider_name(provider: str | None) -> str | None: def extract_opik_metadata( - litellm_metadata: dict[str, Any], - standard_logging_metadata: dict[str, Any], + litellm_metadata: Mapping[str, Any], + standard_logging_metadata: Mapping[str, Any], ) -> dict[str, Any]: """ Merge Opik metadata from three sources in increasing priority order: @@ -97,7 +98,7 @@ def extract_span_identifiers( def extract_tags( - opik_metadata: dict[str, Any], + opik_metadata: Mapping[str, Any], custom_llm_provider: str | None, ) -> list[str]: """ @@ -122,7 +123,7 @@ def apply_proxy_header_overrides( project_name: str, tags: list[str], thread_id: str | None, - proxy_headers: dict[str, Any], + proxy_headers: Mapping[str, str], ) -> tuple[str, list[str], str | None]: """ Apply overrides from proxy request headers (opik_* prefix). @@ -148,7 +149,7 @@ def apply_proxy_header_overrides( thread_id = value elif param_key == "tags": try: - parsed_tags = json.loads(value) + parsed_tags: object = json.loads(value) if isinstance(parsed_tags, list): tags.extend(parsed_tags) except (json.JSONDecodeError, TypeError): @@ -158,11 +159,11 @@ def apply_proxy_header_overrides( def extract_and_build_metadata( - opik_metadata: dict[str, Any], - standard_logging_metadata: dict[str, Any], - standard_logging_object: dict[str, Any], - litellm_kwargs: dict[str, Any], -) -> dict[str, Any]: + opik_metadata: Mapping[str, object], + standard_logging_metadata: Mapping[str, object], + standard_logging_object: Mapping[str, object], + litellm_kwargs: Mapping[str, object], +) -> dict[str, object]: """ Build the complete metadata dictionary from all available sources. diff --git a/litellm/integrations/opik/opik_payload_builder/payload_builders.py b/litellm/integrations/opik/opik_payload_builder/payload_builders.py index e40d72ea542..855b84ba4c8 100644 --- a/litellm/integrations/opik/opik_payload_builder/payload_builders.py +++ b/litellm/integrations/opik/opik_payload_builder/payload_builders.py @@ -17,12 +17,12 @@ def build_trace_payload( end_time: datetime, input_data: Any, output_data: Any, - metadata: dict[str, Any], + metadata: dict[str, object], tags: list[str], thread_id: str | None, ) -> types.TracePayload: """Build a complete trace payload.""" - trace_name: Final = response_obj.get("object", "unknown type") + trace_name: Final[str] = response_obj.get("object", "unknown type") return types.TracePayload( project_name=project_name, @@ -47,7 +47,7 @@ def build_span_payload( end_time: datetime, input_data: Any, output_data: Any, - metadata: dict[str, Any], + metadata: dict[str, object], tags: list[str], usage: dict[str, int], provider: str | None = None, @@ -56,9 +56,9 @@ def build_span_payload( """Build a complete span payload.""" span_id: Final = utils.create_uuid7() - model: Final = response_obj.get("model", "unknown-model") - obj_type: Final = response_obj.get("object", "unknown-object") - created: Final = response_obj.get("created", 0) + model: Final[str] = response_obj.get("model", "unknown-model") + obj_type: Final[str] = response_obj.get("object", "unknown-object") + created: Final[int] = response_obj.get("created", 0) span_name: Final = f"{model}_{obj_type}_{created}" _logging.verbose_logger.debug("OpikLogger creating span with id %s for trace %s", span_id, trace_id) diff --git a/litellm/integrations/otel/mappers/genai.py b/litellm/integrations/otel/mappers/genai.py index b09498f9292..3ac92b04c27 100644 --- a/litellm/integrations/otel/mappers/genai.py +++ b/litellm/integrations/otel/mappers/genai.py @@ -62,6 +62,8 @@ class GenAIMapper: GenAI.RESPONSE_TIME_TO_FIRST_CHUNK: lambda d: d.time_to_first_chunk_seconds, GenAI.USAGE_INPUT_TOKENS: lambda d: d.usage.input_tokens, GenAI.USAGE_OUTPUT_TOKENS: lambda d: d.usage.output_tokens, + GenAI.USAGE_CACHE_CREATION_INPUT_TOKENS: lambda d: d.usage.cache_creation_input_tokens, + GenAI.USAGE_CACHE_READ_INPUT_TOKENS: lambda d: d.usage.cache_read_input_tokens, Error.TYPE: lambda d: d.error.error_type if d.error else None, Server.ADDRESS: lambda d: d.server.address if d.server else None, Server.PORT: lambda d: d.server.port if d.server else None, diff --git a/litellm/integrations/otel/model/payloads.py b/litellm/integrations/otel/model/payloads.py index f70c777e1a7..e8ed269f6cb 100644 --- a/litellm/integrations/otel/model/payloads.py +++ b/litellm/integrations/otel/model/payloads.py @@ -6,6 +6,7 @@ import json from collections.abc import Mapping from dataclasses import dataclass, field from enum import Enum +from types import MappingProxyType from typing import TYPE_CHECKING, ClassVar, Final, cast from urllib.parse import urlsplit @@ -62,6 +63,31 @@ if TYPE_CHECKING: # --- typed sub-structures ---------------------------------------------------- # +def _cache_token_value(*values: object) -> int | None: + explicit_zero = False + invalid_before_zero = False + for raw_value in values: + if raw_value is None: + continue + if isinstance(raw_value, bool): + parsed = None + else: + try: + parsed = as_int(raw_value) + except (OverflowError, ValueError): + parsed = None + if parsed is None: + if not explicit_zero: + invalid_before_zero = True + elif parsed > 0: + return parsed + elif parsed == 0: + explicit_zero = True + elif not explicit_zero: + invalid_before_zero = True + return 0 if explicit_zero and not invalid_before_zero else None + + @dataclass(frozen=True) class LLMRequestParams: temperature: float | None = None @@ -95,6 +121,35 @@ class LLMUsage: input_tokens: int | None = None output_tokens: int | None = None total_tokens: int | None = None + cache_creation_input_tokens: int | None = None + cache_read_input_tokens: int | None = None + + @classmethod + def from_standard_logging_payload(cls, payload: StandardLoggingPayload) -> LLMUsage: + # Cache token counts only exist on the raw provider usage object under metadata + metadata: Final[Mapping[str, object]] = payload.get("metadata") or {} + raw_usage: Final = metadata.get("usage_object") + usage_object: Final[Mapping[str, object]] = raw_usage if isinstance(raw_usage, Mapping) else {} + raw_details: Final = usage_object.get("prompt_tokens_details") + prompt_details: Final[Mapping[str, object]] = ( + raw_details if isinstance(raw_details, Mapping) else MappingProxyType({}) + ) + return cls( + input_tokens=as_int(payload.get("prompt_tokens")), + output_tokens=as_int(payload.get("completion_tokens")), + total_tokens=as_int(payload.get("total_tokens")), + cache_creation_input_tokens=_cache_token_value( + usage_object.get("cache_creation_input_tokens"), + prompt_details.get("cache_write_tokens"), + prompt_details.get("cache_creation_tokens"), + prompt_details.get("cache_creation_input_tokens"), + ), + cache_read_input_tokens=_cache_token_value( + usage_object.get("cache_read_input_tokens"), + prompt_details.get("cached_tokens"), + usage_object.get("prompt_cache_hit_tokens"), + ), + ) @dataclass(frozen=True) @@ -363,11 +418,7 @@ class LLMCallSpanData: response_model=context.response_model, response_id=as_str(response.get("id")), request_params=LLMRequestParams.from_model_parameters(params), - usage=LLMUsage( - input_tokens=as_int(payload.get("prompt_tokens")), - output_tokens=as_int(payload.get("completion_tokens")), - total_tokens=as_int(payload.get("total_tokens")), - ), + usage=LLMUsage.from_standard_logging_payload(payload), finish_reasons=finish_reasons, error=_parse_error(payload), response_cost=as_float(payload.get("response_cost")), diff --git a/litellm/integrations/otel/model/semconv.py b/litellm/integrations/otel/model/semconv.py index 4ad0cb5d1b4..f7a6280f95b 100644 --- a/litellm/integrations/otel/model/semconv.py +++ b/litellm/integrations/otel/model/semconv.py @@ -110,6 +110,8 @@ class GenAI: # usage USAGE_INPUT_TOKENS: Final = "gen_ai.usage.input_tokens" USAGE_OUTPUT_TOKENS: Final = "gen_ai.usage.output_tokens" + USAGE_CACHE_CREATION_INPUT_TOKENS: Final = "gen_ai.usage.cache_creation.input_tokens" + USAGE_CACHE_READ_INPUT_TOKENS: Final = "gen_ai.usage.cache_read.input_tokens" # content (opt-in, gated by capture mode) INPUT_MESSAGES: Final = "gen_ai.input.messages" OUTPUT_MESSAGES: Final = "gen_ai.output.messages" diff --git a/litellm/integrations/otel/plumbing/metrics.py b/litellm/integrations/otel/plumbing/metrics.py index c7e491c002a..e1623f4697f 100644 --- a/litellm/integrations/otel/plumbing/metrics.py +++ b/litellm/integrations/otel/plumbing/metrics.py @@ -11,9 +11,10 @@ identical metrics. The attribute cardinality filter is reused from v1 by import from collections.abc import Mapping from dataclasses import dataclass from datetime import datetime -from typing import Any, Final, TypeAlias +from typing import Any, Final, Literal, Protocol, TypeAlias from opentelemetry.metrics import Histogram, Meter +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -151,6 +152,29 @@ METRIC_ATTRIBUTE_CEILING: Final[frozenset[str]] = frozenset( BOUNDED_HIDDEN_PARAM_KEYS: Final[tuple[str, ...]] = ("model_id",) +class _TokenUsage(TypedDict, total=False): + """The token counts a response's ``usage`` carries, as the recorder reads them.""" + + prompt_tokens: ReadOnly[int] + completion_tokens: ReadOnly[int] + + +class _ResponseView(Protocol): + """The one read the recorder makes on a litellm response object.""" + + def get(self, key: Literal["usage"], /) -> _TokenUsage | None: ... + + +class _MetricKwargs(TypedDict, total=False): + """The logging kwargs the recorder reads directly.""" + + call_type: ReadOnly[str | None] + litellm_params: ReadOnly[Mapping[str, object] | None] + response_cost: ReadOnly[float | None] + completion_start_time: ReadOnly[datetime | float | str | None] + api_call_start_time: ReadOnly[datetime | float | str | None] + + def resolve_error_type(kwargs: Mapping[str, Any]) -> str: """The ``error.type`` value for a failed request. @@ -192,8 +216,8 @@ class GenAIMetricRecorder: def record( self, - kwargs: Mapping[str, Any], - response_obj: Any, + kwargs: _MetricKwargs, + response_obj: _ResponseView | None, start_time: datetime, end_time: datetime, ) -> None: @@ -218,7 +242,7 @@ class GenAIMetricRecorder: def record_failure( self, - kwargs: Mapping[str, Any], + kwargs: _MetricKwargs, start_time: datetime, end_time: datetime, ) -> None: @@ -342,7 +366,7 @@ class GenAIMetricRecorder: # Per-metric recording # ------------------------------------------------------------------ # - def _record_token_usage(self, response_obj: Any, common_attrs: dict) -> None: + def _record_token_usage(self, response_obj: _ResponseView | None, common_attrs: dict) -> None: if not response_obj: return usage: Final = response_obj.get("usage") @@ -353,7 +377,7 @@ class GenAIMetricRecorder: self._metrics.token_usage.record(usage.get("prompt_tokens", 0), attributes=in_attrs) self._metrics.token_usage.record(usage.get("completion_tokens", 0), attributes=out_attrs) - def _record_time_to_first_token(self, kwargs: Mapping[str, Any], common_attrs: dict) -> None: + def _record_time_to_first_token(self, kwargs: _MetricKwargs, common_attrs: dict) -> None: time_to_first_chunk: Final = time_to_first_chunk_seconds(kwargs) if time_to_first_chunk is None: return @@ -361,15 +385,14 @@ class GenAIMetricRecorder: def _record_time_per_output_token( self, - kwargs: Mapping[str, Any], - response_obj: Any, + kwargs: _MetricKwargs, + response_obj: _ResponseView | None, end_time: datetime, duration_s: float, common_attrs: dict, ) -> None: - completion_tokens = None - if response_obj and (usage := response_obj.get("usage")): - completion_tokens = usage.get("completion_tokens") + usage: Final = response_obj.get("usage") if response_obj else None + completion_tokens: Final = usage.get("completion_tokens") if usage else None if completion_tokens is None or completion_tokens <= 0: return diff --git a/litellm/integrations/otel/plumbing/providers.py b/litellm/integrations/otel/plumbing/providers.py index 81b00f788ee..fb74ff85e5b 100644 --- a/litellm/integrations/otel/plumbing/providers.py +++ b/litellm/integrations/otel/plumbing/providers.py @@ -1,7 +1,7 @@ """Provider / exporter factory + the Baggage span processor.""" from collections.abc import Callable, Iterable -from typing import TYPE_CHECKING, Any, Final +from typing import TYPE_CHECKING, Any, Final, Literal from opentelemetry import _logs, baggage, metrics from opentelemetry._events import EventLogger @@ -135,14 +135,36 @@ def parse_headers(raw: str | None) -> dict[str, str]: return dict(parse_env_headers(raw, liberal=True)) +_IN_MEMORY_KINDS: Final = ("in_memory", "inmemory", "memory") +_OTLP_HTTP_KINDS: Final = ("otlp_http", "http", "http/protobuf", "http/json") +_OTLP_GRPC_KINDS: Final = ("otlp_grpc", "grpc") + + +def exporter_transport(kind: str) -> Literal["http", "grpc", "headerless"]: + """How an exporter of this ``kind`` carries credentials, per ``_exporter_from_spec``. + + ``http``/``grpc`` exporters (and any registered factory, which builds an + OTLP exporter) stamp ``spec.headers``; ``console``, ``in_memory``, and any + unrecognized kind (which falls back to a header-ignoring console exporter) + are ``headerless``. Routability decisions must read this rather than a + denylist, so a typo'd or unavailable kind is not mistaken for OTLP. + """ + resolved: Final = kind.lower() + if resolved in _OTLP_HTTP_KINDS or resolved in _EXPORTER_FACTORIES: + return "http" + if resolved in _OTLP_GRPC_KINDS: + return "grpc" + return "headerless" + + def _exporter_from_spec(spec: ExporterSpec) -> SpanExporter: kind: Final = (spec.kind or "console").lower() factory: Final = _EXPORTER_FACTORIES.get(kind) if factory is not None: return factory(spec) - if kind in ("in_memory", "inmemory", "memory"): + if kind in _IN_MEMORY_KINDS: return InMemorySpanExporter() - if kind in ("otlp_http", "http", "http/protobuf", "http/json"): + if kind in _OTLP_HTTP_KINDS: from opentelemetry.exporter.otlp.proto.http.trace_exporter import ( OTLPSpanExporter as HTTPExporter, ) @@ -151,7 +173,7 @@ def _exporter_from_spec(spec: ExporterSpec) -> SpanExporter: endpoint=_otlp_traces_endpoint(spec.endpoint), headers=parse_headers(spec.headers), ) - if kind in ("otlp_grpc", "grpc"): + if kind in _OTLP_GRPC_KINDS: from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import ( OTLPSpanExporter as GRPCExporter, ) diff --git a/litellm/integrations/otel/plumbing/routing.py b/litellm/integrations/otel/plumbing/routing.py index dc2db823a8d..227e18f3663 100644 --- a/litellm/integrations/otel/plumbing/routing.py +++ b/litellm/integrations/otel/plumbing/routing.py @@ -27,6 +27,7 @@ from litellm.constants import OTEL_SERVICE_NAME_METADATA_KEYS from litellm.integrations.otel.model.config import ExporterSpec, OpenTelemetryV2Config from litellm.integrations.otel.plumbing.providers import ( build_tracer_provider, + exporter_transport, get_tracer, ) from litellm.integrations.otel.presets import ( @@ -121,13 +122,27 @@ def _encoded_header_string(headers: Mapping[str, str]) -> str: class TenantRoute: """The tracer to create a span on, plus whether it must root its own trace. - ``detached`` is True when project routing engaged. Phoenix assigns a whole + ``detached`` is True when the routed span exports to a DIFFERENT backend + than the request's root span, which always exports through the default + tracer. A detached span roots a fresh trace with a link back to the request + trace for correlation, so the destination account is not left holding a + child whose parent it never received. It is driven by whether routing + headers were actually applied to an owned exporter, not merely requested: + a credential or project route whose callback owns no exporter those headers + can reach exports through the default backend unchanged, so it stays + parented like an unrouted span. + + Credential routing (a team/key's own vendor account) is one detaching case: + the root, auth, and db spans stay on the operator's default backend while + the LLM-call span exports to the tenant's account, so parenting it into the + request trace makes the tenant account show a fragmented span with a missing + parent. Project routing (Phoenix) is the other: Phoenix assigns a whole trace to one project by whichever of its spans arrives first, so a project-routed span parented into the request trace gets dragged into the project of the default-exported request spans and the header does nothing. - The span must therefore start a fresh trace (with a link back to the - request trace for correlation) — which is also how the v1 Phoenix logger - behaved, exporting each request under its own Phoenix-local parent span. + Both mirror the v1 loggers, which exported each request under its own + backend-local root. Service-name routing does NOT detach: it relabels + ``service.name`` on the SAME operator backend, where the parent is present. """ tracer: Tracer @@ -161,11 +176,20 @@ class TenantTracerCache: self._open_span_counts: dict[TracerProvider, int] = {} # mutable-ok: live refcount state # Oldest-first so an overflow of draining providers sheds the stalest. self._retired: OrderedDict[TracerProvider, None] = OrderedDict() # mutable-ok: draining evicted providers - self._project_routable = any( - spec.owner == callback_name and spec.kind.lower() not in (*_NON_OTLP_KINDS, *_GRPC_KINDS) - for spec in config.exporters + # An owned exporter is routable only when its kind actually resolves to a + # header-carrying OTLP exporter. A denylist would accept a typo'd or + # unavailable kind, which ``_exporter_from_spec`` falls back to a + # header-ignoring console exporter: detaching such a span would strand it + # on the operator's console, never reaching the tenant backend. Project + # headers are HTTP-only; credentials ride gRPC metadata too (Arize's + # default exporter is gRPC), so they accept either OTLP transport. + owned_transports: Final = tuple( + exporter_transport(spec.kind) for spec in config.exporters if spec.owner == callback_name ) + self._project_routable = "http" in owned_transports + self._credential_routable = "http" in owned_transports or "grpc" in owned_transports self._warned_project_unroutable = False + self._warned_credential_unroutable = False def release(self, provider: TracerProvider | None) -> None: """Drop one open-span count; shut a retired provider down once drained. @@ -207,7 +231,7 @@ class TenantTracerCache: concurrent overflow eviction can't shut it down between selection and the caller's span start. The caller must ``release`` it exactly once. """ - credential_headers: Final = dynamic_otlp_headers(self._callback_name, dynamic_params) or _NO_HEADERS + credential_headers: Final = self._credential_headers(dynamic_params) project_headers: Final = self._project_headers(auth_metadata) service_name: Final = tenant_service_name(auth_metadata) if not credential_headers and not project_headers and service_name is None: @@ -231,7 +255,7 @@ class TenantTracerCache: _shutdown_provider(evicted) return TenantRoute( tracer=get_tracer(provider, self._tracer_name), - detached=bool(project_headers), + detached=bool(project_headers) or bool(credential_headers), provider=provider, ) @@ -275,6 +299,26 @@ class TenantTracerCache: self._open_span_counts.pop(overflowed, None) return overflowed + def _credential_headers(self, dynamic_params: StandardCallbackDynamicParams | None) -> Mapping[str, str]: + """The per-request dynamic OTLP credentials, if this cache can apply them. + + A callback owning only a console/in_memory exporter has nowhere to stamp + them, so the span would export to the operator's default backend + unchanged; routing there and detaching would orphan it on the very + backend that holds its parent. Warn once and keep the default tracer. + """ + requested: Final = dynamic_otlp_headers(self._callback_name, dynamic_params) or _NO_HEADERS + if not requested or self._credential_routable: + return requested + if not self._warned_credential_unroutable: + self._warned_credential_unroutable = True + verbose_logger.warning( + "OTel V2: %s request carries dynamic credentials, but the callback owns no " + "OTLP exporter to stamp them onto; spans export to the default backend.", + self._callback_name, + ) + return _NO_HEADERS + def _project_headers(self, auth_metadata: Mapping[str, str] | None) -> Mapping[str, str]: """The per-request project-routing headers, if this cache can apply them. diff --git a/litellm/integrations/posthog.py b/litellm/integrations/posthog.py index db9610a5a3c..4f7dff952e6 100644 --- a/litellm/integrations/posthog.py +++ b/litellm/integrations/posthog.py @@ -12,7 +12,10 @@ For batching specific details see CustomBatchLogger class import asyncio import atexit import os -from typing import Any, Final +from collections.abc import Mapping, Sequence +from typing import Final + +from typing_extensions import ReadOnly, TypedDict from litellm._logging import verbose_logger from litellm._uuid import uuid @@ -34,6 +37,21 @@ from litellm.types.integrations.posthog import ( from litellm.types.utils import StandardCallbackDynamicParams, StandardLoggingPayload +class PostHogBatchPayload(TypedDict): + api_key: ReadOnly[str] + batch: ReadOnly[Sequence[PostHogEventPayload]] + + +class PostHogLiteLLMParams(TypedDict, total=False): + metadata: ReadOnly[Mapping[str, object]] + + +class PostHogLogKwargs(TypedDict, total=False): + standard_logging_object: ReadOnly[StandardLoggingPayload] + standard_callback_dynamic_params: ReadOnly[StandardCallbackDynamicParams] + litellm_params: ReadOnly[PostHogLiteLLMParams] + + class PostHogLogger(CustomBatchLogger): def __init__(self, **kwargs): """ @@ -137,7 +155,7 @@ class PostHogLogger(CustomBatchLogger): if len(self.log_queue) >= self.batch_size: await self.flush_queue() - def create_posthog_event_payload(self, kwargs: dict[str, Any]) -> PostHogEventPayload: + def create_posthog_event_payload(self, kwargs: PostHogLogKwargs) -> PostHogEventPayload: """ Helper function to create a PostHog event payload for logging @@ -171,11 +189,11 @@ class PostHogLogger(CustomBatchLogger): def _create_posthog_properties( self, standard_logging_object: StandardLoggingPayload, - kwargs: dict[str, Any], + kwargs: PostHogLogKwargs, event_name: str, - ) -> dict[str, Any]: + ) -> dict[str, object]: """Create PostHog properties following LLM Analytics spec""" - properties: Final = {} + properties: Final[dict[str, object]] = {} # Core model information properties["$ai_model"] = self._safe_get(standard_logging_object, "model", "") @@ -211,16 +229,19 @@ class PostHogLogger(CustomBatchLogger): properties["$ai_error"] = error_str # Add trace properties - self._add_trace_properties(properties, kwargs) + self._add_trace_properties(properties, standard_logging_object, kwargs) # Add custom metadata fields self._add_custom_metadata_properties(properties, kwargs) return properties - def _add_trace_properties(self, properties: dict[str, Any], kwargs: dict[str, Any]): - standard_logging_object: Final = self._safe_get(kwargs, "standard_logging_object", {}) - + def _add_trace_properties( + self, + properties: dict[str, object], + standard_logging_object: StandardLoggingPayload, + kwargs: PostHogLogKwargs, + ) -> None: trace_id: Final = self._safe_get(standard_logging_object, "trace_id", self._safe_uuid()) properties["$ai_trace_id"] = trace_id @@ -232,7 +253,7 @@ class PostHogLogger(CustomBatchLogger): if parent_id: properties["$ai_parent_id"] = parent_id - def _add_custom_metadata_properties(self, properties: dict[str, Any], kwargs: dict[str, Any]): + def _add_custom_metadata_properties(self, properties: dict[str, object], kwargs: PostHogLogKwargs) -> None: """Add custom metadata fields to PostHog properties""" metadata: Final = self._extract_metadata(kwargs) if not isinstance(metadata, dict): @@ -277,7 +298,7 @@ class PostHogLogger(CustomBatchLogger): if key not in litellm_internal_fields: properties[key] = value - def _get_distinct_id(self, standard_logging_object: StandardLoggingPayload, kwargs: dict[str, Any]) -> str: + def _get_distinct_id(self, standard_logging_object: StandardLoggingPayload, kwargs: PostHogLogKwargs) -> str: metadata: Final = self._extract_metadata(kwargs) user_id: Final = self._safe_get(metadata, "user_id") if user_id: @@ -291,7 +312,7 @@ class PostHogLogger(CustomBatchLogger): return self._safe_uuid() - def _get_credentials_for_request(self, kwargs: dict[str, Any]) -> tuple[str | None, str | None]: + def _get_credentials_for_request(self, kwargs: PostHogLogKwargs) -> tuple[str | None, str | None]: """ Get PostHog credentials for this request. @@ -334,7 +355,7 @@ class PostHogLogger(CustomBatchLogger): verbose_logger.debug("[POSTHOG MOCK] Mock mode enabled - API calls will be intercepted") # Group events by credentials for batch sending - batches_by_credentials: Final[dict[tuple[str, str], list]] = {} + batches_by_credentials: Final[dict[tuple[str, str], list[PostHogEventPayload]]] = {} for item in self.log_queue: key = (item["api_key"], item["api_url"]) if key not in batches_by_credentials: @@ -380,18 +401,19 @@ class PostHogLogger(CustomBatchLogger): verbose_logger.error("PostHog: Failed to initialize async components: %s", e) raise - def _extract_metadata(self, kwargs: dict[str, Any]) -> dict[str, Any]: - litellm_params: Final = kwargs.get("litellm_params", {}) or {} - return litellm_params.get("metadata", {}) or {} + def _extract_metadata(self, kwargs: PostHogLogKwargs) -> Mapping[str, object]: + litellm_params: Final[PostHogLiteLLMParams] = kwargs.get("litellm_params", {}) or {} + metadata: Final[Mapping[str, object]] = litellm_params.get("metadata", {}) or {} + return metadata def _safe_uuid(self) -> str: return str(uuid.uuid4()) - def _create_posthog_payload(self, events: list, api_key: str) -> dict[str, Any]: + def _create_posthog_payload(self, events: Sequence[PostHogEventPayload], api_key: str) -> PostHogBatchPayload: return {"api_key": api_key, "batch": events} - def _safe_get(self, obj: Any, key: str, default: Any = None) -> Any: - if obj is None or not hasattr(obj, "get"): + def _safe_get(self, obj: Mapping[str, object] | None, key: str, default: object = None) -> object: + if not isinstance(obj, Mapping): return default return obj.get(key, default) @@ -412,7 +434,7 @@ class PostHogLogger(CustomBatchLogger): try: # Group events by credentials (same logic as async_send_batch) - batches_by_credentials: Final[dict[tuple[str, str], list]] = {} + batches_by_credentials: Final[dict[tuple[str, str], list[PostHogEventPayload]]] = {} for item in self.log_queue: key = (item["api_key"], item["api_url"]) if key not in batches_by_credentials: diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py index 467ec72dc4a..975a9bd8639 100644 --- a/litellm/integrations/prometheus.py +++ b/litellm/integrations/prometheus.py @@ -8,6 +8,7 @@ import math import os import sys from collections.abc import Awaitable, Callable, Mapping, Sequence +from dataclasses import replace from datetime import datetime, timedelta from typing import TYPE_CHECKING, Any, Final, Literal, Protocol, TypeVar, cast @@ -58,7 +59,10 @@ from litellm.types.utils import ( if TYPE_CHECKING: from apscheduler.schedulers.asyncio import AsyncIOScheduler + from prometheus_client import Gauge from prometheus_client.metrics import MetricWrapperBase + + from litellm.router import Router else: AsyncIOScheduler = Any @@ -67,6 +71,8 @@ _TableRowT: Final = TypeVar("_TableRowT", bound=BaseModel) _DEFAULT_BUDGET_METRICS_PER_REQUEST_TIMEOUT: Final = 5.0 +UNRECOGNIZED_REQUESTED_MODEL_LABEL: Final = "other" + _NON_ENUM_METRIC_LABELS: Final[frozenset[str]] = frozenset( ( "guardrail_name", @@ -154,6 +160,44 @@ def _get_budget_metrics_per_request_timeout() -> float: return parsed +def _get_proxy_llm_router() -> Router | None: + try: + from litellm.proxy.proxy_server import llm_router + except Exception: + return None + return llm_router + + +def _bounded_requested_model_label(requested_model: str | None, router_originated: bool = False) -> str | None: + """ + Bound ``requested_model`` label cardinality: names the router recognizes + (model names, deployment ids, aliases, routing groups, team public model + names) or matches via a global or team wildcard/pattern route keep their + own label value; any other client-supplied string collapses into the + single ``other`` bucket. With no proxy router to vouch for the string, + client-supplied values collapse to ``other`` while ``router_originated`` + values (emitted by an SDK ``Router``'s own deployment failure and + fallback events, where the proxy router never exists) pass through. + """ + if not requested_model: + return requested_model + llm_router: Final = _get_proxy_llm_router() + if llm_router is None: + return requested_model if router_originated else UNRECOGNIZED_REQUESTED_MODEL_LABEL + if llm_router.is_recognized_model(requested_model): + return requested_model + if requested_model in llm_router.team_public_model_names: + return requested_model + if llm_router.pattern_router.route(requested_model) is not None: + return requested_model + if any( + team_pattern_router.route(requested_model) is not None + for team_pattern_router in llm_router.team_pattern_routers.values() + ): + return requested_model + return UNRECOGNIZED_REQUESTED_MODEL_LABEL + + class PrometheusLogger(CustomLogger): # Class variables or attributes @@ -434,6 +478,30 @@ class PrometheusLogger(CustomLogger): labelnames=self.get_labels_for_metric("litellm_remaining_api_key_tokens_for_model"), ) + self.litellm_api_key_rate_limit_allowed_metric = self._gauge_factory( + "litellm_api_key_rate_limit_allowed_metric", + "Configured rate limit for the API Key in the current window (rpm_limit / tpm_limit), by rate_limit_type", + labelnames=self.get_labels_for_metric("litellm_api_key_rate_limit_allowed_metric"), + ) + + self.litellm_api_key_rate_limit_used_metric = self._gauge_factory( + "litellm_api_key_rate_limit_used_metric", + "Requests or tokens the API Key has consumed in the current rate limit window, by rate_limit_type", + labelnames=self.get_labels_for_metric("litellm_api_key_rate_limit_used_metric"), + ) + + self.litellm_team_rate_limit_allowed_metric = self._gauge_factory( + "litellm_team_rate_limit_allowed_metric", + "Configured rate limit for the Team in the current window (team rpm_limit / tpm_limit), by rate_limit_type", + labelnames=self.get_labels_for_metric("litellm_team_rate_limit_allowed_metric"), + ) + + self.litellm_team_rate_limit_used_metric = self._gauge_factory( + "litellm_team_rate_limit_used_metric", + "Requests or tokens the Team has consumed in the current rate limit window, by rate_limit_type", + labelnames=self.get_labels_for_metric("litellm_team_rate_limit_used_metric"), + ) + ######################################## # LLM API Deployment Metrics / analytics ######################################## @@ -1433,6 +1501,11 @@ class PrometheusLogger(CustomLogger): model_id=enum_values.model_id, ) + self._set_key_and_team_rate_limit_metrics( + standard_logging_payload=standard_logging_payload, # pyright: ignore[reportArgumentType] # isinstance(dict) above narrows the TypedDict to dict[Unknown, Unknown] + enum_values=enum_values, + ) + # set latency metrics self._set_latency_metrics( kwargs=kwargs, @@ -1960,17 +2033,102 @@ class PrometheusLogger(CustomLogger): """ if standard_logging_payload is None: return None + return PrometheusLogger._get_int_from_v3_rate_limit_headers( + standard_logging_payload=standard_logging_payload, + header_name=f"x-ratelimit-model_per_key-remaining-{rate_limit_type}", + ) + + @staticmethod + def _get_int_from_v3_rate_limit_headers( + standard_logging_payload: StandardLoggingPayload, + header_name: str, + ) -> int | None: hidden_params: Final = standard_logging_payload.get("hidden_params") if hidden_params is None: return None - additional_headers: Final = hidden_params.get("additional_headers") + additional_headers: Final[Mapping[str, object] | None] = hidden_params.get("additional_headers") if additional_headers is None: return None - value: Final = dict(additional_headers).get(f"x-ratelimit-model_per_key-remaining-{rate_limit_type}") + value: Final = additional_headers.get(header_name) if isinstance(value, bool) or not isinstance(value, int): return None return value + def _set_key_and_team_rate_limit_metrics( + self, + standard_logging_payload: StandardLoggingPayload, + enum_values: UserAPIKeyLabelValues, + ) -> None: + """ + Export the key-level and team-level RPM / TPM limit and current window + usage from the ``x-ratelimit-{api_key,team}-{limit,remaining}-*`` + headers the v3 rate limiter mirrors into the logging payload. The + limiter already read these counters (from Redis when configured) on + the request path, so no extra store lookup happens here. Descriptors + without a configured limit emit no header, so their series is removed + rather than left at the value from before the limit was dropped. + """ + descriptor_gauges: Final[ + tuple[tuple[Literal["api_key", "team"], DEFINED_PROMETHEUS_METRICS, Gauge, Gauge], ...] + ] = ( + ( + "api_key", + "litellm_api_key_rate_limit_allowed_metric", + self.litellm_api_key_rate_limit_allowed_metric, + self.litellm_api_key_rate_limit_used_metric, + ), + ( + "team", + "litellm_team_rate_limit_allowed_metric", + self.litellm_team_rate_limit_allowed_metric, + self.litellm_team_rate_limit_used_metric, + ), + ) + for descriptor_key, metric_name, allowed_gauge, used_gauge in descriptor_gauges: + for rate_limit_type in ("requests", "tokens"): + self._set_rate_limit_allowed_and_used_gauges( + standard_logging_payload=standard_logging_payload, + enum_values=enum_values, + descriptor_key=descriptor_key, + metric_name=metric_name, + allowed_gauge=allowed_gauge, + used_gauge=used_gauge, + rate_limit_type=rate_limit_type, + ) + + def _set_rate_limit_allowed_and_used_gauges( + self, + standard_logging_payload: StandardLoggingPayload, + enum_values: UserAPIKeyLabelValues, + descriptor_key: Literal["api_key", "team"], + metric_name: DEFINED_PROMETHEUS_METRICS, + allowed_gauge: Gauge, + used_gauge: Gauge, + rate_limit_type: Literal["requests", "tokens"], + ) -> None: + limit: Final = self._get_int_from_v3_rate_limit_headers( + standard_logging_payload=standard_logging_payload, + header_name=f"x-ratelimit-{descriptor_key}-limit-{rate_limit_type}", + ) + remaining: Final = self._get_int_from_v3_rate_limit_headers( + standard_logging_payload=standard_logging_payload, + header_name=f"x-ratelimit-{descriptor_key}-remaining-{rate_limit_type}", + ) + labelled_values: Final = replace(enum_values, rate_limit_type=rate_limit_type) + labelnames: Final = self.get_labels_for_metric(metric_name) + labels: Final = prometheus_label_factory( + supported_enum_labels=labelnames, + enum_values=labelled_values, + label_context=PrometheusLabelFactoryContext(labelled_values), + ) + if limit is None or remaining is None: + label_values: Final = tuple(labels.get(label) for label in labelnames) + self._bounded_prometheus_series_tracker.remove_series(allowed_gauge, label_values) + self._bounded_prometheus_series_tracker.remove_series(used_gauge, label_values) + return + allowed_gauge.labels(**labels).set(limit) + used_gauge.labels(**labels).set(limit - remaining) + def _set_virtual_key_rate_limit_metrics( self, user_api_key: str | None, @@ -2407,7 +2565,7 @@ class PrometheusLogger(CustomLogger): team_alias=user_api_key_dict.team_alias, org_id=user_api_key_dict.org_id, org_alias=user_api_key_dict.organization_alias, - requested_model=request_data.get("model", ""), + requested_model=_bounded_requested_model_label(request_data.get("model", "")), status_code=str(status_code), exception_status=str(status_code), exception_class=self._get_exception_class_name(original_exception), @@ -2627,7 +2785,9 @@ class PrometheusLogger(CustomLogger): label_model_id = "" label_api_base = "" label_api_provider = "" - label_requested_model = litellm_model_name or model_group or "" + label_requested_model = ( + _bounded_requested_model_label(litellm_model_name or model_group, router_originated=True) or "" + ) enum_values: Final = UserAPIKeyLabelValues( litellm_model_name=label_litellm_model_name, @@ -3186,7 +3346,7 @@ class PrometheusLogger(CustomLogger): _tags: Final = cast(list[str], kwargs.get("tags") or []) enum_values: Final = UserAPIKeyLabelValues( - requested_model=original_model_group, + requested_model=_bounded_requested_model_label(original_model_group, router_originated=True), fallback_model=_new_model, hashed_api_key=standard_metadata["user_api_key_hash"], api_key_alias=standard_metadata["user_api_key_alias"], @@ -3227,7 +3387,7 @@ class PrometheusLogger(CustomLogger): ) enum_values: Final = UserAPIKeyLabelValues( - requested_model=original_model_group, + requested_model=_bounded_requested_model_label(original_model_group, router_originated=True), fallback_model=_new_model, hashed_api_key=standard_metadata["user_api_key_hash"], api_key_alias=standard_metadata["user_api_key_alias"], diff --git a/litellm/integrations/prometheus_helpers/bounded_prometheus_series_tracker.py b/litellm/integrations/prometheus_helpers/bounded_prometheus_series_tracker.py index c54790b8ae7..c1ccf09d5d6 100644 --- a/litellm/integrations/prometheus_helpers/bounded_prometheus_series_tracker.py +++ b/litellm/integrations/prometheus_helpers/bounded_prometheus_series_tracker.py @@ -60,6 +60,10 @@ class BoundedPrometheusSeriesTracker: break del series[tracked_label_values] + def remove_series(self, metric: object, label_values: tuple[str | None, ...]) -> bool: + """Drop one child series, True when it is gone (removed or never existed).""" + return self._remove_metric_child(metric, label_values) + def _should_run_ttl_cleanup( self, metric_name: str, diff --git a/litellm/integrations/rubrik.py b/litellm/integrations/rubrik.py index a474a11601d..c9e511905a6 100644 --- a/litellm/integrations/rubrik.py +++ b/litellm/integrations/rubrik.py @@ -8,11 +8,12 @@ import uuid from collections import Counter from collections.abc import Awaitable, Mapping, Sequence from dataclasses import dataclass +from datetime import datetime from types import MappingProxyType -from typing import TYPE_CHECKING, Any, Final, Literal, Optional, TypedDict +from typing import TYPE_CHECKING, Final, Literal, Optional, Protocol, TypedDict, overload import httpx -from typing_extensions import Never, ReadOnly +from typing_extensions import Never, ReadOnly, Required from litellm._logging import verbose_logger from litellm.integrations.custom_batch_logger import CustomBatchLogger @@ -30,6 +31,7 @@ from litellm.llms.custom_httpx.http_handler import ( httpxSpecialProvider, ) from litellm.types.guardrails import GuardrailEventHooks +from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolCallChunk from litellm.types.utils import ( ChatCompletionMessageToolCall, Function, @@ -52,17 +54,102 @@ _DROP_WARNING_INTERVAL_SECONDS: Final = 60.0 _EMPTY_MAPPING: Final[Mapping[str, Never]] = MappingProxyType({}) -class _ServiceToolCall(TypedDict): - id: ReadOnly[str] +class _ModerationToolCall(TypedDict, total=False): + id: ReadOnly[Required[str]] -class _ServiceMessage(TypedDict, total=False): +class _ModerationMessage(TypedDict, total=False): + content: ReadOnly[str | None] + tool_calls: ReadOnly[Sequence[_ModerationToolCall] | None] + + +class _ModerationChoice(TypedDict, total=False): + message: ReadOnly[_ModerationMessage | None] + + +class _ModerationResponse(TypedDict, total=False): + choices: ReadOnly[Sequence[_ModerationChoice]] + + +class _LogEventKwargs(TypedDict, total=False): + standard_logging_object: ReadOnly[Required[StandardLoggingPayload]] + litellm_call_id: ReadOnly[str] + + +class _HasCallId(Protocol): + def get(self, key: Literal["litellm_call_id"], /) -> str | None: ... + + +class _HasModelAttr(Protocol): + model: str | None + + +class _ResponseSource(Protocol): + def get(self, key: Literal["response"], /) -> "_HasModelAttr | None": ... + + +class _ModelSource(Protocol): + def get(self, key: Literal["model"], default: str, /) -> str: ... + + +class _FallbackSource(Protocol): + @overload + def get(self, key: Literal["start_time"], /) -> datetime | None: ... + @overload + def get(self, key: str, /) -> object | None: ... + + +class _RequestContextSource(Protocol): + @overload + def get(self, key: Literal["optional_params"], /) -> Mapping[str, object] | None: ... + @overload + def get(self, key: str, /) -> object | None: ... + def __contains__(self, key: object, /) -> bool: ... + def __getitem__(self, key: str, /) -> object: ... + + +class _ToolCallLike(Protocol): + id: str | None + type: str | None + function: Function + + +class _ModerationSourceToolCall(TypedDict, total=False): + function: ReadOnly[Mapping[str, object] | None] + + +class _ModerationSourceMessage(TypedDict, total=False): + role: ReadOnly[str] + function_call: ReadOnly[Mapping[str, object] | None] + tool_calls: ReadOnly[Sequence[_ModerationSourceToolCall | None] | None] + + +class _FlattenedModerationMessage(TypedDict): + role: ReadOnly[str | None] content: ReadOnly[str] - tool_calls: ReadOnly[Sequence[_ServiceToolCall]] -class _ServiceChoice(TypedDict, total=False): - message: ReadOnly[_ServiceMessage] +class _CorrelatablePayload(TypedDict): + id: str # writable-ok: _apply_correlation_id overwrites the provider id on a deep-copied payload + + +class _SystemPromptCarrier(TypedDict, total=False): + messages: object # writable-ok: _prepend_system_prompt rebinds messages on the copied payload by design + + +class _BlockFailurePayload(TypedDict, total=False): + id: object # writable-ok: correlation id is pinned after copying the base payload + model: ReadOnly[object] + model_group: ReadOnly[object] + model_id: ReadOnly[str] + model_parameters: ReadOnly[object] + startTime: ReadOnly[float | None] + endTime: ReadOnly[float | None] + completionStartTime: ReadOnly[float | None] + messages: object # writable-ok: passed to _prepend_system_prompt, which rebinds messages + metadata: ReadOnly[StandardLoggingUserAPIKeyMetadata] + response: str # writable-ok: block failure text replaces the copied response + status: ReadOnly[str] class _MalformedToolBlockingResponseError(Exception): @@ -385,7 +472,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): @staticmethod def _stash_block_context( logging_obj: Optional["LiteLLMLoggingObj"], - request_data: dict, + request_data: dict[str, object], ) -> None: """Stash signals so the deferred success-event skips this request and ``async_post_call_failure_hook`` can build the failure payload. @@ -414,12 +501,16 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): request_data["_rubrik_logging_obj"] = logging_obj @staticmethod - def _normalize_tool_calls(tool_calls: Sequence[object]) -> tuple[ChatCompletionMessageToolCall, ...]: + def _normalize_tool_calls( + tool_calls: Sequence[ChatCompletionToolCallChunk | ChatCompletionMessageToolCall | _ToolCallLike], + ) -> tuple[ChatCompletionMessageToolCall, ...]: """Convert tool_calls from inputs to ChatCompletionMessageToolCall objects.""" return tuple(RubrikLogger._normalize_tool_call(tc) for tc in tool_calls) @staticmethod - def _normalize_tool_call(tc: Any) -> ChatCompletionMessageToolCall: + def _normalize_tool_call( + tc: ChatCompletionToolCallChunk | ChatCompletionMessageToolCall | _ToolCallLike, + ) -> ChatCompletionMessageToolCall: if isinstance(tc, ChatCompletionMessageToolCall): return tc if isinstance(tc, dict): @@ -460,12 +551,15 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): ``content`` is sent so the webhook can moderate the response text; ``None`` when the assistant produced no text (tool-call-only response). """ - message: Final[dict[str, object]] = { + message: Final[Mapping[str, object]] = { "role": "assistant", "content": content or None, + **( + {"tool_calls": tuple(tc.model_dump(exclude_none=True) for tc in tool_calls)} + if tool_calls + else _EMPTY_MAPPING + ), } - if tool_calls: - message["tool_calls"] = tuple(tc.model_dump(exclude_none=True) for tc in tool_calls) return { "id": request_id or f"chatcmpl-{uuid.uuid4()}", "object": "chat.completion", @@ -481,7 +575,9 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): } @staticmethod - def _flatten_messages_for_moderation(messages: Sequence[object] | None) -> tuple[Mapping[str, Any], ...]: + def _flatten_messages_for_moderation( + messages: Sequence[AllMessageValues | None] | None, + ) -> tuple[_FlattenedModerationMessage, ...]: """Collapse each message's content to a plain string for the webhook. litellm normalizes Anthropic ``/v1/messages`` requests to OpenAI shape, @@ -502,7 +598,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): ) @staticmethod - def _moderation_text_parts(message: Mapping[str, Any]) -> tuple[str, ...]: + def _moderation_text_parts(message: _ModerationSourceMessage) -> tuple[str, ...]: """Every attacker-controlled text segment of a message: its content plus the arguments of any tool call or deprecated function call.""" fc: Final = message.get("function_call") @@ -530,16 +626,8 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): ``/v1/messages`` requests too. Optional fields are sent only when present so the payload stays clean. """ - payload: Final[dict[str, object]] = { - "model": inputs.get("model") or request_data.get("model") or "", - "messages": RubrikLogger._flatten_messages_for_moderation(inputs.get("structured_messages")), - } tools: Final = inputs.get("tools") - if tools is not None: - payload["tools"] = tools user: Final = request_data.get("user") - if user: - payload["user"] = user # Fall back to litellm_call_id, the stable cross-provider join key the # response/tool path uses (see _correlation_id). LiteLLM does not # populate request_data["correlation_key"]; it carries litellm_call_id. @@ -547,14 +635,18 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): # when correlation_key is empty, so without this the block fires but no # log is ever written. An explicit correlation_key still wins. correlation_key: Final = request_data.get("correlation_key") or request_data.get("litellm_call_id") - if correlation_key: - payload["correlation_key"] = correlation_key - return payload + return { + "model": inputs.get("model") or request_data.get("model") or "", + "messages": RubrikLogger._flatten_messages_for_moderation(inputs.get("structured_messages")), + **({"tools": tools} if tools is not None else _EMPTY_MAPPING), + **({"user": user} if user else _EMPTY_MAPPING), + **({"correlation_key": correlation_key} if correlation_key else _EMPTY_MAPPING), + } @staticmethod def _extract_request_data( - call_details: Mapping[str, Any], - request_data: Mapping[str, object] | None, + call_details: _RequestContextSource, + request_data: _RequestContextSource | None, ) -> Mapping[str, object]: """Extract original request data from model_call_details for the response moderation service envelope. @@ -590,7 +682,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): } @staticmethod - def _sanitize_proxy_server_request(proxy_server_request: object) -> object: + def _sanitize_proxy_server_request(proxy_server_request: Mapping[str, object] | str | None) -> object: """Allowlist only routing fields (``url``, ``method``) when forwarding ``proxy_server_request`` to an external webhook, dropping inbound ``headers`` (Authorization, Cookie, x-api-key, ...) and the raw @@ -600,18 +692,19 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): return {key: proxy_server_request[key] for key in ("url", "method") if key in proxy_server_request} @staticmethod - def _resolve_model(request_data: Mapping[str, object], call_details: Mapping[str, str]) -> str: + def _resolve_model(request_data: _ResponseSource, call_details: _ModelSource) -> str: """Get the model name for the ModifyResponseException.""" response: Final = request_data.get("response") if response and hasattr(response, "model"): - response_model: Final[str | None] = getattr(response, "model", None) - return response_model or "unknown" + return response.model or "unknown" return call_details.get("model", "unknown") # -- Logging hooks --------------------------------------------------------- @staticmethod - def _correlation_id(call_details: Mapping[str, str], request_data: Mapping[str, str] | None = None) -> str | None: + def _correlation_id( + call_details: _HasCallId | _LogEventKwargs, request_data: _HasCallId | None = None + ) -> str | None: """The id that joins a blocked request's two S3 logs by filename: the moderation (``_blocking``) log and the failure (response) log. @@ -625,7 +718,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): return call_details.get("litellm_call_id") or (request_data or _EMPTY_MAPPING).get("litellm_call_id") @classmethod - def _apply_correlation_id(cls, payload: dict[str, object], source: Mapping[str, str]) -> None: + def _apply_correlation_id(cls, payload: _CorrelatablePayload, source: _HasCallId | _LogEventKwargs) -> None: """Pin ``payload["id"]`` to ``litellm_call_id`` in place so this log shares its S3 filename id with the moderation (``_blocking``) and failure logs for the same request -- for every provider. @@ -645,7 +738,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): payload["id"] = correlated @staticmethod - def _prepend_system_prompt(payload: dict[str, object], source: Mapping[str, object]) -> None: + def _prepend_system_prompt(payload: _SystemPromptCarrier, source: Mapping[str, object]) -> None: """Prepend ``source["system"]`` onto ``payload["messages"]``. Builds a NEW messages list rather than mutating ``payload["messages"]`` @@ -673,9 +766,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): exc_info=True, ) - async def _prepare_log_payload( - self, kwargs: Mapping[str, object], event_type: str - ) -> StandardLoggingPayload | None: + async def _prepare_log_payload(self, kwargs: _LogEventKwargs, event_type: str) -> StandardLoggingPayload | None: """Shared logic for success logging (sampled).""" if random.random() > self.sampling_rate: verbose_logger.debug("Skipping Rubrik %s logging (sampling_rate=%s)", event_type, self.sampling_rate) @@ -684,12 +775,12 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): # Deep-copy so mutations don't affect other callbacks sharing this object standard_logging_payload: Final[StandardLoggingPayload] = safe_deep_copy(kwargs["standard_logging_object"]) - self._apply_correlation_id(standard_logging_payload, kwargs) # pyright: ignore[reportArgumentType] # StandardLoggingPayload is dict[str,Any] at runtime + self._apply_correlation_id(standard_logging_payload, kwargs) self._prepend_system_prompt(standard_logging_payload, kwargs) # pyright: ignore[reportArgumentType] # StandardLoggingPayload is dict[str,Any] at runtime return standard_logging_payload - async def _append_and_maybe_flush(self, payload) -> None: + async def _append_and_maybe_flush(self, payload: Mapping[str, object]) -> None: self._ensure_periodic_flush_task() self.log_queue.append(payload) self._enforce_max_queue_size() @@ -714,7 +805,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): self._dropped_since_warning = 0 self._last_drop_warning_time = now - async def _enqueue_log_event(self, kwargs: Mapping[str, object], event_type: str): + async def _enqueue_log_event(self, kwargs: _LogEventKwargs, event_type: str): try: payload: Final = await self._prepare_log_payload(kwargs, event_type) if payload is None: @@ -835,7 +926,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): logging_obj: "LiteLLMLoggingObj", exception: "ModifyResponseException", user_api_key_dict: "UserAPIKeyAuth", - ) -> StandardLoggingPayload: + ) -> _BlockFailurePayload: """Build a failure-style payload using the exception text as response. Blocked-tool events are security-relevant and **bypass sampling**: @@ -877,9 +968,9 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): call_details: Final = logging_obj.model_call_details exception_text: Final = f"{type(exception).__name__}: {exception.message}" - base: Final = call_details.get("standard_logging_object") + base: Final[StandardLoggingPayload | None] = call_details.get("standard_logging_object") if base is not None: - payload: dict[str, object] = safe_deep_copy(base) + payload: _BlockFailurePayload = self._copy_block_payload_base(base) else: verbose_logger.debug( "Rubrik: standard_logging_object not yet on model_call_details " @@ -901,6 +992,10 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): return payload + @staticmethod + def _copy_block_payload_base(base: StandardLoggingPayload) -> _BlockFailurePayload: + return safe_deep_copy(base) + @staticmethod def _caller_metadata(user_api_key_dict: "UserAPIKeyAuth") -> StandardLoggingUserAPIKeyMetadata: """Identify the caller whose request was blocked. @@ -923,9 +1018,9 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): @classmethod def _build_fallback_payload( cls, - call_details: Mapping[str, Any], + call_details: _FallbackSource, user_api_key_dict: "UserAPIKeyAuth", - ) -> dict[str, object]: + ) -> _BlockFailurePayload: # Convert datetime to a Unix float so json.dumps can serialize it. # httpx's json= parameter uses stdlib json.dumps with no custom encoder. _raw_start: Final = call_details.get("start_time") @@ -959,7 +1054,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): response: Final = await self.async_httpx_client.post( url=self.logging_endpoint, json=data, - headers=self._headers, + headers=dict(self._headers), ) response.raise_for_status() except httpx.HTTPStatusError as e: @@ -1013,7 +1108,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): # -- Webhook services ------------------------------------------------------ - async def _post_json(self, endpoint: str, payload: Mapping[str, object], service_name: str) -> Mapping[str, Any]: + async def _post_json(self, endpoint: str, payload: Mapping[str, object], service_name: str) -> _ModerationResponse: """POST ``payload`` to a Rubrik webhook and return its dict response. Raises: @@ -1023,11 +1118,11 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): verbose_logger.debug("Sending request to %s: %s", service_name, endpoint) http_response: Final = await self.moderation_client.post( endpoint, - json=payload, - headers=self._headers, + json=dict(payload), + headers=dict(self._headers), ) http_response.raise_for_status() - result: Final[object] = http_response.json() + result: Final[_ModerationResponse | None] = http_response.json() if not isinstance(result, dict): raise TypeError( f"{service_name} returned non-dict JSON " @@ -1040,7 +1135,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): self, response_data: Mapping[str, object], request_data: Mapping[str, object], - ) -> Mapping[str, Any]: + ) -> _ModerationResponse: """Post the ``{request, response}`` envelope to the after_completion webhook and return its (possibly rewritten) response. @@ -1056,7 +1151,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): "Response moderation service", ) - async def _post_to_prompt_moderation_endpoint(self, payload: Mapping[str, object]) -> Mapping[str, Any]: + async def _post_to_prompt_moderation_endpoint(self, payload: Mapping[str, object]) -> _ModerationResponse: """Post a bare OpenAI request to the before_prompt webhook. Returns ``{}`` (passthrough) or a synthetic chat.completion (block). @@ -1064,14 +1159,14 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): return await self._post_json(self.prompt_moderation_endpoint, payload, "Prompt moderation service") @staticmethod - def _extract_prompt_refusal(service_response: Mapping[str, Any]) -> str | None: + def _extract_prompt_refusal(service_response: _ModerationResponse) -> str | None: """Return the refusal text when the prompt was blocked, else None. The before_prompt webhook returns ``{}`` (passthrough) or a synthetic chat.completion whose ``choices[0].message.content`` is the refusal explanation. """ - choices: Final[Sequence[_ServiceChoice] | None] = service_response.get("choices") + choices: Final = service_response.get("choices") if not choices: return None message: Final = choices[0].get("message") or _EMPTY_MAPPING @@ -1080,7 +1175,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): @staticmethod def _extract_response_block( - service_response: Mapping[str, Any], + service_response: _ModerationResponse, all_tool_calls: Sequence[ChatCompletionMessageToolCall], sent_content: str, ) -> BlockedResponseResult | None: @@ -1103,7 +1198,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): Expects service_response in OpenAI chat completion format: {"choices": [{"message": {"tool_calls": [...], "content": "..."}}]} """ - choices: Final[Sequence[_ServiceChoice]] = service_response.get("choices") or () + choices: Final = service_response.get("choices") or () if not choices: raise _MalformedToolBlockingResponseError("Response moderation service returned empty response") diff --git a/litellm/integrations/s3.py b/litellm/integrations/s3.py index ddeb410c54a..8ce461eea5b 100644 --- a/litellm/integrations/s3.py +++ b/litellm/integrations/s3.py @@ -1,11 +1,18 @@ #### What this does #### # On success + failure, log events to Supabase +import hashlib from datetime import datetime from typing import Final, cast import litellm from litellm._logging import print_verbose, verbose_logger +from litellm.constants import ( + MAX_S3_OBJECT_DOWNLOAD_FILENAME_BYTES, + MAX_S3_OBJECT_KEY_BYTES, + S3_BOUNDED_OBJECT_KEY_HEAD_BYTES, + S3_PREFIX_DIGEST_CHARS, +) from litellm.types.utils import StandardLoggingPayload @@ -133,9 +140,7 @@ class S3Logger: s3_file_name, ) - s3_object_download_filename: Final = ( - "time-" + start_time.strftime("%Y-%m-%dT%H-%M-%S-%f") + "_" + payload["id"] + ".json" - ) + s3_object_download_filename: Final = get_s3_object_download_filename(start_time, payload["id"]) from litellm.litellm_core_utils.safe_json_dumps import safe_dumps @@ -198,6 +203,47 @@ def resolve_sse_params( return algorithm, valid_key_id +S3_MIN_BOUNDED_FILE_NAME_BYTES: Final = 64 + + +def _truncate_to_utf8_bytes(value: str, max_bytes: int) -> str: + """Trim `value` so its UTF-8 encoding fits `max_bytes`, never splitting a character.""" + if max_bytes <= 0: + return "" + encoded: Final = value.encode("utf-8") + if len(encoded) <= max_bytes: + return value + return encoded[:max_bytes].decode("utf-8", errors="ignore") + + +def get_s3_object_download_filename(start_time: datetime, response_id: str) -> str: + """Content-Disposition filename for the uploaded object, bounded to the metadata header cap.""" + sanitized_response_id: Final = response_id.replace("/", "_").replace('"', "_") + file_name: Final = f"time-{start_time.strftime('%Y-%m-%dT%H-%M-%S-%f')}_{response_id}" + sanitized_file_name: Final = f"time-{start_time.strftime('%Y-%m-%dT%H-%M-%S-%f')}_{sanitized_response_id}" + budget: Final = MAX_S3_OBJECT_DOWNLOAD_FILENAME_BYTES - len(b".json") + if len(sanitized_file_name.encode("utf-8")) <= budget: + return sanitized_file_name + ".json" + return _bounded_s3_file_name(file_name, sanitized_file_name, budget) + ".json" + + +def _bounded_s3_file_name(s3_file_name: str, sanitized_s3_file_name: str, max_bytes: int) -> str: + """As much of the file name as `max_bytes` allows, then the sha256 of the whole name.""" + digest: Final = hashlib.sha256(s3_file_name.encode("utf-8")).hexdigest() + head_budget: Final = min(S3_BOUNDED_OBJECT_KEY_HEAD_BYTES, max_bytes - len(digest) - 1) + head: Final = _truncate_to_utf8_bytes(sanitized_s3_file_name, head_budget) + return f"{head}_{digest}" if head else digest + + +def _bounded_s3_prefix(configured_prefix: str, max_bytes: int) -> str: + """As much of the configured prefix as fits, then a digest segment naming the full prefix.""" + digest_segment: Final = hashlib.sha256(configured_prefix.encode("utf-8")).hexdigest()[:S3_PREFIX_DIGEST_CHARS] + "/" + if max_bytes < len(digest_segment): + return "" + head: Final = _truncate_to_utf8_bytes(configured_prefix, max_bytes - len(digest_segment) - 1).rstrip("/") + return f"{head}/{digest_segment}" if head else digest_segment + + def get_s3_object_key( s3_path: str, prefix: str, @@ -205,12 +251,23 @@ def get_s3_object_key( s3_file_name: str, ) -> str: sanitized_s3_file_name: Final = s3_file_name.replace("/", "_") - s3_object_key = ( - (s3_path.rstrip("/") + "/" if s3_path else "") - + prefix - + start_time.strftime("%Y-%m-%d") - + "/" - + sanitized_s3_file_name - ) # we need the s3 key to include the time, so we log cache hits too - s3_object_key += ".json" - return s3_object_key + configured_prefix: Final = (s3_path.rstrip("/") + "/" if s3_path else "") + prefix + date_segment: Final = start_time.strftime("%Y-%m-%d") + "/" + # we need the s3 key to include the time, so we log cache hits too + s3_object_key: Final = configured_prefix + date_segment + sanitized_s3_file_name + ".json" + if len(s3_object_key.encode("utf-8")) <= MAX_S3_OBJECT_KEY_BYTES: + return s3_object_key + + # shorten the response id first and only trim the configured prefix if that is what does not + # fit, so prefix scoped IAM policies and lifecycle rules keep matching + budget: Final = MAX_S3_OBJECT_KEY_BYTES - len(date_segment.encode("utf-8")) - len(b".json") + prefix_bytes: Final = len(configured_prefix.encode("utf-8")) + if prefix_bytes + S3_MIN_BOUNDED_FILE_NAME_BYTES <= budget: + bounded_file_name: Final = _bounded_s3_file_name(s3_file_name, sanitized_s3_file_name, budget - prefix_bytes) + return configured_prefix + date_segment + bounded_file_name + ".json" + + shortest_file_name: Final = _bounded_s3_file_name( + s3_file_name, sanitized_s3_file_name, S3_MIN_BOUNDED_FILE_NAME_BYTES + ) + bounded_prefix: Final = _bounded_s3_prefix(configured_prefix, budget - len(shortest_file_name.encode("utf-8"))) + return bounded_prefix + date_segment + shortest_file_name + ".json" diff --git a/litellm/integrations/s3_v2.py b/litellm/integrations/s3_v2.py index 9f6ae72fb3a..712ce41d09e 100644 --- a/litellm/integrations/s3_v2.py +++ b/litellm/integrations/s3_v2.py @@ -16,7 +16,11 @@ from urllib.parse import quote import litellm from litellm._logging import print_verbose, verbose_logger from litellm.constants import DEFAULT_S3_BATCH_SIZE, DEFAULT_S3_FLUSH_INTERVAL_SECONDS -from litellm.integrations.s3 import get_s3_object_key, resolve_sse_params +from litellm.integrations.s3 import ( + get_s3_object_download_filename, + get_s3_object_key, + resolve_sse_params, +) from litellm.litellm_core_utils.aws_partition import get_aws_dns_suffix from litellm.litellm_core_utils.safe_json_dumps import safe_dumps from litellm.litellm_core_utils.sensitive_data_masker import SensitiveDataMasker @@ -259,11 +263,11 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): now: Final = datetime.now(timezone.utc) audit_log_id: Final = audit_log.get("id", "unknown") - s3_path = cast(str | None, self.s3_path) or "" - s3_path = s3_path.rstrip("/") + "/" if s3_path else "" - - s3_object_key: Final = ( - f"{s3_path}audit_logs/{now.strftime('%Y-%m-%d')}/{now.strftime('%H-%M-%S')}_{audit_log_id}.json" + s3_object_key: Final = get_s3_object_key( + cast(str | None, self.s3_path) or "", + "audit_logs/", + now, + f"{now.strftime('%H-%M-%S')}_{audit_log_id}", ) element: Final = s3BatchLoggingElement( @@ -463,9 +467,7 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): ) verbose_logger.debug("s3_object_key=%s", s3_object_key) - s3_object_download_filename: Final = ( - f"time-{start_time.strftime('%Y-%m-%dT%H-%M-%S-%f')}_{standard_logging_payload['id']}.json" - ) + s3_object_download_filename: Final = get_s3_object_download_filename(start_time, standard_logging_payload["id"]) return s3BatchLoggingElement( payload=dict(standard_logging_payload), diff --git a/litellm/integrations/shadow_eval_logger.py b/litellm/integrations/shadow_eval_logger.py index cf8aa38d86e..27da785331a 100644 --- a/litellm/integrations/shadow_eval_logger.py +++ b/litellm/integrations/shadow_eval_logger.py @@ -1,8 +1,11 @@ """Shadow Eval Logger: samples a shadowed key's successful LLM requests (chat completions, Anthropic Messages, and Responses API surfaces, each normalized to chat shape), duplicates -each against the job's other arm in a detached task (the auto-router for a forward job, the -fixed baseline model for a reverse one), blind-judges real vs shadow, and appends one -``LiteLLM_ShadowEvalAttempt`` row (verdict or error) as the feature's only hot-path write. +each through every shadow arm in one detached task (each candidate auto-router for a +forward job, the fixed baseline model for a reverse one), blind-judges real vs each arm, +and appends one ``LiteLLM_ShadowEvalAttempt`` row per arm (verdict or error) as the +feature's only hot-path write. A multi-router job's arms therefore score the identical +sampled requests against the identical real responses, which is what makes their win +rates comparable head-to-head. Counts, status, and spend derive from those rows at read time, so nothing can disagree across pods or stop races; the hook reads active jobs through a short-TTL cache.""" @@ -498,12 +501,16 @@ def _decision_classifier_cost(metadata: Mapping[str, object]) -> float: return float(raw) if isinstance(raw, (int, float)) else 0.0 -def _request_was_routed_by(request_metadata: Mapping[str, object], router_name: str) -> bool: - """Whether the router under evaluation served this request, which is what decides - the direction it belongs to. A forward job skips its own router's traffic, since - duplicating it would compare the router to itself: guaranteed ties, judge spend for - zero information. A reverse job samples exactly that traffic and nothing else.""" - return _routing_decision(request_metadata).get("router_model_name") == router_name +def _direction_admits(request_metadata: Mapping[str, object], job: "ActiveShadowEvalJob") -> bool: + """Whether this request belongs to the job's direction. A forward job skips traffic + any of its candidate routers served: duplicating a router's own request compares it + to itself (guaranteed ties), and judging a sibling against another candidate's live + response would score candidates against each other instead of against the incumbent. + A reverse job samples exactly its one router's traffic and nothing else.""" + routed_by: Final = _routing_decision(request_metadata).get("router_model_name") + if job.direction == "reverse": + return routed_by == job.router_name + return routed_by not in job.arm_router_names @dataclass(frozen=True, slots=True) @@ -546,6 +553,7 @@ class ActiveShadowEvalJob(BaseModel): id: str router_name: str + router_names: tuple[str, ...] = () direction: ShadowEvalDirection = "forward" baseline_model: str | None = None shadow_percentage: float @@ -567,12 +575,25 @@ class ActiveShadowEvalJob(BaseModel): raise ValueError("baseline_model is set for exactly the reverse jobs") return self + @model_validator(mode="after") + def _reverse_evaluates_one_router(self) -> "ActiveShadowEvalJob": + """A reverse row naming several routers is unsamplable (there is no one traffic + slice they share) and fails closed.""" + if self.direction == "reverse" and len(self.arm_router_names) > 1: + raise ValueError("a reverse job evaluates exactly one router") + return self + @property - def shadow_target(self) -> str: - """The model the duplicated arm calls: the router itself for a forward job, the - fixed baseline for a reverse one. Total because the validator above pins + def arm_router_names(self) -> tuple[str, ...]: + """The job's full router set; rows from before router_names existed hold it in + router_name alone. The one place that reading lives on the sampling side.""" + return self.router_names or (self.router_name,) + + def arm_target(self, arm_router: str) -> str: + """The model one duplicated arm calls: the candidate router itself for a forward + job, the fixed baseline for a reverse one. Total because the validator above pins baseline_model to reverse jobs and only those.""" - return self.baseline_model or self.router_name + return self.baseline_model or arm_router def _as_active_job(record: object, attempts: int, spend: float) -> ActiveShadowEvalJob | None: @@ -592,7 +613,12 @@ _JOBS_CACHE_KEY: Final = "shadow_eval:active_jobs" class ShadowEvalLogger(CustomLogger): - """Fires blind pairwise shadow evaluations for keys with an active shadow-eval job.""" + """Fires blind pairwise shadow evaluations for targets with an active shadow-eval job. + + A job targets a virtual key, a team, or a user; a request qualifies for a job when + any of its resolved identities (key hash, team id, user id) matches the job's + target, so team and user jobs cover JWT-authenticated traffic, which carries no + key hash at all.""" def __init__( self, @@ -617,10 +643,10 @@ class ShadowEvalLogger(CustomLogger): # generation; the refill absorbs written rows and resets. self._job_starts: dict[str, int] = {} # mutable-ok: per-generation counter - async def _active_jobs(self) -> Mapping[str, tuple[ActiveShadowEvalJob, ...]]: - """Active jobs by api_key_id, cache-first. A key holds at most one job per - direction, so the value is a collection. A DB fault returns empty without - caching, so sampling pauses for that request and the next one retries.""" + async def _active_jobs(self) -> Mapping[tuple[str, str], tuple[ActiveShadowEvalJob, ...]]: + """Active jobs by (target_type, target_id), cache-first. A target holds at most + one job per direction, so the value is a collection. A DB fault returns empty + without caching, so sampling pauses for that request and the next one retries.""" cached: Final = await self._jobs_cache.async_get_cache(_JOBS_CACHE_KEY) if cached is not None: return cached # pyright: ignore[reportReturnType] # cache stores exactly this mapping shape @@ -652,10 +678,10 @@ class ShadowEvalLogger(CustomLogger): ) for row in grouped or [] } - by_key: Final = tuple( + by_target: Final = tuple( sorted( ( - (str(record.api_key_id), job) + ((str(record.target_type), str(record.target_id)), job) for record in records or [] if (job := _as_active_job(record, *attempt_stats.get(str(record.id), (0, 0.0)))) is not None ), @@ -663,7 +689,7 @@ class ShadowEvalLogger(CustomLogger): ) ) jobs: Final = MappingProxyType( - {key: tuple(job for _, job in group) for key, group in groupby(by_key, key=itemgetter(0))} + {target: tuple(job for _, job in group) for target, group in groupby(by_target, key=itemgetter(0))} ) await self._jobs_cache.async_set_cache(_JOBS_CACHE_KEY, jobs) self._job_starts = {} # rebind-ok: new generation, counts absorbed into the fill @@ -691,7 +717,7 @@ class ShadowEvalLogger(CustomLogger): now >= job.ends_at or job.attempts + self._job_starts.get(job.id, 0) >= job.max_turns or (job.max_budget is not None and job.spend >= job.max_budget) - or _request_was_routed_by(request_metadata, job.router_name) != (job.direction == "reverse") + or not _direction_admits(request_metadata, job) ): continue if not _sample_hits(request_id, job.id, job.shadow_percentage): @@ -720,8 +746,18 @@ class ShadowEvalLogger(CustomLogger): if should_redact_message_logging(dict(kwargs)): # mutable-ok: predicate takes a plain dict return metadata: Final = payload.get("metadata") or _EMPTY_METADATA - api_key_hash: Final = metadata.get("user_api_key_hash") - if not api_key_hash: + # Each identity the request resolved to is a candidate target; JWT-auth + # requests carry no key hash but do carry a team and user. + targets: Final = tuple( + (target_type, str(value)) + for target_type, value in ( + ("key", metadata.get("user_api_key_hash")), + ("team", metadata.get("user_api_key_team_id")), + ("user", metadata.get("user_api_key_user_id")), + ) + if value + ) + if not targets: return request_id: Final = payload.get("id") or "" if not request_id: @@ -731,8 +767,11 @@ class ShadowEvalLogger(CustomLogger): return # only surfaces this table can normalize are comparable; unknown types fail closed if ops.wire_params and _request_mutating_guardrail_ran(request_metadata): return # the wire-body snapshot predates the rewrite; replaying it would resurrect stripped content + active_jobs: Final = await self._active_jobs() eligible: Final = self._sampled_jobs( - (await self._active_jobs()).get(str(api_key_hash), ()), request_metadata, request_id + tuple(job for target in targets for job in active_jobs.get(target, ())), + request_metadata, + request_id, ) if not eligible: return @@ -755,7 +794,10 @@ class ShadowEvalLogger(CustomLogger): if self._inflight_shadow_tasks >= _MAX_CONCURRENT_SHADOW_TASKS: self._record_funnel(job.id, "shed") continue - self._job_starts[job.id] = self._job_starts.get(job.id, 0) + 1 + # One start writes one attempt row per arm, and max_turns is a row + # ceiling, so admission must pre-count every arm or a multi-router + # job overshoots the valve N-fold within a cache generation. + self._job_starts[job.id] = self._job_starts.get(job.id, 0) + len(job.arm_router_names) self._inflight_shadow_tasks += 1 asyncio.create_task( self._run_shadow_eval( @@ -794,32 +836,74 @@ class ShadowEvalLogger(CustomLogger): shadow_params: Mapping[str, object], parent_metadata: Mapping[str, object], ) -> None: - """Budget gate -> shadow call -> blind judge -> one attempt row, and every exit - in exactly one coverage bucket: the gates that decline to spend on an admitted - sample (no DB to record into, an over-budget key, an unverifiable or exhausted - eval budget) count it withheld, so eligible traffic still reconciles as - not_sampled + unjudgeable + shed + withheld + attempt rows. The prisma gate sits - above the dispatch so no provider spend happens without a place to record the - outcome, and the budget read lives here rather than in the success hook.""" + """Budget gates once per sampled request, then every router arm in turn: shadow + call -> blind judge -> one attempt row stamped with the arm. The gates that + decline to spend on an admitted sample (no DB to record into, an over-budget key, + an unverifiable or exhausted eval budget) count the REQUEST withheld before any + arm runs, so funnel counters stay per-request and a leg's eligible traffic still + reconciles as not_sampled + unjudgeable + shed + withheld + sampled requests, + where each sampled request writes one attempt row per arm. A budget crossed + mid-loop lets the remaining arms overshoot by one round, the same class of + overshoot as the samples already in flight when the cap is crossed. The prisma + gate sits above the dispatch so no provider spend happens without a place to + record the outcome, and the budget read lives here rather than in the success + hook.""" prisma: Final = self._prisma_provider() + if prisma is None: + self._record_funnel(job.id, "withheld") + return + if await _key_or_team_is_over_budget(parent_metadata): + self._record_funnel(job.id, "withheld") + return + if job.max_budget is not None: + try: + spend: Final = await self._read_job_spend(_job_spend_counter_key(job.id), job.spend, job.max_budget) + except Exception as e: # noqa: BLE001 # unverifiable budget: skip the sample rather than spend on it + verbose_logger.warning("shadow_eval: budget unverifiable for %s, sample skipped: %s", job.id, e) + self._record_funnel(job.id, "withheld") + return + if spend >= job.max_budget: + self._record_funnel(job.id, "withheld") + return + for arm_router in job.arm_router_names: + await self._run_shadow_arm( + prisma=prisma, + job=job, + arm_router=arm_router, + request_id=request_id, + messages=messages, + real_text=real_text, + real_model=real_model, + real_cost=real_cost, + real_classifier_cost=real_classifier_cost, + real_cache_hit=real_cache_hit, + control_tier=control_tier, + shadow_params=shadow_params, + parent_metadata=parent_metadata, + ) + + async def _run_shadow_arm( + self, + prisma: "PrismaClient", + job: ActiveShadowEvalJob, + arm_router: str, + request_id: str, + messages: Sequence[Mapping[str, object]], + real_text: str, + real_model: str, + real_cost: float, + real_classifier_cost: float, + real_cache_hit: bool, + control_tier: str | None, + shadow_params: Mapping[str, object], + parent_metadata: Mapping[str, object], + ) -> None: + """One arm's pipeline: shadow call -> blind judge -> one attempt row, every exit + recording this arm's outcome, so one arm's fault never silences a sibling arm.""" try: - if prisma is None: - self._record_funnel(job.id, "withheld") - return - if await _key_or_team_is_over_budget(parent_metadata): - self._record_funnel(job.id, "withheld") - return - if job.max_budget is not None: - try: - spend: Final = await self._read_job_spend(_job_spend_counter_key(job.id), job.spend, job.max_budget) - except Exception as e: # noqa: BLE001 # unverifiable budget: skip the sample rather than spend on it - verbose_logger.warning("shadow_eval: budget unverifiable for %s, sample skipped: %s", job.id, e) - self._record_funnel(job.id, "withheld") - return - if spend >= job.max_budget: - self._record_funnel(job.id, "withheld") - return - shadow: Final = await self._call_router_shadow(job.shadow_target, messages, shadow_params, parent_metadata) + shadow: Final = await self._call_router_shadow( + job.arm_target(arm_router), messages, shadow_params, parent_metadata + ) except Exception as e: # noqa: BLE001 # detached task: nothing billed yet, record and never raise verbose_logger.debug("shadow_eval: pipeline failed for %s: %s", request_id, e) await self._record_attempt( @@ -827,6 +911,7 @@ class ShadowEvalLogger(CustomLogger): job, request_id, control_tier, + router_name=arm_router, outcome="error", error=f"pipeline error: {e}", real_cost=real_cost, @@ -840,6 +925,7 @@ class ShadowEvalLogger(CustomLogger): job, request_id, control_tier, + router_name=arm_router, outcome="error", error=shadow.error, shadow_cost=shadow.cost, @@ -864,6 +950,7 @@ class ShadowEvalLogger(CustomLogger): job, request_id, control_tier, + router_name=arm_router, outcome="error", error=verdict.error, shadow=shadow, @@ -880,6 +967,7 @@ class ShadowEvalLogger(CustomLogger): job, request_id, control_tier, + router_name=arm_router, outcome=verdict.preference, shadow=shadow, real_model=real_model, @@ -898,6 +986,7 @@ class ShadowEvalLogger(CustomLogger): job, request_id, control_tier, + router_name=arm_router, outcome="error", error=f"pipeline error: {e}", shadow=shadow, @@ -915,6 +1004,7 @@ class ShadowEvalLogger(CustomLogger): request_id: str, control_tier: str | None, *, + router_name: str, outcome: str, real_cost: float, real_classifier_cost: float, @@ -937,6 +1027,7 @@ class ShadowEvalLogger(CustomLogger): data={ # mutable-ok: Prisma payload "job_id": job.id, "request_id": request_id, + "router_name": router_name, "outcome": outcome, "tier": control_tier if job.direction == "reverse" else (shadow.tier if shadow else None), "real_model": real_model or None, @@ -1056,7 +1147,7 @@ class ShadowEvalLogger(CustomLogger): ) -_EMPTY_JOBS: Final[Mapping[str, tuple[ActiveShadowEvalJob, ...]]] = MappingProxyType({}) +_EMPTY_JOBS: Final[Mapping[tuple[str, str], tuple[ActiveShadowEvalJob, ...]]] = MappingProxyType({}) def _default_prisma_provider() -> "PrismaClient | None": diff --git a/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py b/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py index aa29162ba1f..07d4f959489 100644 --- a/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py +++ b/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py @@ -13,7 +13,7 @@ from litellm._logging import verbose_logger from litellm.integrations.custom_logger import CustomLogger from litellm.types.llms.openai import AllMessageValues, ChatCompletionUserMessage from litellm.types.prompts.init_prompts import PromptSpec -from litellm.types.utils import StandardCallbackDynamicParams +from litellm.types.utils import CallTypes, StandardCallbackDynamicParams from litellm.types.vector_stores import ( LiteLLM_ManagedVectorStore, VectorStoreResultContent, @@ -226,7 +226,7 @@ class VectorStorePreCallHook(CustomLogger): self, request_data: dict, response: Any, - call_type: Any | None, + call_type: CallTypes | None, ) -> Any | None: """ Add search results to the response after successful LLM call. @@ -283,7 +283,7 @@ class VectorStorePreCallHook(CustomLogger): self, request_data: dict, response_chunk: Any, - call_type: Any | None, + call_type: CallTypes | None, ) -> Any | None: """ Add search results to the final streaming chunk. diff --git a/litellm/integrations/websearch_interception/handler.py b/litellm/integrations/websearch_interception/handler.py index 13a16947fb4..dc61ee38a8c 100644 --- a/litellm/integrations/websearch_interception/handler.py +++ b/litellm/integrations/websearch_interception/handler.py @@ -10,7 +10,7 @@ import asyncio import math import uuid from collections.abc import AsyncIterator, Mapping, Sequence -from typing import TYPE_CHECKING, Any, Final, TypedDict, cast +from typing import TYPE_CHECKING, Any, Final, Literal, Never, TypedDict, TypeVar, cast from typing_extensions import ReadOnly @@ -46,7 +46,13 @@ from litellm.types.integrations.websearch_interception import ( AnthropicServerToolUseBlock, WebSearchInterceptionConfig, ) -from litellm.types.llms.openai import AllMessageValues +from litellm.types.llms.anthropic import AnthropicThinkingParam +from litellm.types.llms.openai import ( + AllMessageValues, + ChatCompletionAudioParam, + ChatCompletionPredictionContentParam, + OpenAIWebSearchOptions, +) from litellm.types.utils import ( AgenticLoopParams, CallTypes, @@ -56,6 +62,8 @@ from litellm.types.utils import ( from litellm.utils import ProviderConfigManager if TYPE_CHECKING: + from aiohttp import ClientSession + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.llms.base_llm.anthropic_messages.transformation import ( BaseAnthropicMessagesConfig, @@ -77,6 +85,10 @@ WEBSEARCH_EMIT_NATIVE_BLOCKS_KEY: Final = "_websearch_interception_emit_native_b # ``web_search_tool_result`` blocks to inject into the final response. WEBSEARCH_NATIVE_BLOCKS_METADATA_KEY: Final = "websearch_native_blocks" +_RESPONSE_CONTENT_FIELD: Final = "content" + +_ResponseT: Final = TypeVar("_ResponseT") + class _PlanMetadataView(TypedDict): websearch_native_blocks: Sequence[Mapping[str, object]] | None @@ -90,23 +102,98 @@ class _WebSearchSettingsView(TypedDict): websearch_interception_params: WebSearchInterceptionConfig +class _SearchToolLitellmParams(TypedDict, total=False): + search_provider: ReadOnly[str | None] + + class _SearchToolConfig(TypedDict, total=False): search_tool_name: str - litellm_params: Mapping[str, object] | None + litellm_params: ReadOnly[_SearchToolLitellmParams | None] -class _DeploymentKwargsView(TypedDict): - """Typed reads of the untyped request kwargs seen by the deployment hook.""" - +class _LitellmParamsProviderView(TypedDict, total=False): custom_llm_provider: ReadOnly[str] - litellm_params: ReadOnly[Mapping[str, object]] + + +class _DeploymentCallKwargsView(TypedDict): + custom_llm_provider: ReadOnly[str] + litellm_params: ReadOnly[_LitellmParamsProviderView] model: ReadOnly[str] -class _UserAuthView(TypedDict): - """Typed read of the optional team attached to the caller's auth object.""" +class _AcreateNamedParams(TypedDict, total=False): + metadata: ReadOnly[Never] + stop_sequences: ReadOnly[Never] + stream: ReadOnly[bool | None] + system: ReadOnly[str | None] + temperature: ReadOnly[float | None] + thinking: ReadOnly[Never] + tool_choice: ReadOnly[Never] + tools: ReadOnly[Never] + top_k: ReadOnly[int | None] + top_p: ReadOnly[float | None] + container: ReadOnly[Never] - team_id: ReadOnly[str | None] + +class _AsearchNamedParams(TypedDict, total=False): + max_results: ReadOnly[int | None] + search_domain_filter: ReadOnly[Never] + max_tokens_per_page: ReadOnly[int | None] + country: ReadOnly[str | None] + api_key: ReadOnly[str | None] + api_base: ReadOnly[str | None] + timeout: ReadOnly[float | None] + extra_headers: ReadOnly[Never] + + +class _AcompletionNamedParams(TypedDict, total=False): + functions: ReadOnly[Never] + function_call: ReadOnly[str | None] + timeout: ReadOnly[float | None] + temperature: ReadOnly[float | None] + top_p: ReadOnly[float | None] + n: ReadOnly[int | None] + stream: ReadOnly[bool | None] + stream_options: ReadOnly[Never] + stop: ReadOnly[Never] + max_tokens: ReadOnly[int | None] + max_completion_tokens: ReadOnly[int | None] + modalities: ReadOnly[Never] + prediction: ReadOnly[ChatCompletionPredictionContentParam | None] + audio: ReadOnly[ChatCompletionAudioParam | None] + presence_penalty: ReadOnly[float | None] + frequency_penalty: ReadOnly[float | None] + logit_bias: ReadOnly[Never] + user: ReadOnly[str | None] + response_format: ReadOnly[Never] + seed: ReadOnly[int | None] + tools: ReadOnly[Never] + tool_choice: ReadOnly[Never] + parallel_tool_calls: ReadOnly[bool | None] + logprobs: ReadOnly[bool | None] + top_logprobs: ReadOnly[int | None] + deployment_id: ReadOnly[str | None] + reasoning_effort: ReadOnly[Literal["none", "minimal", "low", "medium", "high", "xhigh", "default"] | None] + verbosity: ReadOnly[Literal["low", "medium", "high"] | None] + safety_identifier: ReadOnly[str | None] + service_tier: ReadOnly[str | None] + store: ReadOnly[bool | None] + prompt_cache_key: ReadOnly[str | None] + base_url: ReadOnly[str | None] + api_version: ReadOnly[str | None] + api_key: ReadOnly[str | None] + model_list: ReadOnly[Never] + extra_headers: ReadOnly[Never] + thinking: ReadOnly[AnthropicThinkingParam | None] + web_search_options: ReadOnly[OpenAIWebSearchOptions | None] + include_server_side_tool_invocations: ReadOnly[bool | None] + shared_session: ReadOnly["ClientSession | None"] + enable_json_schema_validation: ReadOnly[bool | None] + + +_NO_ACREATE_NAMED: Final[_AcreateNamedParams] = {} +_NO_ASEARCH_NAMED: Final[_AsearchNamedParams] = {} +_NO_ACOMPLETION_NAMED: Final[_AcompletionNamedParams] = {} class WebSearchInterceptionLogger(CustomLogger): @@ -308,17 +395,17 @@ class WebSearchInterceptionLogger(CustomLogger): """ # Check if this is for an enabled provider # Try top-level kwargs first, then nested litellm_params, then derive from model name - kwargs_view: Final[_DeploymentKwargsView] = { + call_kwargs_view: Final[_DeploymentCallKwargsView] = { "custom_llm_provider": kwargs.get("custom_llm_provider", ""), "litellm_params": kwargs.get("litellm_params", {}), "model": kwargs.get("model", ""), } - custom_llm_provider = kwargs_view["custom_llm_provider"] or kwargs_view["litellm_params"].get( + custom_llm_provider = call_kwargs_view["custom_llm_provider"] or call_kwargs_view["litellm_params"].get( "custom_llm_provider", "" ) if not custom_llm_provider: try: - _, custom_llm_provider, _, _ = litellm.get_llm_provider(model=kwargs_view["model"]) + _, custom_llm_provider, _, _ = litellm.get_llm_provider(model=call_kwargs_view["model"]) except Exception: custom_llm_provider = "" if custom_llm_provider not in self.enabled_providers: @@ -948,17 +1035,17 @@ class WebSearchInterceptionLogger(CustomLogger): ) @staticmethod - def _inject_native_blocks(response: Any, native_blocks: Sequence[Mapping[str, object]]) -> Any: + def _inject_native_blocks(response: _ResponseT, native_blocks: Sequence[Mapping[str, object]]) -> _ResponseT: """Prepend native blocks to response content, dict or object form.""" if not native_blocks: return response if isinstance(response, dict): - existing = response.get("content") or [] - response["content"] = list(native_blocks) + list(existing) + existing = response.get(_RESPONSE_CONTENT_FIELD) or [] + response[_RESPONSE_CONTENT_FIELD] = list(native_blocks) + list(existing) return response - existing = getattr(response, "content", None) or [] + existing = getattr(response, _RESPONSE_CONTENT_FIELD, None) or [] try: - response.content = list(native_blocks) + list(existing) + setattr(response, _RESPONSE_CONTENT_FIELD, list(native_blocks) + list(existing)) except (AttributeError, TypeError): # Object refused write — fall through and leave the response # untouched rather than crash the request. @@ -1214,10 +1301,10 @@ class WebSearchInterceptionLogger(CustomLogger): messages: list[dict], tool_calls: list[dict], thinking_blocks: list[dict], - anthropic_messages_optional_request_params: dict, + anthropic_messages_optional_request_params: Mapping[str, object], logging_obj: "LiteLLMLoggingObj | None", stream: bool, - kwargs: dict, + kwargs: Mapping[str, object], ) -> "AnthropicMessagesResponse | AsyncIterator[object]": """Legacy path: execute search + build patch + run follow-up call.""" request_patch, structured_results = await self._build_anthropic_request_patch( @@ -1225,9 +1312,9 @@ class WebSearchInterceptionLogger(CustomLogger): messages=messages, tool_calls=tool_calls, thinking_blocks=thinking_blocks, - anthropic_messages_optional_request_params=anthropic_messages_optional_request_params, + anthropic_messages_optional_request_params=dict[str, object](anthropic_messages_optional_request_params), logging_obj=logging_obj, - kwargs=kwargs, + kwargs=dict[str, object](kwargs), ) if request_patch.messages is None: raise ValueError("WebSearchInterception: missing follow-up messages") @@ -1242,12 +1329,14 @@ class WebSearchInterceptionLogger(CustomLogger): if max_tokens is None: max_tokens = cast(int, kwargs.get("max_tokens", 1024)) + patch_kwargs: Final = dict[str, object](request_patch.kwargs) response: AnthropicMessagesResponse | AsyncIterator[object] = await anthropic_messages.acreate( max_tokens=max_tokens, messages=request_patch.messages, model=request_patch.model or model, + **_NO_ACREATE_NAMED, **optional_params, - **request_patch.kwargs, + **patch_kwargs, ) # Legacy path: the new path goes through the typed plan + core @@ -1389,12 +1478,13 @@ class WebSearchInterceptionLogger(CustomLogger): search_tool: Final = self._select_search_tool_from_router(llm_router=llm_router) search_provider: str | None = None - search_litellm_params: dict[str, Any] = {} + search_litellm_params: Mapping[str, object] = {} search_tool_name: Final = self._selected_search_tool_name(search_tool=search_tool) if search_tool is not None: await self._authorize_search_tool(search_tool=search_tool, kwargs=kwargs) - search_litellm_params = dict(search_tool.get("litellm_params", {}) or {}) - search_provider = search_litellm_params.get("search_provider") + tool_params: Final[_SearchToolLitellmParams] = search_tool.get("litellm_params", {}) or {} + search_litellm_params = dict[str, object](tool_params) + search_provider = tool_params.get("search_provider") # Fallback to perplexity if no router or no search tools configured if not search_provider: @@ -1422,12 +1512,15 @@ class WebSearchInterceptionLogger(CustomLogger): if key != "search_provider" and value is not None } result: Final = ( - await litellm.asearch(query=query, search_provider=search_provider, **search_kwargs) + await litellm.asearch( + query=query, search_provider=search_provider, **_NO_ASEARCH_NAMED, **search_kwargs + ) if search_metadata is None else await litellm.asearch( query=query, search_provider=search_provider, litellm_metadata=search_metadata, + **_NO_ASEARCH_NAMED, **search_kwargs, ) ) @@ -1467,8 +1560,7 @@ class WebSearchInterceptionLogger(CustomLogger): valid_token=user_api_key_auth, ) - auth_view: Final[_UserAuthView] = {"team_id": getattr(user_api_key_auth, "team_id", None)} - team_id: Final = auth_view["team_id"] + team_id: Final[str | None] = getattr(user_api_key_auth, "team_id", None) if team_id: from litellm.proxy.proxy_server import ( prisma_client, @@ -1541,16 +1633,18 @@ class WebSearchInterceptionLogger(CustomLogger): def _select_search_tool_from_router(self, llm_router: object) -> "_SearchToolConfig | None": if llm_router is None or not hasattr(llm_router, "search_tools"): return None - search_tools: Final = list(getattr(llm_router, "search_tools") or []) + search_tools: Final = tuple(getattr(llm_router, "search_tools", None) or ()) return self._select_search_tool_from_list(search_tools=search_tools, source="router") def _select_search_tool_from_list( self, - search_tools: list[_SearchToolConfig], + search_tools: Sequence[_SearchToolConfig], source: str, ) -> "_SearchToolConfig | None": if self.search_tool_name: - matching_tools = [tool for tool in search_tools if tool.get("search_tool_name") == self.search_tool_name] + matching_tools: Final = tuple( + tool for tool in search_tools if tool.get("search_tool_name") == self.search_tool_name + ) if matching_tools: search_provider = (matching_tools[0].get("litellm_params", {}) or {}).get("search_provider") verbose_logger.debug( @@ -1583,10 +1677,10 @@ class WebSearchInterceptionLogger(CustomLogger): model: str, messages: list[dict], tool_calls: list[dict], - optional_params: dict, + optional_params: Mapping[str, object], logging_obj: "LiteLLMLoggingObj | None", stream: bool, - kwargs: dict, + kwargs: Mapping[str, object], response_format: str = "openai", ) -> "ModelResponse | CustomStreamWrapper": """Legacy path: execute search + build patch + run follow-up call.""" @@ -1594,8 +1688,8 @@ class WebSearchInterceptionLogger(CustomLogger): model=model, messages=messages, tool_calls=tool_calls, - optional_params=optional_params, - kwargs=kwargs, + optional_params=dict[str, object](optional_params), + kwargs=dict[str, object](kwargs), response_format=response_format, ) if request_patch.messages is None: @@ -1603,11 +1697,13 @@ class WebSearchInterceptionLogger(CustomLogger): params: Final = dict(optional_params) params.update(request_patch.optional_params) params.pop("tool_choice", None) + patch_kwargs: Final = dict[str, object](request_patch.kwargs) return await litellm.acompletion( model=request_patch.model or model, messages=request_patch.messages, + **_NO_ACOMPLETION_NAMED, **params, - **request_patch.kwargs, + **patch_kwargs, ) async def _build_chat_completion_request_patch( diff --git a/litellm/litellm_core_utils/audio_utils/subtitle_utils.py b/litellm/litellm_core_utils/audio_utils/subtitle_utils.py index 615873e295d..91427ba09ad 100644 --- a/litellm/litellm_core_utils/audio_utils/subtitle_utils.py +++ b/litellm/litellm_core_utils/audio_utils/subtitle_utils.py @@ -1,19 +1,36 @@ """Provider-agnostic SRT/WebVTT subtitle synthesis from timestamped transcription tokens.""" +import unicodedata from collections.abc import Sequence from dataclasses import dataclass -from itertools import accumulate, chain +from itertools import accumulate, groupby from typing import Final from pydantic import BaseModel, ConfigDict, TypeAdapter, ValidationError -CUE_MAX_TOKENS: Final = 15 -CUE_MAX_DURATION_MS: Final = 5000 +CUE_MAX_CHARS: Final = 84 +CUE_MAX_DURATION_MS: Final = 7000 +CUE_GAP_MS: Final = 700 SRT_RESPONSE_FORMAT: Final = "srt" VTT_RESPONSE_FORMAT: Final = "vtt" SUBTITLE_RESPONSE_FORMATS: Final = frozenset((SRT_RESPONSE_FORMAT, VTT_RESPONSE_FORMAT)) +_SENTENCE_END_CHARS: Final = (".", "!", "?", "。", "!", "?", "؟", "۔", "।", "॥", "։", "።") + +_CJK_RANGES: Final = ( + (0x3400, 0x4DBF), + (0x4E00, 0x9FFF), + (0xF900, 0xFAFF), + (0x3040, 0x309F), + (0x30A0, 0x30FF), + (0x31F0, 0x31FF), +) + +_CJK_NO_BREAK_BEFORE: Final = "、。,.!?:;・ー…」』)〉》】〕" + +_CJK_NO_BREAK_AFTER: Final = "「『(〈《【〔" + @dataclass(frozen=True, slots=True) class SubtitleToken: @@ -31,69 +48,138 @@ class SubtitleCue: @dataclass(frozen=True, slots=True) -class _CueAccumulator: - texts: tuple[str, ...] = () - start_ms: int | None = None - end_ms: int | None = None - speaker: str | int | None = None +class _Word: + text: str + start_ms: int | None + end_ms: int | None + speaker: str | int | None -def _completed_cue(accumulator: _CueAccumulator) -> tuple[SubtitleCue, ...]: - if not accumulator.texts or accumulator.start_ms is None: - return () - text: Final = "".join(accumulator.texts).strip() - if not text: - return () - end_ms: Final = accumulator.end_ms if accumulator.end_ms is not None else accumulator.start_ms - return (SubtitleCue(start_ms=accumulator.start_ms, end_ms=end_ms, text=text),) +def _is_cjk(ch: str) -> bool: + cp: Final = ord(ch) + return any(lo <= cp <= hi for lo, hi in _CJK_RANGES) -def _cue_break_reached(accumulator: _CueAccumulator, token: SubtitleToken) -> bool: - if len(accumulator.texts) >= CUE_MAX_TOKENS: - return True +def _is_cjk_word_boundary(prev_ch: str, next_ch: str) -> bool: + if not (_is_cjk(prev_ch) or _is_cjk(next_ch)): + return False + return next_ch not in _CJK_NO_BREAK_BEFORE and prev_ch not in _CJK_NO_BREAK_AFTER + + +def _text_width(text: str) -> int: + return sum(2 if unicodedata.east_asian_width(ch) in ("W", "F") else 1 for ch in text) + + +def _starts_new_word(prev: SubtitleToken, token: SubtitleToken) -> bool: + prev_last: Final = prev.text[-1:] + first: Final = token.text[0] return ( - accumulator.start_ms is not None - and token.start_ms is not None - and token.start_ms - accumulator.start_ms >= CUE_MAX_DURATION_MS + first.isspace() + or prev_last.isspace() + or token.speaker != prev.speaker + or _is_cjk_word_boundary(prev_last, first) ) -_AbsorbStep = tuple[tuple[SubtitleCue, ...], _CueAccumulator] - - -def _absorb_token(accumulator: _CueAccumulator, token: SubtitleToken) -> _AbsorbStep: - if token.start_ms is None and accumulator.start_ms is None: - return (), accumulator - if token.speaker is not None and token.speaker != accumulator.speaker: - return _completed_cue(accumulator), _CueAccumulator( - texts=(token.text,), - start_ms=token.start_ms, - end_ms=token.end_ms, - speaker=token.speaker, - ) - if _cue_break_reached(accumulator, token): - return _completed_cue(accumulator), _CueAccumulator( - texts=(token.text,), - start_ms=token.start_ms, - end_ms=token.end_ms, - speaker=accumulator.speaker, - ) - return (), _CueAccumulator( - texts=(*accumulator.texts, token.text), - start_ms=accumulator.start_ms if accumulator.start_ms is not None else token.start_ms, - end_ms=token.end_ms if token.end_ms is not None else accumulator.end_ms, - speaker=accumulator.speaker, +def _build_word(group: Sequence[SubtitleToken]) -> _Word: + return _Word( + text="".join(t.text for t in group), + start_ms=next((t.start_ms for t in group if t.start_ms is not None), None), + end_ms=next((t.end_ms for t in reversed(group) if t.end_ms is not None), None), + speaker=group[0].speaker, ) -def _absorb_step(carry: _AbsorbStep, token: SubtitleToken) -> _AbsorbStep: - return _absorb_token(carry[1], token) +def _merge_tokens_into_words(tokens: Sequence[SubtitleToken]) -> tuple[_Word, ...]: + """ + Merge subword tokens (e.g. ``"Hel"``, ``"lo"``) into whole words. + + A token starts a new word when its text begins with whitespace, when the + previous token's text ends with whitespace, when the speaker changes, or + at a CJK character boundary (CJK scripts carry no spaces, so without this + an entire utterance would fuse into a single unbreakable "word"; CJK + punctuation stays attached to the preceding character per kinsoku rules). + Each word carries the first/last available timestamps of its tokens. + """ + kept: Final = tuple(t for t in tokens if t.text != "") + starts: Final = tuple(i for i, t in enumerate(kept) if i == 0 or _starts_new_word(kept[i - 1], t)) + return tuple(_build_word(kept[begin:end]) for begin, end in zip(starts, (*starts[1:], len(kept)))) + + +def _cue_start(ws: Sequence[_Word]) -> int | None: + return next((w.start_ms for w in ws if w.start_ms is not None), None) + + +def _cue_end(ws: Sequence[_Word]) -> int | None: + return next((w.end_ms for w in reversed(ws) if w.end_ms is not None), _cue_start(ws)) + + +def _cue_text(ws: Sequence[_Word]) -> str: + return "".join(w.text for w in ws).strip() + + +def _should_break(cue: Sequence[_Word], word: _Word) -> bool: + speaker_changed: Final = word.speaker is not None and any( + w.speaker is not None and w.speaker != word.speaker for w in cue + ) + cue_start: Final = _cue_start(cue) + cue_end: Final = _cue_end(cue) + gap_exceeded: Final = word.start_ms is not None and cue_end is not None and (word.start_ms - cue_end) >= CUE_GAP_MS + chars_exceeded: Final = _text_width(_cue_text(cue)) + _text_width(word.text) > CUE_MAX_CHARS + word_end: Final = word.end_ms if word.end_ms is not None else word.start_ms + duration_exceeded: Final = ( + word_end is not None and cue_start is not None and (word_end - cue_start) > CUE_MAX_DURATION_MS + ) + return speaker_changed or gap_exceeded or chars_exceeded or duration_exceeded + + +def _cue_start_indices(words: Sequence[_Word]) -> tuple[int, ...]: + def next_start(start: int, index: int) -> int: + if words[index - 1].text.rstrip().endswith(_SENTENCE_END_CHARS): + return index + if _should_break(words[start:index], words[index]): + return index + return start + + if not words: + return () + return tuple(start for start, _ in groupby(accumulate(range(1, len(words)), next_start, initial=0))) + + +def _build_cue(ws: Sequence[_Word]) -> SubtitleCue | None: + text: Final = _cue_text(ws) + start: Final = _cue_start(ws) + if not text or start is None: + return None + end: Final = _cue_end(ws) + return SubtitleCue(start_ms=start, end_ms=end if end is not None else start, text=text) def group_subtitle_tokens_into_cues(tokens: Sequence[SubtitleToken]) -> tuple[SubtitleCue, ...]: - steps: Final = tuple(accumulate(tokens, _absorb_step, initial=((), _CueAccumulator()))) - completed: Final = chain.from_iterable(emitted for emitted, _ in steps) - return (*completed, *_completed_cue(steps[-1][1])) + """ + Group transcription tokens into subtitle cues aligned to the actual speech. + + Cues only ever break at word boundaries (tokens may be subwords, so they + are first merged into words). A new cue starts when: + - the speaker changes (if diarization is on), + - a silence gap of at least CUE_GAP_MS separates two words, so + subtitles never bridge pauses in speech, + - adding the next word would exceed CUE_MAX_CHARS of display width + (~two subtitle lines; East-Asian wide characters count double), or + - adding the next word would make the cue span more than + CUE_MAX_DURATION_MS. + A cue also ends after sentence-final punctuation, which keeps cue breaks + at natural seams. Cue timestamps come straight from token timestamps; + words without timestamps stay attached to the surrounding cue, and a cue + whose words carry no timestamps at all is dropped. + """ + words: Final = _merge_tokens_into_words(tokens) + starts: Final = _cue_start_indices(words) + return tuple( + cue + for begin, end in zip(starts, (*starts[1:], len(words))) + if (cue := _build_cue(words[begin:end])) is not None + ) def _format_timestamp(total_ms: int, millis_separator: str) -> str: diff --git a/litellm/litellm_core_utils/audio_utils/utils.py b/litellm/litellm_core_utils/audio_utils/utils.py index 3b3775a8fe6..dab3e48f91a 100644 --- a/litellm/litellm_core_utils/audio_utils/utils.py +++ b/litellm/litellm_core_utils/audio_utils/utils.py @@ -7,7 +7,13 @@ import os from dataclasses import dataclass from typing import Final -from litellm.types.files import get_file_mime_type_from_extension +from litellm.types.files import ( + AUDIO_FILE_TYPES, + FILE_EXTENSIONS, + FILE_MIME_TYPES, + FileType, + get_file_mime_type_from_extension, +) from litellm.types.utils import FileTypes @@ -323,3 +329,75 @@ def calculate_request_duration(file: FileTypes) -> float | None: except Exception: # Silently fail if duration extraction fails return None + + +DEFAULT_SPEECH_MEDIA_TYPE: Final = "audio/mpeg" + + +def _speech_media_type_for_response_format(response_format: str) -> str | None: + file_type: Final = next( + (candidate for candidate, extensions in FILE_EXTENSIONS.items() if response_format.lower() in extensions), + None, + ) + if file_type is None or file_type not in AUDIO_FILE_TYPES: + return None + return FILE_MIME_TYPES[file_type] + + +def resolve_speech_media_type(upstream_content_type: str | None, response_format: str | None) -> str: + upstream_media_type: Final = (upstream_content_type or "").split(";", 1)[0].strip().lower() + if upstream_media_type.startswith("audio/"): + return upstream_media_type + requested_media_type: Final = ( + None if response_format is None else _speech_media_type_for_response_format(response_format) + ) + return requested_media_type or DEFAULT_SPEECH_MEDIA_TYPE + + +_OGG_OPUS_HEAD_WINDOW: Final = 64 +_ADTS_SYNC_AND_LAYER_MASK: Final = 0xF6 +_ADTS_SYNC_AND_LAYER: Final = 0xF0 +_ADTS_SAMPLE_RATE_INDEX_LIMIT: Final = 13 +_MPEG_SYNC_MASK: Final = 0xE0 +_MPEG_LAYER_MASK: Final = 0x06 +_MPEG_RESERVED_VERSION: Final = 0x01 +_MPEG_INVALID_BITRATE_INDEX: Final = 0x0F +_MPEG_RESERVED_SAMPLE_RATE_INDEX: Final = 0x03 + + +def _adts_aac_frame_media_type(header: bytes) -> str | None: + sample_rate_index: Final = (header[2] >> 2) & 0x0F + return FILE_MIME_TYPES[FileType.AAC] if sample_rate_index < _ADTS_SAMPLE_RATE_INDEX_LIMIT else None + + +def _mpeg_audio_frame_media_type(header: bytes) -> str | None: + version: Final = (header[1] >> 3) & 0x03 + layer: Final = header[1] & _MPEG_LAYER_MASK + bitrate_index: Final = header[2] >> 4 + sample_rate_index: Final = (header[2] >> 2) & 0x03 + if ( + (header[1] & _MPEG_SYNC_MASK) != _MPEG_SYNC_MASK + or version == _MPEG_RESERVED_VERSION + or layer == 0 + or bitrate_index == _MPEG_INVALID_BITRATE_INDEX + or sample_rate_index == _MPEG_RESERVED_SAMPLE_RATE_INDEX + ): + return None + return FILE_MIME_TYPES[FileType.MP3] + + +def speech_media_type_from_audio_bytes(audio: bytes) -> str | None: + if audio[:4] == b"RIFF" and audio[8:12] == b"WAVE": + return FILE_MIME_TYPES[FileType.WAV] + if audio[:4] == b"fLaC": + return FILE_MIME_TYPES[FileType.FLAC] + if audio[:4] == b"OggS": + is_opus: Final = b"OpusHead" in audio[:_OGG_OPUS_HEAD_WINDOW] + return FILE_MIME_TYPES[FileType.OPUS if is_opus else FileType.OGG] + if audio[:3] == b"ID3": + return FILE_MIME_TYPES[FileType.MP3] + if len(audio) < 3 or audio[0] != 0xFF: + return None + if (audio[1] & _ADTS_SYNC_AND_LAYER_MASK) == _ADTS_SYNC_AND_LAYER: + return _adts_aac_frame_media_type(audio) + return _mpeg_audio_frame_media_type(audio) diff --git a/litellm/litellm_core_utils/chat_completion_agentic_loop.py b/litellm/litellm_core_utils/chat_completion_agentic_loop.py index 07bed1f88ad..c8e9e2583ba 100644 --- a/litellm/litellm_core_utils/chat_completion_agentic_loop.py +++ b/litellm/litellm_core_utils/chat_completion_agentic_loop.py @@ -1,6 +1,7 @@ # this is a patch to allow for agentic loops covering llm_http_handler.py and openai sdk based calling flows for the .completion() api import json +from collections.abc import Mapping from typing import Final, cast from litellm._logging import verbose_logger @@ -9,8 +10,11 @@ from litellm.litellm_core_utils.agentic_loop_settings import ( DEFAULT_MAX_AGENTIC_LOOPS, validated_max_agentic_loops, ) +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObject +from litellm.llms.base_llm.base_model_iterator import MockResponseIterator from litellm.types.integrations.custom_logger import ( CHAT_COMPLETION_AGENTIC_SURFACE, + HEADROOM_CONVERTED_STREAM_KEY, NON_CODE_INTERPRETER_INTERCEPTION_INTERNAL_PREFIXES, AgenticLoopPlan, AgenticLoopRequestPatch, @@ -50,6 +54,12 @@ def _post_hook_overridden(callback: CustomLogger) -> bool: return getattr(func, "__func__", func) is not getattr(base, "__func__", base) +def _converted_stream_requested(kwargs: Mapping[str, object]) -> bool: + return bool( + kwargs.get("_code_interpreter_interception_converted_stream") or kwargs.get(HEADROOM_CONVERTED_STREAM_KEY) + ) + + def _coerce_int(value: object, default: int) -> int: return int(value) if isinstance(value, (int, str)) else default @@ -87,16 +97,24 @@ def _check_agentic_loop_safety( return fingerprint -def _wrap_response_as_fake_stream(response: object) -> object: - if getattr(response, "object", None) == "chat.completion.chunk": +def _wrap_response_as_fake_stream( + response: object, + *, + model: str, + custom_llm_provider: str, + logging_obj: object, +) -> object: + if isinstance(response, CustomStreamWrapper): return response - if not hasattr(response, "choices"): + if not isinstance(response, ModelResponse) or not isinstance(logging_obj, LiteLLMLoggingObject): return response - from litellm.llms.base_llm.base_model_iterator import ( - convert_model_response_to_streaming, - ) - return convert_model_response_to_streaming(cast(ModelResponse, response)) + return CustomStreamWrapper( + completion_stream=MockResponseIterator(model_response=response), + model=model, + custom_llm_provider=custom_llm_provider, + logging_obj=logging_obj, + ) def _add_agentic_loop_metadata(kwargs_for_followup: dict[str, object]) -> None: @@ -177,8 +195,13 @@ async def _execute_chat_completion_agentic_plan( model, str(e), ) - if kwargs.get("_code_interpreter_interception_converted_stream") and not depth: - return _wrap_response_as_fake_stream(response_followup) + if _converted_stream_requested(kwargs) and not depth: + return _wrap_response_as_fake_stream( + response_followup, + model=model, + custom_llm_provider=custom_llm_provider, + logging_obj=logging_obj, + ) return response_followup finally: try: @@ -302,9 +325,14 @@ async def maybe_run_chat_completion_agentic_loop( str(e), ) - if kwargs.get("_code_interpreter_interception_converted_stream") and not depth and hasattr(response, "choices"): + if _converted_stream_requested(kwargs) and not depth: return cast( "ModelResponse | CustomStreamWrapper", - _wrap_response_as_fake_stream(response), + _wrap_response_as_fake_stream( + response, + model=model, + custom_llm_provider=custom_llm_provider, + logging_obj=logging_obj, + ), ) return None diff --git a/litellm/litellm_core_utils/get_litellm_params.py b/litellm/litellm_core_utils/get_litellm_params.py index b12c715c9f5..389e6f7f501 100644 --- a/litellm/litellm_core_utils/get_litellm_params.py +++ b/litellm/litellm_core_utils/get_litellm_params.py @@ -50,6 +50,9 @@ OPTIONAL_KWARGS_KEYS: Final = ( "vertex_ai_project", "vertex_ai_location", "vertex_ai_credentials", + "gigachat_scope", + "gigachat_auth_url", + "gigachat_access_token", "tpm", "rpm", "itpm", diff --git a/litellm/litellm_core_utils/get_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py index 74a1d3e5008..ce51fb19970 100644 --- a/litellm/litellm_core_utils/get_llm_provider_logic.py +++ b/litellm/litellm_core_utils/get_llm_provider_logic.py @@ -127,7 +127,7 @@ def handle_anthropic_text_model_custom_llm_provider( return model, custom_llm_provider -def declared_authenticating_provider(model: str, custom_llm_provider: str | None = None) -> str | None: +def declared_authenticating_provider(model: str | None, custom_llm_provider: str | None = None) -> str | None: """The authenticating provider this pair already names, or None. get_llm_provider runs the OAuth device flow for github_copilot and chatgpt, because their @@ -135,7 +135,7 @@ def declared_authenticating_provider(model: str, custom_llm_provider: str | None and for a declared pair the resolver's answer is the declaration itself, so metadata callers adopt the declaration instead of resolving. """ - declared: Final = custom_llm_provider or model.split("/", 1)[0] + declared: Final = custom_llm_provider or (model.split("/", 1)[0] if model and "/" in model else None) return declared if declared in PROVIDERS_THAT_AUTHENTICATE_ON_PROVIDER_INFO else None @@ -369,6 +369,9 @@ def get_llm_provider( elif endpoint == "https://api.meta.ai/v1": custom_llm_provider = "meta" dynamic_api_key = get_secret_str("META_API_KEY") + elif endpoint == "https://gigachat.devices.sberbank.ru/api/v1": + custom_llm_provider = "gigachat" + dynamic_api_key = get_secret_str("GIGACHAT_API_KEY") elif (json_provider := JSONProviderRegistry.get_by_base_url(endpoint)) is not None: custom_llm_provider = json_provider.slug dynamic_api_key = api_key if api_key is not None else get_secret_str(json_provider.api_key_env) @@ -533,6 +536,14 @@ def get_llm_provider( ) +def _dashscope_family_chat_config(custom_llm_provider: str) -> "litellm.DashScopeChatConfig": + if custom_llm_provider == "qwencloud": + return litellm.QwenCloudChatConfig() + if custom_llm_provider == "qwen_ai_platform": + return litellm.QwenAIPlatformChatConfig() + return litellm.DashScopeChatConfig() + + def _get_openai_compatible_provider_info( model: str, api_base: str | None, @@ -782,11 +793,11 @@ def _get_openai_compatible_provider_info( api_base, dynamic_api_key, ) = litellm.HerokuChatConfig()._get_openai_compatible_provider_info(api_base, api_key) - elif custom_llm_provider == "dashscope": + elif custom_llm_provider in ("dashscope", "qwencloud", "qwen_ai_platform"): ( api_base, dynamic_api_key, - ) = litellm.DashScopeChatConfig()._get_openai_compatible_provider_info(api_base, api_key) + ) = _dashscope_family_chat_config(custom_llm_provider)._get_openai_compatible_provider_info(api_base, api_key) elif custom_llm_provider == "modelscope": ( api_base, @@ -867,6 +878,9 @@ def _get_openai_compatible_provider_info( # Manus is OpenAI compatible for responses API api_base = api_base or get_secret_str("MANUS_API_BASE") or "https://api.manus.im" dynamic_api_key = api_key or get_secret_str("MANUS_API_KEY") + elif custom_llm_provider == "gigachat": + api_base = api_base or get_secret_str("GIGACHAT_API_BASE") or "https://gigachat.devices.sberbank.ru/api/v1" + dynamic_api_key = api_key or get_secret_str("GIGACHAT_API_KEY") if api_base is not None and not isinstance(api_base, str): raise Exception(f"api base needs to be a string. api_base={api_base}") diff --git a/litellm/litellm_core_utils/get_model_cost_map.py b/litellm/litellm_core_utils/get_model_cost_map.py index 2043a9e2f89..9cba5db8ab7 100644 --- a/litellm/litellm_core_utils/get_model_cost_map.py +++ b/litellm/litellm_core_utils/get_model_cost_map.py @@ -12,6 +12,7 @@ import asyncio import json import os import random +import time from collections.abc import Awaitable, Callable from dataclasses import dataclass from datetime import datetime, timezone @@ -154,18 +155,6 @@ class GetModelCostMap: return True - @staticmethod - def fetch_remote_model_cost_map(url: str, timeout: int = 5) -> dict: - """ - Fetch the model cost map from a remote URL. - - Returns the parsed JSON dict. Raises on network/parse errors - (caller is expected to handle). - """ - response: Final = httpx.get(url, timeout=timeout) - response.raise_for_status() - return response.json() - RETRYABLE_FETCH_STATUS_CODES: Final = frozenset({429, 500, 502, 503, 504}) MODEL_COST_MAP_FETCH_MAX_ATTEMPTS: Final = 3 @@ -212,6 +201,13 @@ class _AsyncGetClient(Protocol): def get(self, url: str, *, timeout: float | None = None) -> Awaitable[httpx.Response]: ... +class _SyncGetClient(Protocol): + def get(self, url: str, *, timeout: float | None = None) -> httpx.Response: ... + + +_FetchAttemptOutcome = ModelCostMapReloaded | ModelCostMapReloadUnavailable | _FetchAttemptRetryable + + def _default_reload_client() -> _AsyncGetClient: from litellm.llms.custom_httpx.http_handler import get_async_httpx_client from litellm.types.llms.custom_http import httpxSpecialProvider @@ -219,13 +215,30 @@ def _default_reload_client() -> _AsyncGetClient: return get_async_httpx_client(llm_provider=httpxSpecialProvider.ModelCostMap) -async def _attempt_fetch( - client: _AsyncGetClient, url: str, timeout: int -) -> ModelCostMapReloaded | ModelCostMapReloadUnavailable | _FetchAttemptRetryable: +def _classify_fetch_error(error: httpx.HTTPError | httpx.InvalidURL, url: str) -> _FetchAttemptOutcome: + reason: Final = f"{type(error).__name__} fetching {url}: {error}" + if isinstance(error, (httpx.InvalidURL, httpx.UnsupportedProtocol)): + return ModelCostMapReloadUnavailable(reason=reason) + return _FetchAttemptRetryable(reason=reason, retry_after_seconds=None) + + +async def _attempt_fetch(client: _AsyncGetClient, url: str, timeout: int) -> _FetchAttemptOutcome: try: response: Final = await client.get(url, timeout=timeout) - except httpx.HTTPError as e: - return _FetchAttemptRetryable(reason=f"{type(e).__name__} fetching {url}: {e}", retry_after_seconds=None) + except (httpx.HTTPError, httpx.InvalidURL) as e: + return _classify_fetch_error(e, url) + return _classify_fetch_response(response, url) + + +def _attempt_fetch_sync(client: _SyncGetClient, url: str, timeout: int) -> _FetchAttemptOutcome: + try: + response: Final = client.get(url, timeout=timeout) + except (httpx.HTTPError, httpx.InvalidURL) as e: + return _classify_fetch_error(e, url) + return _classify_fetch_response(response, url) + + +def _classify_fetch_response(response: httpx.Response, url: str) -> _FetchAttemptOutcome: if response.status_code in RETRYABLE_FETCH_STATUS_CODES: return _FetchAttemptRetryable( reason=f"HTTP {response.status_code} from {url}", @@ -242,6 +255,22 @@ async def _attempt_fetch( return ModelCostMapReloaded(model_cost_map=parsed) +def _next_retry_wait( + outcome: _FetchAttemptRetryable, attempt: int, max_attempts: int, rng: random.Random +) -> float | ModelCostMapReloadUnavailable: + if attempt == max_attempts: + return ModelCostMapReloadUnavailable(reason=f"{outcome.reason} (after {max_attempts} attempts)") + wait_seconds: Final = _retry_wait_seconds(outcome=outcome, attempt=attempt, rng=rng) + verbose_logger.warning( + "LiteLLM: model cost map fetch attempt %d/%d failed (%s); retrying in %.1fs", + attempt, + max_attempts, + outcome.reason, + wait_seconds, + ) + return wait_seconds + + async def _fetch_remote_model_cost_map_with_retry( url: str, timeout: int, @@ -254,20 +283,32 @@ async def _fetch_remote_model_cost_map_with_retry( outcome = await _attempt_fetch(client=client, url=url, timeout=timeout) if not isinstance(outcome, _FetchAttemptRetryable): return outcome - if attempt == max_attempts: - return ModelCostMapReloadUnavailable(reason=f"{outcome.reason} (after {max_attempts} attempts)") - wait_seconds = _retry_wait_seconds(outcome=outcome, attempt=attempt, rng=rng) - verbose_logger.warning( - "LiteLLM: model cost map fetch attempt %d/%d failed (%s); retrying in %.1fs", - attempt, - max_attempts, - outcome.reason, - wait_seconds, - ) + wait_seconds = _next_retry_wait(outcome=outcome, attempt=attempt, max_attempts=max_attempts, rng=rng) + if isinstance(wait_seconds, ModelCostMapReloadUnavailable): + return wait_seconds await sleep(wait_seconds) return ModelCostMapReloadUnavailable(reason="model cost map fetch failed") +def _fetch_remote_model_cost_map_with_retry_sync( + url: str, + timeout: int, + max_attempts: int, + sleep: Callable[[float], None], + rng: random.Random, + client: _SyncGetClient, +) -> ModelCostMapReloadResult: + for attempt in range(1, max_attempts + 1): + outcome = _attempt_fetch_sync(client=client, url=url, timeout=timeout) + if not isinstance(outcome, _FetchAttemptRetryable): + return outcome + wait_seconds = _next_retry_wait(outcome=outcome, attempt=attempt, max_attempts=max_attempts, rng=rng) + if isinstance(wait_seconds, ModelCostMapReloadUnavailable): + return wait_seconds + sleep(wait_seconds) + return ModelCostMapReloadUnavailable(reason="model cost map fetch failed") + + async def refetch_model_cost_map( url: str, timeout: int = 5, @@ -423,13 +464,21 @@ def _finalize_model_cost_map(model_cost: dict) -> dict: return _expand_model_aliases(model_cost) -def get_model_cost_map(url: str) -> dict: +def get_model_cost_map( + url: str, + timeout: int = 5, + max_attempts: int = MODEL_COST_MAP_FETCH_MAX_ATTEMPTS, + sleep: Callable[[float], None] = time.sleep, + rng: random.Random | None = None, + client: "_SyncGetClient | None" = None, +) -> dict: """ Public entry point — returns the model cost map dict. 1. If ``LITELLM_LOCAL_MODEL_COST_MAP`` is set, uses the local backup only. - 2. Otherwise fetches from ``url``, validates integrity, and falls back - to the local backup on any failure. + 2. Otherwise fetches from ``url``, retrying transient HTTP errors + (429/5xx/transport) with Retry-After-aware backoff, validates + integrity, and falls back to the local backup on any failure. Only the backup model count is cached (a single int) for validation. The full backup dict is only parsed when it must be *returned* as a @@ -448,17 +497,24 @@ def get_model_cost_map(url: str) -> dict: _cost_map_source_info.url = url _cost_map_source_info.is_env_forced = False - try: - content: Final = GetModelCostMap.fetch_remote_model_cost_map(url) - except Exception as e: + result: Final = _fetch_remote_model_cost_map_with_retry_sync( + url=url, + timeout=timeout, + max_attempts=max_attempts, + sleep=sleep, + rng=rng if rng is not None else random.Random(), + client=client if client is not None else httpx, + ) + if isinstance(result, ModelCostMapReloadUnavailable): verbose_logger.warning( "LiteLLM: Failed to fetch remote model cost map from %s: %s. Falling back to local backup.", url, - str(e), + result.reason, ) _cost_map_source_info.source = "local" - _cost_map_source_info.fallback_reason = f"Remote fetch failed: {e}" + _cost_map_source_info.fallback_reason = f"Remote fetch failed: {result.reason}" return _finalize_model_cost_map(GetModelCostMap.load_local_model_cost_map()) + content: Final = result.model_cost_map # Validate using cached count (cheap int comparison, no file I/O) if not GetModelCostMap.validate_model_cost_map( diff --git a/litellm/litellm_core_utils/internal_call_metadata.py b/litellm/litellm_core_utils/internal_call_metadata.py index 34d5797a6d8..4d043701f40 100644 --- a/litellm/litellm_core_utils/internal_call_metadata.py +++ b/litellm/litellm_core_utils/internal_call_metadata.py @@ -25,6 +25,13 @@ from litellm.types.utils import BACKGROUND_RESPONSE_COST_POLL_CALL_ORIGIN, Inter BUDGET_RESERVATION_METADATA_KEYS: Final = frozenset({"user_api_key_budget_reservation"}) +MODEL_ACCESS_GROUP_METADATA_KEY: Final = "user_api_key_matched_model_access_groups" +"""Where auth records the model access groups that authorized the request, for the spend writer. + +The ``user_api_key`` prefix is load-bearing, not cosmetic: when a request carries both +``metadata`` and ``litellm_metadata``, ``get_litellm_metadata_from_kwargs`` returns the latter and +copies a key across only when ``user_api_key`` appears in its name.""" + _USER_API_KEY_AUTH_KEY: Final = "user_api_key_auth" FORWARDABLE_IDENTITY_METADATA_KEYS: Final = frozenset( diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index a8672b5c112..9a6fb11f978 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -64,7 +64,10 @@ from litellm.integrations.mlflow import MlflowLogger from litellm.integrations.sqs import SQSLogger from litellm.litellm_core_utils.core_helpers import is_expected_client_error, reconstruct_model_name from litellm.litellm_core_utils.get_litellm_params import get_litellm_params -from litellm.litellm_core_utils.internal_call_metadata import is_unbilled_non_inference_call +from litellm.litellm_core_utils.internal_call_metadata import ( + MODEL_ACCESS_GROUP_METADATA_KEY, + is_unbilled_non_inference_call, +) from litellm.litellm_core_utils.llm_cost_calc.guardrail_cost import ( cost_breakdown_with_guardrail, guardrail_information_cost, @@ -108,6 +111,7 @@ from litellm.types.mcp import MCPPostCallResponseObject from litellm.types.prompts.init_prompts import PromptSpec from litellm.types.rerank import RerankResponse from litellm.types.utils import ( + DEPLOYMENT_SCOPED_PRICING_FIELDS, CachingDetails, CallTypes, CostBreakdown, @@ -252,6 +256,7 @@ _STANDARD_LOGGING_METADATA_KEYS: Final[frozenset[str]] = frozenset(StandardLoggi # Cache custom pricing keys as frozenset for O(1) lookups instead of looping through 49 keys _CUSTOM_PRICING_KEYS: Final[frozenset[str]] = frozenset(CustomPricingLiteLLMParams.model_fields.keys()) +_MODEL_INFO_CUSTOM_PRICING_KEYS: Final[frozenset[str]] = _CUSTOM_PRICING_KEYS | DEPLOYMENT_SCOPED_PRICING_FIELDS sentry_sdk_instance = None capture_exception = None @@ -544,6 +549,9 @@ class Logging(LiteLLMLoggingBaseClass): # Init Caching related details self.caching_details: CachingDetails | None = None + # Timing for results that cannot carry ``_hidden_params`` (plain-dict /v1/messages + # responses and the bridge stream wrappers); see ``update_response_metadata``. + self.response_timing_metrics: Mapping[str, float] = {} # mutable-ok: kept deep-copyable # Passthrough endpoint guardrails config for field targeting self.passthrough_guardrails_config: dict[str, Any] | None = None @@ -563,6 +571,10 @@ class Logging(LiteLLMLoggingBaseClass): self._defer_async_logging: bool = False self._enqueue_deferred_logging: Callable[[], None] | None = None + def set_response_timing_metrics(self, timing_metrics: Mapping[str, float]) -> None: + """Keep ``_response_ms`` / ``litellm_overhead_time_ms`` for a result that has no ``_hidden_params``.""" + self.response_timing_metrics = dict(timing_metrics) # mutable-ok: kept deep-copyable + def process_dynamic_callbacks(self): """ Initializes CustomLogger compatible callbacks in self.dynamic_* callbacks @@ -2131,6 +2143,9 @@ class Logging(LiteLLMLoggingBaseClass): logging_result: Final = self.normalize_logging_result(result=result) + if isinstance(result, Response) and isinstance(logging_result, (ModelResponse, EmbeddingResponse)): + result = logging_result + if standard_logging_object is None and result is not None and self.stream is not True: if self._is_recognized_call_type_for_logging(logging_result=logging_result) or isinstance( logging_result, (dict, list) @@ -2872,6 +2887,8 @@ class Logging(LiteLLMLoggingBaseClass): batch_cost: Final = kwargs.get("batch_cost", None) batch_usage = kwargs.get("batch_usage", None) batch_models = kwargs.get("batch_models", None) + batch_successful_requests: Final = kwargs.get("batch_successful_requests", None) + batch_failed_requests: Final = kwargs.get("batch_failed_requests", None) has_explicit_batch_data: Final = all(x is not None for x in (batch_cost, batch_usage, batch_models)) should_compute_batch_data: Final = ( @@ -2880,14 +2897,12 @@ class Logging(LiteLLMLoggingBaseClass): if has_explicit_batch_data: result._hidden_params["response_cost"] = batch_cost result._hidden_params["batch_models"] = batch_models + result._hidden_params["batch_successful_requests"] = batch_successful_requests # pyright: ignore[reportPrivateUsage] # rebind-ok: same result._hidden_params pattern as response_cost/batch_models above + result._hidden_params["batch_failed_requests"] = batch_failed_requests # pyright: ignore[reportPrivateUsage] # rebind-ok: same pattern as above result.usage = batch_usage elif should_compute_batch_data: - ( - response_cost, - batch_usage, - batch_models, - ) = await _handle_completed_batch( + batch_result: Final = await _handle_completed_batch( batch=result, custom_llm_provider=self.custom_llm_provider, model_name=self.get_deployment_model_for_cost(), @@ -2895,9 +2910,11 @@ class Logging(LiteLLMLoggingBaseClass): model_info=self.get_router_deployment_model_info(), ) - result._hidden_params["response_cost"] = response_cost - result._hidden_params["batch_models"] = batch_models - result.usage = batch_usage + result._hidden_params["response_cost"] = batch_result.cost + result._hidden_params["batch_models"] = batch_result.models + result._hidden_params["batch_successful_requests"] = batch_result.successful_requests # pyright: ignore[reportPrivateUsage] # rebind-ok: same pattern as above + result._hidden_params["batch_failed_requests"] = batch_result.failed_requests # pyright: ignore[reportPrivateUsage] # rebind-ok: same pattern as above + result.usage = batch_result.usage start_time, end_time, result = self._success_handler_helper_fn( start_time=start_time, @@ -2942,13 +2959,25 @@ class Logging(LiteLLMLoggingBaseClass): "Model=%s not found in completion cost map. Setting 'response_cost' to None", self.model ) self.model_call_details["response_cost"] = None + except Exception: # noqa: BLE001 # cost calculation must never block later callbacks (slot release) + verbose_logger.exception( + "Error calculating streaming response cost for model=%s. Setting 'response_cost' to None", + self.model, + ) + self.model_call_details["response_cost"] = None self._merge_hidden_params_from_response_into_metadata(complete_streaming_response) ## STANDARDIZED LOGGING PAYLOAD - self.model_call_details["standard_logging_object"] = self._build_standard_logging_payload( - complete_streaming_response, start_time, end_time - ) + try: + self.model_call_details["standard_logging_object"] = self._build_standard_logging_payload( + complete_streaming_response, start_time, end_time + ) + except Exception: # noqa: BLE001 # payload build must never block later callbacks (slot release) + verbose_logger.exception( + "LiteLLM.LoggingError: [Non-Blocking] Exception building the standard logging payload " + "for a streaming response; callbacks still run without it" + ) # print standard logging payload if (standard_logging_payload := self.model_call_details.get("standard_logging_object")) is not None: @@ -2988,32 +3017,39 @@ class Logging(LiteLLMLoggingBaseClass): ## LOGGING HOOK ## for callback in callbacks: - if isinstance(callback, CustomGuardrail): - from litellm.types.guardrails import GuardrailEventHooks + try: + if isinstance(callback, CustomGuardrail): + from litellm.types.guardrails import GuardrailEventHooks - if ( - callback.should_run_guardrail( - data=self.model_call_details, - event_type=GuardrailEventHooks.logging_only, + if ( + callback.should_run_guardrail( + data=self.model_call_details, + event_type=GuardrailEventHooks.logging_only, + ) + is not True + ): + continue + + self.model_call_details, result = await callback.async_logging_hook( + kwargs=self.model_call_details, + result=result, + call_type=self.call_type, ) - is not True - ): - continue - - self.model_call_details, result = await callback.async_logging_hook( - kwargs=self.model_call_details, - result=result, - call_type=self.call_type, - ) - elif isinstance(callback, CustomLogger): - result = redact_message_input_output_from_custom_logger( - result=result, litellm_logging_obj=self, custom_logger=callback - ) - self.model_call_details, result = await callback.async_logging_hook( - kwargs=self.model_call_details, - result=result, - call_type=self.call_type, + elif isinstance(callback, CustomLogger): + result = redact_message_input_output_from_custom_logger( + result=result, litellm_logging_obj=self, custom_logger=callback + ) + self.model_call_details, result = await callback.async_logging_hook( + kwargs=self.model_call_details, + result=result, + call_type=self.call_type, + ) + except Exception: # noqa: BLE001 # one failing hook must not skip later callbacks (slot release) + verbose_logger.error( + "LiteLLM.LoggingError: [Non-Blocking] Exception occurred in async_logging_hook %s", + traceback.format_exc(), ) + self._handle_callback_failure(callback=callback) self.has_run_logging(event_type="async_success") @@ -5018,7 +5054,9 @@ def use_custom_pricing_for_model(litellm_params: dict | None) -> bool: """ Check if the model uses custom pricing - Returns True if any of `SPECIAL_MODEL_INFO_PARAMS` are present in `litellm_params` or `model_info` + Returns True if any custom pricing field is present in `litellm_params`, or if + any custom pricing or deployment-scoped pricing field (such as + ``off_peak_pricing``) is present in the metadata ``model_info`` """ if litellm_params is None: return False @@ -5036,7 +5074,7 @@ def use_custom_pricing_for_model(litellm_params: dict | None) -> bool: model_info: dict = metadata.get("model_info", {}) or {} if model_info: - matching_keys = _CUSTOM_PRICING_KEYS & model_info.keys() + matching_keys = _MODEL_INFO_CUSTOM_PRICING_KEYS & model_info.keys() for key in matching_keys: if model_info.get(key) is not None: return True @@ -5049,6 +5087,42 @@ def is_valid_sha256_hash(value: str) -> bool: return bool(re.fullmatch(r"[a-fA-F0-9]{64}", value)) +def coerce_model_access_groups(value: object) -> tuple[str, ...]: + """Model access group names out of untrusted request metadata, deduped and order preserving.""" + if not isinstance(value, (list, tuple)): + return () + return tuple(dict.fromkeys(group for group in value if isinstance(group, str) and group)) + + +def _model_access_groups_on_auth_object(user_api_key_auth: object) -> object: + if isinstance(user_api_key_auth, Mapping): + return user_api_key_auth.get("matched_model_access_groups") + return getattr(user_api_key_auth, "matched_model_access_groups", None) + + +def _model_access_groups_from_metadata(metadata: Mapping[str, object]) -> tuple[str, ...]: + stamped: Final = coerce_model_access_groups(metadata.get(MODEL_ACCESS_GROUP_METADATA_KEY)) + if stamped: + return stamped + return coerce_model_access_groups(_model_access_groups_on_auth_object(metadata.get("user_api_key_auth"))) + + +def request_model_access_groups_from_litellm_params(litellm_params: Mapping[str, object]) -> tuple[str, ...]: + """Access groups the auth layer stamped onto this request, from whichever metadata field carries them. + + Detached internal sub-calls only inherit the identity keys, so the auth object is the + fallback there, exactly as _get_budget_reservation_from_metadata does for reservations. + """ + for metadata_variable_name in ("metadata", "litellm_metadata"): + metadata = litellm_params.get(metadata_variable_name) + if not isinstance(metadata, Mapping): + continue + model_access_groups = _model_access_groups_from_metadata(metadata) + if model_access_groups: + return model_access_groups + return () + + class StandardLoggingPayloadSetup: @staticmethod def cleanup_timestamps( @@ -5422,6 +5496,8 @@ class StandardLoggingPayloadSetup: additional_headers=None, litellm_overhead_time_ms=None, batch_models=None, + batch_successful_requests=None, + batch_failed_requests=None, litellm_model_name=None, usage_object=None, ) @@ -5812,6 +5888,8 @@ def _extract_response_obj_and_hidden_params( response_cost=None, litellm_overhead_time_ms=None, batch_models=None, + batch_successful_requests=None, + batch_failed_requests=None, litellm_model_name=None, usage_object=None, ) @@ -5896,6 +5974,7 @@ def get_standard_logging_object_payload( request_tags: Final = StandardLoggingPayloadSetup._get_request_tags( litellm_params=litellm_params, proxy_server_request=proxy_server_request ) + request_model_access_groups: Final = request_model_access_groups_from_litellm_params(litellm_params) # cleanup timestamps ( @@ -5959,6 +6038,13 @@ def get_standard_logging_object_payload( clean_hidden_params: Final = StandardLoggingPayloadSetup.get_hidden_params(hidden_params) if clean_hidden_params["response_cost"] is None and raw_response_cost is not None: clean_hidden_params["response_cost"] = llm_response_cost + if clean_hidden_params["litellm_overhead_time_ms"] is None and status == "success": + # /v1/messages dict results and the bridge stream wrappers keep it on the logging object; + # failure payloads stay None like every response type that carries its own _hidden_params + timing_metrics: Final = ( + getattr(logging_obj, "response_timing_metrics", None) or {} # mutable-ok: empty fallback + ) + clean_hidden_params["litellm_overhead_time_ms"] = timing_metrics.get("litellm_overhead_time_ms") model_cost_information: Final = StandardLoggingPayloadSetup.get_model_cost_information( base_model=base_model, @@ -6058,6 +6144,7 @@ def get_standard_logging_object_payload( prompt_tokens=usage_dict.get("prompt_tokens", 0), completion_tokens=usage_dict.get("completion_tokens", 0), request_tags=request_tags, + request_model_access_groups=request_model_access_groups, end_user=end_user_id, api_base=StandardLoggingPayloadSetup.strip_trailing_slash(litellm_params.get("api_base", "")) or "", model_group=_model_group, @@ -6091,7 +6178,10 @@ def get_standard_logging_object_payload( def emit_standard_logging_payload(payload: StandardLoggingPayload): if os.getenv("LITELLM_PRINT_STANDARD_LOGGING_PAYLOAD"): - print(json.dumps(payload, indent=4), flush=True) # noqa: T201 + try: + print(json.dumps(payload, indent=4, default=str), flush=True) # noqa: T201 + except Exception as e: # noqa: BLE001 # Safe catch-all for verbose logging + verbose_logger.exception("Error serializing standard logging payload for debug output: %s", e) def get_standard_logging_metadata( @@ -6228,6 +6318,8 @@ def create_dummy_standard_logging_payload() -> StandardLoggingPayload: additional_headers=None, litellm_overhead_time_ms=None, batch_models=None, + batch_successful_requests=None, + batch_failed_requests=None, litellm_model_name=None, usage_object=None, ) @@ -6269,6 +6361,7 @@ def create_dummy_standard_logging_payload() -> StandardLoggingPayload: cache_key=None, saved_cache_cost=saved_cache_cost, request_tags=[], + request_model_access_groups=(), end_user=None, requester_ip_address="127.0.0.1", messages=messages, diff --git a/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py b/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py index 9250b92e268..5504756ceb8 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py +++ b/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py @@ -3,7 +3,7 @@ Helper utilities for tracking the cost of built-in tools. """ from collections.abc import Mapping -from typing import Any, Final, Literal +from typing import Final, Literal import litellm from litellm.constants import OPENAI_FILE_SEARCH_COST_PER_1K_CALLS @@ -16,6 +16,7 @@ from litellm.types.llms.openai import ( WebSearchOptions, ) from litellm.types.utils import ( + ChatCompletionAnnotation, Message, ModelInfo, ModelResponse, @@ -49,7 +50,7 @@ class StandardBuiltInToolCostTracking: @staticmethod def get_cost_for_built_in_tools( model: str, - response_object: Any, + response_object: object, usage: Usage | None = None, custom_llm_provider: str | None = None, standard_built_in_tools_params: StandardBuiltInToolsParams | None = None, @@ -201,8 +202,7 @@ class StandardBuiltInToolCostTracking: model_info: Final = StandardBuiltInToolCostTracking._safe_get_model_info( model=model, custom_llm_provider=custom_llm_provider ) - file_search_raw: Final[Any] = standard_built_in_tools_params.get("file_search", {}) - file_search_usage: Final[FileSearchTool | None] = FileSearchTool(**file_search_raw) if file_search_raw else None + file_search_usage: Final[FileSearchTool | None] = standard_built_in_tools_params.get("file_search") or None # Convert model_info to dict and extract usage parameters model_info_dict: Final = dict(model_info) if model_info is not None else None @@ -245,7 +245,7 @@ class StandardBuiltInToolCostTracking: @staticmethod def _extract_file_search_params( - file_search_usage: Any, + file_search_usage: object, ) -> tuple[float | None, float | None]: """Extract and convert file search parameters safely.""" storage_gb = None @@ -335,7 +335,7 @@ class StandardBuiltInToolCostTracking: @staticmethod def _extract_token_counts( - computer_use_usage: Any, + computer_use_usage: object, ) -> tuple[int | None, int | None]: """Extract and convert token counts safely.""" input_tokens = None @@ -351,9 +351,9 @@ class StandardBuiltInToolCostTracking: return input_tokens, output_tokens @staticmethod - def _safe_convert_to_int(value: Any) -> int | None: + def _safe_convert_to_int(value: object) -> int | None: """Safely convert a value to int.""" - if value is not None: + if isinstance(value, (int, float, str)): try: return int(value) except (TypeError, ValueError): @@ -381,7 +381,7 @@ class StandardBuiltInToolCostTracking: return usage.model_copy(update={"server_tool_use": server_tool_use}) @staticmethod - def response_object_includes_web_search_call(response_object: Any, usage: Usage | None = None) -> bool: + def response_object_includes_web_search_call(response_object: object, usage: Usage | None = None) -> bool: """ Check if the response object includes a web search call. @@ -446,7 +446,7 @@ class StandardBuiltInToolCostTracking: @staticmethod def response_object_includes_file_search_call( - response_object: Any, + response_object: object, ) -> bool: """ Check if the response object includes a file search call. @@ -477,11 +477,11 @@ class StandardBuiltInToolCostTracking: message: Message | None = getattr(choice, "message", None) if message is None: continue - if annotations := getattr(message, "annotations", None): - if len(annotations) > 0: - for annotation in annotations: - if annotation.get("type", None) == annotation_type: - return True + annotations: list[ChatCompletionAnnotation] | None = getattr(message, "annotations", None) + if annotations: + for annotation in annotations: + if annotation.get("type", None) == annotation_type: + return True return False @staticmethod @@ -522,10 +522,8 @@ class StandardBuiltInToolCostTracking: if model_info is None: return 0.0 - search_context_raw: Final[Any] = model_info.get("search_context_cost_per_query", {}) - search_context_pricing: Final[SearchContextCostPerQuery] = ( - SearchContextCostPerQuery(**search_context_raw) if search_context_raw else SearchContextCostPerQuery() - ) + search_context_raw: Final = model_info.get("search_context_cost_per_query") + search_context_pricing: Final[SearchContextCostPerQuery] = search_context_raw or SearchContextCostPerQuery() if web_search_options.get("search_context_size", None) == "low": return search_context_pricing.get("search_context_size_low", 0.0) elif web_search_options.get("search_context_size", None) == "medium": @@ -545,10 +543,8 @@ class StandardBuiltInToolCostTracking: """ if model_info is None: return 0.0 - search_context_raw: Final[Any] = model_info.get("search_context_cost_per_query", {}) or {} - search_context_pricing: Final[SearchContextCostPerQuery] = ( - SearchContextCostPerQuery(**search_context_raw) if search_context_raw else SearchContextCostPerQuery() - ) + search_context_raw: Final = model_info.get("search_context_cost_per_query") + search_context_pricing: Final[SearchContextCostPerQuery] = search_context_raw or SearchContextCostPerQuery() return search_context_pricing.get("search_context_size_medium", 0.0) @staticmethod @@ -714,7 +710,7 @@ class StandardBuiltInToolCostTracking: response_object: ModelResponse, ) -> bool: for _choice in response_object.choices: - message = getattr(_choice, "message", None) + message: Message | None = getattr(_choice, "message", None) if ( message is not None and hasattr(message, "annotations") diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py index 19e3f624268..b34c416cd40 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/utils.py +++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py @@ -2,10 +2,12 @@ ## Helper utilities for cost_per_token() import re -from collections.abc import Mapping +from collections.abc import Mapping, Sequence from dataclasses import dataclass +from datetime import datetime, timezone, tzinfo from types import MappingProxyType from typing import Any, Final, Literal, TypedDict, cast +from zoneinfo import ZoneInfo, ZoneInfoNotFoundError import litellm from litellm._logging import verbose_logger @@ -290,10 +292,187 @@ def _get_tiered_base_costs(model_info: ModelInfo, usage: Usage) -> tuple[float, ) +def _is_within_off_peak_window(off_peak_hours_utc: str | Sequence[str], current_time: datetime | None = None) -> bool: + """Return True if current_time (UTC, defaulting to now) falls inside any off-peak window. + + off_peak_hours_utc is a "HH:MM-HH:MM" string in UTC, or a list of such strings for providers + with multiple daily windows (e.g. ["16:30-00:30", "04:00-06:00"]). A window may wrap past + midnight, and a window whose start equals its end covers the whole day. The start is + inclusive and the end is exclusive; malformed windows are ignored. + + An aware current_time is converted to UTC. A naive one is taken to already be UTC rather + than being localised, so callers must pass datetime.now(timezone.utc), never datetime.now(), + or every window shifts by the host's offset. + """ + reference: Final = current_time if current_time is not None else datetime.now(timezone.utc) + now: Final = (reference.astimezone(timezone.utc) if reference.tzinfo is not None else reference).time() + windows: Final = (off_peak_hours_utc,) if isinstance(off_peak_hours_utc, str) else off_peak_hours_utc + for window in windows: + try: + start_str, end_str = window.split("-") + start = datetime.strptime(start_str.strip(), "%H:%M").replace(tzinfo=timezone.utc).time() + end = datetime.strptime(end_str.strip(), "%H:%M").replace(tzinfo=timezone.utc).time() + except (ValueError, AttributeError): + continue + if start < end: + if start <= now < end: + return True + elif now >= start or now < end: + return True + return False + + +_WEEKDAY_NUMBERS: Final = MappingProxyType( + { + "mon": 1, + "monday": 1, + "tue": 2, + "tues": 2, + "tuesday": 2, + "wed": 3, + "wednesday": 3, + "thu": 4, + "thur": 4, + "thurs": 4, + "thursday": 4, + "fri": 5, + "friday": 5, + "sat": 6, + "saturday": 6, + "sun": 7, + "sunday": 7, + } +) + + +def _normalize_weekday(value: object) -> int | None: + if isinstance(value, bool): + return None + if isinstance(value, int): + return value if 1 <= value <= 7 else None + if isinstance(value, str): + return _WEEKDAY_NUMBERS.get(value.strip().lower()) + return None + + +def _weekday_calendar(weekday_timezone: object) -> tzinfo: + if isinstance(weekday_timezone, str) and weekday_timezone.strip(): + try: + return ZoneInfo(weekday_timezone.strip()) + except (ValueError, ZoneInfoNotFoundError): + return timezone.utc + return timezone.utc + + +def _matches_weekdays(reference_utc: datetime, weekdays: object, weekday_timezone: object) -> bool: + """Return True when reference_utc falls on one of the rule's weekdays, read on the calendar + named by weekday_timezone (default UTC). An absent weekdays means every day. The calendar + matters even when UTC and vendor-local weekdays agree at every currently priced hour: a + window past 16:00 UTC is where an Asia/Shanghai weekday diverges from the UTC one. + """ + if weekdays is None: + return True + if isinstance(weekdays, str) or not isinstance(weekdays, Sequence): + return False + allowed: Final = frozenset(day for day in map(_normalize_weekday, weekdays) if day is not None) + return reference_utc.astimezone(_weekday_calendar(weekday_timezone)).isoweekday() in allowed + + +def _as_window_strings(value: object) -> tuple[str, ...]: + if isinstance(value, str): + return (value,) + if isinstance(value, Sequence): + return tuple(entry for entry in value if isinstance(entry, str)) + return () + + +def _is_off_peak(off_peak: Mapping[str, object], current_time: datetime | None = None) -> bool: + """Return True when current_time (UTC, defaulting to now) is off-peak under the block's + rules: the flat hours_utc windows, which apply every day, or any entry in windows, whose + hours apply only on its weekdays. + """ + reference: Final = current_time if current_time is not None else datetime.now(timezone.utc) + reference_utc: Final = ( + reference.astimezone(timezone.utc) if reference.tzinfo is not None else reference.replace(tzinfo=timezone.utc) + ) + flat_windows: Final = _as_window_strings(off_peak.get("hours_utc")) + if flat_windows and _is_within_off_peak_window(flat_windows, reference_utc): + return True + windows: Final = off_peak.get("windows") + if isinstance(windows, str) or not isinstance(windows, Sequence): + return False + weekday_timezone: Final = off_peak.get("weekday_timezone") + for rule in windows: + if not isinstance(rule, Mapping): + continue + rule_windows = _as_window_strings(rule.get("hours_utc")) + if not rule_windows: + continue + if not _matches_weekdays(reference_utc, rule.get("weekdays"), weekday_timezone): + continue + if _is_within_off_peak_window(rule_windows, reference_utc): + return True + return False + + +def _coerce_off_peak_rate(value: object, default: float) -> float: + if isinstance(value, bool): + return default + if isinstance(value, (int, float)): + return float(value) + if isinstance(value, str): + try: + return float(value) + except ValueError: + return default + return default + + +def _apply_off_peak_pricing( + model_info: ModelInfo, + current_time: datetime | None, + prompt_base_cost: float, + completion_base_cost: float, + cache_read_cost: float, +) -> tuple[float, float, float]: + """Swap in off-peak per-token rates when the current UTC time is inside one of the model's + off_peak_pricing rules, the every-day hours_utc windows or a day-of-week-qualified entry in + windows. An off-peak rate replaces the rate that would otherwise apply rather than + discounting it, so a model that also has tiered or above-threshold pricing bills the flat + off-peak rate for the whole request while the window is open. Any rate left unset in + off_peak_pricing falls back to the standard rate. + """ + off_peak: Final = model_info.get("off_peak_pricing") + if not isinstance(off_peak, Mapping) or not _is_off_peak(off_peak, current_time): + return prompt_base_cost, completion_base_cost, cache_read_cost + return ( + _coerce_off_peak_rate(off_peak.get("input_cost_per_token"), prompt_base_cost), + _coerce_off_peak_rate(off_peak.get("output_cost_per_token"), completion_base_cost), + _coerce_off_peak_rate(off_peak.get("cache_read_input_token_cost"), cache_read_cost), + ) + + +def _apply_off_peak_to_base_costs( + model_info: ModelInfo, + current_time: datetime | None, + base_costs: tuple[float, float, float, float, float], +) -> tuple[float, float, float, float, float]: + """Apply off-peak rates to an already-resolved set of base costs, whichever pricing path + produced them. Cache-creation rates are passed through untouched, since off_peak_pricing + has no field for them. + """ + prompt, completion, cache_creation, cache_creation_above_1hr, cache_read = base_costs + off_peak_prompt, off_peak_completion, off_peak_cache_read = _apply_off_peak_pricing( + model_info, current_time, prompt, completion, cache_read + ) + return (off_peak_prompt, off_peak_completion, cache_creation, cache_creation_above_1hr, off_peak_cache_read) + + def _get_token_base_cost( model_info: ModelInfo, usage: Usage, service_tier: str | None = None, + current_time: datetime | None = None, *, threshold_is_inclusive: bool = False, ) -> tuple[float, float, float, float, float]: @@ -311,7 +490,7 @@ def _get_token_base_cost( """ tiered_base_costs: Final = _get_tiered_base_costs(model_info=model_info, usage=usage) if tiered_base_costs is not None: - return tiered_base_costs + return _apply_off_peak_to_base_costs(model_info, current_time, tiered_base_costs) # Get service tier aware cost keys input_cost_key: Final = _get_service_tier_cost_key("input_cost_per_token", service_tier) @@ -345,12 +524,16 @@ def _get_token_base_cost( k for k in model_info if k.startswith("input_cost_per_token_above_") and not k.endswith(_SERVICE_TIER_SUFFIXES) ] if not threshold_keys: - return ( - prompt_base_cost, - completion_base_cost, - cache_creation_cost, - cache_creation_cost_above_1hr, - cache_read_cost, + return _apply_off_peak_to_base_costs( + model_info, + current_time, + ( + prompt_base_cost, + completion_base_cost, + cache_creation_cost, + cache_creation_cost_above_1hr, + cache_read_cost, + ), ) # Only sort the threshold keys (typically 1-2 keys instead of 66+) @@ -451,12 +634,16 @@ def _get_token_base_cost( except Exception: continue - return ( - prompt_base_cost, - completion_base_cost, - cache_creation_cost, - cache_creation_cost_above_1hr, - cache_read_cost, + return _apply_off_peak_to_base_costs( + model_info, + current_time, + ( + prompt_base_cost, + completion_base_cost, + cache_creation_cost, + cache_creation_cost_above_1hr, + cache_read_cost, + ), ) diff --git a/litellm/litellm_core_utils/llm_response_utils/response_metadata.py b/litellm/litellm_core_utils/llm_response_utils/response_metadata.py index a375560288f..b53a2d36753 100644 --- a/litellm/litellm_core_utils/llm_response_utils/response_metadata.py +++ b/litellm/litellm_core_utils/llm_response_utils/response_metadata.py @@ -1,6 +1,9 @@ import datetime +from collections.abc import Mapping from typing import Any, Final +import httpx + from litellm.constants import LITELLM_DETAILED_TIMING from litellm.litellm_core_utils.core_helpers import process_response_headers from litellm.litellm_core_utils.llm_response_utils.get_api_base import get_api_base @@ -13,6 +16,39 @@ from litellm.types.utils import ( ) +def response_timing_metrics( + start_time: datetime.datetime, + end_time: datetime.datetime, + logging_obj: LiteLLMLoggingObject, + include_overhead: bool = True, +) -> Mapping[str, float]: + """``_response_ms`` for the whole call, plus ``litellm_overhead_time_ms`` when it can be derived. + + On a cache hit the overhead is the total minus the cache read; otherwise it is the total minus + the provider call (``llm_api_duration_ms``). It is omitted when neither duration was recorded, + and when ``include_overhead`` is False because the two durations cover different windows. + """ + total_response_time_ms: Final = (end_time - start_time).total_seconds() * 1000 + if not include_overhead: + return {"_response_ms": total_response_time_ms} # mutable-ok: read-only timing result + caching_details: Final = logging_obj.caching_details + cache_duration_ms: Final = ( + caching_details.get("cache_duration_ms") + if caching_details is not None and caching_details.get("cache_hit") is True + else None + ) + llm_api_duration_ms: Final = logging_obj.model_call_details.get("llm_api_duration_ms") + if cache_duration_ms is not None: + overhead_ms: float | None = total_response_time_ms - cache_duration_ms + elif llm_api_duration_ms is not None: + overhead_ms = round(total_response_time_ms - llm_api_duration_ms, 4) + else: + overhead_ms = None + if overhead_ms is None: + return {"_response_ms": total_response_time_ms} + return {"_response_ms": total_response_time_ms, "litellm_overhead_time_ms": overhead_ms} + + class ResponseMetadata: """ Handles setting and managing `_hidden_params`, `response_time_ms`, and `litellm_overhead_time_ms` for LiteLLM responses @@ -25,11 +61,7 @@ class ResponseMetadata: @property def supports_response_time(self) -> bool: """Check if response type supports timing metrics""" - return ( - isinstance(self.result, ModelResponse) - or isinstance(self.result, EmbeddingResponse) - or isinstance(self.result, TranscriptionResponse) - ) + return isinstance(self.result, (ModelResponse, EmbeddingResponse, TranscriptionResponse)) def set_hidden_params(self, logging_obj: LiteLLMLoggingObject, model: str | None, kwargs: dict) -> None: """Set hidden parameters on the response""" @@ -45,14 +77,14 @@ class ResponseMetadata: result=self.result, litellm_model_name=model, router_model_id=model_id ), "additional_headers": process_response_headers( - self._get_value_from_hidden_params("additional_headers") or {}, + self._get_additional_headers_from_hidden_params() or {}, preserve_litellm_internal_headers=True, ), "litellm_model_name": model, } self._update_hidden_params(new_params) - def _update_hidden_params(self, new_params: dict) -> None: + def _update_hidden_params(self, new_params: Mapping[str, object]) -> None: """ Update hidden params - handles when self._hidden_params is a dict or HiddenParams object """ @@ -64,51 +96,38 @@ class ResponseMetadata: for key, value in new_params.items(): setattr(self._hidden_params, key, value) - def _get_value_from_hidden_params(self, key: str) -> Any | None: - """Get value from hidden params - handles when self._hidden_params is a dict or HiddenParams object""" + def _get_additional_headers_from_hidden_params(self) -> httpx.Headers | dict[str, str] | None: + """Get `additional_headers` from hidden params - handles when self._hidden_params is a dict or HiddenParams object""" if isinstance(self._hidden_params, dict): - return self._hidden_params.get(key, None) + return self._hidden_params.get("additional_headers", None) elif isinstance(self._hidden_params, HiddenParams): - return getattr(self._hidden_params, key, None) + return getattr(self._hidden_params, "additional_headers", None) def set_timing_metrics( self, start_time: datetime.datetime, end_time: datetime.datetime, logging_obj: LiteLLMLoggingObject, + include_overhead: bool = True, ) -> None: """Set response timing metrics""" - total_response_time_ms: Final = (end_time - start_time).total_seconds() * 1000 + timing_metrics: Final = response_timing_metrics(start_time, end_time, logging_obj, include_overhead) + total_response_time_ms: Final = timing_metrics["_response_ms"] # Set total response time if supported if self.supports_response_time: self.result._response_ms = total_response_time_ms ######################################################### - # 1. Add _response_ms total duration + # 1. Add _response_ms total duration and the LiteLLM overhead within it + # (total minus the cache read on a cache hit, else total minus the provider call) ######################################################### - self._update_hidden_params( - { - "_response_ms": total_response_time_ms, - } - ) + self._update_hidden_params(timing_metrics) ######################################################### - # 2. Add LiteLLM overhead duration + # 2. Add callback processing duration ######################################################### - llm_api_duration_ms: Final = logging_obj.model_call_details.get("llm_api_duration_ms") - if llm_api_duration_ms is not None: - overhead_ms = round(total_response_time_ms - llm_api_duration_ms, 4) - self._update_hidden_params( - { - "litellm_overhead_time_ms": overhead_ms, - } - ) - - ######################################################### - # 3. Add callback processing duration - ######################################################### - callback_duration_ms: Final = getattr(logging_obj, "callback_duration_ms", None) + callback_duration_ms: Final[float | None] = getattr(logging_obj, "callback_duration_ms", None) if callback_duration_ms is not None: self._update_hidden_params( { @@ -117,36 +136,21 @@ class ResponseMetadata: ) ######################################################### - # 4. Add duration for reading from cache - # In this case overhead from litellm is the difference between the cache read duration and the total response time - ######################################################### - if ( - logging_obj.caching_details is not None - and logging_obj.caching_details.get("cache_hit") is True - and (cache_duration_ms := logging_obj.caching_details.get("cache_duration_ms")) is not None - ): - overhead_ms = total_response_time_ms - cache_duration_ms - self._update_hidden_params( - { - "litellm_overhead_time_ms": overhead_ms, - } - ) - - ######################################################### - # 5. Detailed per-phase timing (opt-in via env var) + # 3. Detailed per-phase timing (opt-in via env var) ######################################################### + llm_api_duration_ms: Final = logging_obj.model_call_details.get("llm_api_duration_ms") if LITELLM_DETAILED_TIMING and llm_api_duration_ms is not None: - detailed: Final[dict] = { + detailed: Final[dict[str, float]] = { "timing_llm_api_ms": round(llm_api_duration_ms, 4), } # message copy time from Logging.__init__() - msg_copy_ms: Final = getattr(logging_obj, "message_copy_duration_ms", None) + msg_copy_ms: Final[float | None] = getattr(logging_obj, "message_copy_duration_ms", None) if msg_copy_ms is not None: detailed["timing_message_copy_ms"] = round(msg_copy_ms, 4) # pre-processing = time from request start to LLM API call start - api_call_start: Final = logging_obj.model_call_details.get("api_call_start_time") + api_call_start: Final[datetime.datetime | None] = logging_obj.model_call_details.get("api_call_start_time") if api_call_start is not None and start_time is not None: pre_ms: Final = (api_call_start - start_time).total_seconds() * 1000 detailed["timing_pre_processing_ms"] = round(pre_ms, 4) @@ -170,6 +174,7 @@ def update_response_metadata( kwargs: dict, start_time: datetime.datetime, end_time: datetime.datetime, + include_overhead: bool = True, ) -> None: """ Updates response metadata including hidden params and timing metrics @@ -177,11 +182,22 @@ def update_response_metadata( - response._hidden_params - response._hidden_params["litellm_overhead_time_ms"] - response.response_time_ms + A result that cannot hold ``_hidden_params`` gets its timing on ``logging_obj`` instead. + Callers whose ``end_time`` covers more than the recorded provider call (a stream read to + completion) pass ``include_overhead=False``, since the overhead cannot be derived there. """ - if result is None or not hasattr(result, "_hidden_params"): + if result is None: + return + if not hasattr(result, "_hidden_params"): + # /v1/messages returns a plain dict and the Anthropic / Responses bridge stream wrappers + # cannot hold ``_hidden_params``: keep only the timing on the logging object (no cost + # recompute) so the proxy headers and the standard logging payload can still read it. + logging_obj.set_response_timing_metrics( + response_timing_metrics(start_time, end_time, logging_obj, include_overhead) + ) return metadata: Final = ResponseMetadata(result) metadata.set_hidden_params(logging_obj, model, kwargs) - metadata.set_timing_metrics(start_time, end_time, logging_obj) + metadata.set_timing_metrics(start_time, end_time, logging_obj, include_overhead) metadata.apply() diff --git a/litellm/litellm_core_utils/model_response_utils.py b/litellm/litellm_core_utils/model_response_utils.py index 7bf667164ae..dc4f375daa7 100644 --- a/litellm/litellm_core_utils/model_response_utils.py +++ b/litellm/litellm_core_utils/model_response_utils.py @@ -144,7 +144,6 @@ def _is_choice_non_empty(choice: StreamingChoices) -> bool: # Check model_extra for dynamically added fields on the choice choice_extra_fields: Final[Mapping[str, object]] = choice.model_extra or {} for extra_field_name, extra_field_value in choice_extra_fields.items(): - # Skip certain structural fields that are just default/None placeholders if extra_field_name == "index" and extra_field_value == 0: continue if extra_field_name in {"finish_reason", "logprobs"} and extra_field_value is None: @@ -192,7 +191,6 @@ def _is_delta_non_empty(delta: Delta) -> bool: # Check model_extra for dynamically added fields (this is where Pydantic stores them) delta_extra_fields: Final[Mapping[str, object]] = delta.model_extra or {} for extra_field_value in delta_extra_fields.values(): - # Even structural fields are meaningful if they have actual content if _has_meaningful_content(extra_field_value): return True diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py index f0e5086b660..ff46440ff5c 100644 --- a/litellm/litellm_core_utils/prompt_templates/common_utils.py +++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py @@ -10,6 +10,7 @@ from collections.abc import Iterable, Mapping, Sequence from itertools import groupby from os import PathLike from pathlib import Path +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final, Literal, TypeVar, cast from openai.types.chat.chat_completion_custom_tool_param import ( @@ -204,6 +205,41 @@ def is_non_content_values_set(message: AllMessageValues) -> bool: return any(message.get(key, None) is not None for key in message if key not in ignore_keys) +_IMAGE_CONTENT_PART_TYPES: Final = frozenset({"image_url", "input_image", "image"}) +_IMAGE_SCAN_MAX_DEPTH: Final = 4 + + +def _content_parts_contain_image(parts: Sequence[object]) -> bool: + """Depth-bounded frontier walk over nested content lists, iterative because the repo bans + recursion; an Anthropic tool_result nests its image parts exactly one level down.""" + frontier = parts # rebind-ok: depth-bounded frontier walk + for _ in range(_IMAGE_SCAN_MAX_DEPTH): + if any(isinstance(part, Mapping) and part.get("type") in _IMAGE_CONTENT_PART_TYPES for part in frontier): + return True + frontier = tuple( # rebind-ok: depth-bounded frontier walk + nested + for part in frontier + if isinstance(part, Mapping) + for content in (part.get("content"),) + if isinstance(content, list) + for nested in content + ) + if not frontier: + return False + return False + + +def request_contains_image_content(messages: Sequence[Mapping[str, object]]) -> bool: + """Whether any message carries an image content part, across the dialects that reach + pre-routing hooks untranslated: chat-completions ``image_url``, Responses ``input_image``, + and Anthropic Messages ``image``, including images nested inside ``tool_result`` blocks.""" + return any( + isinstance(content, list) and _content_parts_contain_image(content) + for message in messages + for content in (message.get("content"),) + ) + + def _audio_or_image_in_message_content(message: AllMessageValues) -> bool: """ Checks if message content contains an image or audio @@ -519,10 +555,10 @@ def update_messages_with_model_file_ids( def update_responses_input_with_model_file_ids( - input: Any, + input: object, model_id: str | None = None, model_file_id_mapping: dict[str, dict[str, str]] | None = None, -) -> str | list[dict[str, Any]]: +) -> object: """ Updates responses API input with provider-specific file IDs. File IDs are always inside the content array, not as direct input_file items. @@ -603,8 +639,8 @@ def update_responses_input_with_model_file_ids( def _decode_vector_store_ids_in_tools( - tools: list[dict[str, Any]] | None, -) -> list[dict[str, Any]] | None: + tools: list[dict[str, object]] | None, +) -> list[dict[str, object]] | None: """ Decodes unified (LiteLLM-managed) vector_store_ids in file_search tools to provider-native IDs. Non-unified IDs are passed through unchanged. @@ -656,10 +692,10 @@ def _decode_vector_store_ids_in_tools( def update_responses_tools_with_model_file_ids( - tools: list[dict[str, Any]] | None, + tools: list[dict[str, object]] | None, model_id: str | None = None, model_file_id_mapping: dict[str, dict[str, str]] | None = None, -) -> list[dict[str, Any]] | None: +) -> list[dict[str, object]] | None: """ Updates responses API tools with provider-specific file IDs. @@ -852,7 +888,7 @@ def extract_file_data(file_data: FileTypes) -> ExtractedFileData: # --------------------------------------------------------------------------- -def _estimate_json_bytes(obj: Any) -> int: +def _estimate_json_bytes(obj: object) -> int: """Estimate the JSON-serialised byte size of ``obj`` without materialising JSON. Walks iteratively (no recursion stack risk). @@ -1089,6 +1125,175 @@ def sanitize_input_schema_for_anthropic(input_schema: dict) -> "AnthropicInputSc return AnthropicInputSchema(**filtered) +_TOP_LEVEL_SCHEMA_COMBINATORS: Final = ("allOf", "anyOf", "oneOf") +_OPENAI_REJECTED_TOP_LEVEL_SCHEMA_KEYS: Final = ("enum", "const", "not") +_LOCAL_SCHEMA_REF_PREFIXES: Final = (("#/$defs/", "$defs"), ("#/definitions/", "definitions")) +_MAX_SCHEMA_FLATTEN_DEPTH: Final = 32 +_EMPTY_SCHEMA: Final[Mapping[str, object]] = MappingProxyType({}) + + +def _schema_properties(schema: Mapping[str, object]) -> Mapping[str, object]: + properties: Final = schema.get("properties") + return properties if isinstance(properties, dict) else _EMPTY_SCHEMA + + +def _schema_branches(schema: Mapping[str, object], combinator: str) -> tuple[object, ...]: + branches: Final = schema.get(combinator) + return tuple(branches) if isinstance(branches, list) else () + + +def _schema_required_names(schema: Mapping[str, object]) -> frozenset[str]: + required: Final = schema.get("required") + if not isinstance(required, list): + return frozenset() + return frozenset(name for name in required if isinstance(name, str)) + + +def _combinator_required_names(combinator: str, branches: tuple[Mapping[str, object], ...]) -> frozenset[str]: + branch_names: Final = tuple(_schema_required_names(branch) for branch in branches) + if not branch_names: + return frozenset() + if combinator == "allOf": + return branch_names[0].union(*branch_names[1:]) + return branch_names[0].intersection(*branch_names[1:]) + + +def _resolve_local_schema_ref(root: Mapping[str, object], ref: str) -> Mapping[str, object] | None: + matched: Final = next( + ((prefix, container) for prefix, container in _LOCAL_SCHEMA_REF_PREFIXES if ref.startswith(prefix)), + None, + ) + if matched is None: + return None + prefix, container = matched + definitions: Final = root.get(container) + if not isinstance(definitions, dict): + return None + target: Final = definitions.get(ref[len(prefix) :]) + return target if isinstance(target, dict) else None + + +def _mergeable_branch( + root: Mapping[str, object], + branch: object, + seen_refs: frozenset[str], + depth: int, + expanded_refs: dict[str, Mapping[str, object] | None], # mutable-ok: per-call memo bounding repeated $ref work +) -> Mapping[str, object] | None: + if not isinstance(branch, dict) or depth > _MAX_SCHEMA_FLATTEN_DEPTH: + return None + ref: Final = branch.get("$ref") + if not isinstance(ref, str): + flattened: Final = _flatten_schema_against_root(branch, root, seen_refs, depth, expanded_refs) + if any(combinator in flattened for combinator in _TOP_LEVEL_SCHEMA_COMBINATORS): + return None + return flattened + if ref in expanded_refs: + return expanded_refs[ref] + if ref in seen_refs: + return None + target: Final = _resolve_local_schema_ref(root, ref) + expanded: Final = ( + None + if target is None + else _mergeable_branch(root, target, seen_refs | frozenset((ref,)), depth + 1, expanded_refs) + ) + expanded_refs[ref] = expanded + return expanded + + +def _is_object_schema(schema: Mapping[str, object]) -> bool: + return schema.get("type") == "object" or ("type" not in schema and "properties" in schema) + + +def _flatten_schema_against_root( + schema: Mapping[str, object], + root: Mapping[str, object], + seen_refs: frozenset[str], + depth: int, + expanded_refs: dict[str, Mapping[str, object] | None], # mutable-ok: per-call memo bounding repeated $ref work +) -> Mapping[str, object]: + raw_branch_groups: Final = tuple( + ( + combinator, + tuple( + _mergeable_branch(root, branch, seen_refs, depth + 1, expanded_refs) + for branch in _schema_branches(schema, combinator) + ), + ) + for combinator in _TOP_LEVEL_SCHEMA_COMBINATORS + if isinstance(schema.get(combinator), list) + ) + dropped: Final = ( + *(combinator for combinator, _ in raw_branch_groups), + *(key for key in _OPENAI_REJECTED_TOP_LEVEL_SCHEMA_KEYS if key in schema), + ) + if not dropped: + return schema + + if any(branch is None for _, group in raw_branch_groups for branch in group): + return schema + branch_groups: Final = tuple( + (combinator, tuple(branch for branch in group if branch is not None)) for combinator, group in raw_branch_groups + ) + branches: Final = tuple(branch for _, group in branch_groups for branch in group) + is_object_schema: Final = _is_object_schema(schema) or ( + "type" not in schema and branches != () and all(_is_object_schema(branch) for branch in branches) + ) + if not is_object_schema: + return schema + + merged_properties: Final = { # mutable-ok: tool parameters are JSON dicts + name: value for source in (*reversed(branches), schema) for name, value in _schema_properties(source).items() + } + required_names: Final = _schema_required_names(schema).union( + *(_combinator_required_names(combinator, group) for combinator, group in branch_groups) + ) + kept: Final = MappingProxyType({key: value for key, value in schema.items() if key not in dropped}) + required_update: Final = MappingProxyType({"required": sorted(required_names)}) if required_names else _EMPTY_SCHEMA + return { # mutable-ok: tool parameters are JSON dicts + **kept, + "type": "object", + "properties": merged_properties, + **required_update, + } + + +def flatten_top_level_schema_combinators(schema: Mapping[str, object]) -> Mapping[str, object]: + """Merge top-level ``allOf``/``anyOf``/``oneOf`` branches into an object tool schema. + + OpenAI's function-calling validator rejects tool ``parameters`` carrying + 'oneOf'/'anyOf'/'allOf'/'enum'/'const'/'not' at the top level (nested uses + are accepted), while lenient backends such as the ChatGPT backend Codex + talks to natively accept them, so an MCP tool declaring a top-level union + 400s through LiteLLM. Branch properties merge without clobbering (the + top-level schema wins, then earlier branches); ``required`` becomes the + top-level list plus the intersection of the branch lists for anyOf/oneOf + or their union for allOf. Branches that are local ``$ref``s + (``#/$defs/...`` or ``#/definitions/...``) are resolved first, each ref + at most once per call, and branches that are themselves combinators are + flattened recursively up to a fixed depth; a branch that cannot be fully + merged (a boolean schema, an external or cyclic ``$ref``, a non-object + union, or nesting past the depth cap) leaves the whole schema untouched so + OpenAI's own validation still applies. Non-object schemas pass through + unchanged and the input is never mutated. + """ + return _flatten_schema_against_root(schema, schema, frozenset(), 0, {}) # mutable-ok: fresh per-call $ref memo + + +def tool_with_flattened_parameters(tool: Mapping[str, object]) -> Mapping[str, object]: + function: Final = tool.get("function") + if not isinstance(function, dict): + return tool + parameters: Final = function.get("parameters") + if not isinstance(parameters, dict): + return tool + flattened: Final = flatten_top_level_schema_combinators(parameters) + if flattened is parameters: + return tool + return {**tool, "function": {**function, "parameters": flattened}} # mutable-ok: request tools are JSON dicts + + def _get_image_mime_type_from_url(url: str) -> str | None: """ Get mime type for common image URLs @@ -1787,7 +1992,7 @@ def drop_tool_reference_parts_from_tool_messages( return [_drop_tool_reference_parts(message) for message in messages] # mutable-ok: pipelines mutate message lists -def _attempt_json_repair(s: str) -> Any | None: +def _attempt_json_repair(s: str) -> object | None: """ Attempt to repair truncated JSON produced by LLM tool calls. @@ -1903,7 +2108,7 @@ def parse_tool_call_arguments( raise ValueError(error_message) from original_error -def split_concatenated_json_objects(raw: str) -> list[dict[str, Any]]: +def split_concatenated_json_objects(raw: str) -> list[dict[str, object]]: """ Split a string that contains one or more concatenated JSON objects into a list of parsed dicts. @@ -1939,7 +2144,7 @@ def split_concatenated_json_objects(raw: str) -> list[dict[str, Any]]: return [] decoder: Final = json.JSONDecoder() - results: Final[list[dict[str, Any]]] = [] + results: Final[list[dict[str, object]]] = [] idx = 0 length: Final = len(raw) diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py index 795fb36961e..ba59e3fa997 100644 --- a/litellm/litellm_core_utils/prompt_templates/factory.py +++ b/litellm/litellm_core_utils/prompt_templates/factory.py @@ -1694,6 +1694,18 @@ def convert_function_to_anthropic_tool_invoke( raise e +def _find_server_tool_result( + tool_id: str, + web_search_results: Sequence[object] | None, + tool_results: Sequence[object] | None, +) -> dict[str, object] | None: + candidates: Final = (*(web_search_results or ()), *(tool_results or ())) + return next( + (result for result in candidates if isinstance(result, dict) and result.get("tool_use_id") == tool_id), + None, + ) + + def convert_to_anthropic_tool_invoke( tool_calls: list[ChatCompletionAssistantToolCall], web_search_results: list[Any] | None = None, @@ -1758,32 +1770,22 @@ def convert_to_anthropic_tool_invoke( context="Anthropic tool invoke", ) - # Check if this is a server-side tool (web_search, tool_search, etc.) - # Server tool IDs start with "srvtoolu_" - if tool_id.startswith("srvtoolu_"): - # Create server_tool_use block instead of tool_use - _anthropic_server_tool_use: dict[str, object] = { - "type": "server_tool_use", - "id": tool_id, - "name": tool_name, - "input": tool_input, - } - anthropic_tool_invoke.append(_anthropic_server_tool_use) - - # Add corresponding tool result if available. - # Check both web_search_results (web_search_tool_result / web_fetch_tool_result) - # and tool_results (bash_code_execution_tool_result, etc.) - _all_tool_results: list[Any] = [] - if web_search_results: - _all_tool_results.extend(web_search_results) - if tool_results: - _all_tool_results.extend(tool_results) - for result in _all_tool_results: - if result.get("tool_use_id") == tool_id: - anthropic_tool_invoke.append(result) - break + server_tool_result = ( + _find_server_tool_result(tool_id, web_search_results, tool_results) + if tool_id.startswith("srvtoolu_") + else None + ) + if server_tool_result is not None: + anthropic_tool_invoke.append( + { + "type": "server_tool_use", + "id": tool_id, + "name": tool_name, + "input": tool_input, + } + ) + anthropic_tool_invoke.append(server_tool_result) else: - # Regular tool_use sanitized_tool_id = _sanitize_anthropic_tool_use_id(tool_id) _anthropic_tool_use_param = AnthropicMessagesToolUseParam( type="tool_use", @@ -4955,10 +4957,13 @@ def make_valid_bedrock_tool_name(input_tool_name: str) -> str: def add_cache_point_tool_block(tool: dict, model: str | None = None) -> BedrockToolBlock | None: - from litellm.llms.bedrock.common_utils import is_claude_4_5_on_bedrock + from litellm.llms.bedrock.common_utils import ( + bedrock_model_accepts_cache_points, + is_claude_4_5_on_bedrock, + ) cache_control: Final = tool.get("cache_control", None) - if cache_control is not None: + if cache_control is not None and bedrock_model_accepts_cache_points(model): cache_point: Final = cache_control.get("type", "ephemeral") if cache_point == "ephemeral": cache_point_block: Final[CachePointBlock] = {"type": "default"} diff --git a/litellm/litellm_core_utils/realtime_streaming.py b/litellm/litellm_core_utils/realtime_streaming.py index 9125ed6e70a..8479e108d17 100644 --- a/litellm/litellm_core_utils/realtime_streaming.py +++ b/litellm/litellm_core_utils/realtime_streaming.py @@ -1500,6 +1500,6 @@ class RealTimeStreaming: pass -def client_sent_openai_beta_realtime_header(websocket: Any) -> bool: +def client_sent_openai_beta_realtime_header(websocket: _ScopedWebSocket) -> bool: """True when the client WebSocket includes ``OpenAI-Beta: realtime=v1``.""" return RealTimeStreaming._detect_beta_header(websocket) diff --git a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py index 0e2139d688b..3978a01a5db 100644 --- a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py +++ b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py @@ -73,6 +73,18 @@ class _ContentChunk(TypedDict): choices: Sequence[_ContentChoice] +class _FunctionCallDelta(TypedDict): + function_call: ReadOnly[FunctionCall] + + +class _FunctionCallChoice(TypedDict): + delta: ReadOnly[_FunctionCallDelta] + + +class _FunctionCallChunk(TypedDict): + choices: ReadOnly[Sequence[_FunctionCallChoice]] + + class _AudioDelta(TypedDict, total=False): audio: ChatCompletionAudioDelta | None @@ -588,7 +600,7 @@ class ChunkProcessor: return tool_calls_list - def get_combined_function_call_content(self, function_call_chunks: list[dict[str, Any]]) -> FunctionCall: + def get_combined_function_call_content(self, function_call_chunks: Sequence["_FunctionCallChunk"]) -> FunctionCall: argument_list: Final = [] delta = function_call_chunks[0]["choices"][0]["delta"] function_call = delta.get("function_call", "") diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index 1e0b778d244..480b1921c18 100644 --- a/litellm/litellm_core_utils/streaming_handler.py +++ b/litellm/litellm_core_utils/streaming_handler.py @@ -93,7 +93,7 @@ def print_verbose(print_statement: object): @dataclass(frozen=True, slots=True) class _ProviderChunkParsed: - response_obj: dict[str, Any] + response_obj: dict[str, object] @dataclass(frozen=True, slots=True) @@ -862,6 +862,8 @@ class CustomStreamWrapper: model_response: Final = ModelResponseStream(**args) if self.response_id is not None: model_response.id = self.response_id + elif model_response.id: + self.response_id = model_response.id if self.system_fingerprint is not None: model_response.system_fingerprint = self.system_fingerprint @@ -1288,7 +1290,7 @@ class CustomStreamWrapper: for key, value in anthropic_response_obj["provider_specific_fields"].items(): setattr(model_response, key, value) - response_obj = cast(dict[str, Any], anthropic_response_obj) + response_obj = cast(dict[str, object], anthropic_response_obj) elif self.model == "replicate" or self.custom_llm_provider == "replicate": response_obj = self.handle_replicate_chunk(chunk) completion_obj["content"] = response_obj["text"] @@ -1444,7 +1446,7 @@ class CustomStreamWrapper: if not isinstance(chunk, str): raise ValueError(f"chunk is not a string: {chunk}") response_obj = cast( - dict[str, Any], + dict[str, object], litellm.CodestralTextCompletionConfig()._chunk_parser(chunk), ) completion_obj["content"] = response_obj["text"] @@ -2551,7 +2553,7 @@ def calculate_total_usage(chunks: list[ModelResponse]) -> Usage: prompt_tokens: int = 0 completion_tokens: int = 0 - latest_usage_chunk = None + latest_usage_chunk: Usage | Mapping[str, int] | None = None prompt_tokens_details: PromptTokensDetailsWrapper | None = None completion_tokens_details: CompletionTokensDetailsWrapper | None = None cache_creation_token_details: CacheCreationTokenDetails | None = None diff --git a/litellm/litellm_core_utils/token_counter.py b/litellm/litellm_core_utils/token_counter.py index 256bee7b348..3732ffd734c 100644 --- a/litellm/litellm_core_utils/token_counter.py +++ b/litellm/litellm_core_utils/token_counter.py @@ -4,8 +4,9 @@ import base64 import io import struct from collections.abc import Callable, Iterable, Mapping, Sequence -from typing import Any, Final, Literal, cast +from typing import Final, Literal, cast +import httpx import tiktoken import litellm @@ -171,6 +172,10 @@ def calculate_tiles_needed( return total_tiles +def _unpack_ints(fmt: str, buffer: bytes) -> tuple[int, ...]: + return struct.unpack(fmt, buffer) + + def get_image_type(image_data: bytes) -> str | None: """take an image (really only the first ~100 bytes max are needed) and return 'png' 'gif' 'jpeg' 'webp' 'heic' or None. method added to @@ -210,9 +215,9 @@ def get_image_dimensions( if data.startswith(("http://", "https://")): try: client: Final = _get_httpx_client() - response: Final = safe_get(client, data) + response: Final[httpx.Response] = safe_get(client, data) max_bytes: Final = int(MAX_IMAGE_URL_DOWNLOAD_SIZE_MB * 1024 * 1024) - content_length: Final = response.headers.get("Content-Length") + content_length: Final[str | None] = response.headers.get("Content-Length") if content_length is not None and int(content_length) > max_bytes: pass # skip download; img_data stays None else: @@ -229,10 +234,10 @@ def get_image_dimensions( img_type: Final = get_image_type(img_data) if img_type == "png": - w, h = struct.unpack(">LL", img_data[16:24]) + w, h = _unpack_ints(">LL", img_data[16:24]) return w, h elif img_type == "gif": - w, h = struct.unpack("H", fhandle.read(2))[0] - 2 + size = _unpack_ints(">H", fhandle.read(2))[0] - 2 fhandle.seek(1, 1) - h, w = struct.unpack(">HH", fhandle.read(4)) + h, w = _unpack_ints(">HH", fhandle.read(4)) return w, h elif img_type == "webp": # For WebP, the dimensions are stored at different offsets depending on the format # Check for VP8X (extended format) if img_data[12:16] == b"VP8X": - w = struct.unpack("> 14) & 0x3FFF) + 1 return w, h @@ -420,8 +425,8 @@ def token_counter( def _count_function_call_tokens( key: str, - value: Any, - message: Mapping[str, Any], + value: object, + message: Mapping[str, object], count_function: TokenCounterFunction, ) -> int: """ @@ -587,7 +592,7 @@ def _fix_model_name(model: str) -> str: def _count_image_tokens( - image_url: Any, + image_url: object, use_default_image_token_count: bool, ) -> int: """ @@ -627,7 +632,7 @@ def _count_image_tokens( raise ValueError(f"Invalid image_url type: {type(image_url).__name__}. Expected str or dict with 'url' field.") -def _validate_anthropic_content(content: Mapping[str, Any]) -> type: +def _validate_anthropic_content(content: Mapping[str, object]) -> type: """ Validate and determine which Anthropic TypedDict applies. @@ -642,7 +647,7 @@ def _validate_anthropic_content(content: Mapping[str, Any]) -> type: "tool_result": AnthropicMessagesToolResultParam, } - expected_cls: Final = mapping.get(content_type) + expected_cls: Final = mapping.get(content_type) if isinstance(content_type, str) else None if expected_cls is None: raise ValueError(f"Unknown Anthropic content type: '{content_type}'") @@ -693,8 +698,28 @@ def _count_document_tokens( ) +def _count_file_tokens( + file_value: object, + count_function: TokenCounterFunction, + use_default_image_token_count: bool, +) -> int: + """An OpenAI `file` block is the chat-completions spelling of a document, so it prices like one.""" + if not isinstance(file_value, Mapping): + return 0 + filename: Final = file_value.get("filename") + file_data: Final = file_value.get("file_data") + name_tokens: Final = count_function(filename) if isinstance(filename, str) and filename else 0 + if not isinstance(file_data, str) or not file_data: + return name_tokens + return name_tokens + calculate_img_tokens( + data=file_data, + mode="auto", + use_default_image_token_count=use_default_image_token_count, + ) + + def _count_anthropic_content( - content: Mapping[str, Any], + content: Mapping[str, object], count_function: TokenCounterFunction, use_default_image_token_count: bool, default_token_count: int | None, @@ -709,7 +734,7 @@ def _count_anthropic_content( avoiding hardcoded field names. """ typeddict_cls: Final = _validate_anthropic_content(content) - type_hints: Final = getattr(typeddict_cls, "__annotations__", {}) + type_hints: Final[Mapping[str, object]] = getattr(typeddict_cls, "__annotations__", {}) tokens = 0 # Fields to skip (metadata/identifiers that don't contribute to prompt tokens) @@ -778,6 +803,12 @@ def _count_content_list( use_default_image_token_count, default_token_count, ) + elif c["type"] == "file": + num_tokens += _count_file_tokens( + c.get("file"), + count_function, + use_default_image_token_count, + ) elif c["type"] in ("tool_use", "tool_result"): num_tokens += _count_anthropic_content( c, @@ -807,7 +838,7 @@ def _count_content_list( raise ValueError( f"Invalid content item type: {content_type}. " f"Expected str or dict with 'type' field " - f"(text, image_url, image, document, tool_use, tool_result, thinking, tool_reference)." + f"(text, image_url, image, document, file, tool_use, tool_result, thinking, tool_reference)." ) return num_tokens except Exception as e: diff --git a/litellm/litellm_core_utils/url_utils.py b/litellm/litellm_core_utils/url_utils.py index 125baa4743a..1e43117933d 100644 --- a/litellm/litellm_core_utils/url_utils.py +++ b/litellm/litellm_core_utils/url_utils.py @@ -417,7 +417,7 @@ def _extract_redirect_url(response: httpx.Response, request_url: str) -> str: return str(httpx.URL(request_url).join(location)) -def safe_get(client: Any, url: str, **kwargs: Any) -> Any: +def safe_get(client: Any, url: str, **kwargs: Any) -> httpx.Response: """ Fetch a user-supplied URL with SSRF protection on every redirect hop. @@ -460,7 +460,7 @@ def safe_get(client: Any, url: str, **kwargs: Any) -> Any: raise SSRFError("Too many redirects") -async def async_safe_get(client: Any, url: str, **kwargs: Any) -> Any: +async def async_safe_get(client: Any, url: str, **kwargs: Any) -> httpx.Response: """Async version of safe_get.""" if not getattr(litellm, "user_url_validation", True): kwargs.setdefault("follow_redirects", True) diff --git a/litellm/llms/a2a/chat/guardrail_translation/handler.py b/litellm/llms/a2a/chat/guardrail_translation/handler.py index 1c5ba951942..f1c7451796d 100644 --- a/litellm/llms/a2a/chat/guardrail_translation/handler.py +++ b/litellm/llms/a2a/chat/guardrail_translation/handler.py @@ -11,8 +11,11 @@ A2A Protocol Format: """ import json +from collections.abc import Sequence from typing import TYPE_CHECKING, Any, Final, Optional +from typing_extensions import ReadOnly, TypedDict + from litellm._logging import verbose_proxy_logger from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation from litellm.types.utils import GenericGuardrailAPIInputs @@ -23,6 +26,13 @@ if TYPE_CHECKING: from litellm.proxy._types import UserAPIKeyAuth +class _A2ATextPart(TypedDict, total=False): + """The subset of an A2A message part this handler reads text from.""" + + kind: ReadOnly[str] + text: ReadOnly[str] + + class A2AGuardrailHandler(BaseTranslation): """ Handler for processing A2A Protocol messages with guardrails. @@ -41,7 +51,7 @@ class A2AGuardrailHandler(BaseTranslation): data: dict, guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, - ) -> Any: + ) -> dict: """ Process A2A input messages by applying guardrails to text content. @@ -214,12 +224,12 @@ class A2AGuardrailHandler(BaseTranslation): async def process_output_streaming_response( self, - responses_so_far: list[Any], + responses_so_far: list[object], guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, user_api_key_dict: Optional["UserAPIKeyAuth"] = None, request_data: dict | None = None, - ) -> list[Any]: + ) -> list[object]: """ Process A2A streaming output by applying guardrails to accumulated text. @@ -305,11 +315,12 @@ class A2AGuardrailHandler(BaseTranslation): def _parse_streaming_responses( self, - responses_so_far: list[Any], - ) -> tuple[list[dict[str, Any] | None], list[tuple[int, dict[str, Any]]]]: + responses_so_far: list[object], + ) -> tuple[list[dict[str, object] | None], list[tuple[int, dict[str, object]]]]: """Parse JSON-RPC items, returning aligned parsed list and valid entries.""" - parsed: Final[list[dict[str, Any] | None]] = [None] * len(responses_so_far) + parsed: Final[list[dict[str, object] | None]] = [None] * len(responses_so_far) for i, item in enumerate(responses_so_far): + obj: dict[str, object] if isinstance(item, dict): obj = item elif isinstance(item, str): @@ -326,7 +337,7 @@ class A2AGuardrailHandler(BaseTranslation): def _collect_text_from_parsed_chunks( self, - valid_parsed: list[tuple[int, dict[str, Any]]], + valid_parsed: list[tuple[int, dict[str, object]]], ) -> tuple[str, list[int]]: """Collect text from parsed chunks, returning combined text and indices.""" from litellm.llms.a2a.common_utils import extract_text_from_a2a_response @@ -411,7 +422,7 @@ class A2AGuardrailHandler(BaseTranslation): def _extract_texts_from_parts( self, - parts: list[dict[str, Any]], + parts: Sequence[_A2ATextPart], path: tuple[str, ...], texts_to_check: list[str], task_mappings: list[tuple[tuple[str, ...], int]], diff --git a/litellm/llms/anthropic/batches/transformation.py b/litellm/llms/anthropic/batches/transformation.py index 6b39adc511e..4f4d39f09b0 100644 --- a/litellm/llms/anthropic/batches/transformation.py +++ b/litellm/llms/anthropic/batches/transformation.py @@ -1,9 +1,11 @@ import json import time +from collections.abc import Mapping from typing import TYPE_CHECKING, Any, Final, Literal, cast import httpx from httpx import Headers, Response +from typing_extensions import ReadOnly, TypedDict from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.llms.base_llm.batches.transformation import BaseBatchesConfig @@ -21,6 +23,29 @@ else: LoggingClass = Any +class AnthropicBatchRequestCounts(TypedDict, total=False): + """The ``request_counts`` object of an Anthropic Message Batch.""" + + processing: ReadOnly[int] + succeeded: ReadOnly[int] + errored: ReadOnly[int] + canceled: ReadOnly[int] + expired: ReadOnly[int] + + +class AnthropicMessageBatch(TypedDict, total=False): + """The fields of an Anthropic Message Batch that map onto an OpenAI Batch.""" + + id: ReadOnly[str] + processing_status: ReadOnly[str] + created_at: ReadOnly[str | None] + ended_at: ReadOnly[str | None] + expires_at: ReadOnly[str | None] + cancel_initiated_at: ReadOnly[str | None] + archived_at: ReadOnly[str | None] + request_counts: ReadOnly[AnthropicBatchRequestCounts] + + class AnthropicBatchesConfig(BaseBatchesConfig): def __init__(self): from ..chat.transformation import AnthropicConfig @@ -85,7 +110,7 @@ class AnthropicBatchesConfig(BaseBatchesConfig): create_batch_data: CreateBatchRequest, optional_params: dict, litellm_params: dict, - ) -> bytes | str | dict[str, Any]: + ) -> bytes | str | dict[str, object]: """ Transform the batch creation request to Anthropic format. @@ -135,7 +160,7 @@ class AnthropicBatchesConfig(BaseBatchesConfig): batch_id: str, optional_params: dict, litellm_params: dict, - ) -> bytes | str | dict[str, Any]: + ) -> bytes | str | dict[str, object]: """ Transform batch retrieval request for Anthropic. @@ -154,7 +179,7 @@ class AnthropicBatchesConfig(BaseBatchesConfig): ) -> LiteLLMBatch: """Transform Anthropic MessageBatch retrieval response to LiteLLM format.""" try: - response_data: Final = raw_response.json() + response_data: Final[AnthropicMessageBatch] = raw_response.json() except Exception as e: raise ValueError(f"Failed to parse Anthropic batch response: {e}") @@ -163,18 +188,20 @@ class AnthropicBatchesConfig(BaseBatchesConfig): processing_status: Final = response_data.get("processing_status", "in_progress") # Map Anthropic processing_status to OpenAI status - status_mapping: dict[ - str, - Literal[ - "validating", - "failed", - "in_progress", - "finalizing", - "completed", - "expired", - "cancelling", - "cancelled", - ], + status_mapping: Final[ + Mapping[ + str, + Literal[ + "validating", + "failed", + "in_progress", + "finalizing", + "completed", + "expired", + "cancelling", + "cancelled", + ], + ] ] = { "in_progress": "in_progress", "canceling": "cancelling", @@ -281,7 +308,7 @@ class AnthropicBatchesConfig(BaseBatchesConfig): if not line: continue try: - response_json = json.loads(line) + response_json: Mapping[str, Mapping[str, dict[str, object]]] = json.loads(line) # Update model_response with the parsed JSON completion_response = response_json["result"]["message"] transformed_response = self.anthropic_chat_config.transform_parsed_response( diff --git a/litellm/llms/anthropic/chat/guardrail_translation/handler.py b/litellm/llms/anthropic/chat/guardrail_translation/handler.py index b9ca18c7843..c7d12e5cf3a 100644 --- a/litellm/llms/anthropic/chat/guardrail_translation/handler.py +++ b/litellm/llms/anthropic/chat/guardrail_translation/handler.py @@ -16,9 +16,9 @@ import json from collections.abc import Mapping, Sequence from copy import deepcopy from dataclasses import dataclass -from typing import TYPE_CHECKING, Any, Final, cast +from typing import TYPE_CHECKING, Any, Final, Protocol, cast, overload, runtime_checkable -from typing_extensions import assert_never +from typing_extensions import ReadOnly, TypedDict, assert_never from litellm._logging import verbose_proxy_logger from litellm.llms.anthropic.chat.transformation import AnthropicConfig @@ -58,6 +58,8 @@ from litellm.types.utils import ( ) if TYPE_CHECKING: + from fastapi import HTTPException + from litellm.integrations.custom_guardrail import ( CustomGuardrail, ModifyResponseException, @@ -98,6 +100,38 @@ InputWriteBackTarget = ( ) +def _as_str_mapping(value: Mapping[str, object]) -> Mapping[str, object]: + return value + + +def _content_block_at(blocks: Sequence[object], index: int) -> object: + return blocks[index] + + +@runtime_checkable +class _ModelDumpBlock(Protocol): + def model_dump(self) -> Mapping[str, object]: ... + + +@runtime_checkable +class _TextAttrBlock(Protocol): + text: str + + +class _WritableMessage(Protocol): + @overload + def get(self, key: str, /) -> object | None: ... + + @overload + def get(self, key: str, default: object, /) -> object: ... + + def __setitem__(self, key: str, value: object, /) -> None: ... + + +def _as_writable(value: _WritableMessage) -> _WritableMessage: + return value + + @dataclass(frozen=True, slots=True) class ScannedText: text: str @@ -113,6 +147,16 @@ class ExtractedInput: EMPTY_EXTRACTED_INPUT: Final = ExtractedInput(scanned=(), images=()) +class _AnthropicSSEDelta(TypedDict, total=False): + type: ReadOnly[str] + text: ReadOnly[str] + stop_reason: ReadOnly[str | None] + + +class _AnthropicSSEEvent(TypedDict, total=False): + delta: ReadOnly[_AnthropicSSEDelta] + + class AnthropicMessagesHandler(BaseTranslation): """Process Anthropic messages with guardrails. @@ -126,7 +170,7 @@ class AnthropicMessagesHandler(BaseTranslation): @staticmethod def _build_streaming_usage_response( - responses_so_far: list[object], + responses_so_far: Sequence[object], request_data: dict | None, ) -> ModelResponse | None: chunks: Final = tuple(response for response in responses_so_far if isinstance(response, (str, bytes))) @@ -144,7 +188,7 @@ class AnthropicMessagesHandler(BaseTranslation): self, exc: "ModifyResponseException", stream_started: bool = False, - responses_so_far: list[object] | None = None, + responses_so_far: Sequence[object] | None = None, ) -> list[bytes]: """ Build an Anthropic SSE sequence delivering the guardrail block message @@ -162,9 +206,22 @@ class AnthropicMessagesHandler(BaseTranslation): would make Anthropic clients reject the stream. """ if stream_started: - return self._block_continuation_chunks(exc, responses_so_far or []) + return list(self._block_continuation_chunks(exc, responses_so_far or [])) return self._standalone_block_chunks(exc) + def build_stream_error_items( + self, + exc: "HTTPException", + responses_so_far: Sequence[Any] | None = None, + ) -> Sequence[Any] | None: + from litellm.proxy.common_request_processing import ( + serialize_http_exception_detail, + ) + from litellm.proxy.guardrails.anthropic_sse import anthropic_sse_error_frames + + message, _ = serialize_http_exception_detail(exc.detail) + return tuple(anthropic_sse_error_frames(message)) + def _standalone_block_chunks(self, exc: "ModifyResponseException") -> list[bytes]: import uuid @@ -187,7 +244,9 @@ class AnthropicMessagesHandler(BaseTranslation): ) return list(FakeAnthropicMessagesStreamIterator(response=block_response)) - def _block_continuation_chunks(self, exc: "ModifyResponseException", responses_so_far: list[object]) -> list[bytes]: + def _block_continuation_chunks( + self, exc: "ModifyResponseException", responses_so_far: Sequence[object] + ) -> Sequence[bytes]: """Continue an already-started message: close the open content block, append the block message as a new text block, then end the message -- without a second message_start.""" @@ -199,7 +258,7 @@ class AnthropicMessagesHandler(BaseTranslation): def _sse(event_type: str, payload: dict) -> bytes: return f"event: {event_type}\ndata: {json.dumps(payload)}\n\n".encode() - output_tokens: Final = blocked_response_usage(getattr(exc, "original_response", None))["output_tokens"] + output_tokens: Final = blocked_response_usage(getattr(exc, "original_response", None)).get("output_tokens", 0) open_index, max_index = self._content_block_state(responses_so_far) new_index: Final = (max_index + 1) if max_index is not None else 0 chunks: list[bytes] = [] @@ -237,7 +296,7 @@ class AnthropicMessagesHandler(BaseTranslation): @staticmethod def _content_block_state( - responses_so_far: list[object], + responses_so_far: Sequence[object], ) -> tuple[int | None, int | None]: """From the SSE chunks already sent to the client, return (open content-block index or None, highest content-block index seen or None). @@ -263,7 +322,20 @@ class AnthropicMessagesHandler(BaseTranslation): return open_index, max_index @staticmethod - def _iter_sse_events(item: object) -> list[dict[str, object]]: + def _parse_sse_data_line(raw_line: str) -> tuple[Mapping[str, object], ...]: + line: Final = raw_line.strip() + if not line.startswith("data:"): + return () + try: + parsed: Final[object] = json.loads(line[len("data:") :].strip()) + except json.JSONDecodeError: + return () + if not isinstance(parsed, dict): + return () + return (_as_str_mapping(parsed),) + + @staticmethod + def _iter_sse_events(item: object) -> Sequence[Mapping[str, object]]: """Yield the event-data dicts in one stream chunk. Handles both formats this stream can carry (see @@ -271,24 +343,15 @@ class AnthropicMessagesHandler(BaseTranslation): several events separated by a blank line -- and an already-parsed event ``dict``.""" if isinstance(item, dict): - return [item] + return (_as_str_mapping(item),) if not isinstance(item, (bytes, bytearray)): - return [] - events: Final[list[dict[str, object]]] = [] - for block in item.decode("utf-8", errors="replace").split("\n\n"): - for line in block.split("\n"): - line = line.strip() - if not line.startswith("data:"): - continue - try: - parsed: str | int | float | bool | None | Sequence[object] | Mapping[str, object] = json.loads( - line[len("data:") :].strip() - ) - except json.JSONDecodeError: - continue - if isinstance(parsed, dict): - events.append(parsed) - return events + return () + return tuple( + event + for block in item.decode("utf-8", errors="replace").split("\n\n") + for line in block.split("\n") + for event in AnthropicMessagesHandler._parse_sse_data_line(line) + ) def _translate_to_openai(self, data: dict) -> ChatCompletionRequest: """Translate Anthropic request to OpenAI chat completion format.""" @@ -321,7 +384,7 @@ class AnthropicMessagesHandler(BaseTranslation): data: dict, guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: "LiteLLMLoggingObj | None" = None, - ) -> Any: + ) -> Mapping[str, object]: """ Process input messages by applying guardrails to text content. """ @@ -481,7 +544,7 @@ class AnthropicMessagesHandler(BaseTranslation): @staticmethod def _openai_system_message_to_anthropic( - message: dict[str, object], + message: Mapping[str, object], ) -> dict[str, object] | None: # mutable-ok: API message payload """Convert an OpenAI system message to the client's Anthropic-shaped entry.""" content: Final = message.get("content") @@ -561,7 +624,7 @@ class AnthropicMessagesHandler(BaseTranslation): @staticmethod def _defer_systems_inside_tool_exchanges( - structured_messages: list, # mutable-ok: API message payload + structured_messages: Sequence[Mapping[str, object]], ) -> list: """Hold a system row until the tool exchange around it completes so the call/result pair converts together.""" from litellm.litellm_core_utils.prompt_templates.factory import group_tool_exchanges @@ -755,7 +818,7 @@ class AnthropicMessagesHandler(BaseTranslation): if scan_only_tool_results: return EMPTY_EXTRACTED_INPUT - text_str: Final = content_item.get("text", None) + text_str: Final[str | None] = content_item.get("text") return ExtractedInput( scanned=( () if text_str is None else (ScannedText(text_str, ContentBlockTextTarget(msg_idx, content_idx)),) @@ -796,16 +859,32 @@ class AnthropicMessagesHandler(BaseTranslation): @staticmethod def _image_sources(block: Mapping[str, object]) -> tuple[str, ...]: + """Normalize an Anthropic image block into strings a guardrail can read. + + base64 becomes a data URI so the format travels with the payload, which is what + the OpenAI path already puts in this field. A file source yields nothing: those + bytes live behind the Files API and this extractor has no client to fetch them. + """ source: Final = block.get("source") if not isinstance(source, Mapping): return () - # Could be base64 or url + + source_type: Final = source.get("type") + if source_type == "url": + url: Final = source.get("url") + return (url,) if isinstance(url, str) and url else () + data: Final = source.get("data") - return (data,) if data else () + if not isinstance(data, str) or not data: + return () + media_type: Final = source.get("media_type") + if isinstance(media_type, str) and media_type: + return (f"data:{media_type};base64,{data}",) + return (data,) async def _apply_guardrail_responses_to_input( self, - messages: list[dict[str, object]], + messages: Sequence[_WritableMessage], responses: list[str], scanned: tuple[ScannedText, ...], ) -> None: @@ -931,7 +1010,7 @@ class AnthropicMessagesHandler(BaseTranslation): litellm_logging_obj: "LiteLLMLoggingObj | None" = None, user_api_key_dict: "UserAPIKeyAuth | None" = None, request_data: dict | None = None, - ) -> list[Any]: + ) -> Sequence[object]: """ Process output streaming response by applying guardrails to text content. @@ -1027,7 +1106,7 @@ class AnthropicMessagesHandler(BaseTranslation): return request_data @staticmethod - def _get_response_content(response: object) -> list[Any]: + def _get_response_content(response: object) -> Sequence[object]: """Extract content list from a dict or object response.""" if isinstance(response, dict): return response.get("content", []) or [] @@ -1037,7 +1116,7 @@ class AnthropicMessagesHandler(BaseTranslation): def _extract_from_content_blocks( self, - response_content: list[Any], + response_content: Sequence[object], texts_to_check: list[str], images_to_check: list[str], task_mappings: list[tuple[int, int | None]], @@ -1045,21 +1124,10 @@ class AnthropicMessagesHandler(BaseTranslation): ) -> None: """Extract text, images, and tool calls from content blocks.""" for content_idx, content_block in enumerate(response_content): - block_dict: dict[str, object] = {} - if isinstance(content_block, dict): - block_type = content_block.get("type") - block_dict = cast(dict[str, object], content_block) - elif hasattr(content_block, "type"): - block_type = getattr(content_block, "type", None) - if hasattr(content_block, "model_dump"): - block_dict = content_block.model_dump() - else: - block_dict = { - "type": block_type, - "text": getattr(content_block, "text", None), - } - else: + fields = self._output_block_fields(content_block) + if fields is None: continue + block_type, block_dict = fields if block_type in ["text", "tool_use"]: self._extract_output_text_and_images( @@ -1071,6 +1139,21 @@ class AnthropicMessagesHandler(BaseTranslation): tool_calls_to_check=tool_calls_to_check, ) + @staticmethod + def _output_block_fields(content_block: object) -> "tuple[object, Mapping[str, object]] | None": + if isinstance(content_block, dict): + block_dict: Final = _as_str_mapping(content_block) + return block_dict.get("type"), block_dict + if not hasattr(content_block, "type"): + return None + block_type: Final = getattr(content_block, "type", None) + if isinstance(content_block, _ModelDumpBlock): + return block_type, content_block.model_dump() + return block_type, { + "type": block_type, + "text": getattr(content_block, "text", None), + } + @staticmethod def _build_guardrail_inputs( texts_to_check: list[str], @@ -1093,7 +1176,7 @@ class AnthropicMessagesHandler(BaseTranslation): inputs["model"] = response_model return inputs - def get_streaming_string_so_far(self, responses_so_far: list[Any]) -> str: + def get_streaming_string_so_far(self, responses_so_far: Sequence[object]) -> str: """ Parse streaming responses and extract accumulated text content. @@ -1164,8 +1247,8 @@ class AnthropicMessagesHandler(BaseTranslation): # Only process content_block_delta events if event_type == "content_block_delta" and data_line: try: - data = json.loads(data_line) - delta = data.get("delta", {}) + data: _AnthropicSSEEvent = json.loads(data_line) + delta: _AnthropicSSEDelta = data.get("delta", {}) if delta.get("type") == "text_delta": text += delta.get("text", "") except json.JSONDecodeError: @@ -1176,7 +1259,7 @@ class AnthropicMessagesHandler(BaseTranslation): return text - def _check_streaming_has_ended(self, responses_so_far: list[Any]) -> bool: + def _check_streaming_has_ended(self, responses_so_far: Sequence[object]) -> bool: """ Check if streaming response has ended by looking for non-null stop_reason. @@ -1227,9 +1310,9 @@ class AnthropicMessagesHandler(BaseTranslation): # Check for message_delta event with stop_reason if event_type == "message_delta" and data_line: try: - data = json.loads(data_line) - delta = data.get("delta", {}) - stop_reason = delta.get("stop_reason") + data: _AnthropicSSEEvent = json.loads(data_line) + delta: _AnthropicSSEDelta = data.get("delta", {}) + stop_reason: str | None = delta.get("stop_reason") if stop_reason is not None: return True except json.JSONDecodeError: @@ -1271,7 +1354,7 @@ class AnthropicMessagesHandler(BaseTranslation): def _extract_output_text_and_images( self, - content_block: dict[str, object], + content_block: Mapping[str, object], content_idx: int, texts_to_check: list[str], images_to_check: list[str], @@ -1294,7 +1377,7 @@ class AnthropicMessagesHandler(BaseTranslation): task_mappings.append((content_idx, None)) # Extract tool calls - elif content_type == "tool_use": + elif content_type == "tool_use" and isinstance(content_block, dict): tool_call: Final = AnthropicConfig.convert_tool_use_to_openai_format( anthropic_tool_content=content_block, index=content_idx, @@ -1319,7 +1402,7 @@ class AnthropicMessagesHandler(BaseTranslation): content_idx = cast(int, mapping[0]) # Handle both dict and object responses - response_content: list[Any] = [] + response_content: Sequence[object] = [] if isinstance(response, dict): response_content = response.get("content", []) or [] elif hasattr(response, "content"): @@ -1335,14 +1418,15 @@ class AnthropicMessagesHandler(BaseTranslation): if content_idx >= len(response_content): continue - content_block = response_content[content_idx] + content_block = _content_block_at(response_content, content_idx) # Verify it's a text block and update the text field # Handle both dict and Pydantic object content blocks if isinstance(content_block, dict): - if content_block.get("type") == "text": - cast(dict[str, object], content_block)["text"] = guardrail_response + block = _as_writable(content_block) + if block.get("type") == "text": + block["text"] = guardrail_response elif hasattr(content_block, "type") and getattr(content_block, "type", None) == "text": # Update Pydantic object's text attribute - if hasattr(content_block, "text"): + if isinstance(content_block, _TextAttrBlock): content_block.text = guardrail_response diff --git a/litellm/llms/anthropic/chat/handler.py b/litellm/llms/anthropic/chat/handler.py index cd47cdd57d6..c82be07a5c5 100644 --- a/litellm/llms/anthropic/chat/handler.py +++ b/litellm/llms/anthropic/chat/handler.py @@ -66,6 +66,10 @@ if TYPE_CHECKING: from litellm.llms.base_llm.chat.transformation import BaseConfig +def _loads_stream_chunk(payload: str) -> dict[str, object]: + return json.loads(payload) + + async def make_call( client: AsyncHTTPHandler | None, api_base: str, @@ -78,7 +82,7 @@ async def make_call( json_mode: bool, speed: str | None = None, tool_name_reverse_map: dict[str, str] | None = None, -) -> tuple[Any, httpx.Headers]: +) -> tuple["ModelResponseIterator", httpx.Headers]: if client is None: client = litellm.module_level_aclient @@ -93,7 +97,7 @@ async def make_call( ) except httpx.HTTPStatusError as e: error_headers = getattr(e, "headers", None) - error_response: Final = getattr(e, "response", None) + error_response: Final[object] = getattr(e, "response", None) if error_headers is None and error_response: error_headers = getattr(error_response, "headers", None) raise AnthropicError( @@ -138,7 +142,7 @@ def make_sync_call( json_mode: bool, speed: str | None = None, tool_name_reverse_map: dict[str, str] | None = None, -) -> tuple[Any, httpx.Headers]: +) -> tuple["ModelResponseIterator", httpx.Headers]: if client is None: client = litellm.module_level_client # re-use a module level client @@ -153,7 +157,7 @@ def make_sync_call( ) except httpx.HTTPStatusError as e: error_headers = getattr(e, "headers", None) - error_response: Final = getattr(e, "response", None) + error_response: Final[object] = getattr(e, "response", None) if error_headers is None and error_response: error_headers = getattr(error_response, "headers", None) raise AnthropicError( @@ -292,7 +296,7 @@ class AnthropicChatCompletion(BaseLLM): status_code: Final = getattr(e, "status_code", 500) error_headers = getattr(e, "headers", None) error_text = getattr(e, "text", str(e)) - error_response: Final = getattr(e, "response", None) + error_response: Final[object] = getattr(e, "response", None) if error_headers is None and error_response: error_headers = getattr(error_response, "headers", None) if error_response and hasattr(error_response, "text"): @@ -593,7 +597,7 @@ class AnthropicChatCompletion(BaseLLM): status_code: Final = getattr(e, "status_code", 500) error_headers = getattr(e, "headers", None) error_text = getattr(e, "text", str(e)) - error_response: Final = getattr(e, "response", None) + error_response: Final[object] = getattr(e, "response", None) if error_headers is None and error_response: error_headers = getattr(error_response, "headers", None) if error_response and hasattr(error_response, "text"): @@ -664,10 +668,10 @@ class ModelResponseIterator: # Accumulate web_search_tool_result blocks for multi-turn reconstruction # See: https://github.com/BerriAI/litellm/issues/17737 - self.web_search_results: list[dict[str, Any]] = [] + self.web_search_results: list[dict[str, object]] = [] # Accumulate compaction blocks for multi-turn reconstruction - self.compaction_blocks: list[dict[str, Any]] = [] + self.compaction_blocks: list[dict[str, object]] = [] # Accumulate streamed thinking text so final usage can split reasoning # tokens from regular output tokens. @@ -727,7 +731,7 @@ class ModelResponseIterator: str, ChatCompletionToolCallChunk | None, list[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock], - dict[str, Any], + dict[str, object], str | None, ]: """ @@ -735,7 +739,7 @@ class ModelResponseIterator: """ text = "" tool_use: ChatCompletionToolCallChunk | None = None - provider_specific_fields: Final = {} + provider_specific_fields: Final[dict[str, object]] = {} reasoning_content: str | None = None content_block: Final = ContentBlockDelta(**chunk) thinking_blocks: list[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock] = [] @@ -809,8 +813,8 @@ class ModelResponseIterator: def _handle_redacted_thinking_content( self, content_block_start: ContentBlockStart, - provider_specific_fields: dict[str, Any], - ) -> tuple[list[ChatCompletionRedactedThinkingBlock], dict[str, Any]]: + provider_specific_fields: dict[str, object], + ) -> tuple[list[ChatCompletionRedactedThinkingBlock], dict[str, object]]: """ Handle the redacted thinking content """ @@ -878,7 +882,7 @@ class ModelResponseIterator: tool_use: ChatCompletionToolCallChunk | None = None finish_reason = "" usage: Usage | None = None - provider_specific_fields: dict[str, Any] = {} + provider_specific_fields: dict[str, object] = {} reasoning_content: str | None = None thinking_blocks: list[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock] | None = None @@ -1212,7 +1216,7 @@ class ModelResponseIterator: # Try to parse as valid JSON first try: - data_json: Final = json.loads(data_str) + data_json: Final = _loads_stream_chunk(data_str) return self.chunk_parser(chunk=data_json) except json.JSONDecodeError: # Switch to accumulation mode and start accumulating @@ -1330,7 +1334,7 @@ class ModelResponseIterator: str_line = str_line[index:] if str_line.startswith("data:"): - data_json: Final = json.loads(str_line[5:]) + data_json: Final = _loads_stream_chunk(str_line[5:]) return self.chunk_parser(chunk=data_json) else: return ModelResponseStream(id=self.response_id) diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index 47116a8f8fb..aa805ccea71 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -7,6 +7,7 @@ from typing import TYPE_CHECKING, Any, Final, NoReturn, cast import httpx from pydantic import ValidationError +from typing_extensions import ReadOnly, TypedDict import litellm from litellm.constants import ( @@ -125,7 +126,25 @@ else: _ANTHROPIC_TOOL_NAME_INVALID_CHARS: Final = re.compile(r"[^a-zA-Z0-9_-]") _ANTHROPIC_TOOL_NAME_MAX_LEN: Final = 128 -_ENUM_TYPE_CHECKS: Final[Mapping[str, Callable[[Any], bool]]] = MappingProxyType( + +class _AnthropicUsageIteration(TypedDict, total=False): + """One entry of the ``usage.iterations`` array on an Anthropic response.""" + + input_tokens: ReadOnly[int | None] + output_tokens: ReadOnly[int | None] + cache_creation_input_tokens: ReadOnly[int | None] + cache_read_input_tokens: ReadOnly[int | None] + + +class _AnthropicToolResultBlock(TypedDict, total=False): + """A ``*_tool_result`` content block on an Anthropic response.""" + + type: ReadOnly[str] + tool_use_id: ReadOnly[str] + content: ReadOnly[object] + + +_ENUM_TYPE_CHECKS: Final[Mapping[str, Callable[[object], bool]]] = MappingProxyType( { "null": lambda v: v is None, "boolean": lambda v: isinstance(v, bool), @@ -440,7 +459,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): optional_params.pop("speed", None) @staticmethod - def _raise_invalid_reasoning_effort(model: str, value: Any, llm_provider: str) -> NoReturn: + def _raise_invalid_reasoning_effort(model: str, value: object, llm_provider: str) -> NoReturn: """Raise a ``BadRequestError`` for an unrecognised ``reasoning_effort``. Args: @@ -1268,7 +1287,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): ) @staticmethod - def _cap_thinking_budget_to_max_tokens( + def cap_thinking_budget_to_max_tokens( thinking: AnthropicThinkingParam, max_tokens: int | None ) -> AnthropicThinkingParam | None: """Cap a legacy ``thinking.budget_tokens`` below ``max_tokens`` (Anthropic @@ -1466,7 +1485,9 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): ) if _tool_choice is not None: - optional_params["tool_choice"] = _tool_choice + optional_params["tool_choice"] = AnthropicConfig._apply_forced_tool_choice( + model=model, tool_choice=_tool_choice, drop_params=drop_params + ) elif param == "stream" and value is True: optional_params["stream"] = value elif param == "stop" and (isinstance(value, str) or isinstance(value, list)): @@ -1495,7 +1516,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): _tool = self.map_response_format_to_anthropic_tool(value, optional_params, is_thinking_enabled) if _tool is None: continue - if not is_thinking_enabled: + if not is_thinking_enabled and not AnthropicModelInfo.forced_tool_use_unsupported(model): _tool_choice = { "name": RESPONSE_FORMAT_TOOL_NAME, "type": "tool", @@ -1530,7 +1551,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): llm_provider=self._resolved_provider, ) capped_thinking = ( - AnthropicConfig._cap_thinking_budget_to_max_tokens(legacy_thinking, max_tokens) + AnthropicConfig.cap_thinking_budget_to_max_tokens(legacy_thinking, max_tokens) if legacy_thinking is not None else None ) @@ -1992,19 +2013,35 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): return data def _apply_output_config(self, data: dict, model: str, optional_params: dict) -> None: - """Validate and apply output_config to the request data.""" + """Validate and apply output_config to the request data. + + The ``drop_params`` gate here is an effort gate: ``format`` is a + structured-output field, not an effort field, so it survives the drop + and is vetted where it is consumed (the map's + ``supports_native_structured_output`` flag on emission paths). + """ if "output_config" not in optional_params: return output_config: Final = optional_params.get("output_config") if not output_config or not isinstance(output_config, dict): return - if litellm.drop_params is True and not self._model_supports_effort_param(model, self._resolved_provider): + if ( + litellm.drop_params is True + and any(key != "format" for key in output_config) + and not self._model_supports_effort_param(model, self._resolved_provider) + ): litellm.verbose_logger.warning( DROP_UNSUPPORTED_OUTPUT_CONFIG_WARNING, model, ) - optional_params.pop("output_config", None) - data.pop("output_config", None) + preserved_format: Final = output_config.get("format") + if preserved_format is None: + optional_params.pop("output_config", None) + data.pop("output_config", None) + return + format_only: Final = {"format": preserved_format} # mutable-ok: json body + optional_params["output_config"] = format_only # rebind-ok: out-param store + data["output_config"] = format_only # rebind-ok: out-param store return effort: Final = output_config.get("effort") valid_efforts: Final = ["high", "medium", "low", "xhigh", "max"] @@ -2059,22 +2096,22 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): self, completion_response: dict ) -> tuple[ str, - list[Any] | None, + list[object] | None, list[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock] | None, str | None, list[ChatCompletionToolCallChunk], - list[Any] | None, - list[Any] | None, - list[Any] | None, + list[object] | None, + list[_AnthropicToolResultBlock] | None, + list[object] | None, ]: text_content = "" - citations: list[Any] | None = None + citations: list[object] | None = None thinking_blocks: list[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock] | None = None reasoning_content: str | None = None tool_calls: Final[list[ChatCompletionToolCallChunk]] = [] - web_search_results: list[Any] | None = None - tool_results: list[Any] | None = None - compaction_blocks: list[Any] | None = None + web_search_results: list[object] | None = None + tool_results: list[_AnthropicToolResultBlock] | None = None + compaction_blocks: list[object] | None = None for idx, content in enumerate(completion_response["content"]): if content["type"] == "text": text_content += content["text"] @@ -2284,7 +2321,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): raw_speed: Final = _usage.get("speed") resolved_speed: Final = raw_speed if isinstance(raw_speed, str) else speed - iterations: Final[list[Any] | None] = _usage.get("iterations") + iterations: Final[Sequence[_AnthropicUsageIteration] | None] = _usage.get("iterations") if iterations: prompt_tokens = sum(it.get("input_tokens", 0) or 0 for it in iterations) completion_tokens = sum(it.get("output_tokens", 0) or 0 for it in iterations) @@ -2377,7 +2414,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): def _build_code_interpreter_results( self, - tool_results: list[Any], + tool_results: Sequence[_AnthropicToolResultBlock], code_by_id: dict[str, str], container_id: str | None, ) -> list[OutputCodeInterpreterCall]: @@ -2403,11 +2440,11 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): def _build_provider_specific_fields( self, completion_response: dict, - citations: list[Any] | None, + citations: Sequence[object] | None, thinking_blocks: list[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock] | None, - web_search_results: list[Any] | None, - tool_results: list[Any] | None, - compaction_blocks: list[Any] | None, + web_search_results: Sequence[object] | None, + tool_results: Sequence[_AnthropicToolResultBlock] | None, + compaction_blocks: Sequence[object] | None, tool_calls: list[ChatCompletionToolCallChunk], ) -> dict[str, Any]: provider_specific_fields: Final[dict[str, Any]] = { diff --git a/litellm/llms/anthropic/common_utils.py b/litellm/llms/anthropic/common_utils.py index 1ef14362601..b5bfb32c0c6 100644 --- a/litellm/llms/anthropic/common_utils.py +++ b/litellm/llms/anthropic/common_utils.py @@ -28,10 +28,15 @@ from litellm.types.llms.anthropic import ( ANTHROPIC_OAUTH_TOKEN_PREFIX, AllAnthropicToolsValues, AnthropicMcpServerTool, + AnthropicMessagesToolChoice, ) from litellm.types.llms.openai import AllMessageValues from litellm.types.proxy.model_listing import ModelInfoResponse +DROP_FORCED_TOOL_CHOICE_WARNING: Final = ( + "Downgrading forced tool_choice to 'auto' for model=%s (drop_params=True): this model rejects tool_choice type " + "'any'/'tool' with a 400 because thinking is always on and a forced call would skip it." +) DROP_DISABLED_THINKING_WARNING: Final = ( "Dropping `thinking={'type': 'disabled'}` for model=%s: thinking is always on for this model and cannot be " "disabled (the alternative is a provider 400). The model will still think adaptively, its response can contain " @@ -320,6 +325,45 @@ class AnthropicModelInfo(BaseLLMModelInfo): status_code=400, ) + @staticmethod + def forced_tool_use_unsupported(model: str) -> bool: + return AnthropicModelInfo._get_model_capability(model, "supports_forced_tool_use") is False + + @staticmethod + def forced_tool_use_downgraded(model: str, drop_params: bool) -> bool: + """True when the model map flags the model with + ``supports_forced_tool_use: false`` (Fable 5.1 / Mythos 5.1 400 on + ``any``/``tool``) and ``drop_params`` asks for the ``auto`` downgrade; + raises a clean client-side 400 for such models without ``drop_params``.""" + if not AnthropicModelInfo.forced_tool_use_unsupported(model): + return False + if not (litellm.drop_params or drop_params): + raise litellm.utils.UnsupportedParamsError( + message=( + f"{model} does not support forced tool use (tool_choice='required' or a named tool). " + "Use tool_choice='auto' and tell the model in the prompt when to call the tool, or set " + "`litellm.drop_params = True` to downgrade to 'auto' automatically." + ), + status_code=400, + ) + litellm.verbose_logger.warning(DROP_FORCED_TOOL_CHOICE_WARNING, model) + return True + + @staticmethod + def _apply_forced_tool_choice( + model: str, + tool_choice: AnthropicMessagesToolChoice, + drop_params: bool, + ) -> AnthropicMessagesToolChoice: + if tool_choice["type"] not in ("any", "tool"): + return tool_choice + if not AnthropicModelInfo.forced_tool_use_downgraded(model, drop_params): + return tool_choice + disable_parallel: Final = tool_choice.get("disable_parallel_tool_use") + if disable_parallel is None: + return AnthropicMessagesToolChoice(type="auto") + return AnthropicMessagesToolChoice(type="auto", disable_parallel_tool_use=disable_parallel) + @staticmethod def _strip_version_suffix(model: str) -> str: at: Final = model.rfind("@") @@ -865,13 +909,9 @@ class AnthropicModelInfo(BaseLLMModelInfo): f"Failed to fetch models from Anthropic. Status code: {response.status_code}, Response: {response.text}" ) - models: Final = response.json()["data"] + models: Final[Sequence[Mapping[str, str]]] = response.json()["data"] - litellm_model_names: Final = [] - for model in models: - stripped_model_name = model["id"] - litellm_model_name = "anthropic/" + stripped_model_name - litellm_model_names.append(litellm_model_name) + litellm_model_names: Final = ["anthropic/" + model["id"] for model in models] return litellm_model_names def get_token_counter(self) -> BaseTokenCounter | None: @@ -1077,7 +1117,7 @@ def strip_empty_content_blocks_from_anthropic_messages( return out -def _is_empty_text_block(block: Any) -> bool: +def _is_empty_text_block(block: object) -> bool: if not isinstance(block, dict) or block.get("type") != "text": return False text: Final = block.get("text") @@ -1099,6 +1139,25 @@ def is_empty_thinking_block(block: object) -> bool: return not isinstance(thinking, str) or not thinking.strip() +def is_empty_unsigned_thinking_block(block: object) -> bool: + """ + True for an empty ``{"type": "thinking"}`` block carrying no signature. + + The emit-side predicate: response paths drop a thinking block only when it + holds nothing the client could need. A signature-only block is a real + provider response (Bedrock Converse under adaptive thinking emits a + reasoning block with empty text and only a signature) and the client needs + the signature to replay reasoning across tool-use turns, so it must be + emitted. Request paths keep using :func:`is_empty_thinking_block`: + Anthropic rejects empty thinking blocks in request history regardless of + signature, and the inbound strip self-heals a replayed signature-only + block. + """ + if not isinstance(block, dict) or not is_empty_thinking_block(block): + return False + return not block.get("signature") + + def normalize_anthropic_tool_use_id(raw_id: str) -> str: """ Normalize a tool_use / tool_result id for Anthropic's ``^[a-zA-Z0-9_-]+$`` @@ -1112,7 +1171,7 @@ def normalize_anthropic_tool_use_id(raw_id: str) -> str: return sanitized or "tool_use_id" -def _sanitize_tool_use_id_content_block(block: Any) -> Any: +def _sanitize_tool_use_id_content_block(block: object) -> object: if not isinstance(block, dict): return block block_type: Final = block.get("type") diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py index cefd4aa2d77..cc5879df56d 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py @@ -1029,7 +1029,7 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): @staticmethod def _is_blank_delta(chunk: "ModelResponseStream") -> bool: - from litellm.llms.anthropic.common_utils import is_empty_thinking_block + from litellm.llms.anthropic.common_utils import is_empty_unsigned_thinking_block choice: Final = chunk.choices[0] if choice.finish_reason is not None: @@ -1041,11 +1041,14 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): return False if getattr(delta, "reasoning_content", None): return False - # thinking_blocks whose entries are all empty (even if signed) must not + # thinking_blocks whose entries are all empty AND unsigned must not # open a block: the emitted {"type": "thinking", "thinking": ""} gets - # replayed as history and Anthropic rejects it (LIT-6357). + # replayed as history and Anthropic rejects it (LIT-6357). A signed + # entry opens the block so the client receives the replay signature. thinking_blocks: Final = getattr(delta, "thinking_blocks", None) - if thinking_blocks and any(isinstance(b, dict) and not is_empty_thinking_block(b) for b in thinking_blocks): + if thinking_blocks and any( + isinstance(b, dict) and not is_empty_unsigned_thinking_block(b) for b in thinking_blocks + ): return False return True diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py index a9fa00c827a..199a8ab77e7 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py @@ -18,6 +18,24 @@ TOOL_NAME_PREFIX_LENGTH: Final = OPENAI_MAX_TOOL_NAME_LENGTH - TOOL_NAME_HASH_LE PROVIDERS_PROXYING_AN_UNKNOWN_BACKEND: Final = frozenset({"litellm_proxy"}) +def _optional_attr(source: object, name: str) -> object: + return getattr(source, name, None) + + +def _as_string_mapping(value: object) -> Mapping[str, object] | None: + if isinstance(value, Mapping): + return value + return None + + +def _thought_signature(provider_specific_fields: object) -> str | None: + fields: Final = _as_string_mapping(provider_specific_fields) + if fields is None: + return None + signature: Final = fields.get("thought_signature") + return signature if isinstance(signature, str) else None + + _ANTHROPIC_TOOL_SCHEMA_KEYS: Final = frozenset( {"name", "type", "input_schema", "description", "cache_control", "strict"} ) @@ -56,7 +74,7 @@ def truncate_tool_name(name: str) -> str: def create_tool_name_mapping( - tools: list[dict[str, Any]], + tools: Sequence[Mapping[str, object]], ) -> dict[str, str]: """ Create a mapping of truncated tool names to original names. @@ -70,6 +88,8 @@ def create_tool_name_mapping( mapping: Final[dict[str, str]] = {} for tool in tools: original_name = tool.get("name", "") + if not isinstance(original_name, str): + continue truncated_name = truncate_tool_name(original_name) if truncated_name != original_name: mapping[truncated_name] = original_name @@ -90,7 +110,7 @@ from litellm.litellm_core_utils.reasoning_effort_utils import ( reasoning_effort_from_thinking_budget, ) from litellm.llms.anthropic.common_utils import ( - is_empty_thinking_block, + is_empty_unsigned_thinking_block, normalize_anthropic_tool_use_id, ) from litellm.llms.anthropic.experimental_pass_through.context_management import ( @@ -286,44 +306,44 @@ class LiteLLMAnthropicMessagesAdapter: ### FOR [BETA] `/v1/messages` endpoint support - def _extract_signature_from_tool_call(self, tool_call: Any) -> str | None: + def _extract_signature_from_tool_call(self, tool_call: object) -> str | None: """ Extract signature from a tool call's provider_specific_fields. Only checks provider_specific_fields, not thinking blocks. """ - signature = None + fields: Final = _optional_attr(tool_call, "provider_specific_fields") + if fields: + return _thought_signature(fields) - if hasattr(tool_call, "provider_specific_fields") and tool_call.provider_specific_fields: - if "thought_signature" in tool_call.provider_specific_fields: - signature = tool_call.provider_specific_fields["thought_signature"] - elif hasattr(tool_call.function, "provider_specific_fields") and tool_call.function.provider_specific_fields: - if "thought_signature" in tool_call.function.provider_specific_fields: - signature = tool_call.function.provider_specific_fields["thought_signature"] + function_fields: Final = _optional_attr(_optional_attr(tool_call, "function"), "provider_specific_fields") + if function_fields: + return _thought_signature(function_fields) - return signature + return None - def _extract_signature_from_tool_use_content(self, content: dict[str, Any]) -> str | None: + def _extract_signature_from_tool_use_content(self, content: Mapping[str, object]) -> str | None: """ Extract signature from a tool_use content block's provider_specific_fields. """ - provider_specific_fields: Final = content.get("provider_specific_fields", {}) + provider_specific_fields: Final = _as_string_mapping(content.get("provider_specific_fields", {})) if provider_specific_fields: - return provider_specific_fields.get("signature") + signature: Final = provider_specific_fields.get("signature") + return signature if isinstance(signature, str) else None return None def _add_cache_control_if_applicable( self, - source: Any, - target: Any, + source: object, + target: object, model: str | None, ) -> None: """ Extract cache_control from source and add to target if it should be preserved. - This method accepts Any type to support both regular dicts and TypedDict objects. - TypedDict objects (like ChatCompletionTextObject, ChatCompletionImageObject, etc.) - are dicts at runtime but have specific types at type-check time. Using Any allows - this method to work with both while maintaining runtime correctness. + This method accepts an unconstrained type to support both regular dicts and + TypedDict objects. TypedDict objects (like ChatCompletionTextObject, + ChatCompletionImageObject, etc.) are dicts at runtime but have specific types at + type-check time, so the widest parameter type works with both. Args: source: Dict or TypedDict containing potential cache_control field @@ -751,7 +771,7 @@ class LiteLLMAnthropicMessagesAdapter: return new_tools, tool_name_mapping - def translate_anthropic_output_format_to_openai(self, output_format: Any) -> dict[str, object] | None: + def translate_anthropic_output_format_to_openai(self, output_format: object) -> dict[str, object] | None: """ Translate Anthropic's output_format to OpenAI's response_format. @@ -1267,7 +1287,7 @@ class LiteLLMAnthropicMessagesAdapter: if hasattr(choice.message, "thinking_blocks") and choice.message.thinking_blocks: for thinking_block in choice.message.thinking_blocks: if thinking_block.get("type") == "thinking": - if is_empty_thinking_block(thinking_block): + if is_empty_unsigned_thinking_block(thinking_block): continue thinking_value = thinking_block.get("thinking", "") signature_value = thinking_block.get("signature", "") @@ -1366,7 +1386,7 @@ class LiteLLMAnthropicMessagesAdapter: @classmethod def _first_positive_prompt_tokens_detail_value(cls, usage: Usage, field_names: tuple[str, ...]) -> int: - prompt_tokens_details: Final = getattr(usage, "prompt_tokens_details", None) + prompt_tokens_details: Final = _optional_attr(usage, "prompt_tokens_details") if prompt_tokens_details is None: return 0 @@ -1374,7 +1394,7 @@ class LiteLLMAnthropicMessagesAdapter: if isinstance(prompt_tokens_details, dict): value = cls._positive_int(prompt_tokens_details.get(field_name)) else: - value = cls._positive_int(getattr(prompt_tokens_details, field_name, None)) + value = cls._positive_int(_optional_attr(prompt_tokens_details, field_name)) if value > 0: return value return 0 diff --git a/litellm/llms/anthropic/experimental_pass_through/context_management/editors/compact.py b/litellm/llms/anthropic/experimental_pass_through/context_management/editors/compact.py index c8cbbba8784..050ab67c86c 100644 --- a/litellm/llms/anthropic/experimental_pass_through/context_management/editors/compact.py +++ b/litellm/llms/anthropic/experimental_pass_through/context_management/editors/compact.py @@ -13,10 +13,10 @@ Mirrors Anthropic's native ``compact_20260112`` for non-Anthropic providers: """ import re -from collections.abc import Mapping, Sequence -from typing import TYPE_CHECKING, Any, Final, Literal, NotRequired, Optional, TypedDict, Union, cast +from collections.abc import Awaitable, Mapping, Sequence +from typing import TYPE_CHECKING, Final, Literal, Optional, Protocol, TypeVar, Union, cast -from typing_extensions import ReadOnly +from typing_extensions import NotRequired, ReadOnly, TypedDict, Unpack import litellm from litellm._logging import verbose_logger @@ -29,6 +29,7 @@ from litellm.types.llms.anthropic import ( if TYPE_CHECKING: from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper from litellm.proxy._types import UserAPIKeyAuth + from litellm.proxy.hooks.parallel_request_limiter_v3 import RateLimitDescriptor, RateLimitResponse from litellm.router import Router from litellm.types.llms.anthropic import ( AllAnthropicPassThroughMessageValues, @@ -84,6 +85,77 @@ _PROPAGATED_METADATA_KEYS: Final = ( _SUMMARY_TAG_RE: Final = re.compile(r"(.*?)", re.IGNORECASE | re.DOTALL) +_MsgT: Final = TypeVar("_MsgT", bound=Mapping[str, object]) + + +def _as_object(value: object) -> object: + return value + + +def _is_tool_result_block(block: object) -> bool: + return isinstance(block, dict) and block.get("type") in ("tool_result",) + + +class _SummaryCallKwargs(TypedDict): + model: ReadOnly[str] + max_tokens: ReadOnly[int] + timeout: ReadOnly[float] + litellm_metadata: ReadOnly[Mapping[str, object]] + user: ReadOnly[NotRequired[str]] + allowed_model_region: ReadOnly[NotRequired[str]] + + +class _SummaryOptionalKwargs(TypedDict, total=False): + user: ReadOnly[str] + allowed_model_region: ReadOnly[str] + + +class _SummaryAcompletion(Protocol): + def __call__( + self, + *, + messages: Sequence[Mapping[str, object]], + **kwargs: Unpack[_SummaryCallKwargs], # kwargs-ok: forwarded verbatim to acompletion, which owns them + ) -> "Awaitable[ModelResponse | CustomStreamWrapper]": ... + + +class _CreateRateLimitDescriptors(Protocol): + def __call__( + self, + *, + user_api_key_dict: "UserAPIKeyAuth", + data: Mapping[str, str], + rpm_limit_type: object, + tpm_limit_type: object, + model_has_failures: bool, + ) -> "Sequence[RateLimitDescriptor]": ... + + +class _AddModelRateLimitDescriptor(Protocol): + def __call__( + self, + *, + user_api_key_dict: "UserAPIKeyAuth", + requested_model: str, + descriptors: "Sequence[RateLimitDescriptor]", + ) -> None: ... + + +class _CreateOrgRateLimitDescriptors(Protocol): + def __call__( + self, user_api_key_dict: "UserAPIKeyAuth", requested_model: str | None = None + ) -> "Sequence[RateLimitDescriptor]": ... + + +class _ShouldRateLimit(Protocol): + def __call__( + self, + *, + descriptors: "Sequence[RateLimitDescriptor]", + parent_otel_span: object, + read_only: bool, + ) -> "Awaitable[RateLimitResponse]": ... + def _read_summary_model_setting() -> str | None: """Look up the configured summarization model from proxy general_settings.""" @@ -159,11 +231,11 @@ async def _check_summary_model_access( return True key_models: Final = list(getattr(user_api_key_auth, "models", None) or []) - team_id: Final = getattr(user_api_key_auth, "team_id", None) - team_model_aliases: Final = getattr(user_api_key_auth, "team_model_aliases", None) + team_id: Final[str | None] = getattr(user_api_key_auth, "team_id", None) + team_model_aliases: Final[dict[str, str] | None] = getattr(user_api_key_auth, "team_model_aliases", None) team_models: Final = list(getattr(user_api_key_auth, "team_models", None) or []) - user_id: Final = getattr(user_api_key_auth, "user_id", None) - project_id: Final = getattr(user_api_key_auth, "project_id", None) + user_id: Final[str | None] = getattr(user_api_key_auth, "user_id", None) + project_id: Final[str | None] = getattr(user_api_key_auth, "project_id", None) checks: Final[tuple[tuple[Literal["key", "team"], list[str]], ...]] = ( ("key", key_models), @@ -371,8 +443,10 @@ async def _check_summary_model_budget( ) return False - end_user_model_max_budget: Final = getattr(user_api_key_auth, "end_user_model_max_budget", None) - end_user_id: Final = getattr(user_api_key_auth, "end_user_id", None) + end_user_model_max_budget: Final[dict[str, object] | None] = getattr( + user_api_key_auth, "end_user_model_max_budget", None + ) + end_user_id: Final[str | None] = getattr(user_api_key_auth, "end_user_id", None) if isinstance(end_user_model_max_budget, dict) and end_user_model_max_budget and end_user_id is not None: try: await model_max_budget_limiter.is_end_user_within_model_budget( @@ -424,40 +498,57 @@ async def _check_summary_model_rate_limit( except Exception: return True - limiter: Final = getattr(proxy_logging_obj, "max_parallel_request_limiter", None) + limiter: Final[object] = getattr(proxy_logging_obj, "max_parallel_request_limiter", None) + should_rate_limit_check: Final[_ShouldRateLimit | None] = getattr(limiter, "should_rate_limit", None) + create_descriptors: Final[_CreateRateLimitDescriptors | None] = getattr( + limiter, "_create_rate_limit_descriptors", None + ) + add_team_descriptor: Final[_AddModelRateLimitDescriptor | None] = getattr( + limiter, "_add_team_model_rate_limit_descriptor_from_metadata", None + ) + add_project_descriptor: Final[_AddModelRateLimitDescriptor | None] = getattr( + limiter, "_add_project_model_rate_limit_descriptor_from_metadata", None + ) + create_org_descriptors: Final[_CreateOrgRateLimitDescriptors | None] = getattr( + limiter, "create_organization_rate_limit_descriptor", None + ) if ( limiter is None - or not hasattr(limiter, "should_rate_limit") - or not hasattr(limiter, "_create_rate_limit_descriptors") + or should_rate_limit_check is None + or create_descriptors is None + or add_team_descriptor is None + or add_project_descriptor is None + or create_org_descriptors is None ): return True try: - metadata: Final = getattr(user_api_key_auth, "metadata", None) or {} + metadata: Final[Mapping[str, object]] = getattr(user_api_key_auth, "metadata", None) or {} data: Final = {"model": summary_model} - descriptors: Final = limiter._create_rate_limit_descriptors( + base_descriptors: Final = create_descriptors( user_api_key_dict=user_api_key_auth, data=data, rpm_limit_type=metadata.get("rpm_limit_type"), tpm_limit_type=metadata.get("tpm_limit_type"), model_has_failures=False, ) - limiter._add_team_model_rate_limit_descriptor_from_metadata( + add_team_descriptor( user_api_key_dict=user_api_key_auth, requested_model=summary_model, - descriptors=descriptors, + descriptors=base_descriptors, ) - limiter._add_project_model_rate_limit_descriptor_from_metadata( + add_project_descriptor( user_api_key_dict=user_api_key_auth, requested_model=summary_model, - descriptors=descriptors, + descriptors=base_descriptors, ) - descriptors.extend(limiter.create_organization_rate_limit_descriptor(user_api_key_auth, summary_model)) + descriptors: Final = (*base_descriptors, *create_org_descriptors(user_api_key_auth, summary_model)) if not descriptors: return True - response: Final = await limiter.should_rate_limit( + parent_otel_span: Final[object] = getattr(user_api_key_auth, "parent_otel_span", None) + response: Final[RateLimitResponse] = await should_rate_limit_check( descriptors=descriptors, - parent_otel_span=getattr(user_api_key_auth, "parent_otel_span", None), + parent_otel_span=parent_otel_span, read_only=True, ) except Exception as e: @@ -471,7 +562,7 @@ async def _check_summary_model_rate_limit( def _find_latest_compaction_index( - messages: list[dict[str, object]], + messages: Sequence[Mapping[str, object]], ) -> tuple[int | None, int | None]: """Return (message_index, block_index) of the most recent compaction block. @@ -490,8 +581,8 @@ def _find_latest_compaction_index( def _slice_around_compaction_block( - messages: list[dict[str, Any]], -) -> tuple[list[dict[str, object]], dict[str, object] | None]: + messages: Sequence[_MsgT], +) -> tuple[Sequence[_MsgT | dict[str, object]], dict[str, object] | None]: """Apply Anthropic's "drop everything before the compaction block" rule. Returns ``(sliced_messages_with_compaction_block, compaction_block_dict)`` @@ -506,19 +597,21 @@ def _slice_around_compaction_block( original_msg: Final = messages[msg_idx] original_content: Final = original_msg["content"] - compaction_block: Final = cast(dict[str, object], original_content[blk_idx]) + if not isinstance(original_content, list): + return messages, None + original_blocks: Final = cast("Sequence[dict[str, object]]", original_content) + compaction_block: Final = original_blocks[blk_idx] # Per Anthropic's contract everything before the compaction block is # dropped, including earlier blocks within the same assistant message. - sliced_content: Final = list(original_content[blk_idx:]) + sliced_content: Final = list(original_blocks[blk_idx:]) - sliced_messages: Final[list[dict[str, object]]] = [{**original_msg, "content": sliced_content}] - sliced_messages.extend(messages[msg_idx + 1 :]) + sliced_messages: Final = [{**original_msg, "content": sliced_content}, *messages[msg_idx + 1 :]] return sliced_messages, compaction_block def _strip_compaction_blocks( - messages: list[dict[str, object]], + messages: Sequence[dict[str, object]], ) -> list[dict[str, object]]: """Drop any ``compaction`` content blocks from messages. @@ -625,7 +718,7 @@ def _propagate_metadata( def _count_effective_tokens( model: str, - effective_messages: list[dict[str, object]], + effective_messages: Sequence[dict[str, object]], compaction_block: CompactionBlock | None, tools: list[dict[str, object]] | None, system: str | list[dict[str, object]] | None = None, @@ -704,17 +797,18 @@ def _system_to_text( return "" if isinstance(system, str): return system - parts: Final[list[str]] = [] - for block in system: - if isinstance(block, dict) and block.get("type") == "text": - text = block.get("text") - if isinstance(text, str) and text: - parts.append(text) - return "\n".join(parts) + return "\n".join( + text + for block in system + if isinstance(block, dict) + and block.get("type") == "text" + and isinstance(text := block.get("text"), str) + and text + ) def _select_last_user_question( - messages: list[dict[str, object]], + messages: Sequence[dict[str, object]], ) -> list[dict[str, object]]: """Pick the most recent ``user`` turn that is a real question. @@ -729,16 +823,18 @@ def _select_last_user_question( turns, or contained no user turns at all). The downstream call always needs a non-empty user message. """ + blocks: Sequence[object] for msg in reversed(messages): if msg.get("role") != "user": continue content = msg.get("content") if isinstance(content, list): - filtered = [blk for blk in content if not (isinstance(blk, dict) and blk.get("type") == "tool_result")] + blocks = [*map(_as_object, content)] + filtered = [blk for blk in blocks if not _is_tool_result_block(blk)] if not filtered: # Purely tool_result — skip and look for an earlier turn. continue - if len(filtered) < len(content): + if len(filtered) < len(blocks): return [{**msg, "content": filtered}] return [msg] return [ @@ -760,7 +856,7 @@ def _extract_summary_text(raw: str | None) -> str | None: def _system_to_openai_message( - system: str | list[dict[str, Any]] | None, + system: str | list[dict[str, object]] | None, ) -> dict[str, object] | None: """Translate Anthropic-shaped ``system`` to an OpenAI system message. @@ -772,17 +868,19 @@ def _system_to_openai_message( if isinstance(system, str): return {"role": "system", "content": system} if system else None if isinstance(system, list): - parts = [block.get("text", "") for block in system if isinstance(block, dict) and block.get("type") == "text"] - joined: Final = "\n\n".join(part for part in parts if part) + parts: Final[list[object]] = [ + block.get("text", "") for block in system if isinstance(block, dict) and block.get("type") == "text" + ] + joined: Final = "\n\n".join(part for part in parts if isinstance(part, str) and part) return {"role": "system", "content": joined} if joined else None return None def _build_summary_messages( - effective_messages: list[dict[str, object]], + effective_messages: Sequence[dict[str, object]], prompt: str, system: str | list[dict[str, object]] | None = None, -) -> list[dict[str, object]]: +) -> Sequence[Mapping[str, object]]: """Build the OpenAI-shape message list for the summary call. The caller's ``system`` prompt is prepended (the default summarization @@ -810,7 +908,7 @@ def _build_summary_messages( ) openai_messages = stripped - summary_messages: Final[list[dict[str, object]]] = [] + summary_messages: Final[list[Mapping[str, object]]] = [] system_message: Final = _system_to_openai_message(system) if system_message is not None: summary_messages.append(system_message) @@ -845,35 +943,17 @@ def _append_text_to_content(content: object, extra_text: str) -> object: if isinstance(content, str): return f"{content}\n\n{extra_text}" if isinstance(content, list): - appended: Final[list[object]] = [*content, {"type": "text", "text": extra_text}] + appended: Final[Sequence[object]] = [*map(_as_object, content), {"type": "text", "text": extra_text}] return appended return [content, {"type": "text", "text": extra_text}] -class _SummaryCallUserKwarg(TypedDict, total=False): - user: ReadOnly[object] - - -class _SummaryCallRegionKwarg(TypedDict, total=False): - allowed_model_region: ReadOnly[str] - - -class _SummaryCallKwargs(TypedDict): - model: ReadOnly[str] - messages: ReadOnly[list[dict[str, object]]] - max_tokens: ReadOnly[int] - timeout: ReadOnly[float] - litellm_metadata: ReadOnly[Mapping[str, object]] - user: NotRequired[ReadOnly[object]] - allowed_model_region: NotRequired[ReadOnly[str]] - - async def _call_summary_model( *, summary_model: str, - summary_messages: list[dict[str, object]], + summary_messages: Sequence[Mapping[str, object]], metadata: Mapping[str, object], - llm_router: Any, + llm_router: Optional["Router"], allowed_model_region: str | None = None, max_tokens: int = COMPACT_SUMMARY_MAX_TOKENS, ) -> Union["ModelResponse", "CustomStreamWrapper"]: @@ -909,28 +989,37 @@ async def _call_summary_model( # than from ``litellm_metadata``, so without it the summary tokens would not # debit the caller's end-user counters. end_user_id: Final = metadata.get("user_api_key_end_user_id") + user_kwargs: Final = ( + _SummaryOptionalKwargs(user=end_user_id) + if isinstance(end_user_id, str) and end_user_id + else _SummaryOptionalKwargs() + ) + region_kwargs: Final = ( + _SummaryOptionalKwargs(allowed_model_region=allowed_model_region) + if allowed_model_region is not None + else _SummaryOptionalKwargs() + ) call_kwargs: Final[_SummaryCallKwargs] = { "model": summary_model, - "messages": summary_messages, "max_tokens": max_tokens, "timeout": COMPACT_SUMMARY_TIMEOUT_SECONDS, "litellm_metadata": metadata, - **(_SummaryCallUserKwarg(user=end_user_id) if end_user_id else _SummaryCallUserKwarg()), - **( - _SummaryCallRegionKwarg(allowed_model_region=allowed_model_region) - if allowed_model_region is not None - else _SummaryCallRegionKwarg() - ), + **user_kwargs, + **region_kwargs, } - if llm_router is not None and hasattr(llm_router, "acompletion"): - return await llm_router.acompletion(**call_kwargs) - return await litellm.acompletion(**call_kwargs) + router_acompletion: Final[_SummaryAcompletion | None] = getattr(llm_router, "acompletion", None) + if llm_router is not None and router_acompletion is not None: + return await router_acompletion(messages=summary_messages, **call_kwargs) + return await litellm.acompletion(messages=[*summary_messages], **call_kwargs) -def _extract_response_text(response: Any) -> str | None: +def _extract_response_text(response: object) -> str | None: try: - choice: Final = response.choices[0] - message: Final = choice.message + choices: Final[Sequence[object] | None] = getattr(response, "choices", None) + if choices is None: + return None + choice: Final = choices[0] + message: Final = getattr(choice, "message", None) content: Final = getattr(message, "content", None) if isinstance(content, str): return content @@ -946,13 +1035,12 @@ def _extract_response_text(response: Any) -> str | None: def _extract_usage(response: object) -> tuple[int, int]: - usage: Final = getattr(response, "usage", None) + usage: Final[object] = getattr(response, "usage", None) if usage is None: return 0, 0 - return ( - int(getattr(usage, "prompt_tokens", 0) or 0), - int(getattr(usage, "completion_tokens", 0) or 0), - ) + prompt_tokens: Final[int | None] = getattr(usage, "prompt_tokens", 0) + completion_tokens: Final[int | None] = getattr(usage, "completion_tokens", 0) + return int(prompt_tokens or 0), int(completion_tokens or 0) def apply_client_compaction_block_history( diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/streaming_iterator.py b/litellm/llms/anthropic/experimental_pass_through/messages/streaming_iterator.py index a282d5f4d4f..66e36dab2ba 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/streaming_iterator.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/streaming_iterator.py @@ -8,6 +8,10 @@ import httpx from pydantic import TypeAdapter from typing_extensions import TypedDict +from litellm.constants import ( + ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS, + ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE, +) from litellm.litellm_core_utils.core_helpers import process_response_headers from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.litellm_core_utils.logging_worker import GLOBAL_LOGGING_WORKER @@ -21,6 +25,9 @@ from litellm.types.utils import GenericStreamingChunk, ModelResponseStream GLOBAL_PASS_THROUGH_SUCCESS_HANDLER_OBJ: Final = PassThroughEndpointLogging() +_UPSTREAM_PUMP_TASKS: Final[set[asyncio.Task[None]]] = set() # mutable-ok: stdlib strong-ref set for pump tasks +_DETACHED_STREAM_DRAINS: Final[set[asyncio.Task[None]]] = set() # mutable-ok: bounded strong-ref set, detached drains + INCOMPLETE_STREAM_ERROR_MESSAGE: Final = ( "Provider stream ended before emitting a message_stop event; " "the response is incomplete and any partial content (e.g. tool_use input JSON) may be truncated." @@ -79,22 +86,41 @@ def _decoded_sse_data_line(line: bytes) -> object | None: return None -def _anthropic_error_event_payload(chunk: object) -> Mapping[str, object] | None: +def _anthropic_event_payload(chunk: object, event_type: str) -> Mapping[str, object] | None: if isinstance(chunk, dict): - return chunk if chunk.get("type") == "error" else None + return chunk if chunk.get("type") == event_type else None if isinstance(chunk, (bytes, bytearray)): decoded_lines: Final = (_decoded_sse_data_line(line) for line in chunk.splitlines()) return next( ( candidate for candidate in decoded_lines - if isinstance(candidate, dict) and candidate.get("type") == "error" + if isinstance(candidate, dict) and candidate.get("type") == event_type ), None, ) return None +def _anthropic_error_event_payload(chunk: object) -> Mapping[str, object] | None: + return _anthropic_event_payload(chunk, "error") + + +def parse_anthropic_refusal_stop_details(chunk: object) -> Mapping[str, object] | None: + """ + Return the ``stop_details`` object of an Anthropic SSE ``message_delta`` + chunk whose delta carries ``stop_reason: "refusal"`` (a safeguard refusal: + https://platform.claude.com/docs/en/build-with-claude/refusals-and-fallback), + or None for any other chunk, a plain refusal without ``stop_details`` included. + """ + payload: Final = _anthropic_event_payload(chunk, "message_delta") + delta: Final = payload.get("delta") if payload is not None else None + if not isinstance(delta, dict) or delta.get("stop_reason") != "refusal": + return None + stop_details: Final = delta.get("stop_details") + return stop_details if isinstance(stop_details, dict) else None + + def _anthropic_error_body(chunk: object) -> Mapping[str, object] | None: """Return the ``error`` object of an Anthropic SSE ``event: error`` chunk, or None.""" payload: Final = _anthropic_error_event_payload(chunk) @@ -133,6 +159,34 @@ def _is_terminal_stream_chunk(chunk: object) -> bool: return _is_message_stop_chunk(chunk) or _is_provider_error_chunk(chunk) +def _try_claim_detached_drain_slot() -> bool: + """Claim a detached-drain slot for the current task, bounding concurrency. + + Returns True if a slot was claimed (the caller may keep draining upstream + for billing) or False if the cap is already reached (the caller should stop + and bill what it has). Only touched from the event loop, so the check + + insert need no lock. + """ + if len(_DETACHED_STREAM_DRAINS) >= ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS: + return False + current_task: Final = asyncio.current_task() + if current_task is not None: + _DETACHED_STREAM_DRAINS.add(current_task) + current_task.add_done_callback(_DETACHED_STREAM_DRAINS.discard) + return True + + +def _exception_left_unconsumed(queue: "asyncio.Queue[bytes | None | BaseException]", exc: BaseException) -> bool: + """After client detach the relay never reads the queue again, so drain it here. + + The forwarded exception still sitting in the queue means the relay tore + down before re-raising it, so the proxy's failure handling never ran and + the caller must salvage spend itself. + """ + remaining: Final = tuple(queue.get_nowait() for _ in range(queue.qsize())) + return any(item is exc for item in remaining) + + def _sse_event(event_type: str, payload: Mapping[str, object]) -> bytes: return f"event: {event_type}\ndata: {json.dumps(payload)}\n\n".encode() @@ -414,17 +468,167 @@ class BaseAnthropicMessagesStreamingIterator: async def async_sse_wrapper( self, - completion_stream: AsyncIterator[bytes | GenericStreamingChunk | ModelResponseStream | dict], + completion_stream: AsyncIterator[bytes | GenericStreamingChunk | ModelResponseStream | Mapping[str, object]], ) -> AsyncIterator[bytes]: """ Generic async SSE wrapper that converts streaming chunks to SSE format and handles logging. + The upstream read runs in a detached background task (``_pump_upstream``) + so that a client disconnect tears down only this client-facing generator, + never the upstream drain + billing. The provider (e.g. Bedrock) keeps + generating and billing the full response regardless of the client, so + draining it to completion is what lets spend tracking see the real + terminal ``message_delta`` / ``message_stop`` usage instead of a + truncated placeholder count. + + Chunks reach the client through a bounded queue. While the client is + connected the pump blocks on a full queue (racing the disconnect + signal), so a slow reader throttles the upstream read exactly as the old + direct ``yield`` did instead of letting the whole response buffer in + memory. Once the client goes away the pump stops enqueueing and only + keeps a single ``collected_chunks`` copy for billing, and the number of + such post-disconnect drains running at once is capped so client behavior + can't create unbounded worker state; over the cap the pump bills what it + has rather than draining further. Detached-drain lifetime is otherwise + bounded by the upstream stream/read timeout. + + An upstream failure (Bedrock read / decode / chunk-conversion error) + that happens while the client is still connected is forwarded through + the queue and re-raised here, so the original provider exception (and + its status) reaches the proxy's failure handling unchanged rather than + being masked by a generic incomplete-stream event. + This method provides the common logic for both Anthropic and Bedrock implementations. """ - collected_chunks: Final = [] - saw_terminal_event = False + queue: Final[asyncio.Queue[bytes | None | BaseException]] = asyncio.Queue( + maxsize=ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE + ) + client_detached: Final = asyncio.Event() + pump_task: Final = asyncio.create_task(self._pump_upstream_to_queue(completion_stream, queue, client_detached)) + _UPSTREAM_PUMP_TASKS.add(pump_task) + pump_task.add_done_callback(_UPSTREAM_PUMP_TASKS.discard) + + reached_end = False # rebind-ok: flipped once the relay consumes the end-of-stream sentinel + try: + while True: + item = await queue.get() + if item is None: + reached_end = True + break + if isinstance(item, BaseException): + raise item + yield item + finally: + client_detached.set() + if not reached_end: + self._dispatch_pending_deferred_logging() + + def _dispatch_pending_deferred_logging(self) -> None: + """Fire deferred billing that a torn-down response would otherwise drop. + + When the pump finishes draining while the client is still connected it + stores the logging coroutine for ProxyLogging._fire_deferred_stream_logging, + which the proxy only fires on a normally completed response: a client + disconnect (GeneratorExit / CancelledError) re-raises past it. Without + this dispatch that window loses the spend row entirely. + """ + deferred_cb: Final = getattr(self.litellm_logging_obj, "_on_deferred_stream_complete", None) + deferred_args: Final = getattr(self.litellm_logging_obj, "_deferred_stream_complete_args", None) + if deferred_cb is None or deferred_args is None: + return + self.litellm_logging_obj._on_deferred_stream_complete = None + self.litellm_logging_obj._deferred_stream_complete_args = None + GLOBAL_LOGGING_WORKER.ensure_initialized_and_enqueue(async_coroutine=deferred_cb(*deferred_args)) + + async def _bill_collected_chunks( + self, + collected_chunks: list[bytes], # mutable-ok: SSE buffer forwarded to list-typed _handle_streaming_logging + *, + stream_teardown: bool, + ) -> None: + from litellm._logging import verbose_proxy_logger + + try: + await self._handle_streaming_logging(collected_chunks, stream_teardown=stream_teardown) + except Exception as exc: # noqa: BLE001 # billing is best-effort; never crash the pump + verbose_proxy_logger.warning( + "async_sse_wrapper billing failed after %d chunks: %s(%s)", + len(collected_chunks), + type(exc).__name__, + exc, + ) + + @staticmethod + async def _abort_upstream( + completion_stream: AsyncIterator[bytes | GenericStreamingChunk | ModelResponseStream | Mapping[str, object]], + ) -> None: + """Close the upstream provider stream so it stops generating and billing.""" + from litellm._logging import verbose_proxy_logger + + try: + await aclose_if_supported(completion_stream) + except Exception as exc: # noqa: BLE001 # abort is best-effort; log and continue + verbose_proxy_logger.warning( + "async_sse_wrapper failed to abort upstream stream: %s(%s)", + type(exc).__name__, + exc, + ) + + @staticmethod + async def _enqueue_for_client( + queue: "asyncio.Queue[bytes | None | BaseException]", + client_detached: "asyncio.Event", + item: bytes | None | BaseException, + ) -> bool: + """Deliver one item to the client, applying backpressure. + + Returns True if the item was queued, False if the client disconnected + before there was room (the item is then dropped, since a gone client + can't receive it). Never blocks once the client has detached. + """ + if client_detached.is_set(): + return False + try: + queue.put_nowait(item) + except asyncio.QueueFull: + pass + else: + return True + put_task: Final = asyncio.ensure_future(queue.put(item)) + detached_task: Final = asyncio.ensure_future(client_detached.wait()) + try: + await asyncio.wait(frozenset((put_task, detached_task)), return_when=asyncio.FIRST_COMPLETED) + finally: + if not detached_task.done(): + detached_task.cancel() + if put_task.done() and not put_task.cancelled(): + return True + put_task.cancel() + return False + + async def _pump_upstream_to_queue( + self, + completion_stream: AsyncIterator[bytes | GenericStreamingChunk | ModelResponseStream | Mapping[str, object]], + queue: "asyncio.Queue[bytes | None | BaseException]", + client_detached: "asyncio.Event", + ) -> None: + """Drain the whole upstream into ``queue`` (backpressured) and bill once. + + Runs detached so a client disconnect can't interrupt the upstream read; + see ``async_sse_wrapper`` for the full rationale. On a completed drain + the success billing (or deferred park) happens before the end-of-stream + sentinel is enqueued: the relay can only tear down after consuming the + sentinel, so its teardown can never outrun the park and get mistaken + for a client disconnect, and a sentinel the client never consumes falls + back to dispatching the parked billing here. + """ + from litellm._logging import verbose_proxy_logger + + collected_chunks: Final[list[bytes]] = [] # mutable-ok: SSE billing buffer appended to across the drain + saw_terminal_event = False # rebind-ok: accumulates across the upstream loop + draining_detached = False # rebind-ok: set once this pump claims a detached-drain slot try: async for chunk in completion_stream: if self.completion_start_time is None: @@ -432,17 +636,62 @@ class BaseAnthropicMessagesStreamingIterator: saw_terminal_event = saw_terminal_event or _is_terminal_stream_chunk(chunk) encoded_chunk = self._convert_chunk_to_sse_format(chunk) collected_chunks.append(encoded_chunk) - yield encoded_chunk - except (GeneratorExit, asyncio.CancelledError): - # A client disconnect tears the generator down at the yield, so the - # post-loop logging below never runs and the tokens already streamed - # (and billed by the provider) would never reach spend tracking. See LIT-5839. - if collected_chunks: - await self._handle_streaming_logging(collected_chunks, stream_teardown=True) - raise + if not client_detached.is_set(): + await self._enqueue_for_client(queue, client_detached, encoded_chunk) + continue + if not draining_detached: + if not _try_claim_detached_drain_slot(): + verbose_proxy_logger.warning( + "async_sse_wrapper: detached-drain cap (%d) reached; billing %d partial " + "chunks and aborting the upstream stream to stop provider billing", + ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS, + len(collected_chunks), + ) + await self._bill_collected_chunks(collected_chunks, stream_teardown=True) + await self._abort_upstream(completion_stream) + return + draining_detached = True + except Exception as exc: # noqa: BLE001 # upstream errors are handled/forwarded by _handle_pump_upstream_error + await self._handle_pump_upstream_error(queue, client_detached, collected_chunks, exc) + return - if not saw_terminal_event: - yield _incomplete_stream_error_sse_event() + if client_detached.is_set(): + await self._bill_collected_chunks(collected_chunks, stream_teardown=True) + return + if not saw_terminal_event and not await self._enqueue_for_client( + queue, client_detached, _incomplete_stream_error_sse_event() + ): + await self._bill_collected_chunks(collected_chunks, stream_teardown=True) + return + await self._bill_collected_chunks(collected_chunks, stream_teardown=False) + if not await self._enqueue_for_client(queue, client_detached, None): + self._dispatch_pending_deferred_logging() - # Handle logging after all chunks are processed - await self._handle_streaming_logging(collected_chunks) + async def _handle_pump_upstream_error( + self, + queue: "asyncio.Queue[bytes | None | BaseException]", + client_detached: "asyncio.Event", + collected_chunks: list[bytes], # mutable-ok: SSE buffer forwarded to list-typed _bill_collected_chunks + exc: BaseException, + ) -> None: + """Forward a provider error to a still-connected client, else salvage partial spend. + + Handing the original exception to the client-facing generator lets it + re-raise so the proxy's failure handling keeps the provider status and + owns logging (no success-bill). If the client already went away, or + disconnects before ever consuming the queued exception, no failure hook + runs, so bill the partial instead of dropping the request. + """ + from litellm._logging import verbose_proxy_logger + + if not client_detached.is_set() and await self._enqueue_for_client(queue, client_detached, exc): + await client_detached.wait() + if not _exception_left_unconsumed(queue, exc): + return + verbose_proxy_logger.warning( + "async_sse_wrapper upstream pump failed after client disconnect (%d chunks): %s(%s)", + len(collected_chunks), + type(exc).__name__, + exc, + ) + await self._bill_collected_chunks(collected_chunks, stream_teardown=True) diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py index ebd514c2605..3d62b8b4784 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py @@ -40,6 +40,11 @@ DROP_UNSUPPORTED_ADAPTIVE_EFFORT_WARNING: Final = ( "minimum thinking budget." ) +DROP_UNFITTING_REASONING_EFFORT_WARNING: Final = ( + "Dropping `thinking` mapped from reasoning_effort=%s for model=%s: max_tokens=%s " + "is too small to fit the minimum thinking budget." +) + class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): @property @@ -335,11 +340,15 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): return headers, api_base @staticmethod - def _translate_reasoning_effort_to_anthropic(model: str, optional_params: dict, custom_llm_provider: str) -> None: + def _translate_reasoning_effort_to_anthropic( + model: str, optional_params: dict, max_tokens: int | None, custom_llm_provider: str + ) -> None: """Map OpenAI-style ``reasoning_effort`` to native Anthropic params. Caller-supplied ``thinking`` / ``output_config`` win over the alias. - ``effort='none'`` clears both. Invalid efforts raise a 400. + ``effort='none'`` clears both. Invalid efforts raise a 400. A mapped + thinking budget is capped below ``max_tokens`` and dropped when even + the minimum budget cannot fit. """ from litellm.exceptions import BadRequestError as _BadRequestError from litellm.llms.anthropic.chat.transformation import ( @@ -365,7 +374,12 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): optional_params.pop("output_config", None) return - optional_params.setdefault("thinking", mapped_thinking) + fitted_thinking: Final = AnthropicConfig.cap_thinking_budget_to_max_tokens(mapped_thinking, max_tokens) + if fitted_thinking is None: + verbose_logger.warning(DROP_UNFITTING_REASONING_EFFORT_WARNING, reasoning_effort, model, max_tokens) + return + + optional_params.setdefault("thinking", fitted_thinking) if AnthropicModelInfo._is_adaptive_thinking_model(model, custom_llm_provider): mapped_effort: Final = REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT.get(reasoning_effort) if mapped_effort is None: @@ -510,7 +524,7 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): except _BadRequestError as e: raise AnthropicError(message=str(e.message), status_code=400) capped_thinking: Final = ( - AnthropicConfig._cap_thinking_budget_to_max_tokens(legacy_thinking, max_tokens) + AnthropicConfig.cap_thinking_budget_to_max_tokens(legacy_thinking, max_tokens) if legacy_thinking is not None else None ) @@ -582,6 +596,7 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): self._translate_reasoning_effort_to_anthropic( model=model, optional_params=anthropic_messages_optional_request_params, + max_tokens=max_tokens, custom_llm_provider=self._resolved_provider, ) diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/utils.py b/litellm/llms/anthropic/experimental_pass_through/messages/utils.py index 02d82887dde..9deff950724 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/utils.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/utils.py @@ -1,11 +1,40 @@ +from collections.abc import Mapping from functools import lru_cache -from typing import Any, Final, cast, get_type_hints +from typing import TYPE_CHECKING, Any, Final, cast, get_type_hints from litellm.types.llms.anthropic import AnthropicMessagesRequestOptionalParams from litellm.types.llms.anthropic_messages.anthropic_response import ( AnthropicMessagesResponse, ) +if TYPE_CHECKING: + from litellm.exceptions import ContentPolicyViolationError + + +def get_safeguard_refusal_stop_details(response: object) -> Mapping[str, Any] | None: + """ + Return the ``stop_details`` of an Anthropic Messages response refused by a + safeguard (``stop_reason: "refusal"`` carrying ``stop_details``: + https://platform.claude.com/docs/en/build-with-claude/refusals-and-fallback), + or None for any other response, a plain refusal without ``stop_details`` included. + """ + if not isinstance(response, dict) or response.get("stop_reason") != "refusal": + return None + stop_details: Final = response.get("stop_details") + return stop_details if isinstance(stop_details, dict) else None + + +def safeguard_refusal_error(model: str, stop_details: Mapping[str, object]) -> "ContentPolicyViolationError": + """The exception a safeguard-refused Anthropic response converts into so the + content-policy fallback chain can re-dispatch it.""" + from litellm.exceptions import ContentPolicyViolationError + + return ContentPolicyViolationError( + message=f"Anthropic safeguard refusal (category: {stop_details.get('category')}).", + model=model, + llm_provider="anthropic", + ) + @lru_cache(maxsize=1) def _anthropic_messages_optional_param_keys() -> frozenset[str]: @@ -100,14 +129,12 @@ def mock_response( model=model, ) return AnthropicMessagesResponse( - **{ - "content": [{"text": mock_response, "type": "text"}], - "id": "msg_013Zva2CMHLNnXjNJJKqJ2EF", - "model": "claude-sonnet-4-20250514", - "role": "assistant", - "stop_reason": "end_turn", - "stop_sequence": None, - "type": "message", - "usage": {"input_tokens": 2095, "output_tokens": 503}, - } + content=[{"text": mock_response, "type": "text"}], + id="msg_013Zva2CMHLNnXjNJJKqJ2EF", + model="claude-sonnet-4-20250514", + role="assistant", + stop_reason="end_turn", + stop_sequence=None, + type="message", + usage={"input_tokens": 2095, "output_tokens": 503}, ) diff --git a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/handler.py b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/handler.py index b6ec9520e79..ec0560016da 100644 --- a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/handler.py +++ b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/handler.py @@ -160,7 +160,7 @@ class LiteLLMMessagesToResponsesAPIHandler: top_k: int | None = None, top_p: float | None = None, output_format: AnthropicOutputSchema | None = None, - **kwargs, + **kwargs: object, ) -> AnthropicMessagesResponse | AsyncIterator[bytes]: responses_kwargs: Final = _build_responses_kwargs( max_tokens=max_tokens, @@ -214,7 +214,7 @@ class LiteLLMMessagesToResponsesAPIHandler: top_p: float | None = None, output_format: AnthropicOutputSchema | None = None, _is_async: bool = False, - **kwargs, + **kwargs: object, ) -> ( AnthropicMessagesResponse | AsyncIterator[bytes] diff --git a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py index ace7fc25dc9..0eb0e38a46e 100644 --- a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py @@ -179,14 +179,14 @@ class LiteLLMAnthropicToResponsesAPIAdapter: ) @staticmethod - def _assistant_block_group_key(indexed_block: tuple[int, Mapping[str, Any]]) -> str: + def _assistant_block_group_key(indexed_block: tuple[int, Mapping[str, object]]) -> str: """Group a run of consecutive thinking blocks together; keep every other block alone.""" index, block = indexed_block return "thinking" if block.get("type") == "thinking" else f"block:{index}" @classmethod def _assistant_group_to_input_item( - cls, group: tuple[Mapping[str, Any], ...] + cls, group: tuple[Mapping[str, object], ...] ) -> dict[str, Any] | None: # mutable-ok: API message payload first: Final = group[0] btype: Final = first.get("type") @@ -206,7 +206,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: def translate_messages_to_responses_input( self, messages: list[AllAnthropicPassThroughMessageValues], - ) -> list[dict[str, Any]]: + ) -> list[dict[str, object]]: """ Convert Anthropic messages list to Responses API `input` items. @@ -220,7 +220,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: assistant thinking -> reasoning assistant tool_use -> function_call """ - input_items: Final[list[dict[str, Any]]] = [] + input_items: Final[list[dict[str, object]]] = [] for m in messages: if m["role"] == "system": @@ -248,7 +248,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: } ) elif isinstance(content, list): - user_parts: list[dict[str, Any]] = [] + user_parts: list[Mapping[str, object]] = [] tool_image_parts: list[dict[str, Any]] = [] # mutable-ok: json content parts for block in content: if not isinstance(block, dict): @@ -379,9 +379,9 @@ class LiteLLMAnthropicToResponsesAPIAdapter: def translate_tools_to_responses_api( self, tools: list[AllAnthropicToolsValues], - ) -> list[dict[str, Any]]: + ) -> list[dict[str, object]]: """Convert Anthropic tool definitions to Responses API function tools.""" - result: Final[list[dict[str, Any]]] = [] + result: Final[list[dict[str, object]]] = [] for tool in tools: tool_dict = cast(dict[str, Any], tool) tool_type = tool_dict.get("type", "") @@ -392,7 +392,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: continue # Responses turns strict mode on when `strict` is omitted, silently rewriting # `required` to every property. Anthropic tools are non-strict unless asked. - func_tool: dict[str, Any] = { + func_tool: dict[str, object] = { "type": "function", "name": tool_name, "strict": bool(tool_dict.get("strict")), @@ -407,7 +407,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: @staticmethod def translate_tool_choice_to_responses_api( tool_choice: AnthropicMessagesToolChoice, - ) -> str | dict[str, Any]: + ) -> str | dict[str, object]: """Convert Anthropic tool_choice to Responses API tool_choice.""" tc_type: Final = tool_choice.get("type") if tc_type == "any": @@ -420,8 +420,8 @@ class LiteLLMAnthropicToResponsesAPIAdapter: @staticmethod def translate_context_management_to_responses_api( - context_management: dict[str, Any], - ) -> list[dict[str, Any]] | None: + context_management: dict[str, object], + ) -> list[dict[str, object]] | None: """ Convert Anthropic context_management dict to OpenAI Responses API array format. @@ -435,13 +435,13 @@ class LiteLLMAnthropicToResponsesAPIAdapter: if not isinstance(edits, list): return None - result: Final[list[dict[str, Any]]] = [] + result: Final[list[dict[str, object]]] = [] for edit in edits: if not isinstance(edit, dict): continue edit_type = edit.get("type", "") if edit_type == "compact_20260112": - entry: dict[str, Any] = {"type": "compaction"} + entry: dict[str, object] = {"type": "compaction"} trigger = edit.get("trigger") if isinstance(trigger, dict) and trigger.get("value") is not None: entry["compact_threshold"] = int(trigger["value"]) @@ -451,9 +451,9 @@ class LiteLLMAnthropicToResponsesAPIAdapter: @staticmethod def translate_thinking_to_reasoning( - thinking: dict[str, Any], - output_config: dict[str, Any] | None = None, - ) -> dict[str, Any] | None: + thinking: dict[str, object], + output_config: dict[str, object] | None = None, + ) -> dict[str, object] | None: """ Convert Anthropic thinking param to Responses API reasoning param. @@ -473,12 +473,14 @@ class LiteLLMAnthropicToResponsesAPIAdapter: if isinstance(output_config, dict) and output_config.get("effort"): effort = output_config["effort"] elif thinking_type == "enabled": - effort = reasoning_effort_from_thinking_budget(thinking.get("budget_tokens", 0)) + raw_budget: Final = thinking.get("budget_tokens", 0) + budget_tokens: Final = int(raw_budget) if isinstance(raw_budget, (int, float)) else 0 + effort = reasoning_effort_from_thinking_budget(budget_tokens) else: return None auto_summary: Final = is_reasoning_auto_summary_enabled() - result: Final[dict[str, Any]] = {"effort": effort} + result: Final[dict[str, object]] = {"effort": effort} summary: Final = thinking.get("summary") if summary: result["summary"] = summary @@ -570,7 +572,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: # output_format / output_config.format -> text format # output_format: {"type": "json_schema", "schema": {...}} # output_config: {"format": {"type": "json_schema", "schema": {...}}} - output_format: Any = anthropic_request.get("output_format") + output_format: object = anthropic_request.get("output_format") output_config = anthropic_request.get("output_config") if not isinstance(output_format, dict) and isinstance(output_config, dict): output_format = output_config.get("format") @@ -620,7 +622,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: ResponseReasoningItem, ) - content: Final[list[dict[str, Any]]] = [] + content: Final[list[dict[str, object]]] = [] stop_reason: AnthropicFinishReason = "end_turn" for item in response.output: diff --git a/litellm/llms/anthropic/files/handler.py b/litellm/llms/anthropic/files/handler.py index 5fdf2ceff7f..dfd62ca575b 100644 --- a/litellm/llms/anthropic/files/handler.py +++ b/litellm/llms/anthropic/files/handler.py @@ -2,7 +2,7 @@ import asyncio import json import time from collections.abc import Coroutine -from typing import Any, Final +from typing import Final import httpx @@ -116,7 +116,7 @@ class AnthropicFilesHandler: api_key: str | None = None, timeout: float | httpx.Timeout = 600.0, max_retries: int | None = None, - ) -> HttpxBinaryResponseContent | Coroutine[Any, Any, HttpxBinaryResponseContent]: + ) -> HttpxBinaryResponseContent | Coroutine[object, object, HttpxBinaryResponseContent]: """ Retrieve file content from Anthropic. diff --git a/litellm/llms/azure/azure.py b/litellm/llms/azure/azure.py index 980b27cda55..46a9dd1a531 100644 --- a/litellm/llms/azure/azure.py +++ b/litellm/llms/azure/azure.py @@ -2,7 +2,7 @@ import asyncio import json import time from collections.abc import Callable, Coroutine -from typing import Any, Final +from typing import Final import httpx from openai import ( @@ -374,7 +374,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): except Exception as e: status_code: Final = getattr(e, "status_code", 500) error_headers = getattr(e, "headers", None) - error_response: Final = getattr(e, "response", None) + error_response: Final[object] = getattr(e, "response", None) error_body: Final = getattr(e, "body", None) if error_headers is None and error_response: error_headers = getattr(error_response, "headers", None) @@ -392,7 +392,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): model: str, api_base: str, data: dict, - timeout: Any, + timeout: float | httpx.Timeout, dynamic_params: bool, model_response: ModelResponse, logging_obj: LiteLLMLoggingObj, @@ -502,7 +502,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): dynamic_params: bool, data: dict[str, object], model: str, - timeout: Any, + timeout: float | httpx.Timeout, max_retries: int, azure_ad_token: str | None = None, azure_ad_token_provider: Callable | None = None, @@ -578,7 +578,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): dynamic_params: bool, data: dict, model: str, - timeout: Any, + timeout: float | httpx.Timeout, max_retries: int, azure_ad_token: str | None = None, azure_ad_token_provider: Callable | None = None, @@ -634,7 +634,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): except Exception as e: status_code: Final = getattr(e, "status_code", 500) error_headers = getattr(e, "headers", None) - error_response: Final = getattr(e, "response", None) + error_response: Final[object] = getattr(e, "response", None) message: Final = getattr(e, "message", str(e)) error_body: Final = getattr(e, "body", None) if error_headers is None and error_response: @@ -754,7 +754,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): aembedding=None, headers: dict | None = None, litellm_params: dict | None = None, - ) -> EmbeddingResponse | Coroutine[Any, Any, EmbeddingResponse]: + ) -> EmbeddingResponse | Coroutine[object, object, EmbeddingResponse]: if headers: optional_params["extra_headers"] = headers if self._client_session is None: @@ -846,6 +846,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): api_key: str, data: dict, headers: dict, + deployment_name: str | None = None, ) -> httpx.Response: """ Implemented for azure dall-e-2 image gen calls @@ -957,7 +958,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): content=json.dumps(result).encode("utf-8"), request=httpx.Request(method="POST", url="https://api.openai.com/v1"), ) - request_json: Final = azure_deployment_image_generation_json_body(api_base, data) + request_json: Final = azure_deployment_image_generation_json_body(api_base, data, deployment_name) return await async_handler.post( url=api_base, json=request_json, @@ -973,6 +974,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): api_key: str, data: dict, headers: dict, + deployment_name: str | None = None, ) -> httpx.Response: """ Implemented for azure dall-e-2 image gen calls @@ -1073,7 +1075,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): content=json.dumps(result).encode("utf-8"), request=httpx.Request(method="POST", url="https://api.openai.com/v1"), ) - request_json: Final = azure_deployment_image_generation_json_body(api_base, data) + request_json: Final = azure_deployment_image_generation_json_body(api_base, data, deployment_name) return sync_handler.post( url=api_base, json=request_json, @@ -1091,9 +1093,10 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): AzureFoundryMAIImageGenerationConfig, ) - api_base: str = azure_client_params.get("azure_endpoint", "") # "https://example-endpoint.openai.azure.com" - if api_base.endswith("/"): - api_base = api_base.rstrip("/") + # deployment-scoped endpoints are moved to "base_url" by select_azure_base_url_or_endpoint + api_base: str = (azure_client_params.get("azure_endpoint") or azure_client_params.get("base_url") or "").rstrip( + "/" + ) api_version: Final[str] = azure_client_params.get("api_version", "") if model is None: model = "" @@ -1113,6 +1116,14 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): api_version=api_version, ) + v1_url: Final = BaseAzureLLM.get_azure_v1_image_url( + api_base=api_base, + api_version=api_version, + route="/openai/images/generations", + ) + if v1_url is not None: + return v1_url + if "/openai/deployments/" in api_base: base_url_with_deployment = api_base else: @@ -1167,6 +1178,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): api_key=api_key, data=data, headers=headers, + deployment_name=model, ) provider_config: Final = get_azure_image_generation_config(data.get("model", "dall-e-2")) @@ -1256,7 +1268,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): headers["Authorization"] = f"Bearer {azure_ad_token}" # init AzureOpenAI Client - azure_client_params: Final[dict[str, Any]] = self.initialize_azure_sdk_client( + azure_client_params: Final[dict[str, object]] = self.initialize_azure_sdk_client( litellm_params=litellm_params or {}, api_key=api_key, model_name=model or "", @@ -1302,6 +1314,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): api_key=api_key or "", data=data, headers=headers, + deployment_name=model, ) provider_config: Final = get_azure_image_generation_config(data.get("model", "dall-e-2")) if isinstance(provider_config, AzureFoundryMAIImageGenerationConfig): diff --git a/litellm/llms/azure/chat/gpt_transformation.py b/litellm/llms/azure/chat/gpt_transformation.py index 2df4ab731ab..0ac0662205a 100644 --- a/litellm/llms/azure/chat/gpt_transformation.py +++ b/litellm/llms/azure/chat/gpt_transformation.py @@ -1,3 +1,5 @@ +from collections.abc import Mapping +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final from httpx._models import Headers, Response @@ -6,6 +8,7 @@ import litellm from litellm.litellm_core_utils.prompt_templates.common_utils import ( drop_tool_reference_parts_from_tool_messages, hoist_images_from_tool_messages, + tool_with_flattened_parameters, ) from litellm.litellm_core_utils.prompt_templates.factory import ( convert_to_azure_openai_messages, @@ -32,6 +35,19 @@ else: LoggingClass = Any +_NO_TOOLS_UPDATE: Final[Mapping[str, object]] = MappingProxyType({}) + + +def flattened_tools_update(optional_params: Mapping[str, object]) -> Mapping[str, object]: + tools: Final = optional_params.get("tools") + if not isinstance(tools, list): + return _NO_TOOLS_UPDATE + flattened: Final = [ # mutable-ok: request tools are a JSON list + tool_with_flattened_parameters(tool) if isinstance(tool, dict) else tool for tool in tools + ] + return MappingProxyType({"tools": flattened}) + + class AzureOpenAIConfig(BaseConfig): """ Reference: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#chat-completions @@ -261,6 +277,7 @@ class AzureOpenAIConfig(BaseConfig): "model": model, "messages": azure_messages, **optional_params, + **flattened_tools_update(optional_params), } def transform_response( diff --git a/litellm/llms/azure/chat/o_series_transformation.py b/litellm/llms/azure/chat/o_series_transformation.py index 6cbd91bab5d..246bf69cb5f 100644 --- a/litellm/llms/azure/chat/o_series_transformation.py +++ b/litellm/llms/azure/chat/o_series_transformation.py @@ -20,6 +20,7 @@ from litellm.types.llms.openai import AllMessageValues from litellm.utils import get_model_info, supports_reasoning from ...openai.chat.o_series_transformation import OpenAIOSeriesConfig +from .gpt_transformation import flattened_tools_update class AzureOpenAIO1Config(OpenAIOSeriesConfig): @@ -108,4 +109,8 @@ class AzureOpenAIO1Config(OpenAIOSeriesConfig): headers: dict, ) -> dict: model = model.replace("o_series/", "") # handle o_series/my-random-deployment-name - return super().transform_request(model, messages, optional_params, litellm_params, headers) + flattened_params: Final = { # mutable-ok: transform_request's contract takes a plain JSON params dict + **optional_params, + **flattened_tools_update(optional_params), + } + return super().transform_request(model, messages, flattened_params, litellm_params, headers) diff --git a/litellm/llms/azure/common_utils.py b/litellm/llms/azure/common_utils.py index 2c34851d275..6cb7d09cec4 100644 --- a/litellm/llms/azure/common_utils.py +++ b/litellm/llms/azure/common_utils.py @@ -4,6 +4,7 @@ import json import os from collections.abc import Callable, Mapping from functools import lru_cache +from types import MappingProxyType from typing import Any, Final, Literal, NamedTuple, cast import httpx @@ -789,6 +790,32 @@ class BaseAzureLLM(BaseOpenAILLM): return str(final_url) + @staticmethod + def get_azure_v1_image_url(api_base: str, api_version: str | None, route: str) -> str | None: + """ + Azure's v1 surface serves images at ``/openai/v1/images/{generations,edits}`` and routes by + ``model`` in the request body, so any deployment path and stale ``api-version`` in + ``api_base`` have to be dropped. + + Returns None when ``api_version`` is a dated one, which still uses the deployment route. + """ + if not BaseAzureLLM._is_azure_v1_api_version(api_version): + return None + + base_url: Final = httpx.URL(api_base) + openai_path_start: Final = base_url.path.find("/openai") + resource_base: Final = str( + base_url.copy_with( + path=base_url.path if openai_path_start == -1 else base_url.path[:openai_path_start], + params=httpx.QueryParams(tuple((k, v) for k, v in base_url.params.multi_items() if k != "api-version")), + ) + ) + return BaseAzureLLM._get_base_azure_url( + api_base=resource_base, + litellm_params=MappingProxyType({"api_version": api_version}), + route=route, + ) + @staticmethod def _is_azure_v1_api_version(api_version: str | None) -> bool: if api_version is None: diff --git a/litellm/llms/azure/image_edit/transformation.py b/litellm/llms/azure/image_edit/transformation.py index 15592968bad..e4716289a34 100644 --- a/litellm/llms/azure/image_edit/transformation.py +++ b/litellm/llms/azure/image_edit/transformation.py @@ -93,8 +93,6 @@ class AzureImageEditConfig(OpenAIImageEditConfig): raise ValueError( f"api_base is required for Azure AI Studio. Please set the api_base parameter. Passed `api_base={api_base}`" ) - original_url: Final = httpx.URL(api_base) - # Resolve api_version: litellm_params > litellm.api_version > AZURE_API_VERSION env > default. # Mirrors the fallback chain used by the Azure chat path in common_utils.py, # so callers that set a global / env api_version don't get an unversioned URL. @@ -105,6 +103,16 @@ class AzureImageEditConfig(OpenAIImageEditConfig): or litellm.AZURE_DEFAULT_API_VERSION ) + v1_url: Final = BaseAzureLLM.get_azure_v1_image_url( + api_base=api_base, + api_version=api_version, + route="/openai/images/edits", + ) + if v1_url is not None: + return v1_url + + original_url: Final = httpx.URL(api_base) + # Create a new dictionary with existing params query_params: Final = dict(original_url.params) diff --git a/litellm/llms/azure/image_generation/http_utils.py b/litellm/llms/azure/image_generation/http_utils.py index 03c425eeffc..1aa5757ca95 100644 --- a/litellm/llms/azure/image_generation/http_utils.py +++ b/litellm/llms/azure/image_generation/http_utils.py @@ -1,7 +1,9 @@ """HTTP helpers for Azure OpenAI image generation (REST, not SDK).""" +from typing import Final -def azure_deployment_image_generation_json_body(api_base: str, data: dict) -> dict: + +def azure_deployment_image_generation_json_body(api_base: str, data: dict, deployment_name: str | None = None) -> dict: """ Build the JSON body for Azure OpenAI image generation POSTs. @@ -9,9 +11,20 @@ def azure_deployment_image_generation_json_body(api_base: str, data: dict) -> di deployment in the URL only; sending ``model`` in the body (especially the deployment name) breaks some models (e.g. gpt-image-2). See LiteLLM #26316. + For the v1 surface (``.../openai/v1/images/...``), Azure routes by the deployment + name in the body ``model`` field, so the deployment name must replace any base + model name there or Azure answers 404 DeploymentNotFound. + Provider-style URLs (e.g. ``/providers/...`` for FLUX on Azure AI) keep all keys so non–OpenAI-deployment payloads still work. """ - if "images/generations" in api_base and "/openai/deployments/" in api_base: - return {k: v for k, v in data.items() if k != "model"} - return data + drop_model: Final = "images/generations" in api_base and "/openai/deployments/" in api_base + v1_route: Final = "/openai/v1/images/" in api_base and bool(deployment_name) + if not drop_model and not v1_route: + return data + entries: Final = ( + tuple((k, v) for k, v in data.items() if k != "model") + if drop_model + else (*data.items(), ("model", deployment_name)) + ) + return {k: v for k, v in entries} diff --git a/litellm/llms/azure_ai/agents/handler.py b/litellm/llms/azure_ai/agents/handler.py index a13b1300e55..f7382190fca 100644 --- a/litellm/llms/azure_ai/agents/handler.py +++ b/litellm/llms/azure_ai/agents/handler.py @@ -51,15 +51,13 @@ else: AsyncHTTPHandler = Any -class _AzureRawAnnotation(TypedDict, total=False): - type: ReadOnly[str] +class _AzureRawAnnotation(ChatCompletionAnnotation, total=False): text: ReadOnly[str] start_index: ReadOnly[int] end_index: ReadOnly[int] - url_citation: ReadOnly[ChatCompletionAnnotationURLCitation] -_TransformedAnnotation: TypeAlias = ChatCompletionAnnotation | _AzureRawAnnotation +_TransformedAnnotation: TypeAlias = ChatCompletionAnnotation class _AzureText(TypedDict, total=False): @@ -223,18 +221,11 @@ class AzureAIAgentsHandler: """Build the ModelResponse from agent output.""" from litellm.types.utils import Choices, Message, Usage - message_kwargs: Final[dict[str, Any]] = { - "content": content, - "role": "assistant", - } - if annotations: - message_kwargs["annotations"] = annotations - model_response.choices = [ Choices( finish_reason="stop", index=0, - message=Message(**message_kwargs), + message=Message(content=content, role="assistant", annotations=annotations or None), ) ] model_response.model = model @@ -655,9 +646,6 @@ class AzureAIAgentsHandler: if data_str == "[DONE]": # Send final chunk with finish_reason - final_delta_kwargs: dict[str, Any] = {"content": None} - if collected_annotations: - final_delta_kwargs["annotations"] = collected_annotations final_chunk = ModelResponseStream( id=response_id, created=created, @@ -667,7 +655,7 @@ class AzureAIAgentsHandler: StreamingChoices( finish_reason="stop", index=0, - delta=Delta(**final_delta_kwargs), + delta=Delta(content=None, annotations=collected_annotations or None), ) ], ) diff --git a/litellm/llms/base_llm/guardrail_translation/base_translation.py b/litellm/llms/base_llm/guardrail_translation/base_translation.py index ba96ab3dc99..220fcedb0f8 100644 --- a/litellm/llms/base_llm/guardrail_translation/base_translation.py +++ b/litellm/llms/base_llm/guardrail_translation/base_translation.py @@ -1,8 +1,11 @@ from abc import ABC, abstractmethod +from collections.abc import Sequence from dataclasses import dataclass, field from typing import TYPE_CHECKING, Any, Final, Optional if TYPE_CHECKING: + from fastapi import HTTPException + from litellm.integrations.custom_guardrail import ( CustomGuardrail, ModifyResponseException, @@ -73,6 +76,31 @@ class BaseTranslation(ABC): return transformed + @staticmethod + def merge_user_api_key_metadata_into_request( + request_data: dict[str, Any], # mutable-ok: proxy hooks share and mutate the request payload dict in place + user_api_key_dict: Optional["UserAPIKeyAuth"], + ) -> None: + """ + Add the prefixed ``user_api_key_*`` metadata to the request's resolved + metadata bucket without overwriting existing keys. + + Writes must go through ``get_or_create_metadata_bucket``: creating a + ``litellm_metadata`` key on a route whose bucket is ``metadata`` (chat + completions) flips the bucket for every later metadata write, and spend + logging never sees those writes (e.g. guardrail_information). + """ + from litellm.litellm_core_utils.core_helpers import ( + get_or_create_metadata_bucket, + ) + + user_metadata: Final = BaseTranslation.transform_user_api_key_dict_to_metadata(user_api_key_dict) + if not user_metadata: + return + _, metadata_bucket = get_or_create_metadata_bucket(request_data) + for key, value in user_metadata.items(): + metadata_bucket.setdefault(key, value) + @abstractmethod async def process_input_messages( self, @@ -127,8 +155,8 @@ class BaseTranslation(ABC): self, exc: "ModifyResponseException", stream_started: bool = False, - responses_so_far: list[Any] | None = None, - ) -> list[bytes] | None: + responses_so_far: Sequence[Any] | None = None, + ) -> Sequence[bytes] | None: """ Build the streaming chunks that deliver a guardrail block message and cleanly terminate the stream in this provider's wire format. @@ -147,6 +175,26 @@ class BaseTranslation(ABC): """ return None + def build_stream_error_items( + self, + exc: "HTTPException", + responses_so_far: Sequence[Any] | None = None, + ) -> Sequence[Any] | None: + """ + Build the stream items that surface a guardrail HTTPException (a block + with the default exception-on-block config, or a failed scan) after the + response has already started streaming, in this endpoint's wire format. + + Called only once chunks have been sent: the HTTP status is gone, so the + failure must travel as an in-stream error frame. ``responses_so_far`` + holds the chunks the client has already received, for formats whose + error frame continues the stream (e.g. sequence numbers). + + Returns None when the format has no in-stream error frame; the caller + then re-raises ``exc``. Override in endpoint subclasses. + """ + return None + def get_structured_messages(self, data: dict) -> list["AllMessageValues"] | None: """ Convert request data to OpenAI-spec structured messages. diff --git a/litellm/llms/base_llm/guardrail_translation/utils.py b/litellm/llms/base_llm/guardrail_translation/utils.py index f09ee210e6c..9b6f9c47105 100644 --- a/litellm/llms/base_llm/guardrail_translation/utils.py +++ b/litellm/llms/base_llm/guardrail_translation/utils.py @@ -124,6 +124,61 @@ def blocked_responses_api_usage(original_response: object) -> ResponseAPIUsage: ) +def stream_item_field(item: object, field: str) -> object | None: + if isinstance(item, dict): + return item.get(field) + return getattr(item, field, None) + + +def blocked_chat_stream_usage(original_response: object) -> tuple[int, int]: + """ + ``(prompt_tokens, completion_tokens)`` for a synthetic guardrail-blocked + chat completions stream. + + A mid-stream block carries the chunks received so far as a list; real usage + rides on the final chunk when the upstream sent one + (``stream_options.include_usage``). Non-list originals defer to + ``blocked_response_usage``. + """ + if not isinstance(original_response, list): + usage: Final = blocked_response_usage(original_response) + return usage.get("input_tokens", 0), usage.get("output_tokens", 0) + usage_obj: Final = next( + ( + chunk_usage + for item in reversed(original_response) + if (chunk_usage := stream_item_field(item, "usage")) is not None + ), + None, + ) + return ( + _usage_tokens(usage_obj, "prompt_tokens", "input_tokens"), + _usage_tokens(usage_obj, "completion_tokens", "output_tokens"), + ) + + +def blocked_responses_stream_usage(original_response: object) -> ResponseAPIUsage: + """ + ``ResponseAPIUsage`` for a synthetic guardrail-blocked /v1/responses stream. + + A mid-stream block carries the events received so far as a list; real usage + rides on the ``response.completed`` event's response when the upstream sent + one. Non-list originals defer to ``blocked_responses_api_usage``. + """ + if not isinstance(original_response, list): + return blocked_responses_api_usage(original_response) + completed: Final = next( + ( + response + for item in reversed(original_response) + if stream_item_field(item, "type") == "response.completed" + and (response := stream_item_field(item, "response")) is not None + ), + None, + ) + return blocked_responses_api_usage(completed) + + def effective_skip_system_message_for_guardrail(guardrail_to_apply: Any) -> bool: per: Final = getattr(guardrail_to_apply, "skip_system_message_in_guardrail", None) if per is not None: diff --git a/litellm/llms/base_llm/managed_resources/base_managed_resource.py b/litellm/llms/base_llm/managed_resources/base_managed_resource.py index 2a59eddf88a..4fbc0ce51b0 100644 --- a/litellm/llms/base_llm/managed_resources/base_managed_resource.py +++ b/litellm/llms/base_llm/managed_resources/base_managed_resource.py @@ -5,13 +5,15 @@ import base64 import json from abc import ABC, abstractmethod -from typing import TYPE_CHECKING, Any, Final, Generic, TypeVar, cast +from collections.abc import Mapping +from typing import TYPE_CHECKING, Any, Final, Generic, Protocol, TypeVar, cast, runtime_checkable from litellm import verbose_logger from litellm.llms.base_llm.managed_resources.isolation import ( build_list_page, build_owner_filter, can_access_resource, + resolve_resource_owner_id, ) from litellm.proxy._types import UserAPIKeyAuth from litellm.types.utils import SpecialEnums @@ -37,6 +39,30 @@ else: ResourceObjectType = TypeVar("ResourceObjectType") +@runtime_checkable +class _HasIdentifier(Protocol): + id: str + + +class _ManagedResourceRecord(Protocol[ResourceObjectType]): + unified_resource_id: str + resource_object: ResourceObjectType + + def model_dump(self) -> dict[str, object]: ... + + +class _ManagedResourceTable(Protocol[ResourceObjectType]): + async def create(self, *, data: Mapping[str, object]) -> object: ... + + async def find_first(self, *, where: Mapping[str, object]) -> _ManagedResourceRecord[ResourceObjectType] | None: ... + + async def find_many( + self, *, where: Mapping[str, object], take: int, order: Mapping[str, str] + ) -> list[_ManagedResourceRecord[ResourceObjectType]]: ... + + async def delete(self, *, where: Mapping[str, object]) -> object: ... + + class BaseManagedResource(ABC, Generic[ResourceObjectType]): """ Base class for managing resources with target_model_names support. @@ -63,6 +89,9 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): self.internal_usage_cache = internal_usage_cache self.prisma_client = prisma_client + def _resource_table(self) -> _ManagedResourceTable[ResourceObjectType]: + return getattr(self.prisma_client.db, self.table_name) + # ============================================================================ # ABSTRACT METHODS # ============================================================================ @@ -136,7 +165,7 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): litellm_parent_otel_span: Span | None, model_mappings: dict[str, str], user_api_key_dict: UserAPIKeyAuth, - additional_db_fields: dict[str, Any] | None = None, + additional_db_fields: Mapping[str, object] | None = None, ) -> None: """ Store unified resource ID with model mappings in cache and database. @@ -152,12 +181,12 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): verbose_logger.info("Storing LiteLLM Managed %s with id=%s in cache", self.resource_type, unified_resource_id) # Prepare cache data - cache_data: Final = { + cache_data: Final[dict[str, object]] = { "unified_resource_id": unified_resource_id, "resource_object": resource_object, "model_mappings": model_mappings, "flat_model_resource_ids": list(model_mappings.values()), - "created_by": user_api_key_dict.user_id, + "created_by": resolve_resource_owner_id(user_api_key_dict), "team_id": user_api_key_dict.team_id, "updated_by": user_api_key_dict.user_id, } @@ -175,11 +204,11 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): ) # Prepare database data - db_data: Final = { + db_data: Final[dict[str, object]] = { "unified_resource_id": unified_resource_id, "model_mappings": json.dumps(model_mappings), "flat_model_resource_ids": list(model_mappings.values()), - "created_by": user_api_key_dict.user_id, + "created_by": resolve_resource_owner_id(user_api_key_dict), "team_id": user_api_key_dict.team_id, "updated_by": user_api_key_dict.user_id, } @@ -204,7 +233,7 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): db_data.update(additional_db_fields) # Store in database - table: Final = getattr(self.prisma_client.db, self.table_name) + table: Final = self._resource_table() result: Final = await table.create(data=db_data) verbose_logger.debug( @@ -239,7 +268,7 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): return result # Check database - table: Final = getattr(self.prisma_client.db, self.table_name) + table: Final = self._resource_table() db_object: Final = await table.find_first(where={"unified_resource_id": unified_resource_id}) if db_object: @@ -263,7 +292,7 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): The deleted resource object or None if not found """ # Get old value from database - table: Final = getattr(self.prisma_client.db, self.table_name) + table: Final = self._resource_table() initial_value: Final = await table.find_first(where={"unified_resource_id": unified_resource_id}) if initial_value is None: @@ -514,7 +543,7 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): user_api_key_dict: UserAPIKeyAuth, limit: int | None = None, after: str | None = None, - additional_filters: dict[str, Any] | None = None, + additional_filters: Mapping[str, object] | None = None, ) -> dict[str, Any]: """ List resources created by a user. @@ -532,7 +561,7 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): if owner_filter is None: return build_list_page([]) - where_clause: Final[dict[str, Any]] = {**owner_filter} + where_clause: Final[dict[str, object]] = {**owner_filter} if after: where_clause["id"] = {"gt": after} @@ -543,14 +572,14 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): # Fetch resources fetch_limit: Final = limit or 20 - table: Final = getattr(self.prisma_client.db, self.table_name) + table: Final = self._resource_table() resources: Final = await table.find_many( where=where_clause, take=fetch_limit, order={"created_at": "desc"}, ) - resource_objects: Final[list[Any]] = [] + resource_objects: Final[list[object]] = [] for resource in resources: try: # Stop once we have enough @@ -558,12 +587,13 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): break # Parse resource object - resource_data = resource.resource_object - if isinstance(resource_data, str): - resource_data = json.loads(resource_data) + stored_resource = resource.resource_object + resource_data: object = ( + json.loads(stored_resource) if isinstance(stored_resource, str) else stored_resource + ) # Set unified ID - if hasattr(resource_data, "id"): + if isinstance(resource_data, _HasIdentifier): resource_data.id = resource.unified_resource_id elif isinstance(resource_data, dict): resource_data["id"] = resource.unified_resource_id diff --git a/litellm/llms/base_llm/managed_resources/isolation.py b/litellm/llms/base_llm/managed_resources/isolation.py index e1b204214d7..6a71e8e9223 100644 --- a/litellm/llms/base_llm/managed_resources/isolation.py +++ b/litellm/llms/base_llm/managed_resources/isolation.py @@ -3,10 +3,11 @@ Tenant-isolation helpers for managed file/batch/vector-store resources. Returns a Prisma filter and an ownership check that scope managed resources to the caller's identity: proxy admins see everything, user-keyed callers -see records they created, and service-account keys (no user_id) fall back -to the resource's owning team. Callers with no admin role and no -identifying ids are denied so an empty user_id can never select an -unscoped query. +see records they created, service-account keys (no user_id) fall back to +the resource's owning team, and keys with neither a user_id nor a team_id +fall back to their own hashed token so they can still reach the resources +they created. Callers with no admin role and no identifying ids at all +are denied so an empty user_id can never select an unscoped query. """ from typing import Any, Final @@ -19,6 +20,32 @@ from litellm.proxy._types import ( ) +def resolve_resource_owner_id( + user_api_key_dict: UserAPIKeyAuth, +) -> str | None: + """Return the identity to stamp on (and match against) a managed + resource's ``created_by``. + + A key with neither a user_id nor a team_id would otherwise stamp + ``created_by=None`` and be locked out of its own resources, so it owns + them under its hashed token instead, using the ``key:`` scope prefix + already used by ``proxy/common_utils/resource_ownership.py``. ``None`` + means the caller has no usable identity of its own and must fall back + to team scoping, or be denied. + """ + if user_api_key_dict.user_id is not None: + return user_api_key_dict.user_id + + if user_api_key_dict.team_id is not None: + return None + + token: Final = user_api_key_dict.token or user_api_key_dict.api_key + if token: + return f"key:{token}" + + return None + + def build_list_page(items: list[Any], has_more: bool = False) -> dict[str, Any]: """Build the OpenAI-style paginated list response shape used by managed file/batch/vector-store listings. ``first_id`` and ``last_id`` are @@ -39,7 +66,8 @@ def build_owner_filter( to records the caller is allowed to see. - ``{}`` means no scoping (proxy admins). - - ``{"created_by": }`` for user-keyed callers. + - ``{"created_by": }`` for user-keyed callers, and for keys + with no user_id and no team_id (owner id is their hashed token). - ``{"team_id": }`` for service-account callers that have a team but no user_id. - ``{"OR": [...]}`` when the caller has both — listing must include @@ -62,12 +90,13 @@ def build_owner_filter( ] } - if user_id is not None: - return {"created_by": user_id} - if team_id is not None: return {"team_id": team_id} + owner_id: Final = resolve_resource_owner_id(user_api_key_dict) + if owner_id is not None: + return {"created_by": owner_id} + return None @@ -86,8 +115,8 @@ def can_access_resource( if _user_has_admin_view(user_api_key_dict): return True - user_id: Final = user_api_key_dict.user_id - if user_id is not None and created_by is not None and created_by == user_id: + owner_id: Final = resolve_resource_owner_id(user_api_key_dict) + if owner_id is not None and created_by is not None and created_by == owner_id: return True team_id: Final = user_api_key_dict.team_id diff --git a/litellm/llms/base_llm/ocr/transformation.py b/litellm/llms/base_llm/ocr/transformation.py index d1c77186ea8..3b302837032 100644 --- a/litellm/llms/base_llm/ocr/transformation.py +++ b/litellm/llms/base_llm/ocr/transformation.py @@ -75,6 +75,7 @@ class OCRUsageInfo(LiteLLMPydanticObjectBase): """Usage information from OCR response.""" pages_processed: int | None = None + pages_processed_annotation: int | None = None credits: float | None = None doc_size_bytes: int | None = None diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py index 852cfaa24f2..1e634ced29b 100644 --- a/litellm/llms/bedrock/base_aws_llm.py +++ b/litellm/llms/bedrock/base_aws_llm.py @@ -1442,7 +1442,7 @@ class BaseAWSLLM: @tracer.wrap() def get_request_headers( self, - credentials: Credentials, + credentials: Credentials | None, aws_region_name: str, extra_headers: dict | None, endpoint_url: str, @@ -1469,9 +1469,13 @@ class BaseAWSLLM: try: from botocore.auth import SigV4Auth from botocore.awsrequest import AWSRequest + from botocore.exceptions import NoCredentialsError except ImportError: raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.") + if credentials is None: + raise NoCredentialsError() + # Filter headers for AWS signature calculation # AWS SigV4 only includes specific headers in signature calculation aws_signature_headers: Final = self._filter_headers_for_aws_signature(headers) diff --git a/litellm/llms/bedrock/chat/converse_handler.py b/litellm/llms/bedrock/chat/converse_handler.py index ca5f1298360..7d5f99ca893 100644 --- a/litellm/llms/bedrock/chat/converse_handler.py +++ b/litellm/llms/bedrock/chat/converse_handler.py @@ -1,4 +1,6 @@ import json +from collections.abc import Mapping +from types import MappingProxyType from typing import Any, Final import httpx @@ -24,6 +26,22 @@ from ..common_utils import BedrockError, _get_all_bedrock_regions from .invoke_handler import AWSEventStreamDecoder, MockResponseIterator, make_call +def _sigv4_principal(credentials: Credentials | None) -> Mapping[str, str]: + if credentials is None: + return MappingProxyType({}) + return MappingProxyType( + { + key: value + for key, value in ( + ("aws_access_key_id", credentials.access_key), + ("aws_secret_access_key", credentials.secret_key), + ("aws_session_token", credentials.token), + ) + if value is not None + } + ) + + def make_sync_call( client: HTTPHandler | None, api_base: str, @@ -95,7 +113,7 @@ class BedrockConverseLLM(BaseAWSLLM): stream, optional_params: dict, litellm_params: dict, - credentials: Credentials, + credentials: Credentials | None, logger_fn=None, headers={}, client: AsyncHTTPHandler | None = None, @@ -167,7 +185,7 @@ class BedrockConverseLLM(BaseAWSLLM): stream, optional_params: dict, litellm_params: dict, - credentials: Credentials, + credentials: Credentials | None, logger_fn=None, headers: dict = {}, client: AsyncHTTPHandler | None = None, @@ -331,7 +349,7 @@ class BedrockConverseLLM(BaseAWSLLM): litellm_params["aws_region_name"] = aws_region_name # [DO NOT DELETE] important for async calls - credentials: Final[Credentials] = self.get_credentials( + credentials: Final[Credentials | None] = self.get_credentials( aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_access_key, aws_session_token=aws_session_token, @@ -368,19 +386,13 @@ class BedrockConverseLLM(BaseAWSLLM): # The Rust core owns the whole call for the subset it accepts. Ask # before transforming so whichever path runs emits pre_call once, and # hand down the credentials, region and endpoint this handler already - # resolved so both paths sign as the same principal. + # resolved so both paths sign as the same principal. Bearer-token auth + # resolves no SigV4 principal at all, and each path reads that token + # itself. rust_optional_params: Final = { # mutable-ok: json.dumps in the bridge rejects a mappingproxy **optional_params, - **{ # mutable-ok: merged into its mutable parent above - key: value - for key, value in ( - ("aws_access_key_id", credentials.access_key), - ("aws_secret_access_key", credentials.secret_key), - ("aws_session_token", credentials.token), - ("aws_region_name", aws_region_name), - ) - if value is not None - }, + **_sigv4_principal(credentials), + "aws_region_name": aws_region_name, } serves_via_rust: Final = rust_chat_completions_accepts( model=model, diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index d22b225b0bd..5363c3c0366 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -87,6 +87,7 @@ from ..common_utils import ( BedrockError, BedrockModelInfo, bedrock_converse_supports_parallel_tool_use_config, + bedrock_model_accepts_cache_points, get_anthropic_beta_from_headers, get_bedrock_tool_name, is_bedrock_application_inference_profile_arn, @@ -588,6 +589,10 @@ class AmazonConverseConfig(BaseConfig): supported_params.append("context_management") return supported_params + @staticmethod + def _auto_tool_choice() -> ToolChoiceValuesBlock: + return ToolChoiceValuesBlock(auto={}) + def map_tool_choice_values( self, model: str, tool_choice: str | dict, drop_params: bool ) -> ToolChoiceValuesBlock | None: @@ -600,10 +605,14 @@ class AmazonConverseConfig(BaseConfig): status_code=400, ) elif tool_choice == "required": + if AnthropicModelInfo.forced_tool_use_downgraded(model, drop_params): + return self._auto_tool_choice() return ToolChoiceValuesBlock(any={}) elif tool_choice == "auto": - return ToolChoiceValuesBlock(auto={}) + return self._auto_tool_choice() elif isinstance(tool_choice, dict): + if AnthropicModelInfo.forced_tool_use_downgraded(model, drop_params): + return self._auto_tool_choice() # only supported for anthropic + mistral models - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ToolChoice.html specific_tool: Final = SpecificToolChoiceBlock( name=make_valid_bedrock_tool_name(tool_choice.get("function", {}).get("name", "")) @@ -924,7 +933,7 @@ class AmazonConverseConfig(BaseConfig): custom_llm_provider="bedrock", ) capped = ( - AnthropicConfig._cap_thinking_budget_to_max_tokens(legacy_thinking, max_tokens) + AnthropicConfig.cap_thinking_budget_to_max_tokens(legacy_thinking, max_tokens) if legacy_thinking is not None else None ) @@ -1065,6 +1074,7 @@ class AmazonConverseConfig(BaseConfig): if ( litellm.utils.supports_tool_choice(model=model, custom_llm_provider=self.custom_llm_provider) and not is_thinking_enabled + and not AnthropicModelInfo.forced_tool_use_unsupported(model) ): optional_params["tool_choice"] = ToolChoiceValuesBlock( tool=SpecificToolChoiceBlock(name=RESPONSE_FORMAT_TOOL_NAME) @@ -1140,7 +1150,7 @@ class AmazonConverseConfig(BaseConfig): model: str | None = None, ) -> SystemContentBlock | ContentBlock | None: cache_control: Final = message_block.get("cache_control", None) - if cache_control is None: + if cache_control is None or not bedrock_model_accepts_cache_points(model): return None cache_point: Final = self._build_cache_point_block(cache_control, model) @@ -1604,7 +1614,7 @@ class AmazonConverseConfig(BaseConfig): # Append cachePoint to tools if cache_control_injection_points has tool_config cache_injection_points: Final = additional_request_params.pop("cache_control_injection_points", None) - if cache_injection_points and len(bedrock_tools) > 0: + if cache_injection_points and len(bedrock_tools) > 0 and bedrock_model_accepts_cache_points(model): for point in cache_injection_points: if point.get("location") == "tool_config": cache_point = self._build_cache_point_block(point.get("control"), model) @@ -1631,6 +1641,11 @@ class AmazonConverseConfig(BaseConfig): bedrock_tool_config["toolChoice"] = tool_choice_values self._drop_tool_choice_type_conflicting_with_tool_config(additional_request_params) + config_block_entries: Final = tuple( + (config_name, config_class, inference_params.pop(config_name, None)) + for config_name, config_class in self.get_config_blocks().items() + ) + data: Final[CommonRequestObject] = { "inferenceConfig": self._transform_inference_params(inference_params=inference_params), } @@ -1641,9 +1656,7 @@ class AmazonConverseConfig(BaseConfig): if system_content_blocks: data["system"] = system_content_blocks - # Handle all config blocks - for config_name, config_class in self.get_config_blocks().items(): - config_value = inference_params.pop(config_name, None) + for config_name, config_class, config_value in config_block_entries: if config_value is not None: data[config_name] = config_class(**config_value) diff --git a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py index 40b90014f3b..2a4c38e71ea 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py @@ -3,7 +3,7 @@ from typing import TYPE_CHECKING, Any, Final import httpx from litellm.anthropic_beta_headers_manager import filter_and_transform_beta_headers -from litellm.litellm_core_utils.litellm_logging import verbose_logger +from litellm.constants import RESPONSE_FORMAT_TOOL_NAME from litellm.litellm_core_utils.prompt_templates.factory import ( convert_to_anthropic_image_obj, ) @@ -12,21 +12,21 @@ from litellm.litellm_core_utils.prompt_templates.image_handling import ( convert_url_to_base64, ) from litellm.llms.anthropic.chat.transformation import AnthropicConfig +from litellm.llms.anthropic.common_utils import AnthropicModelInfo from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import ( AmazonInvokeConfig, ) from litellm.llms.bedrock.common_utils import ( - convert_bedrock_invoke_output_format_to_inline_schema, + apply_bedrock_invoke_structured_output, get_anthropic_beta_from_headers, normalize_bedrock_opus_output_config_effort, normalize_custom_field_on_tools, normalize_tool_input_schema_types_for_bedrock_invoke, - pop_bedrock_invoke_output_config_format, + strip_unsupported_bedrock_invoke_output_config_keys, ) from litellm.types.llms.anthropic import ANTHROPIC_TOOL_SEARCH_BETA_HEADER from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import ModelResponse -from litellm.utils import _supports_factory if TYPE_CHECKING: import tiktoken @@ -76,10 +76,14 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig): drop_params: bool, ) -> dict: # Force tool-based structured outputs for Bedrock Invoke - # (similar to VertexAI fix in #19201) - # Bedrock Invoke doesn't support output_format parameter + # (similar to VertexAI fix in #19201) unless the model map advertises + # native structured output + from litellm.utils import supports_native_structured_output + original_model: Final = model - if "response_format" in non_default_params: + if "response_format" in non_default_params and not supports_native_structured_output( + model=model, custom_llm_provider="bedrock" + ): # Use a model name that forces tool-based approach model = "claude-3-sonnet-20240229" @@ -103,6 +107,16 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig): # Restore original model name model = original_model + # The stub model hides the original model from the parent's forced-tool-use backstop + response_format_tool_choice: Final = optional_params.get("tool_choice") + if ( + "response_format" in non_default_params + and isinstance(response_format_tool_choice, dict) + and response_format_tool_choice.get("name") == RESPONSE_FORMAT_TOOL_NAME + and AnthropicModelInfo.forced_tool_use_unsupported(original_model) + ): + optional_params.pop("tool_choice") + return optional_params @staticmethod @@ -212,36 +226,14 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig): anthropic_request.pop("model", None) anthropic_request.pop("stream", None) anthropic_request.pop("stream_chunk_size", None) - output_format: Final = anthropic_request.pop("output_format", None) - output_config_format: Final = pop_bedrock_invoke_output_config_format(anthropic_request) - if output_format: - convert_bedrock_invoke_output_format_to_inline_schema( - output_format=output_format, - request_body=anthropic_request, - ) - elif output_config_format: - convert_bedrock_invoke_output_format_to_inline_schema( - output_format=output_config_format, - request_body=anthropic_request, - ) - if not ( - _supports_factory( - model=model, - custom_llm_provider="bedrock", - key="supports_output_config", - ) - or AnthropicConfig._model_supports_effort_param(model, "bedrock") - ): - if anthropic_request.pop("output_config", None) is not None: - verbose_logger.warning( - "Bedrock Invoke: stripping unsupported `output_config` for " - "model=%s — neither `supports_output_config` nor any " - "`supports_*_reasoning_effort` flag is set in " - "model_prices_and_context_window.json. Add the capability " - "flag to the model JSON entry if this model accepts " - "`output_config`.", - model, - ) + apply_bedrock_invoke_structured_output( + model=model, + request_body=anthropic_request, + ) + strip_unsupported_bedrock_invoke_output_config_keys( + model=model, + request_body=anthropic_request, + ) if "anthropic_version" not in anthropic_request: anthropic_request["anthropic_version"] = self.anthropic_version diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py index 72e3cc1b326..66ee5f10679 100644 --- a/litellm/llms/bedrock/common_utils.py +++ b/litellm/llms/bedrock/common_utils.py @@ -177,6 +177,95 @@ def convert_bedrock_invoke_output_format_to_inline_schema( request_body["messages"] = new_messages +def _bedrock_model_supports(model: str, key: str) -> bool: + from litellm.utils import _supports_factory + + return _supports_factory(model=model, custom_llm_provider="bedrock", key=key) + + +def apply_bedrock_invoke_structured_output( + model: str, + request_body: dict[str, object], # mutable-ok: edited in place like siblings +) -> None: + """ + Route Anthropic structured-output params to what the Bedrock model supports. + + Consumes the legacy top-level ``output_format`` and the newer + ``output_config.format``, keeping the pre-existing precedence of the legacy + field when a request carries both. Models flagged + ``supports_native_structured_output`` in the model map get the schema + forwarded as ``output_config.format``, which Bedrock relays to the model for + enforced structured output. For every other model the schema is inlined into + the last user message as best-effort text, with a warning because nothing + enforces it. + """ + legacy_output_format: Final = request_body.pop("output_format", None) + output_config_format: Final = pop_bedrock_invoke_output_config_format(request_body) + schema_format: Final = legacy_output_format if isinstance(legacy_output_format, dict) else output_config_format + if schema_format is None: + return + + if _bedrock_model_supports(model, "supports_native_structured_output"): + existing_output_config: Final = request_body.get("output_config") + if isinstance(existing_output_config, dict): + existing_output_config["format"] = schema_format + else: + request_body["output_config"] = {"format": schema_format} # rebind-ok: out-param # mutable-ok: json + return + + verbose_logger.warning( + "Bedrock Invoke: model=%s does not advertise `supports_native_structured_output` " + "in model_prices_and_context_window.json, so the JSON schema was inlined into " + "the last user message and is NOT enforced by the model.", + model, + ) + convert_bedrock_invoke_output_format_to_inline_schema( + output_format=schema_format, + request_body=request_body, + ) + + +def strip_unsupported_bedrock_invoke_output_config_keys( + model: str, + request_body: dict[str, object], # mutable-ok: edited in place like siblings +) -> None: + """ + Drop ``output_config`` keys the Bedrock model does not accept. + + ``format`` survives unconditionally: it is only attached for models whose map + entry advertises ``supports_native_structured_output``. Effort-bearing keys + survive only when the map flags ``supports_output_config`` or a + ``supports_*_reasoning_effort`` tier; otherwise they are dropped with a + warning so Bedrock does not reject the request. + """ + from litellm.llms.anthropic.chat.transformation import AnthropicConfig + + output_config: Final = request_body.get("output_config") + if not isinstance(output_config, dict): + return + if all(key == "format" for key in output_config): + return + if _bedrock_model_supports(model, "supports_output_config") or AnthropicConfig._model_supports_effort_param( + model, "bedrock" + ): + return + + verbose_logger.warning( + "Bedrock Invoke: stripping unsupported `output_config` keys for " + "model=%s: neither `supports_output_config` nor any " + "`supports_*_reasoning_effort` flag is set in " + "model_prices_and_context_window.json. Add the capability " + "flag to the model JSON entry if this model accepts " + "`output_config`.", + model, + ) + preserved_format: Final = output_config.get("format") + if preserved_format is None: + request_body.pop("output_config", None) + else: + request_body["output_config"] = {"format": preserved_format} # rebind-ok: out-param # mutable-ok: json + + def normalize_custom_field_on_tools(request_body: dict) -> None: """ Drop the ``custom`` field from each tool, first hoisting a boolean @@ -727,6 +816,30 @@ def bedrock_converse_supports_parallel_tool_use_config(model: str) -> bool: ) +def bedrock_model_accepts_cache_points(model: str | None) -> bool: + """ + Whether Converse ``cachePoint`` blocks may be sent to this model. + + Bedrock rejects requests carrying cachePoint blocks for models without prompt + caching support ("You invoked an unsupported model or your request did not allow + prompt caching"), so a model whose cost-map entry does not declare + ``supports_prompt_caching`` must not receive them. A model absent from the map + (an application inference profile ARN, a model newer than the map) keeps emitting + so existing caching setups never silently degrade. ``litellm.utils.supports_prompt_caching`` + is not reusable here: it returns False for unmapped models, the opposite polarity. + """ + if model is None: + return True + entries: Final = tuple( + entry + for candidate in (model, get_bedrock_base_model(model)) + if (entry := litellm.model_cost.get(candidate)) is not None + ) + if not entries: + return True + return any(entry.get("supports_prompt_caching") is True for entry in entries) + + def is_claude_4_5_on_bedrock(model: str) -> bool: """ Check if the model supports Bedrock prompt caching with an extended '1h' TTL @@ -1487,6 +1600,7 @@ class CommonBatchFilesUtils: aws_role_name=optional_params.get("aws_role_name"), aws_web_identity_token=optional_params.get("aws_web_identity_token"), aws_sts_endpoint=optional_params.get("aws_sts_endpoint"), + aws_external_id=optional_params.get("aws_external_id"), ) # Prepare the request data diff --git a/litellm/llms/bedrock/embed/cohere_transformation.py b/litellm/llms/bedrock/embed/cohere_transformation.py index d1c9ceb99d1..8a17bb9d595 100644 --- a/litellm/llms/bedrock/embed/cohere_transformation.py +++ b/litellm/llms/bedrock/embed/cohere_transformation.py @@ -20,7 +20,9 @@ class BedrockCohereEmbeddingConfig: def map_openai_params(self, non_default_params: dict, optional_params: dict) -> dict: for k, v in non_default_params.items(): if k == "encoding_format": - optional_params["embedding_types"] = v if isinstance(v, list) else [v] + optional_params["embedding_types"] = [ + "float" if fmt == "base64" else fmt for fmt in (tuple(v) if isinstance(v, list) else (v,)) + ] elif k == "dimensions": optional_params["output_dimension"] = v return optional_params diff --git a/litellm/llms/bedrock/files/handler.py b/litellm/llms/bedrock/files/handler.py index 13718d41cc1..e74c3802d20 100644 --- a/litellm/llms/bedrock/files/handler.py +++ b/litellm/llms/bedrock/files/handler.py @@ -113,6 +113,7 @@ class BedrockFilesHandler(BaseAWSLLM): aws_role_name=optional_params.get("aws_role_name"), aws_web_identity_token=optional_params.get("aws_web_identity_token"), aws_sts_endpoint=optional_params.get("aws_sts_endpoint"), + aws_external_id=optional_params.get("aws_external_id"), ) # Create S3 client diff --git a/litellm/llms/bedrock/files/transformation.py b/litellm/llms/bedrock/files/transformation.py index f442608a288..33b27943ad8 100644 --- a/litellm/llms/bedrock/files/transformation.py +++ b/litellm/llms/bedrock/files/transformation.py @@ -146,6 +146,7 @@ class _BedrockS3RequestParams(BaseModel): aws_role_name: str | None = None aws_web_identity_token: str | None = None aws_sts_endpoint: str | None = None + aws_external_id: str | None = None s3_region_name: str | None = None s3_endpoint_url: str | None = None @@ -1029,6 +1030,7 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): aws_role_name=optional_params.get("aws_role_name"), aws_web_identity_token=optional_params.get("aws_web_identity_token"), aws_sts_endpoint=optional_params.get("aws_sts_endpoint"), + aws_external_id=optional_params.get("aws_external_id"), ) # Calculate SHA256 hash of the content (REQUIRED for S3) @@ -1290,6 +1292,7 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): aws_role_name=request_params.aws_role_name, aws_web_identity_token=request_params.aws_web_identity_token, aws_sts_endpoint=request_params.aws_sts_endpoint, + aws_external_id=request_params.aws_external_id, ) empty_body_hash: Final = hashlib.sha256(b"").hexdigest() diff --git a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py index f74a290d773..6ff9f0155f9 100644 --- a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py @@ -29,14 +29,14 @@ from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation AmazonInvokeConfig, ) from litellm.llms.bedrock.common_utils import ( - convert_bedrock_invoke_output_format_to_inline_schema, + apply_bedrock_invoke_structured_output, ensure_bedrock_anthropic_messages_tool_names, get_anthropic_beta_from_headers, is_claude_4_5_on_bedrock, normalize_bedrock_opus_output_config_effort, normalize_custom_field_on_tools, normalize_tool_input_schema_types_for_bedrock_invoke, - pop_bedrock_invoke_output_config_format, + strip_unsupported_bedrock_invoke_output_config_keys, ) from litellm.llms.bedrock.request_metadata import ( bedrock_request_metadata_headers, @@ -51,7 +51,6 @@ from litellm.types.llms.openai import AllMessageValues from litellm.types.router import GenericLiteLLMParams from litellm.types.utils import GenericStreamingChunk, ModelResponseStream from litellm.types.utils import GenericStreamingChunk as GChunk -from litellm.utils import _supports_factory if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj @@ -708,52 +707,25 @@ class AmazonAnthropicClaudeMessagesConfig( # 4. Remove `ttl` field from cache_control in messages (Bedrock doesn't support it for older models) self._remove_ttl_from_cache_control(anthropic_messages_request=anthropic_messages_request, model=model) - # 5. Convert structured-output params to inline schema. - # Bedrock Invoke doesn't support top-level `output_format`; its - # accepted `output_config` subset is also narrower than Anthropic's, so - # consume the newer `output_config.format` shape here instead of - # forwarding it as an unknown nested key. + # 5. Route structured-output params (`output_format` / + # `output_config.format`) to native enforcement or the inline-schema + # fallback, then strip `output_config` keys the model does not accept. + # Ref: https://github.com/BerriAI/litellm/issues/22797 existing_output_config: Final = anthropic_messages_request.get("output_config") if isinstance(existing_output_config, dict): anthropic_messages_request["output_config"] = dict(existing_output_config) - output_format: Final = anthropic_messages_request.pop("output_format", None) - output_config_format: Final = pop_bedrock_invoke_output_config_format(anthropic_messages_request) - if output_format: - convert_bedrock_invoke_output_format_to_inline_schema( - output_format=output_format, - request_body=anthropic_messages_request, - ) - elif output_config_format: - convert_bedrock_invoke_output_format_to_inline_schema( - output_format=output_config_format, - request_body=anthropic_messages_request, - ) + apply_bedrock_invoke_structured_output( + model=model, + request_body=anthropic_messages_request, + ) normalize_bedrock_opus_output_config_effort( model=model, output_config=anthropic_messages_request.get("output_config"), ) - - # 5a. Bedrock Invoke supports output_config (effort) for Claude 4.6+ models, - # but older models do not — strip it to avoid request rejection. - # Ref: https://github.com/BerriAI/litellm/issues/22797 - if not ( - _supports_factory( - model=model, - custom_llm_provider="bedrock", - key="supports_output_config", - ) - or AnthropicConfig._model_supports_effort_param(model, "bedrock") - ): - if anthropic_messages_request.pop("output_config", None) is not None: - verbose_logger.warning( - "Bedrock Invoke: stripping unsupported `output_config` for " - "model=%s — neither `supports_output_config` nor any " - "`supports_*_reasoning_effort` flag is set in " - "model_prices_and_context_window.json. Add the capability " - "flag to the model JSON entry if this model accepts " - "`output_config`.", - model, - ) + strip_unsupported_bedrock_invoke_output_config_keys( + model=model, + request_body=anthropic_messages_request, + ) # 5b. Hoist `custom.defer_loading` then drop `custom` (Bedrock doesn't support it) # Ref: https://github.com/BerriAI/litellm/issues/22847 @@ -774,9 +746,11 @@ class AmazonAnthropicClaudeMessagesConfig( if filtered_betas: anthropic_messages_request["anthropic_beta"] = filtered_betas + remaining_output_config: Final = anthropic_messages_request.get("output_config") if ( litellm.drop_params is True - and "output_config" in anthropic_messages_request + and isinstance(remaining_output_config, dict) + and any(key != "format" for key in remaining_output_config) and not AnthropicConfig._model_supports_effort_param(model, "bedrock") ): verbose_logger.warning( diff --git a/litellm/llms/bedrock/realtime/handler.py b/litellm/llms/bedrock/realtime/handler.py index 96d7a79c6d8..42fe8941443 100644 --- a/litellm/llms/bedrock/realtime/handler.py +++ b/litellm/llms/bedrock/realtime/handler.py @@ -7,13 +7,18 @@ This uses aws_sdk_bedrock_runtime for bidirectional streaming with Nova Sonic. import asyncio import contextlib import json +from collections.abc import AsyncIterator, Mapping from typing import Final, Protocol from pydantic import JsonValue, TypeAdapter +import litellm from litellm._logging import _redact_string, verbose_proxy_logger from litellm.litellm_core_utils.aws_partition import get_aws_dns_suffix from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging +from litellm.litellm_core_utils.logging_worker import GLOBAL_LOGGING_WORKER +from litellm.litellm_core_utils.realtime_streaming import DefaultLoggedRealTimeEventTypes +from litellm.types.llms.openai import OpenAIRealtimeEvents from litellm.types.realtime import RealtimeResponseTransformInput from ..base_aws_llm import BaseAWSLLM @@ -32,6 +37,17 @@ def _json_str(value: JsonValue) -> str | None: return value if isinstance(value, str) else None +def _should_log_event(openai_message: Mapping[str, object]) -> bool: + logged_types: Final = ( + litellm.logged_real_time_event_types + if litellm.logged_real_time_event_types is not None + else DefaultLoggedRealTimeEventTypes + ) + if logged_types == "*": + return True + return openai_message.get("type") in logged_types + + class RealtimeClientWebSocket(Protocol): """The client-facing websocket surface the realtime bridge talks to.""" @@ -94,7 +110,7 @@ class BedrockRealtime(BaseAWSLLM): aws_sts_endpoint: str | None = None, aws_bedrock_runtime_endpoint: str | None = None, aws_external_id: str | None = None, - **kwargs, + **kwargs: object, ): """ Establish bidirectional streaming connection with Bedrock Nova Sonic. @@ -166,13 +182,16 @@ class BedrockRealtime(BaseAWSLLM): ) bedrock_client: Final = BedrockRuntimeClient(config=config) + async def open_bidirectional_stream() -> BedrockBidirectionalStream: + return await bedrock_client.invoke_model_with_bidirectional_stream( + InvokeModelWithBidirectionalStreamOperationInput(model_id=model) + ) + transformation_config: Final = BedrockRealtimeConfig() try: # Initialize the bidirectional stream - bedrock_stream: Final = await bedrock_client.invoke_model_with_bidirectional_stream( - InvokeModelWithBidirectionalStreamOperationInput(model_id=model) - ) + bedrock_stream: Final = await open_bidirectional_stream() verbose_proxy_logger.debug("Bedrock Realtime: Bidirectional stream established") @@ -202,16 +221,22 @@ class BedrockRealtime(BaseAWSLLM): ) ) - bedrock_to_client_task: Final = asyncio.create_task( - self._forward_bedrock_to_client( - bedrock_stream, - websocket, - transformation_config, - model, - logging_obj, - session_state, + async def forward_bedrock_and_collect_logged_events() -> tuple[OpenAIRealtimeEvents, ...]: + return tuple( + [ + event + async for event in self._forward_bedrock_to_client( + bedrock_stream, + websocket, + transformation_config, + model, + logging_obj, + session_state, + ) + ] ) - ) + + bedrock_to_client_task: Final = asyncio.create_task(forward_bedrock_and_collect_logged_events()) # Wait for both tasks to complete await asyncio.gather( @@ -220,6 +245,27 @@ class BedrockRealtime(BaseAWSLLM): return_exceptions=True, ) + forwarded_logged_events: Final = ( + bedrock_to_client_task.result() + if not bedrock_to_client_task.cancelled() and bedrock_to_client_task.exception() is None + else () + ) + logged_events: Final = ( + *forwarded_logged_events, + *( + leftover_event + for leftover_event in transformation_config.leftover_usage_done_events() + if _should_log_event(leftover_event) + ), + ) + if logged_events: + GLOBAL_LOGGING_WORKER.ensure_initialized_and_enqueue( + logging_obj.dispatch_success_handlers( + list(logged_events), # mutable-ok: realtime spend logging requires a list result + prefer_async_handlers=True, + ) + ) + except Exception as e: verbose_proxy_logger.exception("Error in BedrockRealtime.async_realtime: %s", e) try: @@ -243,10 +289,11 @@ class BedrockRealtime(BaseAWSLLM): InvokeModelWithBidirectionalStreamInputChunk, ) + def build_input_chunk(payload: bytes) -> object: + return InvokeModelWithBidirectionalStreamInputChunk(value=BidirectionalInputPayloadPart(bytes_=payload)) + async def send_to_bedrock(bedrock_message: str) -> None: - event: Final = InvokeModelWithBidirectionalStreamInputChunk( - value=BidirectionalInputPayloadPart(bytes_=bedrock_message.encode("utf-8")) - ) + event: Final = build_input_chunk(bedrock_message.encode("utf-8")) await bedrock_stream.input_stream.send(event) verbose_proxy_logger.debug("Bedrock Realtime: Sent to Bedrock: %s", bedrock_message[:200]) @@ -300,8 +347,8 @@ class BedrockRealtime(BaseAWSLLM): model: str, logging_obj: LiteLLMLogging, session_state: RealtimeResponseTransformInput, - ): - """Forward messages from Bedrock stream to client WebSocket.""" + ) -> AsyncIterator[OpenAIRealtimeEvents]: + """Forward messages from Bedrock to the client, yielding the ones to record for spend logging.""" try: while True: # Receive from Bedrock @@ -349,11 +396,14 @@ class BedrockRealtime(BaseAWSLLM): ) # Send transformed messages to client - openai_messages = transformed_response.get("response", []) + response_value = transformed_response["response"] + openai_messages = response_value if isinstance(response_value, list) else (response_value,) for openai_message in openai_messages: message_json = json.dumps(openai_message) await client_ws.send_text(message_json) verbose_proxy_logger.debug("Bedrock Realtime: Sent to client: %s", message_json[:200]) + if _should_log_event(openai_message): + yield openai_message except Exception as e: verbose_proxy_logger.debug("Bedrock to client forwarding ended: %s", e, exc_info=True) diff --git a/litellm/llms/bedrock/realtime/transformation.py b/litellm/llms/bedrock/realtime/transformation.py index 951bf636b2f..1f4c81d6491 100644 --- a/litellm/llms/bedrock/realtime/transformation.py +++ b/litellm/llms/bedrock/realtime/transformation.py @@ -7,7 +7,7 @@ Transforms between OpenAI Realtime API format and Bedrock Nova Sonic format. import base64 import json import uuid as uuid_lib -from typing import Any, Final +from typing import Final, cast from pydantic import BaseModel @@ -20,29 +20,54 @@ from litellm.types.llms.openai import ( OpenAIRealtimeContentPartDone, OpenAIRealtimeDoneEvent, OpenAIRealtimeEvents, + OpenAIRealtimeInputAudioBufferSpeechEvent, + OpenAIRealtimeInputAudioTranscriptionCompleted, + OpenAIRealtimeInputAudioTranscriptionDelta, OpenAIRealtimeOutputItemDone, OpenAIRealtimeResponseAudioDone, OpenAIRealtimeResponseContentPartAdded, OpenAIRealtimeResponseDelta, OpenAIRealtimeResponseDoneObject, OpenAIRealtimeResponseTextDone, + OpenAIRealtimeResponseUsage, OpenAIRealtimeStreamResponseBaseObject, OpenAIRealtimeStreamResponseOutputItemAdded, OpenAIRealtimeStreamSession, OpenAIRealtimeStreamSessionEvents, + OpenAIRealtimeUsageTokenDetails, ) from litellm.types.realtime import ( ALL_DELTA_TYPES, RealtimeResponseTransformInput, RealtimeResponseTypedDict, ) -from litellm.utils import get_empty_usage class BedrockContentEnd(BaseModel): stopReason: str | None = None +class BedrockUsageTokenDetails(BaseModel): + speechTokens: int = 0 + textTokens: int = 0 + + +class BedrockUsageDetailsTotal(BaseModel): + input: BedrockUsageTokenDetails = BedrockUsageTokenDetails() + output: BedrockUsageTokenDetails = BedrockUsageTokenDetails() + + +class BedrockUsageDetails(BaseModel): + total: BedrockUsageDetailsTotal = BedrockUsageDetailsTotal() + + +class BedrockUsageEvent(BaseModel): + totalInputTokens: int = 0 + totalOutputTokens: int = 0 + totalTokens: int = 0 + details: BedrockUsageDetails = BedrockUsageDetails() + + TRIGGER_AUDIO_SAMPLE_RATE_HERTZ: Final = 16000 TRIGGER_AUDIO_BYTES_PER_SECOND: Final = TRIGGER_AUDIO_SAMPLE_RATE_HERTZ * 2 TRIGGER_LEADING_SILENCE: Final = bytes(TRIGGER_AUDIO_BYTES_PER_SECOND // 2) @@ -87,6 +112,15 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): # Text configuration self.text_media_type = "text/plain" + # Response-stream state (Bedrock events carry no role on textOutput, + # so the USER/ASSISTANT split from contentStart is tracked here) + self._user_transcript_active = False + self._user_transcript_generation_stage: str | None = None + self._user_item_id: str | None = None + self._user_transcript_buffer = "" + self._cumulative_usage = BedrockUsageEvent() + self._reported_usage = BedrockUsageEvent() + def validate_environment(self, headers: dict, model: str, api_key: str | None = None) -> dict: """Validate environment - no special validation needed for Bedrock.""" return headers @@ -599,7 +633,7 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): List of Bedrock format messages (JSON strings) """ try: - json_message: Final = json.loads(message) + json_message: Final[dict[str, object]] = json.loads(message) except json.JSONDecodeError: verbose_logger.warning("Invalid JSON message: %s", message[:200]) return [] @@ -691,6 +725,11 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): role: Final = content_start.get("role") if role != "ASSISTANT": + if role == "USER" and content_start.get("type") == "TEXT": + self._user_transcript_active = True + self._user_transcript_generation_stage = self._parse_generation_stage( + content_start.get("additionalModelFields") + ) return ( [], current_response_id, @@ -700,6 +739,7 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): ) verbose_logger.debug("Handling ASSISTANT contentStart") + is_new_response: Final = current_response_id is None # Initialize IDs if needed if not current_response_id: @@ -715,7 +755,8 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): returned_messages: Final[list[OpenAIRealtimeEvents]] = [] - # Send response.created + # Send response.created only once per response (a response can contain + # multiple content blocks, e.g. TEXT then AUDIO) response_created: Final = OpenAIRealtimeStreamResponseBaseObject( type="response.created", event_id=f"event_{uuid.uuid4()}", @@ -727,7 +768,8 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): "conversation_id": current_conversation_id, }, ) - returned_messages.append(response_created) + if is_new_response: + returned_messages.append(response_created) # Send response.output_item.added output_item_added: Final = OpenAIRealtimeStreamResponseOutputItemAdded( @@ -767,6 +809,108 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): current_delta_type, ) + @staticmethod + def _parse_generation_stage(additional_model_fields: object) -> str | None: + if not isinstance(additional_model_fields, str): + return None + try: + parsed: Final = json.loads(additional_model_fields) + except json.JSONDecodeError: + return None + stage: Final = parsed.get("generationStage") if isinstance(parsed, dict) else None + return stage if isinstance(stage, str) else None + + def _current_user_item_id(self, new_utterance: bool = False) -> str: + """Item id shared by all events of one user utterance (speech boundaries and transcript).""" + if new_utterance or self._user_item_id is None: + self._user_item_id = f"item_{uuid.uuid4()}" + return self._user_item_id + + def transform_user_speech_event(self, is_speech_start: bool) -> tuple[OpenAIRealtimeEvents, ...]: + """Transform Bedrock userSpeechStart/userSpeechEnd to OpenAI speech boundary events.""" + verbose_logger.debug("Handling userSpeech%s", "Start" if is_speech_start else "End") + speech_event: Final[OpenAIRealtimeInputAudioBufferSpeechEvent] = { + "type": "input_audio_buffer.speech_started" if is_speech_start else "input_audio_buffer.speech_stopped", + "event_id": f"event_{uuid.uuid4()}", + "item_id": self._current_user_item_id(new_utterance=is_speech_start), + } + return (speech_event,) + + def transform_usage_event(self, usage_event: BedrockUsageEvent) -> None: + """Record Bedrock's session-cumulative usage totals for the next response.done.""" + verbose_logger.debug("Handling usageEvent") + self._cumulative_usage = usage_event + + def _take_usage_delta(self) -> OpenAIRealtimeResponseUsage: + """Usage for the response now completing: cumulative totals minus what prior response.done events reported.""" + prior: Final = self._reported_usage + latest: Final = self._cumulative_usage + self._reported_usage = latest + input_details: Final[OpenAIRealtimeUsageTokenDetails] = { + "audio_tokens": latest.details.total.input.speechTokens - prior.details.total.input.speechTokens, + "text_tokens": latest.details.total.input.textTokens - prior.details.total.input.textTokens, + "cached_tokens": 0, + } + output_details: Final[OpenAIRealtimeUsageTokenDetails] = { + "audio_tokens": latest.details.total.output.speechTokens - prior.details.total.output.speechTokens, + "text_tokens": latest.details.total.output.textTokens - prior.details.total.output.textTokens, + } + usage_delta: Final[OpenAIRealtimeResponseUsage] = { + "input_tokens": latest.totalInputTokens - prior.totalInputTokens, + "output_tokens": latest.totalOutputTokens - prior.totalOutputTokens, + "total_tokens": latest.totalTokens - prior.totalTokens, + "input_token_details": input_details, + "output_token_details": output_details, + } + return usage_delta + + def leftover_usage_done_events(self) -> tuple[OpenAIRealtimeEvents, ...]: + """Logged-only response.done for usage Bedrock reports after the final turn's contentEnd.""" + if self._cumulative_usage == self._reported_usage: + return () + usage: Final = self._take_usage_delta() + leftover_done: Final = OpenAIRealtimeDoneEvent( + type="response.done", + event_id=f"event_{uuid.uuid4()}", + response=OpenAIRealtimeResponseDoneObject( + object="realtime.response", + id=f"resp_{uuid.uuid4()}", + status="completed", + conversation_id=f"conv_{uuid.uuid4()}", + usage=dict(usage), # mutable-ok: OpenAIRealtimeResponseDoneObject types usage as plain dict + ), + ) + return (leftover_done,) + + def transform_user_transcript_event(self, transcript: str) -> tuple[OpenAIRealtimeEvents, ...]: + """Transform a USER-role Bedrock textOutput (ASR transcript) to an OpenAI transcription delta.""" + verbose_logger.debug("Handling USER textOutput (ASR transcript)") + delta_event: Final[OpenAIRealtimeInputAudioTranscriptionDelta] = { + "type": "conversation.item.input_audio_transcription.delta", + "event_id": f"event_{uuid.uuid4()}", + "item_id": self._current_user_item_id(), + "content_index": 0, + "delta": transcript, + } + if self._user_transcript_generation_stage != "SPECULATIVE": + self._user_transcript_buffer += transcript + return (delta_event,) + + def user_transcript_completed_events(self) -> tuple[OpenAIRealtimeEvents, ...]: + """One completed event with the full transcript once the FINAL user content block ends.""" + transcript: Final = self._user_transcript_buffer + if not transcript: + return () + self._user_transcript_buffer = "" + completed_event: Final[OpenAIRealtimeInputAudioTranscriptionCompleted] = { + "type": "conversation.item.input_audio_transcription.completed", + "event_id": f"event_{uuid.uuid4()}", + "item_id": self._current_user_item_id(), + "content_index": 0, + "transcript": transcript, + } + return (completed_event,) + def transform_text_output_event( self, event: dict, @@ -985,7 +1129,7 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): if not current_response_id or not current_conversation_id: return [], None, None, None - usage_obj: Final = get_empty_usage() + usage: Final = self._take_usage_delta() response_done: Final = OpenAIRealtimeDoneEvent( type="response.done", event_id=f"event_{uuid.uuid4()}", @@ -995,11 +1139,7 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): status="completed", output=[], conversation_id=current_conversation_id, - usage={ - "prompt_tokens": usage_obj.prompt_tokens, - "completion_tokens": usage_obj.completion_tokens, - "total_tokens": usage_obj.total_tokens, - }, + usage=dict(usage), ), ) @@ -1042,9 +1182,7 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): # Create a function call arguments done event # This is a custom event format that matches what clients expect - from typing import cast - - function_call_event: Final[dict[str, Any]] = { + function_call_event: Final[dict[str, object]] = { "type": "response.function_call_arguments.done", "event_id": f"event_{uuid.uuid4()}", "response_id": current_response_id, @@ -1194,18 +1332,26 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): returned_messages.extend(events) elif "textOutput" in event: - events, current_delta_chunks = self.transform_text_output_event( - event, - current_output_item_id, - current_response_id, - current_delta_chunks, - ) - returned_messages.extend(events) + if self._user_transcript_active: + returned_messages.extend(self.transform_user_transcript_event(event["textOutput"].get("content", ""))) + else: + events, current_delta_chunks = self.transform_text_output_event( + event, + current_output_item_id, + current_response_id, + current_delta_chunks, + ) + returned_messages.extend(events) elif "audioOutput" in event: events = self.transform_audio_output_event(event, current_output_item_id, current_response_id) returned_messages.extend(events) + elif "contentEnd" in event and self._user_transcript_active: + self._user_transcript_active = False + self._user_transcript_generation_stage = None + returned_messages.extend(self.user_transcript_completed_events()) + elif "contentEnd" in event: events, current_delta_chunks = self.transform_content_end_event( event, @@ -1224,6 +1370,12 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): ) = self._response_done_events(current_response_id, current_conversation_id) returned_messages.extend(done_events) + elif "userSpeechStart" in event or "userSpeechEnd" in event: + returned_messages.extend(self.transform_user_speech_event("userSpeechStart" in event)) + + elif "usageEvent" in event: + self.transform_usage_event(BedrockUsageEvent.model_validate(event["usageEvent"])) + elif "toolUse" in event: events, tool_call_id, tool_name = self.transform_tool_use_event( event, current_output_item_id, current_response_id diff --git a/litellm/llms/black_forest_labs/image_edit/handler.py b/litellm/llms/black_forest_labs/image_edit/handler.py index 1ff02a6f8d9..178acb0de0d 100644 --- a/litellm/llms/black_forest_labs/image_edit/handler.py +++ b/litellm/llms/black_forest_labs/image_edit/handler.py @@ -8,9 +8,11 @@ then we poll until the result is ready. import asyncio import time -from typing import Any, Final +from collections.abc import Coroutine, Mapping +from typing import Final, Protocol import httpx +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -33,6 +35,42 @@ from ..common_utils import ( from .transformation import BlackForestLabsImageEditConfig +class _BFLSubmitBody(TypedDict, total=False): + """Decoded body of the BFL submit response, which hands back a polling URL.""" + + errors: ReadOnly[object] + polling_url: ReadOnly[str] + + +class _BFLPollBody(TypedDict, total=False): + """Decoded body of a BFL polling response.""" + + status: ReadOnly[str] + + +class _BFLSubmitResponse(Protocol): + """The submit call's HTTP response, read for its status, body text and decoded body.""" + + @property + def status_code(self) -> int: ... + + @property + def text(self) -> str: ... + + def json(self) -> _BFLSubmitBody: ... + + +class _BFLPollResponse(Protocol): + """A polling call's HTTP response, read only for the task status it carries.""" + + def json(self) -> _BFLPollBody: ... + + +def _poll_status(response: _BFLPollResponse) -> str | None: + """Read the task status out of a BFL polling response body.""" + return response.json().get("status") + + class BlackForestLabsImageEdit: """ Black Forest Labs Image Edit handler. @@ -53,10 +91,10 @@ class BlackForestLabsImageEdit: litellm_params: GenericLiteLLMParams | dict, logging_obj: LiteLLMLoggingObj, timeout: float | httpx.Timeout | None, - extra_headers: dict[str, Any] | None = None, + extra_headers: Mapping[str, object] | None = None, client: HTTPHandler | AsyncHTTPHandler | None = None, aimage_edit: bool = False, - ) -> ImageResponse | Any: + ) -> ImageResponse | Coroutine[object, object, ImageResponse]: """ Main entry point for image edit requests. @@ -185,7 +223,7 @@ class BlackForestLabsImageEdit: litellm_params: GenericLiteLLMParams | dict, logging_obj: LiteLLMLoggingObj, timeout: float | httpx.Timeout | None, - extra_headers: dict[str, Any] | None = None, + extra_headers: Mapping[str, object] | None = None, client: AsyncHTTPHandler | None = None, ) -> ImageResponse: """ @@ -281,7 +319,7 @@ class BlackForestLabsImageEdit: def _poll_for_result_sync( self, - initial_response: httpx.Response, + initial_response: _BFLSubmitResponse, headers: dict, sync_client: HTTPHandler, max_wait: float = DEFAULT_MAX_POLLING_TIME, @@ -356,8 +394,7 @@ class BlackForestLabsImageEdit: message=f"Polling failed: {response.text}", ) - data = response.json() - status = data.get("status") + status = _poll_status(response) verbose_logger.debug("BFL poll status: %s", status) @@ -383,7 +420,7 @@ class BlackForestLabsImageEdit: async def _poll_for_result_async( self, - initial_response: httpx.Response, + initial_response: _BFLSubmitResponse, headers: dict, async_client: AsyncHTTPHandler, max_wait: float = DEFAULT_MAX_POLLING_TIME, @@ -447,8 +484,7 @@ class BlackForestLabsImageEdit: message=f"Polling failed: {response.text}", ) - data = response.json() - status = data.get("status") + status = _poll_status(response) verbose_logger.debug("BFL poll status: %s", status) diff --git a/litellm/llms/black_forest_labs/image_generation/handler.py b/litellm/llms/black_forest_labs/image_generation/handler.py index 03e4999c5aa..879bef37b58 100644 --- a/litellm/llms/black_forest_labs/image_generation/handler.py +++ b/litellm/llms/black_forest_labs/image_generation/handler.py @@ -8,9 +8,11 @@ then we poll until the result is ready. import asyncio import time -from typing import Any, Final +from collections.abc import Coroutine, Mapping +from typing import Final, Protocol, TypedDict import httpx +from typing_extensions import ReadOnly import litellm from litellm._logging import verbose_logger @@ -33,6 +35,23 @@ from ..common_utils import ( from .transformation import BlackForestLabsImageGenerationConfig +class _BFLTaskPayload(TypedDict, total=False): + """The body BFL returns for a submitted or polled generation task.""" + + errors: ReadOnly[object] + polling_url: ReadOnly[str] + status: ReadOnly[str] + + +class _TaskJsonResponse(Protocol): + def json(self) -> _BFLTaskPayload: ... + + +def _task_payload(response: _TaskJsonResponse) -> _BFLTaskPayload: + """The JSON body of a BFL task submission or poll response.""" + return response.json() + + class BlackForestLabsImageGeneration: """ Black Forest Labs Image Generation handler. @@ -53,10 +72,10 @@ class BlackForestLabsImageGeneration: litellm_params: GenericLiteLLMParams | dict, logging_obj: LiteLLMLoggingObj, timeout: float | httpx.Timeout | None, - extra_headers: dict[str, Any] | None = None, + extra_headers: Mapping[str, str] | None = None, client: HTTPHandler | AsyncHTTPHandler | None = None, aimg_generation: bool = False, - ) -> ImageResponse | Any: + ) -> ImageResponse | Coroutine[object, object, ImageResponse]: """ Main entry point for image generation requests. @@ -187,7 +206,7 @@ class BlackForestLabsImageGeneration: litellm_params: GenericLiteLLMParams | dict, logging_obj: LiteLLMLoggingObj, timeout: float | httpx.Timeout | None, - extra_headers: dict[str, Any] | None = None, + extra_headers: Mapping[str, str] | None = None, client: AsyncHTTPHandler | None = None, ) -> ImageResponse: """ @@ -305,7 +324,7 @@ class BlackForestLabsImageGeneration: # Parse initial response to get polling URL try: - response_data: Final = initial_response.json() + response_data: Final = _task_payload(initial_response) except Exception as e: raise BlackForestLabsError( status_code=initial_response.status_code, @@ -350,7 +369,7 @@ class BlackForestLabsImageGeneration: message=f"Polling failed: {response.text}", ) - data = response.json() + data = _task_payload(response) status = data.get("status") verbose_logger.debug("BFL poll status: %s", status) @@ -396,7 +415,7 @@ class BlackForestLabsImageGeneration: # Parse initial response to get polling URL try: - response_data: Final = initial_response.json() + response_data: Final = _task_payload(initial_response) except Exception as e: raise BlackForestLabsError( status_code=initial_response.status_code, @@ -441,7 +460,7 @@ class BlackForestLabsImageGeneration: message=f"Polling failed: {response.text}", ) - data = response.json() + data = _task_payload(response) status = data.get("status") verbose_logger.debug("BFL poll status: %s", status) diff --git a/litellm/llms/chatgpt/chat/streaming_utils.py b/litellm/llms/chatgpt/chat/streaming_utils.py index d4a168a6984..ee3120bacea 100644 --- a/litellm/llms/chatgpt/chat/streaming_utils.py +++ b/litellm/llms/chatgpt/chat/streaming_utils.py @@ -49,10 +49,10 @@ class ChatGPTToolCallNormalizer: def __getattr__(self, name: str) -> object: return getattr(self._stream, name) - def __iter__(self): + def __iter__(self) -> "ChatGPTToolCallNormalizer": return self - def __aiter__(self): + def __aiter__(self) -> "ChatGPTToolCallNormalizer": return self def __next__(self) -> ModelResponseStream: diff --git a/litellm/llms/codestral/completion/handler.py b/litellm/llms/codestral/completion/handler.py index 8c08b2bc33c..f8486d3b274 100644 --- a/litellm/llms/codestral/completion/handler.py +++ b/litellm/llms/codestral/completion/handler.py @@ -4,9 +4,10 @@ import json from collections.abc import Callable from functools import partial -from typing import Final +from typing import Final, Protocol import httpx +from typing_extensions import NotRequired, ReadOnly, TypedDict import litellm from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging @@ -23,6 +24,53 @@ from litellm.types.utils import TextChoices from litellm.utils import CustomStreamWrapper, TextCompletionResponse +class _CodestralChoiceMessage(TypedDict): + """`choices[].message` of a Codestral FIM completion.""" + + role: ReadOnly[NotRequired[str]] + content: ReadOnly[NotRequired[str | None]] + + +class _CodestralChoice(TypedDict): + """One entry of `choices` in a Codestral FIM completion.""" + + index: ReadOnly[int] + message: ReadOnly[NotRequired[_CodestralChoiceMessage]] + finish_reason: ReadOnly[NotRequired[str | None]] + logprobs: ReadOnly[NotRequired[dict[str, object] | None]] + + +class _CodestralUsage(TypedDict): + """Token accounting returned alongside a Codestral FIM completion.""" + + prompt_tokens: ReadOnly[NotRequired[int]] + completion_tokens: ReadOnly[NotRequired[int]] + total_tokens: ReadOnly[NotRequired[int]] + + +class _CodestralCompletionResponse(TypedDict): + """Body returned by the Codestral `/v1/fim/completions` endpoint.""" + + id: ReadOnly[NotRequired[str]] + created: ReadOnly[NotRequired[int]] + model: ReadOnly[NotRequired[str]] + object: ReadOnly[NotRequired[str]] + usage: ReadOnly[NotRequired[_CodestralUsage]] + choices: ReadOnly[NotRequired[list[_CodestralChoice]]] + + +class _CodestralHTTPResponse(Protocol): + """The Codestral completion response as this handler reads it.""" + + @property + def status_code(self) -> int: ... + + @property + def text(self) -> str: ... + + def json(self) -> _CodestralCompletionResponse: ... + + class TextCompletionCodestralError(Exception): def __init__( self, @@ -115,7 +163,7 @@ class CodestralTextCompletion: def process_text_completion_response( self, model: str, - response: httpx.Response, + response: _CodestralHTTPResponse, model_response: TextCompletionResponse, stream: bool, logging_obj: LiteLLMLogging, diff --git a/litellm/llms/compactifai/chat/transformation.py b/litellm/llms/compactifai/chat/transformation.py index 63a5427d211..3c5a889ce63 100644 --- a/litellm/llms/compactifai/chat/transformation.py +++ b/litellm/llms/compactifai/chat/transformation.py @@ -2,13 +2,16 @@ CompactifAI chat completion transformation """ +from collections.abc import Mapping, Sequence from typing import TYPE_CHECKING, Any, Final import httpx +from typing_extensions import ReadOnly, TypedDict from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.llms.openai.common_utils import OpenAIError from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import ModelResponse from ...openai.chat.gpt_transformation import OpenAIGPTConfig @@ -23,6 +26,18 @@ else: LiteLLMLoggingObj = Any +class CompactifAIResponseFields(TypedDict, total=False): + """The chat completion fields of a CompactifAI response body.""" + + id: ReadOnly[str] + choices: ReadOnly[Sequence[Mapping[str, object]]] + created: ReadOnly[int] + model: ReadOnly[str] + system_fingerprint: ReadOnly[str | None] + usage: ReadOnly[Mapping[str, object]] + object: ReadOnly[str] + + class CompactifAIChatConfig(OpenAIGPTConfig): """ Configuration class for CompactifAI chat completions. @@ -47,10 +62,10 @@ class CompactifAIChatConfig(OpenAIGPTConfig): raw_response: httpx.Response, model_response: ModelResponse, logging_obj: LiteLLMLoggingObj, - request_data: dict, - messages: list, - optional_params: dict, - litellm_params: dict, + request_data: Mapping[str, object], + messages: Sequence[AllMessageValues], + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, @@ -81,14 +96,18 @@ class CompactifAIChatConfig(OpenAIGPTConfig): message["content"] = tool_calls[0]["function"].get("arguments", "") message["tool_calls"] = None - returned_response: Final = ModelResponse(**response_json) + response_fields: Final[CompactifAIResponseFields] = response_json + + returned_response: Final = ModelResponse(**response_fields) # Set model name with provider prefix returned_response.model = f"compactifai/{model}" return returned_response - def get_error_class(self, error_message: str, status_code: int, headers: dict | httpx.Headers) -> BaseLLMException: + def get_error_class( + self, error_message: str, status_code: int, headers: dict[str, str] | httpx.Headers + ) -> BaseLLMException: """ Get the appropriate error class for CompactifAI errors. Since CompactifAI is OpenAI-compatible, we use OpenAI error handling. diff --git a/litellm/llms/custom_httpx/aiohttp_transport.py b/litellm/llms/custom_httpx/aiohttp_transport.py index b6586481fd3..73adf9c7455 100644 --- a/litellm/llms/custom_httpx/aiohttp_transport.py +++ b/litellm/llms/custom_httpx/aiohttp_transport.py @@ -3,6 +3,7 @@ import concurrent.futures import contextlib import os import ssl +import sys import typing import urllib.request from collections.abc import Callable, Generator @@ -75,10 +76,22 @@ except ImportError: pass +def _current_task_is_cancelling() -> bool: + task: Final = asyncio.current_task() + if task is None or sys.version_info < (3, 11): + return True + return task.cancelling() > 0 + + @contextlib.contextmanager def map_aiohttp_exceptions() -> Generator[None, None, None]: try: yield + except asyncio.CancelledError as exc: + # a closing connector cancels its shielded DNS task; that surfaces here without the request task being cancelled + if _current_task_is_cancelling(): + raise + raise httpx.ConnectError("aiohttp transport cancelled the request internally") from exc except Exception as exc: mapped_exc: type[Exception] | None = None diff --git a/litellm/llms/custom_httpx/container_handler.py b/litellm/llms/custom_httpx/container_handler.py index 91d68aa3bfb..dd20a8c2ed4 100644 --- a/litellm/llms/custom_httpx/container_handler.py +++ b/litellm/llms/custom_httpx/container_handler.py @@ -6,11 +6,12 @@ endpoint defined in endpoints.json, eliminating the need for individual handler """ import json -from collections.abc import Coroutine +from collections.abc import Coroutine, Mapping, Sequence from pathlib import Path from typing import TYPE_CHECKING, Any, Final import httpx +from typing_extensions import NotRequired, ReadOnly, TypedDict import litellm from litellm.litellm_core_utils.url_utils import encode_url_path_segment @@ -32,26 +33,58 @@ if TYPE_CHECKING: from litellm.llms.base_llm.containers.transformation import BaseContainerConfig +class EndpointConfig(TypedDict): + """One endpoint entry of ``litellm/containers/endpoints.json``.""" + + name: ReadOnly[str] + async_name: ReadOnly[str] + path: ReadOnly[str] + method: ReadOnly[str] + path_params: ReadOnly[Sequence[str]] + query_params: ReadOnly[Sequence[str]] + response_type: ReadOnly[str] + is_multipart: NotRequired[ReadOnly[bool]] + returns_binary: NotRequired[ReadOnly[bool]] + + +class EndpointsConfig(TypedDict): + """The parsed ``litellm/containers/endpoints.json`` document.""" + + endpoints: ReadOnly[Sequence[EndpointConfig]] + + +class ContainerErrorDetail(TypedDict, total=False): + """The ``error`` object of a container API error body.""" + + message: ReadOnly[str] + + +class ContainerResponseBody(TypedDict, total=False): + """The fields this handler reads off a container API JSON body.""" + + error: ReadOnly[ContainerErrorDetail] + + +_ContainerResponseModel = ContainerFileListResponse | ContainerFileObject | DeleteContainerFileResponse + # Response type mapping -RESPONSE_TYPES: Final[dict[str, type]] = { +RESPONSE_TYPES: Final[Mapping[str, type[_ContainerResponseModel]]] = { "ContainerFileListResponse": ContainerFileListResponse, "ContainerFileObject": ContainerFileObject, "DeleteContainerFileResponse": DeleteContainerFileResponse, } -ContainerEndpointResponse = ( - ContainerFileListResponse | ContainerFileObject | DeleteContainerFileResponse | bytes | dict[str, object] -) +ContainerEndpointResponse = _ContainerResponseModel | bytes | ContainerResponseBody -def _load_endpoints_config() -> dict: +def _load_endpoints_config() -> EndpointsConfig: """Load the endpoints configuration from JSON file.""" config_path: Final = Path(__file__).parent.parent.parent / "containers" / "endpoints.json" with open(config_path) as f: return json.load(f) -def _get_endpoint_config(endpoint_name: str) -> dict | None: +def _get_endpoint_config(endpoint_name: str) -> EndpointConfig | None: """Get config for a specific endpoint by name.""" config: Final = _load_endpoints_config() for endpoint in config["endpoints"]: @@ -60,10 +93,15 @@ def _get_endpoint_config(endpoint_name: str) -> dict | None: return None +def _response_model(response_type_name: str) -> type[_ContainerResponseModel] | None: + """The pydantic model a container endpoint's ``response_type`` names.""" + return RESPONSE_TYPES.get(response_type_name) + + def _build_url( api_base: str, path_template: str, - path_params: dict[str, str], + path_params: Mapping[str, object], ) -> str: """Build the full URL by substituting path parameters. @@ -93,16 +131,12 @@ def _build_url( def _build_query_params( - query_param_names: list, - kwargs: dict[str, Any], -) -> dict[str, str]: + query_param_names: Sequence[str], + kwargs: Mapping[str, object], +) -> dict[str, object]: """Build query parameters from kwargs.""" - params: Final = {} - for param_name in query_param_names: - value = kwargs.get(param_name) - if value is not None: - params[param_name] = str(value) if not isinstance(value, str) else value - return params + supplied: Final = ((param_name, kwargs.get(param_name)) for param_name in query_param_names) + return {name: value if isinstance(value, str) else str(value) for name, value in supplied if value is not None} def _error_message_from_response(response: httpx.Response) -> str: @@ -136,24 +170,24 @@ def _transform_response( if returns_binary: return response.content - response_json: Final = response.json() + response_json: Final[ContainerResponseBody] = response.json() if "error" in response_json: raise BaseLLMException( status_code=response.status_code, - message=response_json.get("error", {}).get("message", str(response_json)), + message=response_json["error"].get("message", str(response_json)), headers=dict(response.headers), ) - response_type: Final = RESPONSE_TYPES.get(response_type_name) + response_type: Final = _response_model(response_type_name) if response_type: - return response_type(**response_json) + return response_type.model_validate(response_json) return response_json def _prepare_multipart_file_upload( file: Any, - headers: dict[str, Any], -) -> tuple: + headers: dict[str, object], +) -> tuple[dict[str, tuple[str, bytes, str]], dict[str, object]]: """ Prepare file and headers for multipart upload. @@ -178,6 +212,52 @@ def _prepare_multipart_file_upload( return files, headers_copy +def _request_headers( + container_provider_config: "BaseContainerConfig", + extra_headers: dict[str, object] | None, + litellm_params: GenericLiteLLMParams, +) -> dict[str, object]: + """The provider auth headers for a container request.""" + return container_provider_config.validate_environment( + headers=extra_headers or {}, + api_key=litellm_params.get("api_key", None), + ) + + +def _request_api_base( + container_provider_config: "BaseContainerConfig", + litellm_params: GenericLiteLLMParams, +) -> str: + """The provider base URL for a container request.""" + return container_provider_config.get_complete_url( + api_base=litellm_params.get("api_base", None), + litellm_params=dict(litellm_params), + ) + + +def _sync_http_client( + client: HTTPHandler | AsyncHTTPHandler | None, + litellm_params: GenericLiteLLMParams, +) -> HTTPHandler: + """The sync HTTP client for a container request, reusing the caller's when usable.""" + if client is None or not isinstance(client, HTTPHandler): + return _get_httpx_client(params={"ssl_verify": litellm_params.get("ssl_verify", None)}) + return client + + +def _async_http_client( + client: HTTPHandler | AsyncHTTPHandler | None, + litellm_params: GenericLiteLLMParams, +) -> AsyncHTTPHandler: + """The async HTTP client for a container request, reusing the caller's when usable.""" + if client is None or not isinstance(client, AsyncHTTPHandler): + return get_async_httpx_client( + llm_provider=litellm.LlmProviders.OPENAI, + params={"ssl_verify": litellm_params.get("ssl_verify", None)}, + ) + return client + + class GenericContainerHandler: """ Generic handler for container file API endpoints. @@ -192,13 +272,13 @@ class GenericContainerHandler: container_provider_config: "BaseContainerConfig", litellm_params: GenericLiteLLMParams, logging_obj: "LiteLLMLoggingObj", - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, timeout: float | httpx.Timeout = 600, _is_async: bool = False, client: HTTPHandler | AsyncHTTPHandler | None = None, - **kwargs, - ) -> Any | Coroutine[Any, Any, Any]: + **kwargs: object, + ) -> Any | Coroutine[object, object, Any]: """ Generic handler for any container file endpoint. @@ -245,11 +325,11 @@ class GenericContainerHandler: container_provider_config: "BaseContainerConfig", litellm_params: GenericLiteLLMParams, logging_obj: "LiteLLMLoggingObj", - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, timeout: float | httpx.Timeout = 600, client: HTTPHandler | AsyncHTTPHandler | None = None, - **kwargs, + **kwargs: object, ) -> Any: """Synchronous request handler.""" endpoint_config: Final = _get_endpoint_config(endpoint_name) @@ -257,23 +337,14 @@ class GenericContainerHandler: raise ValueError(f"Unknown endpoint: {endpoint_name}") # Get HTTP client - if client is None or not isinstance(client, HTTPHandler): - http_client = _get_httpx_client(params={"ssl_verify": litellm_params.get("ssl_verify", None)}) - else: - http_client = client + http_client: Final = _sync_http_client(client, litellm_params) # Build request - headers = container_provider_config.validate_environment( - headers=extra_headers or {}, - api_key=litellm_params.get("api_key", None), - ) + headers = _request_headers(container_provider_config, extra_headers, litellm_params) if extra_headers: headers.update(extra_headers) - api_base: Final = container_provider_config.get_complete_url( - api_base=litellm_params.get("api_base", None), - litellm_params=dict(litellm_params), - ) + api_base: Final = _request_api_base(container_provider_config, litellm_params) # Build URL with path params path_params: Final = {p: kwargs.get(p, "") for p in endpoint_config.get("path_params", [])} @@ -334,11 +405,11 @@ class GenericContainerHandler: container_provider_config: "BaseContainerConfig", litellm_params: GenericLiteLLMParams, logging_obj: "LiteLLMLoggingObj", - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, timeout: float | httpx.Timeout = 600, client: HTTPHandler | AsyncHTTPHandler | None = None, - **kwargs, + **kwargs: object, ) -> Any: """Asynchronous request handler.""" endpoint_config: Final = _get_endpoint_config(endpoint_name) @@ -346,26 +417,14 @@ class GenericContainerHandler: raise ValueError(f"Unknown endpoint: {endpoint_name}") # Get HTTP client - if client is None or not isinstance(client, AsyncHTTPHandler): - http_client = get_async_httpx_client( - llm_provider=litellm.LlmProviders.OPENAI, - params={"ssl_verify": litellm_params.get("ssl_verify", None)}, - ) - else: - http_client = client + http_client: Final = _async_http_client(client, litellm_params) # Build request - headers = container_provider_config.validate_environment( - headers=extra_headers or {}, - api_key=litellm_params.get("api_key", None), - ) + headers = _request_headers(container_provider_config, extra_headers, litellm_params) if extra_headers: headers.update(extra_headers) - api_base: Final = container_provider_config.get_complete_url( - api_base=litellm_params.get("api_base", None), - litellm_params=dict(litellm_params), - ) + api_base: Final = _request_api_base(container_provider_config, litellm_params) # Build URL with path params path_params: Final = {p: kwargs.get(p, "") for p in endpoint_config.get("path_params", [])} diff --git a/litellm/llms/custom_httpx/http_handler.py b/litellm/llms/custom_httpx/http_handler.py index 777ab576de2..b6e93f590ca 100644 --- a/litellm/llms/custom_httpx/http_handler.py +++ b/litellm/llms/custom_httpx/http_handler.py @@ -9,7 +9,7 @@ import threading import time from collections.abc import AsyncIterable, Callable, Iterable, Mapping from http.cookiejar import CookieJar, DefaultCookiePolicy -from typing import TYPE_CHECKING, Any, ClassVar, Final, Optional, TypeAlias, TypedDict +from typing import TYPE_CHECKING, Any, ClassVar, Final, NoReturn, Optional, TypeAlias, TypedDict import certifi import httpx @@ -447,7 +447,7 @@ def _safe_read_response(response: httpx.Response, timeout: float | None = None) return b"" -def _raise_masked_sync_error(e: httpx.HTTPStatusError, stream: bool) -> None: +def _raise_masked_sync_error(e: httpx.HTTPStatusError, stream: bool) -> NoReturn: """Raise a MaskedHTTPStatusError for sync HTTP handlers.""" if stream: try: @@ -467,7 +467,7 @@ def _raise_masked_sync_error(e: httpx.HTTPStatusError, stream: bool) -> None: raise MaskedHTTPStatusError(e, message=_text, text=_text) from None -async def _raise_masked_async_error(e: httpx.HTTPStatusError, stream: bool) -> None: +async def _raise_masked_async_error(e: httpx.HTTPStatusError, stream: bool) -> NoReturn: """Raise a MaskedHTTPStatusError for async HTTP handlers.""" if stream: try: diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index 573ba85416f..834f7d564a2 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -2872,6 +2872,7 @@ class BaseLLMHTTPHandler: headers=headers, timeout=timeout or float(response_api_optional_request_params.get("timeout", 0)), stream=stream, + logging_obj=logging_obj, **body_kwargs, ) @@ -2903,6 +2904,7 @@ class BaseLLMHTTPHandler: url=api_base, headers=headers, timeout=timeout or float(response_api_optional_request_params.get("timeout", 0)), + logging_obj=logging_obj, **body_kwargs, ) diff --git a/litellm/llms/dashscope/chat/transformation.py b/litellm/llms/dashscope/chat/transformation.py index 5ab7fbf3658..26e60fa959d 100644 --- a/litellm/llms/dashscope/chat/transformation.py +++ b/litellm/llms/dashscope/chat/transformation.py @@ -54,6 +54,9 @@ class DashScopeChatConfig(OpenAIGPTConfig): dynamic_api_key: Final = api_key or get_secret_str("DASHSCOPE_API_KEY") return api_base, dynamic_api_key + def _resolve_chat_api_base(self, api_base: str | None) -> str: + return api_base or "https://dashscope.aliyuncs.com/compatible-mode/v1" + def get_complete_url( self, api_base: str | None, @@ -66,10 +69,7 @@ class DashScopeChatConfig(OpenAIGPTConfig): """ If api_base is not provided, use the default DashScope /chat/completions endpoint. """ - if not api_base: - api_base = "https://dashscope.aliyuncs.com/compatible-mode/v1" - - if not api_base.endswith("/chat/completions"): - api_base = f"{api_base}/chat/completions" - - return api_base + resolved_api_base: Final = self._resolve_chat_api_base(api_base) + if resolved_api_base.endswith("/chat/completions"): + return resolved_api_base + return f"{resolved_api_base}/chat/completions" diff --git a/litellm/llms/dashscope/common_utils.py b/litellm/llms/dashscope/common_utils.py index 9a7dd4da8d3..b7c97893a15 100644 --- a/litellm/llms/dashscope/common_utils.py +++ b/litellm/llms/dashscope/common_utils.py @@ -2,9 +2,89 @@ Common utilities for the DashScope LLM provider. """ +from typing import TYPE_CHECKING + import httpx from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.secret_managers.main import get_secret_str + +if TYPE_CHECKING: + from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig + from litellm.llms.base_llm.image_generation.transformation import ( + BaseImageGenerationConfig, + ) + from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig + + +def get_dashscope_family_embedding_config(custom_llm_provider: str) -> "BaseEmbeddingConfig": + if custom_llm_provider == "qwencloud": + from litellm.llms.dashscope.qwencloud import QwenCloudEmbeddingConfig + + return QwenCloudEmbeddingConfig() + if custom_llm_provider == "qwen_ai_platform": + from litellm.llms.dashscope.qwen_ai_platform import ( + QwenAIPlatformEmbeddingConfig, + ) + + return QwenAIPlatformEmbeddingConfig() + from litellm.llms.dashscope.embed.transformation import DashScopeEmbeddingConfig + + return DashScopeEmbeddingConfig() + + +def get_dashscope_family_rerank_config(custom_llm_provider: str) -> "BaseRerankConfig": + if custom_llm_provider == "qwencloud": + from litellm.llms.dashscope.qwencloud import QwenCloudRerankConfig + + return QwenCloudRerankConfig() + if custom_llm_provider == "qwen_ai_platform": + from litellm.llms.dashscope.qwen_ai_platform import QwenAIPlatformRerankConfig + + return QwenAIPlatformRerankConfig() + from litellm.llms.dashscope.rerank.transformation import DashScopeRerankConfig + + return DashScopeRerankConfig() + + +def get_dashscope_family_image_generation_config( + custom_llm_provider: str, +) -> "BaseImageGenerationConfig": + if custom_llm_provider == "qwencloud": + from litellm.llms.dashscope.qwencloud import QwenCloudImageGenerationConfig + + return QwenCloudImageGenerationConfig() + if custom_llm_provider == "qwen_ai_platform": + from litellm.llms.dashscope.qwen_ai_platform import ( + QwenAIPlatformImageGenerationConfig, + ) + + return QwenAIPlatformImageGenerationConfig() + from litellm.llms.dashscope.image_generation.transformation import ( + DashScopeImageGenerationConfig, + ) + + return DashScopeImageGenerationConfig() + + +def resolve_dashscope_family_api_key(custom_llm_provider: str, api_key: str | None) -> str | None: + if custom_llm_provider == "dashscope": + return api_key or get_secret_str("DASHSCOPE_API_KEY") + return api_key or get_secret_str(f"{custom_llm_provider.upper()}_API_KEY") or get_secret_str("DASHSCOPE_API_KEY") + + +def missing_dashscope_family_key_message(custom_llm_provider: str) -> str: + if custom_llm_provider == "qwencloud": + return ( + "Missing API key for QwenCloud. Set QWENCLOUD_API_KEY or " + "DASHSCOPE_API_KEY environment variable or pass api_key parameter." + ) + if custom_llm_provider == "qwen_ai_platform": + return ( + "Missing API key for Qwen AI Platform. Set QWEN_AI_PLATFORM_API_KEY or " + "DASHSCOPE_API_KEY environment variable or pass api_key parameter." + ) + return "Missing API key for DashScope. Set DASHSCOPE_API_KEY environment variable or pass api_key parameter." class DashScopeError(BaseLLMException): diff --git a/litellm/llms/dashscope/cost_calculator.py b/litellm/llms/dashscope/cost_calculator.py index 771ce140f66..dd5bee1fe8b 100644 --- a/litellm/llms/dashscope/cost_calculator.py +++ b/litellm/llms/dashscope/cost_calculator.py @@ -110,7 +110,7 @@ def _calculate_completion_cost( return (breakdown.completion_tokens * output_cost) + (breakdown.reasoning_tokens * reasoning_cost) -def cost_per_token(model: str, usage: Usage) -> tuple[float, float]: +def cost_per_token(model: str, usage: Usage, custom_llm_provider: str = "dashscope") -> tuple[float, float]: """ Calculate cost per token for Dashscope models. @@ -119,11 +119,12 @@ def cost_per_token(model: str, usage: Usage) -> tuple[float, float]: Args: model: Model name without provider prefix usage: LiteLLM Usage block + custom_llm_provider: The provider id the request resolved to; dashscope or one of its brand aliases Returns: Tuple[float, float] - (prompt_cost_in_usd, completion_cost_in_usd) """ - model_info: Final = get_model_info(model=model, custom_llm_provider="dashscope") + model_info: Final = get_model_info(model=model, custom_llm_provider=custom_llm_provider) breakdown: Final = _extract_token_breakdown(usage) raw_tiers: Final = model_info.get("tiered_pricing") tiered_pricing: Final = raw_tiers if isinstance(raw_tiers, list) else None diff --git a/litellm/llms/dashscope/embed/transformation.py b/litellm/llms/dashscope/embed/transformation.py index 6d13f1e53f7..63ee984a65c 100644 --- a/litellm/llms/dashscope/embed/transformation.py +++ b/litellm/llms/dashscope/embed/transformation.py @@ -62,6 +62,17 @@ class DashScopeEmbeddingConfig(BaseEmbeddingConfig): # for drop_params=False before this method is called. return optional_params + def _resolve_api_key(self, api_key: str | None) -> str: + resolved_api_key: Final = api_key if api_key is not None else get_secret_str("DASHSCOPE_API_KEY") + if resolved_api_key is None: + raise ValueError( + "DashScope API key is required. Set 'DASHSCOPE_API_KEY' env var or pass api_key explicitly." + ) + return resolved_api_key + + def _resolve_embedding_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("DASHSCOPE_API_BASE") or DEFAULT_API_BASE + def validate_environment( self, headers: dict, @@ -72,17 +83,11 @@ class DashScopeEmbeddingConfig(BaseEmbeddingConfig): api_key: str | None = None, api_base: str | None = None, ) -> dict: - if api_key is None: - api_key = get_secret_str("DASHSCOPE_API_KEY") - if api_key is None: - raise ValueError( - "DashScope API key is required. Set 'DASHSCOPE_API_KEY' env var or pass api_key explicitly." - ) - default_headers: Final = { + return { "Content-Type": "application/json", - "Authorization": f"Bearer {api_key}", + "Authorization": f"Bearer {self._resolve_api_key(api_key)}", + **headers, } - return {**default_headers, **headers} def get_complete_url( self, @@ -93,8 +98,7 @@ class DashScopeEmbeddingConfig(BaseEmbeddingConfig): litellm_params: dict, stream: bool | None = None, ) -> str: - base = api_base or get_secret_str("DASHSCOPE_API_BASE") or DEFAULT_API_BASE - base = base.rstrip("/") + base: Final = self._resolve_embedding_api_base(api_base).rstrip("/") if base.endswith("/embeddings"): return base return f"{base}/embeddings" diff --git a/litellm/llms/dashscope/image_generation/transformation.py b/litellm/llms/dashscope/image_generation/transformation.py index a7f0e98865f..c0e278a96ef 100644 --- a/litellm/llms/dashscope/image_generation/transformation.py +++ b/litellm/llms/dashscope/image_generation/transformation.py @@ -91,6 +91,15 @@ class DashScopeImageGenerationConfig(BaseImageGenerationConfig): mapped[k] = v return mapped + def _resolve_api_key(self, api_key: str | None) -> str: + resolved_api_key: Final = api_key or get_secret_str("DASHSCOPE_API_KEY") + if not resolved_api_key: + raise ValueError("DASHSCOPE_API_KEY is not set") + return resolved_api_key + + def _resolve_image_api_base(self, image_api_base: str | None) -> str: + return image_api_base or get_secret_str("DASHSCOPE_API_BASE_IMAGE") or DEFAULT_API_BASE + def get_complete_url( self, api_base: str | None, @@ -103,7 +112,7 @@ class DashScopeImageGenerationConfig(BaseImageGenerationConfig): image_api_base: Final = ( api_base if api_base and not api_base.rstrip("/").endswith(CHAT_COMPATIBLE_MODE_PATH) else None ) - return image_api_base or get_secret_str("DASHSCOPE_API_BASE_IMAGE") or DEFAULT_API_BASE + return self._resolve_image_api_base(image_api_base) def validate_environment( self, @@ -115,10 +124,7 @@ class DashScopeImageGenerationConfig(BaseImageGenerationConfig): api_key: str | None = None, api_base: str | None = None, ) -> dict: - final_api_key: Final = api_key or get_secret_str("DASHSCOPE_API_KEY") - if not final_api_key: - raise ValueError("DASHSCOPE_API_KEY is not set") - headers["Authorization"] = f"Bearer {final_api_key}" + headers["Authorization"] = f"Bearer {self._resolve_api_key(api_key)}" headers["Content-Type"] = "application/json" return headers diff --git a/litellm/llms/dashscope/qwen_ai_platform.py b/litellm/llms/dashscope/qwen_ai_platform.py new file mode 100644 index 00000000000..9a44eaf574a --- /dev/null +++ b/litellm/llms/dashscope/qwen_ai_platform.py @@ -0,0 +1,62 @@ +from typing import Final + +from litellm.secret_managers.main import get_secret_str + +from .chat.transformation import DashScopeChatConfig +from .embed.transformation import DashScopeEmbeddingConfig +from .image_generation.transformation import DashScopeImageGenerationConfig +from .rerank.transformation import DashScopeRerankConfig + +QWEN_AI_PLATFORM_API_BASE: Final = "https://dashscope.aliyuncs.com/compatible-mode/v1" +QWEN_AI_PLATFORM_RERANK_API_BASE: Final = "https://dashscope.aliyuncs.com/compatible-api/v1/reranks" +QWEN_AI_PLATFORM_IMAGE_API_BASE: Final = ( + "https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation" +) + + +def _resolve_qwen_ai_platform_api_key(api_key: str | None) -> str | None: + return api_key or get_secret_str("QWEN_AI_PLATFORM_API_KEY") or get_secret_str("DASHSCOPE_API_KEY") + + +def _require_qwen_ai_platform_api_key(api_key: str | None) -> str: + resolved: Final = _resolve_qwen_ai_platform_api_key(api_key) + if resolved is None: + raise ValueError( + "Qwen AI Platform API key is required. Set 'QWEN_AI_PLATFORM_API_KEY' or 'DASHSCOPE_API_KEY' env var " + "or pass api_key explicitly." + ) + return resolved + + +class QwenAIPlatformChatConfig(DashScopeChatConfig): + def _get_openai_compatible_provider_info( + self, api_base: str | None, api_key: str | None + ) -> tuple[str | None, str | None]: + return self._resolve_chat_api_base(api_base), _resolve_qwen_ai_platform_api_key(api_key) + + def _resolve_chat_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("QWEN_AI_PLATFORM_API_BASE") or QWEN_AI_PLATFORM_API_BASE + + +class QwenAIPlatformEmbeddingConfig(DashScopeEmbeddingConfig): + def _resolve_api_key(self, api_key: str | None) -> str: + return _require_qwen_ai_platform_api_key(api_key) + + def _resolve_embedding_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("QWEN_AI_PLATFORM_API_BASE") or QWEN_AI_PLATFORM_API_BASE + + +class QwenAIPlatformRerankConfig(DashScopeRerankConfig): + def _resolve_api_key(self, api_key: str | None) -> str: + return _require_qwen_ai_platform_api_key(api_key) + + def _resolve_rerank_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("QWEN_AI_PLATFORM_API_BASE_RERANK") or QWEN_AI_PLATFORM_RERANK_API_BASE + + +class QwenAIPlatformImageGenerationConfig(DashScopeImageGenerationConfig): + def _resolve_api_key(self, api_key: str | None) -> str: + return _require_qwen_ai_platform_api_key(api_key) + + def _resolve_image_api_base(self, image_api_base: str | None) -> str: + return image_api_base or get_secret_str("QWEN_AI_PLATFORM_API_BASE_IMAGE") or QWEN_AI_PLATFORM_IMAGE_API_BASE diff --git a/litellm/llms/dashscope/qwencloud.py b/litellm/llms/dashscope/qwencloud.py new file mode 100644 index 00000000000..d8d53e340ef --- /dev/null +++ b/litellm/llms/dashscope/qwencloud.py @@ -0,0 +1,62 @@ +from typing import Final + +from litellm.secret_managers.main import get_secret_str + +from .chat.transformation import DashScopeChatConfig +from .embed.transformation import DashScopeEmbeddingConfig +from .image_generation.transformation import DashScopeImageGenerationConfig +from .rerank.transformation import DashScopeRerankConfig + +QWENCLOUD_API_BASE: Final = "https://dashscope-intl.aliyuncs.com/compatible-mode/v1" +QWENCLOUD_RERANK_API_BASE: Final = "https://dashscope-intl.aliyuncs.com/compatible-api/v1/reranks" +QWENCLOUD_IMAGE_API_BASE: Final = ( + "https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation" +) + + +def _resolve_qwencloud_api_key(api_key: str | None) -> str | None: + return api_key or get_secret_str("QWENCLOUD_API_KEY") or get_secret_str("DASHSCOPE_API_KEY") + + +def _require_qwencloud_api_key(api_key: str | None) -> str: + resolved: Final = _resolve_qwencloud_api_key(api_key) + if resolved is None: + raise ValueError( + "QwenCloud API key is required. Set 'QWENCLOUD_API_KEY' or 'DASHSCOPE_API_KEY' env var " + "or pass api_key explicitly." + ) + return resolved + + +class QwenCloudChatConfig(DashScopeChatConfig): + def _get_openai_compatible_provider_info( + self, api_base: str | None, api_key: str | None + ) -> tuple[str | None, str | None]: + return self._resolve_chat_api_base(api_base), _resolve_qwencloud_api_key(api_key) + + def _resolve_chat_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("QWENCLOUD_API_BASE") or QWENCLOUD_API_BASE + + +class QwenCloudEmbeddingConfig(DashScopeEmbeddingConfig): + def _resolve_api_key(self, api_key: str | None) -> str: + return _require_qwencloud_api_key(api_key) + + def _resolve_embedding_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("QWENCLOUD_API_BASE") or QWENCLOUD_API_BASE + + +class QwenCloudRerankConfig(DashScopeRerankConfig): + def _resolve_api_key(self, api_key: str | None) -> str: + return _require_qwencloud_api_key(api_key) + + def _resolve_rerank_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("QWENCLOUD_API_BASE_RERANK") or QWENCLOUD_RERANK_API_BASE + + +class QwenCloudImageGenerationConfig(DashScopeImageGenerationConfig): + def _resolve_api_key(self, api_key: str | None) -> str: + return _require_qwencloud_api_key(api_key) + + def _resolve_image_api_base(self, image_api_base: str | None) -> str: + return image_api_base or get_secret_str("QWENCLOUD_API_BASE_IMAGE") or QWENCLOUD_IMAGE_API_BASE diff --git a/litellm/llms/dashscope/rerank/transformation.py b/litellm/llms/dashscope/rerank/transformation.py index 98be4e4f2e7..3dd3996b2ee 100644 --- a/litellm/llms/dashscope/rerank/transformation.py +++ b/litellm/llms/dashscope/rerank/transformation.py @@ -58,19 +58,30 @@ class DashScopeRerankConfig(BaseRerankConfig): def __init__(self) -> None: pass + def _resolve_api_key(self, api_key: str | None) -> str: + resolved_api_key: Final = api_key if api_key is not None else get_secret_str("DASHSCOPE_API_KEY") + if resolved_api_key is None: + raise ValueError( + "DashScope API key is required. Set 'DASHSCOPE_API_KEY' env var or pass api_key explicitly." + ) + return resolved_api_key + + def _resolve_rerank_api_base(self, api_base: str | None) -> str: + if api_base is not None: + return api_base + return get_secret_str("DASHSCOPE_API_BASE_RERANK") or DEFAULT_RERANK_URL + def get_complete_url( self, api_base: str | None, model: str, optional_params: dict | None = None, ) -> str: - if api_base is None: - api_base = get_secret_str("DASHSCOPE_API_BASE_RERANK") or DEFAULT_RERANK_URL + resolved_api_base: Final = self._resolve_rerank_api_base(api_base) + if resolved_api_base == DEFAULT_RERANK_URL: + return resolved_api_base - if api_base == DEFAULT_RERANK_URL: - return DEFAULT_RERANK_URL - - cleaned: Final = api_base.rstrip("/") + cleaned: Final = resolved_api_base.rstrip("/") if cleaned.endswith("/reranks") or cleaned.endswith("/rerank"): return cleaned @@ -88,19 +99,12 @@ class DashScopeRerankConfig(BaseRerankConfig): optional_params: dict | None = None, litellm_params: Mapping[str, object] | None = None, ) -> dict: - if api_key is None: - api_key = get_secret_str("DASHSCOPE_API_KEY") - if api_key is None: - raise ValueError( - "DashScope API key is required. Set 'DASHSCOPE_API_KEY' env var or pass api_key explicitly." - ) - - default_headers: Final = { - "Authorization": f"Bearer {api_key}", + return { + "Authorization": f"Bearer {self._resolve_api_key(api_key)}", "accept": "application/json", "content-type": "application/json", + **headers, } - return {**default_headers, **headers} def get_supported_cohere_rerank_params(self, model: str) -> list: return ["query", "documents", "top_n", "return_documents"] diff --git a/litellm/llms/deepinfra/rerank/transformation.py b/litellm/llms/deepinfra/rerank/transformation.py index e52c56af82b..a3d0482af0a 100644 --- a/litellm/llms/deepinfra/rerank/transformation.py +++ b/litellm/llms/deepinfra/rerank/transformation.py @@ -2,10 +2,11 @@ Translate between Cohere's `/rerank` format and Deepinfra's `/rerank` format. """ -from collections.abc import Mapping -from typing import Any, Final +from collections.abc import Mapping, Sequence +from typing import Final, Protocol import httpx +from typing_extensions import ReadOnly, TypedDict from litellm._uuid import uuid from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj @@ -24,6 +25,36 @@ from litellm.types.rerank import ( ) +class _DeepinfraInferenceStatus(TypedDict, total=False): + """The ``inference_status`` block of a DeepInfra rerank response.""" + + status: ReadOnly[str] + runtime_ms: ReadOnly[float] + cost: ReadOnly[float] + tokens_generated: ReadOnly[int] + tokens_input: ReadOnly[int] + + +class _DeepinfraRerankResponse(TypedDict, total=False): + """Body of a DeepInfra ``/rerank`` response.""" + + scores: ReadOnly[Sequence[float]] + input_tokens: ReadOnly[int] + request_id: ReadOnly[str | None] + inference_status: ReadOnly[_DeepinfraInferenceStatus] + + +class _DeepinfraRerankResponseSource(Protocol): + """The DeepInfra ``/rerank`` HTTP response, read for the body it decodes to.""" + + def json(self) -> _DeepinfraRerankResponse: ... + + +def _deepinfra_rerank_body(response: _DeepinfraRerankResponseSource) -> _DeepinfraRerankResponse: + """Decode the body of a DeepInfra ``/rerank`` response.""" + return response.json() + + class DeepinfraRerankConfig(BaseRerankConfig): """ Deepinfra Rerank - Follows the same Spec as Cohere Rerank @@ -95,7 +126,7 @@ class DeepinfraRerankConfig(BaseRerankConfig): model: str, drop_params: bool, query: str, - documents: list[str | dict[str, Any]], + documents: list[str | dict[str, object]], custom_llm_provider: str | None = None, top_n: int | None = None, rank_fields: list[str] | None = None, @@ -150,7 +181,7 @@ class DeepinfraRerankConfig(BaseRerankConfig): litellm_params: dict = {}, ) -> RerankResponse: try: - response_json: Final = raw_response.json() + response_json: Final = _deepinfra_rerank_body(raw_response) logging_obj.post_call(original_response=raw_response.text) # Extract the scores from the response diff --git a/litellm/llms/e2b/sandbox/transformation.py b/litellm/llms/e2b/sandbox/transformation.py index 9cd9ade77a4..4928ca0c092 100644 --- a/litellm/llms/e2b/sandbox/transformation.py +++ b/litellm/llms/e2b/sandbox/transformation.py @@ -8,7 +8,7 @@ Talks to e2b's REST API directly over httpx (no e2b SDK dependency): """ import json -from typing import Final, cast +from typing import Final import httpx @@ -68,13 +68,10 @@ class E2BSandboxConfig(BaseSandboxConfig): if metadata: body["metadata"] = metadata - response: Final = cast( - httpx.Response, - await self._http(client).post( - url=f"{base}/sandboxes", - headers={"X-API-Key": key, "Content-Type": "application/json"}, - json=body, - ), + response: Final = await self._http(client).post( + url=f"{base}/sandboxes", + headers={"X-API-Key": key, "Content-Type": "application/json"}, + json=body, ) data: Final = response.json() @@ -117,14 +114,11 @@ class E2BSandboxConfig(BaseSandboxConfig): headers["E2B-Traffic-Access-Token"] = traffic_token url: Final = f"https://{JUPYTER_PORT}-{handle.id}.{handle.domain}/execute" - response: Final = cast( - httpx.Response, - await self._http(client).post( - url=url, - headers=headers, - json={"code": code, "context_id": None, "env_vars": env_vars}, - stream=True, - ), + response: Final = await self._http(client).post( + url=url, + headers=headers, + json={"code": code, "context_id": None, "env_vars": env_vars}, + stream=True, ) lines: Final = await self._read_capped_lines(response) return self._parse_lines(lines) @@ -142,12 +136,9 @@ class E2BSandboxConfig(BaseSandboxConfig): key: Final = api_key or handle._hidden_params.get("api_key") or self.validate_environment() base: Final = api_base or handle._hidden_params.get("api_base") or E2B_API_BASE try: - response: Final = cast( - httpx.Response, - await self._http(client).delete( - url=f"{base}/sandboxes/{handle.id}", - headers={"X-API-Key": key}, - ), + response: Final = await self._http(client).delete( + url=f"{base}/sandboxes/{handle.id}", + headers={"X-API-Key": key}, ) except httpx.HTTPStatusError as e: if e.response.status_code == 404: diff --git a/litellm/llms/gdc/chat/transformation.py b/litellm/llms/gdc/chat/transformation.py index 2d0322bf10f..03037512551 100644 --- a/litellm/llms/gdc/chat/transformation.py +++ b/litellm/llms/gdc/chat/transformation.py @@ -6,11 +6,25 @@ import json import os import re import threading -from typing import Any, Final +from collections.abc import Callable +from typing import Any, Final, Protocol from urllib.parse import urlsplit import litellm from litellm.llms.openai_like.chat.transformation import OpenAILikeChatConfig +from litellm.types.llms.openai import AllMessageValues + + +class _GDCHAudienceCredentials(Protocol): + """A GDCH service account credential already bound to an audience, ready to mint a bearer token.""" + + @property + def valid(self) -> bool: ... + + @property + def token(self) -> str: ... + + def refresh(self, request: object) -> None: ... class GDCGeminiConfig(OpenAILikeChatConfig): @@ -21,7 +35,7 @@ class GDCGeminiConfig(OpenAILikeChatConfig): def __init__(self, **kwargs: Any) -> None: super().__init__(**kwargs) self._creds_lock = threading.Lock() - self._gdch_creds_cache: dict = {} + self._gdch_creds_cache: dict[tuple[str, str], _GDCHAudienceCredentials] = {} def get_supported_openai_params(self, model: str) -> list: return [ @@ -110,7 +124,7 @@ class GDCGeminiConfig(OpenAILikeChatConfig): return f"{api_base}/v1/projects/{project}/locations/{location}/chat/completions" - def _read_env_bool(self, val: Any, env_var: str, default: bool = True) -> bool | str: + def _read_env_bool(self, val: bool | str | None, env_var: str, default: bool = True) -> bool | str: def _parse(s: str) -> bool | str: cleaned: Final = s.strip().lower() if cleaned in ("false", "0", "no", "off"): @@ -129,7 +143,7 @@ class GDCGeminiConfig(OpenAILikeChatConfig): return default return _parse(_env_val) - def _fetch_auth(self, gdch_creds: Any, ssl_verify: bool | str) -> None: + def _fetch_auth(self, gdch_creds: _GDCHAudienceCredentials, ssl_verify: bool | str) -> None: import requests from google.auth.transport import requests as auth_requests @@ -138,13 +152,24 @@ class GDCGeminiConfig(OpenAILikeChatConfig): auth_request: Final = auth_requests.Request(session=auth_session) gdch_creds.refresh(auth_request) - def _cached_fetch_token(self, creds: Any, audience: str, ssl_verify: bool | str, api_key: str | None = None) -> str: + def _with_gdch_audience(self, creds: object, audience: str) -> _GDCHAudienceCredentials: + """The credential rebound to ``audience``, which GDCH requires before a token refresh.""" + bind_audience: Final[Callable[[str], _GDCHAudienceCredentials] | None] = getattr( + creds, "with_gdch_audience", None + ) + if bind_audience is None: + raise AttributeError("GDC credentials must expose with_gdch_audience to be bound to a request audience") + return bind_audience(audience) + + def _cached_fetch_token( + self, creds: object, audience: str, ssl_verify: bool | str, api_key: str | None = None + ) -> str: # Key cache by both audience and credential identity to prevent cross-caller contamination cache_key: Final = (audience.rstrip("/"), api_key or str(id(creds))) with self._creds_lock: if cache_key not in self._gdch_creds_cache: - self._gdch_creds_cache[cache_key] = creds.with_gdch_audience(audience.rstrip("/")) + self._gdch_creds_cache[cache_key] = self._with_gdch_audience(creds, audience.rstrip("/")) gdch_creds: Final = self._gdch_creds_cache[cache_key] @@ -155,7 +180,7 @@ class GDCGeminiConfig(OpenAILikeChatConfig): return token - def _load_creds_from_key(self, api_key: str) -> tuple[Any, bool]: + def _load_creds_from_key(self, api_key: str) -> tuple[object | None, bool]: import google.auth try: @@ -175,7 +200,7 @@ class GDCGeminiConfig(OpenAILikeChatConfig): self, headers: dict, model: str, - messages: list[Any], + messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, api_key: str | None = None, @@ -230,7 +255,7 @@ class GDCGeminiConfig(OpenAILikeChatConfig): if self._read_env_bool(litellm_params.get("gdc_token_caching"), "GDC_TOKEN_CACHING", default=False): token = self._cached_fetch_token(creds, audience, ssl_verify, api_key) else: - gdch_creds: Final = creds.with_gdch_audience(audience) + gdch_creds: Final = self._with_gdch_audience(creds, audience) self._fetch_auth(gdch_creds, ssl_verify) token = gdch_creds.token headers["Authorization"] = f"Bearer {token}" @@ -252,7 +277,7 @@ class GDCGeminiConfig(OpenAILikeChatConfig): def transform_request( self, model: str, - messages: list[Any], + messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, headers: dict, diff --git a/litellm/llms/gemini/common_utils.py b/litellm/llms/gemini/common_utils.py index bd2b124605c..78e6e6aaf82 100644 --- a/litellm/llms/gemini/common_utils.py +++ b/litellm/llms/gemini/common_utils.py @@ -2,7 +2,7 @@ import base64 import datetime import json import math -from collections.abc import Sequence +from collections.abc import Mapping, Sequence from typing import Any, Final import httpx @@ -128,24 +128,35 @@ def is_gemini_image_model(model: str) -> bool: return "gemini" in base_model +def _parse_image_config_string(raw_image_config: str, model: str) -> object: + try: + return json.loads(raw_image_config) + except json.JSONDecodeError as exc: + raise litellm.UnsupportedParamsError( + model=model, + message="`imageConfig` must be valid JSON when provided as a string.", + ) from exc + + def map_openai_image_params_to_gemini( - params: dict[str, Any], + params: Mapping[str, object], model: str, supported_params: Sequence[str], - optional_params: dict[str, Any] | None = None, + optional_params: Mapping[str, object] | None = None, parse_image_config_string: bool = False, -) -> dict[str, Any]: - optional_params = optional_params or {} +) -> dict[str, object]: + already_mapped: Final[Mapping[str, object]] = optional_params or {} filtered_params: Final = {key: value for key, value in params.items() if key in supported_params} - mapped_params: Final[dict[str, Any]] = {} + mapped_params: Final[dict[str, object]] = {} - if "n" in filtered_params and "n" not in optional_params: + if "n" in filtered_params and "n" not in already_mapped: mapped_params["sampleCount"] = filtered_params["n"] - if "size" in filtered_params and "size" not in optional_params: + size_param: Final = filtered_params.get("size") + if isinstance(size_param, str) and "size" not in already_mapped: image_config: Final = map_openai_size_to_gemini_image_config( - filtered_params["size"], + size_param, model, ) if image_config is not None: @@ -156,33 +167,30 @@ def map_openai_image_params_to_gemini( if "imageSize" in image_config: mapped_params["imageSize"] = image_config["imageSize"] - image_config_param = filtered_params.get("imageConfig") - if isinstance(image_config_param, str) and parse_image_config_string: - try: - image_config_param = json.loads(image_config_param) - except json.JSONDecodeError as exc: - raise litellm.UnsupportedParamsError( - model=model, - message="`imageConfig` must be valid JSON when provided as a string.", - ) from exc + raw_image_config: Final = filtered_params.get("imageConfig") + image_config_param: Final[object] = ( + _parse_image_config_string(raw_image_config, model) + if isinstance(raw_image_config, str) and parse_image_config_string + else raw_image_config + ) if isinstance(image_config_param, dict): mapped_params["imageConfig"] = image_config_param for key, value in filtered_params.items(): - if key not in ("n", "size", "imageConfig", "tools", "web_search_options") and key not in optional_params: + if key not in ("n", "size", "imageConfig", "tools", "web_search_options") and key not in already_mapped: mapped_params[key] = value return mapped_params -def _dedupe_gemini_search_tools(tools: list[dict[str, Any]]) -> list[dict[str, Any]]: +def _dedupe_gemini_search_tools(tools: list[dict[str, object]]) -> list[dict[str, object]]: from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( VertexGeminiConfig, ) search_tool_keys: Final = VertexGeminiConfig._search_tool_keys() seen_search_keys: Final[set[str]] = set() - deduped_tools: Final[list[dict[str, Any]]] = [] + deduped_tools: Final[list[dict[str, object]]] = [] for tool in tools: if not isinstance(tool, dict): @@ -203,7 +211,7 @@ def _dedupe_gemini_search_tools(tools: list[dict[str, Any]]) -> list[dict[str, A return deduped_tools -def _has_gemini_search_tool(tools: list[Any]) -> bool: +def _has_gemini_search_tool(tools: list[object]) -> bool: from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( VertexGeminiConfig, ) @@ -213,9 +221,9 @@ def _has_gemini_search_tool(tools: list[Any]) -> bool: def map_gemini_image_tools_params( - non_default_params: dict[str, Any], - mapped_params: dict[str, Any], -) -> dict[str, Any]: + non_default_params: Mapping[str, object], + mapped_params: Mapping[str, object], +) -> dict[str, object]: from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( VertexGeminiConfig, ) @@ -239,21 +247,24 @@ def map_gemini_image_tools_params( gemini_config._drop_search_tools_mixed_with_functions(result) - if isinstance(result.get("tools"), list): - result["tools"] = _dedupe_gemini_search_tools(result["tools"]) + resolved_tools: Final = result.get("tools") + if isinstance(resolved_tools, list): + result["tools"] = _dedupe_gemini_search_tools(resolved_tools) return result def get_gemini_image_web_search_requests( - response_data: dict[str, Any], + response_data: Mapping[str, object], ) -> int | None: from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( VertexGeminiConfig, ) - grounding_metadata: Final[list[dict[str, Any]]] = [] - for candidate in response_data.get("candidates", []): + raw_candidates: Final = response_data.get("candidates") + candidates: Final[list[object]] = raw_candidates if isinstance(raw_candidates, list) else [] + grounding_metadata: Final[list[dict[str, object]]] = [] + for candidate in candidates: if not isinstance(candidate, dict): continue candidate_grounding = candidate.get("groundingMetadata") @@ -267,13 +278,14 @@ def get_gemini_image_web_search_requests( def get_gemini_image_generation_config( model: str, - optional_params: dict[str, Any], -) -> dict[str, Any]: - generation_config: Final[dict[str, Any]] = {"response_modalities": ["IMAGE", "TEXT"]} + optional_params: Mapping[str, object], +) -> dict[str, object]: + generation_config: Final[dict[str, object]] = {"response_modalities": ["IMAGE", "TEXT"]} - image_config: Final[dict[str, Any]] = {} - if isinstance(optional_params.get("imageConfig"), dict): - image_config.update(optional_params["imageConfig"]) + raw_image_config: Final = optional_params.get("imageConfig") + image_config: Final[dict[str, object]] = {} + if isinstance(raw_image_config, dict): + image_config.update(raw_image_config) if not supports_gemini_image_size(model): image_config.pop("imageSize", None) @@ -398,7 +410,7 @@ class GeminiModelInfo(BaseLLMModelInfo): f"Failed to fetch models from Gemini. Status code: {response.status_code}, Response: {response.json()}" ) - models: Final = response.json()["models"] + models: Final[list[dict[str, str]]] = response.json()["models"] litellm_model_names: Final = self.process_model_name(models) return litellm_model_names @@ -473,12 +485,12 @@ class GoogleAIStudioTokenCounter(BaseTokenCounter): async def count_tokens( self, model_to_use: str, - messages: list[dict[str, Any]] | None, - contents: list[dict[str, Any]] | None, + messages: list[dict[str, object]] | None, + contents: list[dict[str, object]] | None, deployment: dict[str, Any] | None = None, request_model: str = "", - tools: list[dict[str, Any]] | None = None, - system: Any | None = None, + tools: list[dict[str, object]] | None = None, + system: object | None = None, ) -> TokenCountResponse | None: import copy diff --git a/litellm/llms/gemini/files/transformation.py b/litellm/llms/gemini/files/transformation.py index dee83407cb5..2c62e04c5a3 100644 --- a/litellm/llms/gemini/files/transformation.py +++ b/litellm/llms/gemini/files/transformation.py @@ -5,11 +5,13 @@ For vertex ai, check out the vertex_ai/files/handler.py file. """ import time -from typing import Any, Final, Literal +from collections.abc import Mapping +from typing import Final, Literal, TypedDict from urllib.parse import urlparse import httpx from openai.types.file_deleted import FileDeleted +from typing_extensions import ReadOnly, Required from litellm._logging import verbose_logger from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data @@ -18,7 +20,6 @@ from litellm.llms.base_llm.files.transformation import ( BaseFilesConfig, LiteLLMLoggingObj, ) -from litellm.types.llms.gemini import GeminiCreateFilesResponseObject from litellm.types.llms.openai import ( AllMessageValues, CreateFileRequest, @@ -31,6 +32,25 @@ from litellm.types.utils import LlmProviders from ..common_utils import GeminiModelInfo +class _GeminiFileMetadata(TypedDict, total=False): + name: ReadOnly[str] + uri: ReadOnly[Required[str]] + displayName: ReadOnly[Required[str]] + mimeType: ReadOnly[str] + sizeBytes: ReadOnly[Required[str]] + createTime: ReadOnly[Required[str]] + updateTime: ReadOnly[str] + expirationTime: ReadOnly[str] + sha256Hash: ReadOnly[str] + state: ReadOnly[str] + source: ReadOnly[str] + error: ReadOnly[Mapping[str, object]] + + +class _GeminiCreateFileResponse(TypedDict): + file: ReadOnly[_GeminiFileMetadata] + + class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): def __init__(self): pass @@ -41,14 +61,14 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): def validate_environment( self, - headers: dict[Any, Any], + headers: dict[str, str], model: str, messages: list[AllMessageValues], - optional_params: dict[Any, Any], - litellm_params: dict[Any, Any], + optional_params: dict[str, object], + litellm_params: dict[str, object], api_key: str | None = None, api_base: str | None = None, - ) -> dict[Any, Any]: + ) -> dict[str, str]: """ Validate environment and add Gemini API key to headers. Google AI Studio uses x-goog-api-key header for authentication. @@ -164,9 +184,9 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): Transform Gemini's file upload response into OpenAI-style FileObject """ try: - response_json: Final = raw_response.json() + response_json: Final[_GeminiCreateFileResponse] = raw_response.json() - response_object: Final = GeminiCreateFilesResponseObject(**response_json.get("file", {})) + response_object: Final = response_json["file"] # Extract file information from Gemini response @@ -262,7 +282,7 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): """ try: verbose_logger.debug("Retrieve file response: %s", raw_response.text) - response_json: Final = raw_response.json() + response_json: Final[_GeminiFileMetadata] = raw_response.json() verbose_logger.debug("Response JSON: %s", response_json) # Map Gemini state to OpenAI status gemini_state: Final = response_json.get("state", "STATE_UNSPECIFIED") diff --git a/litellm/llms/gemini/interactions/transformation.py b/litellm/llms/gemini/interactions/transformation.py index dcd2e4e3471..6d0f211ed7b 100644 --- a/litellm/llms/gemini/interactions/transformation.py +++ b/litellm/llms/gemini/interactions/transformation.py @@ -12,9 +12,10 @@ Schema versioning: litellm.use_legacy_interactions_schema = True. Remove flag after June 8, 2026. """ -from typing import TYPE_CHECKING, Any, Final +from typing import TYPE_CHECKING, Any, Final, Protocol, TypeAlias import httpx +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -41,6 +42,53 @@ else: LiteLLMLoggingObj = Any +_JsonObject: TypeAlias = dict[str, object] + + +class _InteractionPayload(TypedDict, total=False): + """JSON body of an Interactions API interaction, keyed as ``InteractionsAPIResponse`` fields.""" + + id: ReadOnly[str | None] + object: ReadOnly[str | None] + model: ReadOnly[str | None] + agent: ReadOnly[str | None] + status: ReadOnly[str | None] + created: ReadOnly[str | None] + updated: ReadOnly[str | None] + outputs: ReadOnly[list[_JsonObject] | None] + steps: ReadOnly[list[_JsonObject] | None] + usage: ReadOnly[_JsonObject | None] + + +class _CancelPayload(TypedDict, total=False): + """JSON body of an Interactions API cancel response.""" + + id: ReadOnly[str | None] + status: ReadOnly[str | None] + + +class _InteractionPayloadSource(Protocol): + """An Interactions API HTTP response, read for the interaction body it decodes to.""" + + def json(self) -> _InteractionPayload: ... + + +class _CancelPayloadSource(Protocol): + """An Interactions API cancel HTTP response, read for the body it decodes to.""" + + def json(self) -> _CancelPayload: ... + + +def _interaction_body(response: _InteractionPayloadSource) -> _InteractionPayload: + """Decode the body of an Interactions API interaction response.""" + return response.json() + + +def _cancel_body(response: _CancelPayloadSource) -> _CancelPayload: + """Decode the body of an Interactions API cancel response.""" + return response.json() + + class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): """ Configuration for Google AI Studio Interactions API. @@ -143,7 +191,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): """ use_legacy: Final[bool] = litellm.use_legacy_interactions_schema - request_body: Final[dict[str, Any]] = {} + request_body: Final[dict[str, object]] = {} # Model or Agent (one required) if model: @@ -189,7 +237,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): and (not isinstance(response_format, dict) or "mime_type" not in response_format) ): # Wrap the legacy schema into the new polymorphic format. - new_rf: Final[dict[str, Any]] = { + new_rf: Final[dict[str, object]] = { "type": "text", "mime_type": response_mime_type, } @@ -215,7 +263,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): if image_config is not None: # Move image_config to response_format with type=image. - image_rf: Final[dict[str, Any]] = {"type": "image", **image_config} + image_rf: Final[_JsonObject] = {"type": "image", **image_config} existing_rf: Final = request_body.get("response_format") if existing_rf is None: request_body["response_format"] = image_rf @@ -239,7 +287,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): original_response=raw_response.text, additional_args={"complete_input_dict": {}}, ) - raw_json: Final = raw_response.json() + raw_json: Final = _interaction_body(raw_response) except Exception: raise GeminiError( message=raw_response.text, @@ -290,7 +338,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): logging_obj: LiteLLMLoggingObj, ) -> InteractionsAPIResponse: try: - raw_json: Final = raw_response.json() + raw_json: Final = _interaction_body(raw_response) except Exception: raise GeminiError( message=raw_response.text, @@ -355,7 +403,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): logging_obj: LiteLLMLoggingObj, ) -> CancelInteractionResult: try: - raw_json: Final = raw_response.json() + raw_json: Final = _cancel_body(raw_response) except Exception: raise GeminiError( message=raw_response.text, diff --git a/litellm/llms/gemini/realtime/transformation.py b/litellm/llms/gemini/realtime/transformation.py index 367619db37d..c92af7de145 100644 --- a/litellm/llms/gemini/realtime/transformation.py +++ b/litellm/llms/gemini/realtime/transformation.py @@ -7,6 +7,8 @@ from collections import OrderedDict from collections.abc import Mapping, Sequence from typing import Any, Final, cast +from typing_extensions import ReadOnly, Required, TypedDict + import litellm from litellm import verbose_logger from litellm._uuid import uuid @@ -96,6 +98,23 @@ def _gemini_live_speech_config(voice: object) -> Mapping[str, object] | None: return VertexGeminiConfig()._map_audio_params({"voice": voice}) +class _GeminiLiveSetupEnvelope(TypedDict, total=False): + setup: ReadOnly[BidiGenerateContentSetup] + + +class _OpenAIRealtimeClientEvent(TypedDict, total=False): + type: ReadOnly[str] + audio: ReadOnly[Required[str]] + session: ReadOnly[dict[str, object]] + item: ReadOnly[dict[str, object]] + + +def _parse_setup(session_configuration_request: str) -> BidiGenerateContentSetup: + envelope: Final[_GeminiLiveSetupEnvelope] = json.loads(session_configuration_request) + empty_setup: Final[BidiGenerateContentSetup] = {} + return envelope.get("setup", empty_setup) + + # Google bills Live transcription at an estimated 25 audio tokens/sec of input and # 175 text tokens/min of output (ai.google.dev/gemini-api/docs/pricing). GEMINI_LIVE_TRANSCRIBE_AUDIO_TOKENS_PER_SECOND: Final = 25 @@ -130,7 +149,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): return True @staticmethod - def _usage_detail_alias(details: Any, defaults: dict[str, int]) -> dict[str, Any]: + def _usage_detail_alias(details: Mapping[str, int | None] | None, defaults: dict[str, int]) -> dict[str, int]: if not isinstance(details, dict): return dict(defaults) return { @@ -139,7 +158,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): } @staticmethod - def _add_pipecat_usage_detail_aliases(usage_dict: dict[str, Any]) -> dict[str, Any]: + def _add_pipecat_usage_detail_aliases(usage_dict: dict[str, Any]) -> dict[str, object]: usage_dict.setdefault( "input_token_details", GeminiRealtimeConfig._usage_detail_alias( @@ -222,8 +241,10 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): if not session_configuration_request: return False try: - setup: Final = json.loads(session_configuration_request).get("setup", {}) - automatic_detection: Final = setup.get("realtimeInputConfig", {}).get("automaticActivityDetection", {}) + setup: Final = _parse_setup(session_configuration_request) + automatic_detection: Final[object] = setup.get("realtimeInputConfig", {}).get( + "automaticActivityDetection", {} + ) return isinstance(automatic_detection, dict) and automatic_detection.get("disabled") is True except (json.JSONDecodeError, TypeError, AttributeError): return False @@ -406,7 +427,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): return "TEXT" if GeminiRealtimeConfig._is_text_only_live_model(model) else "AUDIO" @staticmethod - def _coerce_response_modalities(model: str, modalities: Sequence[Any]) -> tuple[str, ...]: + def _coerce_response_modalities(model: str, modalities: Sequence[object]) -> tuple[str, ...]: """Swap responseModalities a Live model cannot produce: TEXT to AUDIO for audio-only models, AUDIO to TEXT for text-only ones (e.g. transcribe-live).""" normalized: Final = tuple( @@ -431,7 +452,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): def _handle_session_update( self, - json_message: dict, + json_message: _OpenAIRealtimeClientEvent, model: str, session_configuration_request: str | None, ) -> list[str]: @@ -445,7 +466,8 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): with a 1007, tearing the session down). To carry tools/instructions, send them on the first session.update before any conversation content. """ - session_payload = json_message.get("session") or {} + empty_session: Final[dict[str, object]] = {} + session_payload = json_message.get("session") or empty_session # Normalize GA-remapped fields (``output_modalities``, # nested ``audio.input.transcription``, # ``audio.input.turn_detection``) back to their flat beta keys so @@ -486,14 +508,15 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): verbose_logger.debug("Gemini Realtime: Ignoring session.update (setup already sent)") return [] - def _handle_conversation_item(self, json_message: dict) -> list[str]: + def _handle_conversation_item(self, json_message: _OpenAIRealtimeClientEvent) -> list[str]: """ Handle conversation.item.create for user text or function call output. Converts OpenAI format to Gemini's clientContent (for user text) or toolResponse (for function outputs). """ - item: Final = json_message.get("item", {}) + empty_item: Final[dict[str, object]] = {} + item: Final = json_message.get("item", empty_item) item_type: Final = item.get("type") if item_type == "function_call_output": @@ -524,7 +547,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): call_id, ) - function_response: Final[dict[str, Any]] = {"response": output_dict} + function_response: Final[dict[str, object]] = {"response": output_dict} if self._include_function_response_id() and call_id: function_response["id"] = call_id if function_name: @@ -559,7 +582,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): ) -> list[str]: realtime_input_dict: BidiGenerateContentRealtimeInput = {} try: - json_message: Final = json.loads(message) + json_message: Final[_OpenAIRealtimeClientEvent] = json.loads(message) except json.JSONDecodeError: if isinstance(message, bytes): message_str = message.decode("utf-8", errors="replace") @@ -610,9 +633,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): session_configuration_request: str | None = None, ) -> OpenAIRealtimeStreamSessionEvents: if session_configuration_request: - session_configuration_request_dict: BidiGenerateContentSetup = json.loads( - session_configuration_request - ).get("setup", {}) + session_configuration_request_dict: BidiGenerateContentSetup = _parse_setup(session_configuration_request) else: session_configuration_request_dict = {} @@ -663,7 +684,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): session_configuration_request_dict: BidiGenerateContentSetup = {} if session_configuration_request is not None: try: - session_configuration_request_dict = json.loads(session_configuration_request).get("setup", {}) + session_configuration_request_dict = _parse_setup(session_configuration_request) except json.JSONDecodeError: session_configuration_request_dict = {} generation_config: Final = session_configuration_request_dict.get("generationConfig", {}) @@ -931,9 +952,9 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): return events @staticmethod - def get_nested_value(obj: dict, path: str) -> Any: + def get_nested_value(obj: dict, path: str) -> object | None: keys: Final = path.split(".") - current = obj + current: object = obj for key in keys: if isinstance(current, dict) and key in current: current = current[key] @@ -1011,9 +1032,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): current_response_id = f"resp_{uuid.uuid4()}" if session_configuration_request: - session_configuration_request_dict: BidiGenerateContentSetup = json.loads( - session_configuration_request - ).get("setup", {}) + session_configuration_request_dict: BidiGenerateContentSetup = _parse_setup(session_configuration_request) else: session_configuration_request_dict = {} @@ -1337,7 +1356,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): session_setup: BidiGenerateContentSetup = {} if session_configuration_request is not None: try: - session_setup = json.loads(session_configuration_request).get("setup", {}) + session_setup = _parse_setup(session_configuration_request) except (json.JSONDecodeError, TypeError): session_setup = {} tool_call_generation_config = session_setup.get("generationConfig", {}) or {} diff --git a/litellm/llms/gemini/videos/transformation.py b/litellm/llms/gemini/videos/transformation.py index 6a1fc144c42..ff4c675b02f 100644 --- a/litellm/llms/gemini/videos/transformation.py +++ b/litellm/llms/gemini/videos/transformation.py @@ -1,4 +1,5 @@ import base64 +from collections.abc import Mapping from typing import TYPE_CHECKING, Any, Final import httpx @@ -54,8 +55,13 @@ def _convert_image_to_gemini_format(image_file) -> dict[str, str]: return {"bytesBase64Encoded": base64_encoded, "mimeType": mime_type} +def _json_payload(raw_response: httpx.Response) -> object: + """Read an HTTP response body as an opaque JSON payload.""" + return raw_response.json() + + def _usage_video_resolution_from_parameters( - parameters: dict[str, Any], + parameters: Mapping[str, object], ) -> str | None: """Normalize Veo ``parameters.resolution`` for usage and cost tracking.""" res: Final = parameters.get("resolution") @@ -97,7 +103,7 @@ class GeminiVideoConfig(BaseVideoConfig): video_create_optional_params: VideoCreateOptionalRequestParams, model: str, drop_params: bool, - ) -> dict[str, Any]: + ) -> dict[str, object]: """ Map OpenAI-style parameters to Veo format. @@ -111,7 +117,7 @@ class GeminiVideoConfig(BaseVideoConfig): All other params are passed through as-is to support Gemini-specific parameters. """ - mapped_params: Final[dict[str, Any]] = {} + mapped_params: Final[dict[str, object]] = {} # Get supported OpenAI params (exclude "model" and "prompt" which are handled separately) supported_openai_params: Final = self.get_supported_openai_params(model) @@ -312,11 +318,11 @@ class GeminiVideoConfig(BaseVideoConfig): - status: "processing" - usage: includes duration_seconds and optional video_resolution for cost calculation """ - response_data: Final = raw_response.json() + response_data: Final = _json_payload(raw_response) # Parse response using Pydantic model for type safety try: - operation_response: Final = GeminiLongRunningOperationResponse(**response_data) + operation_response: Final = GeminiLongRunningOperationResponse.model_validate(response_data) except Exception as e: raise ValueError(f"Failed to parse operation response: {e}") @@ -336,7 +342,7 @@ class GeminiVideoConfig(BaseVideoConfig): model=model, ) - usage_data: Final[dict[str, Any]] = {} + usage_data: Final[dict[str, float | str]] = {} if request_data: parameters: Final = request_data.get("parameters", {}) duration: Final = parameters.get("durationSeconds") or DEFAULT_GOOGLE_VIDEO_DURATION_SECONDS @@ -367,7 +373,7 @@ class GeminiVideoConfig(BaseVideoConfig): """ operation_name: Final = extract_original_video_id(video_id) url: Final = f"{api_base.rstrip('/')}/v1beta/{operation_name}" - params: Final[dict[str, Any]] = {} + params: Final[dict[str, object]] = {} return url, params @@ -403,9 +409,9 @@ class GeminiVideoConfig(BaseVideoConfig): } } """ - response_data: Final = raw_response.json() + response_data: Final = _json_payload(raw_response) # Parse response using Pydantic model for type safety - operation_response: Final = GeminiLongRunningOperationResponse(**response_data) + operation_response: Final = GeminiLongRunningOperationResponse.model_validate(response_data) operation_name: Final = operation_response.name is_done: Final = operation_response.done @@ -443,9 +449,9 @@ class GeminiVideoConfig(BaseVideoConfig): client: Final = litellm.module_level_client status_response: Final = client.get(url=status_url, headers=headers) status_response.raise_for_status() - response_data: Final = status_response.json() + response_data: Final = _json_payload(status_response) - operation_response: Final = GeminiLongRunningOperationResponse(**response_data) + operation_response: Final = GeminiLongRunningOperationResponse.model_validate(response_data) if not operation_response.done: raise ValueError( @@ -458,7 +464,7 @@ class GeminiVideoConfig(BaseVideoConfig): generated_samples: Final = operation_response.response.generateVideoResponse.generatedSamples download_url: Final = generated_samples[0].video.uri - params: Final[dict[str, Any]] = {} + params: Final[dict[str, object]] = {} return download_url, params @@ -480,7 +486,7 @@ class GeminiVideoConfig(BaseVideoConfig): api_base: str, litellm_params: GenericLiteLLMParams, headers: dict, - extra_body: dict[str, Any] | None = None, + extra_body: Mapping[str, object] | None = None, ) -> tuple[str, dict]: """ Video remix is not supported by Veo API. @@ -506,7 +512,7 @@ class GeminiVideoConfig(BaseVideoConfig): after: str | None = None, limit: int | None = None, order: str | None = None, - extra_query: dict[str, Any] | None = None, + extra_query: Mapping[str, object] | None = None, ) -> tuple[str, dict]: """ Video list is not supported by Veo API. @@ -547,7 +553,7 @@ class GeminiVideoConfig(BaseVideoConfig): """Video delete is not supported.""" raise NotImplementedError("Video delete is not supported by Google Veo.") - def transform_video_create_character_request(self, name, video, api_base, litellm_params, headers): + def transform_video_create_character_request(self, name, video: object, api_base, litellm_params, headers): raise NotImplementedError("video create character is not supported for Gemini") def transform_video_create_character_response(self, raw_response, logging_obj): diff --git a/litellm/llms/gigachat/__init__.py b/litellm/llms/gigachat/__init__.py index 3ddbd7864d9..e7c2206ffaa 100644 --- a/litellm/llms/gigachat/__init__.py +++ b/litellm/llms/gigachat/__init__.py @@ -15,9 +15,11 @@ API Documentation: https://developers.sber.ru/docs/ru/gigachat/api/overview from .chat.transformation import GigaChatConfig, GigaChatError from .embedding.transformation import GigaChatEmbeddingConfig +from .passthrough.transformation import GigaChatPassthroughConfig -__all__ = [ +__all__ = ( "GigaChatConfig", "GigaChatEmbeddingConfig", "GigaChatError", -] + "GigaChatPassthroughConfig", +) diff --git a/litellm/llms/gigachat/authenticator.py b/litellm/llms/gigachat/authenticator.py index 9ef6fe7a93c..d6b217d5746 100644 --- a/litellm/llms/gigachat/authenticator.py +++ b/litellm/llms/gigachat/authenticator.py @@ -7,6 +7,7 @@ Based on official GigaChat SDK authentication flow. import time import uuid +from collections.abc import Mapping from typing import Final import httpx @@ -16,7 +17,7 @@ from litellm.caching.caching import InMemoryCache from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.llms.custom_httpx.http_handler import ( HTTPHandler, - _get_httpx_client, + _get_httpx_client, # pyright: ignore[reportPrivateUsage] # house cached-client factory has no public alias get_async_httpx_client, ) from litellm.secret_managers.main import get_secret_str @@ -63,6 +64,7 @@ def get_access_token( credentials: str | None = None, scope: str | None = None, auth_url: str | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> str: """ Get valid access token, using cache if available. @@ -78,71 +80,88 @@ def get_access_token( Raises: GigaChatAuthError: If authentication fails """ - credentials = credentials or _get_credentials() - if not credentials: + if not litellm_params: + litellm_params = {} # mutable-ok: empty dict default; rebind-ok: provide default + + access_token: Final = litellm_params.get("gigachat_access_token") or get_secret_str("GIGACHAT_ACCESS_TOKEN") + if access_token: + return access_token + + effective_credentials: Final = credentials or _get_credentials() + if not effective_credentials: raise GigaChatAuthError( status_code=401, message="GigaChat credentials not provided. Set GIGACHAT_CREDENTIALS or GIGACHAT_API_KEY environment variable.", ) - scope = scope or _get_scope() - auth_url = auth_url or _get_auth_url() + effective_scope: Final = scope or litellm_params.get("gigachat_scope") or _get_scope() + effective_auth_url: Final = auth_url or litellm_params.get("gigachat_auth_url") or _get_auth_url() # Check cache - cache_key: Final = f"gigachat_token:{credentials[:16]}" + cache_key: Final = f"gigachat_token:{effective_credentials[:16]}" cached: Final = _token_cache.get_cache(cache_key) if cached: - token, expires_at = cached + _token, _expires_at = cached # Check if token is still valid (with buffer) - if time.time() * 1000 < expires_at - TOKEN_EXPIRY_BUFFER_MS: + if time.time() * 1000 < _expires_at - TOKEN_EXPIRY_BUFFER_MS: verbose_logger.debug("Using cached GigaChat access token") - return token + return _token # Request new token - token, expires_at = _request_token_sync(credentials, scope, auth_url) + new_token, new_expires_at = _request_token_sync(effective_credentials, effective_scope, effective_auth_url) # pyright: ignore[reportArgumentType] # credential keys may be broader than str - # Cache token - ttl_seconds: Final = max(0, (expires_at - TOKEN_EXPIRY_BUFFER_MS - time.time() * 1000) / 1000) - if ttl_seconds > 0: - _token_cache.set_cache(cache_key, (token, expires_at), ttl=ttl_seconds) + if new_expires_at: + # Cache token + ttl_seconds: Final = max(0, (new_expires_at - TOKEN_EXPIRY_BUFFER_MS - time.time() * 1000) / 1000) + if ttl_seconds > 0: + _token_cache.set_cache(cache_key, (new_token, new_expires_at), ttl=ttl_seconds) - return token + return new_token async def get_access_token_async( credentials: str | None = None, scope: str | None = None, auth_url: str | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> str: """Async version of get_access_token.""" - credentials = credentials or _get_credentials() - if not credentials: + if not litellm_params: + litellm_params = {} # mutable-ok: empty dict default; rebind-ok: provide default + + access_token: Final = litellm_params.get("gigachat_access_token") or get_secret_str("GIGACHAT_ACCESS_TOKEN") + if access_token: + return access_token + + effective_credentials: Final = credentials or _get_credentials() + if not effective_credentials: raise GigaChatAuthError( status_code=401, message="GigaChat credentials not provided. Set GIGACHAT_CREDENTIALS or GIGACHAT_API_KEY environment variable.", ) - scope = scope or _get_scope() - auth_url = auth_url or _get_auth_url() + effective_scope: Final = scope or litellm_params.get("gigachat_scope") or _get_scope() + effective_auth_url: Final = auth_url or litellm_params.get("gigachat_auth_url") or _get_auth_url() # Check cache - cache_key: Final = f"gigachat_token:{credentials[:16]}" + cache_key: Final = f"gigachat_token:{effective_credentials[:16]}" cached: Final = _token_cache.get_cache(cache_key) if cached: - token, expires_at = cached - if time.time() * 1000 < expires_at - TOKEN_EXPIRY_BUFFER_MS: + _token, _expires_at = cached + if time.time() * 1000 < _expires_at - TOKEN_EXPIRY_BUFFER_MS: verbose_logger.debug("Using cached GigaChat access token") - return token + return _token # Request new token - token, expires_at = await _request_token_async(credentials, scope, auth_url) + new_token, new_expires_at = await _request_token_async(effective_credentials, effective_scope, effective_auth_url) # pyright: ignore[reportArgumentType] # credential keys may be broader than str - # Cache token - ttl_seconds: Final = max(0, (expires_at - TOKEN_EXPIRY_BUFFER_MS - time.time() * 1000) / 1000) - if ttl_seconds > 0: - _token_cache.set_cache(cache_key, (token, expires_at), ttl=ttl_seconds) + if new_expires_at: + # Cache token + ttl_seconds: Final = max(0, (new_expires_at - TOKEN_EXPIRY_BUFFER_MS - time.time() * 1000) / 1000) + if ttl_seconds > 0: + _token_cache.set_cache(cache_key, (new_token, new_expires_at), ttl=ttl_seconds) - return token + return new_token def _request_token_sync( @@ -154,7 +173,7 @@ def _request_token_sync( Request new access token from GigaChat OAuth endpoint (sync). Returns: - Tuple of (access_token, expires_at_ms) + tuple of (access_token, expires_at_ms) """ headers: Final = { "Authorization": f"Basic {credentials}", @@ -169,7 +188,7 @@ def _request_token_sync( client: Final = _get_http_client() response: Final = client.post(auth_url, headers=headers, data=data, timeout=30) response.raise_for_status() - return _parse_token_response(response) + return _parse_token_response(response) # pyright: ignore[reportArgumentType] # httpx Response may be None at type level except httpx.HTTPStatusError as e: raise GigaChatAuthError( status_code=e.response.status_code, @@ -204,7 +223,7 @@ async def _request_token_async( ) response: Final = await client.post(auth_url, headers=headers, data=data, timeout=30) response.raise_for_status() - return _parse_token_response(response) + return _parse_token_response(response) # pyright: ignore[reportArgumentType] # httpx Response may be None at type level except httpx.HTTPStatusError as e: raise GigaChatAuthError( status_code=e.response.status_code, @@ -223,7 +242,7 @@ def _parse_token_response(response: httpx.Response) -> tuple[str, int]: # GigaChat returns either 'tok'/'exp' or 'access_token'/'expires_at' access_token: Final = data.get("tok") or data.get("access_token") - expires_at = data.get("exp") or data.get("expires_at") + expires_at_raw: Final = data.get("exp") or data.get("expires_at") if not access_token: raise GigaChatAuthError( @@ -232,8 +251,11 @@ def _parse_token_response(response: httpx.Response) -> tuple[str, int]: ) # expires_at is in milliseconds - if isinstance(expires_at, str): - expires_at = int(expires_at) + expires_at: int # rebind-ok: conditionally assigned from str or int + if isinstance(expires_at_raw, str): + expires_at = int(expires_at_raw) # rebind-ok: conditionally assigned from str or int + else: + expires_at = expires_at_raw # pyright: ignore[reportAssignmentType] # raw value is int or str; converted above; rebind-ok: conditionally assigned from str or int verbose_logger.debug("GigaChat access token obtained successfully") return access_token, expires_at diff --git a/litellm/llms/gigachat/chat/__init__.py b/litellm/llms/gigachat/chat/__init__.py index eb9492b90b3..0f9be19fedd 100644 --- a/litellm/llms/gigachat/chat/__init__.py +++ b/litellm/llms/gigachat/chat/__init__.py @@ -5,8 +5,8 @@ GigaChat Chat Module from .streaming import GigaChatModelResponseIterator from .transformation import GigaChatConfig, GigaChatError -__all__ = [ +__all__ = ( "GigaChatConfig", "GigaChatError", "GigaChatModelResponseIterator", -] +) diff --git a/litellm/llms/gigachat/chat/streaming.py b/litellm/llms/gigachat/chat/streaming.py index 219209773ea..2875b30232e 100644 --- a/litellm/llms/gigachat/chat/streaming.py +++ b/litellm/llms/gigachat/chat/streaming.py @@ -4,13 +4,15 @@ GigaChat Streaming Response Handler import json import uuid +from collections.abc import Mapping, Sequence from typing import Any, Final +from litellm.llms.gigachat.utils import convert_usage from litellm.types.llms.openai import ( ChatCompletionToolCallChunk, ChatCompletionToolCallFunctionChunk, ) -from litellm.types.utils import GenericStreamingChunk +from litellm.types.utils import ChatCompletionUsageBlock, GenericStreamingChunk class GigaChatModelResponseIterator: @@ -26,14 +28,9 @@ class GigaChatModelResponseIterator: self.response_iterator = self.streaming_response self.json_mode = json_mode - def chunk_parser(self, chunk: dict) -> GenericStreamingChunk: + def chunk_parser(self, chunk: Mapping[str, object]) -> GenericStreamingChunk: """Parse a single streaming chunk from GigaChat.""" - text = "" - tool_use: ChatCompletionToolCallChunk | None = None - is_finished = False - finish_reason: str | None = None - - choices: Final = chunk.get("choices", []) + choices: Sequence = chunk.get("choices") or () # mutable-ok: tuple literal as default if not choices: return GenericStreamingChunk( text="", @@ -45,40 +42,63 @@ class GigaChatModelResponseIterator: ) choice: Final = choices[0] - delta: Final = choice.get("delta", {}) - finish_reason = choice.get("finish_reason") + delta: Mapping[str, object] = choice.get("delta") or {} # mutable-ok: empty dict default for get + chunk_finish_reason: Final = choice.get("finish_reason") # Extract text content - text = delta.get("content", "") or "" + text: Final = delta.get("content", "") or "" + + usage_block: ChatCompletionUsageBlock | None = None # rebind-ok: conditionally assigned after stop detection + tool_use: ChatCompletionToolCallChunk | None = None # rebind-ok: conditionally assigned on function_call + finish_reason: str | None = chunk_finish_reason # Handle function_call in stream - if finish_reason == "function_call" and delta.get("function_call"): - func_call: Final = delta["function_call"] - args = func_call.get("arguments", {}) - - if isinstance(args, dict): - args = json.dumps(args, ensure_ascii=False) + raw_function_call: Final = delta.get("function_call") + if chunk_finish_reason == "function_call" and isinstance(raw_function_call, Mapping) and raw_function_call: + func_call: Final[Mapping[str, object]] = raw_function_call + args_raw: Final[object] = func_call.get("arguments") or {} + args_str: str # rebind-ok: conditionally assigned from dict or str + if isinstance(args_raw, dict): + args_str = json.dumps(args_raw, ensure_ascii=False) # rebind-ok: build from dict + else: + args_str = str(args_raw) + name_raw: Final = func_call.get("name") tool_use = ChatCompletionToolCallChunk( id=f"call_{uuid.uuid4().hex[:24]}", type="function", function=ChatCompletionToolCallFunctionChunk( - name=func_call.get("name", ""), - arguments=args, + name=name_raw if isinstance(name_raw, str) else "", + arguments=args_str, ), index=0, ) finish_reason = "tool_calls" - if finish_reason is not None: - is_finished = True + usage_data: Final = chunk.get("usage") or {} # mutable-ok: empty dict default + if usage_data and isinstance(usage_data, dict): + validated_usage: Final = {k: int(v) for k, v in usage_data.items()} + usage = convert_usage(validated_usage) + _prompt_details: dict | None = ( + usage.prompt_tokens_details.model_dump() if usage.prompt_tokens_details else None + ) # rebind-ok: conditional + _completion_details: dict | None = ( + usage.completion_tokens_details.model_dump() if usage.completion_tokens_details else None + ) # rebind-ok: conditional + usage_block = ChatCompletionUsageBlock( # pyright: ignore[reportCallIssue] # TypedDict kwarg constructor + prompt_tokens=usage.prompt_tokens, + completion_tokens=usage.completion_tokens, + total_tokens=usage.total_tokens, + prompt_tokens_details=_prompt_details, + completion_tokens_details=_completion_details, + ) return GenericStreamingChunk( - text=text, + text=str(text), tool_use=tool_use, - is_finished=is_finished, + is_finished=chunk_finish_reason is not None, finish_reason=finish_reason or "", - usage=None, + usage=usage_block, index=choice.get("index", 0), ) diff --git a/litellm/llms/gigachat/chat/transformation.py b/litellm/llms/gigachat/chat/transformation.py index b859a843251..8f23c5175ec 100644 --- a/litellm/llms/gigachat/chat/transformation.py +++ b/litellm/llms/gigachat/chat/transformation.py @@ -4,19 +4,22 @@ GigaChat Chat Transformation Transforms OpenAI-format requests to GigaChat format and back. """ +from __future__ import annotations + import json import time import uuid -from collections.abc import AsyncIterator, Iterator +from collections.abc import AsyncIterator, Iterator, Mapping, Sequence from typing import TYPE_CHECKING, Any, Final import httpx from litellm._logging import verbose_logger from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException +from litellm.llms.gigachat.utils import convert_usage, get_api_base from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import AllMessageValues -from litellm.types.utils import Choices, Message, ModelResponse, Usage +from litellm.types.utils import Choices, Message, ModelResponse from ..authenticator import get_access_token from ..file_handler import upload_file_sync @@ -30,9 +33,6 @@ if TYPE_CHECKING: else: LiteLLMLoggingObj = Any -# GigaChat API endpoint -GIGACHAT_BASE_URL: Final = "https://gigachat.devices.sberbank.ru/api/v1" - def is_valid_json(value: str) -> bool: """Checks whether the value passed is a valid serialized JSON string""" @@ -90,30 +90,30 @@ class GigaChatConfig(BaseConfig): api_base: str | None, api_key: str | None, model: str, - optional_params: dict, - litellm_params: dict, + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], stream: bool | None = None, ) -> str: """Get complete API URL for chat completions.""" - base: Final = api_base or get_secret_str("GIGACHAT_API_BASE") or GIGACHAT_BASE_URL + base: Final = get_api_base(api_base) return f"{base}/chat/completions" def validate_environment( self, - headers: dict, + headers: dict, # mutable-ok: mutates in place per GigaChat OAuth setup model: str, - messages: list[AllMessageValues], - optional_params: dict, - litellm_params: dict, + messages: Sequence[AllMessageValues], + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], api_key: str | None = None, api_base: str | None = None, - ) -> dict: + ) -> dict: # mutable-ok: base class contract returns dict for httpx """ Set up headers with OAuth token. """ # Get access token credentials: Final = api_key or get_secret_str("GIGACHAT_CREDENTIALS") or get_secret_str("GIGACHAT_API_KEY") - access_token: Final = get_access_token(credentials=credentials) + access_token: Final = get_access_token(credentials=credentials, litellm_params=litellm_params) # Store credentials for image uploads self._current_credentials = credentials @@ -125,9 +125,9 @@ class GigaChatConfig(BaseConfig): return headers - def get_supported_openai_params(self, model: str) -> list[str]: + def get_supported_openai_params(self, model: str) -> list[str]: # mutable-ok: base class contract returns list """Return list of supported OpenAI parameters.""" - return [ + return [ # mutable-ok: base class contract returns list "stream", "temperature", "top_p", @@ -143,11 +143,11 @@ class GigaChatConfig(BaseConfig): def map_openai_params( self, - non_default_params: dict, - optional_params: dict, + non_default_params: Mapping[str, object], + optional_params: dict, # mutable-ok: mutated in place per GigaChat mapping model: str, drop_params: bool, - ) -> dict: + ) -> dict: # mutable-ok: base class contract returns dict """Map OpenAI parameters to GigaChat parameters.""" for param, value in non_default_params.items(): if param == "stream": @@ -167,42 +167,50 @@ class GigaChatConfig(BaseConfig): pass elif param == "tools": # Convert tools to functions format - optional_params["functions"] = self._convert_tools_to_functions(value) + if isinstance(value, Sequence): + optional_params["functions"] = self._convert_tools_to_functions(value) elif param == "tool_choice": # Map OpenAI tool_choice to GigaChat function_call - mapped_choice = self._map_tool_choice(value) - if mapped_choice is not None: - optional_params["function_call"] = mapped_choice + if isinstance(value, (str, Mapping)): + mapped_choice = self._map_tool_choice(value) + if mapped_choice is not None: + optional_params["function_call"] = mapped_choice elif param == "functions": optional_params["functions"] = value elif param == "function_call": optional_params["function_call"] = value elif param == "response_format": # Handle structured output via function calling - if value.get("type") == "json_schema": + if isinstance(value, Mapping) and value.get("type") == "json_schema": json_schema = value.get("json_schema", {}) schema_name = json_schema.get("name", "structured_output") schema = json_schema.get("schema", {}) - function_def = { + function_def = { # mutable-ok: request payload for httpx "name": schema_name, "description": f"Output structured response: {schema_name}", "parameters": schema, } - if "functions" not in optional_params: - optional_params["functions"] = [] - optional_params["functions"].append(function_def) - optional_params["function_call"] = {"name": schema_name} + existing_functions = optional_params.get("functions") + optional_params["functions"] = [ + *( + existing_functions + if isinstance(existing_functions, Sequence) and not isinstance(existing_functions, str) + else () + ), + function_def, + ] + optional_params["function_call"] = {"name": schema_name} # mutable-ok: request payload optional_params["_structured_output"] = True return optional_params - def _convert_tools_to_functions(self, tools: list[dict]) -> list[dict]: + def _convert_tools_to_functions(self, tools: Sequence) -> Sequence[dict]: """Convert OpenAI tools format to GigaChat functions format.""" - functions: Final = [] + functions: Final[list[dict]] = [] # mutable-ok: accumulator for building functions list for tool in tools: - if tool.get("type") == "function": + if isinstance(tool, dict) and tool.get("type") == "function": func = tool.get("function", {}) functions.append( { @@ -213,7 +221,7 @@ class GigaChatConfig(BaseConfig): ) return functions - def _map_tool_choice(self, tool_choice: str | dict) -> str | dict | None: + def _map_tool_choice(self, tool_choice: str | Mapping[str, object]) -> str | Mapping[str, object] | None: """ Map OpenAI tool_choice to GigaChat function_call format. @@ -246,8 +254,9 @@ class GigaChatConfig(BaseConfig): # OpenAI format: {"type": "function", "function": {"name": "func_name"}} # GigaChat format: {"name": "func_name"} if tool_choice.get("type") == "function": - func_name: Final = tool_choice.get("function", {}).get("name") - if func_name: + function_spec: Final = tool_choice.get("function") + func_name: Final = function_spec.get("name") if isinstance(function_spec, Mapping) else None + if isinstance(func_name, str) and func_name: return {"name": func_name} # Default to None (don't set function_call) @@ -273,20 +282,51 @@ class GigaChatConfig(BaseConfig): verbose_logger.error("Failed to upload image: %s", e) return None + def _transform_list_content(self, content: Sequence) -> tuple[str, Sequence[str]]: + """ + Extract text and image attachments from a multimodal message content list. + + Args: + content: List of content parts (OpenAI multimodal format) + + Returns: + Tuple of (combined text, list of attachment file ids) + """ + texts: Final[list[str]] = [] # mutable-ok: accumulator + attachments: Final[list[str]] = [] # mutable-ok: accumulator + for part in content: + if isinstance(part, dict): + if part.get("type") == "text": + texts.append(part.get("text", "")) + elif part.get("type") == "image_url": + # Extract image URL and upload to GigaChat + image_url: object = part.get("image_url", {}) + upload_url: str + if isinstance(image_url, str): + upload_url = image_url + else: + upload_url = str(image_url.get("url", "")) if isinstance(image_url, dict) else "" + if upload_url: + file_id = self._upload_image(upload_url) + if file_id: + attachments.append(file_id) + text: Final = "\n".join(texts) if texts else "" + return text, attachments + def transform_request( self, model: str, - messages: list[AllMessageValues], - optional_params: dict, - litellm_params: dict, - headers: dict, - ) -> dict: + messages: Sequence[AllMessageValues], + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], + headers: Mapping[str, object], + ) -> dict: # mutable-ok: request payload sent to httpx """Transform OpenAI request to GigaChat format.""" # Transform messages giga_messages: Final = self._transform_messages(messages) # Build request - request_data: Final = { + request_data: Final[dict[str, object]] = { "model": model.replace("gigachat/", ""), "messages": giga_messages, } @@ -311,9 +351,9 @@ class GigaChatConfig(BaseConfig): return request_data - def _transform_messages(self, messages: list[AllMessageValues]) -> list[dict]: + def _transform_messages(self, messages: Sequence[AllMessageValues]) -> Sequence[dict]: """Transform OpenAI messages to GigaChat format.""" - transformed: Final = [] + transformed: Final[list[dict]] = [] # mutable-ok: accumulator for building transformed messages for i, msg in enumerate(messages): message = dict(msg) @@ -341,24 +381,7 @@ class GigaChatConfig(BaseConfig): # Handle list content (multimodal) - extract text and images content = message.get("content") if isinstance(content, list): - texts = [] - attachments = [] - for part in content: - if isinstance(part, dict): - if part.get("type") == "text": - texts.append(part.get("text", "")) - elif part.get("type") == "image_url": - # Extract image URL and upload to GigaChat - image_url = part.get("image_url", {}) - if isinstance(image_url, str): - url = image_url - else: - url = image_url.get("url", "") - if url: - file_id = self._upload_image(url) - if file_id: - attachments.append(file_id) - message["content"] = "\n".join(texts) if texts else "" + message["content"], attachments = self._transform_list_content(content) if attachments: message["attachments"] = attachments @@ -393,7 +416,7 @@ class GigaChatConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: "tiktoken.Encoding | None", + encoding: tiktoken.Encoding | None, api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: @@ -408,7 +431,7 @@ class GigaChatConfig(BaseConfig): is_structured_output: Final = optional_params.get("_structured_output", False) - choices: Final = [] + choices: Final[list[Choices]] = [] # mutable-ok: accumulator for building response choices for choice in response_json.get("choices", []): message_data = choice.get("message", {}) finish_reason = choice.get("finish_reason", "stop") @@ -462,11 +485,7 @@ class GigaChatConfig(BaseConfig): # Build usage usage_data: Final = response_json.get("usage", {}) - usage: Final = Usage( - prompt_tokens=usage_data.get("prompt_tokens", 0), - completion_tokens=usage_data.get("completion_tokens", 0), - total_tokens=usage_data.get("total_tokens", 0), - ) + usage: Final = convert_usage(usage_data) model_response.id = response_json.get("id", f"chatcmpl-{uuid.uuid4().hex[:12]}") model_response.created = response_json.get("created", int(time.time())) diff --git a/litellm/llms/gigachat/embedding/transformation.py b/litellm/llms/gigachat/embedding/transformation.py index bb495cea423..2ec8324e33c 100644 --- a/litellm/llms/gigachat/embedding/transformation.py +++ b/litellm/llms/gigachat/embedding/transformation.py @@ -5,6 +5,8 @@ Transforms OpenAI /v1/embeddings format to GigaChat format. API Documentation: https://developers.sber.ru/docs/ru/gigachat/api/reference/rest/post-embeddings """ +from __future__ import annotations + import types from typing import Final @@ -14,14 +16,12 @@ from litellm import LlmProviders from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig +from litellm.llms.gigachat.utils import get_api_base from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues from litellm.types.utils import EmbeddingResponse from ..authenticator import get_access_token -# GigaChat API endpoint -GIGACHAT_BASE_URL: Final = "https://gigachat.devices.sberbank.ru/api/v1" - class GigaChatEmbeddingError(BaseLLMException): """GigaChat Embedding API error.""" @@ -78,9 +78,9 @@ class GigaChatEmbeddingConfig(BaseEmbeddingConfig): Returns provider info for GigaChat. Returns: - Tuple of (custom_llm_provider, api_base, dynamic_api_key) + tuple of (custom_llm_provider, api_base, dynamic_api_key) """ - api_base = api_base or GIGACHAT_BASE_URL + api_base = get_api_base(api_base) return LlmProviders.GIGACHAT.value, api_base, api_key def get_complete_url( @@ -93,7 +93,7 @@ class GigaChatEmbeddingConfig(BaseEmbeddingConfig): stream: bool | None = None, ) -> str: """Get the complete URL for embeddings endpoint.""" - base: Final = api_base or GIGACHAT_BASE_URL + base: Final = get_api_base(api_base) return f"{base}/embeddings" def transform_embedding_request( @@ -114,14 +114,12 @@ class GigaChatEmbeddingConfig(BaseEmbeddingConfig): """ # Normalize input to list if isinstance(input, str): - input_list: list = [input] - elif isinstance(input, list): - input_list = input + input_list: list = [input] # rebind-ok: locally scoped conversion else: - input_list = [input] + input_list = input # Remove gigachat/ prefix from model if present - model = model.removeprefix("gigachat/") + model = model.removeprefix("gigachat/") # rebind-ok: parameter reassignment for normalization return { "model": model, @@ -191,7 +189,7 @@ class GigaChatEmbeddingConfig(BaseEmbeddingConfig): Set up headers with OAuth token for GigaChat. """ # Get access token via OAuth - access_token: Final = get_access_token(api_key) + access_token: Final = get_access_token(credentials=api_key, litellm_params=litellm_params) default_headers: Final = { "Content-Type": "application/json", diff --git a/litellm/llms/gigachat/file_handler.py b/litellm/llms/gigachat/file_handler.py index 4cbde551fa2..163e944f124 100644 --- a/litellm/llms/gigachat/file_handler.py +++ b/litellm/llms/gigachat/file_handler.py @@ -9,6 +9,7 @@ import base64 import hashlib import re import uuid +from collections.abc import Mapping from typing import Final from litellm._logging import verbose_logger @@ -16,13 +17,11 @@ from litellm.llms.custom_httpx.http_handler import ( _get_httpx_client, get_async_httpx_client, ) +from litellm.llms.gigachat.utils import get_api_base from litellm.types.utils import LlmProviders from .authenticator import get_access_token, get_access_token_async -# GigaChat API endpoint -GIGACHAT_BASE_URL: Final = "https://gigachat.devices.sberbank.ru/api/v1" - # Simple in-memory cache for file IDs _file_cache: Final[dict[str, str]] = {} @@ -82,6 +81,7 @@ def upload_file_sync( image_url: str, credentials: str | None = None, api_base: str | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> str | None: """ Upload file to GigaChat and return file_id (sync). @@ -114,10 +114,10 @@ def upload_file_sync( filename: Final = f"{uuid.uuid4()}.{ext}" # Get access token - access_token: Final = get_access_token(credentials) + access_token: Final = get_access_token(credentials=credentials, litellm_params=litellm_params) # Upload to GigaChat - base_url: Final = api_base or GIGACHAT_BASE_URL + base_url: Final = get_api_base(api_base) upload_url: Final = f"{base_url}/files" client: Final = _get_httpx_client(params={"ssl_verify": False}) @@ -147,6 +147,7 @@ async def upload_file_async( image_url: str, credentials: str | None = None, api_base: str | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> str | None: """ Upload file to GigaChat and return file_id (async). @@ -179,10 +180,10 @@ async def upload_file_async( filename: Final = f"{uuid.uuid4()}.{ext}" # Get access token - access_token: Final = await get_access_token_async(credentials) + access_token: Final = await get_access_token_async(credentials=credentials, litellm_params=litellm_params) # Upload to GigaChat - base_url: Final = api_base or GIGACHAT_BASE_URL + base_url: Final = get_api_base(api_base) upload_url: Final = f"{base_url}/files" client: Final = get_async_httpx_client( diff --git a/litellm/llms/gigachat/passthrough/__init__.py b/litellm/llms/gigachat/passthrough/__init__.py new file mode 100644 index 00000000000..a66a078dbeb --- /dev/null +++ b/litellm/llms/gigachat/passthrough/__init__.py @@ -0,0 +1,7 @@ +""" +GigaChat passthrough Module +""" + +from .transformation import GigaChatPassthroughConfig + +__all__ = ("GigaChatPassthroughConfig",) diff --git a/litellm/llms/gigachat/passthrough/transformation.py b/litellm/llms/gigachat/passthrough/transformation.py new file mode 100644 index 00000000000..a0edc6f5682 --- /dev/null +++ b/litellm/llms/gigachat/passthrough/transformation.py @@ -0,0 +1,213 @@ +from __future__ import annotations + +import json +from collections.abc import Mapping, Sequence +from typing import TYPE_CHECKING, Final + +import httpx + +from litellm.llms.base_llm.passthrough.transformation import BasePassthroughConfig +from litellm.llms.gigachat.authenticator import get_access_token +from litellm.llms.gigachat.chat.streaming import GigaChatModelResponseIterator +from litellm.llms.gigachat.utils import GIGACHAT_BASE_URL +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import AllMessageValues +from litellm.types.utils import EmbeddingResponse + +if TYPE_CHECKING: + from httpx import URL, Response + + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.types.utils import CostResponseTypes + + +class GigaChatPassthroughConfig(BasePassthroughConfig): + def is_streaming_request(self, endpoint: str, request_data: Mapping[str, object]) -> bool: + return request_data.get("stream", False) + + def get_complete_url( + self, + api_base: str | None, + api_key: str | None, + model: str, + endpoint: str, + request_query_params: Mapping[str, object] | None, + litellm_params: Mapping[str, object], + ) -> tuple[URL, str]: + """Get complete API URL for chat completions.""" + base_target_url: Final = self.get_api_base(api_base) + + if base_target_url is None: + raise Exception("GigaChat api base not found") + + complete_url: Final = f"{base_target_url}/{endpoint.lstrip('/')}" + + return ( + httpx.URL(complete_url), + base_target_url, + ) + + def validate_environment( + self, + headers: dict, # mutable-ok: mutates in place to set OAuth headers + model: str, + messages: Sequence[AllMessageValues], + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], + api_key: str | None = None, + api_base: str | None = None, + ) -> dict: # mutable-ok: base class contract returns dict for httpx + """ + Set up headers with OAuth token. + """ + # Get access token + access_token: Final = get_access_token(credentials=api_key, litellm_params=litellm_params) + + headers["Authorization"] = f"Bearer {access_token}" # rebind-ok: mutating for OAuth setup + headers["Content-Type"] = "application/json" # rebind-ok: mutating for OAuth setup + headers["Accept"] = "application/json" # rebind-ok: mutating for OAuth setup + + return headers + + def logging_non_streaming_response( + self, + model: str, + custom_llm_provider: str, + httpx_response: Response, + request_data: Mapping[str, object], + logging_obj: LiteLLMLoggingObj, + endpoint: str, + ) -> CostResponseTypes | None: + from litellm import encoding + from litellm.types.utils import LlmProviders, ModelResponse + from litellm.utils import ProviderConfigManager + + # cost tracking only for completions and embeddings + if "completions" in endpoint: + provider_chat_config: Final = ProviderConfigManager.get_provider_chat_config( + provider=LlmProviders(custom_llm_provider), + model=model, + ) + + if provider_chat_config is None: + raise ValueError(f"No provider config found for model: {model}") + + raw_messages: Final = request_data.get("messages") + litellm_model_response: Final = provider_chat_config.transform_response( + model=model, + messages=list(raw_messages) + if isinstance(raw_messages, list) + else [], # mutable-ok: transform_response wants a list + raw_response=httpx_response, + model_response=ModelResponse(), + logging_obj=logging_obj, + optional_params={}, # mutable-ok: empty dict kwarg for transform_response + litellm_params={}, # mutable-ok: empty dict kwarg for transform_response + api_key="", + request_data=dict(request_data), # mutable-ok: transform_response wants a dict + encoding=encoding, + ) + + return litellm_model_response + + if "embeddings" in endpoint: + provider_embedding_config: Final = ProviderConfigManager.get_provider_embedding_config( + provider=LlmProviders(custom_llm_provider), + model=model, + ) + + if provider_embedding_config is None: + raise ValueError(f"No provider config found for model: {model}") + + litellm_embedding_response: Final[EmbeddingResponse] = ( + provider_embedding_config.transform_embedding_response( + model=model, + raw_response=httpx_response, + model_response=EmbeddingResponse(), + logging_obj=logging_obj, + optional_params={}, # mutable-ok: empty dict kwarg for transform_embedding_response + api_key="", + request_data=dict(request_data), # mutable-ok: transform_embedding_response wants a dict + litellm_params={}, # mutable-ok: empty dict kwarg for transform_embedding_response + ) + ) + + return litellm_embedding_response + + return None + + def handle_logging_collected_chunks( + self, + all_chunks: Sequence[str], + litellm_logging_obj: LiteLLMLoggingObj, + model: str, + custom_llm_provider: str, + endpoint: str, + ) -> CostResponseTypes | None: + """ + 1. Convert all_chunks to a ModelResponseStream + 2. combine model_response_stream to model_response + 3. Return the model_response + """ + + from litellm.litellm_core_utils.streaming_handler import ( + convert_generic_chunk_to_model_response_stream, + generic_chunk_has_all_required_fields, + ) + from litellm.main import stream_chunk_builder + from litellm.types.utils import ModelResponseStream + + all_translated_chunks: Final[list[object]] = [] # mutable-ok: accumulator + + for chunk in all_chunks: + chunk = chunk.strip() + if not chunk or chunk == "[DONE]": + continue + chunk = chunk.removeprefix("data: ") + try: + message = json.loads(chunk) + except json.JSONDecodeError: + continue + + gigachat_iterator = GigaChatModelResponseIterator( + streaming_response=None, + sync_stream=False, + ) + translated_chunk = gigachat_iterator.chunk_parser(chunk=message) + + if isinstance(translated_chunk, dict) and generic_chunk_has_all_required_fields( # pyright: ignore[reportUnnecessaryIsInstance] # runtime guard for patched chunk_parser + dict(translated_chunk) + ): + chunk_obj = convert_generic_chunk_to_model_response_stream( + translated_chunk # pyright: ignore[reportArgumentType] # validated TypedDict + ) + elif isinstance(translated_chunk, ModelResponseStream): + chunk_obj = translated_chunk + else: + continue + + all_translated_chunks.append(chunk_obj) + + if len(all_translated_chunks) > 0: + return stream_chunk_builder( + chunks=all_translated_chunks, + logging_obj=litellm_logging_obj, + ) + return None + + @staticmethod + def get_api_base(api_base: str | None = None) -> str | None: + return api_base or get_secret_str("GIGACHAT_API_BASE") or GIGACHAT_BASE_URL + + @staticmethod + def get_api_key( + api_key: str | None = None, + ) -> str | None: + return api_key or get_secret_str("GIGACHAT_API_KEY") + + @staticmethod + def get_base_model(model: str) -> str | None: + return model + + def get_models(self, api_key: str | None = None, api_base: str | None = None) -> list[str]: + return list(super().get_models(api_key, api_base)) diff --git a/litellm/llms/gigachat/utils.py b/litellm/llms/gigachat/utils.py new file mode 100644 index 00000000000..cbb35cd1b57 --- /dev/null +++ b/litellm/llms/gigachat/utils.py @@ -0,0 +1,26 @@ +from collections.abc import Mapping +from typing import Final + +from litellm.secret_managers.main import get_secret_str +from litellm.types.utils import PromptTokensDetailsWrapper, Usage + +# GigaChat API endpoint +GIGACHAT_BASE_URL: Final = "https://gigachat.devices.sberbank.ru/api/v1" + + +def convert_usage(usage_data: Mapping[str, int]) -> Usage: + precached_prompt_tokens: Final = usage_data.get("precached_prompt_tokens", 0) + prompt_tokens_details: Final = ( + PromptTokensDetailsWrapper(cached_tokens=precached_prompt_tokens) if precached_prompt_tokens > 0 else None + ) + + return Usage( + prompt_tokens=usage_data.get("prompt_tokens", 0) + precached_prompt_tokens, + completion_tokens=usage_data.get("completion_tokens", 0), + prompt_tokens_details=prompt_tokens_details, + total_tokens=usage_data.get("total_tokens", 0) + precached_prompt_tokens, + ) + + +def get_api_base(api_base: str | None = None) -> str | None: + return api_base or get_secret_str("GIGACHAT_API_BASE") or GIGACHAT_BASE_URL diff --git a/litellm/llms/hosted_vllm/embedding/README.md b/litellm/llms/hosted_vllm/embedding/README.md index 2c58e16fc23..50474aabdeb 100644 --- a/litellm/llms/hosted_vllm/embedding/README.md +++ b/litellm/llms/hosted_vllm/embedding/README.md @@ -4,13 +4,12 @@ VLLM is a superset of OpenAI's `embedding` endpoint. ## `encoding_format` -For OpenAI-compatible embedding calls (including `openai/...` with a custom `api_base` pointing at vLLM), LiteLLM resolves `encoding_format` when it is not set on the request: +For OpenAI-compatible embedding calls (including `openai/...` with a custom `api_base` pointing at vLLM), LiteLLM resolves `encoding_format` when it is not set on the request. `hosted_vllm/...` models use a separate handler that never adds the field on its own, so this resolution applies to the `openai/...`-style routes only: 1. Explicit value on the embedding call (`encoding_format=...`). 2. Model config (`litellm_params.encoding_format` on the proxy `model_list` entry). 3. Environment variable `LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT` (e.g. in `.env` or container env). -4. Default **`float`**. -That avoids forwarding `encoding_format=None` to the provider/SDK where some servers behave poorly. +If none of those is set, or the winning value is the literal string `none`, the field is omitted from the upstream request entirely (LiteLLM also bypasses the OpenAI SDK's own base64 default), so OpenAI-compatible servers that reject `encoding_format` keep working. -To pass provider-specific parameters, see [provider-specific params](https://docs.litellm.ai/docs/completion/provider_specific_params). \ No newline at end of file +To pass provider-specific parameters, see [provider-specific params](https://docs.litellm.ai/docs/completion/provider_specific_params). diff --git a/litellm/llms/huggingface/embedding/transformation.py b/litellm/llms/huggingface/embedding/transformation.py index d3db3530109..f6fe7f2fa10 100644 --- a/litellm/llms/huggingface/embedding/transformation.py +++ b/litellm/llms/huggingface/embedding/transformation.py @@ -1,8 +1,9 @@ import json import os import time +from collections.abc import Sequence from copy import deepcopy -from typing import TYPE_CHECKING, Any, Final +from typing import TYPE_CHECKING, Any, Final, Protocol import httpx @@ -24,6 +25,8 @@ from litellm.utils import token_counter from ..common_utils import HuggingFaceError, hf_task_list, hf_tasks, output_parser if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj LoggingClass = LiteLLMLoggingObj @@ -31,6 +34,12 @@ else: LoggingClass = Any +class _TokenEncoding(Protocol): + """Tokenizer handle the caller passes in; only `encode` is used, to count completion tokens.""" + + def encode(self, text: str, /) -> Sequence[object]: ... + + tgi_models_cache = None conv_models_cache = None @@ -369,7 +378,7 @@ class HuggingFaceEmbeddingConfig(BaseConfig): model_response: ModelResponse, task: hf_tasks | None, optional_params: dict, - encoding: Any, + encoding: "_TokenEncoding | None", messages: list[AllMessageValues], model: str, ): @@ -439,9 +448,10 @@ class HuggingFaceEmbeddingConfig(BaseConfig): if output_text is not None and len(output_text) > 0: completion_tokens = 0 try: - completion_tokens = len( - encoding.encode(model_response["choices"][0]["message"].get("content", "")) - ) ##[TODO] use the llama2 tokenizer here + if encoding is not None: + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) + ) ##[TODO] use the llama2 tokenizer here except Exception: # this should remain non blocking we should not block a response returning if calculating usage fails pass @@ -469,7 +479,7 @@ class HuggingFaceEmbeddingConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/infinity/rerank/transformation.py b/litellm/llms/infinity/rerank/transformation.py index 2526eb3b6a4..e089b3fecbe 100644 --- a/litellm/llms/infinity/rerank/transformation.py +++ b/litellm/llms/infinity/rerank/transformation.py @@ -4,10 +4,11 @@ Transformation logic from Cohere's /v1/rerank format to Infinity's `/v1/rerank` Why separate file? Make it easy to see how transformation works """ -from collections.abc import Mapping +from collections.abc import Mapping, Sequence from typing import Final import httpx +from typing_extensions import NotRequired, ReadOnly, TypedDict import litellm from litellm._uuid import uuid @@ -26,6 +27,31 @@ from litellm.types.rerank import ( from ..common_utils import InfinityError +class _InfinityRerankUsage(TypedDict, extra_items=ReadOnly[int]): + """The token counters Infinity reports in the ``usage`` block of a rerank response.""" + + +class _InfinityRerankResult(TypedDict): + """One scored document in an Infinity ``/v1/rerank`` response.""" + + index: ReadOnly[int] + relevance_score: ReadOnly[float] + document: ReadOnly[str] + + +class _InfinityRerankResponse(TypedDict): + """The JSON body returned by Infinity's ``/v1/rerank`` endpoint.""" + + id: ReadOnly[NotRequired[str]] + usage: ReadOnly[NotRequired[_InfinityRerankUsage]] + results: ReadOnly[Sequence[_InfinityRerankResult]] + + +def _parse_rerank_response(raw_response: httpx.Response) -> _InfinityRerankResponse: + """Read the untyped JSON body of an Infinity rerank response.""" + return raw_response.json() + + class InfinityRerankConfig(CohereRerankConfig): def get_complete_url( self, @@ -82,7 +108,7 @@ class InfinityRerankConfig(CohereRerankConfig): No transformation required, Infinity follows Cohere API response format """ try: - raw_response_json: Final = raw_response.json() + raw_response_json: Final = _parse_rerank_response(raw_response) except Exception: raise InfinityError(message=raw_response.text, status_code=raw_response.status_code) diff --git a/litellm/llms/litellm_proxy/skills/code_execution.py b/litellm/llms/litellm_proxy/skills/code_execution.py index d435994ce20..fbc287589b3 100644 --- a/litellm/llms/litellm_proxy/skills/code_execution.py +++ b/litellm/llms/litellm_proxy/skills/code_execution.py @@ -13,12 +13,111 @@ Generated files are returned directly in the response - no separate storage need import base64 import json +from collections.abc import Mapping, Sequence from enum import Enum -from typing import Any, Final +from typing import Any, Final, Protocol, TypedDict + +from typing_extensions import ReadOnly from litellm._logging import verbose_logger +class _ToolParameterSchema(TypedDict, total=False): + type: ReadOnly[str] + description: ReadOnly[str] + + +class _ToolArgumentSchema(TypedDict, total=False): + type: ReadOnly[str] + properties: ReadOnly[Mapping[str, _ToolParameterSchema]] + required: ReadOnly[Sequence[str]] + + +class _OpenAIToolFunction(TypedDict, total=False): + name: ReadOnly[str] + description: ReadOnly[str] + parameters: ReadOnly[_ToolArgumentSchema] + + +class _OpenAIToolSpec(TypedDict, total=False): + type: ReadOnly[str] + function: ReadOnly[_OpenAIToolFunction] + + +class _AnthropicToolSpec(TypedDict, total=False): + name: ReadOnly[str] + description: ReadOnly[str] + input_schema: ReadOnly[_ToolArgumentSchema] + + +class _CodeExecutionArguments(TypedDict, total=False): + code: ReadOnly[str] + + +class _GeneratedFile(TypedDict, total=False): + name: ReadOnly[str] + mime_type: ReadOnly[str] + content_base64: ReadOnly[str] + size: ReadOnly[int] + + +class _SandboxGeneratedFile(TypedDict): + name: ReadOnly[str] + mime_type: ReadOnly[str] + content_base64: ReadOnly[str] + + +class _SandboxExecutionResult(TypedDict): + success: ReadOnly[bool] + output: ReadOnly[str] + error: ReadOnly[str] + files: ReadOnly[Sequence[_SandboxGeneratedFile]] + + +class _ExecutionResult(TypedDict, total=False): + iteration: ReadOnly[int] + success: ReadOnly[bool] + output: ReadOnly[str] + error: ReadOnly[str] + files: ReadOnly[Sequence[str]] + + +class _ToolCallFunction(Protocol): + name: str + arguments: str + + +class _ToolCall(Protocol): + id: str + function: _ToolCallFunction + + +class _AssistantMessage(Protocol): + content: str | None + tool_calls: Sequence[_ToolCall] | None + + +class _ResponseChoice(Protocol): + message: _AssistantMessage + finish_reason: str | None + + +class _CompletionResponse(Protocol): + choices: Sequence[_ResponseChoice] + + +class _CodeExecutionOutcome(TypedDict, total=False): + response: ReadOnly[_CompletionResponse | None] + files: ReadOnly[Sequence[_GeneratedFile]] + execution_results: ReadOnly[Sequence[_ExecutionResult]] + messages: ReadOnly[Sequence[dict[str, object]]] + max_iterations_reached: ReadOnly[bool] + + +def _parse_code_execution_arguments(serialized_arguments: str) -> _CodeExecutionArguments: + return json.loads(serialized_arguments) + + class LiteLLMInternalTools(str, Enum): """ Enum for internal LiteLLM tools that are injected into requests. @@ -30,7 +129,7 @@ class LiteLLMInternalTools(str, Enum): CODE_EXECUTION = "litellm_code_execution" -def get_litellm_code_execution_tool() -> dict[str, Any]: +def get_litellm_code_execution_tool() -> _OpenAIToolSpec: """ Returns the litellm_code_execution tool definition in OpenAI format. @@ -51,7 +150,7 @@ def get_litellm_code_execution_tool() -> dict[str, Any]: } -def get_litellm_code_execution_tool_anthropic() -> dict[str, Any]: +def get_litellm_code_execution_tool_anthropic() -> _AnthropicToolSpec: """ Returns the litellm_code_execution tool definition in Anthropic/messages API format. @@ -98,12 +197,12 @@ class CodeExecutionHandler: async def execute_with_code_execution( self, model: str, - messages: list[dict], - tools: list[dict], + messages: list[dict[str, object]], + tools: list[_OpenAIToolSpec], skill_files: dict[str, bytes], skill_id: str | None = None, **kwargs, - ) -> dict[str, Any]: + ) -> _CodeExecutionOutcome: """ Execute an LLM call with automatic code execution handling. @@ -134,8 +233,8 @@ class CodeExecutionHandler: ) current_messages: Final = list(messages) - generated_files: Final[list[dict[str, Any]]] = [] # Files returned directly - execution_results: Final[list[dict]] = [] + generated_files: Final[list[_GeneratedFile]] = [] # Files returned directly + execution_results: Final[list[_ExecutionResult]] = [] executor: Final = SkillsSandboxExecutor(timeout=self.sandbox_timeout) response: Any = None # Initialize to avoid possibly unbound error @@ -151,11 +250,12 @@ class CodeExecutionHandler: **kwargs, ) - assistant_message = response.choices[0].message - stop_reason = response.choices[0].finish_reason + choice: _ResponseChoice = response.choices[0] + assistant_message = choice.message + stop_reason = choice.finish_reason # Build assistant message for conversation history - assistant_msg_dict: dict[str, Any] = { + assistant_msg_dict: dict[str, object] = { "role": "assistant", "content": assistant_message.content, } @@ -190,25 +290,27 @@ class CodeExecutionHandler: if tool_name == LiteLLMInternalTools.CODE_EXECUTION.value: # Execute code in sandbox try: - args = json.loads(tool_call.function.arguments) + args = _parse_code_execution_arguments(tool_call.function.arguments) code = args.get("code", "") verbose_logger.debug("CodeExecutionHandler: Executing code (%s chars)", len(code)) - exec_result = executor.execute( + exec_result: _SandboxExecutionResult = executor.execute( code=code, skill_files=skill_files, ) verbose_logger.debug("CodeExecutionHandler: Execution result: %s", exec_result) + sandbox_files: Sequence[_SandboxGeneratedFile] = exec_result["files"] + execution_results.append( { "iteration": iteration, "success": exec_result["success"], "output": exec_result["output"], "error": exec_result["error"], - "files": [f["name"] for f in exec_result["files"]], + "files": [f["name"] for f in sandbox_files], } ) @@ -216,9 +318,9 @@ class CodeExecutionHandler: tool_result = exec_result["output"] or "" # Collect generated files (returned directly, no storage) - if exec_result["files"]: + if sandbox_files: tool_result += "\n\nGenerated files:" - for f in exec_result["files"]: + for f in sandbox_files: file_content = base64.b64decode(f["content_base64"]) # Add to generated files list (returned in response) generated_files.append( @@ -278,7 +380,7 @@ class CodeExecutionHandler: } -def has_code_execution_tool(tools: list[dict] | None) -> bool: +def has_code_execution_tool(tools: list[_OpenAIToolSpec] | None) -> bool: """Check if litellm_code_execution tool is in the tools list.""" if not tools: return False @@ -289,7 +391,7 @@ def has_code_execution_tool(tools: list[dict] | None) -> bool: return False -def add_code_execution_tool(tools: list[dict] | None) -> list[dict]: +def add_code_execution_tool(tools: list[_OpenAIToolSpec] | None) -> list[_OpenAIToolSpec]: """Add litellm_code_execution tool if not already present.""" tools = tools or [] if not has_code_execution_tool(tools): diff --git a/litellm/llms/nvidia_riva/audio_transcription/audio_utils.py b/litellm/llms/nvidia_riva/audio_transcription/audio_utils.py index 008a5a5780f..046b4e29a0a 100644 --- a/litellm/llms/nvidia_riva/audio_transcription/audio_utils.py +++ b/litellm/llms/nvidia_riva/audio_transcription/audio_utils.py @@ -16,7 +16,7 @@ import io import os import tempfile from dataclasses import dataclass -from typing import Any, Final, cast +from typing import Final, Protocol, cast from litellm.llms.nvidia_riva.audio_transcription.transformation import ( RIVA_TARGET_NUM_CHANNELS, @@ -24,10 +24,30 @@ from litellm.llms.nvidia_riva.audio_transcription.transformation import ( ) from litellm.llms.nvidia_riva.common_utils import NvidiaRivaException -# Keep this as Any: the module intentionally avoids importing numpy at module -# import time (optional dependency), and project-wide mypy config evaluates this -# file in contexts where conditional type aliases can degrade to "FloatArray?". -FloatArray = Any + +class FloatArray(Protocol): + """Structural view of the ``numpy.ndarray`` surface this module relies on.""" + + @property + def ndim(self) -> int: ... + + @property + def shape(self) -> tuple[int, ...]: ... + + @property + def size(self) -> int: ... + + def mean(self, axis: int) -> "FloatArray": ... + + def ravel(self) -> "FloatArray": ... + + def astype(self, dtype: object) -> "FloatArray": ... + + def tobytes(self) -> bytes: ... + + def __getitem__(self, key: object) -> "FloatArray": ... + + def __mul__(self, other: float) -> "FloatArray": ... _INSTALL_HINT = "Install Riva STT extras to enable automatic audio resampling: `pip install 'litellm[stt-nvidia-riva]'`" diff --git a/litellm/llms/oci/chat/cohere.py b/litellm/llms/oci/chat/cohere.py index 384e7ec4cf8..6e9bb83b0a0 100644 --- a/litellm/llms/oci/chat/cohere.py +++ b/litellm/llms/oci/chat/cohere.py @@ -9,7 +9,7 @@ response parsing, and streaming chunk parsing for models served with import datetime import json from collections.abc import Iterable, Mapping, Sequence -from typing import Any, Final +from typing import Final import httpx from pydantic import JsonValue, TypeAdapter, ValidationError @@ -76,7 +76,7 @@ def _content_text(content: str | Iterable[Mapping[str, object]] | None) -> str: return str(content) -def _extract_text_content(content: Any) -> str: +def _extract_text_content(content: str | Iterable[Mapping[str, object]] | None) -> str: """Return the plain-text representation of a message content value.""" return _content_text(content) diff --git a/litellm/llms/oci/common_utils.py b/litellm/llms/oci/common_utils.py index 5c3962bc05d..3f703564b5a 100644 --- a/litellm/llms/oci/common_utils.py +++ b/litellm/llms/oci/common_utils.py @@ -5,10 +5,11 @@ import os import re from dataclasses import dataclass from email.utils import formatdate -from typing import Any, Final, Protocol +from typing import Final, Protocol from urllib.parse import urlparse import httpx +from pydantic import JsonValue from litellm.llms.base_llm.chat.transformation import BaseLLMException @@ -64,7 +65,7 @@ class OCISignerProtocol(Protocol): See: https://docs.oracle.com/en-us/iaas/tools/python/latest/api/signing.html """ - def do_request_sign(self, request: Any, *, enforce_content_headers: bool = False) -> None: + def do_request_sign(self, request: "OCIRequestWrapper", *, enforce_content_headers: bool = False) -> None: pass @@ -113,7 +114,7 @@ def build_signature_string(method: str, path: str, headers: dict, signed_headers return "\n".join(lines) -def load_private_key_from_str(key_str: str) -> Any: +def load_private_key_from_str(key_str: str) -> "rsa.RSAPrivateKey": _require_cryptography() key: Final = serialization.load_pem_private_key( key_str.encode("utf-8"), @@ -124,7 +125,7 @@ def load_private_key_from_str(key_str: str) -> Any: return key -def load_private_key_from_file(file_path: str) -> Any: +def load_private_key_from_file(file_path: str) -> "rsa.RSAPrivateKey": """Loads a private key from a file path.""" try: with open(file_path, "r", encoding="utf-8") as f: @@ -421,16 +422,17 @@ OCI_JSON_TO_PYTHON_TYPES: Final[dict[str, str]] = { } -def resolve_oci_schema_refs(schema: dict[str, Any]) -> dict[str, Any]: +def resolve_oci_schema_refs(schema: JsonValue) -> JsonValue: """Inline all ``$ref``/``$defs`` references — OCI does not support JSON Schema ``$ref``.""" - defs: Final = schema.get("$defs", {}) - resolving_stack: Final[set] = set() + raw_defs: Final = schema.get("$defs") if isinstance(schema, dict) else None + defs: Final[dict[str, JsonValue]] = raw_defs if isinstance(raw_defs, dict) else {} + resolving_stack: Final[set[str]] = set() - def _resolve(obj: Any) -> Any: + def _resolve(obj: JsonValue) -> JsonValue: if isinstance(obj, dict): - if "$ref" in obj: - ref: Final = obj["$ref"] - if ref.startswith("#/$defs/"): + ref: Final = obj.get("$ref") + if ref is not None: + if isinstance(ref, str) and ref.startswith("#/$defs/"): key: Final = ref.split("/")[-1] if key in resolving_stack: return {"type": "object"} # break cycles @@ -451,7 +453,7 @@ def resolve_oci_schema_refs(schema: dict[str, Any]) -> dict[str, Any]: return resolved -def resolve_oci_schema_anyof(obj: Any) -> Any: +def resolve_oci_schema_anyof(obj: JsonValue) -> JsonValue: """Resolve Pydantic v2 ``Optional[T]`` → ``anyOf`` patterns. Pydantic v2 emits ``{"anyOf": [{"type": "T"}, {"type": "null"}]}`` for @@ -459,10 +461,13 @@ def resolve_oci_schema_anyof(obj: Any) -> Any: first non-null branch and merge top-level metadata into it. """ if isinstance(obj, dict): - if "anyOf" in obj and "type" not in obj: - non_null: Final = [t for t in obj["anyOf"] if not (isinstance(t, dict) and t.get("type") == "null")] + raw_any_of: Final = obj.get("anyOf") + if raw_any_of is not None and "type" not in obj: + branches: Final = raw_any_of if isinstance(raw_any_of, list) else [] + non_null: Final = [t for t in branches if not (isinstance(t, dict) and t.get("type") == "null")] if non_null: - resolved: Final = {**obj, **non_null[0]} + first: Final = non_null[0] + resolved: Final[dict[str, JsonValue]] = {**obj, **first} if isinstance(first, dict) else {**obj} resolved.pop("anyOf", None) return resolve_oci_schema_anyof(resolved) return {k: resolve_oci_schema_anyof(v) for k, v in obj.items()} @@ -471,7 +476,7 @@ def resolve_oci_schema_anyof(obj: Any) -> Any: return obj -def sanitize_oci_schema(schema: Any) -> Any: +def sanitize_oci_schema(schema: JsonValue) -> JsonValue: """Recursively remove OCI-incompatible fields from a JSON schema. Strips ``title`` keys, removes ``None``-valued ``default`` entries, @@ -483,7 +488,7 @@ def sanitize_oci_schema(schema: Any) -> Any: if not isinstance(schema, dict): return schema - sanitized: Final[dict[str, Any]] = {} + sanitized: Final[dict[str, JsonValue]] = {} for key, value in schema.items(): if key == "title": continue @@ -513,7 +518,7 @@ def sanitize_oci_schema(schema: Any) -> Any: return sanitized -def enrich_cohere_param_description(description: str, param_schema: dict[str, Any]) -> str: +def enrich_cohere_param_description(description: str, param_schema: dict[str, JsonValue]) -> str: """Embed schema constraints into a Cohere parameter description. ``CohereParameterDefinition`` only has ``type``, ``description``, and diff --git a/litellm/llms/ollama/completion/handler.py b/litellm/llms/ollama/completion/handler.py index 6e490f3ff15..449952217b5 100644 --- a/litellm/llms/ollama/completion/handler.py +++ b/litellm/llms/ollama/completion/handler.py @@ -4,16 +4,32 @@ Ollama /chat/completion calls handled in llm_http_handler.py [TODO]: migrate embeddings to a base handler as well. """ -from typing import Any, Final +from collections.abc import Mapping, Sequence +from typing import Any, Final, Protocol, TypedDict + +from typing_extensions import NotRequired, ReadOnly import litellm from litellm.types.utils import EmbeddingResponse +class TokenEncoder(Protocol): + """The tokenizer surface used to estimate prompt tokens.""" + + def encode(self, text: str, /) -> Sequence[int]: ... + + +class OllamaEmbeddingResponse(TypedDict): + """Body of an Ollama ``/api/embed`` response.""" + + embeddings: ReadOnly[list[list[float]]] + prompt_eval_count: ReadOnly[NotRequired[int]] + + def _prepare_ollama_embedding_payload( - model: str, prompts: list[str], optional_params: dict[str, Any] -) -> dict[str, Any]: - data: Final[dict[str, Any]] = {"model": model, "input": prompts} + model: str, prompts: list[str], optional_params: Mapping[str, object] +) -> dict[str, object]: + data: Final[dict[str, object]] = {"model": model, "input": prompts} special_optional_params: Final = ["truncate", "options", "keep_alive", "dimensions"] for k, v in optional_params.items(): @@ -27,12 +43,12 @@ def _prepare_ollama_embedding_payload( def _process_ollama_embedding_response( - response_json: dict, + response_json: OllamaEmbeddingResponse, prompts: list[str], model: str, model_response: EmbeddingResponse, logging_obj: Any, - encoding: Any, + encoding: TokenEncoder | None, ) -> EmbeddingResponse: output_data: Final = [] embeddings: Final[list[list[float]]] = response_json["embeddings"] @@ -72,7 +88,7 @@ async def ollama_aembeddings( model_response: EmbeddingResponse, optional_params: dict, logging_obj: Any, - encoding: Any, + encoding: TokenEncoder | None, ): if not api_base.endswith("/api/embed"): api_base += "/api/embed" @@ -80,7 +96,7 @@ async def ollama_aembeddings( data: Final = _prepare_ollama_embedding_payload(model, prompts, optional_params) response: Final = await litellm.module_level_aclient.post(url=api_base, json=data) - response_json: Final = response.json() + response_json: Final[OllamaEmbeddingResponse] = response.json() return _process_ollama_embedding_response( response_json=response_json, @@ -99,7 +115,7 @@ def ollama_embeddings( optional_params: dict, model_response: EmbeddingResponse, logging_obj: Any, - encoding: Any = None, + encoding: TokenEncoder | None = None, ): if not api_base.endswith("/api/embed"): api_base += "/api/embed" @@ -107,7 +123,7 @@ def ollama_embeddings( data: Final = _prepare_ollama_embedding_payload(model, prompts, optional_params) response: Final = litellm.module_level_client.post(url=api_base, json=data) - response_json: Final = response.json() + response_json: Final[OllamaEmbeddingResponse] = response.json() return _process_ollama_embedding_response( response_json=response_json, diff --git a/litellm/llms/openai/chat/gpt_transformation.py b/litellm/llms/openai/chat/gpt_transformation.py index 5894658e5d2..9afc6331d96 100644 --- a/litellm/llms/openai/chat/gpt_transformation.py +++ b/litellm/llms/openai/chat/gpt_transformation.py @@ -4,7 +4,8 @@ Support for gpt model family import json import os -from collections.abc import AsyncIterator, Coroutine, Iterator +from collections.abc import AsyncIterator, Coroutine, Iterator, Mapping +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final, Literal, Optional, cast, overload from urllib.parse import urlparse @@ -21,6 +22,7 @@ from litellm.litellm_core_utils.prompt_templates.common_utils import ( drop_tool_reference_parts_from_tool_messages, get_tool_call_names, hoist_images_from_tool_messages, + tool_with_flattened_parameters, ) from litellm.litellm_core_utils.prompt_templates.image_handling import ( async_convert_url_to_base64, @@ -65,6 +67,9 @@ else: LiteLLMLoggingObj = Any +_NO_TOOLS_UPDATE: Final[Mapping[str, object]] = MappingProxyType({}) + + class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): """ Reference: https://platform.openai.com/docs/api-reference/chat/create @@ -170,16 +175,20 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): if model != "gpt-3.5-turbo-16k" and model != "gpt-4": # gpt-4 does not support 'response_format' model_specific_params.append("response_format") - # Normalize model name for responses API (e.g., "responses/gpt-4.1" -> "gpt-4.1") - model_for_check: Final = model.split("responses/", 1)[1] if "responses/" in model else model - if ( - model_for_check in litellm.open_ai_chat_completion_models - ) or model_for_check in litellm.open_ai_text_completion_models: + if OpenAIGPTConfig.is_openai_catalog_model(model): model_specific_params.append( "user" ) # user is not a param supported by all openai-compatible endpoints - e.g. azure ai return base_params + model_specific_params + @staticmethod + def is_openai_catalog_model(model: str) -> bool: + model_for_check: Final = model.split("responses/", 1)[1] if "responses/" in model else model + return ( + model_for_check in litellm.open_ai_chat_completion_models + or model_for_check in litellm.open_ai_text_completion_models + ) + def _map_openai_params( self, non_default_params: dict, @@ -321,7 +330,7 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): @overload def _transform_messages( self, messages: list[AllMessageValues], model: str, is_async: Literal[True] - ) -> Coroutine[Any, Any, list[AllMessageValues]]: + ) -> Coroutine[object, object, list[AllMessageValues]]: ... @overload @@ -337,7 +346,7 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): def _transform_messages( self, messages: list[AllMessageValues], model: str, is_async: bool = False - ) -> list[AllMessageValues] | Coroutine[Any, Any, list[AllMessageValues]]: + ) -> list[AllMessageValues] | Coroutine[object, object, list[AllMessageValues]]: """OpenAI no longer supports image_url as a string, so we need to convert it to a dict""" stripped_messages: Final = drop_tool_reference_parts_from_tool_messages(messages) hoisted_messages: Final = hoist_images_from_tool_messages(stripped_messages) @@ -393,6 +402,21 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): ) return messages, tools + def _targets_openai_hosted_endpoint( + self, + custom_llm_provider: str | None, + api_base: str | None, + ) -> bool: + if custom_llm_provider != "openai": + return False + resolved_api_base = api_base or litellm.api_base or os.getenv("OPENAI_BASE_URL") or os.getenv("OPENAI_API_BASE") + if not resolved_api_base: + return True + hostname: Final = urlparse(resolved_api_base).hostname + if hostname is None: + return True + return hostname == "openai.com" or hostname.endswith(".openai.com") + def _should_preserve_cache_control_for_endpoint( self, custom_llm_provider: str | None, @@ -404,15 +428,34 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): api_base. Those can understand cache_control, so it must survive there. Real OpenAI cannot, so it is still stripped for an openai.com host. """ - if custom_llm_provider != "openai": - return False - resolved_api_base = api_base or litellm.api_base or os.getenv("OPENAI_BASE_URL") or os.getenv("OPENAI_API_BASE") - if not resolved_api_base: - return False - hostname: Final = urlparse(resolved_api_base).hostname - if hostname is None: - return False - return hostname != "openai.com" and not hostname.endswith(".openai.com") + return custom_llm_provider == "openai" and not self._targets_openai_hosted_endpoint( + custom_llm_provider, api_base + ) + + def _flattened_tools_update_for_openai( + self, + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], + ) -> Mapping[str, object]: + """ + OpenAI's chat completions validator rejects tool `parameters` carrying + 'oneOf'/'anyOf'/'allOf'/'enum'/'const'/'not' at the top level for every + model family, unlike the Responses API, where GPT-5+ accepts them. + """ + tools: Final = optional_params.get("tools") + if not isinstance(tools, list): + return _NO_TOOLS_UPDATE + provider: Final = litellm_params.get("custom_llm_provider") + raw_api_base: Final = litellm_params.get("api_base") + if not self._targets_openai_hosted_endpoint( + provider if isinstance(provider, str) else None, + raw_api_base if isinstance(raw_api_base, str) else None, + ): + return _NO_TOOLS_UPDATE + flattened: Final = [ # mutable-ok: request tools are a JSON list + tool_with_flattened_parameters(tool) if isinstance(tool, dict) else tool for tool in tools + ] + return MappingProxyType({"tools": flattened}) def transform_request( self, @@ -439,11 +482,14 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): optional_params["tools"] = tools optional_params.pop("max_retries", None) + if not optional_params.get("tools") and not optional_params.get("functions"): + optional_params.pop("tool_choice", None) return { "model": model, "messages": messages, **optional_params, + **self._flattened_tools_update_for_openai(optional_params, litellm_params), } async def async_transform_request( @@ -469,10 +515,13 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): if tools is not None and len(tools) > 0: optional_params["tools"] = tools if self.__class__._is_base_class: + if not optional_params.get("tools") and not optional_params.get("functions"): + optional_params.pop("tool_choice", None) return { "model": model, "messages": transformed_messages, **optional_params, + **self._flattened_tools_update_for_openai(optional_params, litellm_params), } else: ## allow for any object specific behaviour to be handled @@ -493,8 +542,12 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): return None tool_call_names: Final = get_tool_call_names(optional_params.get("tools", [])) try: - json_content: Final = json.loads(content) - if json_content.get("type") == "function" and json_content.get("name") in tool_call_names: + json_content: Final[object] = json.loads(content) + if ( + isinstance(json_content, dict) + and json_content.get("type") == "function" + and json_content.get("name") in tool_call_names + ): return ChatCompletionMessageToolCall( function=Function( name=json_content.get("name"), @@ -618,7 +671,7 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): ## RESPONSE OBJECT try: - completion_response: Final = raw_response.json() + completion_response: Final[dict[str, object]] = raw_response.json() except Exception as e: response_headers: Final = getattr(raw_response, "headers", None) raise OpenAIError( @@ -755,6 +808,14 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): ) +class OpenAIUnknownModelConfig(OpenAIGPTConfig): + """A model the openai provider does not recognize is typically a LiteLLM proxy alias, so + forward reasoning_effort and let the server decide whether it is supported.""" + + def get_supported_openai_params(self, model: str) -> list: # mutable-ok: inherited contract + return super().get_supported_openai_params(model) + ["reasoning_effort"] # mutable-ok: inherited contract + + class OpenAIChatCompletionStreamingHandler(BaseModelResponseIterator): def _map_reasoning_to_reasoning_content(self, choices: list) -> list: """ diff --git a/litellm/llms/openai/chat/guardrail_translation/handler.py b/litellm/llms/openai/chat/guardrail_translation/handler.py index 54673c77f80..96a5ed663fc 100644 --- a/litellm/llms/openai/chat/guardrail_translation/handler.py +++ b/litellm/llms/openai/chat/guardrail_translation/handler.py @@ -14,8 +14,14 @@ Pattern Overview: This pattern can be replicated for other message formats (e.g., Anthropic). """ +import json +import time +import uuid +from collections.abc import Sequence from typing import TYPE_CHECKING, Any, Final, Union, cast +from typing_extensions import NotRequired, ReadOnly, TypedDict + import litellm from litellm._logging import verbose_proxy_logger from litellm.llms.base_llm.guardrail_translation.base_translation import ( @@ -23,6 +29,7 @@ from litellm.llms.base_llm.guardrail_translation.base_translation import ( StreamTransformSink, ) from litellm.llms.base_llm.guardrail_translation.utils import ( + blocked_chat_stream_usage, effective_scan_only_tool_results_for_guardrail, effective_skip_system_message_for_guardrail, effective_skip_tool_message_for_guardrail, @@ -31,6 +38,7 @@ from litellm.llms.base_llm.guardrail_translation.utils import ( openai_tool_name, role_out_of_guardrail_scope, scoped_structured_message_indices, + stream_item_field, ) from litellm.main import stream_chunk_builder from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolParam @@ -46,8 +54,14 @@ from litellm.types.utils import ( ) if TYPE_CHECKING: - from litellm.integrations.custom_guardrail import CustomGuardrail + from fastapi import HTTPException + + from litellm.integrations.custom_guardrail import ( + CustomGuardrail, + ModifyResponseException, + ) from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.proxy._types import UserAPIKeyAuth class OpenAIChatCompletionsHandler(BaseTranslation): @@ -77,7 +91,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): data: dict, guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: "LiteLLMLoggingObj | None" = None, - ) -> Any: + ) -> dict: """ Process input messages by applying guardrails to text content. """ @@ -326,9 +340,9 @@ class OpenAIChatCompletionsHandler(BaseTranslation): response: "ModelResponse", guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: "LiteLLMLoggingObj | None" = None, - user_api_key_dict: Any | None = None, + user_api_key_dict: "UserAPIKeyAuth | None" = None, request_data: dict | None = None, - ) -> Any: + ) -> ModelResponse: """ Process output response by applying guardrails to text content. @@ -382,11 +396,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): if "response" not in request_data: request_data["response"] = response - # Add user API key metadata with prefixed keys - if "litellm_metadata" not in request_data: - user_metadata: Final = self.transform_user_api_key_dict_to_metadata(user_api_key_dict) - if user_metadata: - request_data["litellm_metadata"] = user_metadata + self.merge_user_api_key_metadata_into_request(request_data, user_api_key_dict) inputs: Final = GenericGuardrailAPIInputs(texts=texts_to_check) if images_to_check: @@ -437,7 +447,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): responses_so_far: list["ModelResponseStream"], guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: "LiteLLMLoggingObj | None" = None, - user_api_key_dict: Any | None = None, + user_api_key_dict: "UserAPIKeyAuth | None" = None, request_data: dict | None = None, stream_transform_sink: StreamTransformSink | None = None, ) -> list["ModelResponseStream"]: @@ -487,7 +497,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): responses_so_far: list["ModelResponseStream"], guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: "LiteLLMLoggingObj | None", - user_api_key_dict: Any | None, + user_api_key_dict: "UserAPIKeyAuth | None", request_data: dict | None, ) -> list["ModelResponseStream"]: """Block-only streaming path: run the guardrail so an in-flight BLOCK can @@ -555,11 +565,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): if "responses" not in request_data: request_data["responses"] = responses_so_far - # Add user API key metadata with prefixed keys - if "litellm_metadata" not in request_data: - user_metadata: Final = self.transform_user_api_key_dict_to_metadata(user_api_key_dict) - if user_metadata: - request_data["litellm_metadata"] = user_metadata + self.merge_user_api_key_metadata_into_request(request_data, user_api_key_dict) inputs: Final = GenericGuardrailAPIInputs(texts=texts_to_check) if images_to_check: @@ -591,6 +597,18 @@ class OpenAIChatCompletionsHandler(BaseTranslation): return responses_so_far + def build_stream_error_items( + self, + exc: "HTTPException", + responses_so_far: Sequence[object] | None = None, + ) -> Sequence[bytes] | None: + import json + + from litellm.proxy.common_request_processing import sse_error_payload + + _, error_obj = sse_error_payload(exc) + return (f'data: {{"error": {json.dumps(error_obj)}}}\n\n'.encode(),) + @staticmethod def _accumulate_string_content_by_choice_index( responses_so_far: list["ModelResponseStream"], @@ -623,7 +641,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): responses_so_far: list["ModelResponseStream"], guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: "LiteLLMLoggingObj | None", - user_api_key_dict: Any | None, + user_api_key_dict: "UserAPIKeyAuth | None", request_data: dict | None, sink: StreamTransformSink, ) -> None: @@ -653,10 +671,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): request_data = {"responses": responses_so_far} elif "responses" not in request_data: request_data["responses"] = responses_so_far - if "litellm_metadata" not in request_data: - user_metadata: Final = self.transform_user_api_key_dict_to_metadata(user_api_key_dict) - if user_metadata: - request_data["litellm_metadata"] = user_metadata + self.merge_user_api_key_metadata_into_request(request_data, user_api_key_dict) inputs: Final = GenericGuardrailAPIInputs(texts=texts_to_check) if responses_so_far and getattr(responses_so_far[0], "model", None): @@ -790,7 +805,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): # Determine content source and tool calls based on choice type content = None - tool_calls: list[Any] | None = None + tool_calls: Sequence[object] | None = None if isinstance(choice, litellm.Choices): content = choice.message.content tool_calls = choice.message.tool_calls @@ -1000,3 +1015,129 @@ class OpenAIChatCompletionsHandler(BaseTranslation): else: # Subsequent chunks - clear the text content_item["text"] = "" + + def _check_streaming_has_ended(self, responses_so_far: Sequence[object]) -> bool: + """ + True once any relayed chunk carries a non-null ``finish_reason``. + + The unified guardrail's ``end_of_stream_only`` streaming path probes + this via ``hasattr`` to withhold the terminal chunks until + end-of-stream moderation runs, so a block can replace the finish + instead of trailing after a ``finish_reason`` the client already saw. + """ + return any( + stream_item_field(choice, "finish_reason") is not None + for item in responses_so_far + for choice in _stream_chunk_choices(item) + ) + + def build_block_sse_chunks( + self, + exc: "ModifyResponseException", + stream_started: bool = False, + responses_so_far: Sequence[object] | None = None, + ) -> Sequence[bytes]: + """ + Build OpenAI chat-completions SSE chunks that deliver the guardrail + block message and terminate the stream cleanly, mirroring the + non-streaming block response: ``finish_reason`` ``content_filter`` plus + the real usage the upstream call consumed. + + - ``stream_started`` False (buffered / pre-stream): nothing has been + sent, so open a standalone completion with a ``role`` delta. + - ``stream_started`` True (sampling / mid-stream): chunks already + reached the client, so continue the in-progress completion (reuse its + id/created/model, content-only delta). + + The proxy's data generator appends ``data: [DONE]`` itself. + """ + chunk_id, created, model = _blocked_stream_identity(exc, responses_so_far or ()) + prompt_tokens, completion_tokens = blocked_chat_stream_usage(exc.original_response) + continuation_delta: Final[_BlockedChunkDelta] = {"content": exc.message} + standalone_delta: Final[_BlockedChunkDelta] = {"role": "assistant", "content": exc.message} + message_chunk: Final[_BlockedChunk] = { + "id": chunk_id, + "object": "chat.completion.chunk", + "created": created, + "model": model, + "choices": ( + { + "index": 0, + "delta": continuation_delta if stream_started else standalone_delta, + "finish_reason": None, + }, + ), + } + final_chunk: Final[_BlockedChunk] = { + "id": chunk_id, + "object": "chat.completion.chunk", + "created": created, + "model": model, + "choices": ({"index": 0, "delta": {}, "finish_reason": "content_filter"},), + "usage": { + "prompt_tokens": prompt_tokens, + "completion_tokens": completion_tokens, + "total_tokens": prompt_tokens + completion_tokens, + }, + } + return _chat_sse_chunk(message_chunk), _chat_sse_chunk(final_chunk) + + +class _BlockedChunkDelta(TypedDict, total=False): + role: ReadOnly[str] + content: ReadOnly[str] + + +class _BlockedChunkChoice(TypedDict): + index: ReadOnly[int] + delta: ReadOnly[_BlockedChunkDelta] + finish_reason: ReadOnly[str | None] + + +class _BlockedChunkUsage(TypedDict): + prompt_tokens: ReadOnly[int] + completion_tokens: ReadOnly[int] + total_tokens: ReadOnly[int] + + +class _BlockedChunk(TypedDict): + id: ReadOnly[str] + object: ReadOnly[str] + created: ReadOnly[int] + model: ReadOnly[str] + choices: ReadOnly[tuple[_BlockedChunkChoice, ...]] + usage: NotRequired[ReadOnly[_BlockedChunkUsage]] + + +def _chat_sse_chunk(payload: _BlockedChunk) -> bytes: + return f"data: {json.dumps(payload)}\n\n".encode() + + +def _stream_chunk_choices(item: object) -> Sequence[object]: + choices: Final = stream_item_field(item, "choices") + if isinstance(choices, Sequence) and not isinstance(choices, (str, bytes)): + return choices + return () + + +def _blocked_stream_identity( + exc: "ModifyResponseException", responses_so_far: Sequence[object] +) -> tuple[str, int, str]: + identified: Final = next( + ( + (chunk_id, item) + for item in responses_so_far + if isinstance(chunk_id := stream_item_field(item, "id"), str) and chunk_id + ), + None, + ) + if identified is None: + return f"chatcmpl-{uuid.uuid4()}", int(time.time()), exc.model + chunk_id, source = identified + created: Final = stream_item_field(source, "created") + model: Final = stream_item_field(source, "model") + return ( + chunk_id, + created if isinstance(created, int) else int(time.time()), + model if isinstance(model, str) and model else exc.model, + ) diff --git a/litellm/llms/openai/common_utils.py b/litellm/llms/openai/common_utils.py index 1b1ab80e85d..4d774f6f165 100644 --- a/litellm/llms/openai/common_utils.py +++ b/litellm/llms/openai/common_utils.py @@ -268,6 +268,7 @@ class BaseOpenAILLM: "max_retries", "organization", "api_base", + "workload_identity_config", ) openai_client_fields: Final = ( BaseOpenAILLM.get_openai_client_initialization_param_fields(client_type=client_type) diff --git a/litellm/llms/openai/containers/transformation.py b/litellm/llms/openai/containers/transformation.py index 6fc50458aa3..1a5211d5ff5 100644 --- a/litellm/llms/openai/containers/transformation.py +++ b/litellm/llms/openai/containers/transformation.py @@ -1,6 +1,8 @@ -from typing import TYPE_CHECKING, Any, Final +from collections.abc import Mapping +from typing import TYPE_CHECKING, Any, Final, Literal import httpx +from typing_extensions import ReadOnly, TypedDict import litellm from litellm.litellm_core_utils.llm_cost_calc.tool_call_cost_tracking import ( @@ -11,9 +13,11 @@ from litellm.secret_managers.main import get_secret_str from litellm.types.containers.main import ( ContainerCreateOptionalRequestParams, ContainerFileListResponse, + ContainerFileObject, ContainerListResponse, ContainerObject, DeleteContainerResult, + ExpiresAfter, ) from litellm.types.router import GenericLiteLLMParams @@ -32,6 +36,46 @@ else: BaseLLMException = Any +class OpenAIContainerPayload(TypedDict): + """The JSON body OpenAI returns for a single container.""" + + id: ReadOnly[str] + object: ReadOnly[Literal["container"]] + created_at: ReadOnly[int] + status: ReadOnly[str] + expires_after: ReadOnly[ExpiresAfter | None] + last_active_at: ReadOnly[int | None] + name: ReadOnly[str | None] + + +class OpenAIContainerListPayload(TypedDict): + """The JSON body OpenAI returns for a page of containers.""" + + object: ReadOnly[Literal["list"]] + data: ReadOnly[list[ContainerObject]] + first_id: ReadOnly[str | None] + last_id: ReadOnly[str | None] + has_more: ReadOnly[bool] + + +class OpenAIContainerDeletedPayload(TypedDict): + """The JSON body OpenAI returns for a deleted container.""" + + id: ReadOnly[str] + object: ReadOnly[Literal["container.deleted"]] + deleted: ReadOnly[bool] + + +class OpenAIContainerFileListPayload(TypedDict): + """The JSON body OpenAI returns for a page of container files.""" + + object: ReadOnly[Literal["list"]] + data: ReadOnly[list[ContainerFileObject]] + first_id: ReadOnly[str | None] + last_id: ReadOnly[str | None] + has_more: ReadOnly[bool] + + class OpenAIContainerConfig(BaseContainerConfig): """Configuration class for OpenAI container API.""" @@ -87,7 +131,7 @@ class OpenAIContainerConfig(BaseContainerConfig): def transform_container_create_request( self, name: str, - container_create_optional_request_params: dict, + container_create_optional_request_params: Mapping[str, object], litellm_params: GenericLiteLLMParams, headers: dict, ) -> dict: @@ -111,10 +155,7 @@ class OpenAIContainerConfig(BaseContainerConfig): logging_obj: LiteLLMLoggingObj, ) -> ContainerObject: """Transform the OpenAI container creation response.""" - response_data: Final = raw_response.json() - - # Transform the response data - container_obj: Final = ContainerObject(**response_data) + container_obj: Final = ContainerObject.model_validate(raw_response.json()) # Add cost for container creation (OpenAI containers are code interpreter sessions) # https://platform.openai.com/docs/pricing @@ -140,7 +181,7 @@ class OpenAIContainerConfig(BaseContainerConfig): after: str | None = None, limit: int | None = None, order: str | None = None, - extra_query: dict[str, Any] | None = None, + extra_query: Mapping[str, object] | None = None, ) -> tuple[str, dict]: """Transform the container list request for OpenAI API. @@ -151,7 +192,7 @@ class OpenAIContainerConfig(BaseContainerConfig): url: Final = api_base # Prepare query parameters - params: Final = {} + params: Final[dict[str, object]] = {} if after is not None: params["after"] = after if limit is not None: @@ -171,10 +212,7 @@ class OpenAIContainerConfig(BaseContainerConfig): logging_obj: LiteLLMLoggingObj, ) -> ContainerListResponse: """Transform the OpenAI container list response.""" - response_data: Final = raw_response.json() - - # Transform the response data - container_list: Final = ContainerListResponse(**response_data) + container_list: Final = ContainerListResponse.model_validate(raw_response.json()) return container_list @@ -191,7 +229,7 @@ class OpenAIContainerConfig(BaseContainerConfig): url: Final = join_container_api_base_path(api_base, f"/{encoded_container_id}") # No additional data needed for GET request - data: Final[dict[str, Any]] = {} + data: Final[dict[str, str]] = {} return url, data @@ -201,9 +239,7 @@ class OpenAIContainerConfig(BaseContainerConfig): logging_obj: LiteLLMLoggingObj, ) -> ContainerObject: """Transform the OpenAI container retrieve response.""" - response_data: Final = raw_response.json() - # Transform the response data - container_obj: Final = ContainerObject(**response_data) + container_obj: Final = ContainerObject.model_validate(raw_response.json()) return container_obj @@ -224,7 +260,7 @@ class OpenAIContainerConfig(BaseContainerConfig): url: Final = join_container_api_base_path(api_base, f"/{encoded_container_id}") # No data needed for DELETE request - data: Final[dict[str, Any]] = {} + data: Final[dict[str, str]] = {} return url, data @@ -234,10 +270,7 @@ class OpenAIContainerConfig(BaseContainerConfig): logging_obj: LiteLLMLoggingObj, ) -> DeleteContainerResult: """Transform the OpenAI container delete response.""" - response_data: Final = raw_response.json() - - # Transform the response data - delete_result: Final = DeleteContainerResult(**response_data) + delete_result: Final = DeleteContainerResult.model_validate(raw_response.json()) return delete_result @@ -250,7 +283,7 @@ class OpenAIContainerConfig(BaseContainerConfig): after: str | None = None, limit: int | None = None, order: str | None = None, - extra_query: dict[str, Any] | None = None, + extra_query: Mapping[str, object] | None = None, ) -> tuple[str, dict]: """Transform the container file list request for OpenAI API. @@ -262,7 +295,7 @@ class OpenAIContainerConfig(BaseContainerConfig): url: Final = join_container_api_base_path(api_base, f"/{encoded_container_id}/files") # Prepare query parameters - params: Final[dict[str, Any]] = {} + params: Final[dict[str, object]] = {} if after is not None: params["after"] = after if limit is not None: @@ -282,10 +315,7 @@ class OpenAIContainerConfig(BaseContainerConfig): logging_obj: LiteLLMLoggingObj, ) -> ContainerFileListResponse: """Transform the OpenAI container file list response.""" - response_data: Final = raw_response.json() - - # Transform the response data - file_list: Final = ContainerFileListResponse(**response_data) + file_list: Final = ContainerFileListResponse.model_validate(raw_response.json()) return file_list @@ -308,7 +338,7 @@ class OpenAIContainerConfig(BaseContainerConfig): url: Final = join_container_api_base_path(api_base, f"/{encoded_container_id}/files/{encoded_file_id}/content") # No query parameters needed - params: Final[dict[str, Any]] = {} + params: Final[dict[str, str]] = {} return url, params diff --git a/litellm/llms/openai/openai.py b/litellm/llms/openai/openai.py index 6e66c998acf..1cfc6e06ee9 100644 --- a/litellm/llms/openai/openai.py +++ b/litellm/llms/openai/openai.py @@ -12,9 +12,14 @@ if TYPE_CHECKING: import openai from openai import AsyncOpenAI, OpenAI +from openai._base_client import make_request_options +from openai._constants import RAW_RESPONSE_HEADER +from openai._legacy_response import LegacyAPIResponse +from openai._types import RequestOptions +from openai.types import CreateEmbeddingResponse from openai.types.beta.assistant_deleted import AssistantDeleted from openai.types.file_deleted import FileDeleted -from pydantic import BaseModel +from pydantic import BaseModel, TypeAdapter from typing_extensions import overload import litellm @@ -43,6 +48,7 @@ from litellm.utils import ( from ...types.llms.openai import * from ..base import BaseLLM from .chat.gpt_5_transformation import OpenAIGPT5Config +from .chat.gpt_transformation import OpenAIGPTConfig, OpenAIUnknownModelConfig from .chat.o_series_transformation import OpenAIOSeriesConfig from .common_utils import ( BaseOpenAILLM, @@ -51,6 +57,7 @@ from .common_utils import ( drop_params_from_unprocessable_entity_error, is_output_token_limit_error, ) +from .workload_identity import resolve_openai_workload_identity_config openaiOSeriesConfig: Final = OpenAIOSeriesConfig() openAIGPT5Config: Final = OpenAIGPT5Config() @@ -188,7 +195,12 @@ class OpenAIConfig(BaseConfig): elif litellm.openAIGPTAudioConfig.is_model_gpt_audio_model(model=model): return litellm.openAIGPTAudioConfig.get_supported_openai_params(model=model) else: - return litellm.openAIGPTConfig.get_supported_openai_params(model=model) + return self._gpt_config_for_model(model).get_supported_openai_params(model=model) + + def _gpt_config_for_model(self, model: str) -> OpenAIGPTConfig: + if type(self) is OpenAIConfig and not OpenAIGPTConfig.is_openai_catalog_model(model): + return OpenAIUnknownModelConfig() + return litellm.openAIGPTConfig def _map_openai_params(self, non_default_params: dict, optional_params: dict, model: str) -> dict: supported_openai_params: Final = self.get_supported_openai_params(model) @@ -230,7 +242,7 @@ class OpenAIConfig(BaseConfig): drop_params=drop_params, ) - return litellm.openAIGPTConfig.map_openai_params( + return self._gpt_config_for_model(model).map_openai_params( non_default_params=non_default_params, optional_params=optional_params, model=model, @@ -322,6 +334,28 @@ class OpenAIChatCompletionResponseIterator(BaseModelResponseIterator): raise e +_EXTRA_HEADERS_ADAPTER: Final = TypeAdapter(dict[str, str] | None) +_EXTRA_QUERY_ADAPTER: Final = TypeAdapter(dict[str, object] | None) +_NO_EXTRA_HEADERS: Final[Mapping[str, str]] = types.MappingProxyType({}) +_SDK_OPTION_KEYS: Final = frozenset(("extra_headers", "extra_query", "extra_body")) + + +def _embedding_request_without_sdk_defaults( + data: Mapping[str, object], timeout: float | httpx.Timeout +) -> tuple[Mapping[str, object], RequestOptions]: + body: Final = { # mutable-ok: the SDK json-encodes the body and needs a plain dict + k: v for k, v in data.items() if k not in _SDK_OPTION_KEYS + } + extra_headers: Final = _EXTRA_HEADERS_ADAPTER.validate_python(data.get("extra_headers")) or _NO_EXTRA_HEADERS + options: Final = make_request_options( + extra_headers=types.MappingProxyType({**extra_headers, RAW_RESPONSE_HEADER: "true"}), + extra_query=_EXTRA_QUERY_ADAPTER.validate_python(data.get("extra_query")), + extra_body=data.get("extra_body"), + timeout=timeout, + ) + return body, options + + class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): def __init__(self) -> None: super().__init__() @@ -349,6 +383,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): client: OpenAI | AsyncOpenAI | None = None, shared_session: Optional["ClientSession"] = None, ) -> OpenAI | AsyncOpenAI | None: + workload_identity_config: Final = resolve_openai_workload_identity_config(api_key=api_key, api_base=api_base) client_initialization_params: Final[dict] = locals() if client is None: if not isinstance(max_retries, int): @@ -364,28 +399,49 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): if cached_client: if isinstance(cached_client, OpenAI) or isinstance(cached_client, AsyncOpenAI): return cached_client - http_client: Final[httpx.Client | httpx.AsyncClient | None] = ( - OpenAIChatCompletion._get_async_http_client(shared_session=shared_session) - if is_async - else OpenAIChatCompletion._get_sync_http_client() - ) if is_async: - _new_client: OpenAI | AsyncOpenAI = AsyncOpenAI( - api_key=api_key, - base_url=api_base, - http_client=http_client, - timeout=timeout, - max_retries=max_retries, - organization=organization, + async_http_client: Final = OpenAIChatCompletion._get_async_http_client(shared_session=shared_session) + http_client: httpx.Client | httpx.AsyncClient | None = async_http_client + _new_client: OpenAI | AsyncOpenAI = ( + AsyncOpenAI( + workload_identity=workload_identity_config.to_sdk_workload_identity(), + base_url=api_base, + http_client=async_http_client, + timeout=timeout, + max_retries=max_retries, + organization=organization, + ) + if workload_identity_config is not None + else AsyncOpenAI( + api_key=api_key, + base_url=api_base, + http_client=async_http_client, + timeout=timeout, + max_retries=max_retries, + organization=organization, + ) ) else: - _new_client = OpenAI( - api_key=api_key, - base_url=api_base, - http_client=http_client, - timeout=timeout, - max_retries=max_retries, - organization=organization, + sync_http_client: Final = OpenAIChatCompletion._get_sync_http_client() + http_client = sync_http_client + _new_client = ( + OpenAI( + workload_identity=workload_identity_config.to_sdk_workload_identity(), + base_url=api_base, + http_client=sync_http_client, + timeout=timeout, + max_retries=max_retries, + organization=organization, + ) + if workload_identity_config is not None + else OpenAI( + api_key=api_key, + base_url=api_base, + http_client=sync_http_client, + timeout=timeout, + max_retries=max_retries, + organization=organization, + ) ) ## SAVE CACHE KEY @@ -1148,19 +1204,15 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): data: dict, timeout: float | httpx.Timeout, logging_obj: LiteLLMLoggingObj, - ): - """ - Helper to: - - call embeddings.create.with_raw_response when litellm.return_response_headers is True - - call embeddings.create by default - """ - try: - raw_response = await openai_aclient.embeddings.with_raw_response.create(**data, timeout=timeout) - headers: Final = dict(raw_response.headers) - response: Final = raw_response.parse() - return headers, response - except Exception as e: - raise e + ) -> LegacyAPIResponse[CreateEmbeddingResponse]: + if "encoding_format" not in data: + body, options = _embedding_request_without_sdk_defaults(data, timeout) + bypass_response: Final = await openai_aclient.post( + "/embeddings", body=body, options=options, cast_to=CreateEmbeddingResponse + ) + assert isinstance(bypass_response, LegacyAPIResponse) + return bypass_response + return await openai_aclient.embeddings.with_raw_response.create(**data, timeout=timeout) @track_llm_api_timing() def make_sync_openai_embedding_request( @@ -1169,20 +1221,15 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): data: dict, timeout: float | httpx.Timeout, logging_obj: LiteLLMLoggingObj, - ): - """ - Helper to: - - call embeddings.create.with_raw_response when litellm.return_response_headers is True - - call embeddings.create by default - """ - try: - raw_response = openai_client.embeddings.with_raw_response.create(**data, timeout=timeout) - - headers: Final = dict(raw_response.headers) - response: Final = raw_response.parse() - return headers, response - except Exception as e: - raise e + ) -> LegacyAPIResponse[CreateEmbeddingResponse]: + if "encoding_format" not in data: + body, options = _embedding_request_without_sdk_defaults(data, timeout) + bypass_response: Final = openai_client.post( + "/embeddings", body=body, options=options, cast_to=CreateEmbeddingResponse + ) + assert isinstance(bypass_response, LegacyAPIResponse) + return bypass_response + return openai_client.embeddings.with_raw_response.create(**data, timeout=timeout) async def aembedding( self, @@ -1207,14 +1254,15 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): client=client, shared_session=shared_session, ) - headers, response = await self.make_openai_embedding_request( + raw_response: Final = await self.make_openai_embedding_request( openai_aclient=openai_aclient, data=data, timeout=timeout, logging_obj=logging_obj, ) + headers: Final = dict(raw_response.headers) logging_obj.model_call_details["response_headers"] = headers - stringified_response: Final = response.model_dump() + stringified_response: Final = raw_response.parse().model_dump() ## LOGGING logging_obj.post_call( input=input, @@ -1306,13 +1354,14 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): ) ## embedding CALL - headers: dict | None = None - headers, sync_embedding_response = self.make_sync_openai_embedding_request( + raw_response: Final = self.make_sync_openai_embedding_request( openai_client=openai_client, data=data, timeout=timeout, logging_obj=logging_obj, ) + headers: Final = dict(raw_response.headers) + sync_embedding_response: Final = raw_response.parse() ## LOGGING logging_obj.model_call_details["response_headers"] = headers diff --git a/litellm/llms/openai/responses/count_tokens/transformation.py b/litellm/llms/openai/responses/count_tokens/transformation.py index 6b2f4535df1..88f04c59e01 100644 --- a/litellm/llms/openai/responses/count_tokens/transformation.py +++ b/litellm/llms/openai/responses/count_tokens/transformation.py @@ -4,7 +4,100 @@ OpenAI Responses API token counting transformation logic. This module handles the transformation of requests to OpenAI's /v1/responses/input_tokens endpoint. """ -from typing import Any, Final +from collections.abc import Mapping, Sequence +from typing import Any, Final, Literal + +from typing_extensions import ReadOnly, TypedDict + + +class ResponsesInputTextPart(TypedDict): + type: ReadOnly[Literal["input_text"]] + text: ReadOnly[str] + + +class ResponsesInputImagePart(TypedDict): + type: ReadOnly[Literal["input_image"]] + image_url: ReadOnly[str] + detail: ReadOnly[str] + + +class ResponsesInputFilePart(TypedDict): + type: ReadOnly[Literal["input_file"]] + filename: ReadOnly[str] + file_data: ReadOnly[str] + + +ResponsesInputPart = ResponsesInputTextPart | ResponsesInputImagePart | ResponsesInputFilePart + +ResponsesContentRole = Literal["user", "assistant"] + + +def _chat_image_block_to_responses_part(image_url: object) -> ResponsesInputImagePart | None: + url: Final = image_url.get("url") if isinstance(image_url, Mapping) else image_url + if not isinstance(url, str) or not url: + return None + detail: Final = image_url.get("detail") if isinstance(image_url, Mapping) else None + part: Final[ResponsesInputImagePart] = { + "type": "input_image", + "image_url": url, + "detail": detail if isinstance(detail, str) and detail else "auto", + } + return part + + +def _chat_file_block_to_responses_part(file_value: object) -> ResponsesInputFilePart | None: + """Only an inline file round trips: OpenAI rejects `file_data` without the `filename` beside it.""" + if not isinstance(file_value, Mapping): + return None + filename: Final = file_value.get("filename") + file_data: Final = file_value.get("file_data") + if not isinstance(filename, str) or not filename or not isinstance(file_data, str) or not file_data: + return None + part: Final[ResponsesInputFilePart] = { + "type": "input_file", + "filename": filename, + "file_data": file_data, + } + return part + + +def _chat_block_to_responses_part(block: object, role: ResponsesContentRole) -> ResponsesInputPart | None: + if isinstance(block, str): + bare: Final[ResponsesInputTextPart] = {"type": "input_text", "text": block} + return bare + if not isinstance(block, Mapping): + return None + match block.get("type"): + case "text": + text_value: Final = block.get("text") + text: Final[ResponsesInputTextPart] = { + "type": "input_text", + "text": text_value if isinstance(text_value, str) else "", + } + return text + case "image_url" if role == "user": + return _chat_image_block_to_responses_part(block.get("image_url")) + case "file" if role == "user": + return _chat_file_block_to_responses_part(block.get("file")) + case _: + return None + + +def chat_content_blocks_to_responses_content( + content: Sequence[object], + role: ResponsesContentRole, +) -> str | tuple[ResponsesInputPart, ...]: + """Text-only content collapses to a joined string, which every role accepts and counts identically. + + Only a user turn may carry an image or file part: the Responses API rejects any part but + output_text and refusal inside an assistant turn. + """ + parts: Final = tuple( + part for part in (_chat_block_to_responses_part(block, role) for block in content) if part is not None + ) + if any(part["type"] != "input_text" for part in parts): + return parts + return "\n".join(part["text"] for part in parts if part["type"] == "input_text") class OpenAICountTokensConfig: @@ -120,18 +213,13 @@ class OpenAICountTokensConfig: instructions_parts.append("\n".join(text_parts)) elif role == "user": if isinstance(content, list): - # Extract text from content blocks for Responses API - text_parts = [] - for block in content: - if isinstance(block, dict) and block.get("type") == "text": - text_parts.append(block.get("text", "")) - elif isinstance(block, str): - text_parts.append(block) - content = "\n".join(text_parts) + content = chat_content_blocks_to_responses_content(content, "user") input_items.append({"role": "user", "content": content}) elif role == "assistant": # Map tool_calls to Responses API function_call items tool_calls = msg.get("tool_calls") + if isinstance(content, list): + content = chat_content_blocks_to_responses_content(content, "assistant") if content: input_items.append({"role": "assistant", "content": content}) if tool_calls: diff --git a/litellm/llms/openai/responses/guardrail_translation/handler.py b/litellm/llms/openai/responses/guardrail_translation/handler.py index 7c5d8ac99ad..1530c154e93 100644 --- a/litellm/llms/openai/responses/guardrail_translation/handler.py +++ b/litellm/llms/openai/responses/guardrail_translation/handler.py @@ -28,12 +28,16 @@ Output: response.output is List[GenericResponseOutputItem] where each has: - text: str """ -from collections.abc import Sequence +import time +import uuid +from collections.abc import Mapping, Sequence +from dataclasses import dataclass +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final, Union, cast from openai.types.responses.response_function_tool_call import ResponseFunctionToolCall from openai.types.responses.tool_param import FunctionToolParam -from pydantic import BaseModel +from pydantic import BaseModel, TypeAdapter from typing_extensions import ReadOnly, TypedDict from litellm._logging import verbose_proxy_logger @@ -41,15 +45,33 @@ from litellm.completion_extras.litellm_responses_transformation.transformation i OpenAiResponsesToChatCompletionStreamIterator, ) from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation +from litellm.llms.base_llm.guardrail_translation.utils import ( + blocked_responses_stream_usage, + stream_item_field, +) from litellm.responses.litellm_completion_transformation.transformation import ( LiteLLMCompletionResponsesConfig, ) from litellm.types.llms.openai import ( AllMessageValues, + BaseLiteLLMOpenAIResponseObject, ChatCompletionToolCallChunk, ChatCompletionToolParam, + ContentPartAddedEvent, + ContentPartDoneEvent, + ContentPartDonePartOutputText, + ErrorEvent, + ErrorEventError, OpenAIMcpServerTool, + OutputItemAddedEvent, + OutputItemDoneEvent, + OutputTextDeltaEvent, + OutputTextDoneEvent, + ResponseAPIUsage, + ResponseCompletedEvent, + ResponsesAPIResponse, ResponsesAPIStreamEvents, + ResponsesAPIStreamingResponse, ) from litellm.types.responses.main import ( GenericResponseOutputItem, @@ -59,11 +81,15 @@ from litellm.types.responses.main import ( from litellm.types.utils import GenericGuardrailAPIInputs if TYPE_CHECKING: - from litellm.integrations.custom_guardrail import CustomGuardrail + from fastapi import HTTPException + + from litellm.integrations.custom_guardrail import ( + CustomGuardrail, + ModifyResponseException, + ) from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.proxy._types import UserAPIKeyAuth from litellm.types.llms.openai import ResponseInputParam - from litellm.types.utils import ResponsesAPIResponse class ResponseOutputEnvelope(TypedDict, total=False): @@ -78,6 +104,18 @@ class ResponsesStreamChunk(TypedDict, total=False): type: ReadOnly[str] text: ReadOnly[str] + delta: ReadOnly[str] + item_id: ReadOnly[str] + output_index: ReadOnly[int] + content_index: ReadOnly[int] + + +def _next_stream_sequence_number(responses_so_far: Sequence[Any] | None) -> int: + sequence_numbers: Final = ( + item.get("sequence_number") if isinstance(item, dict) else getattr(item, "sequence_number", None) + for item in reversed(responses_so_far or ()) + ) + return next((n + 1 for n in sequence_numbers if isinstance(n, int)), 0) class OpenAIResponsesHandler(BaseTranslation): @@ -620,11 +658,58 @@ class OpenAIResponsesHandler(BaseTranslation): } return responses_so_far[-1].get("type") in terminal_types + def build_stream_error_items( + self, + exc: "HTTPException", + responses_so_far: Sequence[Any] | None = None, + ) -> Sequence[Any] | None: + from litellm.proxy.common_request_processing import ( + serialize_http_exception_detail, + ) + + message, _ = serialize_http_exception_detail(exc.detail) + return ( + ErrorEvent( + type=ResponsesAPIStreamEvents.ERROR, + sequence_number=_next_stream_sequence_number(responses_so_far), + error=ErrorEventError( + type="guardrail_error", + code=str(exc.status_code), + message=message, + param=None, + ), + ), + ) + def get_streaming_string_so_far(self, responses_so_far: Sequence[ResponsesStreamChunk]) -> str: """ Get the string so far from the responses so far. + + ``response.output_text.done`` events carry the whole part in ``text``, while + ``response.output_text.delta`` events carry fragments in ``delta``. A stream + that dies before its done event (``response.failed`` / ``response.incomplete``) + has text only in deltas, so per content part the done text wins when present + and the joined deltas fill in otherwise, never both. """ - return "".join([response.get("text", "") for response in responses_so_far]) + keyed_events: Final = tuple( + ( + (event.get("item_id"), event.get("output_index"), event.get("content_index")), + event.get("text"), + event.get("delta"), + ) + for event in responses_so_far + if isinstance(event.get("text"), str) or isinstance(event.get("delta"), str) + ) + + def part_text(part_key: tuple[object, object, object]) -> str: + done_texts: Final = tuple( + text for key, text, _ in keyed_events if key == part_key and isinstance(text, str) + ) + if done_texts: + return done_texts[-1] + return "".join(delta for key, _, delta in keyed_events if key == part_key and isinstance(delta, str)) + + return "".join(part_text(key) for key in dict.fromkeys(key for key, _, _ in keyed_events)) def _has_text_content(self, response: "ResponsesAPIResponse") -> bool: """ @@ -802,3 +887,331 @@ class OpenAIResponsesHandler(BaseTranslation): content[content_idx]["text"] = guardrail_response elif hasattr(content[content_idx], "text"): content[content_idx].text = guardrail_response + + def build_block_sse_chunks( + self, + exc: "ModifyResponseException", + stream_started: bool = False, + responses_so_far: Sequence[object] | None = None, + ) -> Sequence[bytes]: + """ + Build Responses API SSE events that deliver the guardrail block message + and terminate the stream cleanly, mirroring the non-streaming block + response: a completed response whose only output is the violation text, + with the real usage the upstream call consumed. + + - ``stream_started`` False (buffered / pre-stream): nothing has been + sent, so emit the full synthetic sequence (``response.created`` + through ``response.completed``). + - ``stream_started`` True (sampling / mid-stream): events already + reached the client, so continue the in-progress response: close the + output item still open on the wire, deliver the block message as a + new output item under the same response id, and close with a + ``response.completed`` carrying only the replacement item. + + The proxy's data generator appends ``data: [DONE]`` itself. + """ + events: Final = ( + self._block_continuation_events(exc, responses_so_far or ()) + if stream_started + else self._standalone_block_events(exc) + ) + return tuple( + f"data: {event.model_dump_json(exclude_none=True, exclude_unset=True, serialize_as_any=True)}\n\n".encode() + for event in events + ) + + @staticmethod + def _standalone_block_events(exc: "ModifyResponseException") -> Sequence[ResponsesAPIStreamingResponse]: + from litellm.responses.streaming_iterator import build_synthetic_response_events + + return build_synthetic_response_events( + transformed=_blocked_response(exc, response_id=f"resp_{uuid.uuid4()}", model=exc.model), + logging_obj=None, + chunk_size=max(len(exc.message), 1), + ) + + @staticmethod + def _block_continuation_events( + exc: "ModifyResponseException", responses_so_far: Sequence[object] + ) -> Sequence[ResponsesAPIStreamingResponse]: + response_id, model, output_index = _continuation_identity(exc, responses_so_far) + item: Final = _blocked_output_item(exc) + item_id: Final = item.id + part: Final[_BlockedContentPart] = {"type": "output_text", "text": exc.message, "annotations": ()} + done_part: Final[_BlockedDoneContentPart] = { + "type": "output_text", + "text": exc.message, + "annotations": (), + "logprobs": None, + } + return ( + *_open_item_closing_events(responses_so_far), + OutputItemAddedEvent( + type=ResponsesAPIStreamEvents.OUTPUT_ITEM_ADDED, + output_index=output_index, + item=item, + ), + ContentPartAddedEvent( + type=ResponsesAPIStreamEvents.CONTENT_PART_ADDED, + item_id=item_id, + output_index=output_index, + content_index=0, + part=BaseLiteLLMOpenAIResponseObject.model_validate(part), + ), + OutputTextDeltaEvent( + type=ResponsesAPIStreamEvents.OUTPUT_TEXT_DELTA, + item_id=item_id, + output_index=output_index, + content_index=0, + delta=exc.message, + ), + OutputTextDoneEvent( + type=ResponsesAPIStreamEvents.OUTPUT_TEXT_DONE, + item_id=item_id, + output_index=output_index, + content_index=0, + text=exc.message, + ), + ContentPartDoneEvent( + type=ResponsesAPIStreamEvents.CONTENT_PART_DONE, + item_id=item_id, + output_index=output_index, + content_index=0, + part=ContentPartDonePartOutputText.model_validate(done_part), + ), + OutputItemDoneEvent( + type=ResponsesAPIStreamEvents.OUTPUT_ITEM_DONE, + output_index=output_index, + item=item, + ), + ResponseCompletedEvent( + type=ResponsesAPIStreamEvents.RESPONSE_COMPLETED, + response=_blocked_response(exc, response_id=response_id, model=model, output_item=item), + ), + ) + + +class _BlockedContentPart(TypedDict): + type: ReadOnly[str] + text: ReadOnly[str] + annotations: ReadOnly[tuple[object, ...]] + + +class _BlockedDoneContentPart(TypedDict): + type: ReadOnly[str] + text: ReadOnly[str] + annotations: ReadOnly[tuple[object, ...]] + logprobs: ReadOnly[None] + + +class _BlockedItemPayload(TypedDict): + type: ReadOnly[str] + id: ReadOnly[str] + status: ReadOnly[str] + role: ReadOnly[str] + content: ReadOnly[tuple[_BlockedContentPart, ...]] + + +class _BlockedResponsePayload(TypedDict): + id: ReadOnly[str] + object: ReadOnly[str] + created_at: ReadOnly[int] + model: ReadOnly[str] + output: ReadOnly[tuple[GenericResponseOutputItem, ...]] + status: ReadOnly[str] + usage: ReadOnly[ResponseAPIUsage] + + +def _blocked_output_item(exc: "ModifyResponseException") -> GenericResponseOutputItem: + payload: Final[_BlockedItemPayload] = { + "type": "message", + "id": f"msg_{uuid.uuid4()}", + "status": "completed", + "role": "assistant", + "content": ({"type": "output_text", "text": exc.message, "annotations": ()},), + } + return GenericResponseOutputItem.model_validate(payload) + + +def _blocked_response( + exc: "ModifyResponseException", + response_id: str, + model: str, + output_item: GenericResponseOutputItem | None = None, +) -> ResponsesAPIResponse: + payload: Final[_BlockedResponsePayload] = { + "id": response_id, + "object": "response", + "created_at": int(time.time()), + "model": model, + "output": (output_item if output_item is not None else _blocked_output_item(exc),), + "status": "completed", + "usage": blocked_responses_stream_usage(exc.original_response), + } + return ResponsesAPIResponse.model_validate(payload) + + +def _continuation_identity(exc: "ModifyResponseException", responses_so_far: Sequence[object]) -> tuple[str, str, int]: + responses: Final = tuple( + response for item in responses_so_far if (response := stream_item_field(item, "response")) is not None + ) + response_id: Final = next( + (rid for response in responses if isinstance(rid := stream_item_field(response, "id"), str) and rid), + f"resp_{uuid.uuid4()}", + ) + model: Final = next( + (m for response in responses if isinstance(m := stream_item_field(response, "model"), str) and m), + exc.model, + ) + indices: Final = tuple( + index for item in responses_so_far if isinstance(index := stream_item_field(item, "output_index"), int) + ) + return response_id, model, max(indices) + 1 if indices else 0 + + +@dataclass(frozen=True, slots=True) +class _OpenItemState: + item_id: str + item_type: str + role: str + output_index: int + content_index: int + text: str + part_open: bool + payload: object + + +def _open_item_state(responses_so_far: Sequence[object]) -> _OpenItemState | None: + typed: Final = tuple((stream_item_field(event, "type"), event) for event in responses_so_far) + added: Final = tuple( + (added_index, stream_item_field(event, "item")) + for event_type, event in typed + if event_type == "response.output_item.added" + and isinstance(added_index := stream_item_field(event, "output_index"), int) + ) + done_indices: Final = frozenset( + done_index + for event_type, event in typed + if event_type == "response.output_item.done" + and isinstance(done_index := stream_item_field(event, "output_index"), int) + ) + open_added: Final = tuple((index, payload) for index, payload in added if index not in done_indices) + if not open_added: + return None + output_index, item_payload = open_added[-1] + if item_payload is None: + return None + item_id: Final = stream_item_field(item_payload, "id") + if not isinstance(item_id, str) or not item_id: + return None + raw_type: Final = stream_item_field(item_payload, "type") + raw_role: Final = stream_item_field(item_payload, "role") + part_added: Final = tuple( + part_index + for event_type, event in typed + if event_type == "response.content_part.added" + and stream_item_field(event, "item_id") == item_id + and isinstance(part_index := stream_item_field(event, "content_index"), int) + ) + part_done: Final = frozenset( + part_done_index + for event_type, event in typed + if event_type == "response.content_part.done" + and stream_item_field(event, "item_id") == item_id + and isinstance(part_done_index := stream_item_field(event, "content_index"), int) + ) + open_parts: Final = tuple(index for index in part_added if index not in part_done) + text: Final = "".join( + delta + for event_type, event in typed + if event_type == "response.output_text.delta" + and stream_item_field(event, "item_id") == item_id + and isinstance(delta := stream_item_field(event, "delta"), str) + ) + return _OpenItemState( + item_id=item_id, + item_type=raw_type if isinstance(raw_type, str) and raw_type else "message", + role=raw_role if isinstance(raw_role, str) and raw_role else "assistant", + output_index=output_index, + content_index=open_parts[-1] if open_parts else 0, + text=text, + part_open=bool(open_parts), + payload=item_payload, + ) + + +_item_fields_adapter: Final = TypeAdapter(Mapping[str, object]) +_no_item_fields: Final[Mapping[str, object]] = MappingProxyType({}) + + +def _incomplete_item_fields(payload: object) -> Mapping[str, object]: + raw: Final = payload.model_dump() if isinstance(payload, BaseModel) else payload + if not isinstance(raw, dict): + return _no_item_fields + return _item_fields_adapter.validate_python(raw) + + +def _open_item_closing_events(responses_so_far: Sequence[object]) -> Sequence[ResponsesAPIStreamingResponse]: + """Close the output item still in progress on the relayed stream before the + block item is appended: strict Responses clients reject a + ``response.completed`` that arrives while an earlier ``output_item.added`` + was never closed. A message item closes ``completed`` with exactly the text + the client has received so far; any other item type (a function call the + guardrail rejected, for instance) closes ``incomplete`` so the synthetic + done event can never authorize acting on it.""" + open_item: Final = _open_item_state(responses_so_far) + if open_item is None: + return () + if open_item.item_type != "message": + return ( + OutputItemDoneEvent( + type=ResponsesAPIStreamEvents.OUTPUT_ITEM_DONE, + output_index=open_item.output_index, + item=BaseLiteLLMOpenAIResponseObject.model_validate( + MappingProxyType({**_incomplete_item_fields(open_item.payload), "status": "incomplete"}) + ), + ), + ) + partial_part: Final[_BlockedContentPart] = { + "type": "output_text", + "text": open_item.text, + "annotations": (), + } + closed_payload: Final[_BlockedItemPayload] = { + "type": open_item.item_type, + "id": open_item.item_id, + "status": "completed", + "role": open_item.role, + "content": (partial_part,), + } + item_done: Final = OutputItemDoneEvent( + type=ResponsesAPIStreamEvents.OUTPUT_ITEM_DONE, + output_index=open_item.output_index, + item=GenericResponseOutputItem.model_validate(closed_payload), + ) + if not open_item.part_open: + return (item_done,) + partial_done_part: Final[_BlockedDoneContentPart] = { + "type": "output_text", + "text": open_item.text, + "annotations": (), + "logprobs": None, + } + return ( + OutputTextDoneEvent( + type=ResponsesAPIStreamEvents.OUTPUT_TEXT_DONE, + item_id=open_item.item_id, + output_index=open_item.output_index, + content_index=open_item.content_index, + text=open_item.text, + ), + ContentPartDoneEvent( + type=ResponsesAPIStreamEvents.CONTENT_PART_DONE, + item_id=open_item.item_id, + output_index=open_item.output_index, + content_index=open_item.content_index, + part=ContentPartDonePartOutputText.model_validate(partial_done_part), + ), + item_done, + ) diff --git a/litellm/llms/openai/responses/transformation.py b/litellm/llms/openai/responses/transformation.py index 2fa44cfc2e3..01313e95878 100644 --- a/litellm/llms/openai/responses/transformation.py +++ b/litellm/llms/openai/responses/transformation.py @@ -1,8 +1,11 @@ -from typing import TYPE_CHECKING, Any, Final, cast, get_type_hints +from collections.abc import Mapping, Sequence +from types import MappingProxyType +from typing import TYPE_CHECKING, Any, Final, Protocol, cast, get_type_hints import httpx from openai.types.responses import ResponseReasoningItem from pydantic import BaseModel, ValidationError +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -12,6 +15,7 @@ from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response impo ) from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.llms.base_llm.responses.transformation import BaseResponsesAPIConfig +from litellm.responses.litellm_completion_transformation.custom_tools import TOOL_CALL_ITEM_ID_PREFIX_BY_TYPE from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import * from litellm.types.responses.main import * @@ -19,6 +23,7 @@ from litellm.types.router import GenericLiteLLMParams from litellm.types.utils import LlmProviders from ..common_utils import OpenAIError +from ..workload_identity import get_workload_identity_bearer_token, resolve_openai_workload_identity_config OPENAI_RESPONSES_API_MIN_MAX_OUTPUT_TOKENS: Final = 16 @@ -29,6 +34,41 @@ if TYPE_CHECKING: else: LiteLLMLoggingObj = Any +_NO_TOOL_UPDATE: Final[Mapping[str, object]] = MappingProxyType({}) +_MODEL_FAMILIES_REJECTING_TOP_LEVEL_SCHEMA_COMBINATORS: Final = ("gpt-4", "gpt-3.5", "chatgpt-4o", "o1", "o3", "o4") +_PROVIDERS_WITH_COMBINATOR_REJECTING_VALIDATOR: Final = frozenset({LlmProviders.AZURE, LlmProviders.OPENAI}) +_PROVIDERS_VALIDATING_TOOL_CALL_ITEM_IDS: Final = frozenset({LlmProviders.AZURE, LlmProviders.OPENAI}) + + +class _DeleteResponseBody(TypedDict): + """Decoded body of the Responses API delete call.""" + + id: ReadOnly[str | None] + object: ReadOnly[str | None] + deleted: ReadOnly[bool | None] + + +class _DeleteResponse(Protocol): + """The delete call's HTTP response, read for the decoded body it carries.""" + + def json(self) -> _DeleteResponseBody: ... + + +class _JsonObjectResponse(Protocol): + """A Responses API HTTP response, read for the JSON object it decodes to.""" + + def json(self) -> dict[str, object]: ... + + +def _delete_response_body(response: _DeleteResponse) -> _DeleteResponseBody: + """Decode a delete response body into the id, object and deleted fields it carries.""" + return response.json() + + +def _json_object_body(response: _JsonObjectResponse) -> dict[str, object]: + """Decode a Responses API response body into its JSON object form.""" + return response.json() + class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): @property @@ -167,10 +207,14 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): input = self._validate_input_param(input) tools = response_api_optional_request_params.get("tools") input, tools = self.remove_cache_control_flag_from_input_and_tools(model=model, input=input, tools=tools) - if tools is not None: - response_api_optional_request_params["tools"] = tools + sanitized_tools: Final = self._flatten_tool_schema_combinators_for_openai( + model=model, tools=tools, litellm_params=litellm_params + ) + if sanitized_tools is not None: + response_api_optional_request_params["tools"] = sanitized_tools + replay_safe_input: Final = self._drop_foreign_tool_call_item_ids(input) final_request_params: Final = dict( - ResponsesAPIRequestParams(model=model, input=input, **response_api_optional_request_params) + ResponsesAPIRequestParams(model=model, input=replay_safe_input, **response_api_optional_request_params) ) return final_request_params @@ -207,6 +251,96 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): return input, tools + def _drop_foreign_tool_call_item_ids(self, input: str | ResponseInputParam) -> str | ResponseInputParam: + if self.custom_llm_provider not in _PROVIDERS_VALIDATING_TOOL_CALL_ITEM_IDS or not isinstance(input, list): + return input + sanitized_items: Final = [self._without_foreign_tool_call_item_id(item) for item in input] + return cast("ResponseInputParam", sanitized_items) # cast-ok: items keep their shape, minus a rejected id + + @staticmethod + def _without_foreign_tool_call_item_id(item: object) -> object: + if not isinstance(item, dict): + return item + item_type: Final = item.get("type") + item_id: Final = item.get("id") + genuine_prefix: Final = TOOL_CALL_ITEM_ID_PREFIX_BY_TYPE.get(item_type) if isinstance(item_type, str) else None + if genuine_prefix is None or not isinstance(item_id, str) or item_id.startswith(genuine_prefix): + return item + return {key: value for key, value in item.items() if key != "id"} # mutable-ok: outgoing JSON request item + + def _flatten_tool_schema_combinators_for_openai( + self, + model: str, + tools: list[ALL_RESPONSES_API_TOOL_PARAMS] | None, # mutable-ok: request tools are a JSON list + litellm_params: GenericLiteLLMParams, + ) -> list[ALL_RESPONSES_API_TOOL_PARAMS] | None: # mutable-ok: request tools are a JSON list + """Flatten top-level schema combinators only where OpenAI's validator rejects them. + + OpenAI-compatible backends reusing this config (and the ChatGPT backend + Codex talks to natively) accept them, and so do GPT-5 and later models, + which also call tools better with the union intact. Codex wraps MCP tools + inside namespace entries, so nested ``tools`` arrays are walked too. + Azure OpenAI shares the validator but names deployments arbitrarily, so + the router's declared ``model_info.base_model`` wins over the deployment + name and an unrecognized name without one is left untouched. + """ + if tools is None or self.custom_llm_provider not in _PROVIDERS_WITH_COMBINATOR_REJECTING_VALIDATOR: + return tools + gate_model: Final = self._combinator_gate_model(model=model, litellm_params=litellm_params) + if not self._rejects_top_level_schema_combinators(gate_model): + return tools + flattened: Final = [ # mutable-ok: request tools are a JSON list + self._flattened_tool_or_passthrough(tool) for tool in tools + ] + return cast("list[ALL_RESPONSES_API_TOOL_PARAMS]", flattened) # cast-ok: dict spread keeps each tool's shape + + @staticmethod + def _flattened_tool_or_passthrough(tool: object) -> object: + return OpenAIResponsesAPIConfig._flattened_tool_entry(tool) if isinstance(tool, dict) else tool + + @staticmethod + def _rejects_top_level_schema_combinators(model: str) -> bool: + bare_model: Final = model.split("/")[-1] + base_model: Final = bare_model.split(":")[1] if bare_model.startswith("ft:") else bare_model + return base_model.startswith(_MODEL_FAMILIES_REJECTING_TOP_LEVEL_SCHEMA_COMBINATORS) + + @staticmethod + def _combinator_gate_model(model: str, litellm_params: GenericLiteLLMParams) -> str: + model_info: Final[object] = getattr(litellm_params, "model_info", None) + base_model: Final[object] = model_info.get("base_model") if isinstance(model_info, dict) else None + return base_model if isinstance(base_model, str) and base_model else model + + @staticmethod + def _flattened_tool_entry( + entry: Mapping[str, object], + ) -> dict[str, object]: # mutable-ok: request tools are JSON dicts + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + flatten_top_level_schema_combinators, + ) + + parameters: Final = entry.get("parameters") + nested_tools: Final = entry.get("tools") + parameters_update: Final = ( + MappingProxyType({"parameters": flatten_top_level_schema_combinators(parameters)}) + if isinstance(parameters, dict) + else _NO_TOOL_UPDATE + ) + tools_update: Final = ( + MappingProxyType({"tools": OpenAIResponsesAPIConfig._flattened_nested_tools(nested_tools)}) + if isinstance(nested_tools, list) + else _NO_TOOL_UPDATE + ) + return {**entry, **parameters_update, **tools_update} # mutable-ok: request tools are JSON dicts + + @staticmethod + def _flattened_nested_tools( + nested_tools: Sequence[object], + ) -> list[object]: # mutable-ok: namespace tools are a JSON list + return [ # mutable-ok: namespace tools are a JSON list + OpenAIResponsesAPIConfig._flattened_tool_entry(item) if isinstance(item, dict) else item + for item in nested_tools + ] + def _validate_input_param(self, input: str | ResponseInputParam) -> str | ResponseInputParam: """ Ensure all input fields if pydantic are converted to dict @@ -310,6 +444,14 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): litellm_params = litellm_params or GenericLiteLLMParams() api_key = litellm_params.api_key or litellm.api_key or litellm.openai_key or get_secret_str("OPENAI_API_KEY") headers.setdefault("Content-Type", "application/json") + workload_identity_config: Final = ( + resolve_openai_workload_identity_config(api_key=api_key, api_base=litellm_params.api_base) + if self.custom_llm_provider is LlmProviders.OPENAI + else None + ) + if workload_identity_config is not None: + headers["Authorization"] = f"Bearer {get_workload_identity_bearer_token(workload_identity_config)}" + return headers headers["Authorization"] = f"Bearer {api_key}" return headers @@ -378,7 +520,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): return None @staticmethod - def get_event_model_class(event_type: str) -> Any: + def get_event_model_class(event_type: str) -> type[BaseLiteLLMOpenAIResponseObject]: """ Returns the appropriate event model class based on the event type. @@ -492,7 +634,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): Transform the delete response API response into a DeleteResponseResult """ try: - raw_response_json: Final = raw_response.json() + raw_response_json: Final = _delete_response_body(raw_response) except Exception: raise OpenAIError(message=raw_response.text, status_code=raw_response.status_code) return DeleteResponseResult(**raw_response_json) @@ -527,7 +669,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): Transform the get response API response into a ResponsesAPIResponse """ try: - raw_response_json: Final = raw_response.json() + raw_response_json: Final = _json_object_body(raw_response) except Exception: raise OpenAIError(message=raw_response.text, status_code=raw_response.status_code) raw_response_headers: Final = dict(raw_response.headers) @@ -555,7 +697,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): ) -> tuple[str, dict]: encoded_response_id: Final = encode_url_path_segment(response_id, field_name="response_id") url: Final = f"{api_base}/{encoded_response_id}/input_items" - params: Final[dict[str, Any]] = {} + params: Final[dict[str, object]] = {} if after is not None: params["after"] = after if before is not None: @@ -574,7 +716,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): logging_obj: LiteLLMLoggingObj, ) -> dict: try: - return raw_response.json() + return _json_object_body(raw_response) except Exception: raise OpenAIError(message=raw_response.text, status_code=raw_response.status_code) @@ -608,7 +750,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): Transform the cancel response API response into a ResponsesAPIResponse """ try: - raw_response_json: Final = raw_response.json() + raw_response_json: Final = _json_object_body(raw_response) except Exception: raise OpenAIError(message=raw_response.text, status_code=raw_response.status_code) raw_response_headers: Final = dict(raw_response.headers) @@ -646,9 +788,15 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): input = self._validate_input_param(input) tools = response_api_optional_request_params.get("tools") input, tools = self.remove_cache_control_flag_from_input_and_tools(model=model, input=input, tools=tools) - if tools is not None: - response_api_optional_request_params["tools"] = tools - data: Final = dict(ResponsesAPIRequestParams(model=model, input=input, **response_api_optional_request_params)) + sanitized_tools: Final = self._flatten_tool_schema_combinators_for_openai( + model=model, tools=tools, litellm_params=litellm_params + ) + if sanitized_tools is not None: + response_api_optional_request_params["tools"] = sanitized_tools + replay_safe_input: Final = self._drop_foreign_tool_call_item_ids(input) + data: Final = dict( + ResponsesAPIRequestParams(model=model, input=replay_safe_input, **response_api_optional_request_params) + ) return url, data diff --git a/litellm/llms/openai/workload_identity.py b/litellm/llms/openai/workload_identity.py new file mode 100644 index 00000000000..ecec161ed46 --- /dev/null +++ b/litellm/llms/openai/workload_identity.py @@ -0,0 +1,100 @@ +from __future__ import annotations + +from dataclasses import dataclass +from functools import lru_cache +from typing import TYPE_CHECKING, Final +from urllib.parse import urlparse + +import litellm +from litellm.secret_managers.main import get_secret_str, normalize_nonempty_secret_str + +from .common_utils import OpenAIError + +if TYPE_CHECKING: + from collections.abc import Callable + + from openai.auth import SubjectTokenProvider, WorkloadIdentity, WorkloadIdentityAuth + +OPENAI_WIF_CLIENT_ID: Final = "litellm" +_OPENAI_API_HOST: Final = "api.openai.com" +_SDK_UPGRADE_MESSAGE: Final = ( + "OpenAI workload identity federation requires openai>=2.32.0. " + "Upgrade the installed openai package to use OPENAI_IDENTITY_PROVIDER_ID / " + "OPENAI_SERVICE_ACCOUNT_ID / OPENAI_IDENTITY_TOKEN_FILE." +) + + +@dataclass(frozen=True, slots=True) +class OpenAIWorkloadIdentityConfig: + identity_provider_id: str + service_account_id: str + token_file: str + + def to_sdk_workload_identity(self) -> WorkloadIdentity: + k8s_token_provider: Final = _load_sdk_k8s_token_provider() + workload_identity: Final[WorkloadIdentity] = { + "client_id": OPENAI_WIF_CLIENT_ID, + "identity_provider_id": self.identity_provider_id, + "service_account_id": self.service_account_id, + "provider": k8s_token_provider(self.token_file), + } + return workload_identity + + +def resolve_openai_workload_identity_config( + api_key: str | None, + api_base: str | None, +) -> OpenAIWorkloadIdentityConfig | None: + static_api_key: Final = normalize_nonempty_secret_str(api_key) or normalize_nonempty_secret_str( + get_secret_str("OPENAI_API_KEY") + ) + if static_api_key is not None: + return None + effective_api_base: Final = ( + api_base or litellm.api_base or get_secret_str("OPENAI_BASE_URL") or get_secret_str("OPENAI_API_BASE") + ) + if not _targets_openai_api(effective_api_base): + return None + identity_provider_id: Final = get_secret_str("OPENAI_IDENTITY_PROVIDER_ID") + service_account_id: Final = get_secret_str("OPENAI_SERVICE_ACCOUNT_ID") + token_file: Final = get_secret_str("OPENAI_IDENTITY_TOKEN_FILE") + if not identity_provider_id or not service_account_id or not token_file: + return None + return OpenAIWorkloadIdentityConfig( + identity_provider_id=identity_provider_id, + service_account_id=service_account_id, + token_file=token_file, + ) + + +def get_workload_identity_bearer_token(config: OpenAIWorkloadIdentityConfig) -> str: + return _workload_identity_auth(config).get_token() + + +def _targets_openai_api(api_base: str | None) -> bool: + if api_base is None: + return True + parsed: Final = urlparse(api_base) + return parsed.scheme == "https" and parsed.hostname == _OPENAI_API_HOST + + +@lru_cache(maxsize=16) +def _workload_identity_auth(config: OpenAIWorkloadIdentityConfig) -> WorkloadIdentityAuth: + sdk_workload_identity_auth: Final = _load_sdk_workload_identity_auth() + return sdk_workload_identity_auth(workload_identity=config.to_sdk_workload_identity()) + + +def _load_sdk_workload_identity_auth() -> type[WorkloadIdentityAuth]: + try: + from openai.auth import WorkloadIdentityAuth as sdk_workload_identity_auth + except ImportError as e: + raise OpenAIError(status_code=500, message=_SDK_UPGRADE_MESSAGE) from e + return sdk_workload_identity_auth + + +def _load_sdk_k8s_token_provider() -> Callable[[str], SubjectTokenProvider]: + try: + from openai.auth import k8s_service_account_token_provider + except ImportError as e: + raise OpenAIError(status_code=500, message=_SDK_UPGRADE_MESSAGE) from e + return k8s_service_account_token_provider diff --git a/litellm/llms/openai_like/chat/handler.py b/litellm/llms/openai_like/chat/handler.py index 8c548b6b0d6..855c49c320b 100644 --- a/litellm/llms/openai_like/chat/handler.py +++ b/litellm/llms/openai_like/chat/handler.py @@ -5,10 +5,11 @@ For handling OpenAI-like chat completions, like IBM WatsonX, etc. """ import json -from collections.abc import Callable -from typing import Any, Final +from collections.abc import Callable, Mapping, Sequence +from typing import Final, TypedDict import httpx +from typing_extensions import ReadOnly import litellm from litellm import LlmProviders @@ -25,6 +26,23 @@ from ..common_utils import OpenAILikeBase, OpenAILikeError from .transformation import OpenAILikeChatConfig +class _OpenAILikeChatCompletion(TypedDict, total=False): + """The chat-completion JSON body an OpenAI-like provider returns for a non-streamed call.""" + + id: ReadOnly[str] + choices: ReadOnly[Sequence[Mapping[str, object]]] + created: ReadOnly[int] + model: ReadOnly[str] + system_fingerprint: ReadOnly[str] + usage: ReadOnly[Mapping[str, object]] + object: ReadOnly[str] + + +def _fake_streamed_model_response(payload: _OpenAILikeChatCompletion) -> ModelResponse: + """Build the single response a fake-streamed provider call replays as one chunk.""" + return ModelResponse(**payload) + + async def make_call( client: AsyncHTTPHandler | None, api_base: str, @@ -42,9 +60,9 @@ async def make_call( response: Final = await client.post(api_base, headers=headers, data=data, stream=not fake_stream) if streaming_decoder is not None: - completion_stream: Any = streaming_decoder.aiter_bytes(response.aiter_bytes(chunk_size=1024)) + completion_stream = streaming_decoder.aiter_bytes(response.aiter_bytes(chunk_size=1024)) elif fake_stream: - model_response: Final = ModelResponse(**response.json()) + model_response: Final = _fake_streamed_model_response(response.json()) completion_stream = MockResponseIterator(model_response=model_response) else: completion_stream = ModelResponseIterator(streaming_response=response.aiter_lines(), sync_stream=False) @@ -82,7 +100,7 @@ def make_sync_call( if streaming_decoder is not None: completion_stream = streaming_decoder.iter_bytes(response.iter_bytes(chunk_size=1024)) elif fake_stream: - model_response: Final = ModelResponse(**response.json()) + model_response: Final = _fake_streamed_model_response(response.json()) completion_stream = MockResponseIterator(model_response=model_response) else: completion_stream = ModelResponseIterator(streaming_response=response.iter_lines(), sync_stream=True) diff --git a/litellm/llms/opensandbox/sandbox/transformation.py b/litellm/llms/opensandbox/sandbox/transformation.py index 49a7fb08c4a..5126db9bbc9 100644 --- a/litellm/llms/opensandbox/sandbox/transformation.py +++ b/litellm/llms/opensandbox/sandbox/transformation.py @@ -1,7 +1,7 @@ import asyncio import json import time -from typing import Final, cast +from typing import Final import httpx @@ -86,13 +86,10 @@ class OpenSandboxSandboxConfig(BaseSandboxConfig): secure_access=secure_access, ) - response: Final = cast( - httpx.Response, - await self._http(client).post( - url=f"{base}/sandboxes", - headers=self._lifecycle_headers(key), - json=body, - ), + response: Final = await self._http(client).post( + url=f"{base}/sandboxes", + headers=self._lifecycle_headers(key), + json=body, ) data: Final = response.json() sandbox_id: Final = str(data["id"]) @@ -182,12 +179,9 @@ class OpenSandboxSandboxConfig(BaseSandboxConfig): base: Final = str(handle._hidden_params.get("api_base") or self._api_base(api_base)) key: Final = self._api_key(api_key=api_key, handle=handle) try: - response: Final = cast( - httpx.Response, - await self._http(client).delete( - url=f"{base}/sandboxes/{handle.id}", - headers=self._lifecycle_headers(key), - ), + response: Final = await self._http(client).delete( + url=f"{base}/sandboxes/{handle.id}", + headers=self._lifecycle_headers(key), ) except httpx.HTTPStatusError as e: if e.response.status_code == 404: @@ -245,12 +239,9 @@ class OpenSandboxSandboxConfig(BaseSandboxConfig): ) -> None: deadline: Final = time.monotonic() + ready_timeout while True: - response = cast( - httpx.Response, - await self._http(client).get( - url=f"{api_base}/sandboxes/{sandbox_id}", - headers=headers, - ), + response = await self._http(client).get( + url=f"{api_base}/sandboxes/{sandbox_id}", + headers=headers, ) data = response.json() state = self._sandbox_state(data) @@ -306,13 +297,10 @@ class OpenSandboxSandboxConfig(BaseSandboxConfig): use_server_proxy: bool, client: AsyncHTTPHandler | None, ) -> tuple[str, dict[str, str]]: - response: Final = cast( - httpx.Response, - await self._http(client).get( - url=f"{api_base}/sandboxes/{sandbox_id}/endpoints/{OPEN_SANDBOX_EXECD_PORT}", - headers=headers, - params={"use_server_proxy": use_server_proxy}, - ), + response: Final = await self._http(client).get( + url=f"{api_base}/sandboxes/{sandbox_id}/endpoints/{OPEN_SANDBOX_EXECD_PORT}", + headers=headers, + params={"use_server_proxy": use_server_proxy}, ) data: Final = response.json() endpoint: Final = data.get("endpoint") @@ -329,15 +317,12 @@ class OpenSandboxSandboxConfig(BaseSandboxConfig): client: AsyncHTTPHandler | None, ) -> list[str]: timeout: Final = httpx.Timeout(connect=30.0, read=None, write=30.0, pool=None) - response: Final = cast( - httpx.Response, - await self._http(client).post( - url=url, - headers=headers, - timeout=timeout, - json=body, - stream=True, - ), + response: Final = await self._http(client).post( + url=url, + headers=headers, + timeout=timeout, + json=body, + stream=True, ) return await self._read_capped_lines(response) diff --git a/litellm/llms/parallel_ai/search/cost_calculator.py b/litellm/llms/parallel_ai/search/cost_calculator.py new file mode 100644 index 00000000000..809cd280cc8 --- /dev/null +++ b/litellm/llms/parallel_ai/search/cost_calculator.py @@ -0,0 +1,90 @@ +from collections.abc import Mapping, Sequence +from types import MappingProxyType +from typing import Final + +from pydantic import TypeAdapter, ValidationError + +from litellm.utils import get_model_info + +PARALLEL_AI_DEFAULT_RESULTS: Final = 10 +PARALLEL_AI_ADDITIONAL_RESULT_COST: Final = 0.001 +PARALLEL_AI_USAGE_PARAM: Final = "_parallel_ai_usage" +PARALLEL_AI_STANDARD_SEARCH_MODEL: Final = "parallel_ai/search" +PARALLEL_AI_FAST_SEARCH_MODEL: Final = "parallel_ai/search-fast" +PARALLEL_AI_TURBO_SEARCH_MODEL: Final = "parallel_ai/search-turbo" +PARALLEL_AI_PRICING_MODEL_BY_MODE: Final[Mapping[str, str]] = MappingProxyType( + { + "fast": PARALLEL_AI_FAST_SEARCH_MODEL, + "turbo": PARALLEL_AI_TURBO_SEARCH_MODEL, + } +) +ADVANCED_SETTINGS_ADAPTER: Final[TypeAdapter[Mapping[str, object]]] = TypeAdapter(Mapping[str, object]) + + +def _non_negative_int(value: object) -> int | None: + if isinstance(value, bool) or not isinstance(value, int) or value < 0: + return None + return value + + +def _usage_count(usage: Sequence[Mapping[str, object]], sku: str) -> int | None: + counts: Final = tuple( + count + for item in usage + if item.get("name") == sku + if (count := _non_negative_int(item.get("count"))) is not None + ) + return sum(counts) if counts else None + + +def _effective_mode(optional_params: Mapping[str, object]) -> str: + mode: Final = optional_params.get("mode") + if isinstance(mode, str): + return mode + + processor: Final = optional_params.get("processor") + if processor == "pro": + return "advanced" + return "basic" + + +def _effective_max_results(optional_params: Mapping[str, object]) -> int: + try: + advanced_settings: Final = ADVANCED_SETTINGS_ADAPTER.validate_python(optional_params.get("advanced_settings")) + advanced_max_results: Final = _non_negative_int(advanced_settings.get("max_results")) + if advanced_max_results is not None: + return advanced_max_results + except ValidationError: + pass + + max_results: Final = _non_negative_int(optional_params.get("max_results")) + return max_results if max_results is not None else PARALLEL_AI_DEFAULT_RESULTS + + +def _request_cost(mode: str) -> float: + pricing_model: Final = PARALLEL_AI_PRICING_MODEL_BY_MODE.get(mode, PARALLEL_AI_STANDARD_SEARCH_MODEL) + model_info: Final = get_model_info(model=pricing_model, custom_llm_provider="parallel_ai") + return float(model_info.get("input_cost_per_query") or 0.0) + + +def _additional_results( + optional_params: Mapping[str, object], + usage: Sequence[Mapping[str, object]] | None, +) -> int: + usage_count: Final = _usage_count(usage, "sku_search_additional_results") if usage is not None else None + if usage_count is not None: + return usage_count + if usage is not None: + return 0 + return max(_effective_max_results(optional_params) - PARALLEL_AI_DEFAULT_RESULTS, 0) + + +def parallel_ai_search_cost( + optional_params: Mapping[str, object], + usage: Sequence[Mapping[str, object]] | None, +) -> float: + request_cost: Final = _request_cost(_effective_mode(optional_params)) + request_count_from_usage: Final = _usage_count(usage, "sku_search") if usage is not None else None + request_count: Final = request_count_from_usage if request_count_from_usage is not None else 1 + additional_results: Final = _additional_results(optional_params, usage) + return request_count * request_cost + additional_results * PARALLEL_AI_ADDITIONAL_RESULT_COST diff --git a/litellm/llms/parallel_ai/search/transformation.py b/litellm/llms/parallel_ai/search/transformation.py index ea21d1153fe..bde7b7b86db 100644 --- a/litellm/llms/parallel_ai/search/transformation.py +++ b/litellm/llms/parallel_ai/search/transformation.py @@ -4,9 +4,13 @@ Calls Parallel AI's /v1/search endpoint to search the web. Parallel AI API Reference: https://docs.parallel.ai/api-reference/search/search """ +from collections.abc import Mapping, Sequence +from types import MappingProxyType from typing import Final, TypedDict import httpx +from pydantic import BaseModel, ConfigDict +from typing_extensions import ReadOnly from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.llms.base_llm.search.transformation import ( @@ -14,9 +18,29 @@ from litellm.llms.base_llm.search.transformation import ( SearchResponse, SearchResult, ) +from litellm.llms.parallel_ai.search.cost_calculator import PARALLEL_AI_USAGE_PARAM from litellm.secret_managers.main import get_secret_str +class _ParallelAIV1SearchResult(BaseModel): + model_config = ConfigDict(extra="ignore") + + url: str | None = None + title: str | None = None + publish_date: str | None = None + excerpts: Sequence[str] | None = None + + +class _ParallelAIV1SearchResponse(BaseModel): + model_config = ConfigDict(extra="ignore") + + search_id: str | None = None + session_id: str | None = None + results: Sequence[_ParallelAIV1SearchResult] = () + usage: Sequence[Mapping[str, object]] | None = None + warnings: Sequence[Mapping[str, object]] | None = None + + class _ParallelAISourcePolicy(TypedDict, total=False): include_domains: list[str] exclude_domains: list[str] @@ -27,10 +51,16 @@ class _ParallelAIExcerptSettings(TypedDict, total=False): max_chars_per_result: int +class _ParallelAIFetchPolicy(TypedDict, total=False): + max_age_seconds: ReadOnly[int] + timeout_seconds: ReadOnly[float] + disable_cache_fallback: ReadOnly[bool] + + class _ParallelAIAdvancedSettings(TypedDict, total=False): source_policy: _ParallelAISourcePolicy excerpt_settings: _ParallelAIExcerptSettings - fetch_policy: dict + fetch_policy: _ParallelAIFetchPolicy location: str max_results: int @@ -43,14 +73,14 @@ class ParallelAISearchRequest(TypedDict, total=False): search_queries: list[str] # Required - at least one keyword search query objective: str # Optional - natural-language description of search goal - mode: str # Optional - 'turbo', 'basic', or 'advanced' (default 'advanced') + mode: str # Optional - 'turbo', 'fast', 'basic', or 'advanced' (default 'advanced') max_chars_total: int # Optional - upper bound on total excerpt characters session_id: str # Optional - tracks calls across search/extract requests client_model: str # Optional - model consuming the results advanced_settings: _ParallelAIAdvancedSettings -LEGACY_PROCESSOR_TO_MODE: Final = {"base": "basic", "pro": "advanced"} +LEGACY_PROCESSOR_TO_MODE: Final = MappingProxyType({"base": "basic", "pro": "advanced"}) class ParallelAISearchConfig(BaseSearchConfig): @@ -67,16 +97,16 @@ class ParallelAISearchConfig(BaseSearchConfig): api_base: str | None = None, **kwargs, ) -> dict: - api_key = self.resolve_server_api_key( + resolved_api_key: Final = self.resolve_server_api_key( caller_api_key=api_key, caller_api_base=api_base, key_env_vars=("PARALLEL_AI_API_KEY", "PARALLEL_API_KEY"), base_env_var="PARALLEL_AI_API_BASE", default_api_base=self.PARALLEL_AI_API_BASE, ) - if not api_key: + if not resolved_api_key: raise ValueError("PARALLEL_API_KEY is not set. Set `PARALLEL_API_KEY` environment variable.") - headers["x-api-key"] = api_key + headers["x-api-key"] = resolved_api_key headers["Content-Type"] = "application/json" return headers @@ -87,13 +117,12 @@ class ParallelAISearchConfig(BaseSearchConfig): data: dict | list[dict] | None = None, **kwargs, ) -> str: - api_base = api_base or get_secret_str("PARALLEL_AI_API_BASE") or self.PARALLEL_AI_API_BASE + resolved_api_base: Final = api_base or get_secret_str("PARALLEL_AI_API_BASE") or self.PARALLEL_AI_API_BASE - api_base = api_base.rstrip("/") - if not api_base.endswith("/v1/search"): - api_base = f"{api_base.removesuffix('/v1')}/v1/search" - - return api_base + trimmed: Final = resolved_api_base.rstrip("/") + if trimmed.endswith("/v1/search"): + return trimmed + return f"{trimmed.removesuffix('/v1')}/v1/search" def transform_search_request( self, @@ -109,14 +138,17 @@ class ParallelAISearchConfig(BaseSearchConfig): - If string: maps to `search_queries` (single item) and `objective` - If list: maps to `search_queries` (keyword queries) optional_params: Optional parameters for the request - - mode: Search mode ('turbo', 'basic', 'advanced'); defaults to 'basic' + - mode: Search mode ('turbo', 'fast', 'basic', 'advanced'); defaults to 'basic' - processor: Legacy v1beta param; 'base' maps to mode 'basic', 'pro' to 'advanced' - max_results: Maximum number of search results -> `advanced_settings.max_results` - - search_domain_filter: Domains to include -> `advanced_settings.source_policy.include_domains` + - search_domain_filter / include_domains: Domains to include -> `advanced_settings.source_policy.include_domains` - exclude_domains: Domains to exclude -> `advanced_settings.source_policy.exclude_domains` - - country: ISO 3166-1 alpha-2 code -> `advanced_settings.location` + - after_date: RFC 3339 date (YYYY-MM-DD) -> `advanced_settings.source_policy.after_date` + - country / location: ISO 3166-1 alpha-2 code -> `advanced_settings.location` - max_chars_per_result: -> `advanced_settings.excerpt_settings.max_chars_per_result` - - Any other params are passed through to the request body as-is + - fetch_policy: Cache vs live-fetch policy -> `advanced_settings.fetch_policy` + - Any other params (objective, max_chars_total, session_id, client_model, ...) + are passed through to the request body as-is Returns: Dict with request data following the v1 search request spec @@ -137,7 +169,7 @@ class ParallelAISearchConfig(BaseSearchConfig): mode = LEGACY_PROCESSOR_TO_MODE.get(processor, processor) # the v1 API defaults to 'advanced' when mode is omitted; default to 'basic' # instead to keep v1beta's default tier (processor 'base') and litellm's - # $0.004/query cost map entry for `parallel_ai/search` accurate + # cost map entry for `parallel_ai/search` accurate request_data["mode"] = mode or "basic" advanced_settings: Final[_ParallelAIAdvancedSettings] = {} @@ -148,17 +180,29 @@ class ParallelAISearchConfig(BaseSearchConfig): if "country" in params: advanced_settings["location"] = params.pop("country") + if "location" in params: + advanced_settings["location"] = params.pop("location") + if "max_chars_per_result" in params: advanced_settings["excerpt_settings"] = {"max_chars_per_result": params.pop("max_chars_per_result")} + if "fetch_policy" in params: + advanced_settings["fetch_policy"] = params.pop("fetch_policy") + source_policy: Final[_ParallelAISourcePolicy] = {} if "search_domain_filter" in params: source_policy["include_domains"] = params.pop("search_domain_filter") + if "include_domains" in params: + source_policy["include_domains"] = params.pop("include_domains") + if "exclude_domains" in params: source_policy["exclude_domains"] = params.pop("exclude_domains") + if "after_date" in params: + source_policy["after_date"] = params.pop("after_date") + if source_policy: advanced_settings["source_policy"] = source_policy @@ -170,9 +214,11 @@ class ParallelAISearchConfig(BaseSearchConfig): # unified-spec param with no v1 equivalent params.pop("max_tokens_per_page", None) - result_data: Final[dict] = dict(request_data) - result_data.update(params) - return result_data + # reserved for the provider's own reported usage, which prices the request; + # a caller-supplied value would otherwise set its own cost + params.pop(PARALLEL_AI_USAGE_PARAM, None) + + return {**request_data, **params} def transform_search_response( self, @@ -186,26 +232,49 @@ class ParallelAISearchConfig(BaseSearchConfig): Parallel AI -> LiteLLM mappings: - results[].title -> SearchResult.title - results[].url -> SearchResult.url - - results[].excerpts (array) -> SearchResult.snippet (joined string) + - results[].excerpts (array) -> SearchResult.snippet (joined string); the raw + array is preserved as an extra `excerpts` field on each result - results[].publish_date -> SearchResult.date + - search_id / session_id / warnings are preserved as extra fields on the + response; usage is preserved as `parallel_usage` (the `usage` name is + reserved for LiteLLM's token-usage object) """ - response_json: Final = raw_response.json() + parsed: Final = _ParallelAIV1SearchResponse.model_validate(raw_response.json()) - results: Final = [] - for result in response_json.get("results", []): - excerpts = result.get("excerpts") or [] - snippet = " ... ".join(excerpts) if excerpts else "" + # written unconditionally: leaving a caller-supplied value in place when the + # provider reports no usage would let the caller price its own request + logging_obj.optional_params = { + **logging_obj.optional_params, + PARALLEL_AI_USAGE_PARAM: parsed.usage, + } - search_result = SearchResult( - title=result.get("title") or "", - url=result.get("url") or "", - snippet=snippet, - date=result.get("publish_date"), - last_updated=None, + results: Final = tuple( + SearchResult.model_validate( + MappingProxyType( + { + "title": result.title or "", + "url": result.url or "", + "snippet": " ... ".join(result.excerpts or ()), + "date": result.publish_date, + "last_updated": None, + "excerpts": result.excerpts or (), + } + ) ) - results.append(search_result) - - return SearchResponse( - results=results, - object="search", + for result in parsed.results ) + + extra_fields: Final = MappingProxyType( + { + key: value + for key, value in ( + ("search_id", parsed.search_id), + ("session_id", parsed.session_id), + ("parallel_usage", parsed.usage), + ("warnings", parsed.warnings), + ) + if value is not None + } + ) + + return SearchResponse.model_validate(MappingProxyType({"results": results, "object": "search", **extra_fields})) diff --git a/litellm/llms/runwayml/image_generation/transformation.py b/litellm/llms/runwayml/image_generation/transformation.py index cde65addb65..5913709c8a0 100644 --- a/litellm/llms/runwayml/image_generation/transformation.py +++ b/litellm/llms/runwayml/image_generation/transformation.py @@ -1,8 +1,10 @@ import asyncio import time +from collections.abc import Mapping, Sequence from typing import TYPE_CHECKING, Any, Final import httpx +from typing_extensions import ReadOnly, TypedDict from litellm._logging import verbose_logger from litellm.constants import ( @@ -29,6 +31,16 @@ else: LiteLLMLoggingObj = Any +class _RunwayMLTask(TypedDict, total=False): + """The RunwayML task payload returned by POST /v1/text_to_image and GET /v1/tasks/{id}.""" + + id: ReadOnly[str] + status: ReadOnly[str] + output: ReadOnly[Sequence[str | Mapping[str, str]]] + failure: ReadOnly[str] + failureCode: ReadOnly[str] + + class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): """ Configuration for RunwayML image generation models. @@ -80,7 +92,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): @staticmethod def _transform_runwayml_response_to_openai( - response_data: dict[str, Any], + response_data: _RunwayMLTask, model_response: ImageResponse, ) -> ImageResponse: """ @@ -155,7 +167,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): raise TimeoutError(f"RunwayML task polling timed out after {timeout_secs} seconds") @staticmethod - def _check_task_status(response_data: dict[str, Any]) -> str: + def _check_task_status(response_data: _RunwayMLTask) -> str: """ Check RunwayML task status from response. @@ -227,7 +239,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): response = client.get(url=task_url, headers=headers) response.raise_for_status() - response_data = response.json() + response_data: _RunwayMLTask = response.json() # Check task status status = self._check_task_status(response_data=response_data) @@ -276,7 +288,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): response = await client.get(url=task_url, headers=headers) response.raise_for_status() - response_data = response.json() + response_data: _RunwayMLTask = response.json() # Check task status status = self._check_task_status(response_data=response_data) @@ -322,7 +334,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): } """ try: - response_data = raw_response.json() + response_data: _RunwayMLTask = raw_response.json() except Exception as e: raise self.get_error_class( error_message=f"Error transforming image generation response: {e}", @@ -382,7 +394,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): We need to poll the task until it completes (status SUCCEEDED) using async polling. """ try: - response_data = raw_response.json() + response_data: _RunwayMLTask = raw_response.json() except Exception as e: raise self.get_error_class( error_message=f"Error transforming image generation response: {e}", diff --git a/litellm/llms/runwayml/text_to_speech/transformation.py b/litellm/llms/runwayml/text_to_speech/transformation.py index 1da8f0c66f0..19e6d8ff494 100644 --- a/litellm/llms/runwayml/text_to_speech/transformation.py +++ b/litellm/llms/runwayml/text_to_speech/transformation.py @@ -6,10 +6,11 @@ Maps OpenAI TTS spec to RunwayML Text-to-Speech API import asyncio import time -from collections.abc import Coroutine -from typing import TYPE_CHECKING, Any, Final, Union +from collections.abc import Coroutine, Sequence +from typing import TYPE_CHECKING, Any, Final, TypedDict, Union import httpx +from typing_extensions import ReadOnly import litellm from litellm._logging import verbose_logger @@ -31,6 +32,14 @@ else: HttpxBinaryResponseContent = Any +class _RunwayTtsTaskResponse(TypedDict, total=False): + id: ReadOnly[str] + status: ReadOnly[str] + output: ReadOnly[Sequence[object]] + failure: ReadOnly[str] + failureCode: ReadOnly[str] + + class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): """ Configuration for RunwayML Text-to-Speech @@ -64,7 +73,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): litellm_params_dict: dict, logging_obj: "LiteLLMLoggingObj", timeout: float | httpx.Timeout, - extra_headers: dict[str, Any] | None, + extra_headers: dict[str, object] | None, base_llm_http_handler: Any, aspeech: bool, api_base: str | None, @@ -72,7 +81,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): **kwargs: Any, ) -> Union[ "HttpxBinaryResponseContent", - Coroutine[Any, Any, "HttpxBinaryResponseContent"], + Coroutine[object, object, "HttpxBinaryResponseContent"], ]: """ Dispatch method to handle RunwayML TTS requests @@ -242,7 +251,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): raise TimeoutError(f"RunwayML TTS task polling timed out after {timeout_secs} seconds") @staticmethod - def _check_task_status(response_data: dict[str, Any]) -> str: + def _check_task_status(response_data: _RunwayTtsTaskResponse) -> str: """ Check RunwayML task status from response. @@ -314,7 +323,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): response = client.get(url=task_url, headers=headers) response.raise_for_status() - response_data = response.json() + response_data: _RunwayTtsTaskResponse = response.json() # Check task status status = self._check_task_status(response_data=response_data) @@ -362,7 +371,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): response = await client.get(url=task_url, headers=headers) response.raise_for_status() - response_data = response.json() + response_data: _RunwayTtsTaskResponse = response.json() # Check task status status = self._check_task_status(response_data=response_data) @@ -453,7 +462,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): from litellm.types.llms.openai import HttpxBinaryResponseContent try: - response_data: Final = raw_response.json() + response_data: Final[_RunwayTtsTaskResponse] = raw_response.json() except Exception as e: raise self.get_error_class( error_message=f"Error parsing RunwayML TTS response: {e}", @@ -483,7 +492,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): ) # Get the completed task data - task_data: Final = polled_response.json() + task_data: Final[_RunwayTtsTaskResponse] = polled_response.json() verbose_logger.debug("RunwayML TTS polling complete, downloading audio") @@ -522,7 +531,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): from litellm.types.llms.openai import HttpxBinaryResponseContent try: - response_data: Final = raw_response.json() + response_data: Final[_RunwayTtsTaskResponse] = raw_response.json() except Exception as e: raise self.get_error_class( error_message=f"Error parsing RunwayML TTS response: {e}", @@ -552,7 +561,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): ) # Get the completed task data - task_data: Final = polled_response.json() + task_data: Final[_RunwayTtsTaskResponse] = polled_response.json() verbose_logger.debug("RunwayML TTS polling complete (async), downloading audio") diff --git a/litellm/llms/runwayml/videos/transformation.py b/litellm/llms/runwayml/videos/transformation.py index 8dd77db08c0..c7696a1cb29 100644 --- a/litellm/llms/runwayml/videos/transformation.py +++ b/litellm/llms/runwayml/videos/transformation.py @@ -117,6 +117,10 @@ class RunwayMLVideoConfig(BaseVideoConfig): def __init__(self): super().__init__() + @staticmethod + def _parse_task_response(raw_response: httpx.Response) -> _RunwayTaskResponse: + return raw_response.json() + def get_supported_openai_params(self, model: str) -> list: """ Get the list of supported OpenAI parameters for video generation. @@ -141,7 +145,7 @@ class RunwayMLVideoConfig(BaseVideoConfig): video_create_optional_params: VideoCreateOptionalRequestParams, model: str, drop_params: bool, - ) -> dict: + ) -> dict[str, object]: """ Map OpenAI parameters to RunwayML format. @@ -151,37 +155,42 @@ class RunwayMLVideoConfig(BaseVideoConfig): - size -> ratio (convert "WIDTHxHEIGHT" to "WIDTH:HEIGHT") - seconds -> duration (convert to integer) """ - mapped_params: Final[dict[str, object]] = {} + supported_openai_params: Final = self.get_supported_openai_params(model) + return { + **self._prompt_image_param(video_create_optional_params), + **self._ratio_param(video_create_optional_params), + **self._duration_param(video_create_optional_params), + **{key: value for key, value in video_create_optional_params.items() if key not in supported_openai_params}, + } + @staticmethod + def _prompt_image_param(video_create_optional_params: VideoCreateOptionalRequestParams) -> Mapping[str, object]: # Handle input_reference parameter - map to promptImage if "input_reference" in video_create_optional_params: - input_reference: Final = video_create_optional_params["input_reference"] - # RunwayML supports URLs and data URIs directly - mapped_params["promptImage"] = input_reference + return {"promptImage": video_create_optional_params["input_reference"]} + return {} + @staticmethod + def _ratio_param(video_create_optional_params: VideoCreateOptionalRequestParams) -> Mapping[str, str]: # Handle size parameter - convert "1280x720" to "1280:720" if "size" in video_create_optional_params: size: Final = video_create_optional_params["size"] if isinstance(size, str) and "x" in size: - mapped_params["ratio"] = size.replace("x", ":") + return {"ratio": size.replace("x", ":")} + return {} + @staticmethod + def _duration_param(video_create_optional_params: VideoCreateOptionalRequestParams) -> Mapping[str, int]: # Handle seconds parameter - convert to integer if "seconds" in video_create_optional_params: seconds: Final = video_create_optional_params["seconds"] if seconds is not None: try: - mapped_params["duration"] = int(float(seconds)) if isinstance(seconds, str) else int(seconds) + return {"duration": int(float(seconds)) if isinstance(seconds, str) else int(seconds)} except (ValueError, TypeError): # If conversion fails, use default duration pass - - # Pass through other parameters that aren't OpenAI-specific - supported_openai_params: Final = self.get_supported_openai_params(model) - for key, value in video_create_optional_params.items(): - if key not in supported_openai_params: - mapped_params[key] = value - - return mapped_params + return {} def validate_environment( self, @@ -236,7 +245,7 @@ class RunwayMLVideoConfig(BaseVideoConfig): model: str, prompt: str, api_base: str, - video_create_optional_request_params: dict, + video_create_optional_request_params: dict[str, object], litellm_params: GenericLiteLLMParams, headers: dict, ) -> tuple[dict, RequestFiles, str]: @@ -406,20 +415,18 @@ class RunwayMLVideoConfig(BaseVideoConfig): # Get task status to retrieve video URL url: Final = f"{api_base}/tasks/{encoded_video_id}" - params: Final[dict[str, str]] = {} + return url, dict[str, str]() - return url, params - - def _extract_video_url_from_response(self, response_data: dict[str, Any]) -> str: + def _extract_video_url_from_response(self, response_data: _RunwayTaskResponse) -> str: """ Helper method to extract video URL from RunwayML response. Shared between sync and async transforms. """ # Extract video URL from the output field video_url = None - if "output" in response_data and response_data["output"]: - output: Final = response_data["output"] - video_url = output[0] if isinstance(output, list) else output + raw_output: Final = response_data.get("output") + if raw_output: + video_url = raw_output if isinstance(raw_output, str) else raw_output[0] if not video_url: # Check if the video generation failed or is still processing @@ -453,7 +460,7 @@ class RunwayMLVideoConfig(BaseVideoConfig): "output":["https://dnznrvs05pmza.cloudfront.net/.../video.mp4?_jwt=..."] } """ - response_data: Final = raw_response.json() + response_data: Final[_RunwayTaskResponse] = self._parse_task_response(raw_response) video_url: Final = self._extract_video_url_from_response(response_data) # Download the video from the CloudFront URL synchronously @@ -482,7 +489,7 @@ class RunwayMLVideoConfig(BaseVideoConfig): "output":["https://dnznrvs05pmza.cloudfront.net/.../video.mp4?_jwt=..."] } """ - response_data: Final = raw_response.json() + response_data: Final[_RunwayTaskResponse] = self._parse_task_response(raw_response) video_url: Final = self._extract_video_url_from_response(response_data) # Download the video from the CloudFront URL asynchronously @@ -564,9 +571,7 @@ class RunwayMLVideoConfig(BaseVideoConfig): # Construct the URL for task cancellation url: Final = f"{api_base}/tasks/{encoded_video_id}/cancel" - data: Final[dict[str, str]] = {} - - return url, data + return url, dict[str, str]() def transform_video_delete_response( self, @@ -604,9 +609,7 @@ class RunwayMLVideoConfig(BaseVideoConfig): url: Final = f"{api_base}/tasks/{encoded_video_id}" # Empty dict for GET request (no body) - data: Final[dict[str, str]] = {} - - return url, data + return url, dict[str, str]() def transform_video_status_retrieve_response( self, diff --git a/litellm/llms/sap/credentials.py b/litellm/llms/sap/credentials.py index d7743d4d337..a2a93b6114a 100644 --- a/litellm/llms/sap/credentials.py +++ b/litellm/llms/sap/credentials.py @@ -8,9 +8,10 @@ from dataclasses import dataclass from datetime import datetime, timedelta, timezone from pathlib import Path from threading import Lock -from typing import Any, Final +from typing import Any, Final, Protocol import httpx +from typing_extensions import NotRequired, ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -33,8 +34,8 @@ def _get_home() -> str: return os.getenv(HOME_PATH_ENV_VAR, DEFAULT_HOME_PATH) -def _get_nested(d: dict[str, Any] | str, path: Sequence[str]) -> Any: - cur: Any = d +def _get_nested(d: object, path: Sequence[str]) -> object: + cur: object = d if isinstance(cur, str): # This shouldn't happen if service keys are pre-parsed correctly try: @@ -54,7 +55,7 @@ def _get_nested(d: dict[str, Any] | str, path: Sequence[str]) -> Any: return cur -def _load_json_env(var_name: str) -> dict[str, Any] | None: +def _load_json_env(var_name: str) -> dict[str, object] | None: raw: Final = os.environ.get(var_name) if not raw: return None @@ -64,7 +65,7 @@ def _load_json_env(var_name: str) -> dict[str, Any] | None: return None -def _str_or_none(value) -> str | None: +def _str_or_none(value: object) -> str | None: try: return str(value) if value is not None else None except Exception: @@ -124,7 +125,7 @@ CREDENTIAL_VALUES: Final[list[CredentialsValue]] = [ ] -def init_conf(profile: str | None = None) -> dict[str, Any]: +def init_conf(profile: str | None = None) -> dict[str, object]: """ Loads config JSON from: 1) $AICORE_CONFIG if set, otherwise @@ -191,7 +192,7 @@ def resolve_resource_group(sources: list[Source]) -> str | None: def _parse_service_key_once( service_key: str | dict | None, -) -> dict[str, Any] | None: +) -> dict[str, object] | None: """ Pre-parse service_key if it's a string to avoid repeated JSON parsing. @@ -348,8 +349,33 @@ def validate_credentials( ) +class _TokenBody(TypedDict): + """Decoded body of the SAP AI Core OAuth2 token response.""" + + access_token: ReadOnly[str] + expires_in: ReadOnly[NotRequired[int]] + + +class _TokenResponse(Protocol): + """The token endpoint's HTTP response, read for the decoded token body it carries.""" + + def json(self) -> _TokenBody: ... + + +def _bearer_token_and_expiry(response: _TokenResponse) -> tuple[str, datetime]: + """Read a token response into the Authorization header value and the token's absolute expiry.""" + payload: Final = response.json() + expires_in: Final = int(payload.get("expires_in", 3600)) + access_token: Final = payload["access_token"] + return f"Bearer {access_token}", datetime.now(timezone.utc) + timedelta(seconds=expires_in) + + def _request_token( - client_id: str, auth_url: str, timeout: float, cert_pair=None, client_secret=None + client_id: str, + auth_url: str, + timeout: float, + cert_pair: tuple[str, str] | None = None, + client_secret: str | None = None, ) -> tuple[str, datetime]: data: Final = {"grant_type": "client_credentials", "client_id": client_id} if client_secret: @@ -361,15 +387,10 @@ def _request_token( with httpx.Client(cert=cert_pair) as raw_client: handler = HTTPHandler(client=raw_client) resp = handler.post(auth_url, data=data, timeout=timeout) - payload = resp.json() - else: - handler = _get_httpx_client() - resp = handler.post(auth_url, data=data, timeout=timeout) - payload = resp.json() - access_token: Final = payload["access_token"] - expires_in: Final = int(payload.get("expires_in", 3600)) - expiry_date: Final = datetime.now(timezone.utc) + timedelta(seconds=expires_in) - return f"Bearer {access_token}", expiry_date + return _bearer_token_and_expiry(resp) + handler = _get_httpx_client() + resp = handler.post(auth_url, data=data, timeout=timeout) + return _bearer_token_and_expiry(resp) except Exception as e: msg: Final = resp.text if resp is not None else getattr(e, "text", str(e)) raise RuntimeError(f"Token request failed: {msg}") from e diff --git a/litellm/llms/soniox/common_utils.py b/litellm/llms/soniox/common_utils.py index cb33f0b8996..8d83b4c9218 100644 --- a/litellm/llms/soniox/common_utils.py +++ b/litellm/llms/soniox/common_utils.py @@ -138,13 +138,25 @@ def _soniox_token_to_subtitle_token(token: SonioxToken) -> SubtitleToken: ) +def _subtitle_tokens(tokens: Sequence[SonioxToken]) -> tuple[SubtitleToken, ...]: + """ + Convert Soniox tokens for subtitle rendering, excluding translation tokens + (``translation_status == "translation"``): Soniox does not timestamp them, + so they cannot be aligned to the audio and would otherwise mix translated + text into original-language cues. + """ + return tuple( + _soniox_token_to_subtitle_token(token) for token in tokens if token.get("translation_status") != "translation" + ) + + def render_soniox_tokens_as_srt(tokens: Sequence[SonioxToken]) -> str: """ Render Soniox tokens as SRT (SubRip) subtitle format. Returns an empty string if no tokens have timestamp data. """ - return render_subtitle_tokens_as_srt(tuple(_soniox_token_to_subtitle_token(token) for token in tokens)) + return render_subtitle_tokens_as_srt(_subtitle_tokens(tokens)) def render_soniox_tokens_as_vtt(tokens: Sequence[SonioxToken]) -> str: @@ -153,4 +165,4 @@ def render_soniox_tokens_as_vtt(tokens: Sequence[SonioxToken]) -> str: Returns the VTT header even if no cues are present. """ - return render_subtitle_tokens_as_vtt(tuple(_soniox_token_to_subtitle_token(token) for token in tokens)) + return render_subtitle_tokens_as_vtt(_subtitle_tokens(tokens)) diff --git a/litellm/llms/vertex_ai/audio_transcription/gemini_transcribe_transformation.py b/litellm/llms/vertex_ai/audio_transcription/gemini_transcribe_transformation.py new file mode 100644 index 00000000000..f4db5eb110c --- /dev/null +++ b/litellm/llms/vertex_ai/audio_transcription/gemini_transcribe_transformation.py @@ -0,0 +1,216 @@ +import base64 +from collections.abc import Mapping, Sequence +from typing import Final + +from httpx import Headers, Response + +import litellm +from litellm.exceptions import UnsupportedParamsError +from litellm.litellm_core_utils.audio_utils.utils import ( + normalize_transcription_language_to_bcp47, + process_audio_file, +) +from litellm.llms.base_llm.audio_transcription.transformation import ( + AudioTranscriptionRequestData, + BaseAudioTranscriptionConfig, +) +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.llms.vertex_ai.audio_transcription.transformation import ( + SUPPORTED_RESPONSE_FORMATS, + validate_vertex_transcription_location, + validate_vertex_transcription_project_id, +) +from litellm.llms.vertex_ai.common_utils import VertexAIError, get_vertex_base_url +from litellm.llms.vertex_ai.vertex_llm_base import VertexBase +from litellm.types.llms.openai import ( + AllMessageValues, + OpenAIAudioTranscriptionOptionalParams, +) +from litellm.types.llms.vertex_ai_gemini_transcription import ( + VertexGeminiTranscriptionAudioConfig, + VertexGeminiTranscriptionContent, + VertexGeminiTranscriptionGenerationConfig, + VertexGeminiTranscriptionInlineData, + VertexGeminiTranscriptionPart, + VertexGeminiTranscriptionRequest, + VertexGeminiTranscriptionResponse, +) +from litellm.types.utils import ( + FileTypes, + TranscriptionResponse, + TranscriptionUsageInputTokenDetailsObject, + TranscriptionUsageTokensObject, +) + +DEFAULT_GEMINI_TRANSCRIBE_LOCATION: Final = "global" +AUDIO_MODALITY: Final = "AUDIO" + + +class VertexGeminiAudioTranscriptionConfig(BaseAudioTranscriptionConfig, VertexBase): + def __init__(self) -> None: + BaseAudioTranscriptionConfig.__init__(self) + VertexBase.__init__(self) + + def get_supported_openai_params( + self, model: str + ) -> list[OpenAIAudioTranscriptionOptionalParams]: # mutable-ok: BaseAudioTranscriptionConfig signature + return ["language", "response_format"] + + def map_openai_params( + self, + non_default_params: Mapping[str, object], + optional_params: Mapping[str, object], + model: str, + drop_params: bool, + ) -> dict[str, object]: # mutable-ok: BaseAudioTranscriptionConfig signature + supported_params: Final = frozenset(self.get_supported_openai_params(model)) + mapped: Final = { + **optional_params, + **{k: v for k, v in non_default_params.items() if k in supported_params}, + } + response_format: Final = mapped.get("response_format") + if response_format is None or response_format in SUPPORTED_RESPONSE_FORMATS: + return mapped + if drop_params or litellm.drop_params: + return {k: v for k, v in mapped.items() if k != "response_format"} + raise UnsupportedParamsError( + status_code=400, + message=( + f"Vertex AI Gemini transcription does not support response_format={response_format!r}. " + f"Supported values: {', '.join(SUPPORTED_RESPONSE_FORMATS)}. " + "To drop unsupported openai params from the call, set `litellm.drop_params = True`" + ), + ) + + def get_error_class( + self, + error_message: str, + status_code: int, + headers: dict | Headers, # mutable-ok: base signature and VertexAIError take dict | Headers + ) -> BaseLLMException: + return VertexAIError(status_code=status_code, message=error_message, headers=headers) + + def validate_environment( + self, + headers: Mapping[str, str], + model: str, + messages: Sequence[AllMessageValues], + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], + api_key: str | None = None, + api_base: str | None = None, + ) -> dict[str, str]: # mutable-ok: BaseAudioTranscriptionConfig signature + vertex_params: Final = dict(litellm_params) + access_token, project_id = self._ensure_access_token( + credentials=self.safe_get_vertex_ai_credentials(vertex_params), + project_id=self.safe_get_vertex_ai_project(vertex_params), + custom_llm_provider="vertex_ai", + ) + return { + **headers, + "Authorization": f"Bearer {access_token}", + "x-goog-user-project": project_id, + "Content-Type": "application/json", + } + + def get_complete_url( + self, + api_base: str | None, + api_key: str | None, + model: str, + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], + stream: bool | None = None, + ) -> str: + vertex_params: Final = dict(litellm_params) + location: Final = validate_vertex_transcription_location( + self.safe_get_vertex_ai_location(vertex_params), default_location=DEFAULT_GEMINI_TRANSCRIBE_LOCATION + ) + project_id: Final = validate_vertex_transcription_project_id( + self.safe_get_vertex_ai_project(vertex_params) or self._resolve_project_id_from_credentials(vertex_params) + ) + base_url: Final = (api_base or get_vertex_base_url(location)).rstrip("/") + bare_model: Final = model.removeprefix("vertex_ai/") + model_path: Final = f"projects/{project_id}/locations/{location}/publishers/google/models/{bare_model}" + return f"{base_url}/v1/{model_path}:generateContent" + + def _resolve_project_id_from_credentials(self, litellm_params: Mapping[str, object]) -> str: + vertex_params: Final = dict(litellm_params) + _, project_id = self._ensure_access_token( + credentials=self.safe_get_vertex_ai_credentials(vertex_params), + project_id=None, + custom_llm_provider="vertex_ai", + ) + return project_id + + def transform_audio_transcription_request( + self, + model: str, + audio_file: FileTypes, + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], + ) -> AudioTranscriptionRequestData: + processed_audio: Final = process_audio_file(audio_file) + request_body: Final = VertexGeminiTranscriptionRequest( + contents=( + VertexGeminiTranscriptionContent( + role="user", + parts=( + VertexGeminiTranscriptionPart( + inlineData=VertexGeminiTranscriptionInlineData( + mimeType=processed_audio.content_type, + data=base64.b64encode(processed_audio.file_content).decode("utf-8"), + ) + ), + ), + ), + ), + generationConfig=VertexGeminiTranscriptionGenerationConfig( + audioTranscriptionConfig=_audio_transcription_config(optional_params.get("language")) + ), + ) + return AudioTranscriptionRequestData(data=dict(request_body)) + + def transform_audio_transcription_response( + self, + raw_response: Response, + ) -> TranscriptionResponse: + try: + response_json: Final = raw_response.json() + except ValueError: + raise VertexAIError( + status_code=raw_response.status_code, + message=f"Received non-JSON response from Vertex AI Gemini transcription: {raw_response.text}", + ) + parsed: Final = VertexGeminiTranscriptionResponse.model_validate(response_json) + texts: Final = tuple( + part.text + for candidate in parsed.candidates + if candidate.content is not None + for part in candidate.content.parts + if part.text + ) + response: Final = TranscriptionResponse(text=" ".join(texts)) + response["task"] = "transcribe" + usage: Final = parsed.usageMetadata + if usage is not None: + audio_tokens: Final = sum( + detail.tokenCount for detail in usage.promptTokensDetails if detail.modality == AUDIO_MODALITY + ) + response.usage = TranscriptionUsageTokensObject( + type="tokens", + input_tokens=usage.promptTokenCount, + output_tokens=usage.candidatesTokenCount, + total_tokens=usage.totalTokenCount, + input_token_details=TranscriptionUsageInputTokenDetailsObject( + audio_tokens=audio_tokens, + text_tokens=usage.promptTokenCount - audio_tokens, + ), + ) + return response + + +def _audio_transcription_config(language: object) -> VertexGeminiTranscriptionAudioConfig: + if not isinstance(language, str) or not language: + return VertexGeminiTranscriptionAudioConfig() + return VertexGeminiTranscriptionAudioConfig(languageCodes=(normalize_transcription_language_to_bcp47(language),)) diff --git a/litellm/llms/vertex_ai/audio_transcription/transformation.py b/litellm/llms/vertex_ai/audio_transcription/transformation.py index a352b2a34ca..db3504c9a6a 100644 --- a/litellm/llms/vertex_ai/audio_transcription/transformation.py +++ b/litellm/llms/vertex_ai/audio_transcription/transformation.py @@ -35,6 +35,19 @@ SUPPORTED_RESPONSE_FORMATS: Final = ("json", "text") _URL_UNSAFE_PROJECT_CHARS: Final = ("/", "?", "#", "\\", ":", " ", "\t", "\n", "\r") +def validate_vertex_transcription_location(location: str | None, default_location: str) -> str: + try: + return validate_vertex_location(location or default_location) + except ValueError as e: + raise VertexAIError(status_code=400, message=str(e)) from e + + +def validate_vertex_transcription_project_id(project_id: str) -> str: + if not project_id or ".." in project_id or any(c in project_id for c in _URL_UNSAFE_PROJECT_CHARS): + raise VertexAIError(status_code=400, message=f"Invalid vertex_project format: {project_id!r}") + return project_id + + class VertexAIAudioTranscriptionConfig(BaseAudioTranscriptionConfig, VertexBase): def __init__(self) -> None: BaseAudioTranscriptionConfig.__init__(self) @@ -103,27 +116,16 @@ class VertexAIAudioTranscriptionConfig(BaseAudioTranscriptionConfig, VertexBase) litellm_params: dict, stream: bool | None = None, ) -> str: - location: Final = self._validate_location(self.safe_get_vertex_ai_location(litellm_params)) - project_id: Final = self._validate_project_id( + location: Final = validate_vertex_transcription_location( + self.safe_get_vertex_ai_location(litellm_params), default_location=DEFAULT_SPEECH_TO_TEXT_LOCATION + ) + project_id: Final = validate_vertex_transcription_project_id( self.safe_get_vertex_ai_project(litellm_params) or self._resolve_project_id_from_credentials(litellm_params) ) host: Final = "speech.googleapis.com" if location == "global" else f"{location}-speech.googleapis.com" base_url: Final = (api_base or f"https://{host}").rstrip("/") return f"{base_url}/v2/projects/{project_id}/locations/{location}/recognizers/_:recognize" - @staticmethod - def _validate_location(location: str | None) -> str: - try: - return validate_vertex_location(location or DEFAULT_SPEECH_TO_TEXT_LOCATION) - except ValueError as e: - raise VertexAIError(status_code=400, message=str(e)) from e - - @staticmethod - def _validate_project_id(project_id: str) -> str: - if not project_id or ".." in project_id or any(c in project_id for c in _URL_UNSAFE_PROJECT_CHARS): - raise VertexAIError(status_code=400, message=f"Invalid vertex_project format: {project_id!r}") - return project_id - def _resolve_project_id_from_credentials(self, litellm_params: dict) -> str: _, project_id = self._ensure_access_token( credentials=self.safe_get_vertex_ai_credentials(litellm_params), diff --git a/litellm/llms/vertex_ai/common_utils.py b/litellm/llms/vertex_ai/common_utils.py index 1de2337d8eb..a36c920dda0 100644 --- a/litellm/llms/vertex_ai/common_utils.py +++ b/litellm/llms/vertex_ai/common_utils.py @@ -1,7 +1,7 @@ import re from copy import deepcopy from enum import Enum -from typing import Any, Final, Literal, get_type_hints +from typing import Any, Final, Literal, cast, get_type_hints import httpx @@ -31,7 +31,7 @@ class VertexAIError(BaseLLMException): super().__init__(message=message, status_code=status_code, headers=headers) -def redact_vertex_ai_metadata_from_logged_object(obj: Any) -> None: +def redact_vertex_ai_metadata_from_logged_object(obj: object) -> None: if isinstance(obj, dict): for field in VERTEX_AI_PROVIDER_METADATA_FIELDS: if field in obj: @@ -651,7 +651,7 @@ def _build_json_schema(parameters: dict) -> dict: return parameters -def _filter_anyof_fields(schema_dict: dict[str, Any]) -> dict[str, Any]: +def _filter_anyof_fields(schema_dict: dict[str, object]) -> dict[str, object]: """ When anyof is present, only keep the anyof field and its contents - otherwise VertexAI will throw an error - https://github.com/BerriAI/litellm/issues/11164 Filter out other fields in the same dict. @@ -704,7 +704,7 @@ def process_items(schema, depth=0): process_items(item, depth + 1) -def set_schema_property_ordering(schema: dict[str, Any], depth: int = 0) -> dict[str, Any]: +def set_schema_property_ordering(schema: dict[str, object], depth: int = 0) -> dict[str, object]: """ vertex ai and generativeai apis order output of fields alphabetically, unless you specify the order. python dicts retain order, so we just use that. Note that this field only applies to structured outputs, and not tools. @@ -724,14 +724,16 @@ def set_schema_property_ordering(schema: dict[str, Any], depth: int = 0) -> dict # retain propertyOrdering as an escape hatch if user already specifies it if "propertyOrdering" not in schema: schema["propertyOrdering"] = [k for k, v in schema["properties"].items()] - for k, v in schema["properties"].items(): - set_schema_property_ordering(v, depth + 1) - if "items" in schema: - set_schema_property_ordering(schema["items"], depth + 1) + for v in schema["properties"].values(): + if isinstance(v, dict): + set_schema_property_ordering(cast("dict[str, object]", v), depth + 1) # cast-ok: JSON Schema child + items: Final = schema.get("items") + if isinstance(items, dict): + set_schema_property_ordering(cast("dict[str, object]", items), depth + 1) # cast-ok: JSON Schema child return schema -def filter_schema_fields(schema_dict: dict[str, Any], valid_fields: set[str], processed=None) -> dict[str, Any]: +def filter_schema_fields(schema_dict: dict[str, object], valid_fields: set[str], processed=None) -> dict[str, object]: """ Recursively filter a schema dictionary to keep only valid fields. """ @@ -905,7 +907,7 @@ def _convert_schema_types(schema, depth=0): "maxProperties", } - any_of: Final[list[dict[str, Any]]] = [] + any_of: Final[list[dict[str, object]]] = [] for t in type_val: if not isinstance(t, str): continue @@ -916,7 +918,7 @@ def _convert_schema_types(schema, depth=0): # For object/array types, include type-specific fields if t in ("object", "array"): - item_schema = {"type": t} + item_schema: dict[str, object] = {"type": t} # Move type-specific fields into this anyOf item for field in type_specific_fields: if field in schema: @@ -1110,11 +1112,11 @@ class VertexAITokenCounter(BaseTokenCounter): self, model_to_use: str, messages: list[dict[str, Any]] | None, - contents: list[dict[str, Any]] | None, + contents: list[dict[str, object]] | None, deployment: dict[str, Any] | None = None, request_model: str = "", - tools: list[dict[str, Any]] | None = None, - system: Any | None = None, + tools: list[dict[str, object]] | None = None, + system: object | None = None, ) -> TokenCountResponse | None: import copy @@ -1131,25 +1133,26 @@ class VertexAITokenCounter(BaseTokenCounter): partner_models_handler: Final = VertexAIPartnerModels() # Extract vertex-specific params from litellm_params - vertex_project = count_tokens_params_request.get("vertex_project") or count_tokens_params_request.get( + partner_litellm_params: Final[dict[str, object]] = count_tokens_params_request + vertex_project = partner_litellm_params.get("vertex_project") or partner_litellm_params.get( "vertex_ai_project" ) - vertex_location = count_tokens_params_request.get("vertex_location") or count_tokens_params_request.get( + vertex_location = partner_litellm_params.get("vertex_location") or partner_litellm_params.get( "vertex_ai_location" ) # Count tokens not available on global location: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/partner-models/claude/count-tokens - vertex_location = count_tokens_params_request.get("vertex_count_tokens_location") or vertex_location + vertex_location = partner_litellm_params.get("vertex_count_tokens_location") or vertex_location - vertex_credentials: Final = count_tokens_params_request.get( - "vertex_credentials" - ) or count_tokens_params_request.get("vertex_ai_credentials") + vertex_credentials: Final = partner_litellm_params.get("vertex_credentials") or partner_litellm_params.get( + "vertex_ai_credentials" + ) result = await partner_models_handler.count_tokens( model=model_to_use, messages=messages or [], - litellm_params=count_tokens_params_request, + litellm_params=partner_litellm_params, vertex_project=vertex_project, vertex_location=vertex_location, vertex_credentials=vertex_credentials, diff --git a/litellm/llms/vertex_ai/files/transformation.py b/litellm/llms/vertex_ai/files/transformation.py index b7f91bfba0d..b6ad9fbcc04 100644 --- a/litellm/llms/vertex_ai/files/transformation.py +++ b/litellm/llms/vertex_ai/files/transformation.py @@ -12,7 +12,7 @@ from urllib.parse import quote, unquote import httpx from httpx import Headers, Response from openai.types.file_deleted import FileDeleted -from typing_extensions import ReadOnly +from typing_extensions import ReadOnly, Required import litellm from litellm._uuid import uuid @@ -104,6 +104,27 @@ class _VertexBatchRow(TypedDict, total=False): processed_time: ReadOnly[str] +class _VertexEmbeddingVector(TypedDict): + values: ReadOnly[list[float]] + + +class _VertexEmbeddingUsageMetadata(TypedDict, total=False): + promptTokenCount: ReadOnly[int] + + +class _VertexEmbeddingResponse(TypedDict, total=False): + embedding: ReadOnly[Required[_VertexEmbeddingVector]] + usageMetadata: ReadOnly[_VertexEmbeddingUsageMetadata] + tokenCount: ReadOnly[int] + + +class _VertexEmbeddingBatchRow(TypedDict, total=False): + key: ReadOnly[str] + request: ReadOnly[Mapping[str, object]] + status: ReadOnly[Required[str]] + response: ReadOnly[Required[_VertexEmbeddingResponse]] + + class _OpenAIBatchOutputError(TypedDict): code: ReadOnly[str] message: ReadOnly[str] @@ -111,7 +132,7 @@ class _OpenAIBatchOutputError(TypedDict): class _OpenAIBatchOutputResponse(TypedDict): status_code: ReadOnly[int] - request_id: ReadOnly[str] + request_id: ReadOnly[object] body: ReadOnly[Mapping[str, object]] @@ -218,7 +239,7 @@ def _get_litellm_batch_custom_id_from_labels(labels: Mapping[str, object] | None return str(labels.get("litellm_custom_id", "unknown")) -def _is_vertex_embeddings_batch_output_row(vertex_output_row: Mapping[str, Any]) -> bool: +def _is_vertex_embeddings_batch_output_row(vertex_output_row: Mapping[str, object]) -> bool: """ Whether a Vertex batch output row came from an `EmbedContentRequest`. @@ -237,7 +258,7 @@ def _is_vertex_embeddings_batch_output_row(vertex_output_row: Mapping[str, Any]) def _openai_batch_output_row( custom_id: str, - body: Mapping[str, Any] | None = None, + body: Mapping[str, object] | None = None, error_code: str | None = None, error_message: str = "", ) -> _OpenAIBatchOutputRow: @@ -259,7 +280,7 @@ def _openai_batch_output_row( } -def _split_vertex_batch_key(vertex_output_row: Mapping[str, Any]) -> tuple[str, int, int]: +def _split_vertex_batch_key(vertex_output_row: Mapping[str, object]) -> tuple[str, int, int]: """ Resolve `(custom_id, index within that custom_id, group size)` for a Vertex batch output row. @@ -278,7 +299,7 @@ def _split_vertex_batch_key(vertex_output_row: Mapping[str, Any]) -> tuple[str, return unquote(match["custom_id"]), int(match["index"]), int(match["total"]) -def _embedding_prompt_token_count(vertex_response: Mapping[str, Any]) -> int: +def _embedding_prompt_token_count(vertex_response: _VertexEmbeddingResponse) -> int: """ Prompt tokens billed for one Vertex Gemini Embedding batch row. @@ -293,7 +314,7 @@ def _embedding_prompt_token_count(vertex_response: Mapping[str, Any]) -> int: def _vertex_embeddings_rows_to_openai_batch_output_row( custom_id: str, - vertex_output_rows: tuple[Mapping[str, Any], ...], + vertex_output_rows: tuple[_VertexEmbeddingBatchRow, ...], element_indices: tuple[int, ...], element_count: int, model: str | None, @@ -348,7 +369,7 @@ def _vertex_embeddings_rows_to_openai_batch_output_row( def _transform_vertex_embeddings_batch_output_to_openai( - vertex_output_rows: Iterable[Mapping[str, Any]], + vertex_output_rows: Iterable[_VertexEmbeddingBatchRow], model: str | None, ) -> tuple[_OpenAIBatchOutputRow, ...]: """ @@ -388,7 +409,7 @@ def _model_from_managed_gcs_url(url: str) -> str | None: return match.group(1) if match else None -def _is_embeddings_batch_entry(openai_entry: Mapping[str, Any]) -> bool: +def _is_embeddings_batch_entry(openai_entry: Mapping[str, object]) -> bool: """ Whether an OpenAI batch JSONL line targets the embeddings endpoint. @@ -431,7 +452,7 @@ def _vertex_batch_embeddings_key(custom_id: str, index: int, total: int) -> str: return encoded_custom_id if total < 2 else f"{encoded_custom_id}#{index}/{total}" -def _vertex_embeddings_row(key: str | None, embed_content_request: Mapping[str, Any]) -> Mapping[str, Any]: +def _vertex_embeddings_row(key: str | None, embed_content_request: Mapping[str, object]) -> Mapping[str, object]: """ One Vertex Gemini Embedding batch input row. @@ -453,8 +474,8 @@ def _vertex_embeddings_row(key: str | None, embed_content_request: Mapping[str, def _openai_batch_jsonl_entry_to_vertex_embeddings_rows( - openai_entry: Mapping[str, Any], -) -> tuple[Mapping[str, Any], ...]: + openai_entry: Mapping[str, object], +) -> tuple[Mapping[str, object], ...]: """ Transforms a single OpenAI `/v1/embeddings` batch entry into Vertex Gemini Embedding batch rows, one per requested embedding. @@ -512,7 +533,7 @@ def _openai_batch_jsonl_entry_to_vertex_embeddings_rows( def _openai_batch_jsonl_entry_to_vertex_rows( openai_entry: dict[str, Any], map_openai_to_vertex_params: Callable[[dict[str, Any]], dict[str, Any]], -) -> tuple[Mapping[str, Any], ...]: +) -> tuple[Mapping[str, object], ...]: """ Transforms a single OpenAI JSONL batch entry into the Vertex rows it maps to. @@ -533,7 +554,7 @@ def _openai_batch_jsonl_entry_to_vertex_rows( cached_content=None, ) - custom_id: Final = openai_entry.get("custom_id") + custom_id: Final[object] = openai_entry.get("custom_id") if custom_id is not None: if "labels" not in vertex_request_body: vertex_request_body["labels"] = {} diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py index 11c026010ee..e2d62be6a69 100644 --- a/litellm/llms/vertex_ai/gemini/transformation.py +++ b/litellm/llms/vertex_ai/gemini/transformation.py @@ -250,7 +250,7 @@ def _gs_uri_requires_content_type_metadata(url: str) -> bool: def _image_url_payload_may_need_sync_gcs_metadata_fetch( - raw_image_url: Any, + raw_image_url: object, ) -> bool: """ True when this image_url value (content-part image_url or assistant ``images[]`` @@ -326,7 +326,7 @@ def _openai_messages_may_need_sync_gcs_metadata_fetch( def _get_gcs_object_content_type( image_url: str, vertex_project: str | None = None, - vertex_credentials: Any | None = None, + vertex_credentials: object = None, ) -> str | None: """ Resolve content type from GCS object metadata. @@ -479,7 +479,7 @@ def _process_gemini_media( model: str | None = None, video_metadata: dict[str, Any] | None = None, vertex_project: str | None = None, - vertex_credentials: Any | None = None, + vertex_credentials: object = None, ) -> PartType: """ Given a media URL (image, audio, or video), return the appropriate PartType for Gemini @@ -1002,7 +1002,7 @@ def _gemini_convert_messages_with_history( if isinstance(_ss_invocations, list): for invocation in _ss_invocations: # Re-inject toolCall part - tc_part: dict[str, Any] = { + tc_part: dict[str, object] = { "toolCall": { "toolType": invocation.get("tool_type"), "id": invocation.get("id"), @@ -1015,13 +1015,13 @@ def _gemini_convert_messages_with_history( # Re-inject toolResponse part if response is present if "response" in invocation: - tr_dict: dict[str, Any] = { + tr_dict: dict[str, object] = { "id": invocation.get("id"), "response": invocation.get("response"), } if invocation.get("tool_type"): tr_dict["toolType"] = invocation["tool_type"] - tr_part: dict[str, Any] = {"toolResponse": tr_dict} + tr_part: dict[str, object] = {"toolResponse": tr_dict} if "response_thought_signature" in invocation: tr_part["thoughtSignature"] = invocation["response_thought_signature"] assistant_content.append(tr_part) @@ -1090,7 +1090,7 @@ def _pop_and_merge_extra_body(data: RequestBody, optional_params: dict) -> None: data_dict[k] = v -def _has_google_maps_tool(tools: Any | None) -> bool: +def _has_google_maps_tool(tools: object) -> bool: """Return True if any tool object in the list has a 'googleMaps' key.""" if not isinstance(tools, list): return False @@ -1127,7 +1127,7 @@ def _rewrite_mime_type_to_response_format(generation_config: GenerationConfig) - schema = generation_config.pop("response_schema", None) generation_config.pop("response_mime_type", None) - response_format: Final[dict[str, Any]] = {"text": {"mimeType": "APPLICATION_JSON"}} + response_format: Final[dict[str, dict[str, object]]] = {"text": {"mimeType": "APPLICATION_JSON"}} if schema is not None: response_format["text"]["schema"] = schema generation_config["responseFormat"] = response_format @@ -1316,7 +1316,7 @@ async def async_transform_request_body( timeout: float | httpx.Timeout | None, extra_headers: dict | None, optional_params: dict, - logging_obj: litellm.litellm_core_utils.litellm_logging.Logging, + logging_obj: LiteLLMLoggingObj, custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"], litellm_params: dict, vertex_project: str | None, diff --git a/litellm/llms/vertex_ai/image_edit/vertex_gemini_transformation.py b/litellm/llms/vertex_ai/image_edit/vertex_gemini_transformation.py index 5889a8eba06..725a7f39917 100644 --- a/litellm/llms/vertex_ai/image_edit/vertex_gemini_transformation.py +++ b/litellm/llms/vertex_ai/image_edit/vertex_gemini_transformation.py @@ -182,7 +182,6 @@ class VertexAIGeminiImageEditConfig(BaseImageEditConfig, VertexLLM): else None ) - # Generation config with proper structure for image editing generation_config: Final[dict[str, object]] = { key: value for key, value in (("response_modalities", ["IMAGE"]), ("image_config", image_config)) if value } diff --git a/litellm/llms/vertex_ai/text_to_speech/transformation.py b/litellm/llms/vertex_ai/text_to_speech/transformation.py index cf14ab88751..332f892ae6b 100644 --- a/litellm/llms/vertex_ai/text_to_speech/transformation.py +++ b/litellm/llms/vertex_ai/text_to_speech/transformation.py @@ -7,10 +7,14 @@ Reference: https://cloud.google.com/text-to-speech/docs/reference/rest/v1/text/s import base64 from collections.abc import Coroutine +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final, Union import httpx +from litellm.litellm_core_utils.audio_utils.utils import ( + speech_media_type_from_audio_bytes, +) from litellm.llms.base_llm.text_to_speech.transformation import ( BaseTextToSpeechConfig, TextToSpeechRequestData, @@ -457,12 +461,11 @@ class VertexAITextToSpeechConfig(BaseTextToSpeechConfig, VertexBase): if not response_content: raise ValueError("No audioContent in Vertex AI TTS response") - # Decode base64 to get binary content binary_data: Final = base64.b64decode(response_content) - - # Create an httpx.Response object with the binary data + media_type: Final = speech_media_type_from_audio_bytes(binary_data) response: Final = httpx.Response( status_code=200, + headers=None if media_type is None else MappingProxyType({"content-type": media_type}), content=binary_data, ) diff --git a/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py b/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py index c80a02c3683..5c250fc1a7e 100644 --- a/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py +++ b/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py @@ -97,7 +97,7 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase): def __init__(self): super().__init__() - def get_auth_credentials(self, litellm_params: dict) -> BaseVectorStoreAuthCredentials: + def get_auth_credentials(self, litellm_params: Mapping[str, object]) -> BaseVectorStoreAuthCredentials: # Get credentials and project info vertex_credentials: Final = self.get_vertex_ai_credentials(dict(litellm_params)) vertex_project: Final = self.get_vertex_ai_project(dict(litellm_params)) @@ -122,7 +122,9 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase): "write": [("POST", "/ragCorpora")], } - def validate_environment(self, headers: dict, litellm_params: GenericLiteLLMParams | None) -> dict: + def validate_environment( + self, headers: dict[str, str], litellm_params: GenericLiteLLMParams | None + ) -> dict[str, str]: """ Validate and set up authentication for Vertex AI RAG API """ @@ -135,7 +137,7 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase): def get_complete_url( self, api_base: str | None, - litellm_params: dict, + litellm_params: dict[str, object], ) -> str: """ Get the Base endpoint for Vertex AI RAG API @@ -201,7 +203,6 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase): if value is not None } - # Build the request body for Vertex AI RAG API query_body: Final[Mapping[str, object]] = { key: value for key, value in (("text", query), ("rag_retrieval_config", rag_retrieval_config or None)) @@ -292,7 +293,6 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase): # Add metadata if provided metadata: Final = vector_store_create_optional_params.get("metadata") - # Build the request body for Vertex AI RAG Corpus creation request_body: Final[dict[str, object]] = { key: value for key, value in ( diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py index d7ad69593c6..ef03e61a858 100644 --- a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py @@ -153,14 +153,17 @@ class VertexAIAnthropicConfig(AnthropicConfig): drop_params: bool, ) -> dict: """ - Override parent method to ensure VertexAI always uses tool-based structured outputs. - VertexAI doesn't support the output_format parameter, so we force all models - to use the tool-based approach for structured outputs. + Override parent method so VertexAI uses tool-based structured outputs + unless the vertex map entry advertises native structured output + (``output_format``, which Vertex AI Claude forwards for those models). """ - # Temporarily override model name to force tool-based approach - # This ensures Claude Sonnet 4.5 uses tools instead of output_format + from litellm.llms.anthropic.common_utils import AnthropicModelInfo + original_model: Final = model - if "response_format" in non_default_params: + native_structured_output: Final = AnthropicModelInfo._get_provider_resolved_capability( + model, "supports_native_structured_output", "vertex_ai" + ) + if "response_format" in non_default_params and native_structured_output is not True: model = "claude-3-sonnet-20240229" # Use a model that will use tool-based approach # Call parent method with potentially modified model name diff --git a/litellm/llms/vertex_ai/vertex_llm_base.py b/litellm/llms/vertex_ai/vertex_llm_base.py index 75098515deb..1942bc850f1 100644 --- a/litellm/llms/vertex_ai/vertex_llm_base.py +++ b/litellm/llms/vertex_ai/vertex_llm_base.py @@ -9,7 +9,7 @@ import json import os import threading from collections.abc import Mapping -from typing import TYPE_CHECKING, Any, Final, Literal +from typing import TYPE_CHECKING, Any, Final, Literal, Protocol from urllib.parse import urlparse import litellm @@ -27,6 +27,15 @@ from .common_utils import ( get_vertex_base_url, ) + +def _graft_default_vertex_path(api_base: str, default_url: str) -> str: + parsed_api_base: Final = urlparse(api_base) + default_segments: Final = urlparse(default_url).path.lstrip("/").split("/") + graft_segments: Final = default_segments[1:] if default_segments[0] in ("v1", "v1beta1") else default_segments + grafted_path: Final = parsed_api_base.path.rstrip("/") + "/" + "/".join(graft_segments) + return parsed_api_base._replace(path=grafted_path).geturl() + + GOOGLE_IMPORT_ERROR_MESSAGE: Final = ( "Google Cloud SDK not found. Install it with: pip install 'litellm[google]' or pip install google-cloud-aiplatform" ) @@ -38,6 +47,21 @@ else: GoogleCredentialsObject = Any +class _VertexCredentialsObject(Protocol): + """Structural view of the google-auth credentials handle that this class caches and refreshes.""" + + @property + def token(self) -> object: ... + + @property + def quota_project_id(self) -> str | None: ... + + @property + def expired(self) -> object: ... + + def refresh(self, request: object) -> None: ... + + class VertexBase: def __init__(self) -> None: super().__init__() @@ -46,7 +70,7 @@ class VertexBase: self._credentials: GoogleCredentialsObject | None = None self._credentials_project_mapping: dict[ tuple[VERTEX_CREDENTIALS_TYPES | None, str | None], - tuple[GoogleCredentialsObject, str | None], + tuple[_VertexCredentialsObject, str | None], ] = {} self.project_id: str | None = None self.async_handler: AsyncHTTPHandler | None = None @@ -100,7 +124,7 @@ class VertexBase: self, credentials: VERTEX_CREDENTIALS_TYPES | None, project_id: str | None, - ) -> tuple[Any, str]: + ) -> tuple[_VertexCredentialsObject | None, str]: if credentials is not None: if isinstance(credentials, str): _is_path: Final = os.path.exists( @@ -200,7 +224,7 @@ class VertexBase: return creds, project_id # Google Auth Helpers -- extracted for mocking purposes in tests - def _credentials_from_identity_pool(self, json_obj, scopes): + def _credentials_from_identity_pool(self, json_obj, scopes) -> _VertexCredentialsObject: try: from google.auth import identity_pool except ImportError: @@ -211,7 +235,7 @@ class VertexBase: creds = creds.with_scopes(scopes) return creds - def _credentials_from_pluggable(self, json_obj, scopes): + def _credentials_from_pluggable(self, json_obj, scopes) -> _VertexCredentialsObject: try: from google.auth import pluggable except ImportError: @@ -222,7 +246,7 @@ class VertexBase: creds = creds.with_scopes(scopes) return creds - def _credentials_from_identity_pool_with_aws(self, json_obj, scopes): + def _credentials_from_identity_pool_with_aws(self, json_obj, scopes) -> _VertexCredentialsObject: try: from google.auth import aws except ImportError: @@ -233,7 +257,7 @@ class VertexBase: creds = creds.with_scopes(scopes) return creds - def _credentials_from_authorized_user(self, json_obj, scopes): + def _credentials_from_authorized_user(self, json_obj, scopes) -> _VertexCredentialsObject: try: import google.oauth2.credentials except ImportError: @@ -241,7 +265,7 @@ class VertexBase: return google.oauth2.credentials.Credentials.from_authorized_user_info(json_obj, scopes=scopes) - def _credentials_from_service_account(self, json_obj, scopes): + def _credentials_from_service_account(self, json_obj, scopes) -> _VertexCredentialsObject: try: import google.oauth2.service_account except ImportError: @@ -249,7 +273,7 @@ class VertexBase: return google.oauth2.service_account.Credentials.from_service_account_info(json_obj, scopes=scopes) - def _credentials_from_default_auth(self, scopes): + def _credentials_from_default_auth(self, scopes) -> tuple[_VertexCredentialsObject, str | None]: try: import google.auth as google_auth except ImportError: @@ -341,7 +365,7 @@ class VertexBase: ) return api_base - def refresh_auth(self, credentials: Any) -> None: + def refresh_auth(self, credentials: _VertexCredentialsObject) -> None: try: from google.auth.transport.requests import ( Request, @@ -417,7 +441,7 @@ class VertexBase: self, credential_cache_key: tuple, project_id: str | None, - ) -> tuple[str, str, "TokenState", Any, str | None] | None: + ) -> tuple[str, str, "TokenState", _VertexCredentialsObject, str | None] | None: """ Look up cached credentials and return usable token info for FRESH or STALE tokens (both are still valid for outbound requests). STALE @@ -440,7 +464,9 @@ class VertexBase: return None return creds.token, resolved_project, token_state, creds, cached_project_id - def _unpack_cached_credentials(self, credential_cache_key: tuple) -> tuple[Any, str | None]: + def _unpack_cached_credentials( + self, credential_cache_key: tuple + ) -> tuple[_VertexCredentialsObject | None, str | None]: """ Return (credentials, project_id) from the cache, or (None, None) if not cached. Handles both tuple and legacy cache formats. @@ -452,7 +478,7 @@ class VertexBase: return cached_entry return cached_entry, cached_entry.quota_project_id or getattr(cached_entry, "project_id", None) - def _get_token_state(self, credentials: Any) -> "TokenState": + def _get_token_state(self, credentials: _VertexCredentialsObject) -> "TokenState": """ Return the token state using google-auth's TokenState enum. @@ -476,7 +502,7 @@ class VertexBase: credentials: VERTEX_CREDENTIALS_TYPES | None, project_id: str | None, credential_cache_key: tuple, - ) -> tuple[Any, str | None]: + ) -> tuple[_VertexCredentialsObject, str | None]: """Load credentials via load_auth (in thread) and cache the result.""" try: _credentials, credential_project_id = await asyncify(self.load_auth)( @@ -496,7 +522,7 @@ class VertexBase: async def _background_refresh_credentials( self, - credentials: Any, + credentials: _VertexCredentialsObject, credential_cache_key: tuple, credential_project_id: str | None, ) -> None: @@ -548,7 +574,7 @@ class VertexBase: def _schedule_background_refresh( self, - credentials: Any, + credentials: _VertexCredentialsObject, credential_cache_key: tuple, credential_project_id: str | None, ) -> None: @@ -566,7 +592,7 @@ class VertexBase: self._background_refresh_credentials(credentials, credential_cache_key, credential_project_id) ) - def _drop_background_refresh_task(_fut: asyncio.Future[Any]) -> None: + def _drop_background_refresh_task(_fut: asyncio.Future[None]) -> None: if self._background_refresh_tasks.get(credential_cache_key) is _fut: self._background_refresh_tasks.pop(credential_cache_key, None) @@ -621,8 +647,9 @@ class VertexBase: Handles custom api_base for: 1. Gemini (Google AI Studio) - constructs /models/{model}:{endpoint} - 2. Vertex AI with standard proxies - constructs {api_base}:{endpoint}; - if api_base has no path (bare host), grafts the default vertex URL path onto it + 2. Vertex AI with standard proxies - grafts the default vertex URL path onto the + api_base when its path is empty or only an API version (/v1, /v1beta1); + otherwise constructs {api_base}:{endpoint} 3. Vertex AI with PSC endpoints - constructs full path structure {api_base}/v1/projects/{project}/locations/{location}/endpoints/{model}:{endpoint} (only when use_psc_endpoint_format=True) @@ -669,10 +696,14 @@ class VertexBase: ) elif urlparse(api_base).path in ("", "/"): url = api_base.rstrip("/") + urlparse(url).path + elif urlparse(api_base).path.rstrip("/") in ("/v1", "/v1beta1") and "/projects/" in urlparse(url).path: + url = _graft_default_vertex_path(api_base=api_base, default_url=url) else: url = f"{api_base}:{endpoint}" if stream is True: - url = url + "?alt=sse" + parsed_stream_url: Final = urlparse(url) + stream_query: Final = f"{parsed_stream_url.query}&alt=sse" if parsed_stream_url.query else "alt=sse" + url = parsed_stream_url._replace(query=stream_query).geturl() return auth_header, url def _get_token_and_url( @@ -874,7 +905,7 @@ class VertexBase: # Convert dict credentials to string for caching cache_credentials: Final = json.dumps(credentials) if isinstance(credentials, dict) else credentials credential_cache_key: Final = (cache_credentials, project_id) - _credentials: GoogleCredentialsObject | None = None + _credentials: _VertexCredentialsObject | None = None verbose_logger.debug("Checking cached credentials for project_id: %s", project_id) diff --git a/litellm/llms/vertex_ai/videos/transformation.py b/litellm/llms/vertex_ai/videos/transformation.py index 25db3a673a1..e6e3c2739c1 100644 --- a/litellm/llms/vertex_ai/videos/transformation.py +++ b/litellm/llms/vertex_ai/videos/transformation.py @@ -8,12 +8,14 @@ Based on: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/model-refer import base64 import time from collections.abc import Mapping, Sequence -from typing import TYPE_CHECKING, Any, Final, TypedDict, cast +from types import MappingProxyType +from typing import TYPE_CHECKING, Any, ClassVar, Final, TypedDict, cast import httpx from httpx._types import FileContent, RequestFiles from typing_extensions import ReadOnly +import litellm from litellm.constants import DEFAULT_GOOGLE_VIDEO_DURATION_SECONDS from litellm.images.utils import ImageEditRequestUtils from litellm.llms.base_llm.videos.transformation import BaseVideoConfig @@ -119,6 +121,23 @@ class VertexAIVideoConfig(BaseVideoConfig, VertexBase): 3. Extract video data (base64) from response """ + _OPENAI_VIDEO_SIZE_TO_ASPECT_RATIO: ClassVar[Mapping[str, str]] = MappingProxyType( + { + "1280x720": "16:9", + "1920x1080": "16:9", + "720x1280": "9:16", + "1080x1920": "9:16", + } + ) + _OPENAI_VIDEO_SIZE_TO_RESOLUTION: ClassVar[Mapping[str, str]] = MappingProxyType( + { + "1280x720": "720p", + "1920x1080": "1080p", + "720x1280": "720p", + "1080x1920": "1080p", + } + ) + def __init__(self): BaseVideoConfig.__init__(self) VertexBase.__init__(self) @@ -161,6 +180,9 @@ class VertexAIVideoConfig(BaseVideoConfig, VertexBase): - prompt → prompt (in instances) - input_reference → image (in instances) - size → aspectRatio (e.g., "1280x720" → "16:9") + - size → resolution for models with resolution-tier pricing when inferable + ("1280x720"/"720x1280" → "720p", "1920x1080"/"1080x1920" → "1080p"); + skipped if ``resolution`` is already set - seconds → durationSeconds (defaults to 4 seconds if not provided) """ mapped_params: Final[dict[str, object]] = {} @@ -175,6 +197,9 @@ class VertexAIVideoConfig(BaseVideoConfig, VertexBase): if "parameters" in video_create_optional_params: mapped_params["parameters"] = video_create_optional_params["parameters"] + if "resolution" in video_create_optional_params: + mapped_params["resolution"] = video_create_optional_params["resolution"] + # Map size to aspectRatio if "size" in video_create_optional_params: size: Final = video_create_optional_params["size"] @@ -182,6 +207,15 @@ class VertexAIVideoConfig(BaseVideoConfig, VertexBase): aspect_ratio: Final = self._convert_size_to_aspect_ratio(size) if aspect_ratio: mapped_params["aspectRatio"] = aspect_ratio + nested_params: Final = video_create_optional_params.get("parameters") + has_resolution = "resolution" in mapped_params or ( + isinstance(nested_params, dict) and nested_params.get("resolution") is not None + ) + supports_resolution = self._supports_resolution_inference(model) + if supports_resolution and not has_resolution: + inferred_resolution = self._convert_size_to_resolution(size) + if inferred_resolution is not None: + mapped_params["resolution"] = inferred_resolution # Map seconds to durationSeconds, default to 4 seconds (matching OpenAI) if "seconds" in video_create_optional_params: @@ -205,14 +239,16 @@ class VertexAIVideoConfig(BaseVideoConfig, VertexBase): if not size: return None - aspect_ratio_map: Final = { - "1280x720": "16:9", - "1920x1080": "16:9", - "720x1280": "9:16", - "1080x1920": "9:16", - } + return self._OPENAI_VIDEO_SIZE_TO_ASPECT_RATIO.get(size, "16:9") - return aspect_ratio_map.get(size, "16:9") + def _convert_size_to_resolution(self, size: str) -> str | None: + return self._OPENAI_VIDEO_SIZE_TO_RESOLUTION.get(size) + + @staticmethod + def _supports_resolution_inference(model: str) -> bool: + model_key: Final = model if model.startswith("vertex_ai/") else f"vertex_ai/{model}" + model_info: Final = litellm.model_cost.get(model_key) + return model_info is not None and model_info.get("output_cost_per_second_1080p") is not None def validate_environment( self, diff --git a/litellm/main.py b/litellm/main.py index cafa1e4718f..c4c5bbefc4f 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -5507,6 +5507,9 @@ def completion( tpm=kwargs.get("tpm"), rpm=kwargs.get("rpm"), use_xai_oauth=kwargs.get("use_xai_oauth", False), + gigachat_scope=kwargs.get("gigachat_scope"), + gigachat_auth_url=kwargs.get("gigachat_auth_url"), + gigachat_access_token=kwargs.get("gigachat_access_token"), **{key: kwargs[key] for key in FORWARDED_KWARGS_KEYS if key in kwargs}, ) cast(LiteLLMLoggingObj, logging).update_environment_variables( @@ -6289,18 +6292,15 @@ def embedding( if headers is not None and headers != {}: optional_params["extra_headers"] = headers - if encoding_format is not None: - optional_params["encoding_format"] = encoding_format + requested_encoding_format: Final = ( + encoding_format + or optional_params.get("encoding_format") + or get_secret_str("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT") + ) + if requested_encoding_format is None or requested_encoding_format.strip().lower() == "none": + optional_params.pop("encoding_format", None) else: - env_fmt: Final = get_secret_str("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT") - if env_fmt is not None and env_fmt.strip().lower() == "none": - optional_params.pop("encoding_format", None) - else: - _default_fmt: Final = optional_params.get("encoding_format") or env_fmt or "float" - if _default_fmt.strip().lower() == "none": - optional_params.pop("encoding_format", None) - else: - optional_params["encoding_format"] = _default_fmt + optional_params["encoding_format"] = requested_encoding_format api_version = None @@ -6949,12 +6949,18 @@ def embedding( aembedding=aembedding, headers=headers, ) - elif custom_llm_provider == "dashscope": - dashscope_key: Final = api_key or litellm.api_key or get_secret_str("DASHSCOPE_API_KEY") + elif custom_llm_provider in ("dashscope", "qwencloud", "qwen_ai_platform"): + from litellm.llms.dashscope.common_utils import ( + missing_dashscope_family_key_message, + resolve_dashscope_family_api_key, + ) + + dashscope_key: Final = resolve_dashscope_family_api_key( + custom_llm_provider=custom_llm_provider, + api_key=api_key or litellm.api_key, + ) if dashscope_key is None: - raise ValueError( - "Missing API key for DashScope. Set DASHSCOPE_API_KEY environment variable or pass api_key parameter." - ) + raise ValueError(missing_dashscope_family_key_message(custom_llm_provider)) if extra_headers is not None and isinstance(extra_headers, dict): headers = extra_headers else: @@ -8612,9 +8618,9 @@ def _joined_streamed_citations(streamed_citations: "tuple[object, ...]") -> "lis def _stream_builder_model_map_cost(response: ModelResponse) -> float | None: - model_name: Final = getattr(response, "model", None) + model_name: Final = response.model usage: Final = getattr(response, "usage", None) - if not isinstance(model_name, str) or not model_name or not isinstance(usage, Usage): + if not model_name or not isinstance(usage, Usage): return None try: prompt_cost, completion_tokens_cost = litellm.cost_per_token(model=model_name, usage_object=usage) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index bebbcc32181..a3cfb300ea6 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -553,6 +553,27 @@ "supports_response_schema": true, "supports_vision": true }, + "amazon.nova-sonic-v1:0": { + "deprecation_date": "2026-09-14", + "input_cost_per_audio_token": 3.4e-06, + "input_cost_per_token": 6e-08, + "litellm_provider": "bedrock", + "mode": "realtime", + "output_cost_per_audio_token": 1.36e-05, + "output_cost_per_token": 2.4e-07, + "supports_audio_input": true, + "supports_audio_output": true + }, + "amazon.nova-2-sonic-v1:0": { + "input_cost_per_audio_token": 3e-06, + "input_cost_per_token": 3.3e-07, + "litellm_provider": "bedrock", + "mode": "realtime", + "output_cost_per_audio_token": 1.2e-05, + "output_cost_per_token": 2.75e-06, + "supports_audio_input": true, + "supports_audio_output": true + }, "amazon.rerank-v1:0": { "input_cost_per_query": 0.001, "input_cost_per_token": 0.0, @@ -1430,6 +1451,44 @@ "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 512 }, + "anthropic.claude-fable-5-1": { + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 2.5e-07, + "input_cost_per_token": 1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_mid_conversation_system": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_native_structured_output": false, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "bedrock_output_config_effort_ceiling": "xhigh", + "supports_parallel_tool_use_config": true, + "prompt_cache_min_tokens": 512 + }, "global.anthropic.claude-fable-5": { "cache_creation_input_token_cost": 1.25e-05, "cache_creation_input_token_cost_above_1hr": 2e-05, @@ -1467,6 +1526,44 @@ "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 512 }, + "global.anthropic.claude-fable-5-1": { + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 2.5e-07, + "input_cost_per_token": 1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_mid_conversation_system": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_native_structured_output": false, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "bedrock_output_config_effort_ceiling": "xhigh", + "supports_parallel_tool_use_config": true, + "prompt_cache_min_tokens": 512 + }, "us.anthropic.claude-fable-5": { "cache_creation_input_token_cost": 1.375e-05, "cache_creation_input_token_cost_above_1hr": 2.2e-05, @@ -1504,6 +1601,44 @@ "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 512 }, + "us.anthropic.claude-fable-5-1": { + "cache_creation_input_token_cost": 1.375e-05, + "cache_creation_input_token_cost_above_1hr": 2.2e-05, + "cache_read_input_token_cost": 2.75e-07, + "input_cost_per_token": 1.1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5.5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_mid_conversation_system": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_native_structured_output": false, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "bedrock_output_config_effort_ceiling": "xhigh", + "supports_parallel_tool_use_config": true, + "prompt_cache_min_tokens": 512 + }, "eu.anthropic.claude-fable-5": { "cache_creation_input_token_cost": 1.375e-05, "cache_creation_input_token_cost_above_1hr": 2.2e-05, @@ -1541,6 +1676,44 @@ "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 512 }, + "eu.anthropic.claude-fable-5-1": { + "cache_creation_input_token_cost": 1.375e-05, + "cache_creation_input_token_cost_above_1hr": 2.2e-05, + "cache_read_input_token_cost": 2.75e-07, + "input_cost_per_token": 1.1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5.5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_mid_conversation_system": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_native_structured_output": false, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "bedrock_output_config_effort_ceiling": "xhigh", + "supports_parallel_tool_use_config": true, + "prompt_cache_min_tokens": 512 + }, "anthropic.claude-opus-5": { "bedrock_converse_supports_strict_tools": false, "supports_adaptive_thinking": true, @@ -1571,7 +1744,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1607,7 +1780,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1643,7 +1816,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1679,7 +1852,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1715,7 +1888,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1751,7 +1924,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -2044,7 +2217,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2081,7 +2254,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2118,7 +2291,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2155,7 +2328,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2192,7 +2365,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2229,7 +2402,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -3025,6 +3198,7 @@ "prompt_cache_min_tokens": 2048 }, "azure_ai/claude-fable-5": { + "deprecation_date": "2027-12-05", "supports_mid_conversation_system": true, "input_cost_per_token": 1e-05, "output_cost_per_token": 5e-05, @@ -3057,7 +3231,43 @@ "supports_max_reasoning_effort": true, "prompt_cache_min_tokens": 512 }, + "azure_ai/claude-fable-5-1": { + "supports_mid_conversation_system": true, + "input_cost_per_token": 1e-05, + "output_cost_per_token": 5e-05, + "litellm_provider": "azure_ai", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 2.5e-07, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_native_structured_output": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_max_reasoning_effort": true, + "prompt_cache_min_tokens": 512 + }, "azure_ai/claude-opus-5": { + "deprecation_date": "2027-07-08", "supports_mid_conversation_system": true, "supports_adaptive_thinking": true, "input_cost_per_token": 5e-06, @@ -3090,6 +3300,7 @@ "prompt_cache_min_tokens": 512 }, "azure_ai/claude-opus-4-8": { + "deprecation_date": "2027-09-01", "supports_mid_conversation_system": true, "supports_adaptive_thinking": true, "input_cost_per_token": 5e-06, @@ -3168,6 +3379,7 @@ "prompt_cache_min_tokens": 1024 }, "azure_ai/claude-sonnet-5": { + "deprecation_date": "2027-06-30", "supports_mid_conversation_system": true, "cache_creation_input_token_cost": 2.5e-06, "cache_creation_input_token_cost_above_1hr": 4e-06, @@ -3726,7 +3938,7 @@ "output_cost_per_token": 0, "litellm_provider": "azure_ai", "mode": "chat", - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-services/", + "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/aoai/", "comment": "Flat cost of $0.14 per M input tokens for Azure AI Foundry Model Router infrastructure. Use pattern: azure_ai/model_router/ where deployment-name is your Azure deployment (e.g., azure-model-router)" }, "azure/eu/gpt-4o-2024-08-06": { @@ -5328,7 +5540,7 @@ "input_cost_per_second": 0.0002833333333333333, "litellm_provider": "azure", "mode": "audio_transcription", - "source": "https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/gpt-realtime-whisper", + "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/gpt-realtime-whisper", "supported_endpoints": [ "/v1/realtime", "/v1/realtime/transcription_sessions" @@ -9107,7 +9319,7 @@ "mode": "embedding", "output_cost_per_token": 0.0, "output_vector_size": 1024, - "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/cohere.cohere-embed-v3-english-offer?tab=PlansAndPrice", + "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/cohere.cohere-embed-v3-english-offer?tab=PlansAndPrice", "supports_embedding_image_input": true }, "azure_ai/Cohere-embed-v3-multilingual": { @@ -9118,7 +9330,7 @@ "mode": "embedding", "output_cost_per_token": 0.0, "output_vector_size": 1024, - "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/cohere.cohere-embed-v3-english-offer?tab=PlansAndPrice", + "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/cohere.cohere-embed-v3-english-offer?tab=PlansAndPrice", "supports_embedding_image_input": true }, "azure_ai/FLUX-1.1-pro": { @@ -9134,7 +9346,7 @@ "litellm_provider": "azure_ai", "mode": "image_generation", "output_cost_per_image": 0.04, - "source": "https://azuremarketplace.microsoft.com/pt-br/marketplace/apps/cohere.cohere-embed-4-offer?tab=PlansAndPrice", + "source": "https://marketplace.microsoft.com/pt-br/marketplace/apps/cohere.cohere-embed-4-offer?tab=PlansAndPrice", "supported_endpoints": [ "/v1/images/generations" ] @@ -9467,7 +9679,7 @@ "max_tokens": 2048, "mode": "chat", "output_cost_per_token": 3.7e-07, - "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/metagenai.meta-llama-3-2-11b-vision-instruct-offer?tab=Overview", + "source": "https://marketplace.microsoft.com/en/marketplace/apps/metagenai.meta-llama-3-2-11b-vision-instruct-offer?tab=Overview", "supports_function_calling": true, "supports_tool_choice": true, "supports_vision": true @@ -9481,7 +9693,7 @@ "max_tokens": 2048, "mode": "chat", "output_cost_per_token": 2.04e-06, - "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/metagenai.meta-llama-3-2-90b-vision-instruct-offer?tab=Overview", + "source": "https://marketplace.microsoft.com/en/marketplace/apps/metagenai.meta-llama-3-2-90b-vision-instruct-offer?tab=Overview", "supports_function_calling": true, "supports_tool_choice": true, "supports_vision": true @@ -9494,7 +9706,7 @@ "max_tokens": 2048, "mode": "chat", "output_cost_per_token": 7.1e-07, - "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/metagenai.llama-3-3-70b-instruct-offer?tab=Overview", + "source": "https://marketplace.microsoft.com/en/marketplace/apps/metagenai.llama-3-3-70b-instruct-offer?tab=Overview", "supports_function_calling": true, "supports_tool_choice": true }, @@ -9543,7 +9755,7 @@ "max_tokens": 2048, "mode": "chat", "output_cost_per_token": 1.6e-05, - "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-405b-instruct-offer?tab=PlansAndPrice", + "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-405b-instruct-offer?tab=PlansAndPrice", "supports_tool_choice": true }, "azure_ai/Meta-Llama-3.1-70B-Instruct": { @@ -9554,7 +9766,7 @@ "max_tokens": 2048, "mode": "chat", "output_cost_per_token": 3.54e-06, - "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-70b-instruct-offer?tab=PlansAndPrice", + "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-70b-instruct-offer?tab=PlansAndPrice", "supports_tool_choice": true }, "azure_ai/Meta-Llama-3.1-8B-Instruct": { @@ -9566,7 +9778,7 @@ "max_tokens": 2048, "mode": "chat", "output_cost_per_token": 6.1e-07, - "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-8b-instruct-offer?tab=PlansAndPrice", + "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-8b-instruct-offer?tab=PlansAndPrice", "supports_tool_choice": true }, "azure_ai/Phi-3-medium-128k-instruct": { @@ -9756,7 +9968,7 @@ "supported_endpoints": [ "/v1/ocr" ], - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/" + "source": "https://ai.azure.com/catalog/models/mistral-document-ai-2512" }, "azure_ai/doc-intelligence/prebuilt-read": { "litellm_provider": "azure_ai", @@ -9959,6 +10171,22 @@ "supports_reasoning": true, "supports_tool_choice": true }, + "azure_ai/deepseek-v4-flash-0731": { + "cache_read_input_token_cost": 2.8e-08, + "deprecation_date": "2026-12-03", + "input_cost_per_token": 1.9e-07, + "litellm_provider": "azure_ai", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5.1e-07, + "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, "azure_ai/embed-v-4-0": { "input_cost_per_token": 1.2e-07, "litellm_provider": "azure_ai", @@ -9967,7 +10195,7 @@ "mode": "embedding", "output_cost_per_token": 0.0, "output_vector_size": 3072, - "source": "https://azuremarketplace.microsoft.com/pt-br/marketplace/apps/cohere.cohere-embed-4-offer?tab=PlansAndPrice", + "source": "https://marketplace.microsoft.com/pt-br/marketplace/apps/cohere.cohere-embed-4-offer?tab=PlansAndPrice", "supported_endpoints": [ "/v1/embeddings" ], @@ -10151,7 +10379,7 @@ "max_tokens": 8192, "mode": "chat", "output_cost_per_token": 0.00971, - "source": "https://azure.microsoft.com/en-us/products/ai-services/ai-foundry/models/jais-30b-chat" + "source": "https://ai.azure.com/catalog/models/jais-30b-chat" }, "azure_ai/jamba-instruct": { "input_cost_per_token": 5e-07, @@ -10208,7 +10436,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 4e-08, - "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/000-000.ministral-3b-2410-offer?tab=Overview", + "source": "https://marketplace.microsoft.com/en/marketplace/apps/000-000.ministral-3b-2410-offer?tab=Overview", "supports_function_calling": true, "supports_tool_choice": true }, @@ -10231,7 +10459,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-06, - "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/000-000.mistral-ai-large-2407-offer?tab=Overview", + "source": "https://marketplace.microsoft.com/en/marketplace/apps/000-000.mistral-ai-large-2407-offer?tab=Overview", "supports_function_calling": true, "supports_tool_choice": true }, @@ -10243,7 +10471,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-06, - "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/000-000.mistral-ai-large-2407-offer?tab=Overview", + "source": "https://marketplace.microsoft.com/en/marketplace/apps/000-000.mistral-ai-large-2407-offer?tab=Overview", "supports_function_calling": true, "supports_tool_choice": true }, @@ -10280,7 +10508,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 1.5e-07, - "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/000-000.mistral-nemo-12b-2407?tab=PlansAndPrice", + "source": "https://marketplace.microsoft.com/en/marketplace/apps/000-000.mistral-nemo-12b-2407?tab=PlansAndPrice", "supports_function_calling": true }, "azure_ai/mistral-small": { @@ -12267,6 +12495,7 @@ "supports_tool_choice": true }, "cerebras/zai-glm-4.7": { + "deprecation_date": "2026-08-17", "input_cost_per_token": 2.25e-06, "litellm_provider": "cerebras", "max_input_tokens": 128000, @@ -12315,7 +12544,8 @@ "output_cost_per_token": 1e-05, "supported_endpoints": [ "/v1/audio/transcriptions" - ] + ], + "deprecation_date": "2027-02-26" }, "claude-haiku-4-5-20251001": { "deprecation_date": "2026-10-15", @@ -12595,7 +12825,8 @@ "us": 1.1 }, "supports_output_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "source": "https://docs.anthropic.com/en/docs/about-claude/models/overview" }, "claude-sonnet-4-6": { "deprecation_date": "2027-02-17", @@ -12997,7 +13228,49 @@ }, "supports_output_config": true, "prompt_cache_min_tokens": 512, - "supports_native_structured_output": true + "supports_native_structured_output": true, + "source": "https://docs.anthropic.com/en/docs/about-claude/models/overview" + }, + "claude-fable-5-1": { + "deprecation_date": "2027-09-01", + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 2.5e-07, + "input_cost_per_token": 1e-05, + "litellm_provider": "anthropic", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_mid_conversation_system": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + 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Billing reads the per-token dollar fields; the '*_dbu_cost_per_token' fields are the published Databricks rates, kept for reference. Context/max output are the DeepSeek-published model limits (1M context, 384K max output)." + }, + "mode": "chat", + "output_cost_per_token": 2.8e-07, + "output_dbu_cost_per_token": 4e-06, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": false + }, + "databricks/databricks-deepseek-v4-pro-0813": { + "cache_creation_input_token_cost": 1.31999e-06, + "cache_read_input_token_cost": 1.3202e-07, + "input_cost_per_token": 1.31999e-06, + "input_dbu_cost_per_token": 1.8857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 1000000, + "max_output_tokens": 393216, + "max_tokens": 393216, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Billing reads the per-token dollar fields; the '*_dbu_cost_per_token' fields are the published Databricks rates, kept for reference. Context/max output are the DeepSeek-published model limits (1M context, 384K max output)." + }, + "mode": "chat", + "output_cost_per_token": 3.95997e-06, + "output_dbu_cost_per_token": 5.6571e-05, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": false + }, "databricks/databricks-gemini-2-5-flash": { "cache_creation_input_token_cost": 3.0002e-07, "cache_read_input_token_cost": 3.0002e-08, @@ -17764,7 +19998,8 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "deprecation_date": "2027-01-08" }, "eu.anthropic.claude-opus-4-20250514-v1:0": { "cache_creation_input_token_cost": 1.875e-05, @@ -19561,6 +21796,61 @@ "supports_system_messages": true, "supports_tool_choice": true }, + "friendliai/zai-org/GLM-5.3-Flash": { + "litellm_provider": "friendliai", + "supports_reasoning": true, + "supports_function_calling": true, + "max_input_tokens": 1048576, + "max_tokens": 1048576, + "max_output_tokens": 1048576, + "input_cost_per_token": 1.5e-07, + "output_cost_per_token": 5e-07, + "cache_read_input_token_cost": 3e-08, + "supports_prompt_caching": true, + "reasoning_effort_levels": [ + "low", + "high", + "max" + ], + "supports_parallel_function_calling": true, + "supports_response_schema": true, + "supports_native_structured_output": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "mode": "chat", + "comment": "Native multimodal GLM model for efficient coding and long-horizon agent tasks", + "source": "https://api.friendli.ai/serverless/v1/models", + "supports_vision": true, + "supports_image_input": true, + "supports_video_input": true + }, + "friendliai/zai-org/GLM-5.3": { + "litellm_provider": "friendliai", + "supports_reasoning": true, + "supports_function_calling": true, + "max_input_tokens": 1048576, + "max_tokens": 1048576, + "max_output_tokens": 1048576, + "input_cost_per_token": 1.26e-06, + "output_cost_per_token": 3.96e-06, + "cache_read_input_token_cost": 2.34e-07, + "supports_prompt_caching": true, + "reasoning_effort_levels": [ + "low", + "high", + "max" + ], + "supports_parallel_function_calling": true, + "supports_response_schema": true, + "supports_native_structured_output": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "mode": "chat", + "comment": "Flagship GLM model for long-horizon coding, agents, and complex project delivery", + "source": "https://api.friendli.ai/serverless/v1/models", + "supports_vision": false, + "supports_image_input": false + }, "ft:babbage-002": { "deprecation_date": "2026-10-23", "input_cost_per_token": 1.6e-06, @@ -20551,6 +22841,7 @@ "supports_image_size": false }, "gemini-live-2.5-flash-native-audio": { + "deprecation_date": "2026-12-13", "input_cost_per_audio_token": 3e-06, "input_cost_per_token": 5e-07, "litellm_provider": "vertex_ai-language-models", @@ -20562,7 +22853,8 @@ "output_cost_per_token": 2e-06, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supported_endpoints": [ - "/vertex_ai/live" + "/vertex_ai/live", + "/v1/realtime" ], "supported_modalities": [ "text", @@ -22015,6 +24307,49 @@ }, "web_search_billing_unit": "per_query" }, + "gemini/nano-banana-pro-preview": { + "input_cost_per_image": 0.0011, + "input_cost_per_token": 2e-06, + "input_cost_per_token_batches": 1e-06, + "litellm_provider": "gemini", + "max_input_tokens": 131072, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "image_generation", + "output_cost_per_image": 0.134, + "output_cost_per_image_token": 0.00012, + "output_cost_per_token": 1.2e-05, + "rpm": 1000, + "tpm": 4000000, + "output_cost_per_token_batches": 6e-06, + "source": "https://ai.google.dev/gemini-api/docs/pricing", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text", + "image" + ], + "supports_function_calling": false, + "supports_prompt_caching": true, + "supports_reasoning": false, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_vision": true, + "supports_web_search": true, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query" + }, "gemini/gemini-3.1-flash-image": { "input_cost_per_token": 5e-07, "input_cost_per_token_batches": 2.5e-07, @@ -22796,7 +25131,7 @@ "output_cost_per_reasoning_token": 3e-06, "output_cost_per_token": 3e-06, "rpm": 2000, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -22850,7 +25185,7 @@ "output_cost_per_reasoning_token": 9e-06, "output_cost_per_token": 9e-06, "rpm": 2000, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -22915,7 +25250,7 @@ "output_cost_per_token_batches": 1.875e-06, "output_cost_per_token_flex": 1.875e-06, "rpm": 2000, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -22974,7 +25309,7 @@ "output_cost_per_token_batches": 1.875e-06, "output_cost_per_token_flex": 1.875e-06, "rpm": 2000, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -23047,7 +25382,8 @@ "supports_system_messages": true, "supports_video_input": true, "supports_vision": true, - "tpm": 800000 + "tpm": 800000, + "deprecation_date": "2026-09-30" }, "gemini/gemini-3.1-pro-preview": { "prompt_cache_min_tokens": 4096, @@ -23178,7 +25514,7 @@ "mode": "chat", "output_cost_per_reasoning_token": 3e-06, "output_cost_per_token": 3e-06, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -23262,7 +25598,7 @@ "mode": "chat", "output_cost_per_reasoning_token": 9e-06, "output_cost_per_token": 9e-06, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -23325,7 +25661,7 @@ "output_cost_per_token": 3.75e-06, "output_cost_per_token_batches": 1.875e-06, "output_cost_per_token_flex": 1.875e-06, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -23382,7 +25718,7 @@ "output_cost_per_token": 3.75e-06, "output_cost_per_token_batches": 1.875e-06, "output_cost_per_token_flex": 1.875e-06, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -23558,6 +25894,38 @@ "supports_tool_choice": true, "supports_vision": true }, + "gemini/gemma-4-26b-a4b-it": { + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "gemini", + "max_input_tokens": 262144, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, + "source": "https://ai.google.dev/gemini-api/docs/pricing" + }, + "gemini/gemma-4-31b-it": { + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "gemini", + "max_input_tokens": 262144, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, + "source": "https://ai.google.dev/gemini-api/docs/pricing" + }, "gemini/imagen-3.0-fast-generate-001": { "litellm_provider": "gemini", "mode": "image_generation", @@ -23695,8 +26063,10 @@ "max_input_tokens": 1024, "max_tokens": 1024, "mode": "video_generation", - "output_cost_per_second": 0.15, - "source": "https://ai.google.dev/gemini-api/docs/video", + "output_cost_per_second": 0.1, + "output_cost_per_second_1080p": 0.12, + "output_cost_per_second_4k": 0.3, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_modalities": [ "text" ], @@ -23710,7 +26080,8 @@ "max_tokens": 1024, "mode": "video_generation", "output_cost_per_second": 0.4, - "source": "https://ai.google.dev/gemini-api/docs/video", + "output_cost_per_second_4k": 0.6, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_modalities": [ "text" ], @@ -23738,8 +26109,10 @@ "max_input_tokens": 1024, "max_tokens": 1024, "mode": "video_generation", - "output_cost_per_second": 0.15, - "source": "https://ai.google.dev/gemini-api/docs/video", + "output_cost_per_second": 0.1, + "output_cost_per_second_1080p": 0.12, + "output_cost_per_second_4k": 0.3, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_modalities": [ "text" ], @@ -23753,7 +26126,8 @@ "max_tokens": 1024, "mode": "video_generation", "output_cost_per_second": 0.4, - "source": "https://ai.google.dev/gemini-api/docs/video", + "output_cost_per_second_4k": 0.6, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_modalities": [ "text" ], @@ -24262,7 +26636,7 @@ "supports_response_schema": true, "supports_vision": true }, - "gigachat/GigaChat-2-Lite": { + "gigachat/GigaChat-2": { "input_cost_per_token": 0.0, "litellm_provider": "gigachat", "max_input_tokens": 128000, @@ -24324,6 +26698,15 @@ "output_cost_per_token": 0.0, "output_vector_size": 2560 }, + "gigachat/GigaEmbeddings-3B-2025-09": { + "input_cost_per_token": 0.0, + "litellm_provider": "gigachat", + "max_input_tokens": 4096, + "max_tokens": 4096, + "mode": "embedding", + "output_cost_per_token": 0.0, + "output_vector_size": 2048 + }, "gmi/anthropic/claude-opus-4.5": { "input_cost_per_token": 5e-06, "litellm_provider": "gmi", @@ -25247,7 +27630,7 @@ "supports_vision": true }, "gpt-4o-audio-preview": { - "deprecation_date": "2027-01-20", + "deprecation_date": "2026-05-07", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "openai", @@ -25570,7 +27953,7 @@ "supports_vision": true }, "gpt-4o-mini-audio-preview": { - "deprecation_date": "2027-01-20", + "deprecation_date": "2026-05-07", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 1.5e-07, "litellm_provider": "openai", @@ -25608,7 +27991,7 @@ "gpt-4o-mini-realtime-preview": { "cache_creation_input_audio_token_cost": 3e-07, "cache_read_input_token_cost": 3e-07, - "deprecation_date": "2027-01-20", + "deprecation_date": "2026-05-07", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 6e-07, "litellm_provider": "openai", @@ -25707,7 +28090,8 @@ "output_cost_per_token": 5e-06, "supported_endpoints": [ "/v1/audio/transcriptions" - ] + ], + "deprecation_date": "2027-02-26" }, "gpt-4o-mini-tts": { "input_cost_per_token": 2.5e-06, @@ -25729,7 +28113,7 @@ }, "gpt-4o-realtime-preview": { "cache_read_input_token_cost": 2.5e-06, - "deprecation_date": "2027-01-20", + "deprecation_date": "2026-05-07", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 5e-06, "litellm_provider": "openai", @@ -25748,7 +28132,7 @@ }, "gpt-4o-realtime-preview-2024-12-17": { "cache_read_input_token_cost": 2.5e-06, - "deprecation_date": "2027-01-20", + "deprecation_date": "2026-05-07", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 5e-06, "litellm_provider": "openai", @@ -25767,7 +28151,7 @@ }, "gpt-4o-realtime-preview-2025-06-03": { "cache_read_input_token_cost": 2.5e-06, - "deprecation_date": "2027-01-20", + "deprecation_date": "2026-05-07", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 5e-06, "litellm_provider": "openai", @@ -25846,7 +28230,8 @@ "output_cost_per_token": 1e-05, "supported_endpoints": [ "/v1/audio/transcriptions" - ] + ], + "deprecation_date": "2027-02-26" }, "gpt-image-1.5": { "cache_read_input_token_cost": 1.25e-06, @@ -26097,7 +28482,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "low/1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -26108,7 +28494,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "low/1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -26119,7 +28506,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "medium/1024-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.034, @@ -26130,7 +28518,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "medium/1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.05, @@ -26141,7 +28530,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "medium/1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.05, @@ -26152,7 +28542,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "high/1024-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.133, @@ -26163,7 +28554,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "high/1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.2, @@ -26174,7 +28566,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "high/1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.2, @@ -26185,7 +28578,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "standard/1024-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.009, @@ -26196,7 +28590,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "standard/1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -26207,7 +28602,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "standard/1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -26218,7 +28614,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "1024-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.009, @@ -26229,7 +28626,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -26240,7 +28638,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -26251,7 +28650,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "gpt-5": { "cache_read_input_token_cost": 1.25e-07, @@ -26982,7 +29382,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "source": "https://platform.openai.com/docs/models/gpt-5.6-cyber", + "source": "https://developers.openai.com/api/docs/models/gpt-5.6-cyber", "supports_computer_use": true, "supports_parallel_function_calling": true }, @@ -27021,7 +29421,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "source": "https://platform.openai.com/docs/models/daybreak-red-latest", + "source": "https://developers.openai.com/api/docs/models/daybreak-red-latest", "supports_computer_use": true, "supports_parallel_function_calling": true }, @@ -27061,7 +29461,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "source": "https://platform.openai.com/docs/models/daybreak-blue-latest", + "source": "https://developers.openai.com/api/docs/models/daybreak-blue-latest", "supports_parallel_function_calling": true }, "chat-latest": { @@ -27073,7 +29473,7 @@ "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 3e-05, - "source": "https://platform.openai.com/docs/models/chat-latest", + "source": "https://developers.openai.com/api/docs/models/chat-latest", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -30368,7 +32768,7 @@ "search_context_size_low": 0.0025, "search_context_size_medium": 0.0025 }, - "source": "https://dev.meta.ai/docs/getting-started/pricing-rate-limits", + "source": "https://ai.developer.meta.com/docs/pricing-rate-limits", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses", @@ -30409,7 +32809,7 @@ "search_context_size_low": 0.0025, "search_context_size_medium": 0.0025 }, - "source": "https://dev.meta.ai/docs/getting-started/pricing-rate-limits", + "source": "https://ai.developer.meta.com/docs/pricing-rate-limits", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses", @@ -30450,7 +32850,7 @@ "search_context_size_low": 0.0025, "search_context_size_medium": 0.0025 }, - "source": "https://dev.meta.ai/docs/getting-started/pricing-rate-limits", + "source": "https://ai.developer.meta.com/docs/pricing-rate-limits", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses", @@ -30483,7 +32883,7 @@ "max_output_tokens": 4028, "max_tokens": 4028, "mode": "chat", - "source": "https://llama.developer.meta.com/docs/models", + "source": "https://ai.developer.meta.com/docs/models", "supported_modalities": [ "text" ], @@ -30499,7 +32899,7 @@ "max_output_tokens": 4028, "max_tokens": 4028, "mode": "chat", - "source": "https://llama.developer.meta.com/docs/models", + "source": "https://ai.developer.meta.com/docs/models", "supported_modalities": [ "text" ], @@ -30515,7 +32915,7 @@ "max_output_tokens": 4028, "max_tokens": 4028, "mode": "chat", - "source": "https://llama.developer.meta.com/docs/models", + "source": "https://ai.developer.meta.com/docs/models", "supported_modalities": [ "text", "image" @@ -30532,7 +32932,7 @@ "max_output_tokens": 4028, "max_tokens": 4028, "mode": "chat", - "source": "https://llama.developer.meta.com/docs/models", + "source": "https://ai.developer.meta.com/docs/models", "supported_modalities": [ "text", "image" @@ -32477,7 +34877,7 @@ "mode": "chat", "supports_function_calling": true, "supports_reasoning": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/deepseek-ai/DeepSeek-R1-0528": { "max_tokens": 164000, @@ -32489,7 +34889,7 @@ "mode": "chat", "supports_function_calling": true, "supports_reasoning": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/deepseek-ai/DeepSeek-R1-Distill-Llama-70B": { "max_tokens": 128000, @@ -32500,7 +34900,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/deepseek-ai/DeepSeek-V3": { "max_tokens": 128000, @@ -32511,7 +34911,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/deepseek-ai/DeepSeek-V3-0324": { "max_tokens": 128000, @@ -32522,7 +34922,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/google/gemma-3-27b-it": { "max_tokens": 128000, @@ -32534,7 +34934,7 @@ "mode": "chat", "supports_function_calling": true, "supports_vision": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/meta-llama/Llama-3.3-70B-Instruct": { "max_tokens": 128000, @@ -32545,7 +34945,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/meta-llama/Llama-Guard-3-8B": { "max_tokens": 128000, @@ -32555,7 +34955,7 @@ "output_cost_per_token": 6e-08, "litellm_provider": "nebius", "mode": "chat", - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/meta-llama/Meta-Llama-3.1-8B-Instruct": { "max_tokens": 128000, @@ -32566,7 +34966,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/meta-llama/Meta-Llama-3.1-70B-Instruct": { "max_tokens": 128000, @@ -32577,7 +34977,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/meta-llama/Meta-Llama-3.1-405B-Instruct": { "max_tokens": 128000, @@ -32588,7 +34988,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/mistralai/Mistral-Nemo-Instruct-2407": { "max_tokens": 128000, @@ -32599,7 +34999,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/NousResearch/Hermes-3-Llama-3.1-405B": { "max_tokens": 128000, @@ -32610,7 +35010,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/nvidia/Llama-3.1-Nemotron-Ultra-253B-v1": { "max_tokens": 128000, @@ -32621,7 +35021,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/nvidia/Llama-3.3-Nemotron-Super-49B-v1": { "max_tokens": 131072, @@ -32632,7 +35032,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen3-235B-A22B": { "max_tokens": 262144, @@ -32643,7 +35043,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen3-32B": { "max_tokens": 32768, @@ -32654,7 +35054,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen3-30B-A3B": { "max_tokens": 32768, @@ -32665,7 +35065,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen3-14B": { "max_tokens": 32768, @@ -32676,7 +35076,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen3-4B": { "max_tokens": 32768, @@ -32687,7 +35087,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/QwQ-32B": { "max_tokens": 32768, @@ -32699,7 +35099,7 @@ "mode": "chat", "supports_function_calling": true, "supports_reasoning": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen2.5-72B-Instruct": { "max_tokens": 128000, @@ -32710,7 +35110,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen2.5-32B-Instruct": { "max_tokens": 128000, @@ -32721,7 +35121,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen2.5-Coder-7B": { "max_tokens": 32768, @@ -32732,7 +35132,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen2.5-VL-72B-Instruct": { "max_tokens": 131072, @@ -32744,7 +35144,7 @@ "mode": "chat", "supports_function_calling": true, "supports_vision": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen2-VL-72B-Instruct": { "max_tokens": 131072, @@ -32756,7 +35156,7 @@ "mode": "chat", "supports_function_calling": true, "supports_vision": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen2-VL-7B-Instruct": { "max_tokens": 131072, @@ -32767,7 +35167,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_vision": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/BAAI/bge-en-icl": { "max_tokens": 32768, @@ -32776,7 +35176,7 @@ "output_cost_per_token": 0.0, "litellm_provider": "nebius", "mode": "embedding", - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/BAAI/bge-multilingual-gemma2": { "max_tokens": 8192, @@ -32785,7 +35185,7 @@ "output_cost_per_token": 0.0, "litellm_provider": "nebius", "mode": "embedding", - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/intfloat/e5-mistral-7b-instruct": { "max_tokens": 32768, @@ -32794,7 +35194,7 @@ "output_cost_per_token": 0.0, "litellm_provider": "nebius", "mode": "embedding", - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nvidia.nemotron-nano-12b-v2": { "input_cost_per_token": 2e-07, @@ -33535,7 +35935,7 @@ "max_tokens": 4000, "mode": "chat", "output_cost_per_token": 1.56e-06, - "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/", + "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/", "supports_function_calling": true, "supports_response_schema": false, "supports_native_streaming": true @@ -33548,7 +35948,7 @@ "max_tokens": 4000, "mode": "chat", "output_cost_per_token": 1.56e-06, - "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/", + "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/", "supports_function_calling": true, "supports_response_schema": false, "supports_native_streaming": true @@ -33561,7 +35961,7 @@ "max_tokens": 4000, "mode": "chat", "output_cost_per_token": 1.56e-06, - "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/", + "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/", "supports_function_calling": true, "supports_response_schema": false, "supports_native_streaming": true @@ -33618,7 +36018,7 @@ "max_tokens": 8192, "mode": "chat", "output_cost_per_token": 1.56e-06, - "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/", + "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/", "supports_function_calling": true, "supports_response_schema": false, "supports_native_streaming": true, @@ -33632,7 +36032,7 @@ "max_tokens": 8192, "mode": "chat", "output_cost_per_token": 1.56e-06, - "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/", + "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/", "supports_function_calling": false, "supports_response_schema": false, "supports_native_streaming": true @@ -33643,7 +36043,7 @@ "max_input_tokens": 512, "mode": "embedding", "output_vector_size": 1024, - "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/", + "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/", "supports_vision": true }, "oci/cohere.command-a-reasoning-08-2025": { @@ -34540,7 +36940,7 @@ "max_tokens": 2048, "mode": "chat", "output_cost_per_token": 2e-07, - "source": "https://openrouter.ai/api/v1/models/bytedance/ui-tars-1.5-7b", + "source": "https://openrouter.ai/bytedance/ui-tars-1.5-7b", "supports_tool_choice": true }, "openrouter/deepseek/deepseek-chat": { @@ -34775,7 +37175,7 @@ "output_cost_per_reasoning_token": 3e-06, "output_cost_per_token": 3e-06, "rpm": 2000, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -34816,7 +37216,7 @@ "output_cost_per_reasoning_token": 1.5e-06, "output_cost_per_token": 1.5e-06, "rpm": 2000, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -35901,7 +38301,7 @@ "max_tokens": 131000, "mode": "chat", "output_cost_per_token": 6.7e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/deepseek-r1-distill-llama-70b", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, @@ -35915,7 +38315,7 @@ "max_tokens": 131000, "mode": "chat", "output_cost_per_token": 1e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/llama-3-1-8b-instruct", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true @@ -35928,7 +38328,7 @@ "max_tokens": 131000, "mode": "chat", "output_cost_per_token": 6.7e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/meta-llama-3-1-70b-instruct", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": false, "supports_response_schema": false, "supports_tool_choice": false @@ -35941,7 +38341,7 @@ "max_tokens": 131000, "mode": "chat", "output_cost_per_token": 6.7e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/meta-llama-3-3-70b-instruct", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true @@ -35954,7 +38354,7 @@ "max_tokens": 127000, "mode": "chat", "output_cost_per_token": 1e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/mistral-7b-instruct-v0-3", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true @@ -35967,7 +38367,7 @@ "max_tokens": 118000, "mode": "chat", "output_cost_per_token": 1.3e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/mistral-nemo-instruct-2407", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true @@ -35980,7 +38380,7 @@ "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 2.8e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/mistral-small-3-2-24b-instruct-2506", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -35994,7 +38394,7 @@ "max_tokens": 32000, "mode": "chat", "output_cost_per_token": 6.3e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/mixtral-8x7b-instruct-v0-1", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": false, "supports_response_schema": true, "supports_tool_choice": false @@ -36007,7 +38407,7 @@ "max_tokens": 32000, "mode": "chat", "output_cost_per_token": 8.7e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/qwen2-5-coder-32b-instruct", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": false, "supports_response_schema": true, "supports_tool_choice": false @@ -36020,7 +38420,7 @@ "max_tokens": 32000, "mode": "chat", "output_cost_per_token": 9.1e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/qwen2-5-vl-72b-instruct", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": false, "supports_response_schema": true, "supports_tool_choice": false, @@ -36034,7 +38434,7 @@ "max_tokens": 32000, "mode": "chat", "output_cost_per_token": 2.3e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/qwen3-32b", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, @@ -36048,7 +38448,7 @@ "max_tokens": 131000, "mode": "chat", "output_cost_per_token": 4e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/gpt-oss-120b", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": false, "supports_reasoning": true, "supports_response_schema": true, @@ -36062,7 +38462,7 @@ "max_tokens": 131000, "mode": "chat", "output_cost_per_token": 1.5e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/gpt-oss-20b", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": false, "supports_reasoning": true, "supports_response_schema": true, @@ -36076,7 +38476,7 @@ "max_tokens": 32000, "mode": "chat", "output_cost_per_token": 2.9e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/llava-next-mistral-7b", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": false, "supports_response_schema": true, "supports_tool_choice": false, @@ -36090,7 +38490,7 @@ "max_tokens": 256000, "mode": "chat", "output_cost_per_token": 1.9e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/mamba-codestral-7b-v0-1", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": false, "supports_response_schema": true, "supports_tool_choice": false @@ -36156,12 +38556,22 @@ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models" }, "parallel_ai/search": { - "input_cost_per_query": 0.004, + "input_cost_per_query": 0.005, + "litellm_provider": "parallel_ai", + "mode": "search" + }, + "parallel_ai/search-fast": { + "input_cost_per_query": 0.001, "litellm_provider": "parallel_ai", "mode": "search" }, "parallel_ai/search-pro": { - "input_cost_per_query": 0.009, + "input_cost_per_query": 0.005, + "litellm_provider": "parallel_ai", + "mode": "search" + }, + "parallel_ai/search-turbo": { + "input_cost_per_query": 0.001, "litellm_provider": "parallel_ai", "mode": "search" }, @@ -38226,7 +40636,7 @@ "source": "https://docs.mistral.ai/capabilities/code_generation/" }, "text-embedding-004": { - "deprecation_date": "2026-01-14", + "deprecation_date": "2027-04-01", "input_cost_per_character": 2.5e-08, "input_cost_per_token": 1e-07, "litellm_provider": "vertex_ai-embedding-models", @@ -38583,7 +40993,6 @@ "input_cost_per_token": 1.04e-06, "litellm_provider": "together_ai", "max_input_tokens": 131072, - "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 1.04e-06, @@ -38705,7 +41114,6 @@ "input_cost_per_token": 1.5e-07, "litellm_provider": "together_ai", "max_input_tokens": 131072, - "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 6e-07, @@ -38752,7 +41160,6 @@ "input_cost_per_token": 6e-07, "litellm_provider": "together_ai", "max_input_tokens": 200000, - "max_output_tokens": 200000, "max_tokens": 200000, "metadata": { "successor": "together_ai/zai-org/GLM-5.2" @@ -38770,7 +41177,6 @@ "input_cost_per_token": 4.5e-07, "litellm_provider": "together_ai", "max_input_tokens": 200000, - "max_output_tokens": 200000, "max_tokens": 200000, "metadata": { "successor": "together_ai/zai-org/GLM-5.2" @@ -38788,7 +41194,6 @@ "input_cost_per_token": 5e-07, "litellm_provider": "together_ai", "max_input_tokens": 256000, - "max_output_tokens": 256000, "max_tokens": 256000, "metadata": { "successor": "together_ai/moonshotai/Kimi-K3" @@ -38856,7 +41261,7 @@ "max_input_tokens": 262144, "mode": "chat", "output_cost_per_token": 3.6e-06, - "source": "https://www.together.ai/models/Qwen/Qwen3.5-397B-A17B", + "source": "https://www.together.ai/models/qwen3-5-397b-a17b", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -38868,7 +41273,6 @@ "input_cost_per_token": 3e-07, "litellm_provider": "together_ai", "max_input_tokens": 524288, - "max_output_tokens": 524288, "max_tokens": 524288, "mode": "chat", "output_cost_per_token": 1.2e-06, @@ -38885,7 +41289,6 @@ "input_cost_per_token": 0.0, "litellm_provider": "together_ai", "max_input_tokens": 262144, - "max_output_tokens": 262144, "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 0.0, @@ -38895,7 +41298,6 @@ "input_cost_per_token": 1.7e-07, "litellm_provider": "together_ai", "max_input_tokens": 262144, - "max_output_tokens": 262144, "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 2.5e-07, @@ -38911,7 +41313,6 @@ "input_cost_per_token": 5e-07, "litellm_provider": "together_ai", "max_input_tokens": 1000000, - "max_output_tokens": 1000000, "max_tokens": 1000000, "mode": "chat", "output_cost_per_token": 3e-06, @@ -38923,7 +41324,6 @@ "input_cost_per_token": 2.5e-06, "litellm_provider": "together_ai", "max_input_tokens": 1000000, - "max_output_tokens": 1000000, "max_tokens": 1000000, "mode": "chat", "output_cost_per_token": 7.5e-06, @@ -38934,21 +41334,19 @@ "input_cost_per_token": 3.2e-07, "litellm_provider": "together_ai", "max_input_tokens": 1000000, - "max_output_tokens": 1000000, "max_tokens": 1000000, "mode": "chat", "output_cost_per_token": 1.28e-06, "source": "https://docs.together.ai/docs/serverless-models" }, "together_ai/Qwen/Qwen3.8-2.4T-A95B": { - "cache_read_input_token_cost": 2.5e-07, - "input_cost_per_token": 2e-06, + "cache_read_input_token_cost": 5e-07, + "input_cost_per_token": 2.5e-06, "litellm_provider": "together_ai", "max_input_tokens": 1010000, - "max_output_tokens": 1010000, "max_tokens": 1010000, "mode": "chat", - "output_cost_per_token": 6e-06, + "output_cost_per_token": 6.25e-06, "source": "https://docs.together.ai/docs/serverless-models", "supports_prompt_caching": true }, @@ -38956,7 +41354,6 @@ "input_cost_per_token": 1e-07, "litellm_provider": "together_ai", "max_input_tokens": 32768, - "max_output_tokens": 32768, "max_tokens": 32768, "mode": "chat", "output_cost_per_token": 1e-07, @@ -38967,7 +41364,6 @@ "input_cost_per_token": 1.4e-07, "litellm_provider": "together_ai", "max_input_tokens": 1048576, - "max_output_tokens": 1048576, "max_tokens": 1048576, "mode": "chat", "output_cost_per_token": 2.8e-07, @@ -38984,7 +41380,6 @@ "input_cost_per_token": 1.74e-06, "litellm_provider": "together_ai", "max_input_tokens": 512000, - "max_output_tokens": 512000, "max_tokens": 512000, "mode": "chat", "output_cost_per_token": 3.48e-06, @@ -39001,7 +41396,6 @@ "input_cost_per_token": 1.32e-06, "litellm_provider": "together_ai", "max_input_tokens": 1048576, - "max_output_tokens": 1048576, "max_tokens": 1048576, "mode": "chat", "output_cost_per_token": 3.96e-06, @@ -39017,7 +41411,6 @@ "input_cost_per_token": 6e-08, "litellm_provider": "together_ai", "max_input_tokens": 32768, - "max_output_tokens": 32768, "max_tokens": 32768, "mode": "chat", "output_cost_per_token": 1.2e-07, @@ -39027,7 +41420,6 @@ "input_cost_per_token": 3.9e-07, "litellm_provider": "together_ai", "max_input_tokens": 262144, - "max_output_tokens": 262144, "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 9.7e-07, @@ -39053,7 +41445,6 @@ "input_cost_per_token": 2e-07, "litellm_provider": "together_ai", "max_input_tokens": 1048576, - "max_output_tokens": 1048576, "max_tokens": 1048576, "mode": "chat", "output_cost_per_token": 2e-07, @@ -39064,7 +41455,6 @@ "input_cost_per_token": 3.5e-07, "litellm_provider": "together_ai", "max_input_tokens": 131072, - "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 1.5e-06, @@ -39077,7 +41467,6 @@ "input_cost_per_token": 9.5e-07, "litellm_provider": "together_ai", "max_input_tokens": 262144, - "max_output_tokens": 262144, "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 4e-06, @@ -39094,7 +41483,6 @@ "input_cost_per_token": 3e-06, "litellm_provider": "together_ai", "max_input_tokens": 1048576, - "max_output_tokens": 1048576, "max_tokens": 1048576, "mode": "chat", "output_cost_per_token": 1.5e-05, @@ -39118,7 +41506,6 @@ "input_cost_per_token": 6e-07, "litellm_provider": "together_ai", "max_input_tokens": 512288, - "max_output_tokens": 512288, "max_tokens": 512288, "mode": "chat", "output_cost_per_token": 3.6e-06, @@ -39135,7 +41522,6 @@ "input_cost_per_token": 2.8e-07, "litellm_provider": "together_ai", "max_input_tokens": 262144, - "max_output_tokens": 262144, "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 8.6e-07, @@ -39146,7 +41532,6 @@ "input_cost_per_token": 1e-06, "litellm_provider": "together_ai", "max_input_tokens": 524288, - "max_output_tokens": 524288, "max_tokens": 524288, "mode": "chat", "output_cost_per_token": 4.05e-06, @@ -39162,7 +41547,6 @@ "input_cost_per_token": 5e-07, "litellm_provider": "together_ai", "max_input_tokens": 524288, - "max_output_tokens": 524288, "max_tokens": 524288, "mode": "chat", "output_cost_per_token": 1.2e-06, @@ -39174,8 +41558,25 @@ "input_cost_per_token": 1.4e-06, "litellm_provider": "together_ai", "max_input_tokens": 1048575, - "max_output_tokens": 1048575, - "max_tokens": 1048575, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 4.4e-06, + "source": "https://docs.together.ai/docs/serverless-models", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "together_ai/zai-org/GLM-5.3": { + "cache_read_input_token_cost": 2.6e-07, + "input_cost_per_token": 1.4e-06, + "litellm_provider": "together_ai", + "max_input_tokens": 1048575, + "max_output_tokens": 128000, + "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 4.4e-06, "source": "https://docs.together.ai/docs/serverless-models", @@ -39191,8 +41592,8 @@ "input_cost_per_token": 1.5e-07, "litellm_provider": "together_ai", "max_input_tokens": 1048575, - "max_output_tokens": 1048575, - "max_tokens": 1048575, + "max_output_tokens": 128000, + "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 5e-07, "source": "https://docs.together.ai/docs/serverless-models", @@ -41718,6 +44119,42 @@ "supports_max_reasoning_effort": true, "prompt_cache_min_tokens": 512 }, + "vertex_ai/claude-fable-5-1": { + "regional_endpoint_uplift_multiplier": 1.1, + "supports_mid_conversation_system": true, + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 2.5e-07, + "input_cost_per_token": 1e-05, + "litellm_provider": "vertex_ai-anthropic_models", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_native_structured_output": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_max_reasoning_effort": true, + "prompt_cache_min_tokens": 512 + }, "vertex_ai/claude-fable-5@default": { "deprecation_date": "2027-06-08", "regional_endpoint_uplift_multiplier": 1.1, @@ -41753,6 +44190,42 @@ "supports_max_reasoning_effort": true, "prompt_cache_min_tokens": 512 }, + "vertex_ai/claude-fable-5-1@default": { + "regional_endpoint_uplift_multiplier": 1.1, + "supports_mid_conversation_system": true, + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 2.5e-07, + "input_cost_per_token": 1e-05, + "litellm_provider": "vertex_ai-anthropic_models", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_native_structured_output": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_max_reasoning_effort": true, + "prompt_cache_min_tokens": 512 + }, "vertex_ai/claude-opus-5": { "deprecation_date": "2027-01-24", "regional_endpoint_uplift_multiplier": 1.1, @@ -43097,7 +45570,7 @@ "max_tokens": 2000000, "mode": "chat", "output_cost_per_token": 5e-07, - "source": "https://docs.x.ai/docs/models (Vertex AI Model Garden)", + "source": "https://docs.x.ai/developers/models", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -43113,7 +45586,7 @@ "max_tokens": 2000000, "mode": "chat", "output_cost_per_token": 5e-07, - "source": "https://docs.x.ai/docs/models (Vertex AI Model Garden)", + "source": "https://docs.x.ai/developers/models", "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, @@ -43130,7 +45603,7 @@ "max_tokens": 2000000, "mode": "chat", "output_cost_per_token": 6e-06, - "source": "https://docs.x.ai/docs/models (Vertex AI Model Garden)", + "source": "https://docs.x.ai/developers/models", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -43146,7 +45619,7 @@ "max_tokens": 2000000, "mode": "chat", "output_cost_per_token": 6e-06, - "source": "https://docs.x.ai/docs/models (Vertex AI Model Garden)", + "source": "https://docs.x.ai/developers/models", "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, @@ -43266,7 +45739,8 @@ "max_tokens": 1024, "mode": "video_generation", "output_cost_per_second": 0.4, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo", + "output_cost_per_second_4k": 0.6, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supported_modalities": [ "text" ], @@ -43279,8 +45753,10 @@ "max_input_tokens": 1024, "max_tokens": 1024, "mode": "video_generation", - "output_cost_per_second": 0.15, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo", + "output_cost_per_second": 0.1, + "output_cost_per_second_1080p": 0.12, + "output_cost_per_second_4k": 0.3, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supported_modalities": [ "text" ], @@ -43295,7 +45771,8 @@ "max_tokens": 1024, "mode": "video_generation", "output_cost_per_second": 0.4, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo", + "output_cost_per_second_4k": 0.6, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supported_modalities": [ "text" ], @@ -43309,8 +45786,10 @@ "max_input_tokens": 1024, "max_tokens": 1024, "mode": "video_generation", - "output_cost_per_second": 0.15, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo", + "output_cost_per_second": 0.1, + "output_cost_per_second_1080p": 0.12, + "output_cost_per_second_4k": 0.3, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supported_modalities": [ "text" ], @@ -43318,6 +45797,22 @@ "video" ] }, + "vertex_ai/veo-3.1-lite-generate-001": { + "litellm_provider": "vertex_ai-video-models", + "max_input_tokens": 1024, + "max_tokens": 1024, + "mode": "video_generation", + "output_cost_per_second": 0.05, + "output_cost_per_second_1080p": 0.08, + "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing#veo", + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "video" + ] + }, "voyage/rerank-2": { "input_cost_per_token": 5e-08, "litellm_provider": "voyage", @@ -43998,290 +46493,339 @@ "output_cost_per_second": 0.0001, "supported_endpoints": [ "/v1/audio/transcriptions" - ] + ], + "deprecation_date": "2027-02-26" }, "xai/grok-3": { - "cache_read_input_token_cost": 7.5e-07, - "input_cost_per_token": 3e-06, + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1.25e-06, "litellm_provider": "xai", "max_input_tokens": 131072, "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "output_cost_per_token": 1.5e-05, + "output_cost_per_token": 2.5e-06, "source": "https://x.ai/api#pricing", "supports_function_calling": true, "supports_prompt_caching": true, "supports_response_schema": false, "supports_tool_choice": true, "supports_web_search": true, - "deprecation_date": "2026-05-15" + "deprecation_date": "2026-05-15", + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07 }, "xai/grok-3-beta": { - "cache_read_input_token_cost": 7.5e-07, - "input_cost_per_token": 3e-06, + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1.25e-06, "litellm_provider": "xai", "max_input_tokens": 131072, "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "output_cost_per_token": 1.5e-05, + "output_cost_per_token": 2.5e-06, "source": "https://x.ai/api#pricing", "supports_function_calling": true, "supports_prompt_caching": true, "supports_response_schema": false, "supports_tool_choice": true, - "supports_web_search": true + "supports_web_search": true, + "deprecation_date": "2026-05-15", + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07 }, "xai/grok-3-fast-beta": { - "cache_read_input_token_cost": 1.25e-06, - "input_cost_per_token": 5e-06, + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1.25e-06, "litellm_provider": "xai", "max_input_tokens": 131072, "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "output_cost_per_token": 2.5e-05, + "output_cost_per_token": 2.5e-06, "source": "https://x.ai/api#pricing", "supports_function_calling": true, "supports_prompt_caching": true, "supports_response_schema": false, "supports_tool_choice": true, - "supports_web_search": true + "supports_web_search": true, + "deprecation_date": "2026-05-15", + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07 }, "xai/grok-3-fast-latest": { - "cache_read_input_token_cost": 1.25e-06, - "input_cost_per_token": 5e-06, + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1.25e-06, "litellm_provider": "xai", "max_input_tokens": 131072, "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "output_cost_per_token": 2.5e-05, + "output_cost_per_token": 2.5e-06, "source": "https://x.ai/api#pricing", "supports_function_calling": true, "supports_prompt_caching": true, "supports_response_schema": false, "supports_tool_choice": true, - "supports_web_search": true + "supports_web_search": true, + "deprecation_date": "2026-05-15", + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07 }, "xai/grok-3-latest": { - "cache_read_input_token_cost": 7.5e-07, - "input_cost_per_token": 3e-06, + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1.25e-06, "litellm_provider": "xai", "max_input_tokens": 131072, "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - 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"litellm_provider": "xai", "max_input_tokens": 2000000.0, "max_output_tokens": 2000000.0, "max_tokens": 2000000.0, "mode": "chat", - "output_cost_per_token": 5e-07, - "output_cost_per_token_above_128k_tokens": 1e-06, + "output_cost_per_token": 2.5e-06, "source": "https://docs.x.ai/docs/models/grok-4-1-fast-reasoning", "supports_audio_input": true, "supports_function_calling": true, @@ -44290,19 +46834,21 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "supports_web_search": true + "supports_web_search": true, + "deprecation_date": "2026-05-15", + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07 }, "xai/grok-4-1-fast-reasoning": { - "cache_read_input_token_cost": 5e-08, - "input_cost_per_token": 2e-07, - "input_cost_per_token_above_128k_tokens": 4e-07, + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1.25e-06, 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"max_output_tokens": 2000000.0, "max_tokens": 2000000.0, "mode": "chat", - "output_cost_per_token": 5e-07, - "output_cost_per_token_above_128k_tokens": 1e-06, + "output_cost_per_token": 2.5e-06, "source": "https://docs.x.ai/docs/models/grok-4-1-fast-non-reasoning", "supports_audio_input": true, "supports_function_calling": true, @@ -44355,19 +46903,20 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "deprecation_date": "2026-05-15" + "deprecation_date": "2026-05-15", + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07 }, "xai/grok-4-1-fast-non-reasoning-latest": { - "cache_read_input_token_cost": 5e-08, - "input_cost_per_token": 2e-07, - "input_cost_per_token_above_128k_tokens": 4e-07, + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1.25e-06, "litellm_provider": "xai", "max_input_tokens": 2000000.0, "max_output_tokens": 2000000.0, "max_tokens": 2000000.0, "mode": "chat", - "output_cost_per_token": 5e-07, - "output_cost_per_token_above_128k_tokens": 1e-06, + "output_cost_per_token": 2.5e-06, "source": "https://docs.x.ai/docs/models/grok-4-1-fast-non-reasoning", "supports_audio_input": true, "supports_function_calling": true, @@ -44376,7 +46925,10 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "deprecation_date": "2026-05-15" + "deprecation_date": "2026-05-15", + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07 }, "xai/grok-4.20-multi-agent-beta-0309": { "cache_read_input_token_cost": 2e-07, @@ -44930,7 +47482,7 @@ "litellm_provider": "azure", "mode": "video_generation", "output_cost_per_video_per_second": 0.1, - "source": "https://azure.microsoft.com/en-us/products/ai-services/video-generation", + "source": "https://ai.azure.com/catalog/models/sora-2", "supported_modalities": [ "text" ], @@ -44942,7 +47494,7 @@ "litellm_provider": "azure", "mode": "video_generation", "output_cost_per_video_per_second": 0.3, - "source": "https://azure.microsoft.com/en-us/products/ai-services/video-generation", + "source": "https://ai.azure.com/catalog/models/sora-2-pro", "supported_modalities": [ "text" ], @@ -44954,7 +47506,7 @@ "litellm_provider": "azure", "mode": "video_generation", "output_cost_per_video_per_second": 0.5, - "source": "https://azure.microsoft.com/en-us/products/ai-services/video-generation", + "source": "https://ai.azure.com/catalog/models/sora-2-pro", "supported_modalities": [ "text" ], @@ -49164,7 +51716,7 @@ "input_cost_per_second": 0.0002833333333333333, "litellm_provider": "openai", "mode": "audio_transcription", - "source": "https://platform.openai.com/docs/models/gpt-realtime-whisper", + "source": "https://developers.openai.com/api/docs/models/gpt-realtime-whisper", "supported_endpoints": [ "/v1/realtime", "/v1/realtime/transcription_sessions" @@ -50390,7 +52942,7 @@ "max_tokens": 500000, "mode": "chat", "supports_function_calling": true, - "supports_prompt_caching": true, + "supports_prompt_caching": false, "supports_reasoning": true, "supports_tool_choice": true, "supports_vision": true @@ -50405,7 +52957,7 @@ "max_tokens": 500000, "mode": "chat", "supports_function_calling": true, - "supports_prompt_caching": true, + "supports_prompt_caching": false, "supports_reasoning": true, "supports_tool_choice": true, "supports_vision": true @@ -50416,7 +52968,7 @@ "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", - "source": "https://www.volcengine.com/docs/82379/1330310", + "source": "https://docs.volcengine.com/docs/82379/1330310", "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": false, @@ -50454,7 +53006,7 @@ "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", - "source": "https://www.volcengine.com/docs/82379/1330310", + "source": "https://docs.volcengine.com/docs/82379/1330310", "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": false, @@ -50492,7 +53044,7 @@ "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", - "source": "https://www.volcengine.com/docs/82379/1330310", + "source": "https://docs.volcengine.com/docs/82379/1330310", "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": false, @@ -50530,7 +53082,7 @@ "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", - "source": "https://www.volcengine.com/docs/82379/1330310", + "source": "https://docs.volcengine.com/docs/82379/1330310", "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": false, @@ -51279,7 +53831,7 @@ "supports_function_calling": true, "supports_assistant_prefill": true, "supports_reasoning": true, - "source": "https://pinstripes.io/pricing" + "source": "https://pinstripes.io/" }, "pinstripes/ps/qwen3.6-35b-a3b": { "max_tokens": 131072, @@ -51292,7 +53844,7 @@ "supports_function_calling": true, "supports_assistant_prefill": true, "supports_reasoning": true, - "source": "https://pinstripes.io/pricing" + "source": "https://pinstripes.io/" }, "pinstripes/ps/qwen3-30b-a3b": { "max_tokens": 131072, @@ -51305,7 +53857,7 @@ "supports_function_calling": true, "supports_assistant_prefill": true, "supports_reasoning": true, - "source": "https://pinstripes.io/pricing" + "source": "https://pinstripes.io/" }, "pinstripes/ps/qwen3-coder-30b-a3b": { "max_tokens": 131072, @@ -51318,7 +53870,7 @@ "supports_function_calling": true, "supports_assistant_prefill": true, "supports_reasoning": false, - "source": "https://pinstripes.io/pricing" + "source": "https://pinstripes.io/" }, "pinstripes/ps/deepseek-v4-flash": { "max_tokens": 163840, @@ -51331,7 +53883,7 @@ "supports_function_calling": true, "supports_assistant_prefill": true, "supports_reasoning": true, - "source": "https://pinstripes.io/pricing" + "source": "https://pinstripes.io/" }, "pinstripes/ps/minimax-m2.7": { "max_tokens": 1000192, @@ -51344,7 +53896,7 @@ "supports_function_calling": true, "supports_assistant_prefill": true, "supports_reasoning": false, - "source": "https://pinstripes.io/pricing" + "source": "https://pinstripes.io/" }, "darkbloom/gemma-4-26b": { "input_cost_per_token": 3e-08, @@ -51448,7 +54000,7 @@ "input_cost_per_second": 7.5e-05, "litellm_provider": "openai", "mode": "audio_transcription", - "source": "https://platform.openai.com/docs/models/gpt-transcribe", + "source": "https://developers.openai.com/api/docs/models/gpt-transcribe", "supported_endpoints": [ "/v1/audio/transcriptions", "/v1/realtime/transcription_sessions" @@ -51466,7 +54018,7 @@ "input_cost_per_second": 0.0002833333333333333, "litellm_provider": "openai", "mode": "audio_transcription", - "source": "https://platform.openai.com/docs/models/gpt-live-transcribe", + "source": "https://developers.openai.com/api/docs/models/gpt-live-transcribe", "supported_endpoints": [ "/v1/realtime", "/v1/realtime/transcription_sessions" @@ -51487,7 +54039,7 @@ "max_output_tokens": 2000, "max_tokens": 2000, "mode": "realtime", - "source": "https://platform.openai.com/docs/models/gpt-realtime-translate", + "source": "https://developers.openai.com/api/docs/models/gpt-realtime-translate", "supported_modalities": [ "audio" ], @@ -51515,7 +54067,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, - "source": "https://docs.claude.com/en/docs/about-claude/models/overview", + "source": "https://platform.claude.com/docs/en/about-claude/models/overview", "supports_adaptive_thinking": true, "thinking_always_on": true, "supports_mid_conversation_system": true, @@ -51554,7 +54106,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, - "source": "https://docs.claude.com/en/docs/about-claude/models/overview", + "source": "https://platform.claude.com/docs/en/about-claude/models/overview", "supports_adaptive_thinking": true, "thinking_always_on": true, "supports_assistant_prefill": false, @@ -51828,6 +54380,43 @@ "tpm": 250000, "rpm": 10 }, + "vertex_ai/gemini-3.5-transcribe-preview": { + "input_cost_per_audio_token": 2.5e-06, + "input_cost_per_token": 2.5e-06, + "litellm_provider": "vertex_ai", + "mode": "audio_transcription", + "output_cost_per_token": 1.2e-05, + "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ], + "supported_modalities": [ + "text", + "audio" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true + }, + "vertex_ai/gemini-3.5-transcribe-live-preview": { + "input_cost_per_audio_token": 3.5e-06, + "input_cost_per_token": 3.5e-06, + "litellm_provider": "vertex_ai", + "mode": "audio_transcription", + "output_cost_per_token": 2.1e-05, + "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "supported_endpoints": [ + "/v1/realtime" + ], + "supported_modalities": [ + "audio" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true + }, "perplexity/pplx-embed-context-v1-0.6b": { "input_cost_per_token": 8e-09, "litellm_provider": "perplexity", @@ -51947,14 +54536,14 @@ "supports_vision": true }, "fireworks_ai/deepseek-v4-flash-0731": { - "cache_read_input_token_cost": 2.8e-08, - "input_cost_per_token": 1.4e-07, + "cache_read_input_token_cost": 7e-09, + "input_cost_per_token": 2.2e-07, "litellm_provider": "fireworks_ai", "max_input_tokens": 1048576, "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "output_cost_per_token": 2.8e-07, + "output_cost_per_token": 6.6e-07, "source": "https://docs.fireworks.ai/serverless/pricing", "supports_function_calling": true, "supports_reasoning": true, @@ -54596,5 +57185,299 @@ "output_cost_per_token_above_200k_tokens": 5e-06, "cache_read_input_token_cost_above_200k_tokens": 4e-07, "supports_response_schema": true + }, + "groq/qwen/qwen3.8-27b": { + "input_cost_per_token": 8e-07, + "litellm_provider": "groq", + "max_input_tokens": 131042, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 4e-06, + "source": "https://console.groq.com/docs/model/qwen/qwen3.8-27b", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "mistral/mistral-medium-3.5": { + "cache_read_input_token_cost": 1.5e-07, + "input_cost_per_token": 1.5e-06, + "litellm_provider": "mistral", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 7.5e-06, + "source": "https://docs.mistral.ai/models/model-cards/mistral-medium-3-5-26-04", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "mistral/mistral-vibe-cli-latest": { + "cache_read_input_token_cost": 1.5e-07, + "input_cost_per_token": 1.5e-06, + "litellm_provider": "mistral", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 7.5e-06, + "source": "https://docs.mistral.ai/models/model-cards/mistral-medium-3-5-26-04", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "mistral/mistral-vibe-cli-with-tools": { + "cache_read_input_token_cost": 1.5e-07, + "input_cost_per_token": 1.5e-06, + "litellm_provider": "mistral", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 7.5e-06, + "source": "https://docs.mistral.ai/models/model-cards/mistral-medium-3-5-26-04", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "mistral/mistral-vibe-cli-fast": { + "cache_read_input_token_cost": 1.5e-08, + "input_cost_per_token": 1.5e-07, + "litellm_provider": "mistral", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 6e-07, + "source": "https://docs.mistral.ai/models/model-cards/mistral-small-4-0-26-03", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "mistral/mistral-code-latest": { + "cache_read_input_token_cost": 3e-08, + "input_cost_per_token": 3e-07, + "litellm_provider": "mistral", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 9e-07, + "source": "https://docs.mistral.ai/models/model-cards/codestral-25-08", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "mistral/mistral-code-fim-latest": { + "cache_read_input_token_cost": 3e-08, + "input_cost_per_token": 3e-07, + "litellm_provider": "mistral", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 9e-07, + "source": "https://docs.mistral.ai/models/model-cards/codestral-25-08", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "mistral/mistral-code-agent-latest": { + "input_cost_per_token": 4e-07, + "litellm_provider": "mistral", + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "max_tokens": 256000, + "mode": "chat", + "output_cost_per_token": 2e-06, + "source": "https://mistral.ai/news/devstral-2-vibe-cli", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "mistral/mistral-ocr-3": { + "litellm_provider": "mistral", + "ocr_cost_per_page": 0.002, + "annotation_cost_per_page": 0.003, + "mode": "ocr", + "supported_endpoints": [ + "/v1/ocr" + ], + "source": "https://mistral.ai/pricing#api-pricing" + }, + "mistral/mistral-ocr-3-0": { + "litellm_provider": "mistral", + "ocr_cost_per_page": 0.002, + "annotation_cost_per_page": 0.003, + "mode": "ocr", + "supported_endpoints": [ + "/v1/ocr" + ], + "source": "https://mistral.ai/pricing#api-pricing" + }, + "mistral/mistral-ocr-4": { + "annotation_cost_per_page": 0.005, + "litellm_provider": "mistral", + "mode": "ocr", + "ocr_cost_per_page": 0.004, + "source": "https://docs.mistral.ai/models/model-cards/ocr-4-1", + "supported_endpoints": [ + "/v1/ocr" + ] + }, + "mistral/voxtral-mini-latest": { + "input_cost_per_second": 5e-05, + "litellm_provider": "mistral", + "mode": "audio_transcription", + "source": "https://docs.mistral.ai/models/model-cards/voxtral-mini-transcribe-26-02", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ], + "supported_modalities": [ + "audio" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true + }, + "mistral/voxtral-mini-realtime-2602": { + "input_cost_per_second": 0.0001, + "litellm_provider": "mistral", + "mode": "audio_transcription", + "source": "https://docs.mistral.ai/models/model-cards/voxtral-mini-transcribe-realtime-26-02", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ], + "supported_modalities": [ + "audio" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true + }, + "mistral/voxtral-mini-realtime-latest": { + "input_cost_per_second": 0.0001, + "litellm_provider": "mistral", + "mode": "audio_transcription", + "source": "https://docs.mistral.ai/models/model-cards/voxtral-mini-transcribe-realtime-26-02", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ], + "supported_modalities": [ + "audio" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true + }, + "mistral/labs-leanstral-1-5-1": { + "input_cost_per_token": 0.0, + "litellm_provider": "mistral", + "max_input_tokens": 262144, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 0.0, + "source": "https://docs.mistral.ai/models/model-cards/leanstral-1-5", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "fireworks_ai/accounts/fireworks/models/glm-5p3": { + "cache_read_input_token_cost": 2.6e-07, + "input_cost_per_token": 1.4e-06, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 1048576, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 4.4e-06, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": false + }, + "fireworks_ai/accounts/fireworks/models/qwen3-embedding-8b": { + "input_cost_per_token": 1e-07, + "output_cost_per_token": 0.0, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 40960, + "max_output_tokens": 40960, + "max_tokens": 40960, + "mode": "embedding", + "source": "https://docs.fireworks.ai/serverless/pricing" + }, + "zai/glm-5.2": { + "cache_creation_input_token_cost": 0, + "cache_read_input_token_cost": 2.6e-07, + "input_cost_per_token": 1.4e-06, + "litellm_provider": "zai", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 4.4e-06, + "source": "https://docs.z.ai/guides/overview/pricing", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "together_ai/Qwen/Qwen3.8-Flash": { + "input_cost_per_token": 1.5e-07, + "litellm_provider": "together_ai", + "max_input_tokens": 1000000, + "max_tokens": 1000000, + "mode": "chat", + "output_cost_per_token": 4.7e-07, + "source": "https://docs.together.ai/docs/serverless-models" + }, + "cerebras/gemma-4-31b": { + "input_cost_per_token": 9.9e-07, + "litellm_provider": "cerebras", + "max_input_tokens": 131072, + "max_output_tokens": 40960, + "max_tokens": 40960, + "mode": "chat", + "output_cost_per_token": 1.49e-06, + "source": "https://api.cerebras.ai/public/v1/models/gemma-4-31b", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "elevenlabs/scribe_v2": { + "input_cost_per_second": 6.11e-05, + "litellm_provider": "elevenlabs", + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://elevenlabs.io/pricing/api", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] } } diff --git a/litellm/models/base.py b/litellm/models/base.py index 7eedf10212e..8125bfd0205 100644 --- a/litellm/models/base.py +++ b/litellm/models/base.py @@ -33,6 +33,6 @@ class DomainModel(BaseModel): return cls(**record.dict()) return cls(**dict(record)) - def to_db_dict(self, exclude_unset: bool = False) -> dict[str, Any]: + def to_db_dict(self, exclude_unset: bool = False) -> dict[str, object]: """Convert domain model to a dictionary for database operations.""" return self.model_dump(exclude_none=True, exclude_unset=exclude_unset) diff --git a/litellm/models/model.py b/litellm/models/model.py index 209f26d4837..a0c840341ab 100644 --- a/litellm/models/model.py +++ b/litellm/models/model.py @@ -29,6 +29,8 @@ class LiteLLM_ProxyModelTable(LiteLLMPydanticObjectBase): @model_validator(mode="before") @classmethod def check_potential_json_str(cls, values): + if not isinstance(values, dict): + return values if isinstance(values.get("litellm_params"), str): try: values["litellm_params"] = json.loads(values["litellm_params"]) diff --git a/litellm/models/user.py b/litellm/models/user.py index 259c3440d87..82f78c28078 100644 --- a/litellm/models/user.py +++ b/litellm/models/user.py @@ -7,7 +7,7 @@ Canonical definition for ``litellm_usertable``. Re-exported from from datetime import datetime -from pydantic import ConfigDict, Field, model_validator +from pydantic import BaseModel, ConfigDict, Field, model_validator from litellm.models.object_permission import LiteLLM_ObjectPermissionTable from litellm.models.organization_membership import ( @@ -67,3 +67,11 @@ class LiteLLM_UserTable(LiteLLMPydanticObjectBase): if not self.models: return True return model_name in self.models + + +class SCIMPlaceholder(BaseModel): + """A user row keyed by a value that names another account by SSO identity or email.""" + + placeholder_user_id: str + resolved_user_ids: tuple[str, ...] + team_ids: tuple[str, ...] diff --git a/litellm/passthrough/main.py b/litellm/passthrough/main.py index 4b30afb2f98..c4bd03fb1c3 100644 --- a/litellm/passthrough/main.py +++ b/litellm/passthrough/main.py @@ -2,17 +2,22 @@ This module is used to pass through requests to the LLM APIs. """ +from __future__ import annotations + import asyncio import contextvars -from collections.abc import AsyncGenerator, Coroutine, Generator +from collections.abc import AsyncGenerator, AsyncIterator, Awaitable, Coroutine, Generator, Iterator from functools import partial -from typing import TYPE_CHECKING, Any, Final, Optional, cast +from types import TracebackType +from typing import Any, Final, cast import httpx -from httpx._types import CookieTypes, QueryParamTypes, RequestFiles +from httpx._types import CookieTypes, QueryParamTypes, RequestContent, RequestFiles from litellm._logging import verbose_logger from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.base_llm.passthrough.transformation import BasePassthroughConfig from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler from litellm.passthrough.utils import CommonUtils @@ -21,9 +26,222 @@ from litellm.utils import client base_llm_http_handler = BaseLLMHTTPHandler() from .utils import BasePassthroughUtils -if TYPE_CHECKING: - from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj - from litellm.llms.base_llm.passthrough.transformation import BasePassthroughConfig + +async def _as_async_generator(iterable: AsyncIterator[bytes]) -> AsyncGenerator[bytes, bytes]: + async for chunk in iterable: + yield chunk + + +def _as_generator(iterable: Iterator[bytes]) -> Generator[bytes, bytes, None]: + yield from iterable + + +class AsyncPassthroughStreamingResponse(AsyncGenerator[bytes, bytes]): + def __init__( + self, + response: Awaitable[httpx.Response], + litellm_logging_obj: LiteLLMLoggingObj, + provider_config: BasePassthroughConfig, + ) -> None: + self._initialized = False + self._status_code: int = 0 + self._headers = httpx.Headers() + self._response_coro = response + self._response: httpx.Response + self._iterator: AsyncGenerator[bytes, bytes] + self._litellm_logging_obj = litellm_logging_obj + self._provider_config = provider_config + self._raw_bytes: list[bytes] = [] # mutable-ok: instance buffer for streaming chunks + self._flush_scheduled = False + self._background_tasks: set[asyncio.Task] = set() # mutable-ok: instance set for background task tracking + self._hidden_params: dict[str, object] = {} # mutable-ok: router attaches response headers here in place + + @property + def status_code(self) -> int: + if not self._initialized: + raise RuntimeError("AsyncPassthroughStreamingResponse must be awaited before accessing status_code") + return self._status_code + + @status_code.setter + def status_code(self, value: int) -> None: + self._status_code = value + + @property + def headers(self) -> httpx.Headers: + if not self._initialized: + raise RuntimeError("AsyncPassthroughStreamingResponse must be awaited before accessing headers") + return self._headers + + @headers.setter + def headers(self, value: httpx.Headers) -> None: + self._headers = value + + def __await__(self) -> Iterator[Any]: + async def _init(): + if not self._initialized: + self._response = await self._response_coro + self.headers = self._response.headers + self.status_code = self._response.status_code + self._initialized = True + try: + self._response.raise_for_status() + self._iterator = _as_async_generator(self._response.aiter_bytes()) + except Exception: # noqa: BLE001 # Safe catch-all for cleanup logic + try: + await self._response.aread() + except Exception: # noqa: BLE001 S110 # Safe catch-all for cleanup logic + pass + try: + await self._response.aclose() + except Exception: # noqa: BLE001 S110 # Safe catch-all for cleanup logic + pass + raise + return self + + return _init().__await__() + + def _start_flush(self) -> None: + if self._flush_scheduled or not self._raw_bytes: + return + self._flush_scheduled = True + + try: + task: Final = asyncio.create_task( + self._litellm_logging_obj.async_flush_passthrough_collected_chunks( + raw_bytes=self._raw_bytes, + provider_config=self._provider_config, + ) + ) + + # Compliant: Save a strong reference to prevent GC + self._background_tasks.add(task) + + # Remove the task from the set when it finishes to avoid memory leaks + task.add_done_callback(self._background_tasks.discard) + except Exception as e: # noqa: BLE001 # Safe catch-all for verbose logging + verbose_logger.exception( + "Failed to schedule passthrough spend-tracking flush; %d buffered chunks dropped: %s", + len(self._raw_bytes), + e, + ) + + def __aiter__(self) -> AsyncPassthroughStreamingResponse: + return self + + def aiter_bytes(self) -> AsyncPassthroughStreamingResponse: + return self + + async def __anext__(self) -> bytes: + if not self._initialized: + await self # pyright: ignore[reportGeneralTypeIssues] # structural type check misses __await__ + try: + chunk: Final = await anext(self._iterator) + self._raw_bytes.append(chunk) + except Exception: # noqa: BLE001 # Safe catch-all for cleanup logic + self._start_flush() + try: + await self._response.aclose() + except Exception: # noqa: BLE001 S110 # Safe catch-all for cleanup logic + pass + raise + else: + return chunk + + async def asend(self, value: bytes) -> bytes: + if not self._initialized: + await self # pyright: ignore[reportGeneralTypeIssues] # structural type check misses __await__ + return await self._iterator.asend(value) + + async def athrow( + self, + typ: BaseException | type[BaseException], + val: BaseException | object = None, + tb: TracebackType | None = None, + ) -> bytes: + if not self._initialized: + await self # pyright: ignore[reportGeneralTypeIssues] # structural type check misses __await__ + return await self._iterator.athrow(typ, val, tb) # pyright: ignore[reportCallIssue, reportArgumentType] # matches one of the athrow overloads + + async def aclose(self) -> None: + self._start_flush() + try: + if self._initialized: + await self._iterator.aclose() + await self._response.aclose() + except Exception: # noqa: BLE001 S110 # Safe catch-all for cleanup logic + pass + + +class PassthroughStreamingResponse(Generator[bytes, bytes, None]): + def __init__( + self, + response: httpx.Response, + litellm_logging_obj: LiteLLMLoggingObj, + provider_config: BasePassthroughConfig, + ) -> None: + self._response = response + self.headers = response.headers + self.status_code = response.status_code + self._litellm_logging_obj = litellm_logging_obj + self._provider_config = provider_config + self._iterator: Generator[bytes, bytes, None] = _as_generator(response.iter_bytes()) + self._raw_bytes: list[bytes] = [] # mutable-ok: instance buffer for streaming chunks + self._flush_scheduled = False + + def _start_flush(self) -> None: + if self._flush_scheduled or not self._raw_bytes: + return + self._flush_scheduled = True + + from litellm.utils import executor + + try: + executor.submit( + self._litellm_logging_obj.flush_passthrough_collected_chunks, + raw_bytes=self._raw_bytes, + provider_config=self._provider_config, + ) + except Exception as e: # noqa: BLE001 # Safe catch-all for verbose logging + verbose_logger.exception( + "Failed to schedule passthrough spend-tracking flush; %d buffered chunks dropped: %s", + len(self._raw_bytes), + e, + ) + + def __iter__(self) -> PassthroughStreamingResponse: + return self + + def __next__(self) -> bytes: + try: + chunk: Final = next(self._iterator) + self._raw_bytes.append(chunk) + except Exception: # noqa: BLE001 # Safe catch-all for cleanup logic + self._start_flush() + try: + self._response.close() + except Exception: # noqa: BLE001 S110 # Safe catch-all for cleanup logic + pass + raise + else: + return chunk + + def send(self, value: bytes) -> bytes: + return self._iterator.send(value) + + def throw( + self, + typ: BaseException | type[BaseException], + val: BaseException | object = None, + tb: TracebackType | None = None, + ) -> bytes: + return self._iterator.throw(typ, val, tb) # pyright: ignore[reportCallIssue, reportArgumentType] # matches one of the throw overloads + + def close(self) -> None: + self._start_flush() + try: + self._response.close() + except Exception: # noqa: BLE001 S110 # Safe catch-all for cleanup logic + pass @client @@ -37,15 +255,15 @@ async def allm_passthrough_route( api_key: str | None = None, request_query_params: dict | None = None, request_headers: dict | None = None, - content: Any | None = None, + content: RequestContent | None = None, data: dict | None = None, files: RequestFiles | None = None, - json: Any | None = None, + json: object | None = None, params: QueryParamTypes | None = None, cookies: CookieTypes | None = None, client: HTTPHandler | AsyncHTTPHandler | None = None, **kwargs, -) -> httpx.Response | AsyncGenerator[Any, Any]: +) -> httpx.Response | AsyncGenerator[bytes, bytes]: """ Async: Reranks a list of documents based on their relevance to the query """ @@ -64,7 +282,7 @@ async def allm_passthrough_route( from litellm.utils import ProviderConfigManager provider_config = cast( - Optional["BasePassthroughConfig"], kwargs.get("provider_config") + BasePassthroughConfig | None, kwargs.get("provider_config") ) or ProviderConfigManager.get_provider_passthrough_config( provider=LlmProviders(custom_llm_provider), model=model, @@ -132,12 +350,12 @@ async def allm_passthrough_route( if resolved_custom_llm_provider: try: provider_config = cast( - Optional["BasePassthroughConfig"], kwargs.get("provider_config") + BasePassthroughConfig | None, kwargs.get("provider_config") ) or ProviderConfigManager.get_provider_passthrough_config( provider=LlmProviders(resolved_custom_llm_provider), model=model, ) - except Exception: + except Exception: # noqa: BLE001 S110 # If we can't get provider config, pass None pass @@ -162,20 +380,20 @@ def llm_passthrough_route( api_key: str | None = None, request_query_params: dict | None = None, request_headers: dict | None = None, - content: Any | None = None, + content: RequestContent | None = None, data: dict | None = None, files: RequestFiles | None = None, - json: Any | None = None, + json: object | None = None, params: QueryParamTypes | None = None, cookies: CookieTypes | None = None, client: HTTPHandler | AsyncHTTPHandler | None = None, **kwargs, ) -> ( httpx.Response - | Coroutine[Any, Any, httpx.Response] - | Coroutine[Any, Any, httpx.Response | AsyncGenerator[Any, Any]] - | Generator[Any, Any, Any] - | AsyncGenerator[Any, Any] + | Coroutine[object, object, httpx.Response] + | Coroutine[object, object, httpx.Response | AsyncGenerator[bytes, bytes]] + | Generator[bytes, bytes, None] + | AsyncGenerator[bytes, bytes] ): """ Pass through requests to the LLM APIs. @@ -190,7 +408,9 @@ def llm_passthrough_route( _is_async: Final = bool(kwargs.get("allm_passthrough_route", False)) - litellm_logging_obj: Final = cast("LiteLLMLoggingObj", kwargs.get("litellm_logging_obj")) + litellm_logging_obj: Final = cast( + LiteLLMLoggingObj, kwargs.get("litellm_logging_obj") + ) # cast-ok: logging obj is constructed upstream; tests inject mocks model, custom_llm_provider, api_key, api_base = get_llm_provider( model=model, @@ -235,7 +455,7 @@ def llm_passthrough_route( ) provider_config: Final = cast( - Optional["BasePassthroughConfig"], kwargs.get("provider_config") + BasePassthroughConfig | None, kwargs.get("provider_config") ) or ProviderConfigManager.get_provider_passthrough_config( provider=LlmProviders(custom_llm_provider), model=model, @@ -276,10 +496,13 @@ def llm_passthrough_route( forward_headers=False, ) + _request_data: dict | None = ( + data if isinstance(data, dict) else (json if isinstance(json, dict) else None) + ) # rebind-ok: conditional headers, signed_json_body = provider_config.sign_request( headers=headers, litellm_params=litellm_params_dict, - request_data=data if data else json, + request_data=_request_data, api_base=str(updated_url), model=model, ) @@ -301,9 +524,12 @@ def llm_passthrough_route( ) ## IS STREAMING REQUEST + _streaming_request_data: dict = ( + data if isinstance(data, dict) else (json if isinstance(json, dict) else {}) + ) # rebind-ok: conditional is_streaming_request: Final = provider_config.is_streaming_request( endpoint=endpoint, - request_data=data or json or {}, + request_data=_streaming_request_data, ) # Update logging object with streaming status @@ -334,18 +560,26 @@ def llm_passthrough_route( else: # Sync path - client.client.send returns Response directly response: httpx.Response = client.client.send(request=request, stream=is_streaming_request) - response.raise_for_status() + try: + response.raise_for_status() + except Exception: # noqa: BLE001 # Safe catch-all for cleanup logic + try: + response.read() + except Exception: # noqa: BLE001 S110 # Safe catch-all for cleanup logic + pass + try: + response.close() + except Exception: # noqa: BLE001 S110 # Safe catch-all for cleanup logic + pass + raise - if ( - hasattr(response, "iter_bytes") and is_streaming_request - ): # yield the chunk, so we can store it in the logging object - return _sync_streaming(response, litellm_logging_obj, provider_config) + if hasattr(response, "iter_bytes") and is_streaming_request: + return PassthroughStreamingResponse(response, litellm_logging_obj, provider_config) else: - # For non-streaming responses, yield the entire response return response except Exception as e: - if provider_config is None: - raise e + # provider_config is guaranteed non-None here due to the earlier guard + assert provider_config is not None raise base_llm_http_handler._handle_error( e=e, provider_config=provider_config, @@ -356,9 +590,9 @@ async def _async_passthrough_request( client: HTTPHandler | AsyncHTTPHandler, request: httpx.Request, is_streaming_request: bool, - litellm_logging_obj: "LiteLLMLoggingObj", - provider_config: "BasePassthroughConfig", -) -> httpx.Response | AsyncGenerator[Any, Any]: + litellm_logging_obj: LiteLLMLoggingObj, + provider_config: BasePassthroughConfig, +) -> httpx.Response | AsyncGenerator[bytes, bytes]: """ Handle async passthrough requests. Uses async client to send request and properly handles streaming. @@ -369,8 +603,7 @@ async def _async_passthrough_request( # Check if it's a coroutine and await it if asyncio.iscoroutine(response_result): if is_streaming_request: - # Pass the coroutine to _async_streaming which will await it - return _async_streaming( + return await AsyncPassthroughStreamingResponse( # pyright: ignore[reportGeneralTypeIssues] # structural type check misses __await__ response=response_result, litellm_logging_obj=litellm_logging_obj, provider_config=provider_config, @@ -383,84 +616,3 @@ async def _async_passthrough_request( else: # Fallback for sync-like behavior (shouldn't happen in async path) raise Exception("Expected coroutine from async client") - - -def _sync_streaming( - response: httpx.Response, - litellm_logging_obj: "LiteLLMLoggingObj", - provider_config: "BasePassthroughConfig", -): - from litellm.utils import executor - - raw_bytes: Final[list[bytes]] = [] - flush_scheduled = False - try: - for chunk in response.iter_bytes(): - raw_bytes.append(chunk) - yield chunk - finally: - if not flush_scheduled and raw_bytes: - flush_scheduled = True - try: - executor.submit( - litellm_logging_obj.flush_passthrough_collected_chunks, - raw_bytes=raw_bytes, - provider_config=provider_config, - ) - except Exception as e: - verbose_logger.exception( - "Failed to schedule passthrough spend-tracking flush " - "in _sync_streaming; %d buffered chunks dropped: %s", - len(raw_bytes), - e, - ) - - -async def _async_streaming( - response: Coroutine[Any, Any, httpx.Response], - litellm_logging_obj: "LiteLLMLoggingObj", - provider_config: "BasePassthroughConfig", -): - iter_response: Final = await response - - try: - iter_response.raise_for_status() - except Exception: - try: - await iter_response.aclose() - except Exception: - pass - raise - - raw_bytes: Final[list[bytes]] = [] - flush_scheduled = False - try: - async for chunk in iter_response.aiter_bytes(): - raw_bytes.append(chunk) - yield chunk - except Exception: - try: - await iter_response.aclose() - except Exception: - pass - raise - finally: - # GeneratorExit (raised on client disconnect) is not caught by - # `except Exception`; the finally block ensures partial usage - # still gets flushed for spend tracking. See LIT-2642. - if not flush_scheduled and raw_bytes: - flush_scheduled = True - try: - asyncio.create_task( - litellm_logging_obj.async_flush_passthrough_collected_chunks( - raw_bytes=raw_bytes, - provider_config=provider_config, - ) - ) - except Exception as e: - verbose_logger.exception( - "Failed to schedule passthrough spend-tracking flush " - "in _async_streaming; %d buffered chunks dropped: %s", - len(raw_bytes), - e, - ) diff --git a/litellm/provider_endpoints_support_backup.json b/litellm/provider_endpoints_support_backup.json index ead26ab65c5..9d6b1e18f59 100644 --- a/litellm/provider_endpoints_support_backup.json +++ b/litellm/provider_endpoints_support_backup.json @@ -671,6 +671,42 @@ "interactions": true } }, + "qwencloud": { + "display_name": "QwenCloud (`qwencloud`)", + "url": "https://docs.litellm.ai/docs/providers/qwencloud", + "endpoints": { + "chat_completions": true, + "messages": true, + "responses": true, + "embeddings": true, + "image_generations": true, + "audio_transcriptions": false, + "audio_speech": false, + "moderations": false, + "batches": false, + "rerank": true, + "a2a": true, + "interactions": true + } + }, + "qwen_ai_platform": { + "display_name": "Qwen AI Platform (`qwen_ai_platform`)", + "url": "https://docs.litellm.ai/docs/providers/qwencloud", + "endpoints": { + "chat_completions": true, + "messages": true, + "responses": true, + "embeddings": true, + "image_generations": true, + "audio_transcriptions": false, + "audio_speech": false, + "moderations": false, + "batches": false, + "rerank": true, + "a2a": true, + "interactions": true + } + }, "databricks": { "display_name": "Databricks (`databricks`)", "url": "https://docs.litellm.ai/docs/providers/databricks", diff --git a/litellm/proxy/_experimental/mcp_server/db.py b/litellm/proxy/_experimental/mcp_server/db.py index cf74cbd187e..41d0b78b555 100644 --- a/litellm/proxy/_experimental/mcp_server/db.py +++ b/litellm/proxy/_experimental/mcp_server/db.py @@ -600,23 +600,19 @@ async def get_all_mcp_servers( NULL approval_status predates the approval workflow, so those rows are kept explicitly rather than dropped by a bare inequality, which SQL evaluates as NULL and would silently hide them. """ - try: - where: Final[prisma_db_types.LiteLLM_MCPServerTableWhereInput] = ( - {"approval_status": approval_status} - if approval_status is not None - # mutable-ok: prisma where-inputs must be plain dicts, and both `NOT` and `not` drop - # NULL rows (measured), so the OR is the only NULL-preserving way to exclude drafts - else {"OR": [{"approval_status": None}, {"approval_status": {"not": MCPApprovalStatus.draft}}]} - ) - mcp_servers: Final = await _db_find_mcp_server_rows(prisma_client, where) + where: Final[prisma_db_types.LiteLLM_MCPServerTableWhereInput] = ( + {"approval_status": approval_status} + if approval_status is not None + # mutable-ok: prisma where-inputs must be plain dicts, and both `NOT` and `not` drop + # NULL rows (measured), so the OR is the only NULL-preserving way to exclude drafts + else {"OR": [{"approval_status": None}, {"approval_status": {"not": MCPApprovalStatus.draft}}]} + ) + mcp_servers: Final = await _db_find_mcp_server_rows(prisma_client, where) - tables: Final = [LiteLLM_MCPServerTable.model_validate(mcp_server.model_dump()) for mcp_server in mcp_servers] - for table in tables: - decrypt_global_env_var_values(table.env_vars) - return tables - except Exception as e: - verbose_proxy_logger.debug("litellm.proxy._experimental.mcp_server.db.py::get_all_mcp_servers - %s", e) - return [] + tables: Final = [LiteLLM_MCPServerTable.model_validate(mcp_server.model_dump()) for mcp_server in mcp_servers] + for table in tables: + decrypt_global_env_var_values(table.env_vars) + return tables async def get_mcp_server(prisma_client: PrismaClient, server_id: str) -> LiteLLM_MCPServerTable | None: diff --git a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py index 93b85edd88d..94bca9460dd 100644 --- a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py @@ -9,7 +9,7 @@ from typing import TYPE_CHECKING, Any, Final, Literal, Optional from urllib.parse import parse_qsl, urlencode, urlparse, urlunparse import httpx -from fastapi import APIRouter, Form, HTTPException, Request +from fastapi import APIRouter, Depends, Form, HTTPException, Request from fastapi.responses import HTMLResponse, JSONResponse, RedirectResponse, Response from pydantic import BaseModel, ConfigDict, Field, SecretStr, ValidationError @@ -46,6 +46,7 @@ from litellm.proxy._experimental.mcp_server.gateway_dcr_flow import ( aggregate_authorize, aggregate_token, complete_connect_flow, + introspect_gateway_token, is_gateway_dcr_client_id, is_proxy_api_resource, native_client_auth_contract, @@ -67,6 +68,7 @@ from litellm.proxy._experimental.mcp_server.proxy_api_credentials import ( mint_proxy_credential, ) from litellm.proxy.auth.ip_address_utils import IPAddressUtils +from litellm.proxy.auth.user_api_key_auth import user_api_key_auth from litellm.proxy.common_utils.encrypt_decrypt_utils import ( decrypt_value_helper, encrypt_value_helper, @@ -1094,8 +1096,7 @@ async def exchange_token_with_server( headers={"Accept": "application/json", **token_request.headers}, data=token_data, ) - if response is not None: - response.raise_for_status() + response.raise_for_status() except httpx.HTTPStatusError as exc: fault: Final = classify_upstream_token_rejection( exc.response, @@ -1117,11 +1118,6 @@ async def exchange_token_with_server( ) return _bridge_mint_error_response("invalid_refresh") return render_token_fault(fault) - if response is None: - raise HTTPException( - status_code=502, - detail="MCP upstream token endpoint returned no response", - ) token_response = response.json() # Validate token response against server-configured rules before any storage. @@ -1534,16 +1530,10 @@ async def _post_dcr_registration( headers=headers, json=register_data, ) - if response is not None: - response.raise_for_status() + response.raise_for_status() except httpx.HTTPStatusError as exc: status_code, detail = dcr_fault_detail(classify_upstream_dcr_rejection(exc.response, log_context=server_id)) raise HTTPException(status_code=status_code, detail=detail) from exc - if response is None: - raise HTTPException( - status_code=502, - detail="MCP upstream registration endpoint returned no response", - ) return response @@ -1951,6 +1941,26 @@ async def revoke_endpoint(request: Request, token: str = Form(...), client_id: s return await revoke_refresh_token(token=token, client_id=client_id, master_key=master_key, cache=user_api_key_cache) +@router.post("/introspect", dependencies=[Depends(user_api_key_auth)]) +async def introspect_endpoint(token: str = Form(...)) -> Response: + """RFC 7662 introspection for gateway-issued session tokens (``llm_session_`` / + ``llm_srefresh_``), so an external gateway can validate them without the signing + secret. The caller authenticates with a LiteLLM virtual key (section 2.1, enforced by + the route dependency); any token the gateway cannot vouch for answers + ``{"active": false}`` with no further detail.""" + from litellm.proxy.proxy_server import ( # noqa: PLC0415 # circular import at module load + master_key, + user_api_key_cache, + ) + + return await introspect_gateway_token( + token=token, + master_key=master_key, + reload_user=_reload_active_user_by_id, + cache=user_api_key_cache, + ) + + @router.get("/.well-known/litellm-cli-auth") async def native_client_auth_discovery(request: Request) -> JSONResponse: """The versioned contract a native client (``lite login --pkce``, or a CLI in any other @@ -2456,6 +2466,7 @@ def _build_aggregate_authorization_server_response(request: Request) -> dict: "issuer": f"{request_base_url}/mcp", "authorization_endpoint": f"{request_base_url}/authorize", "token_endpoint": f"{request_base_url}/token", + "introspection_endpoint": f"{request_base_url}/introspect", "registration_endpoint": f"{request_base_url}/register", "response_types_supported": ["code"], "scopes_supported": [], diff --git a/litellm/proxy/_experimental/mcp_server/gateway_dcr_flow.py b/litellm/proxy/_experimental/mcp_server/gateway_dcr_flow.py index 853a07972c1..a43e762a456 100644 --- a/litellm/proxy/_experimental/mcp_server/gateway_dcr_flow.py +++ b/litellm/proxy/_experimental/mcp_server/gateway_dcr_flow.py @@ -70,13 +70,19 @@ from litellm.proxy._experimental.mcp_server.outbound_credentials.session_credent open_session_refresh_bearer, ) from litellm.proxy._experimental.mcp_server.outbound_credentials.session_token import ( + SESSION_ISSUER, SESSION_REFRESH_TTL_SECONDS, MintedSessionToken, + OpenedSessionToken, SessionAudience, SessionPrincipal, SessionSigningKeys, + is_session_refresh_token, + is_session_token, mint_session_refresh_token, mint_session_token, + open_session_refresh_token, + open_session_token, ) from litellm.proxy.common_utils.encrypt_decrypt_utils import ( decrypt_value_helper, @@ -885,6 +891,23 @@ class _SingleUseGuard: count = await self._cache.async_increment_cache(key, 1, ttl=ttl_seconds, local_only=True) return "first" if count == 1 else "replayed" + async def peek(self, key: str) -> Literal["unclaimed", "claimed", "unavailable"]: + """Read-only view of a single-use marker, resolved against the same shared authority as + :meth:`claim` so introspection observes exactly the record redemption and revocation wrote. + A backend fault is ``"unavailable"`` (fail closed) rather than a guess either way.""" + from litellm.proxy.proxy_server import redis_usage_cache # noqa: PLC0415 # circular import at module load + + redis_cache: Final = redis_usage_cache or getattr(self._cache, "redis_cache", None) + if redis_cache is not None: + try: + value = await redis_cache.async_get_cache(key) + except Exception as e: # noqa: BLE001 # ANY Redis fault fails the read closed + verbose_logger.warning("mcp gateway single-use peek: shared cache backend unavailable: %s", e) + return "unavailable" + return "unclaimed" if value is None else "claimed" + local: Final = await self._cache.async_get_cache(key, local_only=True) + return "unclaimed" if local is None else "claimed" + def _session_token_pair(principal: SessionPrincipal, keys: SessionSigningKeys, now: datetime) -> Response: access: Final = mint_session_token(principal, keys, now) @@ -1199,3 +1222,83 @@ async def revoke_refresh_token(token: str, client_id: str, master_key: str | Non if burned == "unavailable": return _oauth_error(503, "temporarily_unavailable", _CLAIM_UNAVAILABLE_DESCRIPTION) return Response(content="{}", media_type="application/json", headers=TOKEN_NO_CACHE_HEADERS) + + +def _inactive_introspection_response() -> Response: + """RFC 7662 section 2.2: any token the gateway cannot vouch for, whatever the reason + (wrong family, bad signature, expired, revoked, or a deactivated user), answers 200 + with ``active: false`` and nothing else, so introspection is not a token oracle.""" + return JSONResponse(status_code=200, content={"active": False}, headers=TOKEN_NO_CACHE_HEADERS) + + +def _active_introspection_response(opened: OpenedSessionToken) -> Response: + principal: Final = opened.principal + optional_claims: Final = { + key: value + for key, value in ( + ("token_type", "Bearer" if opened.kind == "session" else None), + ("team_id", principal.team_id), + ("resource_server_id", principal.resource_server_id), + ("audience", principal.audience), + ) + if value is not None + } + return JSONResponse( + status_code=200, + content={ + "active": True, + "iss": SESSION_ISSUER, + "sub": principal.user_id, + "client_id": principal.client_id, + "jti": opened.jti, + "iat": opened.iat, + "exp": opened.exp, + "kind": opened.kind, + **optional_claims, + }, + headers=TOKEN_NO_CACHE_HEADERS, + ) + + +async def introspect_gateway_token( + token: str, + master_key: str | None, + reload_user: ReloadUser, + cache: DualCache, +) -> Response: + """RFC 7662 introspection for the gateway's session tokens, so an external gateway + (Kong, an API management layer) can validate a LiteLLM-issued MCP session credential + without holding the signing secret. The caller is already authenticated by the route + (section 2.1). Active means everything admission itself would require: valid signature + under the configured session signing keys, unexpired, not a revoked or rotated refresh + token, and a litellm user that is still live, so a deactivated user's outstanding + tokens introspect as inactive immediately. A shared-backend or DB outage answers 503 + rather than guessing in either direction.""" + if master_key is None: + verbose_logger.error("mcp_gateway_dcr introspect rejected: no master_key configured") + return _oauth_error(500, "server_error", "the gateway has no master key configured") + keys: Final = active_session_signing_keys(master_key) + if isinstance(keys, SessionSigningConfigError): + verbose_logger.error("mcp_gateway_dcr introspect rejected: %s", keys.detail) + return _oauth_error(500, "server_error", keys.detail) + now: Final = datetime.now(timezone.utc) + if is_session_token(token): + opened = open_session_token(token, keys, now) + elif is_session_refresh_token(token): + opened = open_session_refresh_token(token, keys, now) + else: + return _inactive_introspection_response() + if not isinstance(opened, OpenedSessionToken): + return _inactive_introspection_response() + if opened.kind == "session_refresh": + peeked: Final = await _SingleUseGuard(cache).peek(f"{_USED_REFRESH_CACHE_PREFIX}{opened.jti}") + if peeked == "unavailable": + return _oauth_error(503, "temporarily_unavailable", _CLAIM_UNAVAILABLE_DESCRIPTION) + if peeked == "claimed": + return _inactive_introspection_response() + failure: Final = await reload_user(opened.principal.user_id) + if failure == "unavailable": + return _oauth_error(503, "temporarily_unavailable", "the gateway database is unavailable; retry") + if failure is not None: + return _inactive_introspection_response() + return _active_introspection_response(opened) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 2330120adad..1f552ff3e13 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -13,7 +13,7 @@ import json import os import re import time -from collections.abc import AsyncIterator, Callable, Mapping, Sequence +from collections.abc import AsyncIterator, Awaitable, Callable, Mapping, Sequence from contextlib import asynccontextmanager from dataclasses import dataclass, replace from typing import TYPE_CHECKING, Any, Final, Literal, TypeAlias, TypedDict, cast @@ -1206,7 +1206,7 @@ def _deserialize_json_dict(data: str | _StringMap | None) -> dict[str, str] | No return data -def _deserialize_json_list(data: Any) -> list[dict[str, Any]] | None: +def _deserialize_json_list(data: object) -> list[dict[str, Any]] | None: """Deserialize a JSON array stored in the DB (``env_vars`` and friends). Returns ``None`` for empty / null / unparseable input. Accepts strings @@ -1219,7 +1219,7 @@ def _deserialize_json_list(data: Any) -> list[dict[str, Any]] | None: return None if isinstance(data, str): try: - parsed: Final = json.loads(data) + parsed: Final[object] = json.loads(data) except (json.JSONDecodeError, TypeError): return None data = parsed @@ -1914,7 +1914,7 @@ class MCPServerManager: async def load_servers_from_config( self, - mcp_servers_config: dict[str, Any], + mcp_servers_config: dict[str, MCPServerConfig], mcp_aliases: dict[str, str] | None = None, ): """ @@ -3068,7 +3068,7 @@ class MCPServerManager: return {} cache_key: Final = "toolset_perms:" + ",".join(sorted(toolset_ids)) - cached: Final = await user_api_key_cache.async_get_cache(key=cache_key) + cached: Final[dict[str, list[str]] | None] = await user_api_key_cache.async_get_cache(key=cache_key) if cached is not None: return cached @@ -5154,7 +5154,7 @@ class MCPServerManager: # Wrapped so the bridge runs inside the task: the caller only holds the task and # gathers it later, so there is no other point that still sees a block here. - async def _run_during_call_hook() -> Mapping[str, Any] | None: + async def _run_during_call_hook() -> Mapping[str, object] | None: try: return await proxy_logging_obj.during_call_hook( user_api_key_dict=user_api_key_auth, @@ -5656,7 +5656,7 @@ class MCPServerManager: async def _gather_openapi_tool_tasks( self, - tasks: list[Any], + tasks: Sequence[Awaitable[object]], proxy_logging_obj: ProxyLogging | None, ) -> CallToolResult: """Await OpenAPI tool tasks and return the tool call result.""" diff --git a/litellm/proxy/_experimental/mcp_server/oauth2_flow_backfill.py b/litellm/proxy/_experimental/mcp_server/oauth2_flow_backfill.py index c09106273e1..150900e7ff2 100644 --- a/litellm/proxy/_experimental/mcp_server/oauth2_flow_backfill.py +++ b/litellm/proxy/_experimental/mcp_server/oauth2_flow_backfill.py @@ -36,7 +36,10 @@ a healed fleet has no null rows and the backfill exits after one query. import json from collections import Counter -from typing import Any, Final, Literal +from collections.abc import Mapping, Sequence +from typing import Final, Literal, Protocol + +from pydantic import JsonValue from litellm._logging import verbose_proxy_logger from litellm.proxy._experimental.mcp_server.db import _decode_oauth_payload, decrypt_credentials @@ -55,9 +58,59 @@ BackfillRule = Literal[ _BACKFILL_AUDIT_ACTOR: Final = "oauth2_flow_backfill" -def _decrypted_credentials(raw_credentials: Any) -> MCPCredentials | None: +class _MCPServerRow(Protocol): + """The ``LiteLLM_MCPServerTable`` columns this backfill reads.""" + + @property + def server_id(self) -> str: ... + + @property + def authorization_url(self) -> str | None: ... + + @property + def registration_url(self) -> str | None: ... + + @property + def token_url(self) -> str | None: ... + + @property + def credentials(self) -> str | Mapping[str, JsonValue] | None: ... + + +class _MCPUserCredentialRow(Protocol): + """The ``LiteLLM_MCPUserCredentials`` columns this backfill reads.""" + + @property + def server_id(self) -> str: ... + + @property + def credential_b64(self) -> str: ... + + +class _MCPServerTable(Protocol): + async def find_many(self, *, where: Mapping[str, object]) -> Sequence[_MCPServerRow]: ... + + async def update_many(self, *, where: Mapping[str, object], data: Mapping[str, str]) -> object: ... + + +class _MCPUserCredentialsTable(Protocol): + async def find_many(self, *, where: Mapping[str, object]) -> Sequence[_MCPUserCredentialRow]: ... + + +def _mcp_server_table(prisma_client: PrismaClient) -> _MCPServerTable: + """The MCP server table, typed so the untyped prisma client surface stops here.""" + return prisma_client.db.litellm_mcpservertable + + +def _mcp_user_credentials_table(prisma_client: PrismaClient) -> _MCPUserCredentialsTable: + """The per-user MCP credential table, typed so the untyped prisma client surface stops here.""" + return prisma_client.db.litellm_mcpusercredentials + + +def _decrypted_credentials(raw_credentials: str | Mapping[str, JsonValue] | None) -> MCPCredentials | None: if raw_credentials is None: return None + parsed: JsonValue | Mapping[str, JsonValue] if isinstance(raw_credentials, str): try: parsed = json.loads(raw_credentials) @@ -92,14 +145,14 @@ def classify_null_flow_row( async def backfill_null_oauth2_flows(prisma_client: PrismaClient) -> dict[BackfillRule, int]: """Classify every ``auth_type=oauth2`` row whose ``oauth2_flow`` is null; stamp the provable ones, warn on the ambiguous ones, and return counts per rule.""" - null_rows: Final[list[Any]] = await prisma_client.db.litellm_mcpservertable.find_many( + null_rows: Final[Sequence[_MCPServerRow]] = await _mcp_server_table(prisma_client).find_many( where={"auth_type": "oauth2", "oauth2_flow": None}, ) if not null_rows: return {} server_ids: Final = [row.server_id for row in null_rows] - token_rows: Final[list[Any]] = await prisma_client.db.litellm_mcpusercredentials.find_many( + token_rows: Final[Sequence[_MCPUserCredentialRow]] = await _mcp_user_credentials_table(prisma_client).find_many( where={"server_id": {"in": server_ids}}, ) server_ids_with_oauth_tokens: Final[set[str]] = { @@ -141,7 +194,7 @@ async def backfill_null_oauth2_flows(prisma_client: PrismaClient) -> dict[Backfi stamped_flows: Final = {flow for _, (flow, _) in classified if flow is not None} for stamped_flow in stamped_flows: server_ids_for_flow = [row.server_id for row, (row_flow, _) in classified if row_flow == stamped_flow] - await prisma_client.db.litellm_mcpservertable.update_many( + await _mcp_server_table(prisma_client).update_many( where={"server_id": {"in": server_ids_for_flow}, "oauth2_flow": None}, data={"oauth2_flow": stamped_flow, "updated_by": _BACKFILL_AUDIT_ACTOR}, ) diff --git a/litellm/proxy/_experimental/mcp_server/outbound_credentials/client_credentials.py b/litellm/proxy/_experimental/mcp_server/outbound_credentials/client_credentials.py index da00abfe604..ad18d1bb10f 100644 --- a/litellm/proxy/_experimental/mcp_server/outbound_credentials/client_credentials.py +++ b/litellm/proxy/_experimental/mcp_server/outbound_credentials/client_credentials.py @@ -19,9 +19,9 @@ Implements the client-credentials behavior contract for the v2 resolver: identity. The token-endpoint POST is injected (``M2MTokenEndpointPost``) so the grant orchestration is -testable without a live IdP; ``post_client_credentials_grant`` is the httpx edge and the one -place the untyped response boundary is contained. Failures are values: the source returns -``Result[OAuthToken, CredError]``; only the httpx edge touches exceptions. +testable without a live IdP; ``post_client_credentials_grant`` is the httpx edge. Failures are +values: the source returns ``Result[OAuthToken, CredError]``; only the httpx edge touches +exceptions. """ from __future__ import annotations @@ -95,18 +95,17 @@ async def post_client_credentials_grant( ) -> TokenEndpointOutcome: """POST the grant to the token endpoint and classify the transport outcome. - The httpx edge: litellm's handler is partially typed (and raises ``HTTPStatusError`` itself on - a 4xx/5xx), so the untyped boundary is contained here and every field the caller reads comes - out of a validated ``TokenEndpointOutcome``. + The httpx edge: litellm's handler raises ``HTTPStatusError`` itself on a 4xx/5xx, and every + field the caller reads comes out of a validated ``TokenEndpointOutcome``. """ from litellm.llms.custom_httpx.http_handler import ( # noqa: PLC0415 # defer heavy handler import to call time - get_async_httpx_client, # pyright: ignore[reportUnknownVariableType] # handler is partially typed + get_async_httpx_client, # pyright: ignore[reportUnknownVariableType] # handler factory params are coarsely typed ) from litellm.types.llms.custom_http import httpxSpecialProvider # noqa: PLC0415 # deferred with the handler import try: client: Final = get_async_httpx_client(llm_provider=httpxSpecialProvider.Oauth2Check) - response = await client.post( # pyright: ignore[reportUnknownMemberType, reportUnknownVariableType] # handler is partially typed + response: Final = await client.post( # pyright: ignore[reportUnknownMemberType] # handler params are coarsely typed url, headers={"Accept": "application/json", **headers}, data=form ) except httpx.HTTPStatusError as status_err: @@ -114,8 +113,6 @@ async def post_client_credentials_grant( return TokenEndpointDenied(status_code=status_code, detail=f"token endpoint returned HTTP {status_code}") except Exception as exc: # noqa: BLE001 # any transport failure is the same outcome: unreachable return TokenEndpointUnreachable(detail=str(exc)) - if not isinstance(response, httpx.Response): - return TokenEndpointUnreachable(detail="token endpoint returned no response") try: body: Final = _TOKEN_BODY_ADAPTER.validate_json(response.content) except ValidationError: diff --git a/litellm/proxy/_experimental/mcp_server/outbound_credentials/session_token.py b/litellm/proxy/_experimental/mcp_server/outbound_credentials/session_token.py index 6824f96f927..0fa750a4c4a 100644 --- a/litellm/proxy/_experimental/mcp_server/outbound_credentials/session_token.py +++ b/litellm/proxy/_experimental/mcp_server/outbound_credentials/session_token.py @@ -221,12 +221,17 @@ class MintedSessionToken(BaseModel): class OpenedSessionToken(BaseModel): - """A validated session token of either kind: the principal it was minted for, plus the - ``jti`` so the token endpoint can enforce single-use rotation on a refresh token.""" + """A validated session token of either kind: the principal it was minted for, the + ``jti`` so the token endpoint can enforce single-use rotation on a refresh token, and + the signed ``kind``/``iat``/``exp`` so an introspection response can report the + token's metadata without re-decoding.""" model_config = ConfigDict(frozen=True) principal: SessionPrincipal jti: str + kind: SessionTokenKind + iat: int + exp: int class SessionTokenTooLarge(BaseModel): @@ -458,6 +463,9 @@ def _open( team_id=claims.team_id, ), jti=claims.jti, + kind=claims.kind, + iat=claims.iat, + exp=claims.exp, ) diff --git a/litellm/proxy/_experimental/mcp_server/outbound_credentials/token_endpoint.py b/litellm/proxy/_experimental/mcp_server/outbound_credentials/token_endpoint.py index f6d40b82eda..84f714db449 100644 --- a/litellm/proxy/_experimental/mcp_server/outbound_credentials/token_endpoint.py +++ b/litellm/proxy/_experimental/mcp_server/outbound_credentials/token_endpoint.py @@ -111,9 +111,6 @@ class TokenEndpointClient: return Error( CredError.of_upstream_unavailable("token exchange failed: token endpoint returned a non-JSON response") ) - if raw is None: - verbose_proxy_logger.warning("MCP token endpoint %s returned no response", endpoint) - return Error(CredError.of_upstream_unavailable("token exchange failed: no response from token endpoint")) try: parsed: Final = _TokenEndpointResponse.model_validate(raw) except ValidationError: @@ -199,7 +196,7 @@ def _cache_ttl_seconds(expires_in: int | None) -> int: ) -async def _post_form(endpoint: str, data: dict[str, str]) -> object | None: +async def _post_form(endpoint: str, data: dict[str, str]) -> object: # litellm's httpx handler and httpx.Response are only partially typed; the token endpoint # returns a JSON object that `_TokenEndpointResponse` validates, so the untyped boundary is # contained here. A non-2xx raises `httpx.HTTPStatusError`, an unreachable endpoint raises @@ -208,8 +205,6 @@ async def _post_form(endpoint: str, data: dict[str, str]) -> object | None: # each to a CredError. client = get_async_httpx_client(llm_provider=httpxSpecialProvider.MCP) # pyright: ignore[reportUnknownVariableType] # litellm http handler is untyped response = await client.post(endpoint, data=data) # pyright: ignore[reportUnknownMemberType,reportUnknownVariableType] # litellm http handler is untyped - if response is None: - return None response.raise_for_status() return response.json() # pyright: ignore[reportAny] # untyped JSON; validated by _TokenEndpointResponse in fetch diff --git a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py index 3efb6429326..d1ef73a15cd 100644 --- a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py @@ -1,13 +1,16 @@ import asyncio import importlib -from collections.abc import Awaitable, Callable, Mapping +from collections.abc import Awaitable, Callable, Mapping, Sequence from datetime import datetime +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final, Literal +import anyio import httpx from fastapi import APIRouter, Depends, HTTPException, Query, Request, status from litellm._logging import verbose_logger +from litellm.constants import MCP_CLIENT_TIMEOUT, MCP_TOOL_LISTING_TIMEOUT from litellm.exceptions import ( BlockedPiiEntityError, GuardrailRaisedException, @@ -18,8 +21,11 @@ from litellm.proxy._experimental.mcp_server.exceptions import ( MCPUpstreamAuthError, ) from litellm.proxy._experimental.mcp_server.faults.list_outcomes import ( + ServerListOk, + ServerOutcome, classify_list_exception, list_fault_http_status, + outcome_wire_value, ) from litellm.proxy._experimental.mcp_server.ui_session_utils import ( acting_user_auth, @@ -86,8 +92,6 @@ def _connection_error_message(exc: BaseException) -> str: if MCP_AVAILABLE: - from mcp.types import Tool as MCPTool - from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( _UPSTREAM_OAUTH_DISCOVERY_AUTH_TYPES, global_mcp_server_manager, @@ -99,6 +103,7 @@ if MCP_AVAILABLE: ListMCPToolsRestAPIResponseObject, MCPInfo, MCPServer, + _aggregate_server_key, # pyright: ignore[reportPrivateUsage] # same per-server key as the tools/list _meta outcomes _apply_toolset_scope, _fire_mcp_tool_call_logging, execute_mcp_tool, @@ -803,9 +808,6 @@ if MCP_AVAILABLE: list(allowed_server_ids_set), _rest_client_ip ) - list_tools_result: Final = [] - error_message = None - # If server_id is specified, only query that specific server if server_id: return await _list_tools_for_single_server( @@ -849,22 +851,19 @@ if MCP_AVAILABLE: else {} ) - # Query all servers the user has access to - errors: Final = [] - for allowed_server_id in allowed_server_ids: - server = global_mcp_server_manager.get_mcp_server_by_id(allowed_server_id) - if server is None: - continue - - server_auth_header = _get_server_auth_header(server, mcp_server_auth_headers, mcp_auth_header) - user_oauth_extra_headers = await _get_user_oauth_extra_headers( + async def list_server( + server: MCPServer, + ) -> tuple[Sequence[ListMCPToolsRestAPIResponseObject], ServerOutcome]: + server_auth_header: Final = _get_server_auth_header( + server, mcp_server_auth_headers, mcp_auth_header + ) + user_oauth_extra_headers: Final = await _get_user_oauth_extra_headers( server, user_api_key_dict, prefetched_creds=prefetched_oauth_creds, ) - try: - tools_result = await _get_tools_for_single_server( + tools_result: Final = await _get_tools_for_single_server( server, server_auth_header, raw_headers_from_request, @@ -872,24 +871,36 @@ if MCP_AVAILABLE: extra_headers=user_oauth_extra_headers, apply_tool_filters=apply_tool_filters, ) - list_tools_result.extend(tools_result) except Exception as e: verbose_logger.exception("Error getting tools from %s: %s", server.name, e) - errors.append( - f"{get_server_prefix(server)}: {classify_list_exception(e).tag}" - if isinstance(e, (MCPServerListError, MCPUpstreamAuthError)) - else f"{get_server_prefix(server)}: {e}" - ) - continue + return (), classify_list_exception(e) + return tools_result, ServerListOk(tool_count=len(tools_result)) - if errors and not list_tools_result: - error_message = "Failed to get tools from servers: " + "; ".join(errors) - - return { - "tools": list_tools_result, - "error": "partial_failure" if error_message else None, - "message": (error_message if error_message else "Successfully retrieved tools"), - } + # Query all servers the user has access to + queried_servers: Final = tuple( + server + for server in map(global_mcp_server_manager.get_mcp_server_by_id, allowed_server_ids) + if server is not None + ) + listings: Final = tuple([await list_server(server) for server in queried_servers]) + list_tools_result: Final = [tool for tools, _ in listings for tool in tools] + server_outcomes: Final = MappingProxyType( + {_aggregate_server_key(server): outcome for server, (_, outcome) in zip(queried_servers, listings)} + ) + errors: Final = tuple( + f"{key}: {outcome.tag}" for key, outcome in server_outcomes.items() if outcome.tag != "ok" + ) + error_message: Final = ( + "Failed to get tools from servers: " + "; ".join(errors) + if errors and not list_tools_result + else None + ) + return { + "tools": list_tools_result, + "error": "partial_failure" if error_message else None, + "message": (error_message if error_message else "Successfully retrieved tools"), + "server_outcomes": {key: outcome_wire_value(outcome) for key, outcome in server_outcomes.items()}, + } except MCPUpstreamAuthError as e: # Surface upstream pass-through 401/403 challenges to the client so @@ -1173,6 +1184,7 @@ if MCP_AVAILABLE: transport=request.transport, auth_type=request.auth_type, mcp_info=request.mcp_info, + timeout=request.timeout, command=request.command, args=request.args, env=request.env, @@ -1402,11 +1414,28 @@ if MCP_AVAILABLE: oauth2_headers = MCPRequestHandler._get_oauth2_headers_from_headers(headers) async def _list_tools_operation(client): - async def _list_tools_session_operation(session): - return await session.list_tools() - - list_tools_response: Final = await client.run_with_session(_list_tools_session_operation) - list_tools_result: Final[list[MCPTool]] = list_tools_response.tools + # Bound the whole pagination walk: without this the preview is limited only by the + # per-request timeout times the page cap. max() keeps the pre-pagination guarantee + # that a single slow page within the client timeout still succeeds, and a + # per-server timeout above the global default extends the deadline with it. + listing_deadline: Final = max( + getattr(client, "timeout", MCP_CLIENT_TIMEOUT) or MCP_CLIENT_TIMEOUT, + MCP_TOOL_LISTING_TIMEOUT, + ) + list_tools_result = None # rebind-ok: set inside the timeout scope below + with anyio.move_on_after(listing_deadline): + list_tools_result = await client.list_tools(raise_on_error=True) # rebind-ok: fills the init above + if list_tools_result is None: + verbose_logger.warning( + "MCP tools/list preview timed out after %s seconds while paginating upstream tools", + listing_deadline, + ) + return { # mutable-ok: error response payload + "status": "error", + "error": True, + "message": f"Timed out listing tools after {listing_deadline} seconds. " + "The MCP server may be responding slowly or paginating excessively.", + } model_dumped_tools: Final[list[dict]] = [tool.model_dump() for tool in list_tools_result] return { "tools": model_dumped_tools, diff --git a/litellm/proxy/_experimental/mcp_server/semantic_tool_filter.py b/litellm/proxy/_experimental/mcp_server/semantic_tool_filter.py index 7ec0f4b5192..dcf1b01bc25 100644 --- a/litellm/proxy/_experimental/mcp_server/semantic_tool_filter.py +++ b/litellm/proxy/_experimental/mcp_server/semantic_tool_filter.py @@ -5,6 +5,7 @@ Filters MCP tools semantically for /chat/completions and /responses endpoints. """ import asyncio +from collections.abc import Mapping, Sequence from typing import TYPE_CHECKING, Any, Final from litellm._logging import verbose_logger @@ -74,7 +75,7 @@ class SemanticMCPToolFilter: self.router_instance = litellm_router_instance self.tool_router: SemanticRouter | None = None self.context_window_error: str | None = None - self._tool_map: dict[str, Any] = {} # MCPTool objects or OpenAI function dicts + self._tool_map: dict[str, object] = {} # MCPTool objects or OpenAI function dicts self._index_sync_lock = asyncio.Lock() async def build_router_from_mcp_registry(self) -> None: @@ -182,11 +183,11 @@ class SemanticMCPToolFilter: return raise - def _has_tools_missing_from_index(self, tools: list[Any]) -> bool: + def _has_tools_missing_from_index(self, tools: Sequence[object]) -> bool: """Allocation-free check for any named tool not yet in the semantic index.""" return any(name and name not in self._tool_map for name in (self._extract_tool_info(t)[0] for t in tools)) - def _tools_missing_from_index(self, tools: list[Any]) -> dict[str, Any]: + def _tools_missing_from_index(self, tools: Sequence[object]) -> Mapping[str, object]: """Map name -> tool for every named tool not yet in the semantic index.""" return { name: tool @@ -194,7 +195,7 @@ class SemanticMCPToolFilter: if name and name not in self._tool_map } - async def _ensure_tools_indexed(self, available_tools: list[Any]) -> None: + async def _ensure_tools_indexed(self, available_tools: Sequence[object]) -> None: """ Index request-time tools the startup build never saw. @@ -385,7 +386,7 @@ class SemanticMCPToolFilter: separator: Final = client_name[-len(canonical) - 1] return separator in ("_", "-") - def _get_tools_by_names(self, tool_names: list[str], available_tools: list[Any]) -> list[Any]: + def _get_tools_by_names(self, tool_names: Sequence[str], available_tools: Sequence[object]) -> list[object]: """ Get tools from available_tools by their names, preserving the semantic router's ordering. @@ -401,14 +402,14 @@ class SemanticMCPToolFilter: # Exact matches win over suffix matches when both are present, and # each incoming tool is returned at most once even if two canonical # names happen to be tail-compatible with the same incoming name. - available_by_name: Final[dict[str, Any]] = {} + available_by_name: Final[dict[str, object]] = {} for tool in available_tools: client_name, _ = self._extract_tool_info(tool) if client_name and client_name not in available_by_name: available_by_name[client_name] = tool - matched: Final[list[Any]] = [] - used_ids: Final[set] = set() + matched: Final[list[object]] = [] + used_ids: Final[set[int]] = set() for canonical in tool_names: tool = available_by_name.get(canonical) if tool is None: @@ -430,7 +431,7 @@ class SemanticMCPToolFilter: used_ids.add(id(tool)) return matched - def extract_user_query(self, messages: list[dict[str, Any]]) -> str: + def extract_user_query(self, messages: Sequence[Mapping[str, object]]) -> str: """ Extract user query from messages for /chat/completions or /responses. diff --git a/litellm/proxy/_experimental/mcp_server/toolset_db.py b/litellm/proxy/_experimental/mcp_server/toolset_db.py index 9672383a572..ecaaf35e817 100644 --- a/litellm/proxy/_experimental/mcp_server/toolset_db.py +++ b/litellm/proxy/_experimental/mcp_server/toolset_db.py @@ -1,5 +1,9 @@ import json -from typing import Final +from collections.abc import Mapping, Sequence +from datetime import datetime +from typing import Final, Protocol + +from typing_extensions import NotRequired, ReadOnly, TypedDict from litellm._logging import verbose_proxy_logger from litellm._uuid import uuid @@ -7,18 +11,73 @@ from litellm.proxy.utils import PrismaClient from litellm.repositories.table_repositories import MCPToolsetRepository from litellm.types.mcp_server.mcp_toolset import ( MCPToolset, + MCPToolsetTool, NewMCPToolsetRequest, UpdateMCPToolsetRequest, ) -def _toolset_from_row(row) -> MCPToolset: +class MCPToolsetFields(TypedDict): + """The ``MCPToolset`` constructor keywords a toolset row expands into.""" + + toolset_id: ReadOnly[str] + toolset_name: ReadOnly[str] + description: NotRequired[ReadOnly[str | None]] + tools: NotRequired[ReadOnly[list[MCPToolsetTool]]] + created_at: NotRequired[ReadOnly[datetime | None]] + created_by: NotRequired[ReadOnly[str | None]] + updated_at: NotRequired[ReadOnly[datetime | None]] + updated_by: NotRequired[ReadOnly[str | None]] + + +class MCPToolsetRowData(TypedDict): + """A toolset table row, whose ``tools`` column is stored as JSON.""" + + toolset_id: ReadOnly[str] + toolset_name: ReadOnly[str] + description: NotRequired[ReadOnly[str | None]] + tools: NotRequired[ReadOnly[str | list[MCPToolsetTool]]] + created_at: NotRequired[ReadOnly[datetime | None]] + created_by: NotRequired[ReadOnly[str | None]] + updated_at: NotRequired[ReadOnly[datetime | None]] + updated_by: NotRequired[ReadOnly[str | None]] + + +class MCPToolsetRow(Protocol): + """A row of the toolset table, as the prisma client returns it.""" + + def model_dump(self) -> MCPToolsetRowData: ... + + +class MCPToolsetTable(Protocol): + """The prisma table actions this module runs against the toolset table.""" + + async def create(self, data: Mapping[str, object]) -> MCPToolsetRow: ... + + async def find_unique(self, where: Mapping[str, object]) -> MCPToolsetRow | None: ... + + async def find_first(self, where: Mapping[str, object]) -> MCPToolsetRow | None: ... + + async def find_many(self, where: Mapping[str, object]) -> Sequence[MCPToolsetRow]: ... + + async def update(self, where: Mapping[str, object], data: Mapping[str, object]) -> MCPToolsetRow: ... + + async def delete(self, where: Mapping[str, object]) -> MCPToolsetRow: ... + + +def _toolset_table(prisma_client: PrismaClient) -> MCPToolsetTable: + """The toolset table actions of the prisma client.""" + return MCPToolsetRepository(prisma_client).table + + +def _toolset_from_row(row: MCPToolsetRow) -> MCPToolset: data: Final = row.model_dump() - tools = data.get("tools") or [] - if isinstance(tools, str): - tools = json.loads(tools) - data["tools"] = tools - return MCPToolset(**data) + tools: Final = data.get("tools") or [] + resolved: Final[MCPToolsetFields] = { + **data, + "tools": json.loads(tools) if isinstance(tools, str) else tools, + } + return MCPToolset(**resolved) async def create_mcp_toolset( @@ -31,7 +90,7 @@ async def create_mcp_toolset( data_dict["tools"] = json.dumps(data_dict.get("tools", [])) data_dict["created_by"] = touched_by data_dict["updated_by"] = touched_by - row: Final = await MCPToolsetRepository(prisma_client).table.create(data=data_dict) + row: Final = await _toolset_table(prisma_client).create(data=data_dict) return _toolset_from_row(row) @@ -39,7 +98,7 @@ async def get_mcp_toolset( prisma_client: PrismaClient, toolset_id: str, ) -> MCPToolset | None: - row: Final = await MCPToolsetRepository(prisma_client).table.find_unique(where={"toolset_id": toolset_id}) + row: Final = await _toolset_table(prisma_client).find_unique(where={"toolset_id": toolset_id}) if row is None: return None return _toolset_from_row(row) @@ -47,13 +106,11 @@ async def get_mcp_toolset( async def list_mcp_toolsets( prisma_client: PrismaClient, - toolset_ids: list[str] | None = None, -) -> list[MCPToolset]: + toolset_ids: Sequence[str] | None = None, +) -> Sequence[MCPToolset]: try: - where = {} - if toolset_ids is not None: - where = {"toolset_id": {"in": toolset_ids}} - rows: Final = await MCPToolsetRepository(prisma_client).table.find_many(where=where) + where: Final[Mapping[str, object]] = {} if toolset_ids is None else {"toolset_id": {"in": toolset_ids}} + rows: Final = await _toolset_table(prisma_client).find_many(where=where) return [_toolset_from_row(r) for r in rows] except Exception as e: verbose_proxy_logger.warning("litellm.proxy._experimental.mcp_server.toolset_db::list_mcp_toolsets - %s", e) @@ -64,7 +121,7 @@ async def get_mcp_toolset_by_name( prisma_client: PrismaClient, toolset_name: str, ) -> MCPToolset | None: - row: Final = await MCPToolsetRepository(prisma_client).table.find_first(where={"toolset_name": toolset_name}) + row: Final = await _toolset_table(prisma_client).find_first(where={"toolset_name": toolset_name}) if row is None: return None return _toolset_from_row(row) @@ -80,7 +137,7 @@ async def update_mcp_toolset( data_dict["tools"] = json.dumps(data_dict["tools"]) data_dict["updated_by"] = touched_by try: - row: Final = await MCPToolsetRepository(prisma_client).table.update( + row: Final = await _toolset_table(prisma_client).update( where={"toolset_id": data.toolset_id}, data=data_dict, ) @@ -98,7 +155,7 @@ async def delete_mcp_toolset( toolset_id: str, ) -> MCPToolset | None: try: - row: Final = await MCPToolsetRepository(prisma_client).table.delete(where={"toolset_id": toolset_id}) + row: Final = await _toolset_table(prisma_client).delete(where={"toolset_id": toolset_id}) except Exception as e: from prisma.errors import RecordNotFoundError diff --git a/litellm/proxy/_experimental/out/404.html b/litellm/proxy/_experimental/out/404.html index 52c40147a20..72dc4764ce4 100644 --- a/litellm/proxy/_experimental/out/404.html +++ b/litellm/proxy/_experimental/out/404.html @@ -1 +1 @@ -404: This page could not be found.LiteLLM Dashboard

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ef=((t={}).A2A_Agent="A2A Agent",t.AI21="Ai21",t.AI21_CHAT="Ai21 Chat",t.AIML="AI/ML API",t.AIOHTTP_OPENAI="Aiohttp Openai",t.Anthropic="Anthropic",t.ANTHROPIC_TEXT="Anthropic Text",t.AssemblyAI="AssemblyAI",t.AUTO_ROUTER="Auto Router",t.Bedrock="Amazon Bedrock",t.BedrockMantle="Amazon Bedrock Mantle",t.SageMaker="AWS SageMaker",t.Azure="Azure",t.Azure_AI_Studio="Azure AI Foundry (Studio)",t.AZURE_TEXT="Azure Text",t.BASETEN="Baseten",t.BYTEZ="Bytez",t.Cerebras="Cerebras",t.CLARIFAI="Clarifai",t.CLOUDFLARE="Cloudflare",t.CODESTRAL="Codestral",t.Cognition="Cognition",t.Cohere="Cohere",t.COHERE_CHAT="Cohere Chat",t.COMETAPI="Cometapi",t.COMPACTIFAI="Compactifai",t.Cursor="Cursor",t.Dashscope="Dashscope",t.Databricks="Databricks (Qwen API)",t.DATAROBOT="Datarobot",t.DeepInfra="DeepInfra",t.Deepgram="Deepgram",t.Deepseek="Deepseek",t.DOCKER_MODEL_RUNNER="Docker Model Runner",t.DOTPROMPT="Dotprompt",t.ElevenLabs="ElevenLabs",t.EMPOWER="Empower",t.FalAI="Fal AI",t.FEATHERLESS_AI="Featherless 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-result = client.images.generate( - model="${C}", - prompt="${s}", - n=1 -) - -json_response = json.loads(result.model_dump_json()) - -# Set the directory for the stored image -image_dir = os.path.join(os.curdir, 'images') - -# If the directory doesn't exist, create it -if not os.path.isdir(image_dir): - os.mkdir(image_dir) - -# Initialize the image path -image_filename = f"generated_image_{int(time.time())}.png" -image_path = os.path.join(image_dir, image_filename) - -try: - # Retrieve the generated image - if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): - image_url = json_response["data"][0]["url"] - generated_image = requests.get(image_url).content - with open(image_path, "wb") as image_file: - image_file.write(generated_image) - - print(f"Image saved to {image_path}") - # Display the image - image = Image.open(image_path) - image.show() - else: - print("Could not find image URL in response.") - print("Full response:", json_response) -except Exception as e: - print(f"An error occurred: {e}") - print("Full response:", json_response) -`:` -import base64 -import os -import time -import json -from PIL import Image -import requests - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Helper function to create a file (simplified for this example) -def create_file(image_path): - # In a real implementation, this would upload the file to OpenAI - # For this example, we'll just return a placeholder ID - return f"file_{os.path.basename(image_path).replace('.', '_')}" - -# The prompt entered by the user -prompt = "${_}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${C}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. 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ef=((t={}).A2A_Agent="A2A Agent",t.AI21="Ai21",t.AI21_CHAT="Ai21 Chat",t.AIML="AI/ML API",t.AIOHTTP_OPENAI="Aiohttp Openai",t.Anthropic="Anthropic",t.ANTHROPIC_TEXT="Anthropic Text",t.AssemblyAI="AssemblyAI",t.AUTO_ROUTER="Auto Router",t.Bedrock="Amazon Bedrock",t.BedrockMantle="Amazon Bedrock Mantle",t.SageMaker="AWS SageMaker",t.Azure="Azure",t.Azure_AI_Studio="Azure AI Foundry (Studio)",t.AZURE_TEXT="Azure Text",t.BASETEN="Baseten",t.BYTEZ="Bytez",t.Cerebras="Cerebras",t.CLARIFAI="Clarifai",t.CLOUDFLARE="Cloudflare",t.CODESTRAL="Codestral",t.Cognition="Cognition",t.Cohere="Cohere",t.COHERE_CHAT="Cohere Chat",t.COMETAPI="Cometapi",t.COMPACTIFAI="Compactifai",t.Cursor="Cursor",t.Dashscope="Dashscope",t.Databricks="Databricks (Qwen API)",t.DATAROBOT="Datarobot",t.DeepInfra="DeepInfra",t.Deepgram="Deepgram",t.Deepseek="Deepseek",t.DOCKER_MODEL_RUNNER="Docker Model Runner",t.DOTPROMPT="Dotprompt",t.ElevenLabs="ElevenLabs",t.EMPOWER="Empower",t.FalAI="Fal AI",t.FEATHERLESS_AI="Featherless 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here",v=y.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),S=s.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),C={};l.length>0&&(C.tags=l),p.length>0&&(C.vector_stores=p),d.length>0&&(C.guardrails=d),u.length>0&&(C.policies=u);let j=m||"your-model-name",E="azure"===f?`import openai - -client = openai.AzureOpenAI( - api_key="${x||"YOUR_LITELLM_API_KEY"}", - azure_endpoint="${_}", - api_version="2024-02-01" -)`:`import openai - -client = openai.OpenAI( - api_key="${x||"YOUR_LITELLM_API_KEY"}", - base_url="${_}" -)`;switch(g){case a.CHAT:{let e=Object.keys(C).length>0,i="";if(e){let e=JSON.stringify({metadata:C},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`, - extra_body=${e}`}let o=S.length>0?S:[{role:"user",content:y}];t=` -import base64 - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Example with text only -response = client.chat.completions.create( - model="${j}", - messages=${JSON.stringify(o,null,4)}${i} -) - -print(response) - -# Example with image or PDF (uncomment and provide file path to use) -# base64_file = encode_image("path/to/your/file.jpg") # or .pdf -# response_with_file = client.chat.completions.create( -# model="${j}", -# messages=[ -# { -# "role": "user", -# "content": [ -# { -# "type": "text", -# "text": "${v}" -# }, -# { -# "type": "image_url", -# "image_url": { -# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file} -# } -# } -# ] -# } -# ]${i} -# ) -# print(response_with_file) -`;break}case a.RESPONSES:{let e=Object.keys(C).length>0,i="";if(e){let e=JSON.stringify({metadata:C},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`, - extra_body=${e}`}let o=S.length>0?S:[{role:"user",content:y}];t=` -import base64 - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Example with text only -response = client.responses.create( - model="${j}", - input=${JSON.stringify(o,null,4)}${i} -) - -print(response.output_text) - -# Example with image or PDF (uncomment and provide file path to use) -# base64_file = encode_image("path/to/your/file.jpg") # or .pdf -# response_with_file = client.responses.create( -# model="${j}", -# input=[ -# { -# "role": "user", -# "content": [ -# {"type": "input_text", "text": "${v}"}, -# { -# "type": "input_image", -# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} -# }, -# ], -# } -# ]${i} -# ) -# print(response_with_file.output_text) -`;break}case a.IMAGE:t="azure"===f?` -# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. -# This snippet uses 'client.images.generate' and will create a new image based on your prompt. -# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. -import os -import requests -import json -import time -from PIL import Image - -result = client.images.generate( - model="${j}", - prompt="${r}", - n=1 -) - -json_response = json.loads(result.model_dump_json()) - -# Set the directory for the stored image -image_dir = os.path.join(os.curdir, 'images') - -# If the directory doesn't exist, create it -if not os.path.isdir(image_dir): - os.mkdir(image_dir) - -# Initialize the image path -image_filename = f"generated_image_{int(time.time())}.png" -image_path = os.path.join(image_dir, image_filename) - -try: - # Retrieve the generated image - if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): - image_url = json_response["data"][0]["url"] - generated_image = requests.get(image_url).content - with open(image_path, "wb") as image_file: - image_file.write(generated_image) - - print(f"Image saved to {image_path}") - # Display the image - image = Image.open(image_path) - image.show() - else: - print("Could not find image URL in response.") - print("Full response:", json_response) -except Exception as e: - print(f"An error occurred: {e}") - print("Full response:", json_response) -`:` -import base64 -import os -import time -import json -from PIL import Image -import requests - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Helper function to create a file (simplified for this example) -def create_file(image_path): - # In a real implementation, this would upload the file to OpenAI - # For this example, we'll just return a placeholder ID - return f"file_{os.path.basename(image_path).replace('.', '_')}" - -# The prompt entered by the user -prompt = "${v}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${j}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. Model response:") - print("\\n".join(text_response)) - else: - print("No image data found in response.") - print("Full response for debugging:") - print(response) -`;break;case a.IMAGE_EDITS:t="azure"===f?` -import base64 -import os -import time -import json -from PIL import Image -import requests - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# The prompt entered by the user -prompt = "${v}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${j}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. Model response:") - print("\\n".join(text_response)) - else: - print("No image data found in response.") - print("Full response for debugging:") - print(response) -`:` -import base64 -import os -import time - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Helper function to create a file (simplified for this example) -def create_file(image_path): - # In a real implementation, this would upload the file to OpenAI - # For this example, we'll just return a placeholder ID - return f"file_{os.path.basename(image_path).replace('.', '_')}" - -# The prompt entered by the user -prompt = "${v}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${j}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. 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ef=((t={}).A2A_Agent="A2A Agent",t.AI21="Ai21",t.AI21_CHAT="Ai21 Chat",t.AIML="AI/ML API",t.AIOHTTP_OPENAI="Aiohttp Openai",t.Anthropic="Anthropic",t.ANTHROPIC_TEXT="Anthropic Text",t.AssemblyAI="AssemblyAI",t.AUTO_ROUTER="Auto Router",t.Bedrock="Amazon Bedrock",t.BedrockMantle="Amazon Bedrock Mantle",t.SageMaker="AWS SageMaker",t.Azure="Azure",t.Azure_AI_Studio="Azure AI Foundry (Studio)",t.AZURE_TEXT="Azure Text",t.BASETEN="Baseten",t.BYTEZ="Bytez",t.Cerebras="Cerebras",t.CLARIFAI="Clarifai",t.CLOUDFLARE="Cloudflare",t.CODESTRAL="Codestral",t.Cognition="Cognition",t.Cohere="Cohere",t.COHERE_CHAT="Cohere Chat",t.COMETAPI="Cometapi",t.COMPACTIFAI="Compactifai",t.Cursor="Cursor",t.Dashscope="Dashscope",t.Databricks="Databricks (Qwen API)",t.DATAROBOT="Datarobot",t.DeepInfra="DeepInfra",t.Deepgram="Deepgram",t.Deepseek="Deepseek",t.DOCKER_MODEL_RUNNER="Docker Model Runner",t.DOTPROMPT="Dotprompt",t.ElevenLabs="ElevenLabs",t.EMPOWER="Empower",t.FalAI="Fal AI",t.FEATHERLESS_AI="Featherless 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with text only +response = client.chat.completions.create( + model="${C}", + messages=${JSON.stringify(r,null,4)}${i} +) + +print(response) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.chat.completions.create( +# model="${C}", +# messages=[ +# { +# "role": "user", +# "content": [ +# { +# "type": "text", +# "text": "${_}" +# }, +# { +# "type": "image_url", +# "image_url": { +# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file} +# } +# } +# ] +# } +# ]${i} +# ) +# print(response_with_file) +`;break}case o.RESPONSES:{let e=Object.keys(w).length>0,i="";if(e){let e=JSON.stringify({metadata:w},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`, + extra_body=${e}`}let r=k.length>0?k:[{role:"user",content:x}];t=` +import base64 + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.responses.create( + model="${C}", + input=${JSON.stringify(r,null,4)}${i} +) + +print(response.output_text) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.responses.create( +# model="${C}", +# input=[ +# { +# "role": "user", +# "content": [ +# {"type": "input_text", "text": "${_}"}, +# { +# "type": "input_image", +# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} +# }, +# ], +# } +# ]${i} +# ) +# print(response_with_file.output_text) +`;break}case o.IMAGE:t="azure"===g?` +# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. +# This snippet uses 'client.images.generate' and will create a new image based on your prompt. +# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. +import os +import requests +import json +import time +from PIL import Image + +result = client.images.generate( + model="${C}", + prompt="${s}", + n=1 +) + +json_response = json.loads(result.model_dump_json()) + +# Set the directory for the stored image +image_dir = os.path.join(os.curdir, 'images') + +# If the directory doesn't exist, create it +if not os.path.isdir(image_dir): + os.mkdir(image_dir) + +# Initialize the image path +image_filename = f"generated_image_{int(time.time())}.png" +image_path = os.path.join(image_dir, image_filename) + +try: + # Retrieve the generated image + if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): + image_url = json_response["data"][0]["url"] + generated_image = requests.get(image_url).content + with open(image_path, "wb") as image_file: + image_file.write(generated_image) + + print(f"Image saved to {image_path}") + # Display the image + image = Image.open(image_path) + image.show() + else: + print("Could not find image URL in response.") + print("Full response:", json_response) +except Exception as e: + print(f"An error occurred: {e}") + print("Full response:", json_response) +`:` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${_}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${C}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case o.IMAGE_EDITS:t="azure"===g?` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# The prompt entered by the user +prompt = "${_}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${C}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`:` +import base64 +import os +import time + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${_}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${C}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. 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o?null!=(l=null==(n=t.getState().columnPinning)||null==(n=n[o])?void 0:n.indexOf(e.id))?l:-1:0}},createRow:(e,t)=>{e.getCenterVisibleCells=i(()=>[e._getAllVisibleCells(),t.getState().columnPinning.left,t.getState().columnPinning.right],(e,t,l)=>{let n=[...null!=t?t:[],...null!=l?l:[]];return e.filter(e=>!n.includes(e.column.id))},a(t.options,"debugRows","getCenterVisibleCells")),e.getLeftVisibleCells=i(()=>[e._getAllVisibleCells(),t.getState().columnPinning.left],(e,t)=>(null!=t?t:[]).map(t=>e.find(e=>e.column.id===t)).filter(Boolean).map(e=>({...e,position:"left"})),a(t.options,"debugRows","getLeftVisibleCells")),e.getRightVisibleCells=i(()=>[e._getAllVisibleCells(),t.getState().columnPinning.right],(e,t)=>(null!=t?t:[]).map(t=>e.find(e=>e.column.id===t)).filter(Boolean).map(e=>({...e,position:"right"})),a(t.options,"debugRows","getRightVisibleCells"))},createTable:e=>{e.setColumnPinning=t=>null==e.options.onColumnPinningChange?void 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auto}.grid-cols-\[80px_minmax\(0\,1fr\)\]{grid-template-columns:80px minmax(0,1fr)}.grid-cols-\[160px_minmax\(0\,1fr\)\]{grid-template-columns:160px minmax(0,1fr)}.grid-cols-\[auto\]{grid-template-columns:auto}.grid-cols-\[auto_1fr\]{grid-template-columns:auto 1fr}.grid-cols-\[auto_minmax\(0\,1fr\)\]{grid-template-columns:auto minmax(0,1fr)}.grid-cols-\[max-content_1fr\]{grid-template-columns:max-content 1fr}.grid-cols-\[minmax\(0\,14rem\)_minmax\(0\,1fr\)\]{grid-template-columns:minmax(0,14rem) minmax(0,1fr)}.grid-cols-\[repeat\(auto-fill\,minmax\(220px\,1fr\)\)\]{grid-template-columns:repeat(auto-fill,minmax(220px,1fr))}.grid-cols-\[repeat\(auto-fit\,minmax\(7rem\,1fr\)\)\]{grid-template-columns:repeat(auto-fit,minmax(7rem,1fr))}.grid-rows-\[auto_1fr\]{grid-template-rows:auto 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ef=((t={}).A2A_Agent="A2A Agent",t.AI21="Ai21",t.AI21_CHAT="Ai21 Chat",t.AIML="AI/ML API",t.AIOHTTP_OPENAI="Aiohttp Openai",t.Anthropic="Anthropic",t.ANTHROPIC_TEXT="Anthropic Text",t.AssemblyAI="AssemblyAI",t.AUTO_ROUTER="Auto Router",t.Bedrock="Amazon Bedrock",t.BedrockMantle="Amazon Bedrock Mantle",t.SageMaker="AWS SageMaker",t.Azure="Azure",t.Azure_AI_Studio="Azure AI Foundry (Studio)",t.AZURE_TEXT="Azure Text",t.BASETEN="Baseten",t.BYTEZ="Bytez",t.Cerebras="Cerebras",t.CLARIFAI="Clarifai",t.CLOUDFLARE="Cloudflare",t.CODESTRAL="Codestral",t.Cognition="Cognition",t.Cohere="Cohere",t.COHERE_CHAT="Cohere Chat",t.COMETAPI="Cometapi",t.COMPACTIFAI="Compactifai",t.Cursor="Cursor",t.Dashscope="Dashscope",t.Databricks="Databricks (Qwen API)",t.DATAROBOT="Datarobot",t.DeepInfra="DeepInfra",t.Deepgram="Deepgram",t.Deepseek="Deepseek",t.DOCKER_MODEL_RUNNER="Docker Model Runner",t.DOTPROMPT="Dotprompt",t.ElevenLabs="ElevenLabs",t.EMPOWER="Empower",t.FalAI="Fal AI",t.FEATHERLESS_AI="Featherless 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t=0,s=[];for(;t{"brace"===e.type&&("{"===e.value?s.push("}"):s.splice(s.lastIndexOf("}"),1)),"paren"===e.type&&("["===e.value?s.push("]"):s.splice(s.lastIndexOf("]"),1))}),s.length>0&&s.reverse().map(e=>{"}"===e?t.push({type:"brace",value:"}"}):"]"===e&&t.push({type:"paren",value:"]"})}),r="",t.map(e=>{"string"===e.type?r+='"'+e.value+'"':r+=e.value}),r))},rm="__json_buf";function rh(e){return"tool_use"===e.type||"server_tool_use"===e.type||"mcp_tool_use"===e.type}class rp{constructor(e,t){d.add(this),this.messages=[],this.receivedMessages=[],c.set(this,void 0),u.set(this,null),this.controller=new AbortController,m.set(this,void 0),h.set(this,()=>{}),p.set(this,()=>{}),f.set(this,void 0),g.set(this,()=>{}),x.set(this,()=>{}),b.set(this,{}),y.set(this,!1),v.set(this,!1),j.set(this,!1),w.set(this,!1),_.set(this,void 0),N.set(this,void 0),S.set(this,void 0),T.set(this,e=>{if(tJ(this,v,!0,"f"),tY(e)&&(e=new t1),e instanceof t1)return tJ(this,j,!0,"f"),this._emit("abort",e);if(e instanceof 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this._client.post(s0`/v1/vaults/${r}/credentials/${e}/archive?beta=true`,{...s,headers:sY([{"anthropic-beta":[...a??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}}class rP extends sK{constructor(){super(...arguments),this.credentials=new rA(this._client)}create(e,t){let{betas:s,...r}=e;return this._client.post("/v1/vaults?beta=true",{body:r,...t,headers:sY([{"anthropic-beta":[...s??[],"managed-agents-2026-04-01"].toString()},t?.headers])})}retrieve(e,t={},s){let{betas:r}=t??{};return this._client.get(s0`/v1/vaults/${e}?beta=true`,{...s,headers:sY([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}update(e,t,s){let{betas:r,...a}=t;return this._client.post(s0`/v1/vaults/${e}?beta=true`,{body:a,...s,headers:sY([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}list(e={},t){let{betas:s,...r}=e??{};return 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s=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===s.type&&this._emit("text",e.delta.text,s.text||"");break;case"citations_delta":"text"===s.type&&this._emit("citation",e.delta.citation,s.citations??[]);break;case"input_json_delta":rL(s)&&s.input&&this._emit("inputJson",e.delta.partial_json,s.input);break;case"thinking_delta":"thinking"===s.type&&this._emit("thinking",e.delta.thinking,s.thinking);break;case"signature_delta":"thinking"===s.type&&this._emit("signature",s.signature);break;default:rD(e.delta)}break}case"message_stop":this._addMessageParam(t),this._addMessage(rR(t,tK(this,H,"f"),{logger:tK(this,ei,"f")}),!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":tJ(this,V,t,"f")}},em=function(){if(this.ended)throw new tZ("stream has ended, this shouldn't happen");let e=tK(this,V,"f");if(!e)throw new tZ("request ended without sending any chunks");return tJ(this,V,void 0,"f"),rR(e,tK(this,H,"f"),{logger:tK(this,ei,"f")})},eh=function(e){let t=tK(this,V,"f");if("message_start"===e.type){if(t)throw new tZ(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new tZ(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push({...e.content_block}),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(t.content[e.index]={...s,text:(s.text||"")+e.delta.text});break;case"citations_delta":s?.type==="text"&&(t.content[e.index]={...s,citations:[...s.citations??[],e.delta.citation]});break;case"input_json_delta":if(s&&rL(s)){let r=s[rO]||"";r+=e.delta.partial_json;let a={...s};Object.defineProperty(a,rO,{value:r,enumerable:!1,writable:!0}),r&&(a.input=ru(r)),t.content[e.index]=a}break;case"thinking_delta":s?.type==="thinking"&&(t.content[e.index]={...s,thinking:s.thinking+e.delta.thinking});break;case"signature_delta":s?.type==="thinking"&&(t.content[e.index]={...s,signature:e.delta.signature});break;default:rD(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new sC(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function rD(e){}class rz extends sK{create(e,t){return this._client.post("/v1/messages/batches",{body:e,...t})}retrieve(e,t){return this._client.get(s0`/v1/messages/batches/${e}`,t)}list(e={},t){return this._client.getAPIList("/v1/messages/batches",sO,{query:e,...t})}delete(e,t){return this._client.delete(s0`/v1/messages/batches/${e}`,t)}cancel(e,t){return this._client.post(s0`/v1/messages/batches/${e}/cancel`,t)}async results(e,t){let s=await this.retrieve(e);if(!s.results_url)throw new tZ(`No batch \`results_url\`; Has it finished processing? 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n={};this.idempotencyHeader&&"get"!==s&&(e.idempotencyKey||(e.idempotencyKey=this.defaultIdempotencyKey()),n[this.idempotencyHeader]=e.idempotencyKey);let i=sY([n,{Accept:"application/json","User-Agent":this.getUserAgent(),"X-Stainless-Retry-Count":String(a),...e.timeout?{"X-Stainless-Timeout":String(Math.trunc(e.timeout/1e3))}:{},...t??(t=(()=>{let e="u">typeof Deno&&null!=Deno.build?"deno":"u">typeof EdgeRuntime?"edge":"[object process]"===Object.prototype.toString.call(void 0!==globalThis.process?globalThis.process:0)?"node":"unknown";if("deno"===e)return{"X-Stainless-Lang":"js","X-Stainless-Package-Version":sl,"X-Stainless-OS":sc(Deno.build.os),"X-Stainless-Arch":sd(Deno.build.arch),"X-Stainless-Runtime":"deno","X-Stainless-Runtime-Version":"string"==typeof Deno.version?Deno.version:Deno.version?.deno??"unknown"};if("u">typeof EdgeRuntime)return{"X-Stainless-Lang":"js","X-Stainless-Package-Version":sl,"X-Stainless-OS":"Unknown","X-Stainless-Arch":`other:${EdgeRuntime}`,"X-Stainless-Runtime":"edge","X-Stainless-Runtime-Version":globalThis.process.version};if("node"===e)return{"X-Stainless-Lang":"js","X-Stainless-Package-Version":sl,"X-Stainless-OS":sc(globalThis.process.platform??"unknown"),"X-Stainless-Arch":sd(globalThis.process.arch??"unknown"),"X-Stainless-Runtime":"node","X-Stainless-Runtime-Version":globalThis.process.version??"unknown"};let t=function(){if("u"e.abort()}buildBody({options:{body:e,headers:t}}){if(!e)return{bodyHeaders:void 0,body:void 0};let s=sY([t]);return ArrayBuffer.isView(e)||e instanceof ArrayBuffer||e instanceof DataView||"string"==typeof e&&s.values.has("content-type")||globalThis.Blob&&e instanceof globalThis.Blob||e instanceof FormData||e instanceof URLSearchParams||globalThis.ReadableStream&&e instanceof globalThis.ReadableStream?{bodyHeaders:void 0,body:e}:"object"==typeof e&&(Symbol.asyncIterator in e||Symbol.iterator in e&&"next"in e&&"function"==typeof e.next)?{bodyHeaders:void 0,body:sm(e)}:"object"==typeof e&&"application/x-www-form-urlencoded"===s.values.get("content-type")?{bodyHeaders:{"content-type":"application/x-www-form-urlencoded"},body:this.stringifyQuery(e)}:tK(this,eg,"f").call(this,{body:e,headers:s})}}ef=rG,eg=new WeakMap,ep=new WeakSet,ex=function(){return"https://api.anthropic.com"!==this.baseURL},rG.Anthropic=ef,rG.HUMAN_PROMPT="\\n\\nHuman:",rG.AI_PROMPT="\\n\\nAssistant:",rG.DEFAULT_TIMEOUT=6e5,rG.AnthropicError=tZ,rG.APIError=t0,rG.APIConnectionError=t2,rG.APIConnectionTimeoutError=t4,rG.APIUserAbortError=t1,rG.NotFoundError=t8,rG.ConflictError=t9,rG.RateLimitError=se,rG.BadRequestError=t5,rG.AuthenticationError=t3,rG.InternalServerError=st,rG.PermissionDeniedError=t6,rG.UnprocessableEntityError=t7,rG.toFile=sG;class rJ extends rG{constructor(){super(...arguments),this.completions=new rV(this),this.messages=new rB(this),this.models=new rW(this),this.beta=new rI(this)}}rJ.Completions=rV,rJ.Messages=rB,rJ.Models=rW,rJ.Beta=rI;let rK="toolset:";async function rX(e,t,s,r,a=[],n,i,o,l,d,c,u,m,h,p,f,g,x){if(!r)throw Error("Virtual Key is required");console.log=function(){};let b=p||(0,eU.getProxyBaseUrl)(),y={};a&&a.length>0&&(y["x-litellm-tags"]=a.join(","));let v=new rJ({apiKey:r,baseURL:b,dangerouslyAllowBrowser:!0,defaultHeaders:y});try{let r=Date.now(),a=!1,p={model:s,messages:e.map(e=>({role:e.role,content:e.content})),stream:!0,max_tokens:1024,litellm_trace_id:d},b=function({selectedMCPServers:e,mcpServers:t,mcpToolsets:s,mcpServerToolRestrictions:r}){return e&&0!==e.length?e.includes("__all__")?[{type:"mcp",server_label:"litellm",server_url:"litellm_proxy/mcp",require_approval:"never"}]:e.map(e=>{if(e.startsWith(rK)){let t=e.slice(rK.length),r=s?.find(e=>e.toolset_id===t),a=r?.toolset_name||t;return{type:"mcp",server_label:a,server_url:`litellm_proxy/mcp/${a}`,require_approval:"never"}}let a=t?.find(t=>t.server_id===e),n=a?.server_name||e,i=r?.[e]||[];return{type:"mcp",server_label:n,server_url:`litellm_proxy/mcp/${n}`,require_approval:"never",...i.length>0?{allowed_tools:i}:{}}}):[]}({selectedMCPServers:h,mcpServers:f,mcpToolsets:x,mcpServerToolRestrictions:g});for await(let e of(b.length>0&&(p.tools=b),c&&(p.vector_store_ids=c),u&&(p.guardrails=u),m&&(p.policies=m),v.messages.stream(p,{signal:n}))){if("content_block_delta"===e.type){let n=e.delta;if(!a){a=!0;let e=Date.now()-r;o&&o(e)}"text_delta"===n.type?t("assistant",n.text,s):"reasoning_delta"===n.type&&i&&i(n.text)}if("message_delta"===e.type&&e.usage&&l){let t=e.usage,s={completionTokens:t.output_tokens,promptTokens:t.input_tokens,totalTokens:t.input_tokens+t.output_tokens,...(0,eH.extractPromptCacheTokens)(t)};l(s)}}}catch(e){throw n?.aborted||eL.toast.fromError(`Error occurred while generating model response. 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Please try again. 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with text only +response = client.chat.completions.create( + model="${v}", + messages=${JSON.stringify(a,null,4)}${i} +) + +print(response) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.chat.completions.create( +# model="${v}", +# messages=[ +# { +# "role": "user", +# "content": [ +# { +# "type": "text", +# "text": "${j}" +# }, +# { +# "type": "image_url", +# "image_url": { +# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file} +# } +# } +# ] +# } +# ]${i} +# ) +# print(response_with_file) +`;break}case o.RESPONSES:{let e=Object.keys(S).length>0,i="";if(e){let e=JSON.stringify({metadata:S},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`, + extra_body=${e}`}let a=k.length>0?k:[{role:"user",content:y}];t=` +import base64 + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.responses.create( + model="${v}", + input=${JSON.stringify(a,null,4)}${i} +) + +print(response.output_text) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.responses.create( +# model="${v}", +# input=[ +# { +# "role": "user", +# "content": [ +# {"type": "input_text", "text": "${j}"}, +# { +# "type": "input_image", +# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} +# }, +# ], +# } +# ]${i} +# ) +# print(response_with_file.output_text) +`;break}case o.IMAGE:t="azure"===f?` +# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. +# This snippet uses 'client.images.generate' and will create a new image based on your prompt. +# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. +import os +import requests +import json +import time +from PIL import Image + +result = client.images.generate( + model="${v}", + prompt="${r}", + n=1 +) + +json_response = json.loads(result.model_dump_json()) + +# Set the directory for the stored image +image_dir = os.path.join(os.curdir, 'images') + +# If the directory doesn't exist, create it +if not os.path.isdir(image_dir): + os.mkdir(image_dir) + +# Initialize the image path +image_filename = f"generated_image_{int(time.time())}.png" +image_path = os.path.join(image_dir, image_filename) + +try: + # Retrieve the generated image + if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): + image_url = json_response["data"][0]["url"] + generated_image = requests.get(image_url).content + with open(image_path, "wb") as image_file: + image_file.write(generated_image) + + print(f"Image saved to {image_path}") + # Display the image + image = Image.open(image_path) + image.show() + else: + print("Could not find image URL in response.") + print("Full response:", json_response) +except Exception as e: + print(f"An error occurred: {e}") + print("Full response:", json_response) +`:` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${j}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${v}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case o.IMAGE_EDITS:t="azure"===f?` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# The prompt entered by the user +prompt = "${j}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${v}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`:` +import base64 +import os +import time + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${j}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${v}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case o.EMBEDDINGS:t=` +response = client.embeddings.create( + input="${r||"Your string here"}", + model="${v}", + encoding_format="base64" # or "float" +) + +print(response.data[0].embedding) +`;break;case o.TRANSCRIPTION:t=` +# Open the audio file +audio_file = open("path/to/your/audio/file.mp3", "rb") + +# Make the transcription request +response = client.audio.transcriptions.create( + model="${v}", + file=audio_file${r?`, + prompt="${r.replace(/\\/g,"\\\\").replace(/"/g,'\\"')}"`:""} +) + +print(response.text) +`;break;case o.SPEECH:t=` +# Make the text-to-speech request +response = client.audio.speech.create( + model="${v}", + input="${r||"Your text to convert to speech here"}", + voice="${c}" # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer +) + +# Save the audio to a file 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a=j.length>0?j:[{role:"user",content:y}];t=` import base64 # Helper function to encode images to base64 @@ -61,7 +61,7 @@ def encode_image(image_path): # Example with text only response = client.responses.create( model="${k}", - input=${JSON.stringify(r,null,4)}${i} + input=${JSON.stringify(a,null,4)}${i} ) print(response.output_text) @@ -84,7 +84,7 @@ print(response.output_text) # ]${i} # ) # print(response_with_file.output_text) -`;break}case a.IMAGE:t="azure"===f?` +`;break}case r.IMAGE:t="azure"===f?` # NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. # This snippet uses 'client.images.generate' and will create a new image based on your prompt. # It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. @@ -215,7 +215,7 @@ else: print("No image data found in response.") print("Full response for debugging:") print(response) -`;break;case a.IMAGE_EDITS:t="azure"===f?` +`;break;case r.IMAGE_EDITS:t="azure"===f?` import base64 import os import time @@ -374,7 +374,7 @@ else: print("No image data found in response.") print("Full response for debugging:") print(response) -`;break;case a.EMBEDDINGS:t=` +`;break;case r.EMBEDDINGS:t=` response = client.embeddings.create( input="${n||"Your string here"}", model="${k}", @@ -382,7 +382,7 @@ response = client.embeddings.create( ) print(response.data[0].embedding) -`;break;case a.TRANSCRIPTION:t=` +`;break;case r.TRANSCRIPTION:t=` # Open the audio file audio_file = open("path/to/your/audio/file.mp3", "rb") @@ -394,7 +394,7 @@ response = client.audio.transcriptions.create( ) print(response.text) -`;break;case a.SPEECH:t=` +`;break;case r.SPEECH:t=` # Make the text-to-speech request response = client.audio.speech.create( model="${k}", @@ -417,4 +417,4 @@ print(f"Audio saved to {output_filename}") # ) # response.stream_to_file("output_speech.mp3") `;break;default:t="\n# Code generation 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open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.responses.create( + model="${j}", + input=${JSON.stringify(o,null,4)}${i} +) + +print(response.output_text) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.responses.create( +# model="${j}", +# input=[ +# { +# "role": "user", +# "content": [ +# {"type": "input_text", "text": "${S}"}, +# { +# "type": "input_image", +# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} +# }, +# ], +# } +# ]${i} +# ) +# print(response_with_file.output_text) +`;break}case a.IMAGE:t="azure"===f?` +# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. +# This snippet uses 'client.images.generate' and will create a new image based on your prompt. +# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. +import os +import requests +import json +import time +from PIL import Image + +result = client.images.generate( + model="${j}", + prompt="${r}", + n=1 +) + +json_response = json.loads(result.model_dump_json()) + +# Set the directory for the stored image +image_dir = os.path.join(os.curdir, 'images') + +# If the directory doesn't exist, create it +if not os.path.isdir(image_dir): + os.mkdir(image_dir) + +# Initialize the image path +image_filename = f"generated_image_{int(time.time())}.png" +image_path = os.path.join(image_dir, image_filename) + +try: + # Retrieve the generated image + if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): + image_url = json_response["data"][0]["url"] + generated_image = requests.get(image_url).content + with open(image_path, "wb") as image_file: + image_file.write(generated_image) + + print(f"Image saved to {image_path}") + # Display the image + image = Image.open(image_path) + image.show() + else: + print("Could not find image URL in response.") + print("Full response:", json_response) +except Exception as e: + print(f"An error occurred: {e}") + print("Full response:", json_response) +`:` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${S}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${j}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case a.IMAGE_EDITS:t="azure"===f?` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# The prompt entered by the user +prompt = "${S}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${j}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`:` +import base64 +import os +import time + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${S}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${j}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case a.EMBEDDINGS:t=` +response = client.embeddings.create( + input="${r||"Your string here"}", + model="${j}", + encoding_format="base64" # or "float" +) + +print(response.data[0].embedding) +`;break;case a.TRANSCRIPTION:t=` +# Open the audio file +audio_file = open("path/to/your/audio/file.mp3", "rb") + +# Make the transcription request +response = client.audio.transcriptions.create( + model="${j}", + file=audio_file${r?`, + prompt="${r.replace(/\\/g,"\\\\").replace(/"/g,'\\"')}"`:""} +) + +print(response.text) +`;break;case a.SPEECH:t=` +# Make the text-to-speech request +response = client.audio.speech.create( + model="${j}", + input="${r||"Your text to convert to speech here"}", + voice="${c}" # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer +) + +# Save the audio to a file 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KEY CHANGE + +`,ew=` +curl -X POST --location '/chat/completions' \\ + +-H 'Authorization: Bearer ' \\ + +-H 'Content-Type: application/json' \\ + +-d '{ + "model": "gpt-3.5-turbo', + "messages":[{"role": "user", "content": "Hey, how's it going?"}], + "user": "my-customer-id" +}' # 👈 KEY CHANGE + +`,eD=`from openai import OpenAI +client = OpenAI( + base_url="", + api_key="" +) + +completion = client.chat.completions.create( + model="gpt-3.5-turbo", + messages=[ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Hello!"} + ], + user="my-customer-id" +) + +print(completion.choices[0].message)`;var eM=e.i(708347);let eL=({accessToken:e})=>{let 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Final[tuple[LazyFeature, ...]] = ( "/callback", "/register", "/revoke", + "/introspect", ), # Catches the /{mcp_server_name}/authorize|token|register variants. path_suffixes=("/authorize", "/token", "/register"), diff --git a/litellm/proxy/_lazy_openapi_snapshot.json b/litellm/proxy/_lazy_openapi_snapshot.json index c1e89f8aa75..13c7a4c7cfa 100644 --- a/litellm/proxy/_lazy_openapi_snapshot.json +++ b/litellm/proxy/_lazy_openapi_snapshot.json @@ -461,6 +461,145 @@ "access_groups": { "components": { "schemas": { + "AccessGroupBudget": { + "properties": { + "budget_duration": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Budget Duration" + }, + "budget_id": { + "title": "Budget Id", + "type": "string" + }, + "budget_reset_at": { + "anyOf": [ + { + "format": "date-time", + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Budget Reset At" + }, + "max_budget": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "title": "Max Budget" + }, + "soft_budget": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "title": "Soft Budget" + } + }, + "required": [ + "budget_id" + ], + "title": "AccessGroupBudget", + "type": "object" + }, + "AccessGroupBudgetRequest": { + "additionalProperties": false, + "properties": { + "budget_duration": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Budget Duration" + }, + "budget_id": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Budget Id" + }, + "max_budget": { + "anyOf": [ + { + "minimum": 0.0, + "type": "number" + }, + { + "type": "null" + } + ], + "title": "Max Budget" + }, + "soft_budget": { + "anyOf": [ + { + "minimum": 0.0, + "type": "number" + }, + { + "type": "null" + } + ], + "title": "Soft Budget" + } + }, + "title": "AccessGroupBudgetRequest", + "type": "object" + }, + "AccessGroupBudgetResponse": { + "properties": { + "access_group": { + "title": "Access Group", + "type": "string" + }, + "budget": { + "anyOf": [ + { + "$ref": "#/components/schemas/AccessGroupBudget" + }, + { + "type": "null" + } + ] + }, + "spend": { + "title": "Spend", + "type": "number" + } + }, + "required": [ + "access_group", + "spend" + ], + "title": "AccessGroupBudgetResponse", + "type": "object" + }, "AccessGroupCreateRequest": { "properties": { "access_agent_ids": { @@ -561,6 +700,16 @@ "title": "Access Group", "type": "string" }, + "budget": { + "anyOf": [ + { + "$ref": "#/components/schemas/AccessGroupBudget" + }, + { + "type": "null" + } + ] + }, "deployment_count": { "title": "Deployment Count", "type": "integer" @@ -571,6 +720,17 @@ }, "title": "Model Names", "type": "array" + }, + "spend": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "title": "Spend" } }, "required": [ @@ -782,6 +942,29 @@ "title": "AccessGroupUpdateRequest", "type": "object" }, + "DeleteAccessGroupBudgetResponse": { + "properties": { + "access_group": { + "title": "Access Group", + "type": "string" + }, + "budget_deleted": { + "title": "Budget Deleted", + "type": "boolean" + }, + "message": { + "title": "Message", + "type": "string" + } + }, + "required": [ + "access_group", + "budget_deleted", + "message" + ], + "title": "DeleteAccessGroupBudgetResponse", + "type": "object" + }, "DeleteModelGroupResponse": { "properties": { "access_group": { @@ -1000,7 +1183,7 @@ "paths": { "/access_group/list": { "get": { - "description": "List all access groups.\n\nReturns a list of all access groups with their model names and deployment counts.\n\nExample:\n```bash\ncurl -X GET 'http://localhost:4000/access_group/list' \\\n -H 'Authorization: Bearer sk-1234'\n```\n\nReturns:\n- ListAccessGroupsResponse with all access groups", + "description": "List all access groups.\n\nReturns a list of all access groups with their model names, deployment counts, shared budget\nand the spend drawn against it.\n\nExample:\n```bash\ncurl -X GET 'http://localhost:4000/access_group/list' \\\n -H 'Authorization: Bearer sk-1234'\n```\n\nReturns:\n- ListAccessGroupsResponse with all access groups", "operationId": "list_access_groups_access_group_list_get", "responses": { "200": { @@ -1072,6 +1255,156 @@ ] } }, + "/access_group/{access_group}/budget": { + "delete": { + "description": "Clear the shared budget of an access group, leaving the group itself in place.\n\nExample:\n```bash\ncurl -X DELETE 'http://localhost:4000/access_group/production-models/budget' \\\n -H 'Authorization: Bearer sk-1234'\n```\n\nParameters:\n- access_group: str - The access group name (URL path parameter)\n\nReturns:\n- DeleteAccessGroupBudgetResponse; budget_deleted is false when there was nothing to clear\n\nRaises:\n- HTTPException 404: If access group not found", + "operationId": "delete_access_group_budget_access_group__access_group__budget_delete", + "parameters": [ + { + "in": "path", + "name": "access_group", + "required": true, + "schema": { + "title": "Access Group", + "type": "string" + } + } + ], + "responses": { + "200": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/DeleteAccessGroupBudgetResponse" + } + } + }, + "description": "Successful Response" + }, + "422": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + }, + "description": "Validation Error" + } + }, + "security": [ + { + "APIKeyHeader": [] + } + ], + "summary": "Delete Access Group Budget", + "tags": [ + "access_groups" + ] + }, + "get": { + "description": "Get the shared budget of an access group, and the spend drawn against it.\n\nExample:\n```bash\ncurl -X GET 'http://localhost:4000/access_group/production-models/budget' \\\n -H 'Authorization: Bearer sk-1234'\n```\n\nParameters:\n- access_group: str - The access group name (URL path parameter)\n\nReturns:\n- AccessGroupBudgetResponse; budget is null when the group has no budget set\n\nRaises:\n- HTTPException 404: If access group not found", + "operationId": "get_access_group_budget_access_group__access_group__budget_get", + "parameters": [ + { + "in": "path", + "name": "access_group", + "required": true, + "schema": { + "title": "Access Group", + "type": "string" + } + } + ], + "responses": { + "200": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/AccessGroupBudgetResponse" + } + } + }, + "description": "Successful Response" + }, + "422": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + }, + "description": "Validation Error" + } + }, + "security": [ + { + "APIKeyHeader": [] + } + ], + "summary": "Get Access Group Budget", + "tags": [ + "access_groups" + ] + }, + "put": { + "description": "Set or replace the shared budget of an access group. Idempotent.\n\nEvery key that can reach a model in the group draws from this one budget.\n\nExample:\n```bash\ncurl -X PUT 'http://localhost:4000/access_group/production-models/budget' \\\n -H 'Authorization: Bearer sk-1234' \\\n -H 'Content-Type: application/json' \\\n -d '{\n \"max_budget\": 100.0,\n \"budget_duration\": \"30d\"\n }'\n```\n\nParameters:\n- access_group: str - The access group name (URL path parameter)\n- max_budget: Optional[float] - Requests fail once the group's shared spend exceeds this\n- soft_budget: Optional[float] - Fires an alert when reached; requests still succeed\n- budget_duration: Optional[str] - Frequency of resetting the group's spend (e.g. '30d')\n- budget_id: Optional[str] - Link an existing budget instead of creating one\n\nReturns:\n- AccessGroupBudgetResponse with the stored budget and current spend\n\nRaises:\n- HTTPException 400: If no budget field is given, or budget_duration cannot be parsed\n- HTTPException 404: If access group not found", + "operationId": "set_access_group_budget_access_group__access_group__budget_put", + "parameters": [ + { + "in": "path", + "name": "access_group", + "required": true, + "schema": { + "title": "Access Group", + "type": "string" + } + } + ], + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/AccessGroupBudgetRequest" + } + } + }, + "required": true + }, + "responses": { + "200": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/AccessGroupBudgetResponse" + } + } + }, + "description": "Successful Response" + }, + "422": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + }, + "description": "Validation Error" + } + }, + "security": [ + { + "APIKeyHeader": [] + } + ], + "summary": "Set Access Group Budget", + "tags": [ + "access_groups" + ] + } + }, "/access_group/{access_group}/delete": { "delete": { "description": "Delete an access group.\n\nRemoves the access group from all deployments that have it.\n\nExample:\n```bash\ncurl -X DELETE 'http://localhost:4000/access_group/production-models/delete' \\\n -H 'Authorization: Bearer sk-1234'\n```\n\nParameters:\n- access_group: str - The access group name (URL path parameter)\n\nReturns:\n- DeleteModelGroupResponse with deletion details\n\nRaises:\n- HTTPException 404: If access group not found", @@ -1122,7 +1455,7 @@ }, "/access_group/{access_group}/info": { "get": { - "description": "Get information about a specific access group.\n\nExample:\n```bash\ncurl -X GET 'http://localhost:4000/access_group/production-models/info' \\\n -H 'Authorization: Bearer sk-1234'\n```\n\nParameters:\n- access_group: str - The access group name (URL path parameter)\n\nReturns:\n- AccessGroupInfo with the access group details\n\nRaises:\n- HTTPException 404: If access group not found", + "description": "Get information about a specific access group.\n\nExample:\n```bash\ncurl -X GET 'http://localhost:4000/access_group/production-models/info' \\\n -H 'Authorization: Bearer sk-1234'\n```\n\nParameters:\n- access_group: str - The access group name (URL path parameter)\n\nReturns:\n- AccessGroupInfo with the access group details, its shared budget and its spend\n\nRaises:\n- HTTPException 404: If access group not found", "operationId": "get_access_group_info_access_group__access_group__info_get", "parameters": [ { @@ -6459,6 +6792,109 @@ "title": "ConfigOverrideSettingsResponse", "type": "object" }, + "CyberArkConfig": { + "description": "Configuration for CyberArk Conjur secret manager integration.", + "properties": { + "client_cert": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Path to the client TLS certificate for certificate-based authentication", + "title": "Client Cert" + }, + "client_key": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Path to the client TLS private key for certificate-based authentication", + "title": "Client Key" + }, + "cyberark_account": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "The Conjur organization account name", + "title": "Cyberark Account" + }, + "cyberark_api_base": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "The address of the CyberArk Conjur server (e.g., https://conjur.example.com)", + "title": "Cyberark Api Base" + }, + "cyberark_api_key": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "API key for Conjur API-key authentication", + "title": "Cyberark Api Key" + }, + "cyberark_username": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "The Conjur username (login) to authenticate as", + "title": "Cyberark Username" + }, + "refresh_interval": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Auth token cache TTL in seconds (default: 300)", + "title": "Refresh Interval" + }, + "ssl_verify": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Set to false to disable SSL verification (e.g., for self-signed certificates)", + "title": "Ssl Verify" + } + }, + "title": "CyberArkConfig", + "type": "object" + }, "HTTPValidationError": { "properties": { "detail": { @@ -6654,6 +7090,192 @@ } }, "paths": { + "/config_overrides/cyberark": { + "delete": { + "description": "Delete CyberArk Conjur configuration. Idempotent.", + "operationId": "delete_cyberark_config_config_overrides_cyberark_delete", + "parameters": [ + { + "description": "The litellm-changed-by header enables tracking of actions performed by authorized users on behalf of other users, providing an audit trail for accountability", + "in": "header", + "name": "litellm-changed-by", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "The litellm-changed-by header enables tracking of actions performed by authorized users on behalf of other users, providing an audit trail for accountability", + "title": "Litellm-Changed-By" + } + } + ], + "responses": { + "200": { + "content": { + "application/json": { + "schema": { + "additionalProperties": { + "type": "string" + }, + "title": "Response Delete Cyberark Config Config Overrides Cyberark Delete", + "type": "object" + } + } + }, + "description": "Successful Response" + }, + "422": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + }, + "description": "Validation Error" + } + }, + "security": [ + { + "APIKeyHeader": [] + } + ], + "summary": "Delete Cyberark Config", + "tags": [ + "config_overrides" + ] + }, + "get": { + "description": "Get current CyberArk Conjur configuration.\nReturns decrypted values from DB, or falls back to current env vars.\nSensitive fields are masked before leaving the server.", + "operationId": "get_cyberark_config_config_overrides_cyberark_get", + "responses": { + "200": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/ConfigOverrideSettingsResponse" + } + } + }, + "description": "Successful Response" + } + }, + "security": [ + { + "APIKeyHeader": [] + } + ], + "summary": "Get Cyberark Config", + "tags": [ + "config_overrides" + ] + }, + "post": { + "description": "Update CyberArk Conjur secret manager configuration.\nSets environment variables, encrypts sensitive fields, and stores in DB.\nReinitializes the secret manager on this pod.", + "operationId": "update_cyberark_config_config_overrides_cyberark_post", + "parameters": [ + { + "description": "The litellm-changed-by header enables tracking of actions performed by authorized users on behalf of other users, providing an audit trail for accountability", + "in": "header", + "name": "litellm-changed-by", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "The litellm-changed-by header enables tracking of actions performed by authorized users on behalf of other users, providing an audit trail for accountability", + "title": "Litellm-Changed-By" + } + } + ], + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/CyberArkConfig" + } + } + }, + "required": true + }, + "responses": { + "200": { + "content": { + "application/json": { + "schema": { + "additionalProperties": { + "type": "string" + }, + "title": "Response Update Cyberark Config Config Overrides Cyberark Post", + "type": "object" + } + } + }, + "description": "Successful Response" + }, + "422": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + }, + "description": "Validation Error" + } + }, + "security": [ + { + "APIKeyHeader": [] + } + ], + "summary": "Update Cyberark Config", + "tags": [ + "config_overrides" + ] + } + }, + "/config_overrides/cyberark/test_connection": { + "post": { + "description": "Test the connection to the currently configured CyberArk Conjur server.\nUses the already-initialized secret manager client. Does not modify any state.", + "operationId": "test_cyberark_connection_config_overrides_cyberark_test_connection_post", + "responses": { + "200": { + "content": { + "application/json": { + "schema": { + "additionalProperties": { + "type": "string" + }, + "title": "Response Test Cyberark Connection Config Overrides Cyberark Test Connection Post", + "type": "object" + } + } + }, + "description": "Successful Response" + } + }, + "security": [ + { + "APIKeyHeader": [] + } + ], + "summary": "Test Cyberark Connection", + "tags": [ + "config_overrides" + ] + } + }, "/config_overrides/hashicorp_vault": { "delete": { "description": "Delete Hashicorp Vault configuration. Idempotent.", @@ -16555,6 +17177,19 @@ "title": "Body_authorize_complete_authorize_complete_post", "type": "object" }, + "Body_introspect_endpoint_introspect_post": { + "properties": { + "token": { + "title": "Token", + "type": "string" + } + }, + "required": [ + "token" + ], + "title": "Body_introspect_endpoint_introspect_post", + "type": "object" + }, "Body_revoke_endpoint_revoke_post": { "properties": { "client_id": { @@ -19134,6 +19769,51 @@ ] } }, + "/introspect": { + "post": { + "description": "RFC 7662 introspection for gateway-issued session tokens (``llm_session_`` /\n``llm_srefresh_``), so an external gateway can validate them without the signing\nsecret. The caller authenticates with a LiteLLM virtual key (section 2.1, enforced by\nthe route dependency); any token the gateway cannot vouch for answers\n``{\"active\": false}`` with no further detail.", + "operationId": "introspect_endpoint_introspect_post", + "requestBody": { + "content": { + "application/x-www-form-urlencoded": { + "schema": { + "$ref": "#/components/schemas/Body_introspect_endpoint_introspect_post" + } + } + }, + "required": true + }, + "responses": { + "200": { + "content": { + "application/json": { + "schema": {} + } + }, + "description": "Successful Response" + }, + "422": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + }, + "description": "Validation Error" + } + }, + "security": [ + { + "APIKeyHeader": [] + } + ], + "summary": "Introspect Endpoint", + "tags": [ + "mcp_discoverable" + ] + } + }, "/register": { "post": { "operationId": "register_client_register_post", @@ -20225,6 +20905,223 @@ "title": "LiteLLM_MCPServerTable", "type": "object" }, + "MCPConnectorEntry": { + "properties": { + "args": { + "items": { + "type": "string" + }, + "title": "Args", + "type": "array" + }, + "authorization_token": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Authorization Token" + }, + "command": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Command" + }, + "description": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Description" + }, + "env": { + "additionalProperties": { + "type": "string" + }, + "title": "Env", + "type": "object" + }, + "headers": { + "anyOf": [ + { + "additionalProperties": { + "type": "string" + }, + "type": "object" + }, + { + "type": "null" + } + ], + "title": "Headers" + }, + "name": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Name" + }, + "type": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Type" + }, + "url": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Url" + } + }, + "title": "MCPConnectorEntry", + "type": "object" + }, + "MCPConnectorImportFailure": { + "properties": { + "error": { + "title": "Error", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + } + }, + "required": [ + "name", + "error" + ], + "title": "MCPConnectorImportFailure", + "type": "object" + }, + "MCPConnectorImportRequest": { + "properties": { + "mcp_servers": { + "anyOf": [ + { + "additionalProperties": { + "$ref": "#/components/schemas/MCPConnectorEntry" + }, + "type": "object" + }, + { + "items": { + "$ref": "#/components/schemas/MCPConnectorEntry" + }, + "type": "array" + } + ], + "title": "Mcp Servers" + } + }, + "required": [ + "mcp_servers" + ], + "title": "MCPConnectorImportRequest", + "type": "object" + }, + "MCPConnectorImportResponse": { + "properties": { + "errors": { + "items": { + "$ref": "#/components/schemas/MCPConnectorImportFailure" + }, + "title": "Errors", + "type": "array" + }, + "imported": { + "items": { + "$ref": "#/components/schemas/MCPConnectorImportResult" + }, + "title": "Imported", + "type": "array" + }, + "skipped": { + "items": { + "$ref": "#/components/schemas/MCPConnectorImportSkipped" + }, + "title": "Skipped", + "type": "array" + } + }, + "required": [ + "imported", + "skipped", + "errors" + ], + "title": "MCPConnectorImportResponse", + "type": "object" + }, + "MCPConnectorImportResult": { + "properties": { + "alias": { + "title": "Alias", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "server_id": { + "title": "Server Id", + "type": "string" + } + }, + "required": [ + "name", + "server_id", + "alias" + ], + "title": "MCPConnectorImportResult", + "type": "object" + }, + "MCPConnectorImportSkipped": { + "properties": { + "name": { + "title": "Name", + "type": "string" + }, + "reason": { + "title": "Reason", + "type": "string" + } + }, + "required": [ + "name", + "reason" + ], + "title": "MCPConnectorImportSkipped", + "type": "object" + }, "MCPCredentials": { "properties": { "audience": { @@ -22464,6 +23361,53 @@ ] } }, + "/v1/mcp/server/import": { + "post": { + "description": "Bulk-import MCP connectors from Anthropic mcpServers or mcp_servers JSON", + "operationId": "import_mcp_servers_v1_mcp_server_import_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/MCPConnectorImportRequest" + } + } + }, + "required": true + }, + "responses": { + "200": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/MCPConnectorImportResponse" + } + } + }, + "description": "Successful Response" + }, + "422": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + }, + "description": "Validation Error" + } + }, + "security": [ + { + "APIKeyHeader": [] + } + ], + "summary": "Import Mcp Servers", + "tags": [ + "mcp_management" + ] + } + }, "/v1/mcp/server/oauth/session": { "post": { "description": "Temporarily cache an MCP server in memory without writing to the database", @@ -31058,6 +32002,62 @@ "title": "SCIMPatchOperation", "type": "object" }, + "SCIMPlaceholder": { + "description": "A user row keyed by a value that names another account by SSO identity or email.", + "properties": { + "placeholder_user_id": { + "title": "Placeholder User Id", + "type": "string" + }, + "resolved_user_ids": { + "items": { + "type": "string" + }, + "title": "Resolved User Ids", + "type": "array" + }, + "team_ids": { + "items": { + "type": "string" + }, + "title": "Team Ids", + "type": "array" + } + }, + "required": [ + "placeholder_user_id", + "resolved_user_ids", + "team_ids" + ], + "title": "SCIMPlaceholder", + "type": "object" + }, + "SCIMPlaceholderMergeResult": { + "properties": { + "merged_into_user_id": { + "title": "Merged Into User Id", + "type": "string" + }, + "placeholder_user_id": { + "title": "Placeholder User Id", + "type": "string" + }, + "team_ids": { + "items": { + "type": "string" + }, + "title": "Team Ids", + "type": "array" + } + }, + "required": [ + "placeholder_user_id", + "merged_into_user_id", + "team_ids" + ], + "title": "SCIMPlaceholderMergeResult", + "type": "object" + }, "SCIMServiceProviderConfig": { "properties": { "authenticationSchemes": { @@ -32697,6 +33697,129 @@ "scim" ] } + }, + "/scim/v2/placeholders": { + "get": { + "description": "List user rows whose id is another account's SSO identity or email.\n\nAn earlier release provisioned a group member it could not match as a user keyed\nby the raw member value, and that row now shadows the account the value really\nnames, so every push of that member is refused. This lists those rows so an\noperator can fold each one into the account it shadows with\n``POST /scim/v2/placeholders/{user_id}/merge``. A row that has an SSO identity of\nits own or owns virtual keys is left out: someone uses that account.", + "operationId": "list_placeholders_scim_v2_placeholders_get", + "parameters": [ + { + "in": "query", + "name": "feature", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Feature" + } + } + ], + "responses": { + "200": { + "content": { + "application/json": { + "schema": { + "items": { + "$ref": "#/components/schemas/SCIMPlaceholder" + }, + "title": "Response List Placeholders Scim V2 Placeholders Get", + "type": "array" + } + } + }, + "description": "Successful Response" + }, + "422": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + }, + "description": "Validation Error" + } + }, + "security": [ + { + "APIKeyHeader": [] + } + ], + "summary": "List Placeholders", + "tags": [ + "scim" + ] + } + }, + "/scim/v2/placeholders/{user_id}/merge": { + "post": { + "description": "Fold a placeholder user into the one account its id names by SSO identity or email.\n\nThe account is added to every team the placeholder is on, then the placeholder is\ndeleted the way ``DELETE /scim/v2/Users/{id}`` deletes a user, so the next group\npush resolves the member value to the real account. Refused with 409 when the row\nhas an SSO identity of its own, owns virtual keys, or names no account or several.", + "operationId": "merge_placeholder_scim_v2_placeholders__user_id__merge_post", + "parameters": [ + { + "in": "path", + "name": "user_id", + "required": true, + "schema": { + "title": "User ID", + "type": "string" + } + }, + { + "in": "query", + "name": "feature", + "required": false, + "schema": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Feature" + } + } + ], + "responses": { + "200": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/SCIMPlaceholderMergeResult" + } + } + }, + "description": "Successful Response" + }, + "422": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + }, + "description": "Validation Error" + } + }, + "security": [ + { + "APIKeyHeader": [] + } + ], + "summary": "Merge Placeholder", + "tags": [ + "scim" + ] + } } } }, diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index f40b0632398..18714256a8f 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -241,6 +241,7 @@ class Litellm_EntityType(enum.Enum): PROJECT = "project" TAG = "tag" AGENT = "agent" + MODEL_ACCESS_GROUP = "model_access_group" # global proxy level entity PROXY = "proxy" @@ -421,6 +422,9 @@ class LiteLLMRoutes(enum.Enum): "/responses/{response_id}/cancel", "/v1/responses/{response_id}/cancel", "/openai/v1/responses/{response_id}/cancel", + "/responses/input_tokens", + "/v1/responses/input_tokens", + "/openai/v1/responses/input_tokens", # vector stores "/vector_stores", "/v1/vector_stores", @@ -470,6 +474,7 @@ class LiteLLMRoutes(enum.Enum): "/vllm", "/mistral", "/milvus", + "/gigachat", "/watsonx", ] @@ -504,6 +509,7 @@ class LiteLLMRoutes(enum.Enum): "/mcp-rest/tools/list", "/mcp-rest/tools/call", "/v1/mcp/tools", + "/introspect", ] # MCP server CRUD routes — control-plane. Gated by DISABLE_ADMIN_ENDPOINTS. @@ -1210,6 +1216,13 @@ class GenerateKeyRequest(KeyRequestBase): organization_id: str | None = None project_id: str | None = None + @field_validator("team_id", mode="before") + @classmethod + def treat_cleared_team_id_as_unset(cls, v: object) -> object: + if v == "": + return None + return v + class GenerateKeyResponse(KeyRequestBase): key: str @@ -1404,15 +1417,15 @@ class NewMCPServerRequest(LiteLLMPydanticObjectBase): # BYOM submission fields — set by the endpoint, not by the caller. # Any caller-provided values are silently overridden before persistence. approval_status: str | None = Field( - None, + default=None, description="Server-managed: set by the endpoint; caller values are overridden.", ) submitted_by: str | None = Field( - None, + default=None, description="Server-managed: set by the endpoint; caller values are overridden.", ) submitted_at: datetime | None = Field( - None, + default=None, description="Server-managed: set by the endpoint; caller values are overridden.", ) @@ -2430,9 +2443,22 @@ class ConfigGeneralSettings(LiteLLMPydanticObjectBase): database_socket_timeout: float | None = Field( None, description=( - "Prisma `socket_timeout` URL param (seconds). When set, an idle/slow " - "connection that has not produced data within this window is closed. " - "This is the main knob for capping idle DB connections from LiteLLM." + "Prisma `socket_timeout` URL param (seconds). When set, an in-flight " + "operation that has not produced data within this window is aborted. " + "For capping how long idle pooled connections are kept, see " + "`database_max_idle_connection_lifetime`." + ), + ) + database_max_idle_connection_lifetime: float | None = Field( + 60, + description=( + "Prisma `max_idle_connection_lifetime` URL param (seconds). A pooled " + "connection idle longer than this is closed and replaced instead of " + "being handed to the next request. Defaults to 60 so connections are " + "recycled before common infra idle timeouts (AWS NLB / RDS Proxy " + "~350s, many LBs 60-350s) silently drop them and requests fail with " + "`Error { kind: Closed }`. A value pinned on the DATABASE_URL or set " + "via `database_extra_connection_params` takes precedence." ), ) database_extra_connection_params: dict[str, Any] | None = Field( @@ -2539,7 +2565,7 @@ class ConfigGeneralSettings(LiteLLMPydanticObjectBase): ) alerting: list | None = Field( None, - description="List of alerting integrations. Today, just slack - `alerting: ['slack']`", + description="List of alerting integrations - e.g. `alerting: ['slack', 'webhook', 'email']`. 'slack' posts Slack-format messages to any Slack-compatible webhook (Slack, Rocket.Chat, Mattermost); 'webhook' posts structured JSON budget alerts to WEBHOOK_URL", ) alert_types: list[AlertType] | None = Field( None, @@ -2886,6 +2912,7 @@ class UserAPIKeyAuth(LiteLLM_VerificationTokenView): # the expected response ob ), ) budget_reservation: dict[str, Any] | None = Field(default=None, exclude=True) + matched_model_access_groups: list[str] | None = Field(default=None, exclude=True) budget_throttle_pct: float | None = Field(default=None, exclude=True) user: Any | None = None # Expanded user object when expand=user is used created_by_user: Any | None = None # Expanded created_by user when expand=user is used @@ -3546,6 +3573,19 @@ class AllCallbacks(LiteLLMPydanticObjectBase): ) +class SpendLogsRouterMetadata(TypedDict): + """ + Router provenance stamped on spend logs for deployments flagged with + model_info.internal_router_model, correlating the requested model group + with the provider deployment that served the call + """ + + requested_model: ReadOnly[str | None] + selected_model: ReadOnly[str | None] + selected_provider: ReadOnly[str | None] + router_correlation_id: ReadOnly[str | None] + + class SpendLogsMetadata(TypedDict): """ Specific metadata k,v pairs logged to spendlogs for easier cost tracking @@ -3573,6 +3613,8 @@ class SpendLogsMetadata(TypedDict): status: StandardLoggingPayloadStatus proxy_server_request: str | None batch_models: list[str] | None + batch_successful_requests: int | None # writable-ok: built by assignment like every sibling key in this TypedDict + batch_failed_requests: int | None # writable-ok: built by assignment like every sibling key in this TypedDict error_information: StandardLoggingPayloadErrorInformation | None usage_object: dict | None model_map_information: StandardLoggingModelInformation | None @@ -3586,6 +3628,7 @@ class SpendLogsMetadata(TypedDict): compression_savings: CompressionSavingsMetadata | None autorouter_savings: ReadOnly[float | None] # stamped by the logging payload; None = not auto-routed litellm_gateway_injected_cache: ReadOnly[str | None] + router_metadata: ReadOnly[SpendLogsRouterMetadata | None] # None = deployment not flagged internal_router_model class SpendLogsPayload(TypedDict): @@ -4918,6 +4961,7 @@ class DBSpendUpdateTransactions(TypedDict): org_list_transactions: dict[str, float] | None tag_list_transactions: dict[str, float] | None agent_list_transactions: dict[str, float] | None + model_access_group_list_transactions: ReadOnly[dict[str, float] | None] class SpendUpdateQueueItem(TypedDict, total=False): diff --git a/litellm/proxy/agent_endpoints/a2a_endpoints.py b/litellm/proxy/agent_endpoints/a2a_endpoints.py index bd02cfdf907..31b05320cd3 100644 --- a/litellm/proxy/agent_endpoints/a2a_endpoints.py +++ b/litellm/proxy/agent_endpoints/a2a_endpoints.py @@ -14,7 +14,7 @@ import json from collections.abc import AsyncGenerator, Mapping from copy import deepcopy from types import MappingProxyType -from typing import TYPE_CHECKING, Any, Final +from typing import TYPE_CHECKING, Any, Final, Protocol from urllib.parse import urlparse from fastapi import APIRouter, Depends, HTTPException, Request, Response @@ -215,11 +215,20 @@ def _enforce_inbound_trace_id(agent: "AgentResponse", request: Request) -> None: ) +class _JsonRpcResponse(Protocol): + def json(self) -> dict[str, object]: ... + + +def _jsonrpc_body(response: _JsonRpcResponse) -> dict[str, object]: + """The decoded JSON-RPC body of ``response``.""" + return response.json() + + async def _forward_jsonrpc( agent_url: str, body: dict[str, object], extra_headers: Mapping[str, str] | None = None, -) -> dict[str, Any]: +) -> dict[str, object]: from litellm.llms.custom_httpx.http_handler import get_async_httpx_client from litellm.types.llms.custom_http import httpxSpecialProvider @@ -230,7 +239,7 @@ async def _forward_jsonrpc( ) resp: Final = await handler.post(agent_url, json=body, headers=headers) try: - result: Final = resp.json() + result: Final = _jsonrpc_body(resp) except Exception: resp.raise_for_status() raise @@ -940,8 +949,8 @@ async def invoke_agent_a2a( ) result = await _forward_jsonrpc(agent_url, forward_body, extra_headers=caller_headers) if method == "agent/getAuthenticatedExtendedCard": - if isinstance(result.get("result"), dict): - card: Final = result["result"] + card: Final = result.get("result") + if isinstance(card, dict): proxy_url: Final = get_custom_url(str(request.base_url), route=f"a2a/{agent_id}") # Rewrite the upstream agent URL in both 0.3 (top-level `url`) # and 1.0 (`supportedInterfaces[0].url`) wire formats so that diff --git a/litellm/proxy/agent_endpoints/agent_registry.py b/litellm/proxy/agent_endpoints/agent_registry.py index 6d9a907324d..144de52d0d2 100644 --- a/litellm/proxy/agent_endpoints/agent_registry.py +++ b/litellm/proxy/agent_endpoints/agent_registry.py @@ -401,12 +401,10 @@ class AgentRegistry: The patched agent """ try: - existing_row: Final = await AgentsRepository(prisma_client).table.find_unique( - where={"agent_id": agent_id} # mutable-ok: prisma filters are plain dicts - ) - if existing_row is None: + existing_record: Final = await agents_table(prisma_client).find_unique(where={"agent_id": agent_id}) + if existing_record is None: raise Exception(f"Agent with ID {agent_id} not found") - existing_agent: Final = dict(existing_row) + existing_agent: Final[Mapping[str, object]] = dict(existing_record) augment_agent: Final = {**existing_agent, **agent} update_data: Final[dict[str, object]] = {} @@ -433,7 +431,7 @@ class AgentRegistry: update_data["extra_headers"] = extra_headers_value if extra_headers_value is not None else [] if agent.get("object_permission") is not None: agent_copy: Final = dict(augment_agent) - existing_object_permission_id: Final = existing_agent.get("object_permission_id") + existing_object_permission_id: Final = existing_record.object_permission_id object_permission_id: Final = await handle_update_object_permission_common( agent_copy, existing_object_permission_id, diff --git a/litellm/proxy/anthropic_endpoints/endpoints.py b/litellm/proxy/anthropic_endpoints/endpoints.py index f742965ade2..7f0045c1d93 100644 --- a/litellm/proxy/anthropic_endpoints/endpoints.py +++ b/litellm/proxy/anthropic_endpoints/endpoints.py @@ -22,6 +22,7 @@ from litellm.proxy.auth.user_api_key_auth import user_api_key_auth from litellm.proxy.common_request_processing import ( ProxyBaseLLMRequestProcessing, create_response, + proxy_exception_from_http_exception, ) from litellm.proxy.common_utils.http_parsing_utils import _read_request_body from litellm.types.utils import TokenCountResponse @@ -214,6 +215,9 @@ async def anthropic_response( litellm_logging_obj=None, ) + if isinstance(e, HTTPException): + raise proxy_exception_from_http_exception(e, headers) + error_msg: Final = f"{e}" raise ProxyException( message=getattr(e, "message", error_msg), diff --git a/litellm/proxy/anthropic_endpoints/streaming_model_restamp.py b/litellm/proxy/anthropic_endpoints/streaming_model_restamp.py new file mode 100644 index 00000000000..7da5e5099fc --- /dev/null +++ b/litellm/proxy/anthropic_endpoints/streaming_model_restamp.py @@ -0,0 +1,174 @@ +""" +Restamp the public ``model`` on the Anthropic Messages ``message_start`` event, the only +stream event carrying a model, so streamed responses report the requested model like +non-streaming ones do. + +Chunks reach the serializer either as already-encoded SSE frames (``bytes``/``str``, the +provider passthrough path) or as event dicts (fake-stream and agentic paths). +""" + +import json +import re +from collections.abc import Mapping +from typing import Final + +from pydantic import TypeAdapter, ValidationError + +_MESSAGE_START_EVENT: Final = "message_start" +_MESSAGE_START_MARKER: Final = b"message_start" +_SSE_DATA_FIELD: Final = "data:" +_SSE_FRAME_END_PATTERN: Final = re.compile(rb"\r\n\r\n|\r\r|\n\n") +_MAX_HELD_BYTES: Final = 65536 +_PING_MARKERS: Final = (b"event: ping", b'"type": "ping"', b'"type":"ping"') + +_EVENT_ADAPTER: Final = TypeAdapter(Mapping[str, object]) + + +def _restamped_event(event: Mapping[str, object], requested_model: str) -> Mapping[str, object] | None: + message: Final = event.get("message") + if event.get("type") != _MESSAGE_START_EVENT or not isinstance(message, dict): + return None + if message.get("model") == requested_model: + return None + return {**event, "message": {**message, "model": requested_model}} # mutable-ok: SSE payload, re-serialized as is + + +def _restamped_data_line(line: str, requested_model: str) -> str | None: + stripped: Final = line.strip() + if not stripped.startswith(_SSE_DATA_FIELD): + return None + payload: Final = stripped[len(_SSE_DATA_FIELD) :].strip() + if not payload or payload == "[DONE]": + return None + try: + event: Final = _EVENT_ADAPTER.validate_json(payload) + except ValidationError: + return None + restamped: Final = _restamped_event(event, requested_model) + if restamped is None: + return None + terminator: Final = line[len(line.rstrip("\r\n")) :] + return f"data: {json.dumps(restamped, separators=(',', ':'))}{terminator}" + + +def _restamped_frame(frame: str, requested_model: str) -> str | None: + lines: Final = frame.splitlines(keepends=True) + restamped: Final = tuple(_restamped_data_line(line, requested_model) for line in lines) + if all(line is None for line in restamped): + return None + return "".join(new if new is not None else old for new, old in zip(restamped, lines)) + + +def restamp_anthropic_stream_chunk_model(chunk: object, requested_model: str) -> object: + """ + Return ``chunk`` with the ``message_start`` model replaced by ``requested_model``. + + Chunks that carry no model are returned unchanged. + """ + if isinstance(chunk, dict): + try: + event: Final = _EVENT_ADAPTER.validate_python(chunk) + except ValidationError: + return chunk + return _restamped_event(event, requested_model) or chunk + + if isinstance(chunk, (bytes, bytearray)): + if _MESSAGE_START_EVENT.encode() not in chunk: + return chunk + restamped_bytes: Final = _restamped_frame(chunk.decode("utf-8", errors="ignore"), requested_model) + return chunk if restamped_bytes is None else restamped_bytes.encode("utf-8") + + if isinstance(chunk, str): + if _MESSAGE_START_EVENT not in chunk: + return chunk + restamped_text: Final = _restamped_frame(chunk, requested_model) + return chunk if restamped_text is None else restamped_text + + return chunk + + +def _is_ping_frame(frame: bytes) -> bool: + return any(marker in frame for marker in _PING_MARKERS) + + +class AnthropicStreamModelRestamper: + """ + Per-stream restamper for the encoded passthrough path, where chunks are raw + transport reads: the ``message_start`` SSE frame can arrive split across + chunks or coalesced with later frames. Complete frames (``\\n\\n``, + ``\\r\\n\\r\\n``, or ``\\r\\r`` terminated) are emitted as their terminator + closes them and an incomplete tail is held until it completes, so the + restamp never misses a torn frame; ``flush`` returns whatever is still held + when the stream ends so no bytes are swallowed. Once ``message_start`` has + been handled, or the first real event proves the stream carries none, every + later chunk passes through untouched. + """ + + def __init__(self, requested_model: str) -> None: + self._requested_model: Final = requested_model + self._held = b"" + self._armed = True + + def process(self, chunk: object) -> object: + if not self._armed: + return chunk + if isinstance(chunk, (bytes, bytearray)): + return self._process_encoded(bytes(chunk)) + if isinstance(chunk, str): + return self._process_encoded(chunk.encode("utf-8")) + restamped: Final = restamp_anthropic_stream_chunk_model(chunk, self._requested_model) + if isinstance(chunk, dict) and chunk.get("type") not in (None, "ping"): + self._armed = False + return restamped + + def flush(self) -> bytes: + held: Final = self._held + self._held = b"" + self._armed = False + if not held: + return b"" + restamped: Final = restamp_anthropic_stream_chunk_model(held, self._requested_model) + return restamped if isinstance(restamped, bytes) else held + + def _process_encoded(self, data: bytes) -> bytes: + combined: Final = self._held + data + boundaries: Final = tuple(match.end() for match in _SSE_FRAME_END_PATTERN.finditer(combined)) + if not boundaries: + if len(combined) > _MAX_HELD_BYTES: + self._held = b"" + self._armed = False + return combined + self._held = combined + return b"" + emitted: Final = self._restamped_closed_block(combined[: boundaries[-1]]) + tail: Final = combined[boundaries[-1] :] + if not self._armed: + self._held = b"" + return emitted + tail + self._held = tail + return emitted + + def _restamped_closed_block(self, closed: bytes) -> bytes: + boundaries: Final = tuple(match.end() for match in _SSE_FRAME_END_PATTERN.finditer(closed)) + frames: Final = tuple(closed[start:end] for start, end in zip((0, *boundaries[:-1]), boundaries)) + decider: Final = next( + ( + index + for index, frame in enumerate(frames) + if _MESSAGE_START_MARKER in frame or (b"data:" in frame and not _is_ping_frame(frame)) + ), + None, + ) + if decider is None: + return closed + self._armed = False + if _MESSAGE_START_MARKER not in frames[decider]: + return closed + restamped_text: Final = _restamped_frame( + frames[decider].decode("utf-8", errors="ignore"), self._requested_model + ) + if restamped_text is None: + return closed + return b"".join( + restamped_text.encode("utf-8") if index == decider else frame for index, frame in enumerate(frames) + ) diff --git a/litellm/proxy/auth/auth_checks.py b/litellm/proxy/auth/auth_checks.py index c1f9407cdad..5703c6cd5e8 100644 --- a/litellm/proxy/auth/auth_checks.py +++ b/litellm/proxy/auth/auth_checks.py @@ -32,6 +32,7 @@ from litellm.constants import ( DEFAULT_MAX_RECURSE_DEPTH, EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE, END_USER_RESTRICTED_REGISTRY_MAX_SIZE, + MODEL_ACCESS_GROUP_REGISTRY_MAX_SIZE, REGISTRY_ERROR_NEGATIVE_CACHE_TTL, TAG_REGISTRY_MAX_SIZE, ) @@ -78,11 +79,15 @@ from litellm.proxy.common_utils.http_parsing_utils import ( from litellm.proxy.common_utils.timezone_utils import get_budget_reset_time from litellm.proxy.common_utils.user_api_key_cache import ( END_USER_RESTRICTED_REGISTRY_OVERFLOW_SENTINEL, + MODEL_ACCESS_GROUP_REGISTRY_OVERFLOW_SENTINEL, TAG_REGISTRY_OVERFLOW_SENTINEL, UserApiKeyCache, end_user_cache_key, end_user_restricted_registry_cache_key, get_management_object_ttl, + model_access_group_cache_key, + model_access_group_registry_cache_key, + model_access_group_spend_counter_key, object_permission_cache_key, tag_cache_key, tag_registry_cache_key, @@ -107,12 +112,14 @@ from litellm.repositories.table_repositories import ( EndUserRepository, JWTKeyMappingRepository, ManagedVectorStoresRepository, + ModelAccessGroupBudgetRepository, TagRepository, TeamMembershipRepository, ) from litellm.repositories.team_repository import TeamRepository from litellm.repositories.user_repository import UserRepository from litellm.router import Router +from litellm.types.proxy.model_access_group_budget import ModelAccessGroupBudget from litellm.utils import get_utc_datetime from .auth_checks_organization import ( @@ -251,6 +258,43 @@ def _end_user_table(repo: _PrismaTableHolder[_PrismaEndUserRow]) -> _PrismaAuthT return repo.table +class _PrismaMaxBudgetRow(Protocol): + @property + def max_budget(self) -> float | None: ... + + +class _PrismaModelAccessGroupBudgetRow(Protocol): + access_group_name: str + + @property + def spend(self) -> float | None: ... + + @property + def litellm_budget_table(self) -> _PrismaMaxBudgetRow | None: ... + + +def _model_access_group_budget_table( + repo: _PrismaTableHolder[_PrismaModelAccessGroupBudgetRow], +) -> _PrismaAuthTable[_PrismaModelAccessGroupBudgetRow]: + return repo.table + + +class _MemberModelScope(Protocol): + @property + def allowed_models(self) -> Sequence[str] | None: ... + + +class _TeamMembershipModelScope(Protocol): + @property + def litellm_budget_table(self) -> _MemberModelScope | None: ... + + +def _member_allowed_models(membership: _TeamMembershipModelScope) -> Sequence[str]: + """The member's own model scope, read through a narrowed view of the membership row.""" + budget_table: Final = membership.litellm_budget_table + return () if budget_table is None else (budget_table.allowed_models or ()) + + class _RawCacheRead(Protocol): async def async_get_cache(self, *, key: str) -> object: ... @@ -807,6 +851,7 @@ async def common_checks( 1.1. If project is blocked 2. If team can call model 2.2 If project can call model + 2.3 Which model access groups authorized this request 3. If team is in budget 3.0.2. If project is in budget 3.0.3. If project is over soft budget (alert only) @@ -925,6 +970,18 @@ async def common_checks( proxy_logging_obj=proxy_logging_obj, ) + # 2.3 Which model access groups authorized this request + matched_model_access_groups: Final = await stamp_matched_model_access_groups( + model=_model, + valid_token=valid_token, + team_object=team_object, + project_object=project_object, + llm_router=llm_router, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + proxy_logging_obj=proxy_logging_obj, + ) + # Run before apply_key_tags_pre_auth injects key metadata.tags into request_body. _reject_clientside_metadata_tags_check(general_settings, request_body, route) @@ -1004,6 +1061,13 @@ async def common_checks( proxy_logging_obj=proxy_logging_obj, valid_token=valid_token, ), + _model_access_group_max_budget_check( + matched_model_access_groups=matched_model_access_groups, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + ) + if matched_model_access_groups + else None, _user_max_budget_check(), _check_team_member_budget( team_object=team_object, @@ -1444,6 +1508,7 @@ _REGISTRY_NOT_CACHED: Final = _RegistryNotCached() #: One lock per registry; module-level because the stampede to collapse is worker-wide. _TAG_REGISTRY_LOAD_LOCK: Final = asyncio.Lock() _END_USER_REGISTRY_LOAD_LOCK: Final = asyncio.Lock() +_MODEL_ACCESS_GROUP_REGISTRY_LOAD_LOCK: Final = asyncio.Lock() async def _cached_registry( @@ -1836,6 +1901,105 @@ async def _load_tag_registry( ) +async def _load_model_access_group_registry( + prisma_client: PrismaClient, + user_api_key_cache: UserApiKeyCache, +) -> frozenset[str] | None: + """The set of model access group names that have a row in ``LiteLLM_ModelAccessGroupBudgetTable``.""" + + async def fetch_ids() -> tuple[str, ...]: + registry_rows: Final = await _model_access_group_budget_table( + ModelAccessGroupBudgetRepository(prisma_client) + ).find_many(take=MODEL_ACCESS_GROUP_REGISTRY_MAX_SIZE + 1) + return tuple(row.access_group_name for row in registry_rows) + + return await _load_bounded_registry( + cache_key=model_access_group_registry_cache_key(), + overflow_sentinel=MODEL_ACCESS_GROUP_REGISTRY_OVERFLOW_SENTINEL, + max_size=MODEL_ACCESS_GROUP_REGISTRY_MAX_SIZE, + load_lock=_MODEL_ACCESS_GROUP_REGISTRY_LOAD_LOCK, + fetch_ids=fetch_ids, + user_api_key_cache=user_api_key_cache, + ) + + +async def _fetch_uncached_model_access_group_budgets( + uncached_groups: Sequence[str], + prisma_client: PrismaClient, + user_api_key_cache: UserApiKeyCache, +) -> tuple[tuple[str, ModelAccessGroupBudget], ...]: + """Budget rows for the groups a cache probe missed. + + No registry gate here, unlike the tag path: the names only ever come from + ``matched_model_access_groups``, which :func:`collect_matched_model_access_groups` already + intersected with the registry, so a name that has no row cannot reach this. + """ + if not uncached_groups: + return () + + try: + db_rows: Final = await _model_access_group_budget_table( + ModelAccessGroupBudgetRepository(prisma_client) + ).find_many( + where={"access_group_name": {"in": list(uncached_groups)}}, + include={"litellm_budget_table": True}, + ) + fetched: Final = tuple((row.access_group_name, _model_access_group_budget(row)) for row in db_rows) + for fetched_name, fetched_obj in fetched: + await user_api_key_cache.async_set_cache( + key=model_access_group_cache_key(fetched_name), + value=fetched_obj, + model_type=ModelAccessGroupBudget, + ttl=get_management_object_ttl(user_api_key_cache), + ) + except Exception as e: # noqa: BLE001 # fail-safe: a budget fetch error must yield "no budget rows", never break auth + verbose_proxy_logger.debug("Error batch fetching model access group budgets from database: %s", e) + return () + else: + return fetched + + +def _model_access_group_budget(row: _PrismaModelAccessGroupBudgetRow) -> ModelAccessGroupBudget: + budget_table: Final = row.litellm_budget_table + return ModelAccessGroupBudget( + access_group_name=row.access_group_name, + spend=row.spend or 0.0, + max_budget=None if budget_table is None else budget_table.max_budget, + ) + + +@log_db_metrics +async def get_model_access_group_budgets_batch( + access_group_names: Sequence[str], + prisma_client: PrismaClient | None, + user_api_key_cache: UserApiKeyCache, +) -> dict[str, ModelAccessGroupBudget]: + """Budget rows for the given model access groups, served from cache where possible. + + Shared by the two enforcement paths so they read one row per group per request: the + reservation counters when reservations are on, and :func:`_model_access_group_max_budget_check` + when ``disable_budget_reservation`` turns them off. + """ + if prisma_client is None or not access_group_names: + return {} + + probed: Final = [ + ( + group, + await user_api_key_cache.async_get_cache( + key=model_access_group_cache_key(group), model_type=ModelAccessGroupBudget + ), + ) + for group in access_group_names + ] + fetched: Final = await _fetch_uncached_model_access_group_budgets( + uncached_groups=tuple(group for group, budget in probed if budget is None), + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + ) + return {group: budget for group, budget in (*probed, *fetched) if budget is not None} + + async def _fetch_uncached_tags( uncached_tags: Sequence[str], prisma_client: PrismaClient, @@ -3882,6 +4046,192 @@ def _resolve_key_models_for_auth_check(valid_token: UserAPIKeyAuth) -> list[str] return models +def _model_access_groups_serving_model( + model: str | Sequence[str], + llm_router: Router, + team_id: str | None, +) -> frozenset[str]: + """Every model access group whose deployments serve the requested model(s).""" + requested: Final = (model,) if isinstance(model, str) else tuple(model) + return frozenset( + group + for requested_model in requested + for group in llm_router.get_model_access_groups(model_name=requested_model, team_id=team_id) + ) + + +async def _team_member_granted_models( + valid_token: UserAPIKeyAuth, + team_object: LiteLLM_TeamTable | None, + prisma_client: PrismaClient, + user_api_key_cache: UserApiKeyCache, + proxy_logging_obj: ProxyLogging, +) -> Sequence[str]: + """The member's own ``allowed_models`` scope; empty when the member is not narrowed below the team.""" + if team_object is None or valid_token.user_id is None: + return () + + team_membership: Final = await get_team_membership( + user_id=valid_token.user_id, + team_id=team_object.team_id, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + proxy_logging_obj=proxy_logging_obj, + ) + return () if team_membership is None else _member_allowed_models(team_membership) + + +async def _org_granted_models( + valid_token: UserAPIKeyAuth, + team_object: LiteLLM_TeamTable | None, + prisma_client: PrismaClient, + user_api_key_cache: UserApiKeyCache, + proxy_logging_obj: ProxyLogging, +) -> Sequence[str]: + """The org allowlist reached through the key, or through its team when the key names no org.""" + org_id: Final = valid_token.org_id or (team_object.organization_id if team_object is not None else None) + if org_id is None: + return () + + try: + org_object: Final = await get_org_object( + org_id=org_id, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + proxy_logging_obj=proxy_logging_obj, + ) + except Exception as e: # noqa: BLE001 # fail-safe: attribution degrades to "no org grant", it must never break auth + verbose_proxy_logger.debug("access group attribution: org lookup failed: %s", e) + return () + return org_object.models if org_object is not None else () + + +async def _granted_model_lists( + valid_token: UserAPIKeyAuth, + team_object: LiteLLM_TeamTable | None, + project_object: LiteLLM_ProjectTableCachedObj | None, + prisma_client: PrismaClient, + user_api_key_cache: UserApiKeyCache, + proxy_logging_obj: ProxyLogging, +) -> tuple[Sequence[str], ...]: + """One model allowlist per level that participates in authorizing the request.""" + return ( + _resolve_key_models_for_auth_check(valid_token=valid_token), + team_object.models if team_object is not None else (), + await _team_member_granted_models( + valid_token=valid_token, + team_object=team_object, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + proxy_logging_obj=proxy_logging_obj, + ), + project_object.models if project_object is not None else (), + await _org_granted_models( + valid_token=valid_token, + team_object=team_object, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + proxy_logging_obj=proxy_logging_obj, + ), + ) + + +async def collect_matched_model_access_groups( + model: str | Sequence[str] | None, + valid_token: UserAPIKeyAuth | None, + team_object: LiteLLM_TeamTable | None, + project_object: LiteLLM_ProjectTableCachedObj | None, + llm_router: Router | None, + prisma_client: PrismaClient | None, + user_api_key_cache: UserApiKeyCache, + proxy_logging_obj: ProxyLogging, +) -> tuple[str, ...]: + """ + The budgeted model access groups that authorized this request, sorted and deduplicated. + + A group is charged only when its name appears on an allowlist the caller was granted -- key, + team, team-member scope, project or org -- *and* that group serves the requested model. Asking + for a model that merely belongs to a group attributes nothing, because nothing about the caller + named the group. + + Levels are unioned, never ranked: a team granted ``*`` whose member is scoped to one group is + still a caller gated by that group. An unrestricted allowlist (empty, ``*``) names no group and + so contributes nothing. + + The whole walk is gated on the budget registry, because collecting every match costs a full scan + of each allowlist where the plain access check stops at the first hit. An empty registry means no + group carries a budget, so there is nothing to attribute and no work worth doing. + """ + if model is None or valid_token is None or llm_router is None or prisma_client is None: + return () + + registry: Final = await _load_model_access_group_registry( + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + ) + if registry is not None and not registry: + return () + + covering_groups: Final = _model_access_groups_serving_model( + model=model, + llm_router=llm_router, + team_id=valid_token.team_id, + ) + budgeted_groups: Final = covering_groups if registry is None else covering_groups & registry + if not budgeted_groups: + return () + + granted: Final = frozenset( + granted_model + for granted_models in await _granted_model_lists( + valid_token=valid_token, + team_object=team_object, + project_object=project_object, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + proxy_logging_obj=proxy_logging_obj, + ) + for granted_model in granted_models + ) + return tuple(sorted(budgeted_groups & granted)) + + +async def stamp_matched_model_access_groups( + model: str | Sequence[str] | None, + valid_token: UserAPIKeyAuth | None, + team_object: LiteLLM_TeamTable | None, + project_object: LiteLLM_ProjectTableCachedObj | None, + llm_router: Router | None, + prisma_client: PrismaClient | None, + user_api_key_cache: UserApiKeyCache, + proxy_logging_obj: ProxyLogging, +) -> tuple[str, ...]: + """Record the groups that authorized this request on its auth object, for the post-call spend + writer and the reservation counters, and hand them back for the budget check.""" + if valid_token is None: + return () + + try: + matched: Final = await collect_matched_model_access_groups( + model=model, + valid_token=valid_token, + team_object=team_object, + project_object=project_object, + llm_router=llm_router, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + proxy_logging_obj=proxy_logging_obj, + ) + except Exception as e: # noqa: BLE001 # fail-safe: attribution is spend telemetry, it must never break auth + verbose_proxy_logger.debug("model access group attribution failed: %s", e) + return () + if not matched: + return () + matched_groups: Final = list(matched) # mutable-ok: the auth field is typed list[str] | None + valid_token.matched_model_access_groups = matched_groups # rebind-ok: request-scoped carrier for the writer + return matched + + async def can_key_call_model( model: str | list[str], llm_model_list: list | None, @@ -4476,6 +4826,7 @@ async def _virtual_key_multi_budget_check( max_budget=w["max_budget"], window_entity_type="Key", window_entity_id=valid_token.token, + window_duration=str(w["budget_duration"]), window_start=get_budget_window_start(w), ) if math.isfinite(w["max_budget"]) and window_spend >= w["max_budget"]: @@ -4849,6 +5200,7 @@ async def _team_multi_budget_check( max_budget=w["max_budget"], window_entity_type="Team", window_entity_id=team_object.team_id, + window_duration=str(w["budget_duration"]), window_start=get_budget_window_start(w), ) if math.isfinite(w["max_budget"]) and window_spend >= w["max_budget"]: @@ -5256,6 +5608,61 @@ async def _tag_max_budget_check( ) +async def _model_access_group_max_budget_check( + matched_model_access_groups: Sequence[str], + prisma_client: PrismaClient | None, + user_api_key_cache: UserApiKeyCache, +) -> None: + """Block the request when a model access group that authorized it is over its max budget. + + Only the groups auth already matched are charged and therefore only they are checked, so a + request that no budgeted group authorized costs nothing here. + + Like the tag check this is a plain read with no reservation, so concurrent requests can + overshoot the ceiling slightly. The reservation counters are the precise path; this one covers + the ``disable_budget_reservation`` case. + + The ceiling is exclusive, unlike the tag check it otherwise mirrors: a pool whose recorded + spend has reached ``max_budget`` has nothing left to give, so the next request is refused. + Keys and organizations already draw the line there. A non-positive budget means no budget, + matching what the reservation path treats as unbudgeted. + + Raises: + BudgetExceededError if a matched group is over its max budget. + """ + if prisma_client is None or not matched_model_access_groups: + return + + budgets: Final = await get_model_access_group_budgets_batch( + access_group_names=matched_model_access_groups, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + ) + + from litellm.proxy.proxy_server import get_current_spend + + for group in matched_model_access_groups: + budget = budgets.get(group) + if budget is None or budget.max_budget is None or budget.max_budget <= 0: + continue + + group_spend = await get_current_spend( + counter_key=model_access_group_spend_counter_key(group), + fallback_spend=budget.spend, + max_budget=budget.max_budget, + fallback_authoritative=True, + ) + if group_spend < budget.max_budget: + continue + raise litellm.BudgetExceededError( + current_cost=group_spend, + max_budget=budget.max_budget, + message=f"Budget has been exceeded! Model access group={group} Current cost: {group_spend}, Max budget: {budget.max_budget}", + entity_type=Litellm_EntityType.MODEL_ACCESS_GROUP.value, + entity_id=group, + ) + + def is_model_allowed_by_pattern(model: str, allowed_model_pattern: str) -> bool: """ Check if a model matches an allowed pattern. diff --git a/litellm/proxy/auth/auth_exception_handler.py b/litellm/proxy/auth/auth_exception_handler.py index a42187b3a44..64878a480a7 100644 --- a/litellm/proxy/auth/auth_exception_handler.py +++ b/litellm/proxy/auth/auth_exception_handler.py @@ -2,13 +2,14 @@ Handles Authentication Errors """ +import logging from collections.abc import Mapping from typing import TYPE_CHECKING, Any, Final from fastapi import HTTPException, Request, status import litellm -from litellm._logging import verbose_proxy_logger +from litellm._logging import verbose_proxy_logger, verbose_proxy_stdout_logger from litellm.constants import EMPTY_MAPPING from litellm.integrations.otel.runtime import seed_request_identity from litellm.litellm_core_utils.core_helpers import is_expected_client_error @@ -18,7 +19,11 @@ from litellm.proxy._types import ( ProxyException, UserAPIKeyAuth, ) -from litellm.proxy.auth.auth_utils import _get_request_ip_address +from litellm.proxy.auth.auth_utils import ( + _get_request_ip_address, + is_invalid_virtual_key_error, + mark_invalid_virtual_key_error, +) from litellm.proxy.db.exception_handler import PrismaDBExceptionHandler from litellm.types.services import ServiceTypes @@ -36,6 +41,41 @@ else: Span = Any +def _as_proxy_exception(e: Exception) -> ProxyException: + """Convert an authentication failure into the ProxyException the client receives.""" + if isinstance(e, litellm.BudgetExceededError): + return ProxyException( + message=e.message, + type=ProxyErrorTypes.budget_exceeded, + param=None, + code=getattr(e, "status_code", status.HTTP_429_TOO_MANY_REQUESTS), + ) + if isinstance(e, HTTPException): + return ProxyException( + message=getattr(e, "detail", f"Authentication Error({e})"), + type=ProxyErrorTypes.auth_error, + param=getattr(e, "param", "None"), + code=getattr(e, "status_code", status.HTTP_401_UNAUTHORIZED), + ) + if isinstance(e, ProxyException): + return e + if PrismaDBExceptionHandler.is_database_service_unavailable_error(e): + return ProxyException( + message=( + "Service Unavailable, the authentication database is temporarily unreachable. Please retry shortly." + ), + type=ProxyErrorTypes.no_db_connection, + param="None", + code=status.HTTP_503_SERVICE_UNAVAILABLE, + ) + return ProxyException( + message="Authentication Error, " + str(e), + type=ProxyErrorTypes.auth_error, + param=getattr(e, "param", "None"), + code=status.HTTP_401_UNAUTHORIZED, + ) + + def _with_requester_ip_address(request_data: dict[str, object], requester_ip: str | None) -> dict[str, object]: """Auth gate rejections are raised before `add_litellm_data_to_request` records the caller IP, so their failure logs would otherwise carry no IP nor key/user identity.""" @@ -110,16 +150,21 @@ class UserAPIKeyAuthExceptionHandler: request=request, use_x_forwarded_for=general_settings.get("use_x_forwarded_for") is True, ) - log_fn: Final = ( - verbose_proxy_logger.error - if is_expected_client_error(e) and not litellm.log_client_error_tracebacks - else verbose_proxy_logger.exception - ) - log_fn( + + # Log authentication failures before identity seeding and callbacks, so the log + # survives a raising callback pipeline. Classify and route malformed virtual-key + # rejections to WARNING on stdout (suppressible via LITELLM_LOG=ERROR). + log_extra: Final = {"requester_ip": requester_ip} + is_invalid_virtual_key: Final = is_invalid_virtual_key_error(e) + is_quiet_log: Final = is_invalid_virtual_key and not litellm.log_client_error_tracebacks + logger: Final = verbose_proxy_stdout_logger if is_quiet_log else verbose_proxy_logger + logger.log( + logging.WARNING if is_quiet_log else logging.ERROR, "litellm.proxy.proxy_server.user_api_key_auth(): Exception occured - %s\nRequester IP Address:%s", e, requester_ip, - extra={"requester_ip": requester_ip}, + exc_info=True if litellm.log_client_error_tracebacks or not is_expected_client_error(e) else None, + extra=log_extra, ) # Log this exception to OTEL, Datadog etc. Reuse the identity resolved @@ -167,35 +212,13 @@ class UserAPIKeyAuthExceptionHandler: if transformed_exception is not None: e = transformed_exception - if isinstance(e, litellm.BudgetExceededError): - raise ProxyException( - message=e.message, - type=ProxyErrorTypes.budget_exceeded, - param=None, - code=getattr(e, "status_code", status.HTTP_429_TOO_MANY_REQUESTS), + final_exception: Final = mark_invalid_virtual_key_error(_as_proxy_exception(e), is_invalid_virtual_key) + # If a quiet-logged malformed-key transform yields non-401, escalate to ERROR + if is_quiet_log and str(final_exception.code) != str(status.HTTP_401_UNAUTHORIZED): + verbose_proxy_logger.error( + "litellm.proxy.proxy_server.user_api_key_auth(): Exception occured - %s\nRequester IP Address:%s", + final_exception, + requester_ip, + extra=log_extra, ) - if isinstance(e, HTTPException): - raise ProxyException( - message=getattr(e, "detail", f"Authentication Error({e})"), - type=ProxyErrorTypes.auth_error, - param=getattr(e, "param", "None"), - code=getattr(e, "status_code", status.HTTP_401_UNAUTHORIZED), - ) - elif isinstance(e, ProxyException): - raise e - if PrismaDBExceptionHandler.is_database_service_unavailable_error(e): - raise ProxyException( - message=( - "Service Unavailable, the authentication database is " - "temporarily unreachable. Please retry shortly." - ), - type=ProxyErrorTypes.no_db_connection, - param="None", - code=status.HTTP_503_SERVICE_UNAVAILABLE, - ) - raise ProxyException( - message="Authentication Error, " + str(e), - type=ProxyErrorTypes.auth_error, - param=getattr(e, "param", "None"), - code=status.HTTP_401_UNAUTHORIZED, - ) + raise final_exception diff --git a/litellm/proxy/auth/auth_utils.py b/litellm/proxy/auth/auth_utils.py index 9b1a6ba5aa7..89b2c92cdfd 100644 --- a/litellm/proxy/auth/auth_utils.py +++ b/litellm/proxy/auth/auth_utils.py @@ -15,6 +15,7 @@ from litellm._logging import verbose_proxy_logger from litellm.constants import ( BATCH_ENQUEUED_TOKEN_LIMIT_METADATA_KEY, EMPTY_MAPPING, + INVALID_VIRTUAL_KEY_ERROR_MARKER, MINIMUM_CUSTOM_KEY_LENGTH, STANDARD_CUSTOMER_ID_HEADERS, ) @@ -34,6 +35,43 @@ from litellm.types.router import CONFIGURABLE_CLIENTSIDE_AUTH_PARAMS from litellm.types.utils import CustomPricingLiteLLMParams +def is_invalid_virtual_key_error(exception: BaseException | None) -> bool: + """True when an authentication error rejects a malformed virtual key. + + Classifies only by the marker stamped where that 401 is raised. Message + content is never inspected: other 401s interpolate caller-supplied values + (vector store ids, organization ids) into their messages, so a phrase + match would let a request body demote an authorization failure to the + quiet log path. + """ + if not isinstance(exception, (HTTPException, ProxyException)): + return False + + code: Final[object] = getattr(exception, "code", None) + status_code: Final[object] = code if code is not None else getattr(exception, "status_code", None) + if str(status_code) != str(status.HTTP_401_UNAUTHORIZED): + return False + + return getattr(exception, INVALID_VIRTUAL_KEY_ERROR_MARKER, False) is True + + +def mark_invalid_virtual_key_error(exception: ProxyException, is_invalid_virtual_key: bool) -> ProxyException: + """Return an independently marked malformed-key exception after callback transformations.""" + if not is_invalid_virtual_key or str(exception.code) != str(status.HTTP_401_UNAUTHORIZED): + return exception + marked_exception: Final = ProxyException( + message=exception.message, + type=exception.type, + param=exception.param, + code=exception.code, + headers=exception.headers.copy(), + openai_code=None if exception.openai_code is None else str(exception.openai_code), + provider_specific_fields=exception.provider_specific_fields, + ) + setattr(marked_exception, INVALID_VIRTUAL_KEY_ERROR_MARKER, True) + return marked_exception + + def _get_request_ip_address(request: Request, use_x_forwarded_for: bool | None = False) -> str | None: client_ip = None if use_x_forwarded_for is True and "x-forwarded-for" in request.headers: @@ -956,7 +994,7 @@ def get_key_model_rpm_limit( # 2. Check model_max_budget if user_api_key_dict.model_max_budget: - model_rpm_limit: Final[dict[str, Any]] = {} + model_rpm_limit: Final[dict[str, int]] = {} for model, budget in user_api_key_dict.model_max_budget.items(): if isinstance(budget, dict) and budget.get("rpm_limit") is not None: model_rpm_limit[model] = budget["rpm_limit"] @@ -999,7 +1037,7 @@ def get_key_model_tpm_limit( # 2. Check model_max_budget (iterate per-model like RPM does) if user_api_key_dict.model_max_budget: - model_tpm_limit: Final[dict[str, Any]] = {} + model_tpm_limit: Final[dict[str, int]] = {} for model, budget in user_api_key_dict.model_max_budget.items(): if isinstance(budget, dict) and budget.get("tpm_limit") is not None: model_tpm_limit[model] = budget["tpm_limit"] @@ -1062,7 +1100,7 @@ def _validated_output_token_estimates_per_model(raw: object) -> Mapping[str, int def _estimated_output_tokens_from_metadata( - metadata: Mapping[str, Any] | None, + metadata: Mapping[str, object] | None, model_name: str | None, ) -> int | None: """Resolve the per-model, then global, estimate out of one metadata blob. @@ -1628,7 +1666,7 @@ def _dedupe_model_candidates(candidates: list[str]) -> list[str]: return deduped -def _get_case_insensitive_mapping_value(mapping: Mapping[str, Any] | None, key: str) -> Any: +def _get_case_insensitive_mapping_value(mapping: Mapping[str, object] | None, key: str) -> object: if not mapping: return None if key in mapping: @@ -1732,8 +1770,8 @@ def _resolve_model_id_with_router(model_id: str | None, llm_router: Router | Non def _extract_model_candidates_from_request( request_data: dict, route: str, - request_headers: Mapping[str, Any] | None = None, - request_query_params: Mapping[str, Any] | None = None, + request_headers: Mapping[str, object] | None = None, + request_query_params: Mapping[str, object] | None = None, llm_router: Router | None = None, ) -> list[str]: candidates: Final[list[str]] = [] @@ -1825,8 +1863,8 @@ def request_dispatched_to_pass_through_endpoint(request: Request | None) -> bool def get_model_from_request( request_data: dict, route: str, - request_headers: Mapping[str, Any] | None = None, - request_query_params: Mapping[str, Any] | None = None, + request_headers: Mapping[str, object] | None = None, + request_query_params: Mapping[str, object] | None = None, llm_router: Router | None = None, request: Request | None = None, ) -> str | list[str] | None: diff --git a/litellm/proxy/auth/handle_jwt.py b/litellm/proxy/auth/handle_jwt.py index 39e6ca9a369..0795cee7409 100644 --- a/litellm/proxy/auth/handle_jwt.py +++ b/litellm/proxy/auth/handle_jwt.py @@ -14,8 +14,8 @@ import hashlib import os import re import time -from collections.abc import Awaitable, Callable -from typing import Any, Final, Literal, NoReturn, TypeVar, cast +from collections.abc import Awaitable, Callable, Sequence +from typing import Any, Final, Literal, NoReturn, Protocol, TypeVar, cast import httpx import jwt @@ -24,6 +24,7 @@ from cryptography.hazmat.backends import default_backend from cryptography.hazmat.primitives import serialization from fastapi import HTTPException, status from jwt.api_jwk import PyJWK +from typing_extensions import ReadOnly, TypedDict from litellm._logging import verbose_proxy_logger from litellm.litellm_core_utils.dot_notation_indexing import get_nested_value @@ -93,6 +94,47 @@ UNREACHABLE_CACHE_KEY_PREFIX: Final = "litellm_jwks_unreachable_" _CachedValueT = TypeVar("_CachedValueT", bound=JWKKeyValue | str) +class _JWTAuthSettings(Protocol): + """The JWT auth settings block this handler reads back through ``getattr``, when one is configured.""" + + @property + def issuers(self) -> Sequence[JWTIssuerConfig] | None: ... + + @property + def public_key_ttl(self) -> float: ... + + @property + def public_key_stale_ttl(self) -> float: ... + + +class _OIDCDiscoveryBody(TypedDict, total=False): + """Decoded OIDC discovery document, read for the JWKS endpoint it advertises.""" + + jwks_uri: ReadOnly[str] + + +class _OIDCDiscoveryResponse(Protocol): + """The discovery endpoint's HTTP response, read for the decoded document it carries.""" + + def json(self) -> _OIDCDiscoveryBody: ... + + +class _UserInfoResponse(Protocol): + """The OIDC UserInfo endpoint's HTTP response, read for the identity document it carries.""" + + def json(self) -> dict[str, object]: ... + + +def _discovery_document(response: _OIDCDiscoveryResponse) -> _OIDCDiscoveryBody: + """Decode an OIDC discovery response body.""" + return response.json() + + +def _userinfo_document(response: _UserInfoResponse) -> dict[str, object]: + """Decode an OIDC UserInfo response body into its JSON object form.""" + return response.json() + + def jwks_unavailable_exception(error: JWKSUnreachableError) -> ProxyException: return ProxyException( message=( @@ -794,7 +836,7 @@ class JWTHandler: f"JWT Auth: OIDC discovery endpoint {url} returned status {response.status_code}: {response.text}" ) try: - discovery: Final = response.json() + discovery: Final = _discovery_document(response) except Exception as e: raise Exception(f"JWT Auth: Failed to parse OIDC discovery document at {url}: {e}") @@ -806,13 +848,13 @@ class JWTHandler: return jwks_uri def _get_public_key_cache_ttl(self) -> float: - litellm_jwtauth: Final = getattr(self, "litellm_jwtauth", None) + litellm_jwtauth: Final[_JWTAuthSettings | None] = getattr(self, "litellm_jwtauth", None) if litellm_jwtauth is None: return 600 return litellm_jwtauth.public_key_ttl def _get_public_key_stale_ttl(self) -> float: - litellm_jwtauth: Final = getattr(self, "litellm_jwtauth", None) + litellm_jwtauth: Final[_JWTAuthSettings | None] = getattr(self, "litellm_jwtauth", None) if litellm_jwtauth is None: return DEFAULT_JWKS_STALE_TTL return litellm_jwtauth.public_key_stale_ttl @@ -938,7 +980,7 @@ class JWTHandler: if response.status_code != 200: raise Exception(f"OIDC UserInfo endpoint returned status {response.status_code}: {response.text}") - userinfo: Final = response.json() + userinfo: Final = _userinfo_document(response) verbose_proxy_logger.debug("Received OIDC UserInfo: %s", userinfo) # Cache the userinfo response @@ -996,7 +1038,7 @@ class JWTHandler: } def _get_configured_issuer(self, token: str) -> JWTIssuerConfig | None: - litellm_jwtauth: Final = getattr(self, "litellm_jwtauth", None) + litellm_jwtauth: Final[_JWTAuthSettings | None] = getattr(self, "litellm_jwtauth", None) if litellm_jwtauth is None: return None diff --git a/litellm/proxy/auth/user_api_key_auth.py b/litellm/proxy/auth/user_api_key_auth.py index e92d090a2fb..5fb6dad0cd7 100644 --- a/litellm/proxy/auth/user_api_key_auth.py +++ b/litellm/proxy/auth/user_api_key_auth.py @@ -19,12 +19,15 @@ import fastapi import orjson from fastapi import HTTPException, Request, WebSocket, status from fastapi.security.api_key import APIKeyHeader +from starlette.exceptions import WebSocketException import litellm from litellm._logging import verbose_logger, verbose_proxy_logger from litellm._service_logger import ServiceLogging from litellm.constants import ( GLOBAL_PROXY_SPEND_CACHE_KEY, + INVALID_VIRTUAL_KEY_ERROR_MARKER, + INVALID_VIRTUAL_KEY_ERROR_MESSAGE, LITELLM_PROXY_BUDGET_NAME, LITELLM_PROXY_MASTER_KEY_ALIAS, ) @@ -65,6 +68,7 @@ from litellm.proxy.auth.auth_utils import ( get_model_from_request, get_request_route, get_request_route_template, + is_invalid_virtual_key_error, iter_request_fallback_targets, normalize_request_route, pre_db_read_auth_checks, @@ -539,6 +543,8 @@ async def user_api_key_auth_websocket(websocket: WebSocket): try: return await user_api_key_auth(request=request, api_key=f"Bearer {api_key}") except Exception as e: + if is_invalid_virtual_key_error(e): + raise WebSocketException(code=status.WS_1008_POLICY_VIOLATION) verbose_proxy_logger.exception(e) await websocket.close(code=status.WS_1008_POLICY_VIOLATION) raise HTTPException(status_code=403, detail=str(e)) @@ -1867,13 +1873,17 @@ async def _user_api_key_auth_builder( _masked_key: Final = f"{api_key[:4]}****{api_key[-4:]}" if len(api_key) > 8 else "****" if not api_key.startswith("sk-"): _hint = _JWT_AUTH_DISABLED_HINT if not enable_jwt_auth and JWTHandler.is_jwt(token=api_key) else "" - raise HTTPException( + _malformed_key_error = HTTPException( status_code=status.HTTP_401_UNAUTHORIZED, detail=( - f"LiteLLM Virtual Key expected. Received={_masked_key}, " + f"{INVALID_VIRTUAL_KEY_ERROR_MESSAGE}. Received={_masked_key}, " f"expected to start with 'sk-'.{_hint}" ), ) # prevent token hashes from being used + # Stamp provenance here so log routing classifies this 401 by + # where it was raised, never by its message text. + setattr(_malformed_key_error, INVALID_VIRTUAL_KEY_ERROR_MARKER, True) + raise _malformed_key_error else: verbose_logger.warning( "litellm.proxy.proxy_server.user_api_key_auth(): Warning - Key is not a string. Got type={}".format( diff --git a/litellm/proxy/client/chat.py b/litellm/proxy/client/chat.py index 2953ed7f683..bd4d0df3ed0 100644 --- a/litellm/proxy/client/chat.py +++ b/litellm/proxy/client/chat.py @@ -8,16 +8,19 @@ from .exceptions import UnauthorizedError class ChatClient: - def __init__(self, base_url: str, api_key: str | None = None): + def __init__(self, base_url: str, api_key: str | None = None, timeout: int = 600): """ Initialize the ChatClient. Args: base_url (str): The base URL of the LiteLLM proxy server (e.g., "http://localhost:8000") api_key (Optional[str]): API key for authentication. If provided, it will be sent as a Bearer token. + timeout (int): Request timeout in seconds (default: 600, the OpenAI SDK default, since a completion + can legitimately take minutes) """ self._base_url = base_url.rstrip("/") # Remove trailing slash if present self._api_key = api_key + self._timeout = timeout def _get_headers(self) -> dict[str, str]: """ @@ -96,7 +99,7 @@ class ChatClient: # Prepare and send the request session: Final = requests.Session() try: - response: Final = session.send(request.prepare()) + response: Final = session.send(request.prepare(), timeout=self._timeout) response.raise_for_status() return response.json() except requests.exceptions.HTTPError as e: @@ -161,7 +164,9 @@ class ChatClient: # Make streaming request session: Final = requests.Session() try: - response: Final = session.post(url, headers=self._get_headers(), json=data, stream=True) + response: Final = session.post( + url, headers=self._get_headers(), json=data, stream=True, timeout=self._timeout + ) response.raise_for_status() # Parse SSE stream diff --git a/litellm/proxy/client/cli/README.md b/litellm/proxy/client/cli/README.md index fe417396317..3ddce35b53d 100644 --- a/litellm/proxy/client/cli/README.md +++ b/litellm/proxy/client/cli/README.md @@ -489,7 +489,7 @@ lite codex exec "summarize the repo" Each command resolves your LiteLLM key (logging in via SSO when none is stored and you are at a terminal; otherwise it expects `LITELLM_PROXY_API_KEY` or `--api-key`), checks the key against the proxy so bad credentials fail immediately instead of deep inside the agent, exports the environment variables the agent reads, then replaces itself with the agent process. -The right variables are picked per agent. Claude Code gets `ANTHROPIC_BASE_URL` (the proxy root, so it appends `/v1/messages`) and `ANTHROPIC_AUTH_TOKEN`, with any stray `ANTHROPIC_API_KEY` cleared so the proxy token wins. Codex and OpenCode get `OPENAI_BASE_URL` (the proxy plus `/v1`) and `OPENAI_API_KEY`. Codex ignores `OPENAI_BASE_URL`, so it is additionally pointed at the proxy through a custom provider passed as `-c` config overrides (HTTP/SSE Responses transport, since the proxy does not speak the Responses WebSocket protocol). +The right variables are picked per agent. Claude Code gets `ANTHROPIC_BASE_URL` (the proxy root, so it appends `/v1/messages`) and `ANTHROPIC_AUTH_TOKEN`, with any stray `ANTHROPIC_API_KEY` cleared so the proxy token wins, and `ENABLE_TOOL_SEARCH=true` (unless you already set it) so Claude Code keeps tool search on even though the base URL is a proxy rather than a first-party Anthropic host. Codex and OpenCode get `OPENAI_BASE_URL` (the proxy plus `/v1`) and `OPENAI_API_KEY`. Codex ignores `OPENAI_BASE_URL`, so it is additionally pointed at the proxy through a custom provider passed as `-c` config overrides (HTTP/SSE Responses transport, since the proxy does not speak the Responses WebSocket protocol). Options (these belong to the wrapper, so put them before the agent's own flags): @@ -505,7 +505,7 @@ The credential is short-lived by design (default 24h, configurable via `LITELLM_ ### Route Every Claude Code Session Through the Proxy -`lite claude` wraps a single invocation, but `lite up` goes further: it patches `~/.claude/settings.json`, Claude Code's own config file, so that every Claude Code session started afterward -- from any terminal, launched normally with just `claude`, no wrapper needed -- routes through your LiteLLM proxy. It sets `env.ANTHROPIC_BASE_URL` to the proxy URL and `apiKeyHelper` to a `lite auth print-token` invocation, drops any stray static `ANTHROPIC_API_KEY` so the helper-issued token wins, and leaves every other setting in the file untouched. It backs up the original file before patching it. +`lite claude` wraps a single invocation, but `lite up` goes further: it patches `~/.claude/settings.json`, Claude Code's own config file, so that every Claude Code session started afterward -- from any terminal, launched normally with just `claude`, no wrapper needed -- routes through your LiteLLM proxy. It sets `env.ANTHROPIC_BASE_URL` to the proxy URL, `env.ENABLE_TOOL_SEARCH` to `true` when that key is missing, and `apiKeyHelper` to a `lite auth print-token` invocation, drops any stray static `ANTHROPIC_API_KEY` so the helper-issued token wins, and leaves every other setting in the file untouched. It backs up the original file before patching it. Two things need to already be true: you've run `lite login` (or `lite login --pkce`, whose key the helper renews on its own), since the apiKeyHelper depends on that stored token, and the proxy is already reachable, since `lite up` does not start one for you. @@ -529,7 +529,7 @@ Cursor is not supported: it has no equivalent file-based config to hot-patch thi lite --base-url https://your-proxy.example.com login --config-claude ``` -It writes the same two settings `lite up` does, `env.ANTHROPIC_BASE_URL` and `apiKeyHelper`, but persistently: there is no backup, nothing to restore, and no foreground process to keep alive. Every other key in `~/.claude/settings.json` is preserved, the file is created if it does not exist, and it is written atomically with owner-only permissions. Plain `lite login` is unchanged; nothing happens to your Claude Code config unless you pass the flag. +It writes the same settings `lite up` does, `env.ANTHROPIC_BASE_URL`, `env.ENABLE_TOOL_SEARCH`, and `apiKeyHelper`, but persistently: there is no backup, nothing to restore, and no foreground process to keep alive. Every other key in `~/.claude/settings.json` is preserved, the file is created if it does not exist, and it is written atomically with owner-only permissions. Plain `lite login` is unchanged; nothing happens to your Claude Code config unless you pass the flag. Because the credential is reached through `apiKeyHelper` rather than copied into the file, a later `lite login` refreshes it with no further action: Claude Code re-runs the helper on every request and picks up whatever token the most recent login stored. Nothing secret is written to `settings.json`. diff --git a/litellm/proxy/client/cli/commands/agents.py b/litellm/proxy/client/cli/commands/agents.py index e05e85ae483..c591cbabee1 100644 --- a/litellm/proxy/client/cli/commands/agents.py +++ b/litellm/proxy/client/cli/commands/agents.py @@ -9,10 +9,13 @@ import click import requests from .auth import context_secret_vault, get_stored_api_key, login +from .cmd_quoting import quote_for_cmd ANTHROPIC_BASE_URL_ENV: Final = "ANTHROPIC_BASE_URL" ANTHROPIC_AUTH_TOKEN_ENV: Final = "ANTHROPIC_AUTH_TOKEN" ANTHROPIC_API_KEY_ENV: Final = "ANTHROPIC_API_KEY" +ENABLE_TOOL_SEARCH_ENV: Final = "ENABLE_TOOL_SEARCH" +ENABLE_TOOL_SEARCH_VALUE: Final = "true" OPENAI_BASE_URL_ENV: Final = "OPENAI_BASE_URL" OPENAI_API_KEY_ENV: Final = "OPENAI_API_KEY" @@ -61,7 +64,10 @@ def build_agent_env( Anthropic clients (Claude Code) append /v1/messages to ANTHROPIC_BASE_URL, so it stays the bare proxy root; OpenAI clients (Codex, OpenCode) expect the /v1 suffix on OPENAI_BASE_URL. ANTHROPIC_API_KEY is dropped so a stray - Anthropic key cannot win over the bearer token we set. + Anthropic key cannot win over the bearer token we set. ENABLE_TOOL_SEARCH + defaults to true because Claude Code turns tool search off when + ANTHROPIC_BASE_URL is not a first-party Anthropic host; a value already in + the environment is left alone. """ env: Final = dict(base_env) root: Final = base_url.rstrip("/") @@ -69,6 +75,8 @@ def build_agent_env( env[ANTHROPIC_BASE_URL_ENV] = root env[ANTHROPIC_AUTH_TOKEN_ENV] = api_key env.pop(ANTHROPIC_API_KEY_ENV, None) + if ENABLE_TOOL_SEARCH_ENV not in env: + env[ENABLE_TOOL_SEARCH_ENV] = ENABLE_TOOL_SEARCH_VALUE if PROFILE_OPENAI in profiles: env[OPENAI_BASE_URL_ENV] = root + "/v1" env[OPENAI_API_KEY_ENV] = api_key @@ -144,31 +152,9 @@ def verify_proxy_key( _WINDOWS_SHIM_SUFFIXES: Final[frozenset[str]] = frozenset({".cmd", ".bat"}) -_CMD_PERCENT_GUARD: Final = "%%cd:~,%" _CMD_LINE_BREAKS: Final = ("\r", "\n") -def _double_trailing_backslashes(segment: str) -> str: - bare: Final = segment.rstrip("\\") - return bare + "\\" * 2 * (len(segment) - len(bare)) - - -def _quote_for_cmd(token: str) -> str: - """Quote one token so both parsers that read it see the original text. - - Follows the algorithm the Rust standard library settled on for batch files - after CVE-2024-24576. Two parsers see this token: cmd.exe, which ends a - quoted string on a lone `"` and so wants an embedded one doubled, and the - shim's own interpreter, which re-splits `%*` under C runtime rules where a - backslash escapes the quote that follows it, so every backslash run standing - before a quote is doubled. Quoting cannot stop cmd expanding `%VAR%`, so each - `%` is prefixed with `%%cd:~,`: the zero-length substring of the always - defined `cd` expands to nothing and leaves no `%` pair for cmd to match. - """ - escaped: Final = '""'.join(_double_trailing_backslashes(part) for part in token.split('"')) - return '"' + escaped.replace("%", _CMD_PERCENT_GUARD) + '"' - - def _windows_command(path: str, args: Sequence[str]) -> str | tuple[str, ...]: """Build what CreateProcess runs, routing batch shims through cmd.exe. @@ -195,7 +181,7 @@ def _windows_command(path: str, args: Sequence[str]) -> str | tuple[str, ...]: f"Cannot pass an argument containing a line break to `{os.path.basename(path)}` on " "Windows: cmd.exe ends the command line there, so the agent would silently lose it." ) - inner: Final = " ".join(_quote_for_cmd(token) for token in (path, *rest)) + inner: Final = " ".join(quote_for_cmd(token) for token in (path, *rest)) return f'cmd.exe /d /e:on /v:off /s /c "{inner}"' diff --git a/litellm/proxy/client/cli/commands/auth.py b/litellm/proxy/client/cli/commands/auth.py index 550b11311f5..2fad9f933c1 100644 --- a/litellm/proxy/client/cli/commands/auth.py +++ b/litellm/proxy/client/cli/commands/auth.py @@ -99,11 +99,6 @@ class CliPollData(TypedDict, total=False): team_id: str -class CliPollRequestKwargs(TypedDict, total=False): - timeout: int - headers: dict[str, str] - - class CliSsoStartData(TypedDict): login_id: str poll_secret: str @@ -518,10 +513,7 @@ def _poll_for_ready_data( ) -> CliPollData | None: for attempt in range(total_timeout // poll_interval): try: - request_kwargs: CliPollRequestKwargs = {"timeout": request_timeout} - if headers is not None: - request_kwargs["headers"] = headers - response = requests.get(url, **request_kwargs) + response = requests.get(url, headers=headers, timeout=request_timeout) if response.status_code == 200: data: CliPollData = response.json() status = data.get("status") diff --git a/litellm/proxy/client/cli/commands/autoroute/settings.py b/litellm/proxy/client/cli/commands/autoroute/settings.py index 9fcb11a585b..60729b5410d 100644 --- a/litellm/proxy/client/cli/commands/autoroute/settings.py +++ b/litellm/proxy/client/cli/commands/autoroute/settings.py @@ -9,6 +9,8 @@ API_KEY_HELPER_KEY: Final = "apiKeyHelper" ANTHROPIC_API_KEY_KEY: Final = "ANTHROPIC_API_KEY" ANTHROPIC_AUTH_TOKEN_KEY: Final = "ANTHROPIC_AUTH_TOKEN" ANTHROPIC_BASE_URL_KEY: Final = "ANTHROPIC_BASE_URL" +ENABLE_TOOL_SEARCH_KEY: Final = "ENABLE_TOOL_SEARCH" +ENABLE_TOOL_SEARCH_VALUE: Final = "true" # Force every one of Claude Code's own model tiers to request the auto-router by name. # Router's auto-router registry is keyed by the literal requested model string # (litellm/router.py:10711-10717) with no wildcard/pattern resolution, so a bare "*" @@ -34,6 +36,7 @@ def merge_claude_settings_static_token( raw_env: Final = settings.get(ENV_KEY, {}) base_env: Final = raw_env if isinstance(raw_env, dict) else {} env: Final[dict[str, JsonValue]] = { + ENABLE_TOOL_SEARCH_KEY: ENABLE_TOOL_SEARCH_VALUE, **base_env, ANTHROPIC_BASE_URL_KEY: base_url.rstrip("/"), ANTHROPIC_AUTH_TOKEN_KEY: auth_token, diff --git a/litellm/proxy/client/cli/commands/claude_settings.py b/litellm/proxy/client/cli/commands/claude_settings.py index e18e5b1b7ee..46af641636e 100644 --- a/litellm/proxy/client/cli/commands/claude_settings.py +++ b/litellm/proxy/client/cli/commands/claude_settings.py @@ -8,6 +8,7 @@ live here rather than in either command module. import shlex import shutil +import sys from collections.abc import Mapping, Sequence from dataclasses import dataclass from pathlib import Path @@ -17,10 +18,14 @@ from pydantic import JsonValue, TypeAdapter, ValidationError from litellm.litellm_core_utils.private_json import write_private_json +from .cmd_quoting import quote_for_cmd + ENV_KEY: Final = "env" API_KEY_HELPER_KEY: Final = "apiKeyHelper" ANTHROPIC_BASE_URL_KEY: Final = "ANTHROPIC_BASE_URL" ANTHROPIC_API_KEY_KEY: Final = "ANTHROPIC_API_KEY" +ENABLE_TOOL_SEARCH_KEY: Final = "ENABLE_TOOL_SEARCH" +ENABLE_TOOL_SEARCH_VALUE: Final = "true" CLAUDE_SETTINGS_PATH: Final = Path.home() / ".claude" / "settings.json" BACKUP_PATH: Final = Path.home() / ".litellm" / "claude_settings_backup.json" @@ -70,21 +75,27 @@ def merge_claude_settings( Only env.ANTHROPIC_BASE_URL and the top-level apiKeyHelper are overridden; a stray env.ANTHROPIC_API_KEY is dropped so it cannot outrank the helper-issued - token (same reasoning as build_agent_env in agents.py). Every other key is - preserved untouched. + token (same reasoning as build_agent_env in agents.py). ENABLE_TOOL_SEARCH + defaults to true because Claude Code turns tool search off when + ANTHROPIC_BASE_URL is not a first-party Anthropic host; an existing value is + left alone. Every other key is preserved untouched. """ raw_env: Final = settings.get(ENV_KEY, {}) base_env: Final = raw_env if isinstance(raw_env, dict) else {} env: Final = { + ENABLE_TOOL_SEARCH_KEY: ENABLE_TOOL_SEARCH_VALUE, **{key: value for key, value in base_env.items() if key != ANTHROPIC_API_KEY_KEY}, ANTHROPIC_BASE_URL_KEY: base_url.rstrip("/"), } return {**settings, ENV_KEY: env, API_KEY_HELPER_KEY: api_key_helper} -def resolve_api_key_helper(base_url: str) -> str: +def resolve_api_key_helper(base_url: str, platform: str = sys.platform) -> str: """Build the shell command Claude Code should run for its apiKeyHelper. + Claude Code hands the string to the system shell, `sh` on POSIX and cmd.exe + on Windows, so every token is quoted for the shell that will read it. + Resolves `lite` to an absolute path so the helper works regardless of the PATH visible to whatever subprocess Claude Code spawns it from. Passing --base-url explicitly (rather than relying on the bare invocation Claude @@ -101,7 +112,8 @@ def resolve_api_key_helper(base_url: str) -> str: raise ClaudeSettingsError( "Could not find `lite` on your PATH. Claude Code's apiKeyHelper needs an absolute path to it." ) - return f"{shlex.quote(lite_path)} --base-url {shlex.quote(base_url)} auth print-token" + quote: Final = quote_for_cmd if platform.startswith("win") else shlex.quote + return " ".join(quote(token) for token in (lite_path, "--base-url", base_url, "auth", "print-token")) def write_claude_settings(base_url: str, settings_path: Path, owners: Sequence[SettingsFileOwner]) -> None: @@ -144,6 +156,8 @@ __all__ = ( "AUTOROUTE_BACKUP_PATH", "BACKUP_PATH", "CLAUDE_SETTINGS_PATH", + "ENABLE_TOOL_SEARCH_KEY", + "ENABLE_TOOL_SEARCH_VALUE", "ENV_KEY", "SETTINGS_FILE_OWNERS", "ClaudeSettingsError", diff --git a/litellm/proxy/client/cli/commands/cmd_quoting.py b/litellm/proxy/client/cli/commands/cmd_quoting.py new file mode 100644 index 00000000000..efd6d584527 --- /dev/null +++ b/litellm/proxy/client/cli/commands/cmd_quoting.py @@ -0,0 +1,26 @@ +"""Quoting for command lines that cmd.exe reads before handing them to a program.""" + +from typing import Final + +_CMD_PERCENT_GUARD: Final = "%%cd:~,%" + + +def _double_trailing_backslashes(segment: str) -> str: + bare: Final = segment.rstrip("\\") + return bare + "\\" * 2 * (len(segment) - len(bare)) + + +def quote_for_cmd(token: str) -> str: + """Quote one token so both parsers that read it see the original text. + + Follows the algorithm the Rust standard library settled on for batch files + after CVE-2024-24576. Two parsers see this token: cmd.exe, which ends a + quoted string on a lone `"` and so wants an embedded one doubled, and the + program's own C runtime argv split, where a backslash escapes the quote that + follows it, so every backslash run standing before a quote is doubled. + Quoting cannot stop cmd expanding `%VAR%`, so each `%` is prefixed with + `%%cd:~,`: the zero-length substring of the always defined `cd` expands to + nothing and leaves no `%` pair for cmd to match. + """ + escaped: Final = '""'.join(_double_trailing_backslashes(part) for part in token.split('"')) + return '"' + escaped.replace("%", _CMD_PERCENT_GUARD) + '"' diff --git a/litellm/proxy/client/cli/commands/teams.py b/litellm/proxy/client/cli/commands/teams.py index 1f91d5559d8..1a941786f19 100644 --- a/litellm/proxy/client/cli/commands/teams.py +++ b/litellm/proxy/client/cli/commands/teams.py @@ -34,7 +34,7 @@ def teams(): """Manage teams and team assignments""" -def display_teams_table(teams: list[dict[str, Any]]) -> None: +def display_teams_table(teams: Sequence[dict[str, Any]]) -> None: """Display teams in a formatted table""" console: Final = Console() diff --git a/litellm/proxy/client/client.py b/litellm/proxy/client/client.py index d71802e06c8..de1e45b91be 100644 --- a/litellm/proxy/client/client.py +++ b/litellm/proxy/client/client.py @@ -24,7 +24,8 @@ class Client: Args: base_url (str): The base URL of the LiteLLM proxy server (e.g., "http://localhost:4000") api_key (Optional[str]): API key for authentication. If provided, it will be sent as a Bearer token. - timeout: Request timeout in seconds (default: 30) + timeout: Request timeout in seconds for management calls (default: 30). Chat completions keep + ChatClient's own 600 second default, since a completion can legitimately take minutes """ self._base_url = base_url.rstrip("/") # Only use the stored CLI key when it was issued for this server. @@ -33,9 +34,9 @@ class Client: # Initialize resource clients self.http = HTTPClient(base_url=base_url, api_key=self._api_key, timeout=timeout) - self.models = ModelsManagementClient(base_url=self._base_url, api_key=self._api_key) - self.model_groups = ModelGroupsManagementClient(base_url=self._base_url, api_key=self._api_key) + self.models = ModelsManagementClient(base_url=self._base_url, api_key=self._api_key, timeout=timeout) + self.model_groups = ModelGroupsManagementClient(base_url=self._base_url, api_key=self._api_key, timeout=timeout) self.chat = ChatClient(base_url=self._base_url, api_key=self._api_key) - self.keys = KeysManagementClient(base_url=self._base_url, api_key=self._api_key) - self.credentials = CredentialsManagementClient(base_url=self._base_url, api_key=self._api_key) - self.teams = TeamsManagementClient(base_url=self._base_url, api_key=self._api_key) + self.keys = KeysManagementClient(base_url=self._base_url, api_key=self._api_key, timeout=timeout) + self.credentials = CredentialsManagementClient(base_url=self._base_url, api_key=self._api_key, timeout=timeout) + self.teams = TeamsManagementClient(base_url=self._base_url, api_key=self._api_key, timeout=timeout) diff --git a/litellm/proxy/client/credentials.py b/litellm/proxy/client/credentials.py index 136bdf3f293..a9bff67b1c5 100644 --- a/litellm/proxy/client/credentials.py +++ b/litellm/proxy/client/credentials.py @@ -6,16 +6,18 @@ from .exceptions import UnauthorizedError class CredentialsManagementClient: - def __init__(self, base_url: str, api_key: str | None = None): + def __init__(self, base_url: str, api_key: str | None = None, timeout: int = 30): """ Initialize the CredentialsManagementClient. Args: base_url (str): The base URL of the LiteLLM proxy server (e.g., "http://localhost:8000") api_key (Optional[str]): API key for authentication. If provided, it will be sent as a Bearer token. + timeout (int): Request timeout in seconds (default: 30) """ self._base_url = base_url.rstrip("/") # Remove trailing slash if present self._api_key = api_key + self._timeout = timeout def _get_headers(self) -> dict[str, str]: """ @@ -56,7 +58,7 @@ class CredentialsManagementClient: session: Final = requests.Session() try: - response: Final = session.send(request.prepare()) + response: Final = session.send(request.prepare(), timeout=self._timeout) response.raise_for_status() return response.json() except requests.exceptions.HTTPError as e: @@ -103,7 +105,7 @@ class CredentialsManagementClient: session: Final = requests.Session() try: - response: Final = session.send(request.prepare()) + response: Final = session.send(request.prepare(), timeout=self._timeout) response.raise_for_status() return response.json() except requests.exceptions.HTTPError as e: @@ -140,7 +142,7 @@ class CredentialsManagementClient: session: Final = requests.Session() try: - response: Final = session.send(request.prepare()) + response: Final = session.send(request.prepare(), timeout=self._timeout) response.raise_for_status() return response.json() except requests.exceptions.HTTPError as e: @@ -177,7 +179,7 @@ class CredentialsManagementClient: session: Final = requests.Session() try: - response: Final = session.send(request.prepare()) + response: Final = session.send(request.prepare(), timeout=self._timeout) response.raise_for_status() return response.json() except requests.exceptions.HTTPError as e: diff --git a/litellm/proxy/client/keys.py b/litellm/proxy/client/keys.py index 5b66567363d..fe100c5f676 100644 --- a/litellm/proxy/client/keys.py +++ b/litellm/proxy/client/keys.py @@ -9,16 +9,18 @@ from .exceptions import UnauthorizedError class KeysManagementClient: - def __init__(self, base_url: str, api_key: str | None = None): + def __init__(self, base_url: str, api_key: str | None = None, timeout: int = 30): """ Initialize the KeysManagementClient. Args: base_url (str): The base URL of the LiteLLM proxy server (e.g., "http://localhost:8000") api_key (Optional[str]): API key for authentication. If provided, it will be sent as a Bearer token. + timeout (int): Request timeout in seconds (default: 30) """ self._base_url = base_url.rstrip("/") # Remove trailing slash if present self._api_key = api_key + self._timeout = timeout def _get_headers(self) -> dict[str, str]: """ @@ -99,7 +101,7 @@ class KeysManagementClient: session: Final = requests.Session() try: - response: Final = session.send(request.prepare()) + response: Final = session.send(request.prepare(), timeout=self._timeout) response.raise_for_status() return response.json() except requests.exceptions.HTTPError as e: @@ -174,7 +176,7 @@ class KeysManagementClient: session: Final = requests.Session() try: - response: Final = session.send(request.prepare()) + response: Final = session.send(request.prepare(), timeout=self._timeout) response.raise_for_status() return response.json() except requests.exceptions.HTTPError as e: @@ -218,7 +220,7 @@ class KeysManagementClient: session: Final = requests.Session() try: - response: Final = session.send(request.prepare()) + response: Final = session.send(request.prepare(), timeout=self._timeout) response.raise_for_status() return response.json() except requests.exceptions.HTTPError as e: @@ -279,7 +281,7 @@ class KeysManagementClient: session: Final = requests.Session() response_text: str | None = None try: - response: Final = session.send(request.prepare()) + response: Final = session.send(request.prepare(), timeout=self._timeout) response_text = response.text response.raise_for_status() return response.json() @@ -309,7 +311,7 @@ class KeysManagementClient: session: Final = requests.Session() try: - response: Final = session.send(request.prepare()) + response: Final = session.send(request.prepare(), timeout=self._timeout) response.raise_for_status() return response.json() except requests.exceptions.HTTPError as e: diff --git a/litellm/proxy/client/model_groups.py b/litellm/proxy/client/model_groups.py index 9c7c38dc67c..fef307600c4 100644 --- a/litellm/proxy/client/model_groups.py +++ b/litellm/proxy/client/model_groups.py @@ -6,16 +6,18 @@ from .exceptions import UnauthorizedError class ModelGroupsManagementClient: - def __init__(self, base_url: str, api_key: str | None = None): + def __init__(self, base_url: str, api_key: str | None = None, timeout: int = 30): """ Initialize the ModelGroupsManagementClient. Args: base_url (str): The base URL of the LiteLLM proxy server (e.g., "http://localhost:8000") api_key (Optional[str]): API key for authentication. If provided, it will be sent as a Bearer token. + timeout (int): Request timeout in seconds (default: 30) """ self._base_url = base_url.rstrip("/") # Remove trailing slash if present self._api_key = api_key + self._timeout = timeout def _get_headers(self) -> dict[str, str]: """ @@ -53,7 +55,7 @@ class ModelGroupsManagementClient: # Prepare and send the request session: Final = requests.Session() try: - response: Final = session.send(request.prepare()) + response: Final = session.send(request.prepare(), timeout=self._timeout) response.raise_for_status() return response.json()["data"] except requests.exceptions.HTTPError as e: diff --git a/litellm/proxy/client/models.py b/litellm/proxy/client/models.py index 0f1dd2b5bab..4b16087e15b 100644 --- a/litellm/proxy/client/models.py +++ b/litellm/proxy/client/models.py @@ -7,16 +7,18 @@ from .exceptions import NotFoundError, UnauthorizedError class ModelsManagementClient: - def __init__(self, base_url: str, api_key: str | None = None): + def __init__(self, base_url: str, api_key: str | None = None, timeout: int = 30): """ Initialize the ModelsManagementClient. Args: base_url (str): The base URL of the LiteLLM proxy server (e.g., "http://localhost:8000") api_key (Optional[str]): API key for authentication. If provided, it will be sent as a Bearer token. + timeout (int): Request timeout in seconds (default: 30) """ self._base_url = base_url.rstrip("/") # Remove trailing slash if present self._api_key = api_key + self._timeout = timeout def _get_headers(self) -> dict[str, str]: """ @@ -55,7 +57,7 @@ class ModelsManagementClient: # Prepare and send the request session: Final = requests.Session() try: - response: Final = session.send(request.prepare()) + response: Final = session.send(request.prepare(), timeout=self._timeout) response.raise_for_status() return response.json()["data"] except requests.exceptions.HTTPError as e: @@ -104,7 +106,7 @@ class ModelsManagementClient: # Prepare and send the request session: Final = requests.Session() try: - response: Final = session.send(request.prepare()) + response: Final = session.send(request.prepare(), timeout=self._timeout) response.raise_for_status() return response.json() except requests.exceptions.HTTPError as e: @@ -140,7 +142,7 @@ class ModelsManagementClient: # Prepare and send the request session: Final = requests.Session() try: - response: Final = session.send(request.prepare()) + response: Final = session.send(request.prepare(), timeout=self._timeout) response.raise_for_status() return response.json() except requests.exceptions.HTTPError as e: @@ -232,7 +234,7 @@ class ModelsManagementClient: # Prepare and send the request session: Final = requests.Session() try: - response: Final = session.send(request.prepare()) + response: Final = session.send(request.prepare(), timeout=self._timeout) response.raise_for_status() return response.json()["data"] except requests.exceptions.HTTPError as e: @@ -282,7 +284,7 @@ class ModelsManagementClient: # Prepare and send the request session: Final = requests.Session() try: - response: Final = session.send(request.prepare()) + response: Final = session.send(request.prepare(), timeout=self._timeout) response.raise_for_status() return response.json() except requests.exceptions.HTTPError as e: diff --git a/litellm/proxy/client/teams.py b/litellm/proxy/client/teams.py index ef2ac53f9c4..105060e5ca9 100644 --- a/litellm/proxy/client/teams.py +++ b/litellm/proxy/client/teams.py @@ -11,16 +11,18 @@ from .exceptions import UnauthorizedError class TeamsManagementClient: """Client for managing teams in LiteLLM proxy.""" - def __init__(self, base_url: str, api_key: str | None = None): + def __init__(self, base_url: str, api_key: str | None = None, timeout: int = 30): """ Initialize the TeamsManagementClient. Args: base_url (str): The base URL of the LiteLLM proxy server (e.g., "http://localhost:4000") api_key (Optional[str]): API key for authentication. If provided, it will be sent as a Bearer token. + timeout (int): Request timeout in seconds (default: 30) """ self._base_url = base_url.rstrip("/") # Remove trailing slash if present self._api_key = api_key + self._timeout = timeout def _get_headers(self) -> dict[str, str]: """ @@ -60,7 +62,7 @@ class TeamsManagementClient: if organization_id: params["organization_id"] = organization_id - response: Final = requests.get(url, headers=self._get_headers(), params=params) + response: Final = requests.get(url, headers=self._get_headers(), params=params, timeout=self._timeout) if response.status_code == 401: raise UnauthorizedError("Authentication failed. Check your API key.") @@ -117,7 +119,7 @@ class TeamsManagementClient: if sort_by: params["sort_by"] = sort_by - response: Final = requests.get(url, headers=self._get_headers(), params=params) + response: Final = requests.get(url, headers=self._get_headers(), params=params, timeout=self._timeout) if response.status_code == 401: raise UnauthorizedError("Authentication failed. Check your API key.") @@ -138,7 +140,7 @@ class TeamsManagementClient: """ url: Final = f"{self._base_url}/team/available" - response: Final = requests.get(url, headers=self._get_headers()) + response: Final = requests.get(url, headers=self._get_headers(), timeout=self._timeout) if response.status_code == 401: raise UnauthorizedError("Authentication failed. Check your API key.") diff --git a/litellm/proxy/client/users.py b/litellm/proxy/client/users.py index df5f9aad23e..3f11fe94043 100644 --- a/litellm/proxy/client/users.py +++ b/litellm/proxy/client/users.py @@ -6,9 +6,10 @@ from .exceptions import NotFoundError, UnauthorizedError class UsersManagementClient: - def __init__(self, base_url: str, api_key: str | None = None): + def __init__(self, base_url: str, api_key: str | None = None, timeout: int = 30): self.base_url = base_url.rstrip("/") self.api_key = api_key + self.timeout = timeout def _get_headers(self) -> dict[str, str]: headers: Final = {"Content-Type": "application/json"} @@ -19,7 +20,7 @@ class UsersManagementClient: def list_users(self, params: dict[str, Any] | None = None) -> list[dict[str, Any]]: """List users (GET /user/list)""" url: Final = f"{self.base_url}/user/list" - response: Final = requests.get(url, headers=self._get_headers(), params=params) + response: Final = requests.get(url, headers=self._get_headers(), params=params, timeout=self.timeout) if response.status_code == 401: raise UnauthorizedError(response.text) response.raise_for_status() @@ -29,7 +30,7 @@ class UsersManagementClient: """Get user info (GET /user/info)""" url: Final = f"{self.base_url}/user/info" params: Final = {"user_id": user_id} if user_id else {} - response: Final = requests.get(url, headers=self._get_headers(), params=params) + response: Final = requests.get(url, headers=self._get_headers(), params=params, timeout=self.timeout) if response.status_code == 401: raise UnauthorizedError(response.text) if response.status_code == 404: @@ -41,7 +42,7 @@ class UsersManagementClient: """Get user info v2 - lightweight, returns only user object (GET /v2/user/info)""" url: Final = f"{self.base_url}/v2/user/info" params: Final = {"user_id": user_id} if user_id else {} - response: Final = requests.get(url, headers=self._get_headers(), params=params) + response: Final = requests.get(url, headers=self._get_headers(), params=params, timeout=self.timeout) if response.status_code == 401: raise UnauthorizedError(response.text) if response.status_code == 404: @@ -52,7 +53,7 @@ class UsersManagementClient: def create_user(self, user_data: dict[str, Any]) -> dict[str, Any]: """Create a new user (POST /user/new)""" url: Final = f"{self.base_url}/user/new" - response: Final = requests.post(url, headers=self._get_headers(), json=user_data) + response: Final = requests.post(url, headers=self._get_headers(), json=user_data, timeout=self.timeout) if response.status_code == 401: raise UnauthorizedError(response.text) response.raise_for_status() @@ -61,7 +62,9 @@ class UsersManagementClient: def delete_user(self, user_ids: list[str]) -> dict[str, Any]: """Delete users (POST /user/delete)""" url: Final = f"{self.base_url}/user/delete" - response: Final = requests.post(url, headers=self._get_headers(), json={"user_ids": user_ids}) + response: Final = requests.post( + url, headers=self._get_headers(), json={"user_ids": user_ids}, timeout=self.timeout + ) if response.status_code == 401: raise UnauthorizedError(response.text) response.raise_for_status() diff --git a/litellm/proxy/common_request_processing.py b/litellm/proxy/common_request_processing.py index ff6c8d1b1f8..05ddef822f1 100644 --- a/litellm/proxy/common_request_processing.py +++ b/litellm/proxy/common_request_processing.py @@ -176,6 +176,9 @@ if TYPE_CHECKING: ProxyConfig = _ProxyConfig else: ProxyConfig = Any +from litellm.proxy.anthropic_endpoints.streaming_model_restamp import ( + AnthropicStreamModelRestamper, +) from litellm.proxy.litellm_pre_call_utils import ( add_litellm_data_to_request, refresh_proxy_server_request_body_snapshot, @@ -502,7 +505,7 @@ def _as_success_dispatcher(logging_obj: _DispatchesSuccessHandlers) -> _Dispatch return logging_obj -def _serialize_http_exception_detail( +def serialize_http_exception_detail( detail: object, ) -> tuple[str, dict | None]: """ @@ -533,6 +536,21 @@ def _serialize_http_exception_detail( return str(detail), None +def proxy_exception_from_http_exception(exc: HTTPException, headers: dict[str, str]) -> ProxyException: + raw_detail: Final = _getattr_object(exc, "detail", str(exc)) + message, structured_fields = serialize_http_exception_detail(raw_detail) + existing_fields: Final = getattr(exc, "provider_specific_fields", None) or {} + merged_fields: Final = {**existing_fields, **structured_fields} if structured_fields else (existing_fields or None) + return ProxyException( + message=message, + type=getattr(exc, "type", "None"), + param=getattr(exc, "param", "None"), + code=getattr(exc, "status_code", status.HTTP_400_BAD_REQUEST), + provider_specific_fields=merged_fields, + headers=headers, + ) + + def _collect_response_file_search_vector_store_ids(data: Mapping[str, object]) -> set[str]: vector_store_ids: Final[set[str]] = set() tools: Final = data.get("tools") @@ -803,7 +821,7 @@ async def _buffer_first_chunk_honoring_disconnect( raise _ClientDisconnectedBeforeFirstChunk() -def _sse_error_payload(exc: BaseException) -> tuple[int, Mapping[str, object]]: +def sse_error_payload(exc: BaseException) -> tuple[int, Mapping[str, object]]: """Build the ProxyException-shaped ``{"error": ...}`` body used in SSE error frames. Matches ``ProxyException.to_dict()`` so streaming and non-streaming error frames @@ -812,7 +830,7 @@ def _sse_error_payload(exc: BaseException) -> tuple[int, Mapping[str, object]]: # Preserve status code from HTTPException (e.g. guardrail blocks) error_status: Final = getattr(exc, "status_code", status.HTTP_500_INTERNAL_SERVER_ERROR) raw_detail: Final = _getattr_object(exc, "detail", "Error processing stream start") - message, structured_fields = _serialize_http_exception_detail(raw_detail) + message, structured_fields = serialize_http_exception_detail(raw_detail) existing_fields: Final = getattr(exc, "provider_specific_fields", None) or {} merged_fields: Final = {**existing_fields, **structured_fields} if structured_fields else (existing_fields or None) @@ -927,7 +945,7 @@ async def create_response( # Unexpected error consuming first chunk. verbose_proxy_logger.exception("Error consuming first chunk from generator: %s", e) - error_status, error_obj = _sse_error_payload(e) + error_status, error_obj = sse_error_payload(e) async def error_gen_message() -> AsyncGenerator[str, None]: for frame in _sse_error_frames(error_obj): @@ -1104,7 +1122,7 @@ async def open_sse_before_first_byte( # would never fire and the failure would go unaudited. The hook # also gets to sanitize what reaches the client, by returning or # raising a replacement, so its answer decides the frame. - _, error_obj = _sse_error_payload(await _sanitized_late_failure(exc, on_late_failure)) + _, error_obj = sse_error_payload(await _sanitized_late_failure(exc, on_late_failure)) for frame in _sse_error_frames(error_obj): yield frame.encode() return @@ -1362,6 +1380,8 @@ def _classifier_cost_from_request_data(request_data: Mapping[str, object] | None routes and in `metadata` on chat-style routes, so both buckets are consulted, in the same precedence `get_or_create_metadata_bucket` writes them. """ + from litellm.proxy.spend_tracking.savings import classifier_cost_from_decision + data: Final = request_data or {} for metadata_key in ("litellm_metadata", "metadata"): metadata = data.get(metadata_key) @@ -1370,10 +1390,10 @@ def _classifier_cost_from_request_data(request_data: Mapping[str, object] | None decision = metadata.get("routing_decision") if not isinstance(decision, dict): continue - cost = decision.get("classifier_cost") - if isinstance(cost, bool) or not isinstance(cost, (int, float)): + cost = classifier_cost_from_decision(decision) + if cost is None: continue - return float(cost) + return cost return None @@ -1458,10 +1478,55 @@ async def _await_llm_call_cancelling_on_disconnect( monitor.cancel() +def _timing_values( + *, + hidden_params: Mapping[str, object], + logging_obj: LiteLLMLoggingObj | None, + use_logging_obj: bool, +) -> Mapping[str, object]: + """Both timing values from one source, so the two headers always describe the same window. + + /v1/messages returns a plain dict and the Anthropic / Responses bridge stream wrappers carry no + ``_hidden_params``, so ``update_response_metadata`` leaves their timing on the logging object. + """ + if hidden_params.get("_response_ms") is not None or not use_logging_obj or logging_obj is None: + return hidden_params + return getattr(logging_obj, "response_timing_metrics", None) or {} # mutable-ok: empty fallback + + class ProxyBaseLLMRequestProcessing: def __init__(self, data: dict): self.data = data + @staticmethod + def _merge_passthrough_streaming_headers( + response_headers: httpx.Headers | dict | None, + custom_headers: dict, + ) -> dict: + """ + Merge upstream passthrough headers with proxy/custom headers. + + Proxy/custom headers win on key collisions. + """ + excluded_headers: Final = { # mutable-ok: set of header names to exclude from forwarding + "transfer-encoding", + "content-encoding", + "set-cookie", + "connection", + "keep-alive", + "proxy-authenticate", + "proxy-authorization", + "te", + "trailer", + "upgrade", + } + + merged_headers: Final = { # mutable-ok: dict comprehension for merged headers forwarded to httpx + key: value for key, value in dict(response_headers or {}).items() if key.lower() not in excluded_headers + } + merged_headers.update(custom_headers) + return merged_headers + @staticmethod def get_custom_headers( *, @@ -1478,10 +1543,16 @@ class ProxyBaseLLMRequestProcessing: request_data: dict | None = {}, timeout: float | httpx.Timeout | None = None, litellm_logging_obj: LiteLLMLoggingObj | None = None, + read_timing_from_logging_obj: bool = True, **kwargs, ) -> dict: exclude_values: Final = {"", None, "None"} hidden_params = hidden_params or {} + timing_values: Final = _timing_values( + hidden_params=hidden_params, + logging_obj=litellm_logging_obj, + use_logging_obj=read_timing_from_logging_obj, + ) cost_breakdown: Final = _get_cost_breakdown_from_logging_obj( litellm_logging_obj=litellm_logging_obj, response_cost=response_cost @@ -1549,8 +1620,8 @@ class ProxyBaseLLMRequestProcessing: "x-litellm-key-rpm-limit": str(user_api_key_dict.rpm_limit), "x-litellm-key-max-budget": str(user_api_key_dict.max_budget), "x-litellm-key-spend": str(updated_spend), - "x-litellm-response-duration-ms": str(hidden_params.get("_response_ms", None)), - "x-litellm-overhead-duration-ms": str(hidden_params.get("litellm_overhead_time_ms", None)), + "x-litellm-response-duration-ms": str(timing_values.get("_response_ms")), + "x-litellm-overhead-duration-ms": str(timing_values.get("litellm_overhead_time_ms")), "x-litellm-callback-duration-ms": str(hidden_params.get("callback_duration_ms", None)), **( { @@ -2341,54 +2412,25 @@ class ProxyBaseLLMRequestProcessing: if requested_model_from_client: self.data["_litellm_client_requested_model"] = requested_model_from_client - # Streaming: attach a closure that fires after all guardrail - # end-of-stream blocks complete. CSW.__anext__ stores the - # assembled response on logging_obj; the outer consumer - # (ProxyLogging._fire_deferred_stream_logging) fires the - # closure after the full streaming pipeline finishes. - # The closure runs non-apply_guardrail hooks on the - # assembled response, then fires success logging. - # Only for CustomStreamWrapper — raw async generators from - # passthrough routes bypass CSW and would orphan the closure. - from litellm.litellm_core_utils.streaming_handler import ( - CustomStreamWrapper, - ) - - if _post_call_guardrails_active and isinstance(response, CustomStreamWrapper): - # Intentionally a live reference (not a copy) — mirrors - # ProxyLogging.post_call_success_hook which also mutates - # data["guardrail_to_apply"] during iteration. - _captured_data: Final = self.data - _captured_user_api_key_dict: Final = user_api_key_dict - _captured_logging_obj: Final = logging_obj - - async def _on_deferred_stream_complete(assembled_response: object, cache_hit: object) -> None: - await ProxyBaseLLMRequestProcessing._run_deferred_stream_guardrails( - captured_data=_captured_data, - captured_user_api_key_dict=_captured_user_api_key_dict, - captured_logging_obj=_captured_logging_obj, - assembled_response=assembled_response, - cache_hit=cache_hit, - ) - - logging_obj._on_deferred_stream_complete = _on_deferred_stream_complete - elif ( - _post_call_guardrails_active - and route_type == "anthropic_messages" - and self._is_streaming_response(response) - ): - from litellm.litellm_core_utils.logging_worker import ( - GLOBAL_LOGGING_WORKER, + if _post_call_guardrails_active: + self._arm_deferred_stream_dispatch( + response=response, + route_type=route_type, + user_api_key_dict=user_api_key_dict, + logging_obj=logging_obj, ) - async def _on_deferred_native_stream_complete( - logging_coroutine: Coroutine[object, object, object], - ) -> None: - GLOBAL_LOGGING_WORKER.ensure_initialized_and_enqueue(async_coroutine=logging_coroutine) - - logging_obj._on_deferred_stream_complete = _on_deferred_native_stream_complete - if route_type == "allm_passthrough_route": + upstream_response_headers: Final = getattr(response, "headers", None) + streaming_headers: Final = ( + ProxyBaseLLMRequestProcessing._merge_passthrough_streaming_headers( + response_headers=upstream_response_headers, + custom_headers=custom_headers, + ) + if upstream_response_headers is not None + else custom_headers + ) + # Check if response is an async generator if self._is_streaming_response(response): if asyncio.iscoroutine(response): @@ -2418,11 +2460,11 @@ class ProxyBaseLLMRequestProcessing: # For passthrough routes, stream directly without error parsing # since we're dealing with raw binary data (e.g., AWS event streams) - return StreamingResponse( - content=generator, - status_code=status.HTTP_200_OK, + return _UpstreamClosingStreamingResponse( + content=generator, # pyright: ignore[reportArgumentType] # generator-configured StreamingResponse + status_code=getattr(response, "status_code", status.HTTP_200_OK), media_type=self._passthrough_event_stream_media_type(), - headers=custom_headers, + headers=streaming_headers, ) else: _early = await self._handle_non_streaming_allm_passthrough_route( @@ -2437,7 +2479,7 @@ class ProxyBaseLLMRequestProcessing: return StreamingResponse( content=response.aiter_bytes(), status_code=response.status_code, - headers=custom_headers, + headers=streaming_headers, ) elif route_type == "anthropic_messages": # Check if response is actually a streaming response (async generator) @@ -2451,6 +2493,9 @@ class ProxyBaseLLMRequestProcessing: request_data=self.data, proxy_logging_obj=proxy_logging_obj, request=request, + restamp_model=( + None if _should_return_raw_model_name(self.data) else requested_model_from_client + ), ) return await create_response( generator=wrap_sse_stream_with_keepalive_pings( @@ -3057,6 +3102,94 @@ class ProxyBaseLLMRequestProcessing: except Exception as e: verbose_proxy_logger.exception("Error firing deferred logging: %s", e) + def _arm_deferred_stream_dispatch( + self, + response: object, + route_type: str, + user_api_key_dict: "UserAPIKeyAuth", + logging_obj: LiteLLMLoggingObj, + ) -> None: + """ + Streaming with post-call guardrails active: attach a closure that + ProxyLogging._fire_deferred_stream_logging fires after all guardrail + end-of-stream blocks complete, so the spend log sees + guardrail_information. + + Three closure shapes, matching who owns logging for the stream: + - CustomStreamWrapper (chat completions) stores + (assembled_response, cache_hit); the closure also runs + non-apply_guardrail post-call hooks via + _run_deferred_stream_guardrails. + - Bridged /v1/responses (LiteLLMCompletionStreamingIterator) shares + its inner CustomStreamWrapper's logging_obj, so it stores the same + (assembled_response, cache_hit) shape; the closure only dispatches + success logging, matching the route's pre-existing hook surface. + - Native anthropic_messages/aresponses iterators store a single + ready-made logging coroutine to enqueue. + + Raw async generators from passthrough routes bypass all three and + would orphan the closure, so they are not armed here. + + The router wraps iterators that cannot carry _hidden_params in + HiddenParamsAsyncIteratorWrapper, so class sniffing runs on the + unwrapped inner iterator. + """ + from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper + from litellm.router_utils.add_retry_fallback_headers import HiddenParamsAsyncIteratorWrapper + + unwrapped: Final = response._inner if isinstance(response, HiddenParamsAsyncIteratorWrapper) else response + + if isinstance(unwrapped, CustomStreamWrapper): + # Intentionally a live reference (not a copy) — mirrors + # ProxyLogging.post_call_success_hook which also mutates + # data["guardrail_to_apply"] during iteration. + _captured_data: Final = self.data + _captured_user_api_key_dict: Final = user_api_key_dict + _captured_logging_obj: Final = logging_obj + + async def _on_deferred_stream_complete(assembled_response: object, cache_hit: object) -> None: + await ProxyBaseLLMRequestProcessing._run_deferred_stream_guardrails( + captured_data=_captured_data, + captured_user_api_key_dict=_captured_user_api_key_dict, + captured_logging_obj=_captured_logging_obj, + assembled_response=assembled_response, + cache_hit=cache_hit, + ) + + logging_obj._on_deferred_stream_complete = _on_deferred_stream_complete + return + + if route_type not in ("anthropic_messages", "aresponses") or not self._is_streaming_response(response): + return + + from litellm.responses.litellm_completion_transformation.streaming_iterator import ( + LiteLLMCompletionStreamingIterator, + ) + + if isinstance(unwrapped, LiteLLMCompletionStreamingIterator): + _captured_bridge_logging_obj: Final = logging_obj + + async def _on_deferred_bridged_stream_complete(assembled_response: object, cache_hit: object) -> None: + await _as_success_dispatcher(_captured_bridge_logging_obj).dispatch_success_handlers( + assembled_response, + cache_hit=cache_hit, + start_time=None, + end_time=None, + prefer_async_handlers=True, + ) + + logging_obj._on_deferred_stream_complete = _on_deferred_bridged_stream_complete + return + + from litellm.litellm_core_utils.logging_worker import GLOBAL_LOGGING_WORKER + + async def _on_deferred_native_stream_complete( + logging_coroutine: Coroutine[object, object, object], + ) -> None: + GLOBAL_LOGGING_WORKER.ensure_initialized_and_enqueue(async_coroutine=logging_coroutine) + + logging_obj._on_deferred_stream_complete = _on_deferred_native_stream_complete + @staticmethod async def _run_deferred_stream_guardrails( captured_data: dict, @@ -3207,6 +3340,8 @@ class ProxyBaseLLMRequestProcessing: request_data=self.data, timeout=timeout, litellm_logging_obj=_litellm_logging_obj, + # a failed request reports no timing, matching /v1/chat/completions + read_timing_from_logging_obj=False, ) # Extract headers from exception - check both e.headers and e.response.headers headers = getattr(e, "headers", None) or {} @@ -3244,21 +3379,7 @@ class ProxyBaseLLMRequestProcessing: raise e if isinstance(e, HTTPException): - raw_detail: Final = _getattr_object(e, "detail", str(e)) - message, structured_fields = _serialize_http_exception_detail(raw_detail) - existing_fields: Final = getattr(e, "provider_specific_fields", None) or {} - if structured_fields: - merged_fields: dict | None = {**existing_fields, **structured_fields} - else: - merged_fields = existing_fields or None - raise ProxyException( - message=message, - type=getattr(e, "type", "None"), - param=getattr(e, "param", "None"), - code=getattr(e, "status_code", status.HTTP_400_BAD_REQUEST), - provider_specific_fields=merged_fields, - headers=safe_headers, - ) + raise proxy_exception_from_http_exception(e, safe_headers) elif isinstance(e, httpx.HTTPStatusError): # Handle httpx.HTTPStatusError - extract actual error from response # This matches the original behavior before the refactor in commit 511d435f6f @@ -3327,6 +3448,16 @@ class ProxyBaseLLMRequestProcessing: else: return chunk + @staticmethod + def _sse_chunk_serializer(restamper: AnthropicStreamModelRestamper | None) -> StreamChunkSerializer: + if restamper is None: + return ProxyBaseLLMRequestProcessing.return_sse_chunk + + def serialize(chunk: object) -> str: + return ProxyBaseLLMRequestProcessing.return_sse_chunk(restamper.process(chunk)) + + return serialize + @staticmethod async def _finalize_streaming_generator_cleanup( request: Request | None, @@ -3387,11 +3518,16 @@ class ProxyBaseLLMRequestProcessing: serialize_chunk: StreamChunkSerializer, serialize_error: StreamErrorSerializer, request: Request | None = None, + flush_tail: Callable[[], bytes] | None = None, ) -> AsyncGenerator[str, None]: """ Shared streaming data generator: runs proxy iterator hook, per-chunk hook, cost injection, then yields chunks via serialize_chunk; on exception runs failure hook and yields via serialize_error. Use for SSE or NDJSON. + + ``flush_tail`` runs once after the upstream iterator completes cleanly and + its non-empty result is yielded, so a serializer that buffers bytes across + chunks can emit anything still held at end of stream. """ verbose_proxy_logger.debug("inside generator") # Resolve per-stream (not per-chunk) whether the heavy per-chunk path @@ -3454,6 +3590,9 @@ class ProxyBaseLLMRequestProcessing: # so it must not suppress that refund. delivered_chunk = delivered_chunk or chunk != STREAM_SSE_KEEPALIVE_PING_BYTES yield serialize_chunk(chunk) + held_tail: Final = flush_tail() if flush_tail is not None else b"" + if held_tail: + yield serialize_chunk(held_tail) stream_completed = True except (asyncio.CancelledError, GeneratorExit): # Client disconnected mid-stream. CancelledError / GeneratorExit @@ -3464,8 +3603,7 @@ class ProxyBaseLLMRequestProcessing: # billing and release exactly once. This is the outermost generator # Starlette closes on disconnect, so the nested iterator hook (which # only sees GeneratorExit on GC) cannot own the refund. - if not stream_completed: - client_disconnected = True + client_disconnected = not stream_completed if not delivered_chunk and not _withheld_provider_output(response): from litellm.proxy.spend_tracking.budget_reservation import ( release_budget_reservation_on_cancel, @@ -3519,6 +3657,7 @@ class ProxyBaseLLMRequestProcessing: request_data: dict, proxy_logging_obj: ProxyLogging, request: Request | None = None, + restamp_model: str | None = None, ) -> AsyncGenerator[str, None]: """ Anthropic /messages and Google /generateContent streaming data generator require SSE events. @@ -3527,17 +3666,23 @@ class ProxyBaseLLMRequestProcessing: SSE serializers directly (rather than re-wrapping it in another ``async for: yield`` trampoline), so a streamed chunk traverses one fewer async-generator layer / coroutine resume on the hot path. + + ``restamp_model`` publishes that name on the Anthropic ``message_start`` + event in place of the provider's model, matching what the non-streaming + response reports. """ + restamper: Final = AnthropicStreamModelRestamper(restamp_model) if restamp_model else None return ProxyBaseLLMRequestProcessing.async_streaming_data_generator( response=response, user_api_key_dict=user_api_key_dict, request_data=request_data, proxy_logging_obj=proxy_logging_obj, - serialize_chunk=ProxyBaseLLMRequestProcessing.return_sse_chunk, + serialize_chunk=ProxyBaseLLMRequestProcessing._sse_chunk_serializer(restamper), serialize_error=lambda proxy_exc: ( f"{STREAM_SSE_DATA_PREFIX}{json.dumps({'error': proxy_exc.to_dict()})}\n\n" ), request=request, + flush_tail=None if restamper is None else restamper.flush, ) @overload diff --git a/litellm/proxy/common_utils/callback_utils.py b/litellm/proxy/common_utils/callback_utils.py index 9379a8577a3..39e74d2c8bd 100644 --- a/litellm/proxy/common_utils/callback_utils.py +++ b/litellm/proxy/common_utils/callback_utils.py @@ -1,6 +1,6 @@ import copy import os -from collections.abc import Callable, Iterable +from collections.abc import Callable, Iterable, Mapping from dataclasses import dataclass from typing import TYPE_CHECKING, Any, Final, Literal, NoReturn, Optional, TypeAlias @@ -525,16 +525,16 @@ LITELLM_PROXY_INTERNAL_METADATA_KEYS: Final = frozenset( def sanitize_openai_provider_metadata( - metadata: dict[str, Any] | None, -) -> dict[str, str] | None: + metadata: Mapping[str, object] | None, +) -> Mapping[str, object] | None: """ Keep only provider-safe OpenAI metadata entries (string keys -> string values). Strips LiteLLM proxy-internal tracking fields that must not be forwarded to OpenAI batch/file APIs. """ - if not metadata: - return metadata + if metadata is None: + return None sanitized: Final[dict[str, str]] = {} for key, value in metadata.items(): if key in LITELLM_PROXY_INTERNAL_METADATA_KEYS: @@ -547,7 +547,7 @@ def sanitize_openai_provider_metadata( key, type(value).__name__, ) - return sanitized or None + return None if metadata and not sanitized else sanitized def add_guardrail_to_applied_guardrails_header(request_data: dict, guardrail_name: str | None): @@ -644,13 +644,13 @@ def process_callback(_callback: str, callback_type: str, environment_variables: return {"name": _callback, "variables": env_vars_dict, "type": callback_type} -def normalize_callback_names(callbacks: Iterable[Any]) -> list[Any]: +def normalize_callback_names(callbacks: Iterable[object] | None) -> list[object]: if callbacks is None: return [] return [c.lower() if isinstance(c, str) else c for c in callbacks] -def strip_callback_config(metadata: dict[str, Any] | None) -> dict[str, Any] | None: +def strip_callback_config(metadata: dict[str, object] | None) -> dict[str, object] | None: """Return key/team metadata without the slots that carry callback credentials.""" if not isinstance(metadata, dict): return metadata @@ -674,7 +674,7 @@ def decrypt_callback_vars(metadata: Any) -> Any: return _transform_callback_vars(metadata, _decrypt_or_passthrough) -def _transform_callback_vars(metadata: Any, transform: Callable[[str, Any], Any]) -> Any: +def _transform_callback_vars(metadata: object, transform: Callable[[str, Any], Any]) -> object: if not isinstance(metadata, dict): return metadata out: Final = copy.deepcopy(metadata) @@ -704,7 +704,7 @@ def is_sensitive_callback_key( return _CALLBACK_VAR_MASKER.is_sensitive_key(key) -def _encrypt_if_plaintext(key: str, value: Any) -> Any: +def _encrypt_if_plaintext(key: str, value: object) -> object: if not isinstance(value, str) or not value: return value if not is_sensitive_callback_key(key): @@ -725,7 +725,7 @@ def _encrypt_if_plaintext(key: str, value: Any) -> Any: return value -def _decrypt_or_passthrough(key: str, value: Any) -> Any: +def _decrypt_or_passthrough(key: str, value: object) -> object: if not isinstance(value, str) or not value: return value if not value.startswith(_CALLBACK_VAR_ENCRYPTED_PREFIX): diff --git a/litellm/proxy/common_utils/custom_openapi_spec.py b/litellm/proxy/common_utils/custom_openapi_spec.py index bc7b80801fe..2a20e7b07ce 100644 --- a/litellm/proxy/common_utils/custom_openapi_spec.py +++ b/litellm/proxy/common_utils/custom_openapi_spec.py @@ -1,8 +1,12 @@ from collections.abc import Mapping, Sequence -from typing import Any, Final +from typing import Final, TypeAlias, Union from litellm._logging import verbose_proxy_logger +JsonValue: TypeAlias = Union["JsonObject", "JsonArray", str, int, float, bool, None] +JsonObject: TypeAlias = dict[str, JsonValue] +JsonArray: TypeAlias = list[JsonValue] + class CustomOpenAPISpec: """ @@ -27,7 +31,20 @@ class CustomOpenAPISpec: RESPONSES_API_PATHS = ["/v1/responses", "/responses"] @staticmethod - def get_pydantic_schema(model_class) -> Mapping[str, object] | None: + def _as_object(node: JsonValue) -> JsonObject: + return node if isinstance(node, dict) else {} + + @staticmethod + def _as_array(node: JsonValue) -> JsonArray: + return node if isinstance(node, list) else [] + + @staticmethod + def _components_schemas(openapi_schema: JsonObject) -> JsonObject: + components: Final = CustomOpenAPISpec._as_object(openapi_schema.setdefault("components", {})) + return CustomOpenAPISpec._as_object(components.setdefault("schemas", {})) + + @staticmethod + def get_pydantic_schema(model_class) -> JsonObject | None: """ Get JSON schema from a Pydantic model, handling both v1 and v2 APIs. @@ -54,9 +71,7 @@ class CustomOpenAPISpec: return None @staticmethod - def add_schema_to_components( - openapi_schema: dict[str, Any], schema_name: str, schema_def: Mapping[str, object] - ) -> None: + def add_schema_to_components(openapi_schema: JsonObject, schema_name: str, schema_def: JsonObject) -> None: """ Add a schema definition to the OpenAPI components/schemas section. @@ -66,16 +81,25 @@ class CustomOpenAPISpec: schema_def: The schema definition """ # Ensure components/schemas structure exists - if "components" not in openapi_schema: - openapi_schema["components"] = {} - if "schemas" not in openapi_schema["components"]: - openapi_schema["components"]["schemas"] = {} + _ = CustomOpenAPISpec._components_schemas(openapi_schema) # Add the schema CustomOpenAPISpec._move_defs_to_components(openapi_schema, {schema_name: schema_def}) @staticmethod - def add_request_body_to_paths(openapi_schema: dict[str, Any], paths: Sequence[str], schema_ref: str) -> None: + def _expanded_request_field(field_name: str, field_def: JsonValue) -> JsonValue: + expanded: Final = CustomOpenAPISpec._rewrite_defs_refs( + CustomOpenAPISpec._expand_field_definition(CustomOpenAPISpec._as_object(field_def)) + ) + if field_name != "messages": + return expanded + return { + **CustomOpenAPISpec._as_object(expanded), + "example": [{"role": "user", "content": "Hello, how are you?"}], + } + + @staticmethod + def add_request_body_to_paths(openapi_schema: JsonObject, paths: Sequence[str], schema_ref: str) -> None: """ Add request body with expanded form fields for better Swagger UI display. This keeps the request body but expands it to show individual fields in the UI. @@ -86,54 +110,58 @@ class CustomOpenAPISpec: schema_ref: Reference to the schema component (e.g., "#/components/schemas/ModelName") """ for path in paths: - if path in openapi_schema.get("paths", {}) and "post" in openapi_schema["paths"][path]: - # Get the actual schema to extract ALL field definitions - schema_name = schema_ref.split("/")[-1] # Extract "ProxyChatCompletionRequest" from the ref - actual_schema = openapi_schema.get("components", {}).get("schemas", {}).get(schema_name, {}) - schema_properties = actual_schema.get("properties", {}) - required_fields = actual_schema.get("required", []) + path_item = CustomOpenAPISpec._as_object( + CustomOpenAPISpec._as_object(openapi_schema.get("paths")).get(path) + ) + if "post" not in path_item: + continue - # Extract $defs and add them to components/schemas - # This fixes Pydantic v2 $defs not being resolvable in Swagger/OpenAPI - if "$defs" in actual_schema: - CustomOpenAPISpec._move_defs_to_components(openapi_schema, actual_schema["$defs"]) + post_operation = CustomOpenAPISpec._as_object(path_item["post"]) - # Create an expanded inline schema instead of just a $ref - # This makes Swagger UI show all individual fields in the request body editor - expanded_schema = { - "type": "object", - "required": required_fields, - "properties": {}, - } + # Get the actual schema to extract ALL field definitions + schema_name = schema_ref.split("/")[-1] # Extract "ProxyChatCompletionRequest" from the ref + components = CustomOpenAPISpec._as_object(openapi_schema.get("components")) + actual_schema = CustomOpenAPISpec._as_object( + CustomOpenAPISpec._as_object(components.get("schemas")).get(schema_name) + ) + schema_properties = CustomOpenAPISpec._as_object(actual_schema.get("properties")) + required_fields = actual_schema.get("required", []) - # Add all properties with their full definitions - for field_name, field_def in schema_properties.items(): - expanded_field = CustomOpenAPISpec._expand_field_definition(field_def) + # Extract $defs and add them to components/schemas + # This fixes Pydantic v2 $defs not being resolvable in Swagger/OpenAPI + if "$defs" in actual_schema: + CustomOpenAPISpec._move_defs_to_components( + openapi_schema, CustomOpenAPISpec._as_object(actual_schema["$defs"]) + ) - # Rewrite $defs references to use components/schemas instead - expanded_field = CustomOpenAPISpec._rewrite_defs_refs(expanded_field) + # Create an expanded inline schema instead of just a $ref + # This makes Swagger UI show all individual fields in the request body editor + expanded_schema: JsonObject = { + "type": "object", + "required": required_fields, + "properties": { + field_name: CustomOpenAPISpec._expanded_request_field(field_name, field_def) + for field_name, field_def in schema_properties.items() + }, + } - # Add a simple example for the messages field - if field_name == "messages": - expanded_field["example"] = [{"role": "user", "content": "Hello, how are you?"}] + # Set the request body with the expanded schema + post_operation["requestBody"] = { + "required": True, + "content": {"application/json": {"schema": expanded_schema}}, + } - expanded_schema["properties"][field_name] = expanded_field - - # Set the request body with the expanded schema - openapi_schema["paths"][path]["post"]["requestBody"] = { - "required": True, - "content": {"application/json": {"schema": expanded_schema}}, - } - - # Keep any existing parameters (like path parameters) but remove conflicting query params - if "parameters" in openapi_schema["paths"][path]["post"]: - existing_params = openapi_schema["paths"][path]["post"]["parameters"] - # Only keep path parameters, remove query params that conflict with request body - filtered_params = [param for param in existing_params if param.get("in") == "path"] - openapi_schema["paths"][path]["post"]["parameters"] = filtered_params + # Keep any existing parameters (like path parameters) but remove conflicting query params + if "parameters" in post_operation: + # Only keep path parameters, remove query params that conflict with request body + post_operation["parameters"] = [ + param + for param in CustomOpenAPISpec._as_array(post_operation["parameters"]) + if CustomOpenAPISpec._as_object(param).get("in") == "path" + ] @staticmethod - def _move_defs_to_components(openapi_schema: dict[str, Any], defs: Mapping[str, Mapping[str, Any]]) -> None: + def _move_defs_to_components(openapi_schema: JsonObject, defs: Mapping[str, JsonValue]) -> None: """ Move $defs from Pydantic v2 schema to OpenAPI components/schemas. This makes the definitions resolvable in Swagger/OpenAPI viewers. @@ -146,23 +174,31 @@ class CustomOpenAPISpec: return # Ensure components/schemas exists - if "components" not in openapi_schema: - openapi_schema["components"] = {} - if "schemas" not in openapi_schema["components"]: - openapi_schema["components"]["schemas"] = {} + schemas: Final = CustomOpenAPISpec._components_schemas(openapi_schema) # Add each definition to components/schemas for def_name, def_schema in defs.items(): # Recursively rewrite any nested $defs references within this definition - rewritten_def = CustomOpenAPISpec._rewrite_defs_refs(def_schema) - openapi_schema["components"]["schemas"][def_name] = rewritten_def + schemas[def_name] = CustomOpenAPISpec._rewrite_defs_refs(def_schema) # If this definition also has $defs, process them recursively - if "$defs" in def_schema: - CustomOpenAPISpec._move_defs_to_components(openapi_schema, def_schema["$defs"]) + def_object = CustomOpenAPISpec._as_object(def_schema) + if "$defs" in def_object: + CustomOpenAPISpec._move_defs_to_components( + openapi_schema, CustomOpenAPISpec._as_object(def_object["$defs"]) + ) @staticmethod - def _rewrite_defs_refs(schema: Any) -> Any: + def _rewritten_defs_entry(key: str, value: JsonValue) -> JsonValue: + if key == "$ref" and isinstance(value, str) and value.startswith("#/$defs/"): + # Rewrite the reference to use components/schemas + def_name: Final = value.replace("#/$defs/", "") + return f"#/components/schemas/{def_name}" + # Recursively process nested structures + return CustomOpenAPISpec._rewrite_defs_refs(value) + + @staticmethod + def _rewrite_defs_refs(schema: JsonValue) -> JsonValue: """ Recursively rewrite $ref values from #/$defs/... to #/components/schemas/... This converts Pydantic v2 references to OpenAPI-compatible references. @@ -174,26 +210,17 @@ class CustomOpenAPISpec: Schema with rewritten references """ if isinstance(schema, dict): - result: Final = {} - for key, value in schema.items(): - if key == "$ref" and isinstance(value, str) and value.startswith("#/$defs/"): - # Rewrite the reference to use components/schemas - def_name = value.replace("#/$defs/", "") - result[key] = f"#/components/schemas/{def_name}" - elif key == "$defs": - # Remove $defs from the schema since they're moved to components - continue - else: - # Recursively process nested structures - result[key] = CustomOpenAPISpec._rewrite_defs_refs(value) - return result - elif isinstance(schema, list): + return { + key: CustomOpenAPISpec._rewritten_defs_entry(key, value) + for key, value in schema.items() + if key != "$defs" + } + if isinstance(schema, list): return [CustomOpenAPISpec._rewrite_defs_refs(item) for item in schema] - else: - return schema + return schema @staticmethod - def _extract_field_schema(field_def: dict[str, Any]) -> dict[str, Any]: + def _extract_field_schema(field_def: JsonObject) -> JsonValue: """ Extract a simple schema from a Pydantic field definition for parameter display. @@ -209,10 +236,10 @@ class CustomOpenAPISpec: # Handle anyOf (Optional fields in Pydantic v2) if "anyOf" in field_def: - any_of: Final = field_def["anyOf"] + any_of: Final = CustomOpenAPISpec._as_array(field_def["anyOf"]) # Find the non-null type for option in any_of: - if option.get("type") != "null": + if CustomOpenAPISpec._as_object(option).get("type") != "null": return option # Fallback to string if all else fails return {"type": "string"} @@ -221,7 +248,7 @@ class CustomOpenAPISpec: return {"type": "string"} @staticmethod - def _expand_field_definition(field_def: dict[str, object]) -> dict[str, object]: + def _expand_field_definition(field_def: JsonObject) -> JsonObject: """ Expand a Pydantic field definition for inline use in OpenAPI schema. This creates a full field definition that Swagger UI can render as individual form fields. @@ -237,12 +264,12 @@ class CustomOpenAPISpec: @staticmethod def add_request_schema( - openapi_schema: dict[str, object], + openapi_schema: JsonObject, model_class: type, schema_name: str, paths: Sequence[str], operation_name: str, - ) -> dict[str, object]: + ) -> JsonObject: """ Generic method to add a request schema to OpenAPI specification. @@ -282,8 +309,8 @@ class CustomOpenAPISpec: @staticmethod def add_chat_completion_request_schema( - openapi_schema: dict[str, object], - ) -> dict[str, object]: + openapi_schema: JsonObject, + ) -> JsonObject: """ Add ProxyChatCompletionRequest schema to chat completion endpoints for documentation. This shows the request body in Swagger without runtime validation. @@ -309,7 +336,7 @@ class CustomOpenAPISpec: return openapi_schema @staticmethod - def add_embedding_request_schema(openapi_schema: dict[str, object]) -> dict[str, object]: + def add_embedding_request_schema(openapi_schema: JsonObject) -> JsonObject: """ Add EmbeddingRequest schema to embedding endpoints for documentation. This shows the request body in Swagger without runtime validation. @@ -336,8 +363,8 @@ class CustomOpenAPISpec: @staticmethod def add_responses_api_request_schema( - openapi_schema: dict[str, object], - ) -> dict[str, object]: + openapi_schema: JsonObject, + ) -> JsonObject: """ Add ResponsesAPIRequestParams schema to responses API endpoints for documentation. This shows the request body in Swagger without runtime validation. @@ -364,8 +391,8 @@ class CustomOpenAPISpec: @staticmethod def add_llm_api_request_schema_body( - openapi_schema: dict[str, object], - ) -> dict[str, object]: + openapi_schema: JsonObject, + ) -> JsonObject: """ Add LLM API request schema bodies to OpenAPI specification for documentation. @@ -376,12 +403,10 @@ class CustomOpenAPISpec: OpenAPI schema with added request body schemas """ # Add chat completion request schema - openapi_schema = CustomOpenAPISpec.add_chat_completion_request_schema(openapi_schema) + with_chat_completions: Final = CustomOpenAPISpec.add_chat_completion_request_schema(openapi_schema) # Add embedding request schema - openapi_schema = CustomOpenAPISpec.add_embedding_request_schema(openapi_schema) + with_embeddings: Final = CustomOpenAPISpec.add_embedding_request_schema(with_chat_completions) # Add responses API request schema - openapi_schema = CustomOpenAPISpec.add_responses_api_request_schema(openapi_schema) - - return openapi_schema + return CustomOpenAPISpec.add_responses_api_request_schema(with_embeddings) diff --git a/litellm/proxy/common_utils/debug_utils.py b/litellm/proxy/common_utils/debug_utils.py index 3a1d18b48cc..554a6ae8d1a 100644 --- a/litellm/proxy/common_utils/debug_utils.py +++ b/litellm/proxy/common_utils/debug_utils.py @@ -6,9 +6,11 @@ import os import sys import tracemalloc from collections import Counter -from typing import Any, Final +from collections.abc import Mapping, Sequence +from typing import Any, Final, NamedTuple, Protocol, TypedDict from fastapi import APIRouter, Depends, HTTPException, Query +from typing_extensions import ReadOnly from litellm import get_secret_str from litellm._logging import verbose_proxy_logger @@ -194,6 +196,42 @@ async def memory_usage_in_mem_cache_items( } +class _ProcessMemoryInfo(Protocol): + """The resident and virtual sizes psutil reports for a process.""" + + @property + def rss(self) -> int: ... + + @property + def vms(self) -> int: ... + + +class _ProcessHandle(Protocol): + """The psutil process handle members this module reads.""" + + def memory_info(self) -> _ProcessMemoryInfo: ... + + def memory_percent(self) -> float: ... + + +class _ProcessMemoryUsage(NamedTuple): + """Memory usage of a single worker process.""" + + resident_megabytes: float + virtual_megabytes: float + percent: float + + +def _process_memory_usage(process: _ProcessHandle) -> _ProcessMemoryUsage: + """Read resident/virtual megabytes and system memory share for ``process``.""" + memory_info: Final = process.memory_info() + return _ProcessMemoryUsage( + resident_megabytes=memory_info.rss / (1024 * 1024), + virtual_megabytes=memory_info.vms / (1024 * 1024), + percent=process.memory_percent(), + ) + + @router.get("/debug/memory/summary", include_in_schema=False) async def get_memory_summary( _: UserAPIKeyAuth = Depends(user_api_key_auth), @@ -227,10 +265,9 @@ async def get_memory_summary( try: import psutil - process: Final = psutil.Process() - memory_info: Final = process.memory_info() - memory_mb: Final = memory_info.rss / (1024 * 1024) - memory_percent: Final = process.memory_percent() + usage: Final = _process_memory_usage(psutil.Process()) + memory_mb: Final = usage.resident_megabytes + memory_percent: Final = usage.percent process_memory = { "summary": f"{memory_mb:.1f} MB ({memory_percent:.1f}% of system memory)", @@ -252,7 +289,7 @@ async def get_memory_summary( process_memory["error"] = str(e) # Get cache information - caches: Final[dict[str, Any]] = {} + caches: Final[dict[str, object]] = {} total_cache_items = 0 try: @@ -313,7 +350,7 @@ async def get_memory_summary( } -def _get_gc_statistics() -> dict[str, Any]: +def _get_gc_statistics() -> Mapping[str, object]: """Get garbage collector statistics.""" return { "enabled": gc.isenabled(), @@ -341,30 +378,42 @@ def _get_gc_statistics() -> dict[str, Any]: } -def _get_object_type_counts(top_n: int) -> tuple[int, list[dict[str, Any]]]: +class _ObjectTypeCount(TypedDict): + """One row of the tracked-object histogram.""" + + type: ReadOnly[str] + count: ReadOnly[int] + count_readable: ReadOnly[str] + + +def _type_name_counts(objects: Sequence[object]) -> Counter[str]: + """Count ``objects`` by the name of their type.""" + return Counter(type(obj).__name__ for obj in objects) + + +def _get_object_type_counts(top_n: int) -> tuple[int, list[_ObjectTypeCount]]: """Count objects by type and return total count and top N types.""" - type_counts: Final[Counter] = Counter() - total_objects = 0 + type_counts: Final = _type_name_counts(gc.get_objects()) - for obj in gc.get_objects(): - total_objects += 1 - obj_type = type(obj).__name__ - type_counts[obj_type] += 1 - - top_object_types: Final = [ + top_object_types: Final[list[_ObjectTypeCount]] = [ {"type": obj_type, "count": count, "count_readable": f"{count:,}"} for obj_type, count in type_counts.most_common(top_n) ] - return total_objects, top_object_types + return sum(type_counts.values()), top_object_types -def _get_uncollectable_objects_info() -> dict[str, Any]: +def _type_names(objects: Sequence[object]) -> Sequence[str]: + """The type name of each object in ``objects``.""" + return [type(obj).__name__ for obj in objects] + + +def _get_uncollectable_objects_info() -> Mapping[str, object]: """Get information about uncollectable objects (potential memory leaks).""" uncollectable: Final = gc.garbage return { "count": len(uncollectable), - "sample_types": [type(obj).__name__ for obj in uncollectable[:10]], + "sample_types": _type_names(uncollectable[:10]), "warning": ( "If count > 0, you may have reference cycles preventing garbage collection" if len(uncollectable) > 0 @@ -373,9 +422,11 @@ def _get_uncollectable_objects_info() -> dict[str, Any]: } -def _get_cache_memory_stats(user_api_key_cache, llm_router, proxy_logging_obj, redis_usage_cache) -> dict[str, Any]: +def _get_cache_memory_stats( + user_api_key_cache, llm_router, proxy_logging_obj, redis_usage_cache +) -> Mapping[str, object]: """Calculate memory usage for all caches.""" - cache_stats: Final[dict[str, Any]] = {} + cache_stats: Final[dict[str, object]] = {} try: # User API key cache user_cache_size: Final = sys.getsizeof(user_api_key_cache.in_memory_cache.cache_dict) @@ -439,9 +490,9 @@ def _get_cache_memory_stats(user_api_key_cache, llm_router, proxy_logging_obj, r return cache_stats -def _get_router_memory_stats(llm_router) -> dict[str, Any]: +def _get_router_memory_stats(llm_router) -> Mapping[str, object]: """Get memory usage statistics for LiteLLM router.""" - litellm_router_memory: dict[str, Any] = {} + litellm_router_memory: dict[str, object] = {} try: if llm_router is not None: # Model list memory size @@ -505,7 +556,7 @@ def _get_router_memory_stats(llm_router) -> dict[str, Any]: return litellm_router_memory -def _get_process_memory_info(worker_pid: int, include_process_info: bool) -> dict[str, Any] | None: +def _get_process_memory_info(worker_pid: int, include_process_info: bool) -> Mapping[str, object] | None: """Get process-level memory information using psutil.""" if not include_process_info: return None @@ -514,10 +565,10 @@ def _get_process_memory_info(worker_pid: int, include_process_info: bool) -> dic import psutil process: Final = psutil.Process() - memory_info: Final = process.memory_info() - ram_usage_mb: Final = round(memory_info.rss / (1024 * 1024), 2) - virtual_memory_mb: Final = round(memory_info.vms / (1024 * 1024), 2) - memory_percent: Final = round(process.memory_percent(), 2) + usage: Final = _process_memory_usage(process) + ram_usage_mb: Final = round(usage.resident_megabytes, 2) + virtual_memory_mb: Final = round(usage.virtual_megabytes, 2) + memory_percent: Final = round(usage.percent, 2) return { "pid": worker_pid, diff --git a/litellm/proxy/common_utils/reset_budget_job.py b/litellm/proxy/common_utils/reset_budget_job.py index d065b062517..1682cf12f4e 100644 --- a/litellm/proxy/common_utils/reset_budget_job.py +++ b/litellm/proxy/common_utils/reset_budget_job.py @@ -4,7 +4,7 @@ import math import time from collections.abc import Awaitable, Callable, Iterable, Mapping, Sequence from dataclasses import dataclass, field -from datetime import datetime, timezone +from datetime import datetime, timedelta, timezone from enum import Enum from types import MappingProxyType from typing import Final, Literal, Protocol, TypeVar, assert_never @@ -20,10 +20,12 @@ from litellm.constants import ( RESET_BUDGET_JOB_MAX_CHUNKS_PER_RUN, RESET_BUDGET_JOB_NAME, ) +from litellm.litellm_core_utils.duration_parser import duration_in_seconds from litellm.proxy._types import ( DB_RETRY_SAFE_ERROR_TYPES, LiteLLM_BudgetTableFull, LiteLLM_EndUserTable, + Litellm_EntityType, LiteLLM_TeamTable, LiteLLM_UserTable, LiteLLM_VerificationToken, @@ -33,7 +35,12 @@ from litellm.proxy.common_utils.timezone_utils import ( compute_budget_reset_at, get_budget_reset_settings, ) -from litellm.proxy.common_utils.user_api_key_cache import tag_cache_key +from litellm.proxy.common_utils.user_api_key_cache import ( + model_access_group_cache_key, + model_access_group_spend_counter_key, + tag_cache_key, +) +from litellm.proxy.db.budget_window_spend_writer import roll_window_spend_row from litellm.proxy.db.db_transaction_queue.pod_lock_manager import PodLockManager from litellm.proxy.db.exception_handler import call_with_db_reconnect_retry from litellm.proxy.utils import PrismaClient, ProxyLogging @@ -41,6 +48,7 @@ from litellm.repositories.organization_repository import OrganizationRepository from litellm.repositories.prisma_protocols import SpendLinkedTable from litellm.repositories.table_repositories import ( EndUserRepository, + ModelAccessGroupBudgetRepository, TagRepository, TeamMembershipRepository, ) @@ -92,6 +100,11 @@ class _TagRow(_BudgetLinkedRow, Protocol): def tag_name(self) -> str: ... +class _ModelAccessGroupRow(_BudgetLinkedRow, Protocol): + @property + def access_group_name(self) -> str: ... + + class _EndUserRow(_BudgetLinkedRow, Protocol): @property def user_id(self) -> str: ... @@ -154,6 +167,14 @@ def _tag_cache_keys(row: _TagRow) -> tuple[str, ...]: return (tag_cache_key(row.tag_name),) +def _model_access_group_counter_key(row: _ModelAccessGroupRow) -> str: + return model_access_group_spend_counter_key(row.access_group_name) + + +def _model_access_group_cache_keys(row: _ModelAccessGroupRow) -> tuple[str, ...]: + return (model_access_group_cache_key(row.access_group_name),) + + def _budget_link_where( budget_ids: Sequence[str], extra: Mapping[str, object] = MappingProxyType({}), @@ -329,6 +350,7 @@ class _WindowSource: table: str id_column: str + entity_type: Litellm_EntityType counter_prefix: str log_subject: str retry_subject: str @@ -353,6 +375,7 @@ _WINDOW_SOURCES: Final[tuple[_WindowSource, ...]] = ( _WindowSource( table="LiteLLM_VerificationToken", id_column="token", + entity_type=Litellm_EntityType.KEY, counter_prefix="spend:key", log_subject="keys", retry_subject="key", @@ -361,6 +384,7 @@ _WINDOW_SOURCES: Final[tuple[_WindowSource, ...]] = ( _WindowSource( table="LiteLLM_TeamTable", id_column="team_id", + entity_type=Litellm_EntityType.TEAM, counter_prefix="spend:team", log_subject="teams", retry_subject="team", @@ -610,6 +634,11 @@ class ResetBudgetJob: where=_budget_link_where(budget_ids, _SPENT_ROWS_WHERE), log_subject="tags", ) + model_access_groups: Final[tuple[_ModelAccessGroupRow, ...]] = await self._fetch_linked_rows( + table=ModelAccessGroupBudgetRepository(self.prisma_client).table, + where=_budget_link_where(budget_ids, _SPENT_ROWS_WHERE), + log_subject="model access groups", + ) rollover_caps: Final[Mapping[str, float]] = MappingProxyType( { # mutable-ok: MappingProxyType wraps a one-shot dict comprehension b.budget_id: cap @@ -639,6 +668,10 @@ class ResetBudgetJob: *((_key_counter_key(row), _row_carried_spend(row, rollover_caps)) for row in keys), *((_org_counter_key(row), _row_carried_spend(row, rollover_caps)) for row in orgs), *((_tag_counter_key(row), _row_carried_spend(row, rollover_caps)) for row in tags), + *( + (_model_access_group_counter_key(row), _row_carried_spend(row, rollover_caps)) + for row in model_access_groups + ), ), rollover_caps=rollover_caps, cache_keys=( @@ -646,6 +679,7 @@ class ResetBudgetJob: *(key for row in keys for key in _key_cache_keys(row)), *(key for row in orgs for key in _org_cache_keys(row)), *(key for row in tags for key in _tag_cache_keys(row)), + *(key for row in model_access_groups for key in _model_access_group_cache_keys(row)), ), ) @@ -671,6 +705,7 @@ class ResetBudgetJob: _queue_budget_linked_resets(uow.keys, cascade, extra=_LINKED_KEYS_WHERE) _queue_budget_linked_resets(uow.organizations, cascade, extra=_SPENT_ROWS_WHERE) _queue_budget_linked_resets(uow.tags, cascade, extra=_SPENT_ROWS_WHERE) + _queue_budget_linked_resets(uow.model_access_groups, cascade, extra=_SPENT_ROWS_WHERE) _queue_enduser_resets(uow.endusers, cascade) for budget_id, budget_reset_at in cascade.budget_resets: uow.budgets.queue_window_advance(budget_id=budget_id, budget_reset_at=budget_reset_at) @@ -714,7 +749,8 @@ class ResetBudgetJob: async def reset_budget_for_litellm_budget_table(self) -> None: """ Resets the spend a budget tier gates (end users, team members, keys, - orgs, tags) and advances the tier's budget_reset_at, atomically. + orgs, tags, model access groups) and advances the tier's + budget_reset_at, atomically. Caches are invalidated only after the transaction commits, so a failed run cannot leave a zeroed counter in front of an un-reset DB row. @@ -745,8 +781,9 @@ class ResetBudgetJob: return _ChunkOutcome(fetched=len(cascade.budgets), advanced=advanced) case _BudgetCascadeFailed(cascade=cascade, error=error): verbose_proxy_logger.exception( - "Failed to reset the budget table cascade (team member, enduser, org and tag spend, plus " - "budget_reset_at); nothing was committed and the budgets stay due for the next run: %s", + "Failed to reset the budget table cascade (team member, enduser, org, tag and model access " + "group spend, plus budget_reset_at); nothing was committed and the budgets stay due for the " + "next run: %s", error, exc_info=error, ) @@ -1210,6 +1247,9 @@ class ResetBudgetJob: spend_counter_cache: DualCache, now: datetime, reset_settings: BudgetResetSettings, + prisma_client: PrismaClient, + entity_type: Litellm_EntityType, + entity_id: str, ) -> bool: """Reset a single budget window if expired. Returns True if the window was reset.""" reset_at_str: Final = window.get("reset_at") @@ -1225,11 +1265,56 @@ class ResetBudgetJob: await spend_counter_cache.redis_cache.async_set_cache(key=counter_key, value=new_value) except Exception as redis_err: verbose_proxy_logger.warning("Failed to reset Redis counter %s: %s", counter_key, redis_err) - window["reset_at"] = compute_budget_reset_at( - budget_duration=window["budget_duration"], settings=reset_settings - ).isoformat() + budget_duration: Final = window["budget_duration"] + next_reset_at: Final = compute_budget_reset_at(budget_duration=budget_duration, settings=reset_settings) + window["reset_at"] = next_reset_at.isoformat() + await ResetBudgetJob._roll_window_spend_row( + prisma_client=prisma_client, + entity_type=entity_type, + entity_id=entity_id, + budget_duration=budget_duration, + next_reset_at=next_reset_at, + ) return True + @staticmethod + async def _roll_window_spend_row( + prisma_client: PrismaClient, + entity_type: Litellm_EntityType, + entity_id: str, + budget_duration: str, + next_reset_at: datetime, + ) -> None: + """Move this window's LiteLLM_BudgetWindowSpend row onto the window + that just started, so the maintained total the read path uses starts + from zero alongside the counter. + + Best effort: the row is an optimization over aggregating + LiteLLM_SpendLogs, so a failure here must not stop the remaining + windows from having their counters reset. + """ + try: + window_start: Final = next_reset_at - timedelta(seconds=duration_in_seconds(budget_duration)) + except Exception as e: # noqa: BLE001 # duration_in_seconds raises bare exceptions on bad input + verbose_proxy_logger.warning("Unparseable budget_duration %s: %s", budget_duration, e) + return + try: + await roll_window_spend_row( + prisma_client=prisma_client, + entity_type=entity_type.value, + entity_id=entity_id, + window_duration=budget_duration, + new_window_start=window_start, + ) + except Exception as e: # noqa: BLE001 # the row is best effort; counter resets must still land + verbose_proxy_logger.warning( + "Failed to roll budget window spend row for %s=%s window=%s: %s", + entity_type.value, + entity_id, + budget_duration, + e, + ) + @staticmethod async def _window_carried_spend( window: Mapping[str, object], counter_key: str, spend_counter_cache: DualCache @@ -1325,6 +1410,9 @@ class ResetBudgetJob: spend_counter_cache, now, self.reset_settings, + prisma_client=self.prisma_client, + entity_type=source.entity_type, + entity_id=row_id, ): changed = True if changed: diff --git a/litellm/proxy/common_utils/user_api_key_cache.py b/litellm/proxy/common_utils/user_api_key_cache.py index b8df0105b7b..76982d30306 100644 --- a/litellm/proxy/common_utils/user_api_key_cache.py +++ b/litellm/proxy/common_utils/user_api_key_cache.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import Any, Final, TypeVar, cast, overload +from typing import TYPE_CHECKING, Any, Final, TypeVar, cast, overload from pydantic import BaseModel @@ -9,6 +9,9 @@ from litellm.caching.dual_cache import DualCache from litellm.constants import DEFAULT_MANAGEMENT_OBJECT_IN_MEMORY_CACHE_TTL from litellm.proxy.common_utils.cache_pydantic_utils import CacheCodec +if TYPE_CHECKING: + from opentelemetry.trace import Span + T = TypeVar("T", bound=BaseModel) @@ -40,31 +43,32 @@ class UserApiKeyCache(DualCache): @overload def get_cache( self, - key: Any, - parent_otel_span: Any = None, + key: str, + parent_otel_span: Span | None = None, local_only: bool = False, *, model_type: type[T], - **kwargs: Any, + **kwargs: object, ) -> T | None: ... @overload def get_cache( self, - key: Any, - parent_otel_span: Any = None, + key: str, + parent_otel_span: Span | None = None, local_only: bool = False, - **kwargs: Any, + model_type: None = None, + **kwargs: object, ) -> Any: ... def get_cache( self, - key, - parent_otel_span=None, + key: str, + parent_otel_span: Span | None = None, local_only: bool = False, model_type: type[BaseModel] | None = None, - **kwargs, - ) -> Any | BaseModel | None: + **kwargs: object, + ) -> object: if model_type is None and "model_type" in kwargs: model_type = cast(type[BaseModel] | None, kwargs.pop("model_type", None)) cached: Final = super().get_cache(key=key, parent_otel_span=parent_otel_span, local_only=local_only, **kwargs) @@ -85,31 +89,32 @@ class UserApiKeyCache(DualCache): @overload async def async_get_cache( self, - key: Any, - parent_otel_span: Any = None, + key: str, + parent_otel_span: Span | None = None, local_only: bool = False, *, model_type: type[T], - **kwargs: Any, + **kwargs: object, ) -> T | None: ... @overload async def async_get_cache( self, - key: Any, - parent_otel_span: Any = None, + key: str, + parent_otel_span: Span | None = None, local_only: bool = False, - **kwargs: Any, + model_type: None = None, + **kwargs: object, ) -> Any: ... async def async_get_cache( self, - key, - parent_otel_span=None, + key: str, + parent_otel_span: Span | None = None, local_only: bool = False, model_type: type[BaseModel] | None = None, - **kwargs, - ) -> Any | BaseModel | None: + **kwargs: object, + ) -> object: if model_type is None and "model_type" in kwargs: model_type = cast(type[BaseModel] | None, kwargs.pop("model_type", None)) cached: Final = await super().async_get_cache( @@ -129,17 +134,17 @@ class UserApiKeyCache(DualCache): return None return decoded - def set_cache(self, key, value, local_only: bool = False, **kwargs): + def set_cache(self, key: str | None, value: object, local_only: bool = False, **kwargs: object): model_type: Final = cast(type[BaseModel] | None, kwargs.pop("model_type", None)) - payload: Final = CacheCodec.serialize(value, model_type=model_type) + payload: Final[object] = CacheCodec.serialize(value, model_type=model_type) return super().set_cache(key=key, value=payload, local_only=local_only, **kwargs) - async def async_set_cache(self, key, value, local_only: bool = False, **kwargs): + async def async_set_cache(self, key: str | None, value: object, local_only: bool = False, **kwargs: object): model_type: Final = cast(type[BaseModel] | None, kwargs.pop("model_type", None)) - payload: Final = CacheCodec.serialize(value, model_type=model_type) + payload: Final[object] = CacheCodec.serialize(value, model_type=model_type) return await super().async_set_cache(key=key, value=payload, local_only=local_only, **kwargs) - async def async_set_cache_pipeline(self, cache_list: list, local_only: bool = False, **kwargs) -> None: + async def async_set_cache_pipeline(self, cache_list: list, local_only: bool = False, **kwargs: object) -> None: """ Batch writes with the same Codec boundary as ``async_set_cache`` without ``model_type``: ``BaseModel`` values become JSON-safe dicts; dicts/scalars unchanged. @@ -185,6 +190,32 @@ def tag_registry_cache_key() -> str: return "tag_registry" +#: Cached under ``model_access_group_registry_cache_key`` when the table exceeds +#: ``MODEL_ACCESS_GROUP_REGISTRY_MAX_SIZE``: registry unusable, fall back to the per-group lookup. +MODEL_ACCESS_GROUP_REGISTRY_OVERFLOW_SENTINEL: Final = "__model_access_group_registry_overflow__" + + +def model_access_group_cache_key(access_group_name: str) -> str: + """Cache key one model access group budget row is stored under; shared so auth, spend tracking and the management endpoints cannot drift.""" + return f"model_access_group:{access_group_name}" + + +def model_access_group_registry_cache_key() -> str: + """Cache key for the set of model access group names that have a budget row.""" + return "model_access_group_registry" + + +def model_access_group_spend_counter_key(access_group_name: str) -> str: + """Spend counter key for one model access group; shared so its four owners cannot drift. + + The reservation path writes it up front, the cost callback writes it after the call, auth + reads it to enforce ``max_budget``, and the reset job clears it on rollover. A copy that + drifts in any one of them silently resets or reads a counter nobody else touches, which shows + up as a budget that never trips or never resets. + """ + return f"spend:model_access_group:{access_group_name}" + + #: Cached under ``end_user_restricted_registry_cache_key`` when the restricted set exceeds #: ``END_USER_RESTRICTED_REGISTRY_MAX_SIZE``: registry unusable, fall back to the per-id fetch. END_USER_RESTRICTED_REGISTRY_OVERFLOW_SENTINEL: Final = "__end_user_restricted_registry_overflow__" diff --git a/litellm/proxy/db/autorouter_session_rollup.py b/litellm/proxy/db/autorouter_session_rollup.py index 96192b884d8..9c637a62dc1 100644 --- a/litellm/proxy/db/autorouter_session_rollup.py +++ b/litellm/proxy/db/autorouter_session_rollup.py @@ -185,8 +185,10 @@ def build_autorouter_turn_transaction( of a request through the router) are excluded by their internal_call_origin stamp: they are not traffic a user sent, so counting them would manufacture sessions and savings in the adoption metrics. Failed requests served nothing and are excluded. - Cache facts are derived from the payload's own usage record through the savings - owner, never handed in beside it. + The classifier's charge still lands here exactly once, via the decision's own + classifier_cost folded into this turn's spend: the excluded classifier row is how + it was billed, the decision is how it is attributed. Cache facts are derived from + the payload's own usage record through the savings owner, never handed in beside it. """ if payload.get("status") != "success": return None @@ -204,9 +206,12 @@ def build_autorouter_turn_transaction( turn_at: Final = _turn_time_utc(str(payload.get("startTime") or "")) if turn_at is None: return None + from litellm.proxy.spend_tracking.savings import classifier_cost_from_decision + usage_object_raw: Final = metadata.get("usage_object") cache: Final = turn_cache_facts(usage_object_raw if isinstance(usage_object_raw, Mapping) else None) tier_raw: Final = routing_decision.get("tier") + classifier_cost: Final = classifier_cost_from_decision(routing_decision) return AutoRouterTurnTransaction( api_key=api_key, session_id=_bounded_session_id(session_id), @@ -216,7 +221,7 @@ def build_autorouter_turn_transaction( model=model, turn_at=turn_at, total_tokens=int(payload.get("prompt_tokens") or 0) + int(payload.get("completion_tokens") or 0), - spend=float(payload.get("spend") or 0.0), + spend=float(payload.get("spend") or 0.0) + (classifier_cost or 0.0), saved_spend=saved_spend, covered=cache.covered, cache_hit=cache.read_tokens > 0, diff --git a/litellm/proxy/db/budget_window_spend_writer.py b/litellm/proxy/db/budget_window_spend_writer.py new file mode 100644 index 00000000000..8cf2f737063 --- /dev/null +++ b/litellm/proxy/db/budget_window_spend_writer.py @@ -0,0 +1,333 @@ +""" +Writer for LiteLLM_BudgetWindowSpend. + +The table holds one row per configured budget window whose window_start rolls +forward in place, so budget enforcement can read a maintained running total +instead of aggregating LiteLLM_SpendLogs every time a window counter goes cold +(issue #35766). Raw SQL rather than the Prisma upsert helper because the +conditional roll cannot be expressed through the query builder. + +Seeding a row that does not exist yet reads LiteLLM_SpendLogs once and takes +off what the increments being flushed will add, so neither source counts the +same request twice. A row therefore lags real spend by at most one flush +interval of increments queued elsewhere: the same lag the SpendLogs aggregate +it replaces (and every other spend column) already has. +""" + +from collections.abc import Sequence +from dataclasses import dataclass +from datetime import datetime, timedelta, timezone +from typing import TYPE_CHECKING, Final, Protocol + +from litellm._logging import verbose_proxy_logger +from litellm.proxy._types import Litellm_EntityType +from litellm.proxy.db.db_transaction_queue.window_spend_update_queue import ( + WindowSpendTransaction, + to_naive_utc, + window_spend_group_key, +) + +if TYPE_CHECKING: + from litellm.proxy.utils import PrismaClient + + +_SELECT_EXISTING_ROWS_SQL: Final = ( + 'SELECT entity_type, entity_id, window_duration FROM "LiteLLM_BudgetWindowSpend" ' + "WHERE (entity_type, entity_id, window_duration) " + "IN (SELECT * FROM unnest($1::text[], $2::text[], $3::text[]))" +) + +_UPSERT_WINDOW_SPEND_SQL: Final = ( + 'INSERT INTO "LiteLLM_BudgetWindowSpend" ' + "(entity_type, entity_id, window_duration, window_start, spend, created_at, updated_at) " + "VALUES ($1, $2, $3, ($4::timestamptz AT TIME ZONE 'UTC'), $5, " + "($7::timestamptz AT TIME ZONE 'UTC'), ($7::timestamptz AT TIME ZONE 'UTC')) " + "ON CONFLICT (entity_type, entity_id, window_duration) DO UPDATE SET " + "spend = CASE " + 'WHEN "LiteLLM_BudgetWindowSpend".window_start >= EXCLUDED.window_start ' + 'THEN "LiteLLM_BudgetWindowSpend".spend + $6 ' + "ELSE EXCLUDED.spend " + "END, " + 'window_start = GREATEST("LiteLLM_BudgetWindowSpend".window_start, EXCLUDED.window_start), ' + "updated_at = ($7::timestamptz AT TIME ZONE 'UTC')" +) + +_ROLL_WINDOW_SPEND_SQL: Final = ( + 'UPDATE "LiteLLM_BudgetWindowSpend" SET ' + "window_start = ($4::timestamptz AT TIME ZONE 'UTC'), " + "spend = 0, " + "updated_at = ($5::timestamptz AT TIME ZONE 'UTC') " + "WHERE entity_type = $1 AND entity_id = $2 AND window_duration = $3 " + "AND window_start < ($4::timestamptz AT TIME ZONE 'UTC')" +) + +_SEED_FROM_SPEND_LOGS_KEY_SQL: Final = ( + "SELECT COALESCE(SUM(spend), 0.0) AS total, " + "COALESCE(SUM(spend) FILTER (WHERE \"startTime\" < ($3::timestamptz AT TIME ZONE 'UTC')), 0.0) AS before_batch " + 'FROM "LiteLLM_SpendLogs" ' + "WHERE api_key = $1 AND \"startTime\" >= ($2::timestamptz AT TIME ZONE 'UTC')" +) + +_SEED_FROM_SPEND_LOGS_TEAM_SQL: Final = ( + "SELECT COALESCE(SUM(spend), 0.0) AS total, " + "COALESCE(SUM(spend) FILTER (WHERE \"startTime\" < ($3::timestamptz AT TIME ZONE 'UTC')), 0.0) AS before_batch " + 'FROM "LiteLLM_SpendLogs" ' + "WHERE team_id = $1 AND \"startTime\" >= ($2::timestamptz AT TIME ZONE 'UTC')" +) + +_SEED_FROM_SPEND_LOGS_KEY_UNBOUNDED_SQL: Final = ( + "SELECT COALESCE(SUM(spend), 0.0) AS total, COALESCE(SUM(spend), 0.0) AS before_batch " + 'FROM "LiteLLM_SpendLogs" ' + "WHERE api_key = $1 AND \"startTime\" >= ($2::timestamptz AT TIME ZONE 'UTC')" +) + +_SEED_FROM_SPEND_LOGS_TEAM_UNBOUNDED_SQL: Final = ( + "SELECT COALESCE(SUM(spend), 0.0) AS total, COALESCE(SUM(spend), 0.0) AS before_batch " + 'FROM "LiteLLM_SpendLogs" ' + "WHERE team_id = $1 AND \"startTime\" >= ($2::timestamptz AT TIME ZONE 'UTC')" +) + +_UPSERT_TRANSACTION_TIMEOUT: Final = timedelta(seconds=60) + + +@dataclass(frozen=True, slots=True) +class WindowSeedTotals: + """The two sums a seed needs: everything persisted for the window, and the + part of it that predates the batch being flushed.""" + + total: float + before_batch: float + + +class WindowSpendLogsAggregate(Protocol): + """Sums LiteLLM_SpendLogs for one entity since window_start, split at the + batch's earliest request. + + Injected so the flush can be exercised without a database and so the + expensive aggregate stays swappable. + """ + + async def __call__( + self, + prisma_client: "PrismaClient", + entity_type: str, + entity_id: str, + window_start: datetime, + batch_started_at: datetime | None, + ) -> WindowSeedTotals | None: ... + + +async def spend_logs_seed_totals( + prisma_client: "PrismaClient", + entity_type: str, + entity_id: str, + window_start: datetime, + batch_started_at: datetime | None, +) -> WindowSeedTotals | None: + """LiteLLM_SpendLogs spend for one entity since window_start, both in full + and up to the start of the batch being flushed, in one scan. + + The spend log writer drains its own queue on a ~2s poll whenever anything + is queued, while window increments flush on the much slower batch tick, so + by the time a window row is seeded its batch's log rows are normally + already in the table. Counting them in the seed and again in the increment + is what made a fresh row land at twice the true spend. + + Both halves are needed because neither is safe alone: the full sum + double-counts this batch, and the sum before the batch drops spend another + pod has already persisted but not yet incremented. _seed_base picks between + them. Without a known batch start the two are the same sum, so the seed + counts everything: that can only over-count once, which enforcement + tolerates, whereas under-counting is a budget bypass. + """ + if entity_type == Litellm_EntityType.KEY.value: + bounded_sql, unbounded_sql = _SEED_FROM_SPEND_LOGS_KEY_SQL, _SEED_FROM_SPEND_LOGS_KEY_UNBOUNDED_SQL + elif entity_type == Litellm_EntityType.TEAM.value: + bounded_sql, unbounded_sql = _SEED_FROM_SPEND_LOGS_TEAM_SQL, _SEED_FROM_SPEND_LOGS_TEAM_UNBOUNDED_SQL + else: + return None + rows: Final = ( + await prisma_client.db.query_raw(unbounded_sql, entity_id, window_start) + if batch_started_at is None + else await prisma_client.db.query_raw( + bounded_sql, + entity_id, + window_start, + _exclusion_upper_bound(batch_started_at), + ) + ) + if not rows: + return WindowSeedTotals(total=0.0, before_batch=0.0) + return WindowSeedTotals( + total=float(rows[0].get("total") or 0.0), + before_batch=float(rows[0].get("before_batch") or 0.0), + ) + + +def _exclusion_upper_bound(started_at: datetime) -> datetime: + """LiteLLM_SpendLogs.startTime is TIMESTAMP(3); floor to the second so a + millisecond rounding of the batch's own earliest row cannot slip under it.""" + return to_naive_utc(started_at).replace(microsecond=0) + + +def _primary_key(transaction: WindowSpendTransaction) -> tuple[str, str, str]: + return ( + transaction["entity_type"], + transaction["entity_id"], + transaction["window_duration"], + ) + + +async def _existing_primary_keys( + prisma_client: "PrismaClient", + transactions: tuple[WindowSpendTransaction, ...], +) -> frozenset[tuple[str, str, str]]: + rows: Final = await prisma_client.db.query_raw( + _SELECT_EXISTING_ROWS_SQL, + tuple(transaction["entity_type"] for transaction in transactions), + tuple(transaction["entity_id"] for transaction in transactions), + tuple(transaction["window_duration"] for transaction in transactions), + ) + return frozenset((row["entity_type"], row["entity_id"], row["window_duration"]) for row in rows or ()) + + +async def _seed_base_for_missing_row( + prisma_client: "PrismaClient", + transaction: WindowSpendTransaction, + existing_primary_keys: frozenset[tuple[str, str, str]], + spend_logs_aggregate: WindowSpendLogsAggregate, +) -> float: + """Spend already recorded for a window that has no row yet. + + This is the LiteLLM_SpendLogs aggregate the window counter reseed runs on + every cold counter today, but here it runs once per window lifetime and off + the request path, and it discounts the queued increments so they are + counted once. + """ + if _primary_key(transaction) in existing_primary_keys: + return 0.0 + totals: Final = await spend_logs_aggregate( + prisma_client=prisma_client, + entity_type=transaction["entity_type"], + entity_id=transaction["entity_id"], + window_start=datetime.fromisoformat(transaction["window_start"]).replace(tzinfo=timezone.utc), + batch_started_at=_transaction_started_at(transaction), + ) + if totals is None: + return 0.0 + return _seed_base(totals=totals, batch_spend=transaction["spend"]) + + +def _seed_base(totals: WindowSeedTotals, batch_spend: float) -> float: + """What the window already held before the increments about to be applied. + + Subtracting the batch's own spend from the full sum keeps every other + request in the seed, including the ones another pod persisted and has not + incremented yet, which a plain cutoff would drop for good if that pod died. + When this batch's own log rows have not landed yet the subtraction takes + spend that was never counted, so the sum before the batch is the floor. + """ + return max(totals.total - batch_spend, totals.before_batch) + + +def _transaction_started_at(transaction: WindowSpendTransaction) -> datetime | None: + started_at: Final = transaction.get("started_at") + if started_at is None: + return None + return datetime.fromisoformat(started_at).replace(tzinfo=timezone.utc) + + +def _upsert_params( + transaction: WindowSpendTransaction, + seed_base: float, + now: datetime, +) -> tuple[str, str, str, datetime, float, float, datetime]: + """$5 is what a brand new row starts at (pre-existing spend plus this + increment); $6 is the increment alone, which is all an already-current row + may add. They are equal for every row that already existed, so a row is + never seeded twice when two pods flush the same new window.""" + increment: Final = float(transaction["spend"]) + return ( + transaction["entity_type"], + transaction["entity_id"], + transaction["window_duration"], + datetime.fromisoformat(transaction["window_start"]), + seed_base + increment, + increment, + now, + ) + + +async def commit_window_spend_updates( + prisma_client: "PrismaClient", + transactions: Sequence[WindowSpendTransaction], + spend_logs_aggregate: WindowSpendLogsAggregate = spend_logs_seed_totals, +) -> None: + """Apply aggregated window increments to LiteLLM_BudgetWindowSpend. + + An increment at or behind the row's window_start adds into the row (this is + how in-flight requests that raced a reset carry into the new window); an + increment ahead of it rolls the window and starts from that increment. + + Statements are ordered by primary key so concurrent pods take row locks in + the same order, with window_start breaking ties so an older window is + applied before the roll that supersedes it. + """ + if not transactions: + return + + ordered: Final = tuple(sorted(transactions, key=window_spend_group_key)) + existing_primary_keys: Final = await _existing_primary_keys( + prisma_client=prisma_client, + transactions=ordered, + ) + seed_bases: Final = tuple( + [ + await _seed_base_for_missing_row( + prisma_client=prisma_client, + transaction=transaction, + existing_primary_keys=existing_primary_keys, + spend_logs_aggregate=spend_logs_aggregate, + ) + for transaction in ordered + ] + ) + + now: Final = to_naive_utc(datetime.now(timezone.utc)) + verbose_proxy_logger.debug( + "Spend tracking - committing %d budget window spend upserts over %d existing rows", + len(ordered), + len(existing_primary_keys), + ) + async with ( + prisma_client.db.tx(timeout=_UPSERT_TRANSACTION_TIMEOUT) as db_transaction, + db_transaction.batch_() as batcher, + ): + for transaction, seed_base in zip(ordered, seed_bases): + batcher.execute_raw( + _UPSERT_WINDOW_SPEND_SQL, + *_upsert_params(transaction=transaction, seed_base=seed_base, now=now), + ) + + +async def roll_window_spend_row( + prisma_client: "PrismaClient", + entity_type: str, + entity_id: str, + window_duration: str, + new_window_start: datetime, +) -> None: + """Move a row onto the window that just started and zero its spend. + + Conditional on the stored window_start still being behind the new one so a + pod that already rolled the row (or increments that arrived under the new + window) are not clobbered. + """ + await prisma_client.db.execute_raw( + _ROLL_WINDOW_SPEND_SQL, + entity_type, + entity_id, + window_duration, + to_naive_utc(new_window_start), + to_naive_utc(datetime.now(timezone.utc)), + ) diff --git a/litellm/proxy/db/db_spend_update_writer.py b/litellm/proxy/db/db_spend_update_writer.py index 3f2777ff1f3..e6880d521f1 100644 --- a/litellm/proxy/db/db_spend_update_writer.py +++ b/litellm/proxy/db/db_spend_update_writer.py @@ -12,6 +12,7 @@ import os import random import time import traceback +from collections.abc import Mapping, Sequence from datetime import datetime, timedelta, timezone from typing import TYPE_CHECKING, Any, Final, Literal, Protocol, cast, overload @@ -23,6 +24,7 @@ from litellm.constants import ( DB_SPEND_UPDATE_JOB_NAME, INTERNAL_CALL_ORIGIN_METADATA_KEY, ) +from litellm.litellm_core_utils.litellm_logging import coerce_model_access_groups from litellm.litellm_core_utils.safe_json_loads import safe_json_loads from litellm.proxy._types import ( DB_RETRY_SAFE_ERROR_TYPES, @@ -54,6 +56,10 @@ from litellm.proxy.db.db_transaction_queue.spend_update_queue import SpendUpdate from litellm.proxy.db.db_transaction_queue.tool_discovery_queue import ( ToolDiscoveryQueue, ) +from litellm.proxy.db.db_transaction_queue.window_spend_update_queue import ( + WindowSpendTransaction, + WindowSpendUpdateQueue, +) from litellm.proxy.route_llm_request import ROUTE_ENDPOINT_MAPPING from litellm.proxy.spend_tracking.compression_savings import ( extract_compression_saved_tokens, @@ -86,6 +92,7 @@ class _SpendBatch(Protocol): litellm_organizationtable: BatchTable litellm_tagtable: BatchTable litellm_agentstable: BatchTable + litellm_modelaccessgroupbudgettable: BatchTable class _SpendBatchManager(Protocol): @@ -109,7 +116,7 @@ def _spend_update_tx(prisma_client: PrismaClient) -> _SpendTransactionManager: return tx -def _get_llm_router(): +def get_llm_router(): """The proxy's router, or None outside a running proxy. Injected rather than imported where it is used, so the savings computation stays @@ -123,6 +130,52 @@ def _get_llm_router(): return None +class _DeploymentLookup(Protocol): + def get_model_info(self, id: str) -> Mapping[str, object] | None: ... + + +def _served_model_access_groups( + router: _DeploymentLookup | None, + served_model_id: str | None, +) -> frozenset[str] | None: + """Access groups declared by the deployment that actually served the request. + + None when the served deployment cannot be identified, in which case the set + attributed at auth time stands unchanged. + """ + if router is None or not served_model_id: + return None + deployment: Final = router.get_model_info(id=served_model_id) + if deployment is None: + return None + model_info: Final = deployment.get("model_info") + if not isinstance(model_info, Mapping): + return None + declared: Final = model_info.get("access_groups") + if not isinstance(declared, (list, tuple)): + return frozenset() + return frozenset(group for group in declared if isinstance(group, str)) + + +def debitable_model_access_groups( + attributed: Sequence[str] | None, + served_model_id: str | None, + router: _DeploymentLookup | None, +) -> tuple[str, ...]: + """Groups to debit: the set attributed at auth time, narrowed to those the served model belongs to. + + The router may fall back to a model outside the pool auth reserved against, so the + attributed set is the hard upper bound: a group absent from it is never debited. + """ + ordered: Final = coerce_model_access_groups(attributed) + if not ordered: + return () + served: Final = _served_model_access_groups(router=router, served_model_id=served_model_id) + if served is None: + return ordered + return tuple(group for group in ordered if group in served) + + class DBSpendUpdateWriter: """ Module responsible for @@ -146,6 +199,7 @@ class DBSpendUpdateWriter: self.daily_agent_spend_update_queue = DailySpendUpdateQueue() self.daily_org_spend_update_queue = DailySpendUpdateQueue() self.daily_tag_spend_update_queue = DailySpendUpdateQueue() + self.window_spend_update_queue = WindowSpendUpdateQueue() async def update_database( # LiteLLM management object fields @@ -157,11 +211,11 @@ class DBSpendUpdateWriter: org_id: str | None, # Completion object fields kwargs: dict | None, - completion_response: litellm.ModelResponse | Any | Exception | None, + completion_response: object, start_time: datetime | None, end_time: datetime | None, response_cost: float | None, - ): + ) -> None: from litellm.proxy.proxy_server import ( disable_spend_logs, litellm_proxy_budget_name, @@ -187,6 +241,7 @@ class DBSpendUpdateWriter: ## CREATE SPEND LOG PAYLOAD ## from litellm.proxy.spend_tracking.spend_tracking_utils import ( get_logging_payload, + get_request_model_access_groups, ) payload: Final = get_logging_payload( @@ -239,6 +294,7 @@ class DBSpendUpdateWriter: prisma_client=prisma_client, litellm_proxy_budget_name=litellm_proxy_budget_name, payload=payload, + request_model_access_groups=get_request_model_access_groups(kwargs), ) ) @@ -262,11 +318,12 @@ class DBSpendUpdateWriter: org_id, end_user_id, ) + return async def _enqueue_tool_usage_transaction( self, payload: SpendLogsPayload, - completion_response: "litellm.ModelResponse | Any | Exception | None", + completion_response: object, prisma_client: "PrismaClient | None", kwargs: "dict | None" = None, ) -> None: @@ -320,7 +377,7 @@ class DBSpendUpdateWriter: routing_decision=metadata.get("routing_decision"), usage_object=usage_object_raw if isinstance(usage_object_raw, dict) else None, model_id=payload.get("model_id"), - llm_router=_get_llm_router, + llm_router=get_llm_router, cost_breakdown=metadata.get("cost_breakdown"), recorded_autorouter_savings=metadata.get("autorouter_savings"), ) @@ -339,7 +396,7 @@ class DBSpendUpdateWriter: def _enqueue_tool_registry_upsert( self, kwargs: dict | None, - completion_response: Any | None, + completion_response: object, hashed_token: str | None = None, team_id: str | None = None, ) -> None: @@ -431,9 +488,10 @@ class DBSpendUpdateWriter: prisma_client: PrismaClient | None, litellm_proxy_budget_name: str | None, payload: SpendLogsPayload, + request_model_access_groups: Sequence[str] = (), ): """ - Runs all 11 spend-update helpers sequentially inside a single asyncio task. + Runs all 13 spend-update helpers sequentially inside a single asyncio task. Each helper is wrapped in try/except so one failure doesn't prevent the others. @@ -505,6 +563,14 @@ class DBSpendUpdateWriter: traceback.format_exc(), ) + await self._update_model_access_group_db( + response_cost=response_cost, + request_model_access_groups=request_model_access_groups, + served_model_id=payload_copy.get("model_id"), + prisma_client=prisma_client, + router=get_llm_router(), + ) + _agent_id_for_spend: Final = payload_copy.get("agent_id") try: await self._update_agent_db( @@ -783,7 +849,7 @@ class DBSpendUpdateWriter: return # Parse tags from JSON string - tags = [] + tags: Sequence[object] = [] if isinstance(request_tags, str): tags = safe_json_loads(request_tags, default=[]) if not tags: @@ -814,6 +880,50 @@ class DBSpendUpdateWriter: ) raise e + async def _update_model_access_group_db( + self, + response_cost: float | None, + request_model_access_groups: Sequence[str] | None, + served_model_id: str | None, + prisma_client: PrismaClient | None, + router: _DeploymentLookup | None = None, + ) -> None: + """ + Update spend for every model access group this request is billed against. + + Args: + response_cost: Cost of the request, charged in full to each group + request_model_access_groups: Groups attributed at auth time, the upper bound on what may be debited + served_model_id: Deployment id actually served, used to narrow the attributed set + prisma_client: Prisma client instance + router: Deployment lookup used to re-resolve groups after a fallback + """ + try: + if prisma_client is None: + return + + for model_access_group in debitable_model_access_groups( + attributed=request_model_access_groups, + served_model_id=served_model_id, + router=router, + ): + await self.spend_update_queue.add_update( + update=SpendUpdateQueueItem( + entity_type=Litellm_EntityType.MODEL_ACCESS_GROUP, + entity_id=model_access_group, + response_cost=response_cost, + ) + ) + except Exception as e: # noqa: BLE001 # isolation: a helper failure must not stop the batch + spend_log_error( + "Spend tracking - failed to enqueue model access group spend update. " + "model_access_groups=%s, response_cost=%s - %s", + request_model_access_groups, + response_cost, + str(e), + exc=e, + ) + async def _insert_spend_log_to_db( self, payload: dict | SpendLogsPayload, @@ -895,6 +1005,7 @@ class DBSpendUpdateWriter: daily_org_spend_update_queue=self.daily_org_spend_update_queue, daily_end_user_spend_update_queue=self.daily_end_user_spend_update_queue, daily_agent_spend_update_queue=self.daily_agent_spend_update_queue, + window_spend_update_queue=self.window_spend_update_queue, ) # Only commit from redis to db if this pod is the leader @@ -913,6 +1024,7 @@ class DBSpendUpdateWriter: daily_org_spend_update_transactions, daily_end_user_spend_update_transactions, daily_agent_spend_update_transactions, + window_spend_update_transactions, ) = await self.redis_update_buffer.get_all_transactions_from_redis_buffer_pipeline() uncommitted = { # mutable-ok: drives which popped categories still need re-queuing @@ -922,12 +1034,14 @@ class DBSpendUpdateWriter: "daily_org_spend_update_transactions": daily_org_spend_update_transactions, "daily_end_user_spend_update_transactions": daily_end_user_spend_update_transactions, "daily_agent_spend_update_transactions": daily_agent_spend_update_transactions, + "window_spend_update_transactions": window_spend_update_transactions, } if db_spend_update_transactions is not None: verbose_proxy_logger.info( "Spend tracking - committing spend updates from Redis to DB: " - "keys=%d, users=%d, teams=%d, orgs=%d, end_users=%d, team_members=%d, tags=%d, agents=%d", + "keys=%d, users=%d, teams=%d, orgs=%d, end_users=%d, team_members=%d, tags=%d, agents=%d, " + "model_access_groups=%d", len(db_spend_update_transactions.get("key_list_transactions") or {}), len(db_spend_update_transactions.get("user_list_transactions") or {}), len(db_spend_update_transactions.get("team_list_transactions") or {}), @@ -936,6 +1050,7 @@ class DBSpendUpdateWriter: len(db_spend_update_transactions.get("team_member_list_transactions") or {}), len(db_spend_update_transactions.get("tag_list_transactions") or {}), len(db_spend_update_transactions.get("agent_list_transactions") or {}), + len(db_spend_update_transactions.get("model_access_group_list_transactions") or {}), ) await self._commit_spend_updates_to_db( prisma_client=prisma_client, @@ -989,6 +1104,12 @@ class DBSpendUpdateWriter: daily_spend_transactions=daily_agent_spend_update_transactions, ) uncommitted.pop("daily_agent_spend_update_transactions", None) + if window_spend_update_transactions is not None: + await DBSpendUpdateWriter._commit_window_spend_updates( + prisma_client=prisma_client, + window_spend_transactions=window_spend_update_transactions, + ) + uncommitted.pop("window_spend_update_transactions", None) except Exception as e: spend_log_error( "Spend tracking - failed to commit spend updates from Redis to DB. " @@ -1104,6 +1225,27 @@ class DBSpendUpdateWriter: daily_spend_transactions=daily_agent_spend_update_transactions, ) + ################## Budget Window Spend Update Transactions ################## + # Aggregate all in memory budget window spend transactions and commit to db + window_spend_update_transactions: Final = ( + await self.window_spend_update_queue.flush_and_get_aggregated_window_spend_transactions() + ) + + try: + await DBSpendUpdateWriter._commit_window_spend_updates( + prisma_client=prisma_client, + window_spend_transactions=window_spend_update_transactions, + ) + except Exception as e: # noqa: BLE001 # the increments go back on the queue; the rest of the flush must run + spend_log_error( + "Spend tracking - failed to commit budget window spend updates. " + "Re-queued %d window increments for retry on next tick. Error: %s", + len(window_spend_update_transactions), + str(e), + exc=e, + ) + await self.window_spend_update_queue.update_queue.put(window_spend_update_transactions) + ################## Tool Registry Upserts ################## await self._flush_tool_discovery_queue(prisma_client=prisma_client) @@ -1168,6 +1310,28 @@ class DBSpendUpdateWriter: cronjob_id=DB_DAILY_TAG_SPEND_UPDATE_JOB_NAME, ) + @staticmethod + async def _commit_window_spend_updates( + prisma_client: PrismaClient, + window_spend_transactions: Sequence[WindowSpendTransaction], + ) -> None: + """ + Commit per-budget-window spend increments to LiteLLM_BudgetWindowSpend. + + Raises on failure so the caller re-queues the increments: budget + enforcement trusts a current row without reconciling it against + LiteLLM_SpendLogs, so a dropped increment would let the entity spend + past its window limit after the next counter reseed. + """ + from litellm.proxy.db.budget_window_spend_writer import ( + commit_window_spend_updates, + ) + + await commit_window_spend_updates( + prisma_client=prisma_client, + transactions=window_spend_transactions, + ) + async def _drain_and_commit_daily_tag_spend_from_redis( self, prisma_client: PrismaClient, @@ -1433,6 +1597,20 @@ class DBSpendUpdateWriter: proxy_logging_obj=proxy_logging_obj, ) + ### UPDATE MODEL ACCESS GROUP TABLE ### + model_access_group_list_transactions: Final = db_spend_update_transactions.get( + "model_access_group_list_transactions" + ) + await DBSpendUpdateWriter._update_entity_spend_in_db( + entity_name="Model access group", + transactions=model_access_group_list_transactions, + table_accessor="litellm_modelaccessgroupbudgettable", + where_field="access_group_name", + n_retry_times=n_retry_times, + prisma_client=prisma_client, + proxy_logging_obj=proxy_logging_obj, + ) + ### UPDATE AGENT TABLE ### agent_list_transactions: Final = db_spend_update_transactions["agent_list_transactions"] await DBSpendUpdateWriter._update_entity_spend_in_db( @@ -1449,7 +1627,7 @@ class DBSpendUpdateWriter: async def _update_entity_spend_in_db( entity_name: str, transactions: dict[str, float] | None, - table_accessor: Literal["litellm_tagtable", "litellm_agentstable"], + table_accessor: Literal["litellm_tagtable", "litellm_agentstable", "litellm_modelaccessgroupbudgettable"], where_field: str, n_retry_times: int, prisma_client: PrismaClient, @@ -1884,7 +2062,7 @@ class DBSpendUpdateWriter: gateway_injected_cache=marks_gateway_injection(_metadata, payload.get("model_id")), routing_decision=_metadata.get("routing_decision"), model_id=payload.get("model_id"), - llm_router=_get_llm_router, + llm_router=get_llm_router, usage_object=usage_obj, cost_breakdown=_metadata.get("cost_breakdown"), recorded_autorouter_savings=_metadata.get("autorouter_savings"), @@ -2082,7 +2260,7 @@ class DBSpendUpdateWriter: verbose_proxy_logger.debug("request_tags is None for request. Skipping incrementing tag spend.") return - request_tags = [] + request_tags: Sequence[str] = [] if isinstance(payload["request_tags"], str): request_tags = json.loads(payload["request_tags"]) elif isinstance(payload["request_tags"], list): diff --git a/litellm/proxy/db/db_transaction_queue/redis_update_buffer.py b/litellm/proxy/db/db_transaction_queue/redis_update_buffer.py index ad92902221a..c06f2e04aca 100644 --- a/litellm/proxy/db/db_transaction_queue/redis_update_buffer.py +++ b/litellm/proxy/db/db_transaction_queue/redis_update_buffer.py @@ -23,6 +23,7 @@ from litellm.constants import ( REDIS_DAILY_TAG_SPEND_UPDATE_BUFFER_KEY, REDIS_DAILY_TEAM_SPEND_UPDATE_BUFFER_KEY, REDIS_UPDATE_BUFFER_KEY, + REDIS_WINDOW_SPEND_UPDATE_BUFFER_KEY, ) from litellm.litellm_core_utils.safe_json_dumps import safe_dumps from litellm.proxy._types import ( @@ -42,6 +43,11 @@ from litellm.proxy.db.db_transaction_queue.daily_spend_update_queue import ( DailySpendUpdateQueue, ) from litellm.proxy.db.db_transaction_queue.spend_update_queue import SpendUpdateQueue +from litellm.proxy.db.db_transaction_queue.window_spend_update_queue import ( + WindowSpendTransaction, + WindowSpendUpdateQueue, + to_wire_payload, +) from litellm.secret_managers.main import str_to_bool from litellm.types.caching import ( RedisPipelineLpopOperation, @@ -65,6 +71,7 @@ _SpendTransactionField: TypeAlias = Literal[ "org_list_transactions", "tag_list_transactions", "agent_list_transactions", + "model_access_group_list_transactions", ] _SPEND_TRANSACTION_FIELDS: Final[tuple[_SpendTransactionField, ...]] = ( @@ -76,6 +83,7 @@ _SPEND_TRANSACTION_FIELDS: Final[tuple[_SpendTransactionField, ...]] = ( "org_list_transactions", "tag_list_transactions", "agent_list_transactions", + "model_access_group_list_transactions", ) _ValueT = TypeVar("_ValueT") @@ -180,6 +188,7 @@ class RedisUpdateBuffer: daily_org_spend_update_queue: DailySpendUpdateQueue, daily_end_user_spend_update_queue: DailySpendUpdateQueue, daily_agent_spend_update_queue: DailySpendUpdateQueue, + window_spend_update_queue: WindowSpendUpdateQueue | None = None, ): """ Stores the in-memory spend updates to Redis @@ -248,6 +257,11 @@ class RedisUpdateBuffer: daily_agent_spend_update_transactions: Final = ( await daily_agent_spend_update_queue.flush_and_get_aggregated_daily_spend_update_transactions() ) + window_spend_update_transactions: Final = ( + await window_spend_update_queue.flush_and_get_aggregated_window_spend_transactions() + if window_spend_update_queue is not None + else () + ) verbose_proxy_logger.debug("ALL DB SPEND UPDATE TRANSACTIONS: %s", db_spend_update_transactions) verbose_proxy_logger.debug("ALL DAILY SPEND UPDATE TRANSACTIONS: %s", daily_spend_update_transactions) @@ -284,6 +298,11 @@ class RedisUpdateBuffer: REDIS_DAILY_AGENT_SPEND_UPDATE_BUFFER_KEY, ServiceTypes.REDIS_DAILY_AGENT_SPEND_UPDATE_QUEUE, ), + ( + tuple(map(to_wire_payload, window_spend_update_transactions)), + REDIS_WINDOW_SPEND_UPDATE_BUFFER_KEY, + ServiceTypes.REDIS_WINDOW_SPEND_UPDATE_QUEUE, + ), ] rpush_list: Final[list[RedisPipelineRpushOperation]] = [] @@ -324,12 +343,14 @@ class RedisUpdateBuffer: daily_org_spend_update_transactions=daily_org_spend_update_transactions, daily_end_user_spend_update_transactions=daily_end_user_spend_update_transactions, daily_agent_spend_update_transactions=daily_agent_spend_update_transactions, + window_spend_update_transactions=window_spend_update_transactions, spend_update_queue=spend_update_queue, daily_spend_update_queue=daily_spend_update_queue, daily_team_spend_update_queue=daily_team_spend_update_queue, daily_org_spend_update_queue=daily_org_spend_update_queue, daily_end_user_spend_update_queue=daily_end_user_spend_update_queue, daily_agent_spend_update_queue=daily_agent_spend_update_queue, + window_spend_update_queue=window_spend_update_queue, ) return @@ -349,12 +370,14 @@ class RedisUpdateBuffer: daily_org_spend_update_transactions: dict[str, BaseDailySpendTransaction] | None, daily_end_user_spend_update_transactions: dict[str, BaseDailySpendTransaction] | None, daily_agent_spend_update_transactions: dict[str, BaseDailySpendTransaction] | None, + window_spend_update_transactions: tuple[WindowSpendTransaction, ...] | None, spend_update_queue: SpendUpdateQueue, daily_spend_update_queue: DailySpendUpdateQueue, daily_team_spend_update_queue: DailySpendUpdateQueue, daily_org_spend_update_queue: DailySpendUpdateQueue, daily_end_user_spend_update_queue: DailySpendUpdateQueue, daily_agent_spend_update_queue: DailySpendUpdateQueue, + window_spend_update_queue: WindowSpendUpdateQueue | None, ) -> None: """ Put drained-but-unpushed transactions back into in-memory queues. @@ -397,6 +420,10 @@ class RedisUpdateBuffer: Litellm_EntityType.AGENT, db_spend_update_transactions.get("agent_list_transactions"), ), + ( + Litellm_EntityType.MODEL_ACCESS_GROUP, + db_spend_update_transactions.get("model_access_group_list_transactions"), + ), ] for entity_type, entities in entity_entries: if not entities: @@ -424,6 +451,9 @@ class RedisUpdateBuffer: if daily_txns: await daily_queue.update_queue.put(daily_txns) + if window_spend_update_transactions and window_spend_update_queue is not None: + await window_spend_update_queue.update_queue.put(window_spend_update_transactions) + async def restore_transactions_to_redis( self, db_spend_update_transactions: DBSpendUpdateTransactions | None = None, @@ -433,6 +463,7 @@ class RedisUpdateBuffer: daily_end_user_spend_update_transactions: Mapping[str, BaseDailySpendTransaction] | None = None, daily_agent_spend_update_transactions: Mapping[str, BaseDailySpendTransaction] | None = None, daily_tag_spend_update_transactions: Mapping[str, BaseDailySpendTransaction] | None = None, + window_spend_update_transactions: Sequence[WindowSpendTransaction] | None = None, ) -> None: """ Re-push transactions that were popped from Redis but not committed to the DB. @@ -454,6 +485,12 @@ class RedisUpdateBuffer: (daily_end_user_spend_update_transactions, REDIS_DAILY_END_USER_SPEND_UPDATE_BUFFER_KEY), (daily_agent_spend_update_transactions, REDIS_DAILY_AGENT_SPEND_UPDATE_BUFFER_KEY), (daily_tag_spend_update_transactions, REDIS_DAILY_TAG_SPEND_UPDATE_BUFFER_KEY), + ( + None + if window_spend_update_transactions is None + else tuple(map(to_wire_payload, window_spend_update_transactions)), + REDIS_WINDOW_SPEND_UPDATE_BUFFER_KEY, + ), ) rpush_list: Final = tuple( @@ -571,20 +608,22 @@ class RedisUpdateBuffer: dict[str, DailyOrganizationSpendTransaction] | None, dict[str, DailyEndUserSpendTransaction] | None, dict[str, DailyAgentSpendTransaction] | None, + tuple[WindowSpendTransaction, ...] | None, ]: """ - Drains the main 6 Redis buffer queues in a single pipeline round-trip. + Drains the main 7 Redis buffer queues in a single pipeline round-trip. - Returns a 6-tuple of parsed results in this order: + Returns a 7-tuple of parsed results in this order: 0: DBSpendUpdateTransactions 1: daily user spend 2: daily team spend 3: daily org spend 4: daily end-user spend 5: daily agent spend + 6: budget window spend """ if self.redis_cache is None: - return None, None, None, None, None, None + return None, None, None, None, None, None, None lpop_list: Final[list[RedisPipelineLpopOperation]] = [ RedisPipelineLpopOperation(key=REDIS_UPDATE_BUFFER_KEY, count=MAX_REDIS_BUFFER_DEQUEUE_COUNT), @@ -608,12 +647,16 @@ class RedisUpdateBuffer: key=REDIS_DAILY_AGENT_SPEND_UPDATE_BUFFER_KEY, count=MAX_REDIS_BUFFER_DEQUEUE_COUNT, ), + RedisPipelineLpopOperation( + key=REDIS_WINDOW_SPEND_UPDATE_BUFFER_KEY, + count=MAX_REDIS_BUFFER_DEQUEUE_COUNT, + ), ] raw_results: Final = await self.redis_cache.async_lpop_pipeline(lpop_list=lpop_list) # Pad with None if pipeline returned fewer results than expected - while len(raw_results) < 6: + while len(raw_results) < 7: raw_results.append(None) # Slot 0: DBSpendUpdateTransactions @@ -634,6 +677,14 @@ class RedisUpdateBuffer: aggregated = DailySpendUpdateQueue.get_aggregated_daily_spend_update_transactions(list_of_daily) daily_results.append(aggregated) + window_spend: Final = ( + WindowSpendUpdateQueue.get_aggregated_window_spend_transactions( + tuple(json.loads(transaction) for transaction in raw_results[6]) + ) + if raw_results[6] is not None + else None + ) + return ( db_spend, cast(dict[str, DailyUserSpendTransaction] | None, daily_results[0]), @@ -641,6 +692,7 @@ class RedisUpdateBuffer: cast(dict[str, DailyOrganizationSpendTransaction] | None, daily_results[2]), cast(dict[str, DailyEndUserSpendTransaction] | None, daily_results[3]), cast(dict[str, DailyAgentSpendTransaction] | None, daily_results[4]), + window_spend, ) async def store_in_memory_daily_tag_spend_updates_in_redis( @@ -826,6 +878,9 @@ class RedisUpdateBuffer: org_list_transactions=_merged_entity_transactions(list_of_transactions, "org_list_transactions"), tag_list_transactions=_merged_entity_transactions(list_of_transactions, "tag_list_transactions"), agent_list_transactions=_merged_entity_transactions(list_of_transactions, "agent_list_transactions"), + model_access_group_list_transactions=_merged_entity_transactions( + list_of_transactions, "model_access_group_list_transactions" + ), ) async def _emit_new_item_added_to_redis_buffer_event( diff --git a/litellm/proxy/db/db_transaction_queue/spend_update_queue.py b/litellm/proxy/db/db_transaction_queue/spend_update_queue.py index 57cb5e73b64..8c0076b10c1 100644 --- a/litellm/proxy/db/db_transaction_queue/spend_update_queue.py +++ b/litellm/proxy/db/db_transaction_queue/spend_update_queue.py @@ -139,6 +139,7 @@ class SpendUpdateQueue(BaseUpdateQueue): org_list_transactions={}, tag_list_transactions={}, agent_list_transactions={}, + model_access_group_list_transactions={}, ) # Map entity types to their corresponding transaction dictionary keys @@ -151,6 +152,7 @@ class SpendUpdateQueue(BaseUpdateQueue): Litellm_EntityType.ORGANIZATION: "org_list_transactions", Litellm_EntityType.TAG: "tag_list_transactions", Litellm_EntityType.AGENT: "agent_list_transactions", + Litellm_EntityType.MODEL_ACCESS_GROUP: "model_access_group_list_transactions", } for update in updates: @@ -190,6 +192,8 @@ class SpendUpdateQueue(BaseUpdateQueue): transactions_dict = db_spend_update_transactions["tag_list_transactions"] elif dict_key == "agent_list_transactions": transactions_dict = db_spend_update_transactions["agent_list_transactions"] + elif dict_key == "model_access_group_list_transactions": + transactions_dict = db_spend_update_transactions["model_access_group_list_transactions"] else: continue diff --git a/litellm/proxy/db/db_transaction_queue/window_spend_update_queue.py b/litellm/proxy/db/db_transaction_queue/window_spend_update_queue.py new file mode 100644 index 00000000000..372a6666c02 --- /dev/null +++ b/litellm/proxy/db/db_transaction_queue/window_spend_update_queue.py @@ -0,0 +1,195 @@ +""" +In memory buffer for per-budget-window spend increments. + +Kept separate from SpendUpdateQueue: an increment is only meaningful together +with the window it landed in, so two increments for the same entity must not be +merged when their window_start differs. +""" + +import asyncio +import math +from collections.abc import Sequence +from datetime import datetime, timezone +from itertools import chain, groupby +from typing import Final, TypedDict + +from typing_extensions import ReadOnly + +from litellm._logging import verbose_proxy_logger +from litellm.constants import LITELLM_ASYNCIO_QUEUE_MAXSIZE +from litellm.proxy.db.db_transaction_queue.base_update_queue import BaseUpdateQueue + + +class WindowSpendTransaction(TypedDict): + """One increment for a single (entity, budget window) pair. + + window_start is an ISO-8601 string rather than a datetime so the + transaction survives the JSON round trip through the Redis buffer. + + started_at is the earliest request start in the batch. The one-time seed for + a window that has no row yet uses it to tell this batch's own + LiteLLM_SpendLogs rows from everything else, because the spend log writer + flushes on its own ~2s poll and will usually have persisted this batch's + rows before the window queue flushes; without that split the seed and the + increment would each count them. + """ + + entity_type: ReadOnly[str] + entity_id: ReadOnly[str] + window_duration: ReadOnly[str] + window_start: ReadOnly[str] + spend: ReadOnly[float] + started_at: ReadOnly[str | None] + + +class WindowSpendWirePayload(WindowSpendTransaction): + """How an increment is encoded in the shared Redis buffer. + + request_ids is dead weight here: workers built before this field was + dropped index it while merging whatever they pop, and the pop is + destructive, so a leader still running one of those during a rolling deploy + would raise on a payload without the key and lose those increments. It is + always empty, which only makes such a leader seed without exclusions. + + TODO: remove once no supported version reads it, i.e. one release after the + field stopped being written. + """ + + request_ids: ReadOnly[Sequence[str]] + + +def to_wire_payload(transaction: WindowSpendTransaction) -> WindowSpendWirePayload: + return WindowSpendWirePayload( + entity_type=transaction["entity_type"], + entity_id=transaction["entity_id"], + window_duration=transaction["window_duration"], + window_start=transaction["window_start"], + spend=transaction["spend"], + started_at=transaction.get("started_at"), + request_ids=(), + ) + + +def to_naive_utc(value: datetime) -> datetime: + """LiteLLM_BudgetWindowSpend.window_start is TIMESTAMP(3), which holds naive UTC.""" + if value.tzinfo is None: + return value + return value.astimezone(timezone.utc).replace(tzinfo=None) + + +def window_spend_group_key(transaction: WindowSpendTransaction) -> tuple[str, str, str, str]: + """Identity of a window increment: the row's primary key plus the window it + belongs to. Two increments only aggregate when all four match.""" + return ( + transaction["entity_type"], + transaction["entity_id"], + transaction["window_duration"], + transaction["window_start"], + ) + + +def build_window_spend_transaction( + entity_type: str, + entity_id: str, + window_duration: str, + window_start: datetime, + spend: float, + started_at: datetime | None = None, +) -> WindowSpendTransaction: + return WindowSpendTransaction( + entity_type=entity_type, + entity_id=entity_id, + window_duration=window_duration, + window_start=to_naive_utc(window_start).isoformat(timespec="microseconds"), + spend=spend, + started_at=None + if started_at is None + else to_naive_utc(started_at.astimezone(timezone.utc)).isoformat(timespec="microseconds"), + ) + + +def _merge_window_spend_transactions( + payloads: tuple[WindowSpendTransaction, ...], +) -> WindowSpendTransaction: + first: Final = payloads[0] + started_ats: Final = tuple( + started_at for payload in payloads if (started_at := payload.get("started_at")) is not None + ) + return WindowSpendTransaction( + entity_type=first["entity_type"], + entity_id=first["entity_id"], + window_duration=first["window_duration"], + window_start=first["window_start"], + spend=math.fsum(payload["spend"] for payload in payloads), + started_at=min(started_ats) if started_ats else None, + ) + + +class WindowSpendUpdateQueue(BaseUpdateQueue): + """ + In memory buffer for budget-window spend increments committed to + LiteLLM_BudgetWindowSpend. + + Add an update with the payload built by build_window_spend_transaction: + window_spend_update_queue.add_update( + build_window_spend_transaction( + entity_type="key", + entity_id="", + window_duration="30d", + window_start=datetime(2026, 8, 1, tzinfo=timezone.utc), + spend=0.02, + ) + ) + """ + + def __init__(self) -> None: + super().__init__() + self.update_queue: asyncio.Queue[tuple[WindowSpendTransaction, ...]] = asyncio.Queue( + maxsize=LITELLM_ASYNCIO_QUEUE_MAXSIZE + ) + + async def add_update(self, update: WindowSpendTransaction) -> None: + """Enqueue an update.""" + verbose_proxy_logger.debug("Adding budget window spend update to queue: %s", update) + await self.update_queue.put((update,)) + if self.update_queue.qsize() >= self.MAX_SIZE_IN_MEMORY_QUEUE: + verbose_proxy_logger.warning( + "Budget window spend update queue is full. Aggregating all entries in queue to concatenate entries." + ) + await self.aggregate_queue_updates() + + async def aggregate_queue_updates(self) -> None: + """Collapse everything currently queued into a single aggregated update.""" + updates: Final = await self.flush_all_updates_from_in_memory_queue() + await self.update_queue.put(WindowSpendUpdateQueue.get_aggregated_window_spend_transactions(updates)) + + async def flush_and_get_aggregated_window_spend_transactions( + self, + ) -> tuple[WindowSpendTransaction, ...]: + """Drain the queue and return the increments aggregated per window.""" + updates: Final = await self.flush_all_updates_from_in_memory_queue() + if len(updates) > 0: + verbose_proxy_logger.info( + "Spend tracking - flushed %d budget window spend update batches from in-memory queue", + len(updates), + ) + return WindowSpendUpdateQueue.get_aggregated_window_spend_transactions(updates) + + @staticmethod + def get_aggregated_window_spend_transactions( + updates: Sequence[Sequence[WindowSpendTransaction]], + ) -> tuple[WindowSpendTransaction, ...]: + """Sum spend per (entity_type, entity_id, window_duration, window_start). + + Increments belonging to different windows stay separate even when they + share a primary key, so a window boundary crossed mid-tick does not fold + the new window's spend into the previous window's total. + + The result is ordered by that same key, which is the order the flush + needs: primary key first for cross-pod lock ordering, then window_start + so an older window is applied before the roll that supersedes it. + """ + ordered: Final = tuple(sorted(chain.from_iterable(updates), key=window_spend_group_key)) + return tuple( + _merge_window_spend_transactions(tuple(group)) for _, group in groupby(ordered, key=window_spend_group_key) + ) diff --git a/litellm/proxy/db/db_url_settings.py b/litellm/proxy/db/db_url_settings.py index 1a39016b3a3..01f66e4f3c5 100644 --- a/litellm/proxy/db/db_url_settings.py +++ b/litellm/proxy/db/db_url_settings.py @@ -82,10 +82,30 @@ CONNECTION_PARAM_KEYS: Final[frozenset[str]] = frozenset( "pool_timeout", "connect_timeout", "socket_timeout", + "max_idle_connection_lifetime", "pgbouncer", } ) +# Quaint never tests pooled connections on checkout and keeps them idle for +# 300s by default, past many infra idle timeouts, so dead sockets surface as +# `Error { kind: Closed }`. 60s recycles them first; explicit values win. +DEFAULT_MAX_IDLE_CONNECTION_LIFETIME: Final = 60 +IDLE_LIFETIME_DEFAULT_PARAMS: Final[Mapping[str, int]] = MappingProxyType( + {"max_idle_connection_lifetime": DEFAULT_MAX_IDLE_CONNECTION_LIFETIME} +) + + +def idle_lifetime_params(configured: float | None) -> Mapping[str, str | int | float]: + """The `max_idle_connection_lifetime` to add to URLs that do not pin one. + + Applied via ``add_missing_query_params`` so a URL-pinned value always wins, + whether the operator configured `database_max_idle_connection_lifetime` or not. + """ + if configured is None: + return IDLE_LIFETIME_DEFAULT_PARAMS + return MappingProxyType({"max_idle_connection_lifetime": configured}) + def add_missing_query_params(url: str, params: Mapping[str, str | int | float]) -> str: """Return ``url`` with the ``params`` it does not already carry appended. diff --git a/litellm/proxy/db/routing_prisma_wrapper.py b/litellm/proxy/db/routing_prisma_wrapper.py index 1929e7d3fc8..be515392a17 100644 --- a/litellm/proxy/db/routing_prisma_wrapper.py +++ b/litellm/proxy/db/routing_prisma_wrapper.py @@ -61,6 +61,26 @@ class _RoutedActions: return getattr(self._writer_actions, name) +class WriterPinnedClient: + """PrismaClient-shaped view whose `.db` resolves to the writer while it is available. + + Read-after-write paths (e.g. the model reconcile a /model/new triggers to + verify its own just-committed row) must not read through a lagging read + replica: the row is not replayed there yet, so the reconcile concludes the + write is missing and fails the request even though it is durable (#38556). + + While the writer is degraded (`writer_unavailable`), the pin yields to the + routed wrapper so reconcile reads keep working from the replica: a proxy + that starts during a primary outage must still load DB-backed models, and + no read-after-write hazard exists then because writes are failing anyway. + """ + + __slots__ = ("db",) + + def __init__(self, db: "PrismaWrapper | RoutingPrismaWrapper") -> None: + self.db: Final = db.writer if isinstance(db, RoutingPrismaWrapper) and not db.writer_unavailable else db + + class RoutingPrismaWrapper: """ Routes Prisma operations between a writer and a reader Prisma client. diff --git a/litellm/proxy/db/spend_counter_reseed.py b/litellm/proxy/db/spend_counter_reseed.py index deb9cd5ae25..7b3c261036e 100644 --- a/litellm/proxy/db/spend_counter_reseed.py +++ b/litellm/proxy/db/spend_counter_reseed.py @@ -14,14 +14,18 @@ memory in long-lived deployments. import asyncio from collections import OrderedDict -from datetime import datetime +from collections.abc import Mapping +from datetime import datetime, timezone +from types import MappingProxyType from typing import TYPE_CHECKING, ClassVar, Final, Optional from litellm._logging import verbose_proxy_logger from litellm.constants import SPEND_COUNTER_RESEED_LOCKS_MAX_SIZE from litellm.litellm_core_utils.duration_parser import duration_in_seconds +from litellm.proxy._types import Litellm_EntityType from litellm.repositories.organization_repository import OrganizationRepository from litellm.repositories.table_repositories import ( + BudgetWindowSpendRepository, SpendLogsRepository, TeamMembershipRepository, ) @@ -36,6 +40,25 @@ if TYPE_CHECKING: from litellm.proxy.utils import PrismaClient +_WINDOW_SPEND_ENTITY_TYPES: Final[Mapping[str, str]] = MappingProxyType( + { + "Key": Litellm_EntityType.KEY.value, + "Team": Litellm_EntityType.TEAM.value, + } +) + +_WINDOW_SPEND_LOG_FIELDS: Final[Mapping[str, str]] = MappingProxyType( + { + "Key": "api_key", + "Team": "team_id", + } +) + + +def _as_utc(value: datetime) -> datetime: + return value if value.tzinfo is not None else value.replace(tzinfo=timezone.utc) + + class SpendCounterReseed: """ Reseeds spend counters from the authoritative DB and warms the cache, @@ -205,6 +228,92 @@ class SpendCounterReseed: raise return current_value + @staticmethod + async def window_from_table( + prisma_client: Optional["PrismaClient"], + entity_type: str, + entity_id: str, + window_duration: str, + expected_window_start: datetime, + ) -> float | None: + """ + Read the maintained per-window spend row by primary key. + + Returns the row's spend only when the row belongs to the window the + caller is enforcing, i.e. ``row.window_start >= expected_window_start``. + A row at or past the expected start was rolled by a pod whose reset_at + was at least as fresh as this caller's, so it is trusted; an older row + means the window boundary was crossed and nothing has rolled the row + yet, so its spend belongs to a previous window. + + Returns None for a missing, stale or unreadable row so the caller falls + back to the spend-logs aggregate. ``entity_type`` is the counter-facing + label ("Key"/"Team"); anything else has no row and returns None. + """ + if prisma_client is None: + return None + row_entity_type: Final = _WINDOW_SPEND_ENTITY_TYPES.get(entity_type) + if row_entity_type is None: + return None + + try: + row: Final = await BudgetWindowSpendRepository(prisma_client).table.find_unique( + where={ + "entity_type_entity_id_window_duration": { + "entity_type": row_entity_type, + "entity_id": entity_id, + "window_duration": window_duration, + } + } + ) + except Exception: # noqa: BLE001 # any read failure (DB, stale prisma client) must degrade to the aggregate path + verbose_proxy_logger.exception( + "SpendCounterReseed.window_from_table: failed for %s=%s window=%s", + entity_type, + entity_id, + window_duration, + ) + return None + + if row is None: + return None + if _as_utc(row.window_start) < _as_utc(expected_window_start): + return None + return float(row.spend or 0.0) + + @staticmethod + async def window_from_db( + prisma_client: Optional["PrismaClient"], + entity_type: str, + entity_id: str, + window_duration: str | None, + window_start: datetime, + ) -> float | None: + """ + Authoritative window spend: the maintained row first, falling back to + the spend-logs aggregate only when no current row exists. + + The aggregate range-scans an unindexed table, so it must stay a + transitional path (window configured before the row existed) rather + than a steady-state read. + """ + if window_duration is not None: + from_table: Final = await SpendCounterReseed.window_from_table( + prisma_client=prisma_client, + entity_type=entity_type, + entity_id=entity_id, + window_duration=window_duration, + expected_window_start=window_start, + ) + if from_table is not None: + return from_table + return await SpendCounterReseed.window_from_spend_logs( + prisma_client=prisma_client, + entity_type=entity_type, + entity_id=entity_id, + window_start=window_start, + ) + @staticmethod async def window_from_spend_logs( prisma_client: Optional["PrismaClient"], @@ -215,20 +324,13 @@ class SpendCounterReseed: if prisma_client is None: return None - if entity_type == "Key": - group_field = "api_key" - where = { - "api_key": entity_id, - "startTime": {"gte": window_start}, - } - elif entity_type == "Team": - group_field = "team_id" - where = { - "team_id": entity_id, - "startTime": {"gte": window_start}, - } - else: + group_field: Final = _WINDOW_SPEND_LOG_FIELDS.get(entity_type) + if group_field is None: return None + where: Final = { + group_field: entity_id, + "startTime": {"gte": window_start}, + } try: response: Final = await SpendLogsRepository(prisma_client).table.group_by( @@ -258,6 +360,7 @@ class SpendCounterReseed: counter_key: str, entity_type: str, entity_id: str, + window_duration: str | None, window_start: datetime, ) -> float | None: lock: Final = await SpendCounterReseed._get_lock(counter_key) @@ -276,10 +379,11 @@ class SpendCounterReseed: if val is not None: return float(val) - window_spend: Final = await SpendCounterReseed.window_from_spend_logs( + window_spend: Final = await SpendCounterReseed.window_from_db( prisma_client=prisma_client, entity_type=entity_type, entity_id=entity_id, + window_duration=window_duration, window_start=window_start, ) if window_spend is None: diff --git a/litellm/proxy/db/tool_registry_writer.py b/litellm/proxy/db/tool_registry_writer.py index 367552e783e..cd0aa75b859 100644 --- a/litellm/proxy/db/tool_registry_writer.py +++ b/litellm/proxy/db/tool_registry_writer.py @@ -6,9 +6,11 @@ Admins use the management endpoints to read and update input_policy / output_pol """ import uuid -from collections.abc import Mapping +from collections.abc import Mapping, Sequence from datetime import datetime, timezone -from typing import TYPE_CHECKING, Any, Final +from typing import TYPE_CHECKING, Final, Protocol + +from pydantic import TypeAdapter from litellm._logging import verbose_proxy_logger from litellm.proxy._types import ToolDiscoveryQueueItem @@ -27,6 +29,13 @@ if TYPE_CHECKING: from litellm.proxy.utils import PrismaClient +class _ModelDumpMethod(Protocol): + def __call__(self) -> Mapping: ... + + +_ROW_DICT: Final = TypeAdapter(dict) + + def _tool_table_actions(prisma_client: "PrismaClient") -> "TableActions[prisma_db_models.LiteLLM_ToolTable]": table: Final[TableActions[prisma_db_models.LiteLLM_ToolTable]] = ToolRepository(prisma_client).table return table @@ -41,33 +50,35 @@ def _object_permission_table_actions( return table -def _row_to_model(row: dict | Any) -> LiteLLM_ToolTableRow: +def _row_to_model(row: object) -> LiteLLM_ToolTableRow: """Convert a Prisma model instance or dict to LiteLLM_ToolTableRow.""" - model_dump: Final = getattr(row, "model_dump", None) + model_dump: Final[_ModelDumpMethod | None] = getattr(row, "model_dump", None) if callable(model_dump): row = model_dump() elif not isinstance(row, dict): - row = { - k: getattr(row, k, None) - for k in ( - "tool_id", - "tool_name", - "origin", - "input_policy", - "output_policy", - "call_count", - "assignments", - "key_hash", - "team_id", - "key_alias", - "user_agent", - "last_used_at", - "created_at", - "updated_at", - "created_by", - "updated_by", - ) - } + row = _ROW_DICT.validate_python( + { + k: getattr(row, k, None) + for k in ( + "tool_id", + "tool_name", + "origin", + "input_policy", + "output_policy", + "call_count", + "assignments", + "key_hash", + "team_id", + "key_alias", + "user_agent", + "last_used_at", + "created_at", + "updated_at", + "created_by", + "updated_by", + ) + } + ) return LiteLLM_ToolTableRow( tool_id=row.get("tool_id", ""), tool_name=row.get("tool_name", ""), @@ -190,7 +201,7 @@ async def update_tool_policy( _updated_by: Final = updated_by or "system" now: Final = datetime.now(timezone.utc) - create_data: Final[dict[str, object]] = { + create_data: Final[Mapping[str, str | datetime]] = { "tool_id": str(uuid.uuid4()), "tool_name": tool_name, "input_policy": input_policy or "untrusted", @@ -200,14 +211,16 @@ async def update_tool_policy( "created_at": now, "updated_at": now, } - update_data: Final[dict[str, object]] = { - "updated_by": _updated_by, - "updated_at": now, + update_data: Final[Mapping[str, str | datetime]] = { + key: value + for key, value in ( + ("updated_by", _updated_by), + ("updated_at", now), + ("input_policy", input_policy), + ("output_policy", output_policy), + ) + if value is not None } - if input_policy is not None: - update_data["input_policy"] = input_policy - if output_policy is not None: - update_data["output_policy"] = output_policy await _tool_table_actions(prisma_client).upsert( where={"tool_name": tool_name}, @@ -338,7 +351,7 @@ class ToolPolicyRegistry: self._blocked_tools_by_op_id = {} for row in perms: op_id = getattr(row, "object_permission_id", None) - blocked = getattr(row, "blocked_tools", None) or [] + blocked: Sequence[str] = getattr(row, "blocked_tools", None) or [] if op_id: self._blocked_tools_by_op_id[op_id] = list(blocked) @@ -370,10 +383,12 @@ class ToolPolicyRegistry: """ if not tool_names: return {} - blocked: Final[set[str]] = set() - for op_id in (object_permission_id, team_object_permission_id): - if op_id and op_id.strip(): - blocked.update(self._blocked_tools_by_op_id.get(op_id.strip(), [])) + blocked: Final[frozenset[str]] = frozenset( + tool + for op_id in (object_permission_id, team_object_permission_id) + if op_id and op_id.strip() + for tool in self._blocked_tools_by_op_id.get(op_id.strip(), []) + ) result: Final[dict[str, str]] = {} for name in tool_names: if name in blocked: @@ -408,13 +423,12 @@ async def add_tool_to_object_permission_blocked( ) if row is None: return False - current: Final = list(getattr(row, "blocked_tools", []) or []) + current: Final[Sequence[str]] = getattr(row, "blocked_tools", []) or [] if tool_name in current: return True - current.append(tool_name) await _object_permission_table_actions(prisma_client).update( where={"object_permission_id": object_permission_id}, - data={"blocked_tools": current}, + data={"blocked_tools": [*current, tool_name]}, ) return True except Exception as e: @@ -436,13 +450,12 @@ async def remove_tool_from_object_permission_blocked( ) if row is None: return False - current = list(getattr(row, "blocked_tools", []) or []) + current: Final[Sequence[str]] = getattr(row, "blocked_tools", []) or [] if tool_name not in current: return False - current = [t for t in current if t != tool_name] await _object_permission_table_actions(prisma_client).update( where={"object_permission_id": object_permission_id}, - data={"blocked_tools": current}, + data={"blocked_tools": [t for t in current if t != tool_name]}, ) return True except Exception as e: diff --git a/litellm/proxy/guardrails/guardrail_hooks/akto/akto.py b/litellm/proxy/guardrails/guardrail_hooks/akto/akto.py index 3716d00774f..2c27531cea1 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/akto/akto.py +++ b/litellm/proxy/guardrails/guardrail_hooks/akto/akto.py @@ -162,10 +162,10 @@ class AktoGuardrail(CustomGuardrail): def build_request_body( inputs: GenericGuardrailAPIInputs, request_data: dict | None = None, - ) -> dict[str, Any]: + ) -> dict[str, object]: """Build the LLM request body from guardrail inputs (messages, model, tools).""" model: Final = inputs.get("model", "") or "" - body: Final[dict[str, Any]] = {"model": model} + body: Final[dict[str, object]] = {"model": model} structured: Final = inputs.get("structured_messages") if structured: @@ -194,7 +194,7 @@ class AktoGuardrail(CustomGuardrail): def build_response_body( inputs: GenericGuardrailAPIInputs, request_data: dict | None = None, - ) -> dict[str, Any]: + ) -> dict[str, object]: """Build the LLM response body, preferring the actual model response if available.""" model_response: Final = request_data.get("response") if request_data else None if model_response is not None and hasattr(model_response, "model_dump"): @@ -224,7 +224,7 @@ class AktoGuardrail(CustomGuardrail): *, status_code: int = 200, include_response: bool = False, - ) -> dict[str, Any]: + ) -> dict[str, object]: """Build the flat MIRRORING payload sent to Akto's HTTP proxy endpoint. All body fields use double-encoding: json.dumps({"body": json.dumps(actual_body)}) diff --git a/litellm/proxy/guardrails/guardrail_hooks/alice/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/alice/__init__.py new file mode 100644 index 00000000000..75ea16f7a88 --- /dev/null +++ b/litellm/proxy/guardrails/guardrail_hooks/alice/__init__.py @@ -0,0 +1,34 @@ +from typing import TYPE_CHECKING, Final + +from litellm.types.guardrails import SupportedGuardrailIntegrations + +from .alice import AliceGuardrail + +if TYPE_CHECKING: + from litellm.types.guardrails import Guardrail, LitellmParams + + +def initialize_guardrail(litellm_params: "LitellmParams", guardrail: "Guardrail"): + import litellm + + _alice_guardrail_callback: Final = AliceGuardrail( + api_key=litellm_params.api_key, + api_base=litellm_params.api_base, + unreachable_fallback=getattr(litellm_params, "unreachable_fallback", "fail_closed"), + guardrail_name=guardrail.get("guardrail_name", ""), + event_hook=litellm_params.mode, + default_on=litellm_params.default_on, + ) + + litellm.logging_callback_manager.add_litellm_callback(_alice_guardrail_callback) + return _alice_guardrail_callback + + +guardrail_initializer_registry: Final = { # mutable-ok: module-level registry, built once and never mutated + SupportedGuardrailIntegrations.ALICE.value: initialize_guardrail, +} + + +guardrail_class_registry: Final = { # mutable-ok: module-level registry, built once and never mutated + SupportedGuardrailIntegrations.ALICE.value: AliceGuardrail, +} diff --git a/litellm/proxy/guardrails/guardrail_hooks/alice/alice.py b/litellm/proxy/guardrails/guardrail_hooks/alice/alice.py new file mode 100644 index 00000000000..27018769909 --- /dev/null +++ b/litellm/proxy/guardrails/guardrail_hooks/alice/alice.py @@ -0,0 +1,369 @@ +# +-------------------------------------------------------------+ +# +# Use Alice for your LLM calls +# https://alice.io/ +# +# +-------------------------------------------------------------+ + +import json +import os +from collections.abc import Mapping +from typing import ( + TYPE_CHECKING, + Any, # noqa: TID251 # **kwargs forwards verbatim to CustomGuardrail.__init__; see ruff-strict.toml + Final, + Literal, + Optional, +) + +import httpx +from typing_extensions import NotRequired, ReadOnly, TypedDict + +from litellm._logging import verbose_proxy_logger +from litellm.exceptions import GuardrailRaisedException, Timeout +from litellm.integrations.custom_guardrail import ( + CustomGuardrail, + log_guardrail_information, +) +from litellm.llms.custom_httpx.http_handler import ( + get_async_httpx_client, + httpxSpecialProvider, +) +from litellm.types.guardrails import GuardrailEventHooks +from litellm.types.utils import GenericGuardrailAPIInputs + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + +GUARDRAIL_NAME: Final = "alice" + +_DEFAULT_API_BASE: Final = "https://api.alice.io" +_EVALUATE_PATH: Final = "/v2/evaluate/litellm" + +_VERDICT_ALLOW: Final = "ALLOW" +_VERDICT_BLOCK: Final = "BLOCK" +_VERDICT_MASK: Final = "MASK" +_VERDICT_DETECT: Final = "DETECT" +_KNOWN_VERDICTS: Final = frozenset({_VERDICT_ALLOW, _VERDICT_BLOCK, _VERDICT_MASK, _VERDICT_DETECT}) + +_DEFAULT_BLOCK_MESSAGE: Final = "Blocked by your organization's content policy." + +# apply_guardrail selects nothing: it forwards whichever of these came populated and lets Alice +# decide what is worth evaluating. Only skip the call when every one of them is empty — there is +# then genuinely nothing to send. +_SELECTABLE_INPUT_FIELDS: Final = ("texts", "images", "tools", "tool_calls", "structured_messages") + +# Caps on the outbound copy of request_data. A payload deeper or wider than this is malformed +# rather than large, and serializing it would cost more than the evaluation it feeds. +_MAX_DEPTH: Final = 12 +_MAX_ITEMS: Final = 5000 + +# request_data carries the caller's raw credentials under these keys, at any nesting depth — +# a real captured payload puts inbound headers at request_data["proxy_server_request"]["headers"], +# again under ["metadata"]["headers"] / ["litellm_metadata"]["headers"], and again under +# ["metadata"]["requester_metadata"]["headers"], any of which can carry an Authorization or +# x-api-key value. LiteLLM's own spend-log sanitizer excludes `secret_fields` for the same reason +# (spend_tracking_utils._SENSITIVE_REQUEST_BODY_KEYS): `secret_fields.raw_headers` holds the +# caller's Authorization / x-api-key in the clear, and `api_key` can carry a forwarded provider +# credential. Stripping by key name rather than by path means a new nesting path can never +# reintroduce the leak. Posting any of these to a third-party guardrail endpoint would be worse +# than what the proxy already refuses to persist in its own audit trail — so none of them leave +# the process. +_CREDENTIAL_KEYS_TO_STRIP: Final = frozenset( + {"secret_fields", "api_key", "raw_headers", "headers", "provider_specific_header"} +) + + +class AliceReplacement(TypedDict): + """A masked substitution, positional against the texts that were submitted.""" + + index: ReadOnly[NotRequired[int]] + text: ReadOnly[NotRequired[str]] + + +class AliceVerdict(TypedDict): + """Body returned by Alice's LiteLLM evaluate endpoint.""" + + verdict: ReadOnly[NotRequired[str]] + categories: ReadOnly[NotRequired["tuple[str, ...]"]] + correlation_id: ReadOnly[NotRequired[str]] + message: ReadOnly[NotRequired[str]] + replacements: ReadOnly[NotRequired["tuple[AliceReplacement, ...]"]] + + +class AliceGuardrailMissingSecrets(Exception): + """Raised when the Alice API key is not configured.""" + + +class AliceGuardrail(CustomGuardrail): + """ + Alice — policy-based guardrails for prompts and model responses. + + This forwards the hook's arguments as it received them and enforces the verdict that comes + back, with one deliberate exception: any key named `secret_fields`, `api_key`, `raw_headers`, + `headers`, or `provider_specific_header` is dropped from `request_data` at any nesting depth + before it is serialized, and never reaches Alice. Short of that, it selects nothing and + renames nothing: which parts of a conversation are worth evaluating, and how a verdict is + reached, are decided by Alice — so changing either is a change on their side rather than a + LiteLLM upgrade. A batch with nothing selectable at all (no `texts`, `images`, `tools`, + `tool_calls`, or `structured_messages`) still skips the call, since there would be nothing to + send. + + Known limitation: the unified guardrail's `streaming_transform_mode` defaults to + `block_only`, whose streaming path discards any returned text rewrite. A MASK verdict is + therefore a no-op on a streamed response — the original, unmasked text still reaches the + caller — while BLOCK continues to function on both streamed and non-streamed responses. + This is `during_call`'s documented behavior generally, not specific to Alice; configure a + masking-aware `streaming_transform_mode` if that gap matters for your traffic. + + Alice evaluates against policies configured per *application*, and one proxy typically fronts + several, so the application is named on the virtual key rather than in this config: + + curl $PROXY/key/generate -H "Authorization: Bearer $LITELLM_MASTER_KEY" \\ + -d '{"key_alias": "payments-bot", + "metadata": {"alice_app_id": "payments-bot"}}' + + Alice reads that off the authenticated key. Because the proxy strips caller-supplied + `user_api_key_*` from the request before a guardrail sees it, a caller cannot point its own + traffic at an application with laxer policies than the one its key was issued for. + + Configuration example (litellm config YAML): + guardrails: + - guardrail_name: alice + litellm_params: + guardrail: alice + mode: [pre_call, post_call] + api_key: os.environ/ALICE_API_KEY + api_base: https://api.alice.io # optional + unreachable_fallback: fail_closed # optional + """ + + def __init__( + self, + api_key: str | None = None, + api_base: str | None = None, + unreachable_fallback: Literal["fail_closed", "fail_open"] = "fail_closed", + **kwargs: Any, # kwargs-ok: forwarded verbatim to CustomGuardrail.__init__, whose param list is wide and evolving + ) -> None: + self.async_handler = get_async_httpx_client(llm_provider=httpxSpecialProvider.GuardrailCallback) + + alice_api_key: Final = api_key or os.environ.get("ALICE_API_KEY") + if not alice_api_key: + raise AliceGuardrailMissingSecrets( + "Alice API key is required. Set the `ALICE_API_KEY` environment variable or " + "pass `api_key` in the guardrail config." + ) + self.alice_api_key: str = alice_api_key + + base: Final = (api_base or os.environ.get("ALICE_API_BASE") or _DEFAULT_API_BASE).rstrip("/") + self.api_base: str = f"{base}{_EVALUATE_PATH}" + self.unreachable_fallback: Literal["fail_closed", "fail_open"] = unreachable_fallback + + if "supported_event_hooks" not in kwargs: + kwargs["supported_event_hooks"] = [ # mutable-ok: CustomGuardrail.__init__ requires a list here + GuardrailEventHooks.pre_call, + GuardrailEventHooks.during_call, + GuardrailEventHooks.post_call, + ] + + super().__init__(**kwargs) + + @log_guardrail_information + async def apply_guardrail( + self, + inputs: GenericGuardrailAPIInputs, + request_data: dict[str, object], # mutable-ok: overrides CustomGuardrail.apply_guardrail's plain-dict contract + input_type: Literal["request", "response"], + logging_obj: Optional["LiteLLMLoggingObj"] = None, + ) -> GenericGuardrailAPIInputs: + if not any(inputs.get(field) for field in _SELECTABLE_INPUT_FIELDS): + return inputs + + try: + verdict: AliceVerdict = await self._evaluate( + inputs=inputs, request_data=request_data, input_type=input_type + ) + except Timeout as e: + return self._on_unreachable(e, inputs) + except httpx.HTTPStatusError as e: + status_code: Final = getattr(getattr(e, "response", None), "status_code", None) + # Any 5xx is an outage on Alice's side, not our misconfiguration — route the whole + # class through the configured policy. A 4xx (rejected credential, bad request) is + # ours to fix and must never fail open, so it is deliberately left to propagate. + if isinstance(status_code, int) and 500 <= status_code < 600: + return self._on_unreachable(e, inputs) + raise + except httpx.RequestError as e: + return self._on_unreachable(e, inputs) + except (json.JSONDecodeError, UnicodeDecodeError, TypeError) as e: + # A body that cannot be decoded, cannot be parsed as JSON, or parses to something + # other than an object, is as unreachable as a dropped connection: this deployment's + # policy decides, not a raw exception. UnicodeDecodeError is named explicitly because + # it is a sibling of JSONDecodeError under ValueError, not a subclass of it. + return self._on_unreachable(e, inputs) + + return self._enforce(verdict, inputs) + + async def _evaluate( + self, + inputs: GenericGuardrailAPIInputs, + request_data: Mapping[str, object], + input_type: str, + ) -> AliceVerdict: + response: Final = await self.async_handler.post( + url=self.api_base, + json={ # mutable-ok: one-shot HTTP request body, never mutated after construction + "input_type": input_type, + "inputs": _json_safe(inputs), + "request_data": _json_safe(request_data, strip_keys=_CREDENTIAL_KEYS_TO_STRIP), + }, + headers={ # mutable-ok: one-shot HTTP headers, never mutated after construction + "Content-Type": "application/json", + "af-api-key": self.alice_api_key, + }, + ) + response.raise_for_status() + body = response.json() + if not isinstance(body, dict): + raise TypeError("Alice returned a non-object body") + return body + + def _enforce(self, verdict: AliceVerdict, inputs: GenericGuardrailAPIInputs) -> GenericGuardrailAPIInputs: + """Act on the verdict. An answer we cannot read is treated as unavailable, never as a pass.""" + name: Final = verdict.get("verdict") + if name not in _KNOWN_VERDICTS: + return self._on_unreachable(ValueError(f"unrecognized verdict: {name!r}"), inputs) + + if name == _VERDICT_BLOCK: + raise GuardrailRaisedException( + guardrail_name=GUARDRAIL_NAME, + message=verdict.get("message") or _DEFAULT_BLOCK_MESSAGE, + should_wrap_with_default_message=False, + blocked_content=True, + ) + + if name == _VERDICT_DETECT: + # Recorded by Alice and allowed through. The correlation id is what ties this request + # to that record; the evaluated text itself is never logged. + verbose_proxy_logger.warning( + "Alice guardrail: detection recorded, request allowed (correlation_id=%s, categories=%s)", + verdict.get("correlation_id"), + verdict.get("categories"), + ) + return inputs + + if name == _VERDICT_MASK: + self._apply_replacements(verdict, inputs) + + return inputs + + def _apply_replacements(self, verdict: AliceVerdict, inputs: GenericGuardrailAPIInputs) -> None: + """ + Write each replacement onto the text it names. + + Only `texts` is touched. The chat translation layer maps a returned `texts` list back onto + the request positionally, but takes a different branch entirely when `structured_messages` + comes back as a new object — which would drop these edits. + + All-or-nothing: a single out-of-range or malformed replacement blocks the whole verdict + rather than being silently skipped, so content Alice meant to replace can never reach the + model unmasked alongside content that was replaced. + """ + texts: Final = inputs.get("texts") or [] # mutable-ok: empty-list fallback, replaced wholesale below + replacements: Final = verdict.get("replacements") or [] # mutable-ok: empty-list fallback for iteration only + + if not replacements: + raise self._mask_rejected(verdict) + + for replacement in replacements: + index = replacement.get("index") + text = replacement.get("text") + if not (isinstance(index, int) and isinstance(text, str) and 0 <= index < len(texts)): + raise self._mask_rejected(verdict) + texts[index] = text # mutable-ok: item assignment into the local working copy above + + inputs["texts"] = texts + + def _mask_rejected(self, verdict: AliceVerdict) -> GuardrailRaisedException: + """A MASK verdict that cannot be applied in full is refused outright, never partially — + see `_apply_replacements`.""" + return GuardrailRaisedException( + guardrail_name=GUARDRAIL_NAME, + message=verdict.get("message") or _DEFAULT_BLOCK_MESSAGE, + should_wrap_with_default_message=False, + blocked_content=True, + ) + + def _on_unreachable(self, error: Exception, inputs: GenericGuardrailAPIInputs) -> GenericGuardrailAPIInputs: + """Apply the configured policy when Alice cannot be reached or cannot be understood.""" + if self.unreachable_fallback == "fail_open": + verbose_proxy_logger.critical( + "Alice guardrail unreachable, allowing request per unreachable_fallback: %s", + error, + ) + return inputs + raise GuardrailRaisedException( + guardrail_name=GUARDRAIL_NAME, + message="Alice guardrail is unavailable and this request cannot be checked", + should_wrap_with_default_message=False, + ) from error + + @staticmethod + def get_config_model() -> type | None: + from litellm.types.proxy.guardrails.guardrail_hooks.alice import ( + AliceGuardrailConfigModel, + ) + + return AliceGuardrailConfigModel + + +def _json_safe( + value: object, + depth: int = 0, + seen: frozenset[int] = frozenset(), + strip_keys: frozenset[str] = frozenset(), +) -> object: + """ + Copy `value` into something `json.dumps` accepts, dropping only what cannot cross. + + `request_data` carries live Python objects — an OpenTelemetry span among them — so it cannot + be serialized as it stands. What is dropped is decided by a mechanical rule rather than a + field list: a list drifts from what the far side needs, a rule cannot. Serializing naively + raises, and that error would be read as "guardrail unavailable" on every single request. + + `strip_keys` drops a dict key by name at every depth it appears, not just the root — a caller + passes `_CREDENTIAL_KEYS_TO_STRIP` here so a credential nested under any path is caught the + same way a top-level one is, without maintaining a list of paths. The source object is never + mutated: every branch below builds a new container. + """ + if isinstance(value, (str, int, float, bool)) or value is None: + return value + if depth >= _MAX_DEPTH or id(value) in seen: + return None + + nested: Final = seen | {id(value)} # mutable-ok: one-shot set literal, unioned into a frozenset immediately + + if isinstance(value, dict): + out: dict[str, object] = {} # mutable-ok: bounded accumulator local to this call, never escapes as-is + for key, item in list(value.items())[:_MAX_ITEMS]: # mutable-ok: list() only to slice an unordered view + if isinstance(key, str) and key not in strip_keys: + out[key] = _json_safe(item, depth + 1, nested, strip_keys) + return out + + if isinstance(value, (list, tuple, set, frozenset)): + return [ # mutable-ok: return value is a one-shot list, discarded by the caller after use + _json_safe(item, depth + 1, nested, strip_keys) + for item in list(value)[:_MAX_ITEMS] # mutable-ok: list() only to slice an unordered view + ] + + dump: Final = getattr(value, "model_dump", None) + if callable(dump): + try: + return _json_safe(dump(mode="json"), depth + 1, nested, strip_keys) + except Exception: # noqa: BLE001 # a model that will not dump is one we drop + return None + + # Everything json.dumps handles natively — str, int, float, bool, None, dict, list — is + # caught above, and a dict/list subclass is caught by isinstance. So whatever reaches here + # (bytes, datetime, an OpenTelemetry span) cannot cross the wire. + return None diff --git a/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py b/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py index dd76a27c80f..30526d30dc5 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py +++ b/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py @@ -18,6 +18,7 @@ import time from collections.abc import AsyncGenerator, Mapping, Sequence from datetime import datetime, timezone from itertools import accumulate, groupby +from types import MappingProxyType from typing import TYPE_CHECKING, Any, ClassVar, Final, Literal, NamedTuple, Optional, cast import httpx @@ -30,7 +31,11 @@ from litellm.caching import DualCache from litellm.constants import BEDROCK_APPLY_GUARDRAIL_CHUNK_BUDGET_CHARS from litellm.exceptions import ModifyResponseException from litellm.integrations.custom_guardrail import CustomGuardrail +from litellm.litellm_core_utils.api_route_to_call_types import get_call_types_for_route from litellm.litellm_core_utils.core_helpers import redact_nested_match_and_regex_keys +from litellm.litellm_core_utils.litellm_logging import ( + _get_masked_values, # pyright: ignore[reportPrivateUsage] # the shared header-masking helper has no public name +) from litellm.litellm_core_utils.llm_cost_calc.guardrail_cost import bedrock_guardrail_cost from litellm.llms.anthropic.chat.guardrail_translation.handler import AnthropicMessagesHandler from litellm.llms.base_llm.guardrail_translation.utils import ( @@ -42,7 +47,7 @@ from litellm.llms.custom_httpx.http_handler import ( httpxSpecialProvider, ) from litellm.proxy._types import UserAPIKeyAuth -from litellm.proxy.common_request_processing import _serialize_http_exception_detail +from litellm.proxy.common_request_processing import serialize_http_exception_detail from litellm.proxy.common_utils.sse_keepalive import keepalive_ping_has_fired from litellm.proxy.guardrails.anthropic_sse import ( anthropic_sse_chunks_from_response, @@ -52,7 +57,12 @@ from litellm.proxy.guardrails.anthropic_sse import ( model_response_text, ) from litellm.secret_managers.main import get_secret_str -from litellm.types.guardrails import BedrockChecksConfigModel, GuardrailEventHooks +from litellm.types.guardrails import ( + BedrockChecksConfigModel, + BedrockGuardrailStreamingParams, + GuardrailEventHooks, + LitellmParams, +) from litellm.types.llms.openai import AllMessageValues, ChatCompletionUserMessage from litellm.types.proxy.guardrails.guardrail_hooks.bedrock_guardrails import ( BedrockChecksMessage, @@ -206,6 +216,16 @@ def _redact_assessment_match_fields(assessments: list[dict]) -> list[dict]: return redacted if isinstance(redacted, list) else assessments +_RESPONSES_API_CALL_TYPES: Final = frozenset({CallTypes.responses, CallTypes.aresponses}) + + +def _is_responses_api_route(request_route: str | None) -> bool: + if request_route is None: + return False + call_types: Final = get_call_types_for_route(request_route) + return call_types is not None and any(call_type in _RESPONSES_API_CALL_TYPES for call_type in call_types) + + class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): # During-call must use async_moderation_hook (not unified apply_guardrail), otherwise # OpenAI translation always passes input_type="request" and spend/UI show PRE-CALL. @@ -221,9 +241,23 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): prompt_attack_threshold: float | None = 0.5, pii_confidence_threshold: float | None = 0.5, chunk_budget_chars: int = BEDROCK_APPLY_GUARDRAIL_CHUNK_BUDGET_CHARS, + streaming_buffer_until_moderated: bool | None = None, + streaming_sampling_rate: int | None = None, + streaming_end_of_stream_only: bool | None = None, **kwargs, ): self.async_handler = get_async_httpx_client(llm_provider=httpxSpecialProvider.GuardrailCallback) + self._set_streaming_params( + BedrockGuardrailStreamingParams.from_extras( + MappingProxyType( + { + "streaming_buffer_until_moderated": streaming_buffer_until_moderated, + "streaming_sampling_rate": streaming_sampling_rate, + "streaming_end_of_stream_only": streaming_end_of_stream_only, + } + ) + ) + ) self.guardrailIdentifier = guardrailIdentifier self.guardrailVersion = guardrailVersion self.guardrail_provider = "bedrock" @@ -232,7 +266,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): # Resource-less, detect-only InvokeGuardrailChecks mode. Present `checks` # routes the guardrail to InvokeGuardrailChecks; absent => ApplyGuardrail. - self.checks: dict[str, Any] | None = self._normalize_checks(checks) + self.checks: dict[str, object] | None = self._normalize_checks(checks) # Per-check block thresholds; a score >= threshold blocks. None => the # check is detect-only (logged, never blocks). self.content_filter_threshold = content_filter_threshold @@ -278,6 +312,18 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): list(self.checks.keys()) if self.checks else None, ) + def _set_streaming_params(self, streaming_params: BedrockGuardrailStreamingParams) -> None: + self.streaming_buffer_until_moderated = streaming_params.streaming_buffer_until_moderated + self.streaming_sampling_rate = streaming_params.streaming_sampling_rate + self.streaming_end_of_stream_only = streaming_params.streaming_end_of_stream_only + + def update_in_memory_litellm_params(self, litellm_params: LitellmParams) -> None: + super().update_in_memory_litellm_params(litellm_params) + self._set_streaming_params(BedrockGuardrailStreamingParams.from_extras(litellm_params.model_extra)) + + def _streams_incrementally(self) -> bool: + return not self.streaming_buffer_until_moderated and not self.mask_response_content + @classmethod def get_supported_event_hooks(cls) -> list[GuardrailEventHooks]: return [ @@ -289,7 +335,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): ] @staticmethod - def _normalize_checks(checks: BedrockChecksConfigModel | Mapping[str, object] | None) -> dict[str, Any] | None: + def _normalize_checks(checks: BedrockChecksConfigModel | Mapping[str, object] | None) -> dict[str, object] | None: """Normalize the configured `checks` into a plain dict for the API body. Accepts a pydantic ``BedrockChecksConfigModel`` or a raw dict; drops None / @@ -340,7 +386,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): def _create_bedrock_output_content_request( self, - response: Any | ModelResponse, + response: object, messages: list[AllMessageValues] | None = None, ) -> BedrockRequest: """ @@ -364,9 +410,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): bedrock_request["content"] = bedrock_request_content return bedrock_request - def _build_response_content_items( - self, response: Any | ModelResponse, has_grounding: bool - ) -> list[BedrockContentItem]: + def _build_response_content_items(self, response: object, has_grounding: bool) -> list[BedrockContentItem]: """Build content item(s) from the model response. When the request supplied grounding, the response is qualified ``guard_content`` so Bedrock can score it. """ @@ -390,7 +434,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): self, source: Literal["INPUT", "OUTPUT"], messages: list[AllMessageValues] | None = None, - response: Any | ModelResponse | None = None, + response: object | None = None, ) -> BedrockRequest: """ Convert the litellm messages/response to the bedrock request format. @@ -913,7 +957,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): async def _apply_guardrail_content_with_chunking( self, content: Sequence[BedrockContentItem], - base_request_data: Mapping[str, Any], + base_request_data: Mapping[str, object], credentials: "Credentials", aws_region_name: str, api_key: str | None, @@ -1051,7 +1095,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): async def _post_apply_guardrail_content_with_retry( self, content: Sequence[BedrockContentItem], - base_request_data: Mapping[str, Any], + base_request_data: Mapping[str, object], credentials: "Credentials", aws_region_name: str, api_key: str | None, @@ -1101,7 +1145,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): async def _post_apply_guardrail_content( self, content: Sequence[BedrockContentItem], - base_request_data: Mapping[str, Any], + base_request_data: Mapping[str, object], credentials: "Credentials", aws_region_name: str, api_key: str | None, @@ -1140,11 +1184,12 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): aws_region_name=aws_region_name, api_key=api_key, ) + headers_dict: Final = dict(prepared_request.headers) # mutable-ok: the masking helper requires a dict verbose_proxy_logger.debug( "Bedrock AI request body: %s, url %s, headers: %s", bedrock_request_data, prepared_request.url, - prepared_request.headers, + _get_masked_values(headers_dict), ) httpx_response: Final = await self._sign_and_post( @@ -1829,7 +1874,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): return BedrockGuardrailResponse() credentials, aws_region_name = self._load_credentials() - body: Final[dict[str, Any]] = {"messages": checks_messages, "checks": self.checks} + body: Final[dict[str, object]] = {"messages": checks_messages, "checks": self.checks} api_key: Final[str | None] = request_data.get("api_key") if request_data else None prepared_request: Final = self._prepare_request( @@ -2311,7 +2356,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): guardrail_name=self.guardrail_name, ) - detail: Final[dict[str, Any]] = { + detail: Final[dict[str, object]] = { "error": "Violated guardrail policy", "bedrock_guardrail_response": bedrock_guardrail_output_text, } @@ -2660,6 +2705,39 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): Collect content from the stream and run the bedrock OUTPUT scan (post_call only validates the response). """ + if self._streams_incrementally(): + from litellm.proxy.guardrails.guardrail_hooks.unified_guardrail.unified_guardrail import ( + UnifiedLLMGuardrails, + ) + + async for streamed_chunk in UnifiedLLMGuardrails().async_post_call_streaming_iterator_hook( + user_api_key_dict=user_api_key_dict, + response=response, + request_data=request_data, + guardrail_to_apply=self, + buffer_until_moderated_default=False, + ): + yield streamed_chunk + return + + # Responses-API events are neither chat-completions chunks nor raw + # Anthropic SSE, so the assembly below cannot scan them; the unified + # guardrail's translation layer can, with buffering semantics kept. + if _is_responses_api_route(user_api_key_dict.request_route): + from litellm.proxy.guardrails.guardrail_hooks.unified_guardrail.unified_guardrail import ( + UnifiedLLMGuardrails, + ) + + async for translated_chunk in UnifiedLLMGuardrails().async_post_call_streaming_iterator_hook( + user_api_key_dict=user_api_key_dict, + response=response, + request_data=request_data, + guardrail_to_apply=self, + buffer_until_moderated_default=True, + ): + yield translated_chunk + return + # Import here to avoid circular imports from litellm.llms.base_llm.base_model_iterator import MockResponseIterator from litellm.main import stream_chunk_builder @@ -2716,7 +2794,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): ) if not raw_sse or (not is_block and not headers_flushed): raise - block_message, _ = _serialize_http_exception_detail(block_detail) + block_message, _ = serialize_http_exception_detail(block_detail) for error_frame in anthropic_sse_error_frames( block_message if is_block else f"{block_exc.status_code}: {block_message}" ): @@ -2855,7 +2933,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): return updated_messages def _mask_content_list( - self, content_list: list[Any], masked_texts: list[str], masking_index: int + self, content_list: Sequence[object], masked_texts: list[str], masking_index: int ) -> tuple[list[Any], int]: """ Apply masking to a list of content items. @@ -2868,7 +2946,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): Returns: Updated content list with masked items """ - new_content: Final[list[dict | str]] = [] + new_content: Final[list[dict[str, object] | str]] = [] for item in content_list: if isinstance(item, dict) and "text" in item: new_item = item.copy() @@ -2887,7 +2965,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): def _apply_masking_to_response( self, - response: ModelResponse | Any, + response: object, bedrock_guardrail_response: BedrockGuardrailResponse, ) -> None: """ diff --git a/litellm/proxy/guardrails/guardrail_hooks/cisco_ai_defense/cisco_ai_defense_mcp.py b/litellm/proxy/guardrails/guardrail_hooks/cisco_ai_defense/cisco_ai_defense_mcp.py index 8398ec9f141..5a6be1089b6 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/cisco_ai_defense/cisco_ai_defense_mcp.py +++ b/litellm/proxy/guardrails/guardrail_hooks/cisco_ai_defense/cisco_ai_defense_mcp.py @@ -5,8 +5,9 @@ The public guardrail class imports this private mixin from while preserving the existing public import path. """ +from collections.abc import Sequence from datetime import datetime -from typing import TYPE_CHECKING, Any, Final, Optional +from typing import TYPE_CHECKING, Final, Optional from fastapi import HTTPException @@ -23,7 +24,7 @@ if TYPE_CHECKING: from .cisco_ai_defense import _ScanContext -def _serialize_mcp_content_item(item: object) -> dict[str, Any]: +def _serialize_mcp_content_item(item: object) -> dict[str, object]: """Serialize an MCP content item to a JSON-friendly dict. Handles raw dicts, MCP SDK Pydantic models, and simple ``.text`` objects. @@ -57,7 +58,7 @@ class _CiscoAIDefenseMcpMixin: def should_run_guardrail(self, data: dict, event_type: GuardrailEventHooks) -> bool: ... - async def _post_inspection(self, url: str, payload: dict[str, Any], surface: str) -> dict[str, Any]: ... + async def _post_inspection(self, url: str, payload: dict[str, object], surface: str) -> dict[str, object]: ... def _handle_api_error( self, @@ -67,16 +68,16 @@ class _CiscoAIDefenseMcpMixin: start_time: datetime | None = ..., surface: str = ..., direction: str = ..., - ) -> dict[str, Any]: ... + ) -> dict[str, object]: ... def _finalize_inspection( self, - inspect_response: dict[str, Any], + inspect_response: dict[str, object], request_data: dict, context: "_ScanContext", start_time: datetime, response_obj: object = ..., - ) -> dict[str, Any]: ... + ) -> dict[str, object]: ... # ------------------------------------------------------------------ # MCP post-tool hook (dispatcher contract) @@ -95,7 +96,7 @@ class _CiscoAIDefenseMcpMixin: if self.inspection_type != "mcp": return None - request_data: Final[dict[str, Any]] = {} + request_data: Final[dict[str, object]] = {} for key in ( "name", "litellm_call_id", @@ -188,9 +189,9 @@ class _CiscoAIDefenseMcpMixin: original_hidden: Final = getattr(original_response_obj, "hidden_params", None) if isinstance(original_hidden, HiddenParams): - hidden_params: Any = original_hidden + hidden_params: HiddenParams = original_hidden else: - response_cost: Final = getattr(original_hidden, "response_cost", None) + response_cost: Final[float | None] = getattr(original_hidden, "response_cost", None) hidden_params = HiddenParams(response_cost=response_cost) if response_cost is not None else HiddenParams() return MCPPostCallResponseObject( @@ -200,11 +201,11 @@ class _CiscoAIDefenseMcpMixin: @staticmethod def _replace_mcp_tool_response(response_obj: object, replacement_obj: object) -> bool: - replacement: Final = getattr(replacement_obj, "mcp_tool_call_response", None) + replacement: Final[list[object] | None] = getattr(replacement_obj, "mcp_tool_call_response", None) if replacement is None: return False - inner: Final = getattr(response_obj, "mcp_tool_call_response", None) + inner: Final[object | None] = getattr(response_obj, "mcp_tool_call_response", None) if inner is not None: if _CiscoAIDefenseMcpMixin._replace_mcp_tool_response(inner, replacement_obj): return True @@ -276,7 +277,7 @@ class _CiscoAIDefenseMcpMixin: self, data: dict, user_api_key_dict: UserAPIKeyAuth, - ) -> dict[str, Any]: + ) -> dict[str, object]: del user_api_key_dict # carried via logging metadata, not the wire payload url: Final = f"{self.api_base}{self.inspect_path}" payload: Final = self._build_mcp_request_payload(data=data) @@ -312,7 +313,7 @@ class _CiscoAIDefenseMcpMixin: response: object, user_api_key_dict: UserAPIKeyAuth | None = None, redact_response_obj: object = None, - ) -> dict[str, Any]: + ) -> dict[str, object]: del user_api_key_dict # carried via logging metadata, not the wire payload url: Final = f"{self.api_base}{self.inspect_path}" payload: Final = self._build_mcp_response_payload( @@ -349,7 +350,7 @@ class _CiscoAIDefenseMcpMixin: def _build_mcp_request_payload( self, data: dict, - ) -> dict[str, Any] | None: + ) -> dict[str, object] | None: """Build the JSON-RPC ``tools/call`` envelope sent to ``/inspect/mcp``. The Cisco AI Defense MCP inspect endpoint expects the JSON-RPC @@ -390,7 +391,7 @@ class _CiscoAIDefenseMcpMixin: self, request_data: dict, response: object, - ) -> dict[str, Any] | None: + ) -> dict[str, object] | None: """Build the MCP response-inspection body sent to ``/inspect/mcp``.""" request_payload: Final = self._build_mcp_request_payload(data=request_data) if request_payload is None: @@ -415,7 +416,7 @@ class _CiscoAIDefenseMcpMixin: return payload @staticmethod - def _hydrate_mcp_tool_context(request_data: dict[str, Any]) -> None: + def _hydrate_mcp_tool_context(request_data: dict[str, object]) -> None: metadata = request_data.get("mcp_tool_call_metadata") if metadata is None: nested: Final = request_data.get("metadata") or request_data.get("litellm_metadata") @@ -440,7 +441,7 @@ class _CiscoAIDefenseMcpMixin: request_data.setdefault("server_name", server_name) @staticmethod - def _normalize_mcp_response(response: object) -> dict[str, Any] | None: + def _normalize_mcp_response(response: object) -> dict[str, object] | None: """Normalize an MCP tool response into a JSON-RPC envelope. Handles JSON-RPC dicts, raw content lists, MCP SDK models, and @@ -502,10 +503,10 @@ class _CiscoAIDefenseMcpMixin: @staticmethod def _build_mcp_result( - content: list[Any], + content: Sequence[object], source: object = None, - ) -> dict[str, Any]: - result: Final[dict[str, Any]] = {"content": [_serialize_mcp_content_item(item) for item in content]} + ) -> dict[str, object]: + result: Final[dict[str, object]] = {"content": [_serialize_mcp_content_item(item) for item in content]} for key in ("structuredContent", "isError"): value = source.get(key) if isinstance(source, dict) else getattr(source, key, None) if value is not None and (key != "isError" or isinstance(value, bool)): @@ -522,7 +523,7 @@ class _CiscoAIDefenseMcpMixin: if response_obj is None: return False - inner: Final = getattr(response_obj, "mcp_tool_call_response", None) + inner: Final[object | None] = getattr(response_obj, "mcp_tool_call_response", None) if inner is not None: return _CiscoAIDefenseMcpMixin._set_mcp_tool_response_text(inner, text) @@ -559,7 +560,7 @@ class _CiscoAIDefenseMcpMixin: pass elif isinstance(response_obj, dict): result: Final = response_obj.get("result") - target: Final[dict[Any, Any]] = result if isinstance(result, dict) else response_obj + target: Final[dict[object, object]] = result if isinstance(result, dict) else response_obj if "structuredContent" in target: target["structuredContent"] = replacement replaced = True @@ -567,11 +568,11 @@ class _CiscoAIDefenseMcpMixin: return replaced @staticmethod - def _coerce_to_content_list(response_obj: object) -> list[Any] | None: + def _coerce_to_content_list(response_obj: object) -> list[object] | None: """Find the MCP content list inside supported response shapes.""" if response_obj is None: return None - inner: Final = getattr(response_obj, "mcp_tool_call_response", None) + inner: Final[object | None] = getattr(response_obj, "mcp_tool_call_response", None) if inner is not None: return _CiscoAIDefenseMcpMixin._coerce_to_content_list(inner) content: Final = getattr(response_obj, "content", None) @@ -594,8 +595,8 @@ class _CiscoAIDefenseMcpMixin: @staticmethod def _extract_sanitized_mcp_arguments( - inspect_response: dict[str, Any], - ) -> dict[str, Any] | None: + inspect_response: dict[str, object], + ) -> dict[str, object] | None: """Pull sanitized MCP tool-call arguments off the verdict. Cisco can return them at the top level (``params.arguments``) or diff --git a/litellm/proxy/guardrails/guardrail_hooks/compresr/compresr.py b/litellm/proxy/guardrails/guardrail_hooks/compresr/compresr.py index 5c14d03f50e..1fc3c06e6bf 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/compresr/compresr.py +++ b/litellm/proxy/guardrails/guardrail_hooks/compresr/compresr.py @@ -748,7 +748,7 @@ class CompresrGuardrail(CustomGuardrail): } try: - raw_response: HttpxResponse | None = await self.async_handler.post( # pyright: ignore[reportUnknownMemberType] # AsyncHTTPHandler.post is untyped + raw_response: HttpxResponse = await self.async_handler.post( # pyright: ignore[reportUnknownMemberType] # AsyncHTTPHandler.post is untyped url=url, json=payload, headers=self._request_headers(), @@ -778,11 +778,11 @@ class CompresrGuardrail(CustomGuardrail): {"detail": str(e)}, ) return None - if raw_response is None or not 200 <= raw_response.status_code < 300: + if not 200 <= raw_response.status_code < 300: self._handle_compress_failure( "Compresr compression service returned an error", { - "status_code": getattr(raw_response, "status_code", None), + "status_code": raw_response.status_code, "body": _safe_response_text(raw_response), }, ) diff --git a/litellm/proxy/guardrails/guardrail_hooks/grayswan/grayswan.py b/litellm/proxy/guardrails/guardrail_hooks/grayswan/grayswan.py index 955a868a0d6..48832f8ed5e 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/grayswan/grayswan.py +++ b/litellm/proxy/guardrails/guardrail_hooks/grayswan/grayswan.py @@ -2,9 +2,10 @@ import os import time -from typing import TYPE_CHECKING, Any, Final, Literal, Optional +from typing import TYPE_CHECKING, Any, Final, Literal, Optional, Protocol from fastapi import HTTPException +from typing_extensions import NotRequired, ReadOnly, TypedDict from litellm._logging import verbose_proxy_logger from litellm.integrations.custom_guardrail import ( @@ -27,6 +28,33 @@ if TYPE_CHECKING: GRAYSWAN_BLOCK_ERROR_MSG: Final = "Blocked by Gray Swan Guardrail" +class _GraySwanMonitorResponse(TypedDict): + """Body returned by Gray Swan's `/cygnal/monitor` endpoint.""" + + violation: ReadOnly[NotRequired[float | None]] + violated_rules: ReadOnly[NotRequired[list[object]]] + violated_rule_descriptions: ReadOnly[NotRequired[list[object]]] + mutation: ReadOnly[NotRequired[bool | None]] + ipi: ReadOnly[NotRequired[bool | None]] + + +class _GraySwanMonitorHTTPResponse(Protocol): + def raise_for_status(self) -> object: ... + + def json(self) -> _GraySwanMonitorResponse: ... + + +class _GraySwanMonitorHTTPClient(Protocol): + async def post( + self, + *, + url: str, + headers: dict[str, str], + json: dict[str, object], + timeout: float, + ) -> _GraySwanMonitorHTTPResponse: ... + + class GraySwanGuardrailMissingSecrets(Exception): """Raised when the Gray Swan API key is missing.""" @@ -77,7 +105,9 @@ class GraySwanGuardrail(CustomGuardrail): guardrail_timeout: float | None = 30.0, **kwargs: Any, ) -> None: - self.async_handler = get_async_httpx_client(llm_provider=httpxSpecialProvider.GuardrailCallback) + self.async_handler: _GraySwanMonitorHTTPClient = get_async_httpx_client( + llm_provider=httpxSpecialProvider.GuardrailCallback + ) api_key_value: Final = api_key or os.getenv("GRAYSWAN_API_KEY") if not api_key_value: @@ -266,7 +296,7 @@ class GraySwanGuardrail(CustomGuardrail): # Legacy Test Interface (for backward compatibility) # ------------------------------------------------------------------ - async def run_grayswan_guardrail(self, payload: dict) -> dict[str, Any]: + async def run_grayswan_guardrail(self, payload: dict[str, object]) -> _GraySwanMonitorResponse: """ Run the GraySwan guardrail on a payload. @@ -285,7 +315,7 @@ class GraySwanGuardrail(CustomGuardrail): def _process_grayswan_response( self, - response_json: dict, + response_json: _GraySwanMonitorResponse, data: dict | None = None, hook_type: GuardrailEventHooks | None = None, ) -> None: @@ -385,7 +415,7 @@ class GraySwanGuardrail(CustomGuardrail): # Core GraySwan API interaction # ------------------------------------------------------------------ - async def _call_grayswan_api(self, payload: dict) -> dict[str, Any]: + async def _call_grayswan_api(self, payload: dict[str, object]) -> _GraySwanMonitorResponse: """Call the GraySwan monitoring API.""" headers: Final = self._prepare_headers() @@ -406,7 +436,7 @@ class GraySwanGuardrail(CustomGuardrail): def _process_response_internal( self, - response_json: dict[str, Any], + response_json: _GraySwanMonitorResponse, request_data: dict, inputs: GenericGuardrailAPIInputs, is_output: bool, @@ -534,8 +564,8 @@ class GraySwanGuardrail(CustomGuardrail): dynamic_body: dict, request_data: dict, logging_obj: Optional["LiteLLMLoggingObj"] = None, - ) -> dict[str, Any] | None: - payload: Final[dict[str, Any]] = {"messages": messages} + ) -> dict[str, object] | None: + payload: Final[dict[str, object]] = {"messages": messages} categories: Final = dynamic_body.get("categories") or self.categories if categories: @@ -563,13 +593,13 @@ class GraySwanGuardrail(CustomGuardrail): {**existing_headers, **inbound_headers} if isinstance(existing_headers, dict) else inbound_headers ) if cleaned_litellm_metadata: - sanitized: Final = safe_json_loads(safe_dumps(cleaned_litellm_metadata), default={}) + sanitized: Final[object] = safe_json_loads(safe_dumps(cleaned_litellm_metadata), default={}) if isinstance(sanitized, dict) and sanitized: payload["litellm_metadata"] = sanitized return payload - def _format_violation_message(self, detection_info: Any, is_output: bool = False) -> str: + def _format_violation_message(self, detection_info: object, is_output: bool = False) -> str: """ Format detection info into a user-friendly violation message. diff --git a/litellm/proxy/guardrails/guardrail_hooks/headroom/headroom.py b/litellm/proxy/guardrails/guardrail_hooks/headroom/headroom.py index b9cc90107e2..d8c8c2f4974 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/headroom/headroom.py +++ b/litellm/proxy/guardrails/guardrail_hooks/headroom/headroom.py @@ -37,8 +37,12 @@ from litellm.proxy.guardrails.guardrail_hooks.content_text import ( from litellm.proxy.spend_tracking.compression_savings import HEADROOM_GUARDRAIL_PROVIDER from litellm.secret_managers.main import get_secret_str from litellm.types.guardrails import GuardrailEventHooks, Mode -from litellm.types.integrations.custom_logger import AgenticLoopPlan, AgenticLoopRequestPatch -from litellm.types.utils import GenericGuardrailAPIInputs +from litellm.types.integrations.custom_logger import ( + HEADROOM_CONVERTED_STREAM_KEY, + AgenticLoopPlan, + AgenticLoopRequestPatch, +) +from litellm.types.utils import CallTypes, GenericGuardrailAPIInputs if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj @@ -429,7 +433,7 @@ class HeadroomGuardrail(CustomGuardrail): payload["model"] = model try: - raw_response: HttpxResponse | None = await self.async_handler.post( # pyright: ignore[reportUnknownMemberType] + raw_response: HttpxResponse = await self.async_handler.post( # pyright: ignore[reportUnknownMemberType] # AsyncHTTPHandler.post is untyped url=f"{self.headroom_api_base}/v1/compress", json=payload, headers=self._request_headers(), @@ -454,16 +458,6 @@ class HeadroomGuardrail(CustomGuardrail): False, {}, ) - if raw_response is None: - return ( - self._handle_compress_failure( - messages, - "Headroom compression service returned no response", - {}, - ), - False, - {}, - ) response: Final[HttpxResponse] = raw_response if response.status_code != 200: @@ -576,7 +570,7 @@ class HeadroomGuardrail(CustomGuardrail): params["query"] = query try: - raw_response: HttpxResponse | None = await self.async_handler.get( # pyright: ignore[reportUnknownMemberType] + raw_response: HttpxResponse = await self.async_handler.get( # pyright: ignore[reportUnknownMemberType] # AsyncHTTPHandler.get is untyped url=f"{self.headroom_api_base}/v1/retrieve/{hash_value}", params=params, headers=self._request_headers(), @@ -585,7 +579,7 @@ class HeadroomGuardrail(CustomGuardrail): verbose_proxy_logger.warning("Headroom: retrieve failed for hash=%s: %s", hash_value, e) return f"[Headroom: retrieval failed for hash={hash_value}]" - if raw_response is None or raw_response.status_code == 404: + if raw_response.status_code == 404: return f"[Headroom: hash={hash_value} not found or expired]" if raw_response.status_code != 200: @@ -713,6 +707,25 @@ class HeadroomGuardrail(CustomGuardrail): return {**inputs, "structured_messages": compressed, "tools": merged_tools} # pyright: ignore[reportReturnType] + async def async_pre_call_deployment_hook( + self, + kwargs: dict[str, Any], + call_type: CallTypes | None, + ) -> dict[str, Any] | None: # mutable-ok: overrides CustomLogger hook whose contract is a plain dict + base_result: Final = await super().async_pre_call_deployment_hook(kwargs, call_type) + effective: Final = base_result if base_result is not None else kwargs + if call_type not in (CallTypes.completion, CallTypes.acompletion): + return base_result + if not effective.get("stream"): + return base_result + if not has_headroom_retrieve_tool(effective.get("tools")): + return base_result + return { # mutable-ok: the hook contract is a plain dict the router merges into the request kwargs + **effective, + "stream": False, + HEADROOM_CONVERTED_STREAM_KEY: True, + } + async def async_should_run_agentic_loop( self, response: Any, diff --git a/litellm/proxy/guardrails/guardrail_hooks/hiddenlayer/hiddenlayer.py b/litellm/proxy/guardrails/guardrail_hooks/hiddenlayer/hiddenlayer.py index c595952bec2..68914a1989e 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/hiddenlayer/hiddenlayer.py +++ b/litellm/proxy/guardrails/guardrail_hooks/hiddenlayer/hiddenlayer.py @@ -2,7 +2,7 @@ from __future__ import annotations import os from collections.abc import Mapping, Sequence -from typing import TYPE_CHECKING, Any, Final, Literal, Protocol, TypedDict +from typing import TYPE_CHECKING, Final, Literal, Protocol from urllib.parse import urlparse from uuid import uuid4 @@ -11,7 +11,7 @@ import requests from fastapi import HTTPException from httpx import HTTPStatusError from requests.auth import HTTPBasicAuth -from typing_extensions import ReadOnly +from typing_extensions import ReadOnly, TypedDict, Unpack from litellm._logging import verbose_proxy_logger from litellm.integrations.custom_guardrail import ( @@ -37,14 +37,24 @@ if TYPE_CHECKING: from litellm.types.proxy.guardrails.guardrail_hooks.base import GuardrailConfigModel +_AUTH_TIMEOUT_SECONDS: Final[float] = 30.0 + + +class _CustomGuardrailOptions(TypedDict, total=False, extra_items=object): + """Base-class constructor options carried by this guardrail's forwarded keyword arguments.""" + + guardrail_name: ReadOnly[str | None] + supported_event_hooks: list[GuardrailEventHooks] | None + + class _HiddenlayerEvaluation(TypedDict, total=False): - action: str - threat_level: str + action: ReadOnly[str] + threat_level: ReadOnly[str] class _HiddenlayerAnalysisEntry(TypedDict, total=False): - name: str - detected: bool + name: ReadOnly[str] + detected: ReadOnly[bool] class _HiddenlayerModifiedMessage(TypedDict): @@ -56,9 +66,9 @@ class _HiddenlayerModifiedSide(TypedDict): class _HiddenlayerResponse(TypedDict, total=False): - evaluation: _HiddenlayerEvaluation - analysis: Sequence[_HiddenlayerAnalysisEntry] - modified_data: Mapping[str, _HiddenlayerModifiedSide] + evaluation: ReadOnly[_HiddenlayerEvaluation] + analysis: ReadOnly[Sequence[_HiddenlayerAnalysisEntry]] + modified_data: ReadOnly[Mapping[str, _HiddenlayerModifiedSide]] class _ProxyServerRequest(TypedDict, total=False): @@ -146,6 +156,31 @@ def _header_value(headers: Mapping[str, str], key: str, default: str) -> str: return headers.get(key, default) +def _is_image_part(item: object) -> bool: + """Whether a structured-message content part carries an image rather than text.""" + + if not isinstance(item, Mapping): + return False + + part: Final[Mapping[object, object]] = item + return part.get("type") == "image_url" + + +def _scannable_text(content: object) -> str: + """Flatten a structured message's content into the single string the v1 detection endpoint takes. + + Image parts are dropped: the endpoint accepts one string, so an image would only reach it as + its stringified source (a base64 blob or a URL), which is not text the scanner can evaluate. + """ + + if not isinstance(content, list): + return str(content or "") + + parts: Final[Sequence[object]] = content + text_parts: Final = [item for item in parts if not _is_image_part(item)] # mutable-ok: sent as a list repr + return str(text_parts or "") + + def is_saas(host: str) -> bool: """Checks whether the connection is to the SaaS platform""" @@ -157,10 +192,10 @@ def is_saas(host: str) -> bool: return False -def _get_jwt(auth_url, api_id, api_key) -> str: +def _get_jwt(auth_url, api_id, api_key, timeout: float = _AUTH_TIMEOUT_SECONDS) -> str: token_url: Final = f"{auth_url}/oauth2/token?grant_type=client_credentials" - resp: Final = requests.post(token_url, auth=HTTPBasicAuth(api_id, api_key)) + resp: Final = requests.post(token_url, auth=HTTPBasicAuth(api_id, api_key), timeout=timeout) if not resp.ok: raise RuntimeError( @@ -191,7 +226,7 @@ class HiddenlayerGuardrail(CustomGuardrail): api_key: str | None = None, api_base: str | None = None, auth_url: str | None = None, - **kwargs: Any, + **kwargs: Unpack[_CustomGuardrailOptions], ) -> None: kwargs.setdefault("supported_event_hooks", list(self.get_supported_event_hooks())) self.hiddenlayer_client_id = api_id or os.getenv("HIDDENLAYER_CLIENT_ID") @@ -260,7 +295,7 @@ class HiddenlayerGuardrail(CustomGuardrail): "messages": [ { "role": last_msg.get("role", "user"), - "content": str(last_msg.get("content", "")), + "content": _scannable_text(last_msg.get("content")), } ] }, @@ -396,7 +431,7 @@ class HiddenlayerGuardrailV2(CustomGuardrail): api_key: str | None = None, api_base: str | None = None, auth_url: str | None = None, - **kwargs: Any, + **kwargs: Unpack[_CustomGuardrailOptions], ) -> None: self.hiddenlayer_client_id = api_id or os.getenv("HIDDENLAYER_CLIENT_ID") self.hiddenlayer_client_secret = api_key or os.getenv("HIDDENLAYER_CLIENT_SECRET") @@ -527,7 +562,7 @@ class HiddenlayerGuardrailV2(CustomGuardrail): self, payload: _HiddenlayerV2Payload, input_type: Literal["request", "response"], - hl_headers: dict[str, str], + hl_headers: Mapping[str, str], ) -> httpx.Response: if input_type == "request": path = "detection/v2/request-evaluations" diff --git a/litellm/proxy/guardrails/guardrail_hooks/lakera_ai_v2.py b/litellm/proxy/guardrails/guardrail_hooks/lakera_ai_v2.py index 7791adeb41e..2f98a9afbd8 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/lakera_ai_v2.py +++ b/litellm/proxy/guardrails/guardrail_hooks/lakera_ai_v2.py @@ -191,6 +191,25 @@ def _breakdown_has_pii_violation(lakera_response: LakeraAIResponse | None) -> bo ) +def _unmaskable_reason( + guardrail: "LakeraAIGuardrail", + data: dict[str, object], + lakera_response: LakeraAIResponse | None, +) -> str | None: + """Why a PII-only violation on ``data`` can't be masked in place, or None when it can.""" + if has_non_string_content(data): + return "multimodal content, masking would drop the image/audio parts" + if _has_combined_messages_and_input(data): + return "messages and input are both present, so the write-back is positionally ambiguous" + if "messages" in data and not isinstance(data.get("messages"), list): + return "a messages key that isn't a list, so there's nothing to merge the redacted content into" + if not _has_responses_instructions(guardrail, data): + return "no write-back path for the redacted content" + if not (lakera_response or {}).get("payload"): + return "Lakera reported no locations to redact, so payload=true is likely off" + return None + + def _build_lakera_inspection_messages(data: Mapping[str, object]) -> Sequence[Mapping[str, str]]: """Like build_inspection_messages, but also covers the Responses-API ``instructions`` field, placed first since litellm later converts it @@ -301,12 +320,12 @@ class LakeraAIGuardrail(CustomGuardrail): explicit sync below a hot reload that changes mode would pass validation but keep dispatching on the stale event_hook. """ - new_event_hook: Final = getattr(litellm_params, "mode", None) or self.event_hook - prospective_payload: Final = getattr(litellm_params, "payload", None) - prospective_breakdown: Final = getattr(litellm_params, "breakdown", None) + new_event_hook: Final = litellm_params.mode or self.event_hook + prospective_payload: Final = litellm_params.payload + prospective_breakdown: Final = litellm_params.breakdown self._validate_advisory_config( - on_flagged=getattr(litellm_params, "on_flagged", None) or self.on_flagged, - advisory_system_message=getattr(litellm_params, "advisory_system_message", None), + on_flagged=litellm_params.on_flagged or self.on_flagged, + advisory_system_message=litellm_params.advisory_system_message, payload=self.payload if prospective_payload is None else prospective_payload, breakdown=self.breakdown if prospective_breakdown is None else prospective_breakdown, ) @@ -473,6 +492,32 @@ class LakeraAIGuardrail(CustomGuardrail): msg["content"] = content return messages + def _mask_unwritable_instructions_pii_in_place( + self, + data: dict[str, object], # mutable-ok: writes the redacted result back into the caller's request dict in place + inspected_messages: Sequence[AllMessageValues], + lakera_response: LakeraAIResponse | None, + masked_entity_count: dict[str, int], + ) -> bool: + """Mask a body whose only obstacle to mask-in-place is the Responses-API + ``instructions`` field, writing the redacted instructions straight into + ``data["instructions"]``: apply_redacted_messages_back has no path for + that field and would fold the instructions text into ``data["input"]``. + Returns False without masking anything when _unmaskable_reason names an + obstacle this can't get around.""" + if _unmaskable_reason(self, data, lakera_response) is not None: + return False + redacted: Final = self._mask_pii_in_messages( + messages=inspected_messages, + lakera_response=lakera_response, + masked_entity_count=masked_entity_count, + ) + # _build_lakera_inspection_messages puts instructions first and + # _filter_skipped_messages kept it, so index 0 is the instructions. + data["instructions"] = redacted[0]["content"] + _apply_redacted_messages_back_preserving_fields(self, data, redacted[1:]) + return True + async def async_pre_call_hook( self, user_api_key_dict: UserAPIKeyAuth, @@ -537,11 +582,12 @@ class LakeraAIGuardrail(CustomGuardrail): ########## 2. Handle flagged content ########## ######################################################### if lakera_guardrail_response.get("flagged") is True: + is_pii_only_violation: Final = self._is_only_pii_violation(lakera_guardrail_response) # PII-only violations get masked in place regardless of on_flagged: there's # no reason to expose raw PII to satisfy an advisory note, and masking is # strictly safer than either blocking or appending an advisory message next # to unredacted PII. - if self._is_only_pii_violation(lakera_guardrail_response) and not is_multimodal_input: + if is_pii_only_violation and not is_multimodal_input: redacted_messages: Final = self._mask_pii_in_messages( messages=new_messages, lakera_response=lakera_guardrail_response, @@ -583,18 +629,35 @@ class LakeraAIGuardrail(CustomGuardrail): # blocking rather than silently letting the flagged request # through with no advisory ever reaching the model. raise self._get_http_exception_for_blocked_guardrail(lakera_guardrail_response) - else: - # Check on_flagged setting - if self.on_flagged == "monitor": + elif self.on_flagged == "monitor": + # Monitor means "don't block", not "don't redact": until the mask + # branch above started skipping shapes it can't write back to, a + # PII-only violation was masked whatever on_flagged said. + masked_in_place: Final = is_pii_only_violation and self._mask_unwritable_instructions_pii_in_place( + data=data, + inspected_messages=new_messages, + lakera_response=lakera_guardrail_response, + masked_entity_count=masked_entity_count, + ) + if masked_in_place: + verbose_proxy_logger.warning( + "Lakera Guardrail: Monitoring mode - PII detected, masked in place and allowing request" + ) + elif is_pii_only_violation: + verbose_proxy_logger.error( + "Lakera Guardrail: Monitoring mode - PII detected but NOT masked, forwarding unredacted " + "content to the model (reason: %s)", + _unmaskable_reason(self, data, lakera_guardrail_response), + ) + else: verbose_proxy_logger.warning( "Lakera Guardrail: Monitoring mode - violation detected but allowing request" ) - # Log violation but continue - elif self.on_flagged == "block": - # Either non-PII violations, or PII on multimodal input - # (which cannot be masked in place without dropping - # image/audio parts) — raise the standard block error. - raise self._get_http_exception_for_blocked_guardrail(lakera_guardrail_response) + elif self.on_flagged == "block": + # Either non-PII violations, or PII on multimodal input + # (which cannot be masked in place without dropping + # image/audio parts) — raise the standard block error. + raise self._get_http_exception_for_blocked_guardrail(lakera_guardrail_response) ######################################################### ########## 3. Add the guardrail to the applied guardrails header ########## diff --git a/litellm/proxy/guardrails/guardrail_hooks/lasso/lasso.py b/litellm/proxy/guardrails/guardrail_hooks/lasso/lasso.py index ea022510309..cf5da27e9ca 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/lasso/lasso.py +++ b/litellm/proxy/guardrails/guardrail_hooks/lasso/lasso.py @@ -8,6 +8,7 @@ import json import os import uuid +from collections.abc import Mapping, Sequence from typing import TYPE_CHECKING, Any, Final, Literal, TypedDict try: @@ -128,7 +129,7 @@ class LassoGuardrail(CustomGuardrail): @staticmethod def _extract_tool_call_fields( - call: Any, + call: object, ) -> tuple[str | None, str | None, dict[str, object] | None]: """Extract (call_id, name, parsed_input) from a tool call. @@ -476,7 +477,7 @@ class LassoGuardrail(CustomGuardrail): def _map_masked_messages_back( self, original_messages: list[dict[str, Any]], - masked_messages: list[dict[str, Any]], + masked_messages: Sequence[Mapping[str, object]], ) -> list[dict[str, object]]: """Map Lasso-format masked messages back onto the original OpenAI-format messages. @@ -638,7 +639,7 @@ class LassoGuardrail(CustomGuardrail): }, ) - def _expand_messages_for_classification(self, messages: list[dict[str, Any]]) -> list[dict[str, Any]]: + def _expand_messages_for_classification(self, messages: list[dict[str, Any]]) -> list[dict[str, object]]: """ Convert raw OpenAI-format messages to Lasso API format with content blocks. @@ -646,7 +647,7 @@ class LassoGuardrail(CustomGuardrail): - role=tool messages → developer role + tool_result block - plain text messages pass through unchanged """ - expanded: Final[list[dict[str, Any]]] = [] + expanded: Final[list[dict[str, object]]] = [] for msg in messages: role = msg.get("role", "") content = msg.get("content") @@ -917,7 +918,7 @@ class LassoGuardrail(CustomGuardrail): def _apply_masking_to_model_response( self, model_response: litellm.ModelResponse, - masked_messages: list[dict[str, Any]], + masked_messages: Sequence[Mapping[str, object]], ) -> None: """Apply masking to the actual model response when mask=True and masked content is available.""" # Index masked tool_use blocks by id for O(1) lookup. diff --git a/litellm/proxy/guardrails/guardrail_hooks/mcp_jwt_signer/mcp_jwt_signer.py b/litellm/proxy/guardrails/guardrail_hooks/mcp_jwt_signer/mcp_jwt_signer.py index e2d7c06f7c5..a269ad31a6b 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/mcp_jwt_signer/mcp_jwt_signer.py +++ b/litellm/proxy/guardrails/guardrail_hooks/mcp_jwt_signer/mcp_jwt_signer.py @@ -80,7 +80,7 @@ import jwt from cryptography.hazmat.primitives import serialization from cryptography.hazmat.primitives.asymmetric import rsa from cryptography.hazmat.primitives.asymmetric.rsa import RSAPrivateKey, RSAPublicKey -from typing_extensions import NotRequired, TypedDict +from typing_extensions import NotRequired, ReadOnly, TypedDict from litellm._logging import verbose_proxy_logger from litellm.caching import DualCache @@ -89,6 +89,7 @@ from litellm.integrations.custom_guardrail import ( log_guardrail_information, ) from litellm.proxy._types import UserAPIKeyAuth +from litellm.types.guardrail_base_init import GuardrailBaseInitKwargs from litellm.types.guardrails import GuardrailEventHooks from litellm.types.utils import CallTypesLiteral @@ -107,6 +108,19 @@ class _JWTDecodeKwargs(TypedDict): issuer: NotRequired[str] +class _DebugHeaderClaims(TypedDict, total=False): + sub: ReadOnly[object] + iss: ReadOnly[object] + exp: ReadOnly[object] + scope: ReadOnly[str] + + +class _SignedClaimSummary(TypedDict): + sub: ReadOnly[object] + act: ReadOnly[Mapping[str, object]] + exp: ReadOnly[object] + + # Module-level singleton for the JWKS discovery endpoint to access. _mcp_jwt_signer_instance: Optional["MCPJWTSigner"] = None @@ -265,7 +279,8 @@ class MCPJWTSigner(CustomGuardrail): **kwargs: Any, ) -> None: kwargs.setdefault("supported_event_hooks", list(self.get_supported_event_hooks())) - super().__init__(**kwargs) + base_kwargs: Final[GuardrailBaseInitKwargs] = kwargs + super().__init__(**base_kwargs) # --- Signing key setup --- key_material: Final = os.environ.get(self.SIGNING_KEY_ENV) @@ -677,7 +692,7 @@ class MCPJWTSigner(CustomGuardrail): data: dict, jwt_claims: Mapping[str, object] | None = None, call_type: CallTypesLiteral | None = None, - ) -> dict[str, Any]: + ) -> dict[str, object]: """ Build JWT claims for the outbound MCP access token. @@ -752,7 +767,7 @@ class MCPJWTSigner(CustomGuardrail): # ------------------------------------------------------------------ @staticmethod - def _build_debug_header(claims: dict[str, Any], kid: str) -> str: + def _build_debug_header(claims: _DebugHeaderClaims, kid: str) -> str: """ Build the x-litellm-mcp-debug header value. @@ -873,16 +888,18 @@ class MCPJWTSigner(CustomGuardrail): # FR-9: Debug header # ------------------------------------------------------------------ if self.debug_headers: - new_headers["x-litellm-mcp-debug"] = self._build_debug_header(claims, self._kid) + debug_claims: Final[_DebugHeaderClaims] = claims + new_headers["x-litellm-mcp-debug"] = self._build_debug_header(debug_claims, self._kid) hook_data["extra_headers"] = new_headers + logged_claims: Final[_SignedClaimSummary] = claims verbose_proxy_logger.debug( "MCPJWTSigner: signed JWT sub=%s act=%s tool=%s exp=%d verified=%s channel=%s call_type=%s", - claims.get("sub"), - claims.get("act", {}).get("sub"), + logged_claims.get("sub"), + logged_claims.get("act", {}).get("sub"), hook_data.get("mcp_tool_name"), - claims["exp"], + logged_claims["exp"], jwt_claims is not None, bool(self.channel_token_audience), call_type, diff --git a/litellm/proxy/guardrails/guardrail_hooks/noma/noma_v2.py b/litellm/proxy/guardrails/guardrail_hooks/noma/noma_v2.py index e9cd6addef8..292f395053b 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/noma/noma_v2.py +++ b/litellm/proxy/guardrails/guardrail_hooks/noma/noma_v2.py @@ -7,8 +7,9 @@ import enum import json import os +from collections.abc import Callable, Mapping from datetime import datetime -from typing import TYPE_CHECKING, Any, Final, Literal, Optional, cast +from typing import TYPE_CHECKING, Any, Final, Literal, Optional, TypeAlias, cast from urllib.parse import urlparse from litellm._logging import verbose_proxy_logger @@ -23,6 +24,7 @@ from litellm.llms.custom_httpx.http_handler import ( httpxSpecialProvider, ) from litellm.proxy.guardrails.guardrail_hooks.noma.noma import NomaBlockedMessage +from litellm.types.guardrail_base_init import GuardrailBaseInitKwargs from litellm.types.guardrails import GuardrailEventHooks from litellm.types.utils import GenericGuardrailAPIInputs, GuardrailStatus @@ -36,6 +38,8 @@ _AIDR_SCAN_ENDPOINT: Final = "/litellm/guardrail" _INTERVENED_INPUT_FIELDS: Final = ("texts", "images", "tools", "tool_calls") _DEFAULT_API_BASE_HOSTNAME: Final = urlparse(_DEFAULT_API_BASE).hostname +_GuardrailJsonResponse: TypeAlias = Exception | str | dict[str, object] + _KEYS_DUPLICATING_SCAN_INPUTS: Final = ("messages", "input") _LOGGING_KEYS_DUPLICATING_SCAN_INPUTS: Final = _KEYS_DUPLICATING_SCAN_INPUTS + ( "additional_args", @@ -80,7 +84,8 @@ class NomaV2Guardrail(CustomGuardrail): kwargs.setdefault("supported_event_hooks", list(self.get_supported_event_hooks())) - super().__init__(**kwargs) + base_kwargs: Final[GuardrailBaseInitKwargs] = kwargs + super().__init__(**base_kwargs) @staticmethod def get_config_model() -> type["GuardrailConfigModel"] | None: @@ -111,7 +116,7 @@ class NomaV2Guardrail(CustomGuardrail): return parsed.hostname == _DEFAULT_API_BASE_HOSTNAME @staticmethod - def _get_non_empty_str(value: Any) -> str | None: + def _get_non_empty_str(value: object) -> str | None: if not isinstance(value, str): return None stripped: Final = value.strip() @@ -119,7 +124,7 @@ class NomaV2Guardrail(CustomGuardrail): def _resolve_action_from_response( self, - response_json: dict, + response_json: Mapping[str, object], ) -> _Action: action: Final = response_json.get("action") if isinstance(action, str): @@ -153,7 +158,7 @@ class NomaV2Guardrail(CustomGuardrail): else model_call_details ) - payload: Final[dict[str, Any]] = { + payload: Final[dict[str, object]] = { "inputs": inputs, "request_data": payload_request_data, "input_type": input_type, @@ -165,10 +170,11 @@ class NomaV2Guardrail(CustomGuardrail): @staticmethod def _sanitize_payload_for_transport(payload: dict) -> dict: - def _default(obj: Any) -> Any: - if hasattr(obj, "model_dump"): + def _default(obj: object) -> object: + model_dump: Final[Callable[[], Mapping[str, object]] | None] = getattr(obj, "model_dump", None) + if model_dump is not None: try: - return obj.model_dump() + return model_dump() except Exception: pass return str(obj) @@ -178,7 +184,7 @@ class NomaV2Guardrail(CustomGuardrail): except (ValueError, TypeError): json_str = safe_dumps(payload) - safe_payload: Final = safe_json_loads(json_str, default={}) + safe_payload: Final[object] = safe_json_loads(json_str, default={}) if safe_payload == {} and payload: verbose_proxy_logger.warning( "Noma v2 guardrail: payload serialization failed, falling back to empty payload" @@ -196,7 +202,7 @@ class NomaV2Guardrail(CustomGuardrail): async def _call_noma_scan( self, payload: dict, - ) -> dict: + ) -> dict[str, object]: headers: Final[dict[str, str]] = {"Content-Type": "application/json"} authorization_header: Final = self._get_authorization_header() if authorization_header: @@ -215,7 +221,7 @@ class NomaV2Guardrail(CustomGuardrail): response.text, ) response.raise_for_status() - response_json: Final = response.json() + response_json: Final[dict[str, object]] = response.json() verbose_proxy_logger.debug( "Noma v2 AIDR response parsed: %s", json.dumps(response_json, default=str), @@ -227,7 +233,7 @@ class NomaV2Guardrail(CustomGuardrail): request_data: dict, start_time: datetime, guardrail_status: GuardrailStatus, - guardrail_json_response: Any, + guardrail_json_response: _GuardrailJsonResponse, ) -> None: end_time: Final = datetime.now() duration: Final = (end_time - start_time).total_seconds() @@ -270,11 +276,11 @@ class NomaV2Guardrail(CustomGuardrail): ) -> GenericGuardrailAPIInputs: start_time: Final = datetime.now() guardrail_status: GuardrailStatus = "success" - guardrail_json_response: Any = {} + guardrail_json_response: _GuardrailJsonResponse = {} dynamic_params = self.get_guardrail_dynamic_request_body_params(request_data) if not isinstance(dynamic_params, dict): dynamic_params = {} - response_json: dict | None = None + response_json: dict[str, object] | None = None # Per-request dynamic params can override configured application context. application_id = self._get_non_empty_str(dynamic_params.get("application_id")) @@ -320,8 +326,9 @@ class NomaV2Guardrail(CustomGuardrail): except NomaBlockedMessage as e: guardrail_status = "guardrail_intervened" + blocked_detail: Final[dict[str, object]] = {"error": "blocked"} guardrail_json_response = ( - response_json if isinstance(response_json, dict) else getattr(e, "detail", {"error": "blocked"}) + response_json if isinstance(response_json, dict) else getattr(e, "detail", blocked_detail) ) raise except Exception as e: diff --git a/litellm/proxy/guardrails/guardrail_hooks/pillar/pillar.py b/litellm/proxy/guardrails/guardrail_hooks/pillar/pillar.py index 78639ce4fd0..7021d41475b 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/pillar/pillar.py +++ b/litellm/proxy/guardrails/guardrail_hooks/pillar/pillar.py @@ -8,11 +8,12 @@ # Standard library imports import json import os -from typing import TYPE_CHECKING, Any, Final, Literal +from typing import TYPE_CHECKING, Any, Final, Literal, Protocol from urllib.parse import quote # Third-party imports from fastapi import HTTPException +from typing_extensions import NotRequired, ReadOnly, TypedDict # LiteLLM imports from litellm import DualCache @@ -42,7 +43,34 @@ if TYPE_CHECKING: MAX_PILLAR_HEADER_VALUE_BYTES: Final = 8 * 1024 -def _encode_json_for_header(data: Any) -> str: +class _PillarProtectResponse(TypedDict): + """Body returned by Pillar's `/api/v1/protect` endpoint.""" + + flagged: ReadOnly[NotRequired[bool]] + session_id: ReadOnly[NotRequired[str]] + scanners: ReadOnly[NotRequired[dict[str, object]]] + evidence: ReadOnly[NotRequired[list[object]]] + masked_session_messages: ReadOnly[NotRequired[list[object]]] + + +class _PillarProtectHTTPResponse(Protocol): + def raise_for_status(self) -> object: ... + + def json(self) -> _PillarProtectResponse: ... + + +class _PillarProtectHTTPClient(Protocol): + async def post( + self, + *, + url: str, + headers: dict[str, str], + json: dict[str, object], + timeout: float, + ) -> _PillarProtectHTTPResponse: ... + + +def _encode_json_for_header(data: object) -> str: """ JSON-serialize and URL-encode data for safe header transmission. """ @@ -50,7 +78,9 @@ def _encode_json_for_header(data: Any) -> str: return quote(json_payload, safe="") -def _truncate_evidence_payload(evidence: Any, max_bytes: int = MAX_PILLAR_HEADER_VALUE_BYTES) -> tuple[Any, str, bool]: +def _truncate_evidence_payload( + evidence: object, max_bytes: int = MAX_PILLAR_HEADER_VALUE_BYTES +) -> tuple[object, str, bool]: """ Truncate evidence payload so the encoded header value stays within max_bytes. @@ -66,12 +96,12 @@ def _truncate_evidence_payload(evidence: Any, max_bytes: int = MAX_PILLAR_HEADER truncated_value: Final = "[truncated]" return truncated_value, _encode_json_for_header(truncated_value), True - truncated: Final[list[Any]] = [] + truncated: Final[list[object]] = [] encoded = _encode_json_for_header(truncated) truncated_flag = False for entry in evidence: - working_entry: Any + working_entry: object if isinstance(entry, dict): working_entry = dict(entry) else: @@ -105,7 +135,7 @@ def _truncate_evidence_payload(evidence: Any, max_bytes: int = MAX_PILLAR_HEADER return truncated, encoded, truncated_flag -def build_pillar_response_headers(metadata_store: dict[str, Any]) -> dict[str, str]: +def build_pillar_response_headers(metadata_store: dict[str, object]) -> dict[str, str]: """ Create URL-safe Pillar response headers and apply truncation metadata. """ @@ -191,7 +221,9 @@ class PillarGuardrail(CustomGuardrail): LiteLLM virtual key context (user_id, team_id, key_alias, etc.) is always automatically passed as X-LiteLLM-* headers to enable application/user tracking. """ - self.async_handler = get_async_httpx_client(llm_provider=httpxSpecialProvider.GuardrailCallback) + self.async_handler: _PillarProtectHTTPClient = get_async_httpx_client( + llm_provider=httpxSpecialProvider.GuardrailCallback + ) self.api_key = api_key or os.environ.get("PILLAR_API_KEY") if self.api_key is None: @@ -686,7 +718,7 @@ class PillarGuardrail(CustomGuardrail): ) return payload - async def _call_pillar_api(self, headers: dict[str, str], payload: dict[str, Any]) -> dict[str, Any]: + async def _call_pillar_api(self, headers: dict[str, str], payload: dict[str, Any]) -> _PillarProtectResponse: """ Call the Pillar API and return the response. @@ -714,7 +746,7 @@ class PillarGuardrail(CustomGuardrail): verbose_proxy_logger.debug("Pillar Guardrail: Analysis complete - flagged=%s, session=%s", flagged, session_id) return res - def _process_pillar_response(self, pillar_response: dict[str, Any], original_data: dict) -> None: + def _process_pillar_response(self, pillar_response: _PillarProtectResponse, original_data: dict) -> None: """ Process the Pillar API response and handle detections based on configuration. @@ -774,7 +806,7 @@ class PillarGuardrail(CustomGuardrail): build_pillar_response_headers(metadata_store) - def _raise_pillar_detection_exception(self, pillar_response: dict[str, Any]) -> None: + def _raise_pillar_detection_exception(self, pillar_response: _PillarProtectResponse) -> None: """ Raise an HTTPException for Pillar security detections. @@ -784,7 +816,7 @@ class PillarGuardrail(CustomGuardrail): Raises: HTTPException: Always raises with security detection details """ - pillar_response_dict: Final = { + pillar_response_dict: Final[dict[str, object]] = { "session_id": pillar_response.get("session_id"), } diff --git a/litellm/proxy/guardrails/guardrail_hooks/presidio.py b/litellm/proxy/guardrails/guardrail_hooks/presidio.py index 8d78393d687..da51a905ae3 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/presidio.py +++ b/litellm/proxy/guardrails/guardrail_hooks/presidio.py @@ -11,10 +11,10 @@ import asyncio import json import threading -from collections.abc import AsyncGenerator, Sequence +from collections.abc import AsyncGenerator, AsyncIterable, Awaitable, Sequence from contextlib import asynccontextmanager from datetime import datetime -from typing import TYPE_CHECKING, Any, Final, Literal, Optional, TypedDict, cast +from typing import TYPE_CHECKING, Any, Final, Literal, Optional, Protocol, TypedDict, cast import aiohttp from typing_extensions import NotRequired, ReadOnly @@ -68,6 +68,14 @@ class _PresidioAnonymizeResponse(TypedDict): items: ReadOnly[NotRequired[list[_PresidioAnonymizeItem]]] +class _JsonResponse(Protocol): + def json(self) -> Awaitable[object]: ... + + +async def _json_body(response: _JsonResponse) -> object: + return await response.json() + + _LoopSemaphores = dict[asyncio.AbstractEventLoop, asyncio.Semaphore] @@ -389,7 +397,7 @@ class _OPTIONAL_PresidioPIIMasking(CustomGuardrail): f"expected application/json Content-Type but received '{content_type}'; body: '{error_body[:200]}'" ) - analyze_results: Final = await response.json() + analyze_results: Final = await _json_body(response) verbose_proxy_logger.debug("analyze_results: %s", analyze_results) # Handle error responses from Presidio (e.g., {'error': 'No text provided'}) @@ -997,7 +1005,7 @@ class _OPTIONAL_PresidioPIIMasking(CustomGuardrail): except Exception as e: raise e - def logging_hook(self, kwargs: dict, result: Any, call_type: str) -> tuple[dict, Any]: + def logging_hook(self, kwargs: dict, result: object, call_type: str) -> tuple[dict, object]: from concurrent.futures import ThreadPoolExecutor def run_in_new_loop(): @@ -1025,7 +1033,7 @@ class _OPTIONAL_PresidioPIIMasking(CustomGuardrail): # No running event loop, we can safely run in this thread return run_in_new_loop() - async def async_logging_hook(self, kwargs: dict, result: Any, call_type: str) -> tuple[dict, Any]: + async def async_logging_hook(self, kwargs: dict, result: object, call_type: str) -> tuple[dict, object]: """ Masks the input and output before logging to langfuse, datadog, etc. """ @@ -1092,9 +1100,9 @@ class _OPTIONAL_PresidioPIIMasking(CustomGuardrail): and not isinstance(result.choices[0], StreamingChoices) ): await self._process_response_for_pii(response=result, request_data=kwargs, mode="mask") - elif self._is_anthropic_message_response(result): + elif isinstance(result, dict) and self._is_anthropic_message_response(result): await self._process_anthropic_response_for_pii( - response=cast(dict, result), # cast-ok: _is_anthropic_message_response narrows via isinstance + response=result, request_data=kwargs, mode="mask", ) @@ -1321,7 +1329,7 @@ class _OPTIONAL_PresidioPIIMasking(CustomGuardrail): async def _stream_apply_output_masking( self, - response: Any, + response: AsyncIterable[object], request_data: dict, ) -> AsyncGenerator[ModelResponseStream | bytes, None]: """Apply Presidio masking to streaming output (apply_to_output=True path).""" @@ -1425,7 +1433,7 @@ class _OPTIONAL_PresidioPIIMasking(CustomGuardrail): return "\n".join(result_lines).encode("utf-8") - def _unmask_responses_api_completed_chunk(self, chunk: Any, pii_tokens: dict[str, str]) -> None: + def _unmask_responses_api_completed_chunk(self, chunk: object, pii_tokens: dict[str, str]) -> None: """ Unmask PII tokens in-place for a ``response.completed`` Responses API event. @@ -1434,7 +1442,7 @@ class _OPTIONAL_PresidioPIIMasking(CustomGuardrail): blocks; text blocks expose a ``.text`` string attribute. We walk the tree and replace every PII token with its original value. """ - response_obj: Final = getattr(chunk, "response", None) + response_obj: Final[object] = getattr(chunk, "response", None) if response_obj is None: return @@ -1450,7 +1458,7 @@ class _OPTIONAL_PresidioPIIMasking(CustomGuardrail): async def _stream_pii_unmasking( self, - response: Any, + response: AsyncIterable[object], request_data: dict, ) -> AsyncGenerator[ModelResponseStream | bytes, None]: """Apply PII unmasking to streaming output (output_parse_pii=True path).""" @@ -1526,7 +1534,7 @@ class _OPTIONAL_PresidioPIIMasking(CustomGuardrail): async def async_post_call_streaming_iterator_hook( self, user_api_key_dict: UserAPIKeyAuth, - response: Any, + response: AsyncIterable[object], request_data: dict, ) -> AsyncGenerator[ModelResponseStream | bytes, None]: """ diff --git a/litellm/proxy/guardrails/guardrail_hooks/prompt_security/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/prompt_security/__init__.py index fa1f9f3d36d..0aaba4016cd 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/prompt_security/__init__.py +++ b/litellm/proxy/guardrails/guardrail_hooks/prompt_security/__init__.py @@ -20,6 +20,7 @@ def initialize_guardrail(litellm_params: "LitellmParams", guardrail: "Guardrail" guardrail_name=guardrail.get("guardrail_name", ""), event_hook=litellm_params.mode, default_on=litellm_params.default_on, + file_sanitization_fail_open=getattr(litellm_params, "file_sanitization_fail_open", None), ) litellm.logging_callback_manager.add_litellm_callback(_prompt_security_callback) diff --git a/litellm/proxy/guardrails/guardrail_hooks/prompt_security/prompt_security.py b/litellm/proxy/guardrails/guardrail_hooks/prompt_security/prompt_security.py index 809d5e0fb31..84c4f118b00 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/prompt_security/prompt_security.py +++ b/litellm/proxy/guardrails/guardrail_hooks/prompt_security/prompt_security.py @@ -4,10 +4,12 @@ import os from collections.abc import Mapping, Sequence from typing import TYPE_CHECKING, Final, Literal, Optional +import httpx from fastapi import HTTPException from typing_extensions import ReadOnly, TypedDict from litellm._logging import verbose_proxy_logger +from litellm.exceptions import Timeout as LiteLLMTimeout from litellm.integrations.custom_guardrail import ( CustomGuardrail, log_guardrail_information, @@ -24,6 +26,9 @@ if TYPE_CHECKING: from litellm.types.proxy.guardrails.guardrail_hooks.base import GuardrailConfigModel +_SANITIZE_FILE_FAIL_OPEN_TIMEOUT_SECONDS: Final = 30.0 + + class PromptSecurityGuardrailMissingSecrets(Exception): pass @@ -63,6 +68,13 @@ class _SanitizeStatusResponse(TypedDict, total=False): metadata: ReadOnly[_SanitizeMetadata] +class _SanitizeResult(TypedDict): + action: ReadOnly[str] + content: ReadOnly[str | None] + metadata: ReadOnly[_SanitizeMetadata] + violations: ReadOnly[Sequence[str]] + + class PromptSecurityGuardrail(CustomGuardrail): @classmethod def get_supported_event_hooks(cls) -> list[GuardrailEventHooks]: @@ -79,6 +91,8 @@ class PromptSecurityGuardrail(CustomGuardrail): user: str | None = None, system_prompt: str | None = None, check_tool_results: bool | None = None, + file_sanitization_timeout: float = _SANITIZE_FILE_FAIL_OPEN_TIMEOUT_SECONDS, + file_sanitization_fail_open: bool | None = None, **kwargs, ): kwargs.setdefault("supported_event_hooks", list(self.get_supported_event_hooks())) @@ -108,6 +122,8 @@ class PromptSecurityGuardrail(CustomGuardrail): # Configuration for file sanitization self.max_poll_attempts = 30 # Maximum number of polling attempts self.poll_interval = 2 # Seconds between polling attempts + self.file_sanitization_timeout = file_sanitization_timeout + self.file_sanitization_fail_open = file_sanitization_fail_open is not False super().__init__(**kwargs) @@ -397,6 +413,39 @@ class PromptSecurityGuardrail(CustomGuardrail): Sanitize file content using Prompt Security API. Returns: dict with keys 'action', 'content', 'metadata' """ + try: + return await asyncio.wait_for( + self._sanitize_file_content(file_data, filename, user_api_key_alias), + timeout=self.file_sanitization_timeout, + ) + except (asyncio.TimeoutError, httpx.TimeoutException, LiteLLMTimeout) as exc: + if not self.file_sanitization_fail_open: + verbose_proxy_logger.error( + "Prompt Security Guardrail: file sanitization for %s timed out with %s; failing closed", + filename, + type(exc).__name__, + ) + raise HTTPException(status_code=408, detail="File sanitization timeout") from exc + + verbose_proxy_logger.error( + "Prompt Security Guardrail: file sanitization for %s timed out with %s; failing open", + filename, + type(exc).__name__, + ) + fail_open_result: Final[_SanitizeResult] = { + "action": "allow", + "content": None, + "metadata": {}, + "violations": (), + } + return fail_open_result + + async def _sanitize_file_content( + self, + file_data: bytes, + filename: str, + user_api_key_alias: str | None, + ) -> _SanitizeResult: headers: Final = {"APP-ID": self.api_key} if user_api_key_alias: headers["X-LiteLLM-Key-Alias"] = user_api_key_alias diff --git a/litellm/proxy/guardrails/guardrail_hooks/qualifire/qualifire.py b/litellm/proxy/guardrails/guardrail_hooks/qualifire/qualifire.py index daeb91eb2bd..f834426d619 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/qualifire/qualifire.py +++ b/litellm/proxy/guardrails/guardrail_hooks/qualifire/qualifire.py @@ -121,7 +121,7 @@ class QualifireGuardrail(CustomGuardrail): the live instance untouched instead of raising after it's already been corrupted. Mirrors LakeraAIGuardrail's own override of this same method. """ - prospective_on_flagged: Final = getattr(litellm_params, "on_flagged", None) or self.on_flagged + prospective_on_flagged: Final = litellm_params.on_flagged or self.on_flagged self._validate_on_flagged(prospective_on_flagged) super().update_in_memory_litellm_params(litellm_params=litellm_params) diff --git a/litellm/proxy/guardrails/guardrail_hooks/repelloai/repelloai.py b/litellm/proxy/guardrails/guardrail_hooks/repelloai/repelloai.py index 5f73a169215..8925cc5b3a6 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/repelloai/repelloai.py +++ b/litellm/proxy/guardrails/guardrail_hooks/repelloai/repelloai.py @@ -197,14 +197,11 @@ class RepelloAIGuardrail(CustomGuardrail): repelloai_response: RepelloAIAnalyzeResponse | None = None try: verbose_proxy_logger.debug("RepelloAI Argus request: %s", request) - raw_response: HttpxResponse | None = await self.async_handler.post( # pyright: ignore[reportUnknownMemberType] + response: Final[HttpxResponse] = await self.async_handler.post( # pyright: ignore[reportUnknownMemberType] # AsyncHTTPHandler.post is untyped url=endpoint, headers={"X-API-Key": self.repelloai_api_key}, json=request, ) - if raw_response is None: - raise ValueError("RepelloAI Argus returned no response") - response: Final[HttpxResponse] = raw_response self._raise_for_config_error(response) response.raise_for_status() try: diff --git a/litellm/proxy/guardrails/guardrail_hooks/semantic_guard/semantic_guard.py b/litellm/proxy/guardrails/guardrail_hooks/semantic_guard/semantic_guard.py index e34beec4d3e..2fbd50b5863 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/semantic_guard/semantic_guard.py +++ b/litellm/proxy/guardrails/guardrail_hooks/semantic_guard/semantic_guard.py @@ -6,7 +6,7 @@ via embedding similarity. Smarter than regex (understands intent), lighter than an LLM call (~20-50ms per request for embedding). """ -from typing import TYPE_CHECKING, Any, Final +from typing import TYPE_CHECKING, Any, Final, Protocol from litellm._logging import verbose_logger from litellm.integrations.custom_guardrail import ( @@ -50,7 +50,7 @@ class SemanticGuardrail(CustomGuardrail): similarity_threshold: float, route_templates: list[str] | None = None, custom_routes_file: str | None = None, - custom_routes: list[dict[str, Any]] | None = None, + custom_routes: list[dict[str, object]] | None = None, on_flagged_action: str = "block", event_hook: GuardrailEventHooks | list[GuardrailEventHooks] | Mode | None = None, default_on: bool = False, @@ -157,7 +157,14 @@ class SemanticGuardrail(CustomGuardrail): return response -def _get_top_route_choice(result: Any) -> Any: +class _RouteChoice(Protocol): + """The semantic-router match this guardrail reads: the route that fired, if any.""" + + @property + def name(self) -> str | None: ... + + +def _get_top_route_choice(result: _RouteChoice | list[_RouteChoice] | None) -> _RouteChoice | None: """Extract the top RouteChoice from SemanticRouter result. SemanticRouter.__call__ can return RouteChoice or List[RouteChoice]. @@ -194,7 +201,7 @@ def _extract_response_text(response: Any) -> str: return "" -def _content_to_text(content: Any) -> str: +def _content_to_text(content: object) -> str: if isinstance(content, str): return content if isinstance(content, list): diff --git a/litellm/proxy/guardrails/guardrail_hooks/tool_permission.py b/litellm/proxy/guardrails/guardrail_hooks/tool_permission.py index 3c5625bc272..a8b33109900 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/tool_permission.py +++ b/litellm/proxy/guardrails/guardrail_hooks/tool_permission.py @@ -1,9 +1,10 @@ import json import re from collections.abc import AsyncGenerator, AsyncIterable, Mapping, Sequence -from typing import Any, Final, Literal +from typing import Any, Final, Literal, TypedDict from fastapi import HTTPException +from typing_extensions import ReadOnly, Required from litellm import ChatCompletionToolParam from litellm._logging import verbose_proxy_logger @@ -51,6 +52,27 @@ def _object_list(value: object) -> Sequence[object] | None: return value if isinstance(value, list) else None +class _ToolPermissionRuleFields(TypedDict, total=False): + """The config-file shape a :class:`ToolPermissionRule` is built from.""" + + id: ReadOnly[Required[str]] + tool_name: ReadOnly[str | None] + tool_type: ReadOnly[str | None] + decision: ReadOnly[Required[Literal["allow", "deny"]]] + allowed_param_patterns: ReadOnly[dict[str, str] | None] + + +def _rule_from_fields(fields: _ToolPermissionRuleFields) -> ToolPermissionRule: + """Validate one config-file rule entry into a :class:`ToolPermissionRule`.""" + return ToolPermissionRule(**fields) + + +def _is_tool_use_block(block: object) -> bool: + """Whether ``block`` is an Anthropic ``tool_use`` content block.""" + fields: Final = _object_mapping(block) + return fields is not None and fields.get("type") == "tool_use" + + class ToolPermissionGuardrail(CustomGuardrail): def __init__( self, @@ -101,7 +123,7 @@ class ToolPermissionGuardrail(CustomGuardrail): compiled_patterns: Final[dict[str, dict[str, re.Pattern]]] = {} for rule_item in rules or []: - rule = rule_item if isinstance(rule_item, ToolPermissionRule) else ToolPermissionRule(**rule_item) + rule = rule_item if isinstance(rule_item, ToolPermissionRule) else _rule_from_fields(rule_item) target_patterns: dict[str, re.Pattern | None] = { "tool_name": None, @@ -440,7 +462,7 @@ class ToolPermissionGuardrail(CustomGuardrail): return is_allowed, None, message @staticmethod - def _get_mapping_value(item: Any, key: str) -> Any: + def _get_mapping_value(item: object, key: str) -> Any: if isinstance(item, dict): return item.get(key) return getattr(item, key, None) @@ -450,7 +472,7 @@ class ToolPermissionGuardrail(CustomGuardrail): return f"legacy_function_call_{choice_index}" def _legacy_function_call_to_tool_call( - self, function_call: Any, choice_index: int + self, function_call: object, choice_index: int ) -> ChatCompletionMessageToolCall | None: if function_call is None: return None @@ -549,7 +571,7 @@ class ToolPermissionGuardrail(CustomGuardrail): def _modify_anthropic_content_with_permission_errors( self, response: object, - content: tuple[Any, ...], + content: tuple[object, ...], denied_tools: tuple[tuple[ChatCompletionMessageToolCall, PermissionError], ...], ) -> None: if not denied_tools or not isinstance(response, dict): @@ -557,27 +579,33 @@ class ToolPermissionGuardrail(CustomGuardrail): verbose_proxy_logger.info("Blocking %s unauthorized tool uses", len(denied_tools)) - error_by_tool_use_id: Final = { # mutable-ok: read-only lookup, never mutated after construction + error_by_tool_use_id: Final[ + Mapping[object, str] + ] = { # mutable-ok: read-only lookup, never mutated after construction tool_call.id: self._create_permission_error_result(tool_call, error).content for tool_call, error in denied_tools } - denied_block_ids: Final = frozenset(error_by_tool_use_id) - def _is_denied(block: object) -> bool: - return isinstance(block, dict) and block.get("type") == "tool_use" and block.get("id") in denied_block_ids + def _denied_message(block: object) -> str | None: + fields: Final = _object_mapping(block) + if fields is None or fields.get("type") != "tool_use": + return None + return error_by_tool_use_id.get(fields.get("id")) - error_messages: Final = tuple(error_by_tool_use_id[block["id"]] for block in content if _is_denied(block)) - kept_blocks: Final = tuple(block for block in content if not _is_denied(block)) + error_messages: Final = tuple( + message for message in (_denied_message(block) for block in content) if message is not None + ) + kept_blocks: Final = tuple(block for block in content if _denied_message(block) is None) new_content: Final = [ # mutable-ok: response content is a JSON array on the wire *kept_blocks, {"type": "text", "text": "\n".join(error_messages)}, # mutable-ok: content block is a JSON object ] response["content"] = new_content # rebind-ok: the guardrail rewrites the provider response in place - if not any(isinstance(block, dict) and block.get("type") == "tool_use" for block in kept_blocks): + if not any(_is_tool_use_block(block) for block in kept_blocks): response["stop_reason"] = "end_turn" # rebind-ok: dropping every tool_use ends the turn - def _get_request_tool_name(self, tool: Any) -> tuple[str | None, str | None]: + def _get_request_tool_name(self, tool: object) -> tuple[str | None, str | None]: tool_type: Final = self._get_mapping_value(tool, "type") if tool_type != "function": return None, tool_type @@ -586,7 +614,7 @@ class ToolPermissionGuardrail(CustomGuardrail): tool_name: Final = self._get_mapping_value(function, "name") return tool_name, tool_type - def _get_legacy_function_name(self, function: Any) -> str | None: + def _get_legacy_function_name(self, function: object) -> str | None: return self._get_mapping_value(function, "name") def _get_named_tool_choice(self, data: dict) -> str | None: diff --git a/litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py b/litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py index e95e97bfe74..46b00829b74 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py +++ b/litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py @@ -57,6 +57,9 @@ class _EndpointTranslation(Protocol): @property def build_block_sse_chunks(self) -> "Callable[..., Sequence[bytes] | None]": ... + @property + def build_stream_error_items(self) -> "Callable[..., Sequence[object] | None]": ... + def _as_endpoint_translation(translation: _EndpointTranslation) -> _EndpointTranslation: return translation @@ -408,14 +411,32 @@ class UnifiedLLMGuardrails(CustomLogger): call_type: str | None, responses_so_far: Sequence[object], request_data: dict, + endpoint_translation: _EndpointTranslation | None = None, + stream_started: bool = False, + responses_yielded: Sequence[object] | None = None, ) -> AsyncGenerator[object, None]: - """Surface a mid-stream HTTPException. For A2A call types the response has - already started, so emit an in-stream JSON-RPC error chunk; otherwise - re-raise so the proxy can report it. + """Surface a mid-stream HTTPException (a guardrail block with the default + exception-on-block config, or a failed scan). + + A2A call types emit an in-stream JSON-RPC error chunk. For other call + types, once chunks have already reached the client the HTTP status is + gone, so the failure is delegated to the endpoint translation's + ``build_stream_error_items`` and travels as an in-stream error frame in + that endpoint's wire format. Before the first chunk (or when the format + has no in-stream error frame) the exception is re-raised so the proxy + can report it with a real HTTP status. """ if call_type is not None and CallTypes(call_type) in A2A_CALL_TYPES: yield _a2a_jsonrpc_error_chunk(exc, _get_a2a_request_id(responses_so_far, request_data)) return + if stream_started and endpoint_translation is not None: + error_items: Final = endpoint_translation.build_stream_error_items( + exc, responses_so_far=tuple(responses_yielded) if responses_yielded is not None else None + ) + if error_items is not None: + for error_item in error_items: + yield error_item + return raise exc def _build_transform_chunk( @@ -586,7 +607,15 @@ class UnifiedLLMGuardrails(CustomLogger): yield block_chunk raise _StreamTerminated() except HTTPException as e: - async for error_item in self._emit_streaming_http_error(e, call_type, responses_so_far, request_data): + async for error_item in self._emit_streaming_http_error( + e, + call_type, + responses_so_far, + request_data, + endpoint_translation=endpoint_translation, + stream_started=bool(responses_yielded), + responses_yielded=responses_yielded, + ): yield error_item raise _StreamTerminated() @@ -1070,11 +1099,17 @@ class UnifiedLLMGuardrails(CustomLogger): return except HTTPException as e: # Response already started (we already yielded chunks); cannot send 400. - # For A2A, yield an in-stream JSON-RPC error so the client sees it. - if call_type is not None and CallTypes(call_type) in A2A_CALL_TYPES: - yield _a2a_jsonrpc_error_chunk(e, _get_a2a_request_id(responses_so_far, request_data)) - return - raise + async for error_item in self._emit_streaming_http_error( + e, + call_type, + responses_so_far, + request_data, + endpoint_translation=endpoint_translation, + stream_started=chunks_yielded, + responses_yielded=responses_yielded, + ): + yield error_item + return chunks_yielded = True responses_yielded.append(original_item) yield original_item @@ -1133,7 +1168,13 @@ class UnifiedLLMGuardrails(CustomLogger): yield block_chunk return except HTTPException as e: - if call_type is not None and CallTypes(call_type) in A2A_CALL_TYPES: - yield _a2a_jsonrpc_error_chunk(e, _get_a2a_request_id(responses_so_far, request_data)) - else: - raise + async for error_item in self._emit_streaming_http_error( + e, + call_type, + responses_so_far, + request_data, + endpoint_translation=endpoint_translation, + stream_started=bool(responses_yielded), + responses_yielded=responses_yielded, + ): + yield error_item diff --git a/litellm/proxy/guardrails/guardrail_hooks/vigil_guard/vigil_guard.py b/litellm/proxy/guardrails/guardrail_hooks/vigil_guard/vigil_guard.py index 6b8148645aa..a5945a39589 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/vigil_guard/vigil_guard.py +++ b/litellm/proxy/guardrails/guardrail_hooks/vigil_guard/vigil_guard.py @@ -433,7 +433,7 @@ class VigilGuardGuardrail(CustomGuardrail): return collected @staticmethod - def _clamp_metadata_value(value: Any) -> _MetadataValue | None: + def _clamp_metadata_value(value: object) -> _MetadataValue | None: if isinstance(value, bool): return None if isinstance(value, str): diff --git a/litellm/proxy/guardrails/guardrail_hooks/xecguard/xecguard.py b/litellm/proxy/guardrails/guardrail_hooks/xecguard/xecguard.py index ddb40dc3ca0..831df43692b 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/xecguard/xecguard.py +++ b/litellm/proxy/guardrails/guardrail_hooks/xecguard/xecguard.py @@ -310,7 +310,7 @@ class XecGuardGuardrail(CustomGuardrail): scan_type: str, suppress_errors: bool = False, ) -> dict | None: - payload: Final[dict[str, Any]] = { + payload: Final[dict[str, object]] = { "model": self.xecguard_model, "scan_type": scan_type, "messages": messages, @@ -385,7 +385,7 @@ class XecGuardGuardrail(CustomGuardrail): def _build_full_history( self, request_data: dict, - inputs: Any, + inputs: GenericGuardrailAPIInputs, input_type: str, ) -> list[dict]: """Assemble the full message list that will be sent to XecGuard. diff --git a/litellm/proxy/guardrails/guardrail_initializers.py b/litellm/proxy/guardrails/guardrail_initializers.py index 47aea62f4c2..76dea1b7784 100644 --- a/litellm/proxy/guardrails/guardrail_initializers.py +++ b/litellm/proxy/guardrails/guardrail_initializers.py @@ -11,6 +11,7 @@ def initialize_bedrock(litellm_params: LitellmParams, guardrail: Guardrail): BedrockGuardrail, ) + streaming_params: Final = BedrockGuardrailStreamingParams.from_extras(litellm_params.model_extra) _bedrock_callback: Final = BedrockGuardrail( guardrail_name=guardrail.get("guardrail_name", ""), event_hook=litellm_params.mode, @@ -38,6 +39,9 @@ def initialize_bedrock(litellm_params: LitellmParams, guardrail: Guardrail): aws_bedrock_runtime_endpoint=litellm_params.aws_bedrock_runtime_endpoint, experimental_use_latest_role_message_only=litellm_params.experimental_use_latest_role_message_only, only_scan_new_messages=litellm_params.only_scan_new_messages or False, + streaming_buffer_until_moderated=streaming_params.streaming_buffer_until_moderated, + streaming_sampling_rate=streaming_params.streaming_sampling_rate, + streaming_end_of_stream_only=streaming_params.streaming_end_of_stream_only, ) litellm.logging_callback_manager.add_litellm_callback(_bedrock_callback) return _bedrock_callback diff --git a/litellm/proxy/guardrails/guardrail_registry.py b/litellm/proxy/guardrails/guardrail_registry.py index 90d5f6f4970..dc13c09dd38 100644 --- a/litellm/proxy/guardrails/guardrail_registry.py +++ b/litellm/proxy/guardrails/guardrail_registry.py @@ -413,14 +413,15 @@ class GuardrailRegistry: raise Exception(f"Error getting guardrail from DB: {e}") -def _apply_configured_bool_override(instance: CustomGuardrail, litellm_params: LitellmParams, param_name: str) -> None: - """Override ``instance.`` only when ``litellm_params`` explicitly - sets it, preserving whatever default the guardrail's own constructor chose +def _apply_configured_bool_overrides(instance: CustomGuardrail, litellm_params: LitellmParams) -> None: + """Override the parallel/raw-scan flags only when ``litellm_params`` explicitly + sets them, preserving whatever default the guardrail's own constructor chose otherwise (its constructor default may be True, so blindly copying an absent/None config value would silently clobber it back to False).""" - configured: Final = getattr(litellm_params, param_name, None) - if configured is not None: - setattr(instance, param_name, bool(configured)) + if litellm_params.run_in_parallel is not None: + instance.run_in_parallel = bool(litellm_params.run_in_parallel) + if litellm_params.scan_raw_request is not None: + instance.scan_raw_request = bool(litellm_params.scan_raw_request) class InMemoryGuardrailHandler: @@ -544,8 +545,7 @@ class InMemoryGuardrailHandler: "skip_tool_message_in_guardrail are enabled together, which excludes every message from " "scanning, so no request content would ever be scanned. Remove one of the two." ) - for override_param in ("run_in_parallel", "scan_raw_request"): - _apply_configured_bool_override(custom_guardrail_callback, litellm_params, override_param) + _apply_configured_bool_overrides(custom_guardrail_callback, litellm_params) parsed_guardrail: Final = Guardrail( guardrail_id=guardrail.get("guardrail_id"), @@ -803,7 +803,6 @@ class InMemoryGuardrailHandler: previous_guardrail: Final = self.IN_MEMORY_GUARDRAILS.get(guardrail_id) previous_source: Final = self._sources.get(guardrail_id, source) - # Remove from memory if exists (also removes from callbacks) if guardrail_id in self.IN_MEMORY_GUARDRAILS: self.delete_in_memory_guardrail(guardrail_id) diff --git a/litellm/proxy/hooks/litellm_skills/main.py b/litellm/proxy/hooks/litellm_skills/main.py index 569ec32c1a0..9edbc6dbf1c 100644 --- a/litellm/proxy/hooks/litellm_skills/main.py +++ b/litellm/proxy/hooks/litellm_skills/main.py @@ -67,6 +67,24 @@ class _ChatMessage(Protocol): def tool_calls(self) -> Sequence[_ChatToolCall] | None: ... +class _ChatChoice(Protocol): + @property + def message(self) -> _ChatMessage: ... + + @property + def finish_reason(self) -> str | None: ... + + +class _ChatCompletion(Protocol): + @property + def choices(self) -> Sequence[_ChatChoice]: ... + + +def _first_choice(response: _ChatCompletion) -> _ChatChoice: + """The first choice of an OpenAI shaped completion response.""" + return response.choices[0] + + class SkillsInjectionHook(CustomLogger): """ Pre/Post-call hook that processes skills from container.skills parameter. @@ -738,8 +756,9 @@ print('No executable skill module found') for iteration in range(self.max_iterations): # OpenAI format response has choices[0].message - assistant_message: _ChatMessage = current_response.choices[0].message - stop_reason: str | None = current_response.choices[0].finish_reason + choice: _ChatChoice = _first_choice(current_response) + assistant_message: _ChatMessage = choice.message + stop_reason: str | None = choice.finish_reason # Build assistant message for conversation history assistant_msg_dict: dict[str, object] = { diff --git a/litellm/proxy/hooks/mcp_semantic_filter/hook.py b/litellm/proxy/hooks/mcp_semantic_filter/hook.py index 3ce406eef73..8b82842353c 100644 --- a/litellm/proxy/hooks/mcp_semantic_filter/hook.py +++ b/litellm/proxy/hooks/mcp_semantic_filter/hook.py @@ -5,10 +5,11 @@ Pre-call hook that filters MCP tools semantically before LLM inference. Reduces context window size and improves tool selection accuracy. """ -from collections.abc import Mapping, Sequence -from typing import TYPE_CHECKING, Any, Final, Optional +from collections.abc import Iterable, Mapping, Sequence +from typing import TYPE_CHECKING, Final, Optional from fastapi import HTTPException +from typing_extensions import ReadOnly, TypedDict from litellm._logging import verbose_proxy_logger from litellm.constants import ( @@ -30,6 +31,13 @@ if TYPE_CHECKING: from litellm.router import Router +class SemanticToolFilterConfig(TypedDict, total=False): + enabled: ReadOnly[bool] + embedding_model: ReadOnly[str] + top_k: ReadOnly[int] + similarity_threshold: ReadOnly[float] + + def _truncate_csv_at_tool_name_boundary(tool_names_csv: str, max_length: int) -> str: """Cap a CSV of tool names to max_length, dropping any name that does not fit whole.""" if len(tool_names_csv) <= max_length: @@ -68,7 +76,7 @@ class SemanticToolFilterHook(CustomLogger): semantic_filter.top_k, ) - def _should_expand_mcp_tools(self, tools: list[Any]) -> bool: + def _should_expand_mcp_tools(self, tools: Iterable[Mapping[str, object]]) -> bool: """ Check if tools contain MCP references with server_url="litellm_proxy". @@ -82,9 +90,9 @@ class SemanticToolFilterHook(CustomLogger): async def _expand_mcp_tools( self, - tools: list[Any], + tools: Iterable[Mapping[str, object]], user_api_key_dict: "UserAPIKeyAuth", - ) -> list[dict[str, Any]]: + ) -> list[dict[str, object]]: """ Expand MCP references to actual tool definitions. @@ -111,7 +119,7 @@ class SemanticToolFilterHook(CustomLogger): ) # Convert Pydantic models to dicts for compatibility - openai_tools_as_dicts: Final = [] + openai_tools_as_dicts: Final[list[dict[str, object]]] = [] for tool in openai_tools: if hasattr(tool, "model_dump"): tool_dict = tool.model_dump(exclude_none=True) @@ -141,8 +149,8 @@ class SemanticToolFilterHook(CustomLogger): async def _filter_expanded_tools( self, data: dict, - expanded_tools: list[dict[str, Any]], - ) -> list[dict[str, Any]]: + expanded_tools: list[dict[str, object]], + ) -> list[dict[str, object]]: """ Apply the semantic filter to expanded MCP tool definitions. @@ -159,7 +167,7 @@ class SemanticToolFilterHook(CustomLogger): return await self.filter.filter_tools(query=user_query, available_tools=expanded_tools) - def _selected_tool_names(self, filtered_tools: list[dict[str, Any]]) -> list[str]: + def _selected_tool_names(self, filtered_tools: Sequence[object]) -> list[str]: """Names of the semantically selected tools, as produced by the MCP expansion.""" names: Final = (self.filter._extract_tool_info(tool)[0] for tool in filtered_tools) return [name for name in names if name] @@ -217,10 +225,10 @@ class SemanticToolFilterHook(CustomLogger): def _emit_filter_metadata( self, data: dict, - mcp_tools: list[object], - filtered_mcp_tools: list[object], - native_tools: list[object], - filtered_tools: list[object], + mcp_tools: Sequence[object], + filtered_mcp_tools: Sequence[object], + native_tools: Sequence[object], + filtered_tools: Sequence[object], ) -> None: """ Emit response-header metadata when MCP tools were filtered. @@ -252,10 +260,10 @@ class SemanticToolFilterHook(CustomLogger): def _emit_filter_metadata_safe( self, data: dict, - mcp_tools: list[object], - filtered_mcp_tools: list[object], - native_tools: list[object], - filtered_tools: list[object], + mcp_tools: Sequence[object], + filtered_mcp_tools: Sequence[object], + native_tools: Sequence[object], + filtered_tools: Sequence[object], ) -> None: """ Emit filter metadata without letting an emission failure abort the @@ -375,7 +383,7 @@ class SemanticToolFilterHook(CustomLogger): ) if mcp_tools: - filtered_mcp_tools = await self.filter.filter_tools( + filtered_mcp_tools: list[object] = await self.filter.filter_tools( query=user_query, available_tools=mcp_tools, ) @@ -419,9 +427,9 @@ class SemanticToolFilterHook(CustomLogger): self, data: dict, user_api_key_dict: "UserAPIKeyAuth", - response: Any, + response: object, request_headers: dict[str, str] | None = None, - litellm_call_info: dict[str, Any] | None = None, + litellm_call_info: dict[str, object] | None = None, ) -> dict[str, str] | None: """Add semantic filter stats and tool names to response headers.""" from litellm.constants import MAX_MCP_SEMANTIC_FILTER_TOOLS_HEADER_LENGTH @@ -446,7 +454,7 @@ class SemanticToolFilterHook(CustomLogger): return headers - def _get_tool_names_csv(self, tools: list[Any]) -> str: + def _get_tool_names_csv(self, tools: Sequence[object]) -> str: """Extract tool names and return as CSV string.""" if not tools: return "" @@ -461,7 +469,7 @@ class SemanticToolFilterHook(CustomLogger): @staticmethod async def initialize_from_config( - config: dict[str, Any] | None, + config: SemanticToolFilterConfig | None, llm_router: Optional["Router"], ) -> Optional["SemanticToolFilterHook"]: """ diff --git a/litellm/proxy/hooks/parallel_request_limiter_v3.py b/litellm/proxy/hooks/parallel_request_limiter_v3.py index 1e65da5b867..63129602082 100644 --- a/litellm/proxy/hooks/parallel_request_limiter_v3.py +++ b/litellm/proxy/hooks/parallel_request_limiter_v3.py @@ -8,7 +8,7 @@ import asyncio import binascii import os import uuid -from collections.abc import Callable, Mapping, Sequence, Set +from collections.abc import Awaitable, Callable, Mapping, Sequence, Set from contextvars import ContextVar from dataclasses import dataclass, field from datetime import datetime @@ -386,6 +386,12 @@ CacheCounterValues: TypeAlias = Sequence[CacheCounterValue | None] ParallelGaugeCacheValue: TypeAlias = dict[str, object] | int | float | str | bytes +class _AsyncLuaScript(Protocol): + """A Lua script registered against the async Redis client, called with KEYS and ARGV.""" + + def __call__(self, *, keys: Sequence[str], args: Sequence[object]) -> Awaitable[list[CacheCounterValue]]: ... + + class RateLimitDescriptorRateLimitObject(TypedDict, total=False): requests_per_unit: int | None tokens_per_unit: int | None @@ -577,6 +583,14 @@ def _parse_output_cap_value(raw_value: object) -> int | None: class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): + batch_rate_limiter_script: _AsyncLuaScript | None + token_increment_script: _AsyncLuaScript | None + check_and_increment_by_n_script: _AsyncLuaScript | None + window_guarded_token_increment_script: _AsyncLuaScript | None + parallel_acquire_script: _AsyncLuaScript | None + parallel_release_script: _AsyncLuaScript | None + parallel_count_script: _AsyncLuaScript | None + def __init__( self, internal_usage_cache: InternalUsageCache, @@ -3855,7 +3869,7 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): expected_window_start = operation.get("expected_window_start") if window_key is None or expected_window_start is None: continue - active_window_start = await self.internal_usage_cache.async_get_cache( + active_window_start: CacheCounterValue | None = await self.internal_usage_cache.async_get_cache( key=window_key, litellm_parent_otel_span=parent_otel_span, local_only=True, @@ -4144,7 +4158,7 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): def _collect_tpm_scope_targets( self, standard_logging_metadata: dict[str, Any], - kwargs: Any, + kwargs: object, model_group: str | None, ) -> list[tuple[str, str]]: """ @@ -4301,8 +4315,8 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): def _build_success_event_pipeline_operations( self, - kwargs: Any, - response_obj: Any, + kwargs: dict[str, Any], + response_obj: object, rate_limit_type: Literal["output", "input", "total"], ) -> list[RedisPipelineIncrementOperation]: """Build Redis pipeline increment ops for TPM / parallel-request counters.""" diff --git a/litellm/proxy/hooks/proxy_track_cost_callback.py b/litellm/proxy/hooks/proxy_track_cost_callback.py index f593e94b36f..47aafda2337 100644 --- a/litellm/proxy/hooks/proxy_track_cost_callback.py +++ b/litellm/proxy/hooks/proxy_track_cost_callback.py @@ -1,5 +1,6 @@ import asyncio import traceback +from collections.abc import Sequence from datetime import datetime from typing import TYPE_CHECKING, Any, Final, cast @@ -20,6 +21,10 @@ from litellm.proxy.auth.auth_checks import ( log_db_metrics, ) from litellm.proxy.auth.route_checks import RouteChecks +from litellm.proxy.db.db_spend_update_writer import ( + debitable_model_access_groups, + get_llm_router, +) from litellm.proxy.litellm_pre_call_utils import LiteLLMProxyRequestSetup from litellm.proxy.spend_tracking.spend_log_error_logger import ( should_suppress_spend_log_tracebacks, @@ -27,6 +32,7 @@ from litellm.proxy.spend_tracking.spend_log_error_logger import ( ) from litellm.proxy.spend_tracking.spend_tracking_utils import ( _sanitize_error_information_for_spend_logs, + get_request_model_access_groups, ) from litellm.proxy.utils import ProxyUpdateSpend from litellm.types.utils import ( @@ -258,6 +264,11 @@ class _ProxyDBLogger(CustomLogger): sl_object=sl_object, metadata=metadata, ) + model_access_groups: Final = debitable_model_access_groups( + attributed=get_request_model_access_groups(kwargs), + served_model_id=sl_object.get("model_id") if sl_object is not None else None, + router=get_llm_router(), + ) if response_cost is not None: user_api_key: Final = metadata.get("user_api_key", None) @@ -296,6 +307,7 @@ class _ProxyDBLogger(CustomLogger): response_cost=response_cost, budget_reservation=budget_reservation, request_tags=tags, + model_access_groups=model_access_groups, ) # update cache (fire-and-forget for backward compat: @@ -572,6 +584,7 @@ async def _update_database_and_spend_counters( response_cost: float, budget_reservation: dict | None, request_tags: list[str] | None = None, + model_access_groups: Sequence[str] | None = None, ) -> None: try: await proxy_logging_obj.db_spend_update_writer.update_database( @@ -610,6 +623,8 @@ async def _update_database_and_spend_counters( budget_reservation=budget_reservation, end_user_id=end_user_id, tags=request_tags, + request_started_at=start_time, + model_access_groups=model_access_groups, ) except Exception: if budget_reservation is not None: diff --git a/litellm/proxy/litellm_pre_call_utils.py b/litellm/proxy/litellm_pre_call_utils.py index b7688790e54..20f83085286 100644 --- a/litellm/proxy/litellm_pre_call_utils.py +++ b/litellm/proxy/litellm_pre_call_utils.py @@ -4,9 +4,10 @@ import json import re import time from collections import OrderedDict -from collections.abc import Mapping, MutableMapping +from collections.abc import Mapping, MutableMapping, Sequence +from datetime import datetime from types import MappingProxyType -from typing import TYPE_CHECKING, Any, Final +from typing import TYPE_CHECKING, Any, Final, cast from fastapi import HTTPException, Request from pydantic import ValidationError as PydanticValidationError @@ -29,6 +30,7 @@ from litellm.litellm_core_utils.initialize_dynamic_callback_params import ( _request_blocked_callback_params, iter_client_callback_metadata_dicts, ) +from litellm.litellm_core_utils.internal_call_metadata import MODEL_ACCESS_GROUP_METADATA_KEY from litellm.litellm_core_utils.safe_json_loads import safe_json_loads from litellm.litellm_core_utils.url_utils import ( is_url_destination_allowed_by_host, @@ -54,7 +56,7 @@ from litellm.proxy.common_utils.http_parsing_utils import _safe_get_request_head from litellm.types.integrations.anthropic_cache_control_hook import GATEWAY_INJECTED_CACHE_METADATA_KEY # Cache special headers as a frozenset for O(1) lookup performance -_SPECIAL_HEADERS_CACHE: Final = frozenset(v.value.lower() for v in SpecialHeaders._member_map_.values()) +_SPECIAL_HEADERS_CACHE: Final = frozenset(str(v.value).lower() for v in SpecialHeaders) _REDACTED_HEADER_VALUE: Final = "***REDACTED***" _CREDENTIAL_HEADER_NAMES: Final = SpecialHeaders.litellm_credential_header_names() | frozenset( @@ -125,7 +127,7 @@ def _stampable_key_hash(user_api_key_dict: UserAPIKeyAuth) -> str | None: _ANTHROPIC_SESSION_ID_VALUE_RE: Final = re.compile(r"^[a-zA-Z0-9_\-]+$") -def _sanitize_for_log(value: Any) -> str: +def _sanitize_for_log(value: object) -> str: """ Basic log sanitization helper to reduce log-injection risk. @@ -163,7 +165,7 @@ _ENABLE_TEAM_STALE_ALIAS_BYPASS: bool | None = None if TYPE_CHECKING: from litellm.proxy.proxy_server import ProxyConfig as _ProxyConfig - from litellm.types.proxy.policy_engine import PolicyMatchContext + from litellm.types.proxy.policy_engine import Policy, PolicyMatchContext ProxyConfig = _ProxyConfig else: @@ -253,6 +255,7 @@ _UNTRUSTED_ROOT_CONTROL_FIELDS: Final = ( "_code_interpreter_interception_converted_stream", "_code_interpreter_interception_sandbox_key", "_code_interpreter_interception_session_scoped", + "_headroom_interception_converted_stream", "max_agentic_loops", # Recomputed below from the actual caller-controlled timeout sources (headers and # body fields); a client-forged value here would let a request either dodge cooldown @@ -326,7 +329,7 @@ _ALLOW_CLIENT_PRICING_OVERRIDE_METADATA_KEY: Final = "allow_client_pricing_overr _URL_DESTINATION_REQUEST_FIELDS: Final = ("model", "file_id") -def _reject_url_valued_destinations(data: dict[str, Any]) -> None: +def _reject_url_valued_destinations(data: dict[str, object]) -> None: """Reject URL-valued ``model``/``file_id`` unless admin-allowlisted. Some providers (HuggingFace, Oobabooga, Gemini files) accept a URL in the @@ -385,7 +388,7 @@ def _invalid_metadata_type_error(field: str, value: object) -> ProxyException: ) -def _normalized_metadata_object(field: str, value: object) -> Mapping[str, Any]: +def _normalized_metadata_object(field: str, value: object) -> Mapping[str, object]: """Return ``value`` as a metadata object or raise a 400 like OpenAI does. A JSON string that parses to an object is accepted because multipart/form-data @@ -400,6 +403,23 @@ def _normalized_metadata_object(field: str, value: object) -> Mapping[str, Any]: raise _invalid_metadata_type_error(field=field, value=value) +def _normalized_metadata_slot( + request_data: MutableMapping[str, object], metadata_variable_name: str +) -> dict[str, object]: + """Return the request's metadata slot as a dict, normalising it in place first. + + Metadata can arrive as a JSON string (multipart/form-data, ``extra_body``). Parsing it here keeps + existing entries alive through a merge instead of silently overwriting them with an empty dict. + """ + raw: Final = request_data.get(metadata_variable_name) + if isinstance(raw, dict): + return raw + parsed: Final = safe_json_loads(raw) if isinstance(raw, str) else None + normalized: Final[dict[str, object]] = parsed if isinstance(parsed, dict) else {} + request_data[metadata_variable_name] = normalized + return normalized + + def _strip_untrusted_request_header_controls( headers: Any, *, @@ -415,7 +435,7 @@ def _strip_untrusted_request_header_controls( headers.pop(header_name, None) -def _is_false_like(value: Any) -> bool: +def _is_false_like(value: object) -> bool: if isinstance(value, bool): return value is False if isinstance(value, str): @@ -460,7 +480,7 @@ def _key_or_team_allows_client_pricing_override( ) -def _strip_client_message_redaction_opt_out(data: dict[str, Any]) -> None: +def _strip_client_message_redaction_opt_out(data: dict[str, object]) -> None: stripped: Final[list[str]] = [] if "turn_off_message_logging" in data and _is_false_like(data["turn_off_message_logging"]): stripped.append("turn_off_message_logging") @@ -511,7 +531,7 @@ def _strip_client_callback_credentials( ) -def _strip_client_pricing_overrides(data: dict[str, Any]) -> None: +def _strip_client_pricing_overrides(data: dict[str, object]) -> None: """Drop pricing overrides from the request body and any metadata variant. Skipped only when the calling key/team carries @@ -578,9 +598,9 @@ def _get_metadata_variable_name(request: Request) -> str: def _promoted_trace_control_fields( - requester_metadata: Mapping[str, Any], - litellm_metadata: Mapping[str, Any], -) -> tuple[tuple[str, Any], ...]: + requester_metadata: Mapping[str, object], + litellm_metadata: Mapping[str, object], +) -> tuple[tuple[str, object], ...]: """Return the caller's trace-control fields that ``litellm_metadata`` does not already set.""" return tuple( (key, value) @@ -1191,7 +1211,7 @@ class LiteLLMProxyRequestSetup: def add_litellm_data_for_backend_llm_call( *, headers: dict, - request_data: Mapping[str, Any], + request_data: Mapping[str, object], user_api_key_dict: UserAPIKeyAuth, general_settings: dict[str, Any] | None = None, ) -> LitellmDataForBackendLLMCall: @@ -1325,6 +1345,8 @@ class LiteLLMProxyRequestSetup: def get_sanitized_user_information_from_key( user_api_key_dict: UserAPIKeyAuth, ) -> StandardLoggingUserAPIKeyMetadata: + stripped_metadata: Final = strip_callback_config(user_api_key_dict.metadata) + auth_metadata: Final = cast("dict[str, str] | None", stripped_metadata) # cast-ok: metadata is free-form JSON user_api_key_logged_metadata: Final = StandardLoggingUserAPIKeyMetadata( user_api_key_hash=user_api_key_dict.api_key, # just the hashed token user_api_key_alias=user_api_key_dict.key_alias, @@ -1347,7 +1369,7 @@ class LiteLLMProxyRequestSetup: user_api_key_budget_reset_at=( user_api_key_dict.budget_reset_at.isoformat() if user_api_key_dict.budget_reset_at else None ), - user_api_key_auth_metadata=strip_callback_config(user_api_key_dict.metadata), + user_api_key_auth_metadata=auth_metadata, ) return user_api_key_logged_metadata @@ -1377,6 +1399,10 @@ class LiteLLMProxyRequestSetup: ) if user_api_key_dict.budget_reservation is not None: data[_metadata_variable_name]["user_api_key_budget_reservation"] = user_api_key_dict.budget_reservation + if user_api_key_dict.matched_model_access_groups: + data[_metadata_variable_name][MODEL_ACCESS_GROUP_METADATA_KEY] = ( + user_api_key_dict.matched_model_access_groups + ) # UserAPIKeyAuth object for MCP server access control data[_metadata_variable_name]["user_api_key_auth"] = user_api_key_dict.model_copy( update={ @@ -1571,14 +1597,7 @@ class LiteLLMProxyRequestSetup: return _metadata_variable_name: Final = get_metadata_variable_name_from_kwargs(request_data) - metadata = request_data.get(_metadata_variable_name) - if isinstance(metadata, str): - parsed: Final = safe_json_loads(metadata) - metadata = parsed if isinstance(parsed, dict) else {} - request_data[_metadata_variable_name] = metadata - elif not isinstance(metadata, dict): - metadata = {} - request_data[_metadata_variable_name] = metadata + metadata: Final = _normalized_metadata_slot(request_data, _metadata_variable_name) existing_tags: Final = metadata.get("tags") metadata["tags"] = LiteLLMProxyRequestSetup._merge_tags( @@ -1630,18 +1649,7 @@ class LiteLLMProxyRequestSetup: # from (litellm_metadata vs metadata) so the merged tags are visible # to _tag_max_budget_check. _metadata_variable_name: Final = get_metadata_variable_name_from_kwargs(request_data) - metadata = request_data.get(_metadata_variable_name) - # metadata can arrive as a JSON string (multipart/form-data, extra_body). - # Parse it so existing tags survive the merge — overwriting the string - # with {} would let a caller bypass _tag_max_budget_check on an - # over-budget body tag by also sending a within-budget header tag. - if isinstance(metadata, str): - parsed: Final = safe_json_loads(metadata) - metadata = parsed if isinstance(parsed, dict) else {} - request_data[_metadata_variable_name] = metadata - elif not isinstance(metadata, dict): - metadata = {} - request_data[_metadata_variable_name] = metadata + metadata: Final = _normalized_metadata_slot(request_data, _metadata_variable_name) existing_tags: Final = metadata.get("tags") metadata["tags"] = LiteLLMProxyRequestSetup._merge_tags( @@ -1781,7 +1789,7 @@ async def add_litellm_data_to_request( # admin-injection strip below so the audit / spend-tracking consumers of # proxy_server_request["body"] see the cleaned metadata rather than # attacker-forged user_api_key_* fields. - _litellm_received_at: Final = getattr(request.state, "litellm_received_at", None) + _litellm_received_at: Final[datetime | None] = getattr(request.state, "litellm_received_at", None) arrival_time: Final = _litellm_received_at.timestamp() if _litellm_received_at is not None else time.time() data["proxy_server_request"] = { "url": str(request.url), @@ -2466,16 +2474,16 @@ def _resolve_provider_from_deployment( if deployment is None: continue - litellm_params = getattr(deployment, "litellm_params", None) + litellm_params: object = getattr(deployment, "litellm_params", None) if litellm_params is None: continue custom_provider = getattr(litellm_params, "custom_llm_provider", None) - if custom_provider: + if isinstance(custom_provider, str) and custom_provider: return custom_provider - deployment_model = getattr(litellm_params, "model", "") or "" - if "/" in deployment_model: + deployment_model = getattr(litellm_params, "model", "") + if isinstance(deployment_model, str) and "/" in deployment_model: return deployment_model.split("/", 1)[0] return None @@ -2898,8 +2906,8 @@ def _extract_policy_id(s: str) -> str | None: def _match_and_track_policies( data: dict, context: "PolicyMatchContext", - request_body_policies: Any, - policies_override: dict[str, Any] | None = None, + request_body_policies: Sequence[str], + policies_override: dict[str, "Policy"] | None = None, ) -> tuple[list[str], dict[str, str]]: """ Match policies via attachments and request body, track them in metadata. @@ -2957,7 +2965,7 @@ def _apply_resolved_guardrails_to_metadata( metadata_variable_name: str, context: "PolicyMatchContext", policy_names: list[str] | None = None, - policies: dict[str, Any] | None = None, + policies: dict[str, "Policy"] | None = None, ) -> None: """Apply resolved guardrails and pipelines to request metadata.""" from litellm._logging import verbose_proxy_logger @@ -3087,7 +3095,7 @@ async def add_guardrails_from_policy_engine( request_body_names.append(item) # Resolve policy versions by ID from in-memory cache (populated by sync job; no DB in hot path) - merged_policies: Final[dict[str, Any]] = dict(registry.get_all_policies()) + merged_policies: Final[dict[str, Policy]] = dict(registry.get_all_policies()) fetched_policy_names: Final[list[str]] = [] for policy_id in request_body_version_ids: result = registry.get_policy_by_id_for_request(policy_id=policy_id) diff --git a/litellm/proxy/management_endpoints/auto_router_endpoints.py b/litellm/proxy/management_endpoints/auto_router_endpoints.py index b5533d548e5..21e652114bc 100644 --- a/litellm/proxy/management_endpoints/auto_router_endpoints.py +++ b/litellm/proxy/management_endpoints/auto_router_endpoints.py @@ -7,7 +7,7 @@ POST /auto_router/validate_complexity_router_config - Dry-run the complexity-rou from collections.abc import Mapping, Sequence from datetime import datetime, timedelta, timezone -from itertools import groupby +from itertools import chain, groupby from operator import attrgetter from types import MappingProxyType from typing import TYPE_CHECKING, Annotated, Final, Protocol @@ -58,10 +58,11 @@ from litellm.types.management_endpoints.auto_router_endpoints import ( ComplexityRouterConfigValidationResponse, RequestComplexityRouterConfig, ShadowEvalDirection, - ShadowEvalJobKeyResponse, ShadowEvalJobResponse, + ShadowEvalJobTargetResponse, ShadowEvalResult, ShadowEvalSlice, + ShadowEvalTargetType, StartShadowEvalRequest, ) @@ -104,10 +105,43 @@ class _VerificationTokenTable(Protocol): async def find_many(self, *, where: Mapping[str, object]) -> Sequence[_VerificationTokenRow]: ... +class _TeamRow(Protocol): + @property + def team_id(self) -> str: ... + + @property + def team_alias(self) -> str | None: ... + + +class _TeamRowsTable(Protocol): + async def find_many(self, *, where: Mapping[str, object]) -> Sequence[_TeamRow]: ... + + +class _UserRow(Protocol): + @property + def user_id(self) -> str: ... + + @property + def user_email(self) -> str | None: ... + + +class _UserRowsTable(Protocol): + async def find_many(self, *, where: Mapping[str, object]) -> Sequence[_UserRow]: ... + + class _ShadowEvalJobRow(Protocol): @property def id(self) -> str: ... + @property + def group_id(self) -> str: ... + + @property + def target_type(self) -> str: ... + + @property + def target_id(self) -> str: ... + class _ShadowEvalJobTable(Protocol): async def find_many(self, *, where: Mapping[str, object]) -> Sequence[_ShadowEvalJobRow]: ... @@ -138,6 +172,14 @@ def _verification_tokens(prisma_client: "PrismaClient") -> _VerificationTokenTab return prisma_client.db.litellm_verificationtoken +def _team_rows(prisma_client: "PrismaClient") -> _TeamRowsTable: + return prisma_client.db.litellm_teamtable + + +def _user_rows(prisma_client: "PrismaClient") -> _UserRowsTable: + return prisma_client.db.litellm_usertable + + def _shadow_eval_jobs(prisma_client: "PrismaClient") -> _ShadowEvalJobTable: return prisma_client.db.litellm_shadowevaljob @@ -791,7 +833,7 @@ def _judge_collisions_for_team( return tuple( (role, model) for role, model in ( - *_router_arm_models(llm_router, data.router_name), + *(arm for name in data.router_names for arm in _router_arm_models(llm_router, name)), *((("baseline", data.baseline_model),) if data.baseline_model is not None else ()), ) if judge & judge_target(llm_router, model, team_id).models @@ -836,7 +878,7 @@ def _validate_judge_is_not_a_candidate( def _is_unique_violation(error: Exception) -> bool: - """Whether a Prisma create failed on a unique index. One active job per key and + """Whether a Prisma create failed on a unique index. One active job per target and direction lives in a partial unique index (raw SQL in the migration; schema.prisma cannot express partial indexes), so the read-then-create check above it is advisory: two concurrent starts pass the read, and the loser must surface as the same 409 @@ -862,7 +904,7 @@ class _AttemptAggRow(BaseModel): _ATTEMPT_AGG_ROWS: Final = TypeAdapter(list[_AttemptAggRow]) -_ATTEMPT_AGG_SELECT: Final = """ +_ATTEMPT_AGG_COLUMNS: Final = """ COUNT(*)::int AS turn_count, COUNT(*) FILTER (WHERE outcome = 'real')::int AS real_wins, COUNT(*) FILTER (WHERE outcome = 'shadow')::int AS shadow_wins, @@ -871,21 +913,40 @@ _ATTEMPT_AGG_SELECT: Final = """ COALESCE(SUM(real_cost + real_classifier_cost) FILTER (WHERE real_cost IS NOT NULL AND NOT real_cache_hit), 0)::float AS real_spend, COALESCE(SUM(shadow_cost + shadow_classifier_cost) FILTER (WHERE real_cost IS NOT NULL AND NOT real_cache_hit), 0)::float AS shadow_spend, COUNT(*) FILTER (WHERE real_cache_hit)::int AS cache_hit_turns +""" + +_ATTEMPT_AGG_SELECT: Final = ( + _ATTEMPT_AGG_COLUMNS + + """ FROM "LiteLLM_ShadowEvalAttempt" WHERE job_id = ANY($1::text[]) AND outcome != 'error' GROUP BY 1 """ +) _ATTEMPT_AGG_BY_TIER_SQL: Final = "SELECT COALESCE(tier, 'UNCLASSIFIED') AS grp," + _ATTEMPT_AGG_SELECT _ATTEMPT_AGG_BY_MODEL_SQL: Final = "SELECT COALESCE(real_model, 'unknown') AS grp," + _ATTEMPT_AGG_SELECT _ATTEMPT_AGG_BY_LEG_SQL: Final = "SELECT job_id AS grp," + _ATTEMPT_AGG_SELECT +# Attempt rows from before arm stamping carry no router_name; they belong to the job's +# own router, which the join reads off the leg. +_ATTEMPT_AGG_BY_ROUTER_SQL: Final = ( + "SELECT COALESCE(a.router_name, j.router_name) AS grp," + + _ATTEMPT_AGG_COLUMNS + + """ +FROM "LiteLLM_ShadowEvalAttempt" a +JOIN "LiteLLM_ShadowEvalJob" j ON j.id = a.job_id +WHERE a.job_id = ANY($1::text[]) AND a.outcome != 'error' +GROUP BY 1 +""" +) + # These guards derive spend from attempt rows, the cross-pod authority; the sampler also # reads the live counter, so admission can stop before a row-based guard would fire (safe # direction, and mid-deploy rows from old pods price as judge-only until the deploy ends). _SWEEP_FINISHED_JOBS_SQL: Final = """ UPDATE "LiteLLM_ShadowEvalJob" j SET stopped_at = (NOW() AT TIME ZONE 'utc') -WHERE j.api_key_id = ANY($1::text[]) AND j.stopped_at IS NULL +WHERE j.target_type = $2 AND j.target_id = ANY($1::text[]) AND j.stopped_at IS NULL AND ( j.ends_at <= (NOW() AT TIME ZONE 'utc') OR (SELECT COUNT(*) FROM "LiteLLM_ShadowEvalAttempt" a WHERE a.job_id = j.id) >= j.max_turns @@ -966,10 +1027,10 @@ WHERE group_id IN ( ) """ -_LIST_LEGS_BY_KEY_SQL: Final = """ +_LIST_LEGS_BY_TARGET_SQL: Final = """ SELECT * FROM "LiteLLM_ShadowEvalJob" WHERE group_id IN ( - SELECT group_id FROM "LiteLLM_ShadowEvalJob" WHERE api_key_id = $2 + SELECT group_id FROM "LiteLLM_ShadowEvalJob" WHERE target_type = $2 AND target_id = $3 GROUP BY group_id ORDER BY MAX(created_at) DESC LIMIT $1::int ) """ @@ -1007,16 +1068,18 @@ def _slices(rows: Sequence[_AttemptAggRow]) -> tuple[ShadowEvalSlice, ...]: class _LegRow(BaseModel): """One LiteLLM_ShadowEvalJob row, validated off the untyped prisma record. A row is - one key's leg of a job; the legs of a job share group_id and identical config, written - together by one create_many. The API's job id is the group id, so leg ids never leave - the server (attempts reference them internally).""" + one target's leg of a job; the legs of a job share group_id and identical config, + written together by one create_many. The API's job id is the group id, so leg ids + never leave the server (attempts reference them internally).""" model_config = ConfigDict(from_attributes=True) id: str group_id: str - api_key_id: str + target_type: ShadowEvalTargetType + target_id: str router_name: str + router_names: tuple[str, ...] = () direction: ShadowEvalDirection baseline_model: str | None = None judge_model: str @@ -1028,6 +1091,12 @@ class _LegRow(BaseModel): stopped_at: datetime | None = None stopped_by: str | None = None + @property + def arm_router_names(self) -> tuple[str, ...]: + """The job's full router set; rows from before router_names existed hold it in + router_name alone. The one place that reading lives on the endpoint side.""" + return self.router_names or (self.router_name,) + @field_validator("created_at", "ends_at", "stopped_at") @classmethod def _as_aware_utc(cls, value: datetime | None) -> datetime | None: @@ -1068,18 +1137,19 @@ def _group_response( first: Final = legs[0] return ShadowEvalJobResponse( job_id=group_id, - keys=tuple( - ShadowEvalJobKeyResponse( - api_key_id=leg.api_key_id, + targets=tuple( + ShadowEvalJobTargetResponse( + target_type=leg.target_type, + target_id=leg.target_id, max_turns=leg.max_turns, max_budget=leg.max_budget, stopped_at=leg.stopped_at, attempt_count=stats.attempt_count if (stats := attempt_counts.get(leg.id)) else 0, spend=round(stats.spend, 6) if stats else 0.0, ) - for leg in sorted(legs, key=lambda leg: leg.api_key_id) + for leg in sorted(legs, key=lambda leg: (leg.target_type, leg.target_id)) ), - router_name=first.router_name, + router_names=first.arm_router_names, direction=first.direction, baseline_model=first.baseline_model, judge_model=first.judge_model, @@ -1090,34 +1160,85 @@ def _group_response( ) -_NO_KEY_LABELS: Final[tuple[str | None, str | None]] = (None, None) +_NO_TARGET_LABELS: Final[tuple[str | None, str | None]] = (None, None) -async def _with_key_labels( +def _target_labels( + key_rows: Sequence[_VerificationTokenRow], + team_rows: Sequence[_TeamRow], + user_rows: Sequence[_UserRow], +) -> Mapping[tuple[str, str], tuple[str | None, str | None]]: + """Display labels by (target_type, target_id): a key's (alias, masked name), a + team's (alias, None), a user's (email, None).""" + return MappingProxyType( + { # mutable-ok: MappingProxyType needs a dict to wrap + key: value + for key, value in chain( + ((("key", row.token), (row.key_alias, row.key_name)) for row in key_rows), + ((("team", row.team_id), (row.team_alias, None)) for row in team_rows), + ((("user", row.user_id), (row.user_email, None)) for row in user_rows), + ) + } + ) + + +def _target_ids_of(responses: Sequence[ShadowEvalJobResponse], target_type: ShadowEvalTargetType) -> tuple[str, ...]: + return tuple( + sorted( + frozenset( + target.target_id + for response in responses + for target in response.targets + if target.target_type == target_type + ) + ) + ) + + +async def _with_target_labels( prisma_client: "PrismaClient", responses: Sequence[ShadowEvalJobResponse] ) -> tuple[ShadowEvalJobResponse, ...]: - """Resolve every scoped key's hash to its alias and masked name in one batched read, - so the UI can say whose traffic a job shadows. Deleted keys resolve to None.""" + """Resolve every scoped target's id to a display label in one batched read per kind, + so the UI can say whose traffic a job shadows: a key's alias and masked name, a + team's alias, a user's email. Deleted targets resolve to None.""" if not responses: return () - tokens: Final = sorted(frozenset(key.api_key_id for response in responses for key in response.keys)) - key_rows: Final = await _verification_tokens(prisma_client).find_many( - where={"token": {"in": tokens}} # mutable-ok: Prisma filter + tokens: Final = _target_ids_of(responses, "key") + team_ids: Final = _target_ids_of(responses, "team") + user_ids: Final = _target_ids_of(responses, "user") + key_rows: Final = ( + await _verification_tokens(prisma_client).find_many( + where={"token": {"in": list(tokens)}} # mutable-ok: Prisma filter + ) + if tokens + else () ) - labels: Final[Mapping[str, tuple[str | None, str | None]]] = { - row.token: (row.key_alias, row.key_name) for row in key_rows or () - } + team_rows: Final = ( + await _team_rows(prisma_client).find_many( + where={"team_id": {"in": list(team_ids)}} # mutable-ok: Prisma filter + ) + if team_ids + else () + ) + user_rows: Final = ( + await _user_rows(prisma_client).find_many( + where={"user_id": {"in": list(user_ids)}} # mutable-ok: Prisma filter + ) + if user_ids + else () + ) + labels: Final = _target_labels(key_rows or (), team_rows or (), user_rows or ()) return tuple( response.model_copy( update={ # mutable-ok: pydantic update payload - "keys": tuple( - key.model_copy( + "targets": tuple( + target.model_copy( update={ # mutable-ok: pydantic update payload - "key_alias": labels.get(key.api_key_id, _NO_KEY_LABELS)[0], - "key_name": labels.get(key.api_key_id, _NO_KEY_LABELS)[1], + "target_alias": labels.get((target.target_type, target.target_id), _NO_TARGET_LABELS)[0], + "key_name": labels.get((target.target_type, target.target_id), _NO_TARGET_LABELS)[1], } ) - for key in response.keys + for target in response.targets ) } ) @@ -1125,29 +1246,40 @@ async def _with_key_labels( ) -async def _shadow_eval_results(prisma_client: "PrismaClient", legs: Sequence[_LegRow]) -> ShadowEvalResult | None: - """All three stratifications of one job's verdicts. Tier answers "where does the router - do well"; the model stratification groups by whichever model served the real arm, so it - answers "which of the models these keys use today would the router beat" forward, and - "for the turns the router sent to X, did X beat the baseline" in reverse; key answers - "which key's traffic does the router suit". Reads are bounded by the job's own attempts - (<= the sum of its keys' max_turns) via the job_id index.""" +async def _shadow_eval_results( + prisma_client: "PrismaClient", legs: Sequence[_LegRow] +) -> tuple[ShadowEvalResult | None, Mapping[tuple[str, str], ShadowEvalSlice]]: + """One job's stratified verdicts, plus each target's own slice keyed by the + (target_type, target_id) pair so a key, team, and user sharing an id can never + collapse into one entry. Tier answers "where does the router do well"; the model + stratification groups by whichever model served the real arm, so it answers "which + of the models these targets use today would the router beat" forward, and "for the + turns the router sent to X, did X beat the baseline" in reverse; the per-target + slices answer "which target's traffic does the router suit". Reads are bounded by + the job's own attempts (<= the sum of its targets' max_turns) via the job_id index.""" leg_ids: Final = [leg.id for leg in legs] # mutable-ok: query param by_tier: Final = _ATTEMPT_AGG_ROWS.validate_python( await _query_raw(prisma_client, _ATTEMPT_AGG_BY_TIER_SQL, leg_ids) or () ) if not by_tier: - return None + return None, MappingProxyType({}) by_model: Final = _ATTEMPT_AGG_ROWS.validate_python( await _query_raw(prisma_client, _ATTEMPT_AGG_BY_MODEL_SQL, leg_ids) or () ) - key_by_leg: Final = MappingProxyType({leg.id: leg.api_key_id for leg in legs}) + target_by_leg: Final = MappingProxyType({leg.id: (leg.target_type, leg.target_id) for leg in legs}) by_leg: Final = _ATTEMPT_AGG_ROWS.validate_python( await _query_raw(prisma_client, _ATTEMPT_AGG_BY_LEG_SQL, leg_ids) or () ) - by_key: Final = tuple( - row.model_copy(update={"grp": key_by_leg[row.grp]}) # mutable-ok: pydantic update payload - for row in by_leg + verdicts_by_target: Final[Mapping[tuple[str, str], ShadowEvalSlice]] = MappingProxyType( + { # mutable-ok: MappingProxyType needs a dict to wrap + target_by_leg[slice.group]: slice.model_copy( + update={"group": target_by_leg[slice.group][1]} # mutable-ok: pydantic update payload + ) + for slice in _slices(by_leg) + } + ) + by_router: Final = _ATTEMPT_AGG_ROWS.validate_python( + await _query_raw(prisma_client, _ATTEMPT_AGG_BY_ROUTER_SQL, leg_ids) or () ) total_turns: Final = sum(r.turn_count for r in by_tier) funnel_rows: Final = await _query_raw(prisma_client, _FUNNEL_TOTALS_SQL, leg_ids) @@ -1155,10 +1287,10 @@ async def _shadow_eval_results(prisma_client: "PrismaClient", legs: Sequence[_Le # Coverage only when EVERY leg has a funnel row: a partial seed (one leg's insert # failed) must read as unknown, not as job-level counts missing a leg's traffic. funnel: Final = counted if counted is not None and counted.legs_with_rows == len(leg_ids) else None - return ShadowEvalResult( + result: Final = ShadowEvalResult( by_tier=_slices(by_tier), by_current_model=_slices(by_model), - by_key=_slices(by_key), + by_router=_slices(by_router), overall_shadow_win_rate_pct=_pct_of(sum(r.shadow_wins for r in by_tier), total_turns), overall_tie_rate_pct=_pct_of(sum(r.ties for r in by_tier), total_turns), sampled_real_spend=sum(r.real_spend for r in by_tier), @@ -1168,6 +1300,7 @@ async def _shadow_eval_results(prisma_client: "PrismaClient", legs: Sequence[_Le shed_count=funnel.shed if funnel is not None else None, withheld_count=funnel.withheld if funnel is not None else None, ) + return result, verdicts_by_target @router.post( @@ -1182,59 +1315,126 @@ async def start_shadow_eval( user_api_key_dict: Annotated[UserAPIKeyAuth, Depends(user_api_key_auth)], ) -> ShadowEvalJobResponse: """ - Start a shadow eval: duplicate a sampled slice of one or more keys' live traffic against - a second arm, judge the two responses blind, and stratify win rates by tier, by the model - that served the real arm, and by key. + Start a shadow eval: duplicate a sampled slice of one or more targets' live traffic + against a second arm, judge the two responses blind, and stratify win rates by tier, + by the model that served the real arm, and by target. - A forward job answers whether the keys should adopt router_name: it samples the requests - the router did not serve and duplicates them through it. A reverse job answers whether a - key already on the router still gains from it: it samples the requests the router did - serve and duplicates them against baseline_model. A key can hold one active job per - direction, so both questions can run at once. + A target is a virtual key, a team, or a user. Team and user targets match on the + identity every request resolves to at auth time, so they cover JWT-authenticated + traffic, which presents no virtual key; a user target samples that user's traffic + across all their teams, whether it arrives on a JWT or a key they own. - Shadow responses are never served to users. Each key samples until its recorded eval - spend, the shadow and judge calls' own cost, reaches max_budget dollars, the job's - window ends, or the job is stopped, so one key running out of budget does not end - sampling for the others; sampling changes propagate to pods within about 10 seconds. - Shadow and judge calls bill to the shadowed key but are excluded from request counts - and auto-router adoption metrics. + A forward job answers whether the targets should adopt router_name: it samples the + requests the router did not serve and duplicates them through it. A reverse job + answers whether a target already on the router still gains from it: it samples the + requests the router did serve and duplicates them against baseline_model. A target + can hold one active job per direction, so both questions can run at once, and a + request matching several jobs' targets (say its key and its team) is sampled by + each, separately budgeted. + + Shadow responses are never served to users. Each target samples until its recorded + eval spend, the shadow and judge calls' own cost, reaches max_budget dollars, the + job's window ends, or the job is stopped, so one target running out of budget does + not end sampling for the others; sampling changes propagate to pods within about 10 + seconds. Shadow and judge calls bill to the sampled request's own identity but are + excluded from request counts and auto-router adoption metrics. """ from litellm.proxy.proxy_server import llm_router, prisma_client _require_admin_writer(user_api_key_dict, "start a shadow eval") if prisma_client is None: raise HTTPException(status_code=500, detail=CommonProxyErrors.db_not_connected_error.value) - if llm_router is None or not _is_configured_pre_routing_strategy(llm_router, data.router_name): - raise HTTPException(status_code=400, detail=f"'{data.router_name}' is not a configured auto-router") - token_rows: Final = await _verification_tokens(prisma_client).find_many( - where={"token": {"in": list(data.api_key_ids)}} # mutable-ok: Prisma filter + unconfigured: Final = tuple( + name + for name in data.router_names + if llm_router is None or not _is_configured_pre_routing_strategy(llm_router, name) ) - unknown: Final = tuple(sorted(frozenset(data.api_key_ids) - frozenset(row.token for row in token_rows or ()))) - if unknown: + if unconfigured: raise HTTPException( - status_code=400, - detail=( - f"api_key_ids not on this proxy: {', '.join(unknown)}; pass each key's token hash, " - "the value the key list and key info endpoints report" - ), + status_code=400, detail=f"Not a configured auto-router: {', '.join(repr(n) for n in unconfigured)}" ) + token_rows: Final = ( + await _verification_tokens(prisma_client).find_many( + where={"token": {"in": list(data.api_key_ids)}} # mutable-ok: Prisma filter + ) + if data.api_key_ids + else () + ) + team_rows: Final = ( + await _team_rows(prisma_client).find_many( + where={"team_id": {"in": list(data.team_ids)}} # mutable-ok: Prisma filter + ) + if data.team_ids + else () + ) + user_rows: Final = ( + await _user_rows(prisma_client).find_many( + where={"user_id": {"in": list(data.user_ids)}} # mutable-ok: Prisma filter + ) + if data.user_ids + else () + ) + unknown_keys: Final = sorted(frozenset(data.api_key_ids) - frozenset(row.token for row in token_rows or ())) + unknown_teams: Final = sorted(frozenset(data.team_ids) - frozenset(row.team_id for row in team_rows or ())) + unknown_users: Final = sorted(frozenset(data.user_ids) - frozenset(row.user_id for row in user_rows or ())) + unknown_parts: Final = tuple( + part + for part in ( + ( + f"api_key_ids not on this proxy: {', '.join(unknown_keys)}; pass each key's token hash, " + "the value the key list and key info endpoints report" + ) + if unknown_keys + else None, + f"team_ids not on this proxy: {', '.join(unknown_teams)}" if unknown_teams else None, + f"user_ids not on this proxy: {', '.join(unknown_users)}" if unknown_users else None, + ) + if part is not None + ) + if unknown_parts: + raise HTTPException(status_code=400, detail=". ".join(unknown_parts)) # Every model check below runs once per team the job samples for, since that is the # identity the shadow and judge calls carry and therefore what the router selects on. - team_ids: Final = tuple(dict.fromkeys(row.team_id for row in token_rows or ())) + # A user target's traffic can span teams, so it validates unscoped (None); each + # sampled attempt still resolves the judge under its own request's team at eval time. + team_ids: Final = tuple( + dict.fromkeys( + ( + *(row.team_id for row in token_rows or ()), + *data.team_ids, + *((None,) if data.user_ids else ()), + ) + ) + ) _validate_plain_model(llm_router, data.judge_model, "judge_model", team_ids) if data.baseline_model is not None: _validate_plain_model(llm_router, data.baseline_model, "baseline_model", team_ids) _validate_judge_is_not_a_candidate(llm_router, data, team_ids) + requested_targets: Final[tuple[tuple[ShadowEvalTargetType, str], ...]] = ( + *(("key", key) for key in data.api_key_ids), + *(("team", team) for team in data.team_ids), + *(("user", user) for user in data.user_ids), + ) + requested_by_type: Final[tuple[tuple[ShadowEvalTargetType, tuple[str, ...]], ...]] = tuple( + (target_type, ids) + for target_type, ids in (("key", data.api_key_ids), ("team", data.team_ids), ("user", data.user_ids)) + if ids + ) # A job whose window passed or whose budget ran out stopped sampling on its own, - # but its legs still hold their slots in the per-key, per-direction partial unique index - # until stamped; free them so a new eval can start. Sweeping both directions is deliberate. - requested: Final = list(data.api_key_ids) # mutable-ok: query param - await prisma_client.db.execute_raw(_SWEEP_FINISHED_JOBS_SQL, requested) + # but its legs still hold their slots in the per-target, per-direction partial unique + # index until stamped; free them so a new eval can start. Sweeping both directions is + # deliberate. Sweep and claim filter on exact (target_type, id) pairs so a team id + # that happens to equal a key hash never matches the other kind's slot. + for target_type, ids in requested_by_type: + await prisma_client.db.execute_raw(_SWEEP_FINISHED_JOBS_SQL, list(ids), target_type) # mutable-ok: query param claimed: Final = await _shadow_eval_jobs(prisma_client).find_many( where={ # mutable-ok: Prisma filter - "api_key_id": {"in": requested}, # mutable-ok: Prisma filter + "OR": [ # mutable-ok: Prisma filter + {"target_type": target_type, "target_id": {"in": list(ids)}} # mutable-ok: Prisma filter + for target_type, ids in requested_by_type + ], "direction": data.direction, "stopped_at": None, }, @@ -1244,7 +1444,7 @@ async def start_shadow_eval( status_code=409, detail=( f"Already in an active {data.direction} shadow eval job: " - + ", ".join(sorted(f"{row.api_key_id} (job {row.group_id})" for row in claimed)) + + ", ".join(sorted(f"{row.target_type} {row.target_id} (job {row.group_id})" for row in claimed)) + ". Stop it first." ), ) @@ -1253,7 +1453,9 @@ async def start_shadow_eval( ends_at: Final = now + timedelta(days=data.duration_days) shared_config: Final = { # mutable-ok: Prisma payload "group_id": group_id, - "router_name": data.router_name, + # a pre-router_names pod samples router_name alone, so it must be a real arm + "router_name": data.router_names[0], + "router_names": list(data.router_names), # mutable-ok: Prisma payload "direction": data.direction, "baseline_model": data.baseline_model, "judge_model": data.judge_model, @@ -1268,10 +1470,16 @@ async def start_shadow_eval( # Leg ids are minted here rather than by the DB default so the funnel seed below # writes from the same values with no read-back, which a lagging read replica # (DATABASE_URL_READ_REPLICA) could otherwise return empty. - leg_ids: Final = tuple(str(uuid4()) for _ in data.api_key_ids) + leg_ids: Final = tuple(str(uuid4()) for _ in requested_targets) await _shadow_eval_jobs(prisma_client).create_many( data=[ # mutable-ok: Prisma payload - {**shared_config, "id": leg_id, "api_key_id": key} for leg_id, key in zip(leg_ids, data.api_key_ids) + { # mutable-ok: Prisma payload + **shared_config, + "id": leg_id, + "target_type": target_type, + "target_id": target_id, + } # mutable-ok: Prisma payload + for leg_id, (target_type, target_id) in zip(leg_ids, requested_targets) ] ) except Exception as e: @@ -1280,7 +1488,8 @@ async def start_shadow_eval( raise HTTPException( status_code=409, detail=( - f"A requested key was claimed by another {data.direction} shadow eval job concurrently. Stop it first." + f"A requested target was claimed by another {data.direction} shadow eval job concurrently. " + "Stop it first." ), ) from e # Seed a zero funnel row per leg NOW: a fully covered job never skips a request, so @@ -1293,20 +1502,21 @@ async def start_shadow_eval( ) except Exception as seed_err: # noqa: BLE001 # coverage is advisory; the job must still start verbose_proxy_logger.error("shadow_eval: funnel seed failed for job %s: %s", group_id, seed_err) - labels: Final = MappingProxyType({row.token: row for row in token_rows}) + labels: Final = _target_labels(token_rows or (), team_rows or (), user_rows or ()) return ShadowEvalJobResponse( job_id=group_id, - keys=tuple( - ShadowEvalJobKeyResponse( - api_key_id=api_key_id, + targets=tuple( + ShadowEvalJobTargetResponse( + target_type=target_type, + target_id=target_id, max_turns=SHADOW_EVAL_TURN_VALVE, max_budget=data.max_budget, - key_alias=labels[api_key_id].key_alias, - key_name=labels[api_key_id].key_name, + target_alias=labels.get((target_type, target_id), _NO_TARGET_LABELS)[0], + key_name=labels.get((target_type, target_id), _NO_TARGET_LABELS)[1], ) - for api_key_id in sorted(data.api_key_ids) + for target_type, target_id in sorted(requested_targets) ), - router_name=data.router_name, + router_names=data.router_names, direction=data.direction, baseline_model=data.baseline_model, judge_model=data.judge_model, @@ -1324,22 +1534,29 @@ async def start_shadow_eval( ) async def list_shadow_eval_jobs( user_api_key_dict: Annotated[UserAPIKeyAuth, Depends(user_api_key_auth)], - api_key_id: Annotated[ - str | None, Query(description="Filter to jobs that shadow this key, alone or alongside others") + target_type: Annotated[ + ShadowEvalTargetType | None, Query(description="Kind of target to filter on; requires target_id") + ] = None, + target_id: Annotated[ + str | None, Query(description="Filter to jobs that shadow this target, alone or alongside others") ] = None, limit: Annotated[int, Query(ge=1, le=200, description="Newest jobs to return")] = 50, ) -> tuple[ShadowEvalJobResponse, ...]: - """List shadow eval jobs, newest first, each key with its attempt count so status is - accurate. Judged counts, spend, and results ride the detail endpoint only.""" + """List shadow eval jobs, newest first, each target with its attempt count so status + is accurate. Judged counts, spend, and results ride the detail endpoint only.""" from litellm.proxy.proxy_server import prisma_client _require_admin_viewer(user_api_key_dict, "view shadow evals") if prisma_client is None: raise HTTPException(status_code=500, detail=CommonProxyErrors.db_not_connected_error.value) + filter_type: Final = target_type if isinstance(target_type, str) else None + filter_id: Final = target_id if isinstance(target_id, str) else None + if (filter_type is None) != (filter_id is None): + raise HTTPException(status_code=400, detail="target_type and target_id filter together; pass both or neither") legs: Final = _LEG_ROWS.validate_python( ( - await _query_raw(prisma_client, _LIST_LEGS_BY_KEY_SQL, limit, api_key_id) - if api_key_id + await _query_raw(prisma_client, _LIST_LEGS_BY_TARGET_SQL, limit, filter_type, filter_id) + if filter_type and filter_id else await _query_raw(prisma_client, _LIST_LEGS_SQL, limit) ) or () @@ -1354,7 +1571,7 @@ async def list_shadow_eval_jobs( by_group, key=lambda group_id: max(leg.created_at for leg in by_group[group_id]), reverse=True ) counts: Final = await _leg_attempt_counts(prisma_client, legs) - return await _with_key_labels( + return await _with_target_labels( prisma_client, tuple(_group_response(group_id, by_group[group_id], counts) for group_id in newest_first) ) @@ -1391,16 +1608,25 @@ async def get_shadow_eval_job( where={"job_id": {"in": leg_ids}, "outcome": "error"}, # mutable-ok: Prisma filter order={"created_at": "desc"}, # mutable-ok: Prisma order ) - labeled: Final = await _with_key_labels( + labeled: Final = await _with_target_labels( prisma_client, (_group_response(job_id, legs, await _leg_attempt_counts(prisma_client, legs)),) ) + results, verdicts_by_target = await _shadow_eval_results(prisma_client, legs) return labeled[0].model_copy( update={ # mutable-ok: pydantic update payload "judged_count": totals[0].judged_count if totals else 0, "error_count": totals[0].error_count if totals else 0, "judge_spend": round(totals[0].judge_spend, 6) if totals else 0.0, "last_error": latest_error.error if latest_error else None, - "results": await _shadow_eval_results(prisma_client, legs), + "results": results, + "targets": tuple( + target.model_copy( + update={ # mutable-ok: pydantic update payload + "verdicts": verdicts_by_target.get((target.target_type, target.target_id)) + } + ) + for target in labeled[0].targets + ), } ) @@ -1415,8 +1641,8 @@ async def stop_shadow_eval_job( job_id: str, user_api_key_dict: Annotated[UserAPIKeyAuth, Depends(user_api_key_auth)], ) -> ShadowEvalJobResponse: - """Stop an active shadow eval job, every key it scopes at once. Attempts are kept; - sampling halts within ~10s. Keys that already stopped on their own budget keep the + """Stop an active shadow eval job, every target it scopes at once. Attempts are kept; + sampling halts within ~10s. Targets that already stopped on their own budget keep the stopped_at they earned. The statement is the whole state machine: it claims the job only while a leg still samples inside the window with no stop recorded, so a racing operator, a same-instant budget spend, and a repeat stop all read the same 400 with @@ -1443,5 +1669,5 @@ async def stop_shadow_eval_job( current: Final = _group_response(job_id, legs, counts) if claimed == 0: raise HTTPException(status_code=400, detail=f"Job {job_id} is already {current.status}") - labeled: Final = await _with_key_labels(prisma_client, (current,)) + labeled: Final = await _with_target_labels(prisma_client, (current,)) return labeled[0] diff --git a/litellm/proxy/management_endpoints/config_override_endpoints.py b/litellm/proxy/management_endpoints/config_override_endpoints.py index 7f4faddf178..84593460704 100644 --- a/litellm/proxy/management_endpoints/config_override_endpoints.py +++ b/litellm/proxy/management_endpoints/config_override_endpoints.py @@ -3,7 +3,7 @@ import json import os from collections.abc import Mapping, Sequence from datetime import datetime, timezone -from typing import TYPE_CHECKING, Any, Final, Protocol +from typing import TYPE_CHECKING, Final, Protocol from fastapi import APIRouter, Depends, Header, HTTPException from pydantic import BaseModel, TypeAdapter @@ -36,10 +36,12 @@ from litellm.repositories.table_repositories import ConfigOverridesRepository from litellm.types.llms.custom_http import httpxSpecialProvider from litellm.types.proxy.management_endpoints.config_overrides import ( ConfigOverrideSettingsResponse, + CyberArkConfig, HashicorpVaultConfig, ) if TYPE_CHECKING: + from litellm.proxy.proxy_server import ProxyConfig from litellm.proxy.utils import PrismaClient router: Final = APIRouter() @@ -83,18 +85,19 @@ def _log_audit_task_exception(task: "asyncio.Task[None]") -> None: return exc: Final = task.exception() if exc is not None: - verbose_proxy_logger.warning("Failed to write hashicorp-vault config audit log: %s", exc) + verbose_proxy_logger.warning("Failed to write config override audit log: %s", exc) -async def _emit_hashicorp_vault_audit_log( +async def _emit_config_override_audit_log( *, + object_id: str, action: AUDIT_ACTIONS, before_config: Mapping[str, object] | None, after_config: Mapping[str, object] | None, user_api_key_dict: UserAPIKeyAuth, litellm_changed_by: str | None, ) -> None: - """Emit an audit-log row for a /config_overrides/hashicorp_vault mutation. + """Emit an audit-log row for a /config_overrides/{object_id} mutation. Mirrors the ``store_audit_logs``-gated pattern from ``team_callback_endpoints.py``. Captured under @@ -118,7 +121,7 @@ async def _emit_hashicorp_vault_audit_log( changed_by=litellm_changed_by or user_api_key_dict.user_id or litellm_proxy_admin_name, changed_by_api_key=user_api_key_dict.api_key, table_name=LitellmTableNames.CONFIG_OVERRIDES_TABLE_NAME, - object_id="hashicorp_vault", + object_id=object_id, action=action, updated_values=json.dumps({"config": _redact_config(after_config)}, default=str), before_value=json.dumps({"config": _redact_config(before_config)}, default=str), @@ -150,6 +153,24 @@ HASHICORP_SENSITIVE_FIELDS: Final[set[str]] = { "client_key", } +# --- CyberArk Conjur constants --- + +CYBERARK_ENV_VAR_MAPPING: Final[dict[str, str]] = { # mutable-ok: module-level env mapping + "cyberark_api_base": "CYBERARK_API_BASE", + "cyberark_account": "CYBERARK_ACCOUNT", + "cyberark_username": "CYBERARK_USERNAME", + "cyberark_api_key": "CYBERARK_API_KEY", + "client_cert": "CYBERARK_CLIENT_CERT", + "client_key": "CYBERARK_CLIENT_KEY", + "ssl_verify": "CYBERARK_SSL_VERIFY", + "refresh_interval": "CYBERARK_REFRESH_INTERVAL", +} + +CYBERARK_SENSITIVE_FIELDS: Final[set[str]] = { # mutable-ok: module-level constant, mirrors HASHICORP_SENSITIVE_FIELDS + "cyberark_api_key", + "client_key", +} + _sensitive_masker: Final = SensitiveDataMasker() @@ -215,9 +236,12 @@ def _parse_config_value(raw: str | Mapping[str, object]) -> dict[str, object]: return dict(raw) -def _set_env_vars(config_data: Mapping[str, object]) -> None: - """Set HCP_VAULT_* env vars from config data. Unsets vars for missing/None/empty fields.""" - for field_name, env_var_name in HASHICORP_ENV_VAR_MAPPING.items(): +def _set_env_vars( + config_data: Mapping[str, object], + env_var_mapping: Mapping[str, str] = HASHICORP_ENV_VAR_MAPPING, +) -> None: + """Set mapped env vars from config data. Unsets vars for missing/None/empty fields.""" + for field_name, env_var_name in env_var_mapping.items(): value = config_data.get(field_name) if value is not None and value != "": os.environ[env_var_name] = str(value) @@ -225,13 +249,74 @@ def _set_env_vars(config_data: Mapping[str, object]) -> None: os.environ.pop(env_var_name, None) -def _clear_hashicorp_vault_state(proxy_config: Any) -> None: +def _clear_hashicorp_vault_state(proxy_config: "ProxyConfig") -> None: """Clear all Hashicorp Vault state: env vars, secret manager, and change-detection cache.""" _set_env_vars({}) if litellm._key_management_system == KeyManagementSystem.HASHICORP_VAULT: litellm.secret_manager_client = None litellm._key_management_system = None - proxy_config._last_hashicorp_vault_config = None + proxy_config._last_hashicorp_vault_config = None # pyright: ignore[reportPrivateUsage] # proxy-internal change-detection cache + + +def _snapshot_cyberark_boot_env(proxy_config: "ProxyConfig") -> None: + """Capture deployment-provided CYBERARK_* env vars once, before the first DB-driven overwrite.""" + if proxy_config._cyberark_boot_env is None: # pyright: ignore[reportPrivateUsage] # proxy-internal boot snapshot + proxy_config._cyberark_boot_env = _get_current_env_values(CYBERARK_ENV_VAR_MAPPING) # pyright: ignore[reportPrivateUsage] # proxy-internal boot snapshot + + +def _restore_cyberark_runtime(proxy_config: "ProxyConfig", env_values: Mapping[str, str | None]) -> None: + """Restore CYBERARK_* env vars and reinitialize (or drop) the secret manager to match them.""" + _set_env_vars(env_values, CYBERARK_ENV_VAR_MAPPING) + if env_values.get("cyberark_api_base"): + try: + proxy_config.initialize_secret_manager(key_management_system="cyberark") + except Exception: # noqa: BLE001 # restore is best-effort; fall through to dropping the manager + verbose_proxy_logger.exception("Failed to restore previous CyberArk configuration") + else: + return + if litellm._key_management_system != KeyManagementSystem.CYBERARK: # pyright: ignore[reportPrivateUsage] # proxy-internal helper, mirrors hashicorp endpoint usage + return + litellm.secret_manager_client = None + litellm._key_management_system = None # pyright: ignore[reportPrivateUsage] # proxy-internal helper, mirrors hashicorp endpoint usage + # Force the vault reload to re-init from its own row so no manager is stranded inactive + proxy_config._last_hashicorp_vault_config = None # pyright: ignore[reportPrivateUsage] # proxy-internal change-detection cache + if os.environ.get("HCP_VAULT_ADDR"): + try: + proxy_config.initialize_secret_manager(key_management_system="hashicorp_vault") + except Exception: # noqa: BLE001 # restore is best-effort; the vault reload loop retries from its own row + verbose_proxy_logger.exception("Failed to reinitialize Hashicorp Vault after CyberArk rollback") + + +def _clear_cyberark_state(proxy_config: "ProxyConfig") -> None: + """Drop DB-driven CyberArk state, restoring deployment-provided env vars if any.""" + boot_env: Final[Mapping[str, str | None]] = ( + proxy_config._cyberark_boot_env or {} # pyright: ignore[reportPrivateUsage] # proxy-internal boot snapshot + ) + _restore_cyberark_runtime(proxy_config, boot_env) + proxy_config._last_cyberark_config = None # pyright: ignore[reportPrivateUsage] # proxy-internal helper, mirrors hashicorp endpoint usage + + +async def _persist_cyberark_config( + prisma_client: "PrismaClient", + proxy_config: "ProxyConfig", + config_data: Mapping[str, object], +) -> dict[str, object]: + """Encrypt and upsert the CyberArk config row; returns the stored (encrypted) payload.""" + encrypted_data: Final = proxy_config._encrypt_env_variables(dict(config_data)) # pyright: ignore[reportPrivateUsage] # proxy-internal helper, mirrors hashicorp endpoint usage + config_value: Final = safe_dumps(encrypted_data) + await _config_overrides_table(prisma_client).upsert( + where={"config_type": "cyberark"}, # mutable-ok: prisma upsert payload + data={ # mutable-ok: prisma upsert payload + "create": { # mutable-ok: prisma upsert payload + "config_type": "cyberark", + "config_value": config_value, + }, + "update": { # mutable-ok: prisma upsert payload + "config_value": config_value, + }, + }, + ) + return safe_json_loads(config_value) # --- Hashicorp Vault endpoints --- @@ -358,7 +443,8 @@ async def update_hashicorp_vault_config( # row was absent or its ``config_value`` was NULL. before_config: Final = existing_decrypted if existing_decrypted is not None else env_values action: Final[AUDIT_ACTIONS] = "updated" if existing_record is not None else "created" - await _emit_hashicorp_vault_audit_log( + await _emit_config_override_audit_log( + object_id="hashicorp_vault", action=action, before_config=before_config, after_config=config_data, @@ -484,7 +570,8 @@ async def delete_hashicorp_vault_config( # Only emit audit log if a row was actually removed; an idempotent # delete on a non-existent row produces no security-relevant change. if deleted: - await _emit_hashicorp_vault_audit_log( + await _emit_config_override_audit_log( + object_id="hashicorp_vault", action="deleted", before_config=before_config, after_config=None, @@ -529,7 +616,7 @@ async def test_hashicorp_vault_connection( # Step 1: Authenticate (exercises AppRole login, TLS cert login, or direct token) try: - headers: Final[dict[str, str]] = await asyncio.to_thread(client._get_request_headers) + headers: Final[Mapping[str, str]] = await asyncio.to_thread(client._get_request_headers) except Exception as e: raise HTTPException( status_code=502, @@ -554,3 +641,298 @@ async def test_hashicorp_vault_connection( "status": "success", "message": f"Successfully connected to Vault at {client.vault_addr}", } + + +# --- CyberArk Conjur endpoints --- + + +@router.post( + "/config_overrides/cyberark", + tags=["Config Overrides"], # mutable-ok: FastAPI route decorator metadata + dependencies=[Depends(user_api_key_auth)], # mutable-ok: FastAPI route decorator metadata +) +async def update_cyberark_config( + config: CyberArkConfig, + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), # noqa: B008 # FastAPI dependency injection + litellm_changed_by: str | None = Header( + None, + description="The litellm-changed-by header enables tracking of actions performed by authorized users on behalf of other users, providing an audit trail for accountability", + ), +) -> dict[str, str]: + """ + Update CyberArk Conjur secret manager configuration. + Sets environment variables, encrypts sensitive fields, and stores in DB. + Reinitializes the secret manager on this pod. + """ + from litellm.proxy.proxy_server import prisma_client, proxy_config + + if user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN: + raise HTTPException( + status_code=403, + detail="Only admin users can update config overrides", + ) + + if prisma_client is None: + raise HTTPException( + status_code=500, + detail=CommonProxyErrors.db_not_connected_error.value, + ) + + config_data: dict[str, object] = config.model_dump(exclude_none=True) # mutable-ok: merged # rebind-ok: stripped + + # Merge ALL fields the user didn't send: try DB first, fall back to env vars. + # Omitted field = keep existing; empty string = clear/remove the field. + existing_record: Final = await _config_overrides_table(prisma_client).find_unique( + where={"config_type": "cyberark"} # mutable-ok: prisma where clause + ) + existing_decrypted: dict[str, object] | None = None # mutable-ok: DB payload # rebind-ok: set when record exists + env_values: dict[str, str | None] = {} # mutable-ok: env snapshot # rebind-ok: populated when no DB record exists + if existing_record is not None and existing_record.config_value is not None: + existing_data: Final = _parse_config_value(existing_record.config_value) + existing_decrypted = proxy_config._decrypt_db_variables(existing_data) # pyright: ignore[reportPrivateUsage] # rebind-ok: populated when a prior record decrypts + for field in CYBERARK_ENV_VAR_MAPPING: + if field not in config_data and existing_decrypted.get(field): + config_data[field] = existing_decrypted[field] + else: + env_values = _get_current_env_values(CYBERARK_ENV_VAR_MAPPING) # rebind-ok: populated when no DB record exists + for field in CYBERARK_ENV_VAR_MAPPING: + if field not in config_data and env_values.get(field): + config_data[field] = env_values[field] + + config_data = {k: v for k, v in config_data.items() if v != ""} # mutable-ok: dict # rebind-ok: "" means clear + + has_api_base: Final = bool(config_data.get("cyberark_api_base")) + has_api_key_auth: Final = bool(config_data.get("cyberark_api_key")) + has_tls_cert_auth: Final = bool(config_data.get("client_cert") and config_data.get("client_key")) + + if not has_api_base: + raise HTTPException( + status_code=400, + detail="CyberArk API Base is required", + ) + + if not has_api_key_auth and not has_tls_cert_auth: + raise HTTPException( + status_code=400, + detail="At least one authentication method is required: " + "provide an API Key, or both Client Certificate and Client Key", + ) + + _snapshot_cyberark_boot_env(proxy_config) + previous_env: Final = _get_current_env_values(CYBERARK_ENV_VAR_MAPPING) + _set_env_vars(config_data, CYBERARK_ENV_VAR_MAPPING) + + try: + proxy_config.initialize_secret_manager(key_management_system="cyberark") + except Exception as e: # noqa: BLE001 # any init failure must roll back env vars + _set_env_vars(previous_env, CYBERARK_ENV_VAR_MAPPING) + verbose_proxy_logger.exception("Error reinitializing CyberArk secret manager: %s", str(e)) + raise HTTPException( + status_code=500, + detail=f"Failed to initialize secret manager: {e}", + ) + + try: + proxy_config._last_cyberark_config = await _persist_cyberark_config( # pyright: ignore[reportPrivateUsage] # proxy-internal helper, mirrors hashicorp endpoint usage + prisma_client, proxy_config, config_data + ) + except Exception as e: # noqa: BLE001 # persistence failure must roll back the runtime state set above + _restore_cyberark_runtime(proxy_config, previous_env) + verbose_proxy_logger.exception("Error persisting CyberArk configuration: %s", str(e)) + raise HTTPException( + status_code=500, + detail=f"Failed to persist CyberArk configuration: {e}", + ) + + before_config: Final = existing_decrypted if existing_decrypted is not None else env_values + action: Final[AUDIT_ACTIONS] = "updated" if existing_record is not None else "created" + await _emit_config_override_audit_log( + object_id="cyberark", + action=action, + before_config=before_config, + after_config=config_data, + user_api_key_dict=user_api_key_dict, + litellm_changed_by=litellm_changed_by, + ) + + return { # mutable-ok: JSON response payload + "message": "CyberArk configuration updated successfully", + "status": "success", + } + + +@router.get( + "/config_overrides/cyberark", + tags=["Config Overrides"], # mutable-ok: FastAPI route decorator metadata + dependencies=[Depends(user_api_key_auth)], # mutable-ok: FastAPI route decorator metadata + response_model=ConfigOverrideSettingsResponse, +) +async def get_cyberark_config( + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), # noqa: B008 # FastAPI dependency injection +) -> ConfigOverrideSettingsResponse: + """ + Get current CyberArk Conjur configuration. + Returns decrypted values from DB, or falls back to current env vars. + Sensitive fields are masked before leaving the server. + """ + from litellm.proxy.management_endpoints.common_utils import ( + _user_has_admin_view, # pyright: ignore[reportPrivateUsage] # proxy-internal helper, mirrors hashicorp endpoint usage + ) + from litellm.proxy.proxy_server import prisma_client, proxy_config + + if not _user_has_admin_view(user_api_key_dict): + raise HTTPException( + status_code=403, + detail="Only admin users can view config overrides", + ) + + if prisma_client is None: + raise HTTPException( + status_code=500, + detail=CommonProxyErrors.db_not_connected_error.value, + ) + + field_schema: Final = _build_field_schema(CyberArkConfig) + + db_record: Final = await _config_overrides_table(prisma_client).find_unique( + where={"config_type": "cyberark"} + ) # mutable-ok: prisma where clause + + if db_record is not None and db_record.config_value is not None: + config_data: Final = _parse_config_value(db_record.config_value) + decrypted_data: Final[Mapping[str, object]] = proxy_config._decrypt_db_variables(config_data) # pyright: ignore[reportPrivateUsage] # proxy-internal helper, mirrors hashicorp endpoint usage + masked_data: Final = _mask_sensitive_fields(decrypted_data, CYBERARK_SENSITIVE_FIELDS) + + return ConfigOverrideSettingsResponse( + config_type="cyberark", + values=masked_data, + field_schema=field_schema, + ) + + env_values: Final = _get_current_env_values(CYBERARK_ENV_VAR_MAPPING) + masked_env_values: Final = _mask_sensitive_fields(env_values, CYBERARK_SENSITIVE_FIELDS) + + return ConfigOverrideSettingsResponse( + config_type="cyberark", + values=masked_env_values, + field_schema=field_schema, + ) + + +@router.delete( + "/config_overrides/cyberark", + tags=["Config Overrides"], # mutable-ok: FastAPI route decorator metadata + dependencies=[Depends(user_api_key_auth)], # mutable-ok: FastAPI route decorator metadata +) +async def delete_cyberark_config( + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), # noqa: B008 # FastAPI dependency injection + litellm_changed_by: str | None = Header( + None, + description="The litellm-changed-by header enables tracking of actions performed by authorized users on behalf of other users, providing an audit trail for accountability", + ), +) -> dict[str, str]: + """Delete CyberArk Conjur configuration. Idempotent.""" + from litellm.proxy.proxy_server import prisma_client, proxy_config + + if user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN: + raise HTTPException( + status_code=403, + detail="Only admin users can delete config overrides", + ) + + if prisma_client is None: + raise HTTPException( + status_code=500, + detail=CommonProxyErrors.db_not_connected_error.value, + ) + + existing_record: Final = await _config_overrides_table(prisma_client).find_unique( + where={"config_type": "cyberark"} # mutable-ok: prisma where clause + ) + before_config: dict[str, object] | None = None # mutable-ok: audit snapshot # rebind-ok: set when decrypts + if existing_record is not None and existing_record.config_value is not None: + try: + before_config = proxy_config._decrypt_db_variables(_parse_config_value(existing_record.config_value)) # pyright: ignore[reportPrivateUsage] # rebind-ok: populated when the prior record decrypts + except Exception: # noqa: BLE001 # undecryptable prior config must not block deletion + before_config = None # rebind-ok: reset when decryption fails + + deleted = False # rebind-ok: set true once the DB row is removed + try: + await _config_overrides_table(prisma_client).delete( + where={"config_type": "cyberark"} + ) # mutable-ok: prisma where clause + deleted = True # rebind-ok: set true once the DB row is removed + except RecordNotFoundError: + verbose_proxy_logger.debug("No existing CyberArk config record to delete") + + _clear_cyberark_state(proxy_config) + + if deleted: + await _emit_config_override_audit_log( + object_id="cyberark", + action="deleted", + before_config=before_config, + after_config=None, + user_api_key_dict=user_api_key_dict, + litellm_changed_by=litellm_changed_by, + ) + + return { # mutable-ok: JSON response payload + "message": "CyberArk configuration deleted successfully", + "status": "success", + } + + +@router.post( + "/config_overrides/cyberark/test_connection", + tags=["Config Overrides"], # mutable-ok: FastAPI route decorator metadata + dependencies=[Depends(user_api_key_auth)], # mutable-ok: FastAPI route decorator metadata +) +async def test_cyberark_connection( + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), # noqa: B008 # FastAPI dependency injection +) -> dict[str, str]: + """ + Test the connection to the currently configured CyberArk Conjur server. + Uses the already-initialized secret manager client. Does not modify any state. + """ + from litellm.secret_managers.cyberark_secret_manager import CyberArkSecretManager + + if user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN: + raise HTTPException( + status_code=403, + detail="Only admin users can test CyberArk connection", + ) + + client: Final = litellm.secret_manager_client + if not isinstance(client, CyberArkSecretManager): + raise HTTPException( + status_code=400, + detail="CyberArk is not configured. Save a configuration first.", + ) + + try: + headers: Final[Mapping[str, str]] = await asyncio.to_thread(client._get_request_headers) # pyright: ignore[reportPrivateUsage] # proxy-internal helper, mirrors hashicorp endpoint usage + except Exception as e: # noqa: BLE001 # surface any auth failure as a 502 with detail + raise HTTPException( + status_code=502, + detail=f"CyberArk authentication failed: {e}", + ) + + try: + async_client: Final = get_async_httpx_client( + llm_provider=httpxSpecialProvider.SecretManager, + params={"ssl_verify": client.ssl_verify}, # mutable-ok: httpx client params + ) + whoami_url: Final = f"{client.conjur_addr}/whoami" + response: Final = await async_client.get(whoami_url, headers=headers) + response.raise_for_status() + except Exception as e: # noqa: BLE001 # surface any connectivity/TLS failure as a 502 with detail + raise HTTPException( + status_code=502, + detail=f"CyberArk token validation failed: {e}", + ) + + return { # mutable-ok: JSON response payload + "status": "success", + "message": f"Successfully connected to CyberArk Conjur at {client.conjur_addr}", + } diff --git a/litellm/proxy/management_endpoints/customer_endpoints.py b/litellm/proxy/management_endpoints/customer_endpoints.py index 9ef3d2defef..d2d87331d55 100644 --- a/litellm/proxy/management_endpoints/customer_endpoints.py +++ b/litellm/proxy/management_endpoints/customer_endpoints.py @@ -626,11 +626,7 @@ async def update_end_user( # get non default values for key non_default_values: Final = dict[str, object]() for k, v in data_json.items(): - if v is not None and v not in ( - [], - {}, - 0, - ): # models default to [], spend defaults to 0, we should not reset these values + if v is not None and ((isinstance(v, bool) and k in data.fields_set()) or v not in ([], {}, 0)): non_default_values[k] = v ## Get end user table data ## diff --git a/litellm/proxy/management_endpoints/internal_user_endpoints.py b/litellm/proxy/management_endpoints/internal_user_endpoints.py index 9a98bdbb6b1..c08ca5b7783 100644 --- a/litellm/proxy/management_endpoints/internal_user_endpoints.py +++ b/litellm/proxy/management_endpoints/internal_user_endpoints.py @@ -17,7 +17,7 @@ import json import traceback from collections.abc import Awaitable, Mapping, Sequence from datetime import datetime, timezone -from typing import Any, Final, Literal, cast +from typing import Any, Final, Literal, Protocol, cast, overload import fastapi from fastapi import APIRouter, Depends, Header, HTTPException, Request, status @@ -735,10 +735,44 @@ def _enforce_user_info_access(user_id: str | None, user_api_key_dict: UserAPIKey ) -async def _get_user_info_teams( - prisma_client: Any, +class _UserInfoDataClient(Protocol): + @overload + async def get_data(self, *, user_id: str) -> "prisma_models.LiteLLM_UserTable | None": ... + + @overload + async def get_data( + self, + *, + user_id: str | None, + table_name: Literal["key"], + query_type: Literal["find_all"], + ) -> "Sequence[LiteLLM_VerificationToken] | None": ... + + @overload + async def get_data( + self, + *, + team_id_list: list[str], + table_name: Literal["team"], + query_type: Literal["find_all"], + ) -> "Sequence[TeamListResponseObject] | None": ... + + +async def _get_user_info_keys( + prisma_client: "_UserInfoDataClient", user_id: str | None, - user_info: Any | None, +) -> "Sequence[LiteLLM_VerificationToken] | None": + return await prisma_client.get_data( + user_id=user_id, + table_name="key", + query_type="find_all", + ) + + +async def _get_user_info_teams( + prisma_client: "_UserInfoDataClient", + user_id: str | None, + user_info: "prisma_models.LiteLLM_UserTable", user_api_key_dict: UserAPIKeyAuth, ) -> tuple[list[TeamListResponseObject], list[TeamListResponseObject] | None]: """Fetch and merge teams from membership + user.teams field.""" @@ -759,7 +793,7 @@ async def _get_user_info_teams( team_list = teams_1 team_id_list = [team.team_id for team in teams_1] - teams_2: list[TeamListResponseObject] | None = None + teams_2: Sequence[TeamListResponseObject] | None = None target_team_ids: Final = getattr(user_info, "teams", None) if target_team_ids and isinstance(target_team_ids, list): @@ -769,8 +803,8 @@ async def _get_user_info_teams( query_type="find_all", ) elif user_api_key_dict.user_id is not None and user_id is None: - caller_user_info: Final[object] = await prisma_client.get_data(user_id=user_api_key_dict.user_id) - caller_team_ids: Final = getattr(caller_user_info, "teams", None) + caller_user_info: Final = await prisma_client.get_data(user_id=user_api_key_dict.user_id) + caller_team_ids: Final = caller_user_info.teams if caller_user_info is not None else None if caller_team_ids: teams_2 = await prisma_client.get_data( team_id_list=caller_team_ids, @@ -807,7 +841,7 @@ def _redact_scim_enterprise_metadata( def _build_user_info_response( user_id: str | None, user_info: Any | None, - keys: list[LiteLLM_VerificationToken] | None, + keys: Sequence[LiteLLM_VerificationToken] | None, team_list: list[TeamListResponseObject], teams_1: list[TeamListResponseObject] | None, model_max_budget_usage: dict[str, dict[str, object]] | None = None, @@ -894,11 +928,7 @@ async def user_info( ) ## GET ALL KEYS ## - keys: Final = await prisma_client.get_data( - user_id=user_id, - table_name="key", - query_type="find_all", - ) + keys: Final = await _get_user_info_keys(prisma_client, user_id) response_data: Final = _build_user_info_response( user_id=user_id, @@ -997,6 +1027,14 @@ async def user_info_v2( This is the v2 replacement for /user/info, designed to avoid the "god endpoint" problem where the old endpoint loaded all keys and teams into memory. + Note on `spend`: this is the user's running budget counter, which the budget reset job + resets whenever `budget_reset_at` elapses (see `budget_duration`): to zero by default, + or to the overage above `max_budget` when `budget_rollover` is enabled. It is NOT + lifetime or per-period historical spend. For historical spend over a date range, use + `/user/daily/activity` or `/user/daily/activity/aggregated`, which read daily spend + records that only ever accumulate and are never reset. The two values are expected to + diverge once a budget reset has occurred within the queried period. + Access control: - Proxy admins can query any user - Team admins can query users within their teams @@ -1077,6 +1115,12 @@ async def user_info_v2( raise handle_exception_on_proxy(e) +async def _fetch_admin_teams_and_keys_rows( + prisma_client: "PrismaClient", sql_query: str +) -> Sequence[Mapping[str, Sequence[Mapping[str, object]] | None]]: + return await prisma_client.db.query_raw(sql_query) + + async def _get_user_info_for_proxy_admin(user_api_key_dict: UserAPIKeyAuth): """ Admin UI Endpoint - Returns All Teams and Keys when Proxy Admin is querying @@ -1100,22 +1144,25 @@ async def _get_user_info_for_proxy_admin(user_api_key_dict: UserAPIKeyAuth): "Database not connected. Connect a database to your proxy - https://docs.litellm.ai/docs/simple_proxy#managing-auth---virtual-keys" ) - results: Final = await prisma_client.db.query_raw(sql_query) + results: Final = await _fetch_admin_teams_and_keys_rows(prisma_client, sql_query) verbose_proxy_logger.debug("results_keys: %s", results) - _keys_in_db: Final[Sequence[dict[str, object]]] = results[0]["keys"] or [] + _keys_in_db: Final[Sequence[Mapping[str, object]]] = results[0]["keys"] or [] # cast all keys to LiteLLM_VerificationToken keys_in_db: Final = [] for key in _keys_in_db: - if key.get("models") is None: - key["models"] = [] - keys_in_db.append(LiteLLM_VerificationToken.model_validate(key)) + key_payload = dict[str, object](key) + if key_payload.get("models") is None: + key_payload["models"] = [] + keys_in_db.append(LiteLLM_VerificationToken.model_validate(key_payload)) # cast all teams to LiteLLM_TeamTable - _teams_in_db: list[LiteLLM_TeamTable] = results[0]["teams"] or [] - _teams_in_db = [LiteLLM_TeamTable.model_validate(team) for team in _teams_in_db] - _teams_in_db.sort(key=lambda x: getattr(x, "team_alias", "") or "") + _teams_rows: Final[Sequence[Mapping[str, object]]] = results[0]["teams"] or [] + _teams_in_db: Final = sorted( + (LiteLLM_TeamTable.model_validate(team) for team in _teams_rows), + key=lambda x: getattr(x, "team_alias", "") or "", + ) returned_keys: Final = _process_keys_for_user_info(keys=keys_in_db, all_teams=_teams_in_db) # Get admin's own user_id and user_info @@ -1140,7 +1187,7 @@ async def _get_user_info_for_proxy_admin(user_api_key_dict: UserAPIKeyAuth): def _process_keys_for_user_info( - keys: list[LiteLLM_VerificationToken] | None, + keys: Sequence[LiteLLM_VerificationToken] | None, all_teams: list[LiteLLM_TeamTable] | list[TeamListResponseObject] | None, ): from litellm.constants import UI_SESSION_TOKEN_TEAM_ID @@ -1231,7 +1278,7 @@ def _update_internal_user_params(data_json: dict, data: UpdateUserRequest | Upda async def _schedule_user_update_audit_log( - response: dict[str, Any], + response: Mapping[str, object], existing_user_row: BaseModel | None, litellm_changed_by: str | None, user_api_key_dict: UserAPIKeyAuth, @@ -2687,6 +2734,11 @@ async def get_user_daily_activity( Meant to optimize querying spend data for analytics for a user. + Reads daily spend records that only ever accumulate and are never affected by budget + resets. Their total can legitimately exceed the `spend` field returned by + `/v2/user/info`, which is a running budget counter that every budget reset sets back + to zero (or to the overage above `max_budget` when `budget_rollover` is enabled). + Returns: (by date) - spend @@ -2800,6 +2852,11 @@ async def get_user_daily_activity_aggregated( """ Aggregated analytics for a user's daily activity without pagination. Returns the same response shape as the paginated endpoint with page metadata set to single-page. + + Reads daily spend records that only ever accumulate and are never affected by budget + resets. Their total can legitimately exceed the `spend` field returned by + `/v2/user/info`, which is a running budget counter that every budget reset sets back + to zero (or to the overage above `max_budget` when `budget_rollover` is enabled). """ from litellm.proxy.proxy_server import prisma_client diff --git a/litellm/proxy/management_endpoints/jwt_key_mapping_endpoints.py b/litellm/proxy/management_endpoints/jwt_key_mapping_endpoints.py index 9f561eadfbd..ccfd5338ec4 100644 --- a/litellm/proxy/management_endpoints/jwt_key_mapping_endpoints.py +++ b/litellm/proxy/management_endpoints/jwt_key_mapping_endpoints.py @@ -1,4 +1,6 @@ -from typing import Final +from collections.abc import Mapping, Sequence +from datetime import datetime +from typing import Final, Protocol from fastapi import APIRouter, Depends, HTTPException, Query @@ -18,7 +20,59 @@ from litellm.repositories.table_repositories import JWTKeyMappingRepository router: Final = APIRouter() -def _to_response(mapping) -> JWTKeyMappingResponse: +class _JWTKeyMappingRecord(Protocol): + """A ``LiteLLM_JWTKeyMapping`` row, viewed through the columns these endpoints read.""" + + @property + def id(self) -> str: ... + + @property + def jwt_claim_name(self) -> str: ... + + @property + def jwt_claim_value(self) -> str: ... + + @property + def description(self) -> str | None: ... + + @property + def is_active(self) -> bool: ... + + @property + def created_at(self) -> datetime: ... + + @property + def updated_at(self) -> datetime: ... + + @property + def created_by(self) -> str | None: ... + + @property + def updated_by(self) -> str | None: ... + + +class _JWTKeyMappingTable(Protocol): + """The Prisma table actions these endpoints issue against the JWT key mapping table.""" + + async def create(self, *, data: Mapping[str, object]) -> _JWTKeyMappingRecord: ... + + async def find_unique(self, *, where: Mapping[str, object]) -> _JWTKeyMappingRecord | None: ... + + async def update(self, *, where: Mapping[str, object], data: Mapping[str, object]) -> _JWTKeyMappingRecord: ... + + async def delete(self, *, where: Mapping[str, object]) -> _JWTKeyMappingRecord | None: ... + + async def find_many(self, *, skip: int, take: int, order: Mapping[str, str]) -> Sequence[_JWTKeyMappingRecord]: ... + + async def count(self) -> int: ... + + +def _mapping_table(prisma_client: object) -> _JWTKeyMappingTable: + """View the JWT key mapping repository's untyped Prisma table through the actions used here.""" + return JWTKeyMappingRepository(prisma_client).table + + +def _to_response(mapping: _JWTKeyMappingRecord) -> JWTKeyMappingResponse: """Convert a Prisma mapping object to a safe response (no hashed token).""" return JWTKeyMappingResponse( id=mapping.id, @@ -62,7 +116,7 @@ async def create_jwt_key_mapping( if data.description is not None: create_data["description"] = data.description - new_mapping: Final = await JWTKeyMappingRepository(prisma_client).table.create(data=create_data) + new_mapping: Final = await _mapping_table(prisma_client).create(data=create_data) # Invalidate cache cache_key: Final = f"jwt_key_mapping:{data.jwt_claim_name}:{data.jwt_claim_value}" @@ -110,7 +164,7 @@ async def update_jwt_key_mapping( try: # Get old mapping for cache invalidation - old_mapping: Final = await JWTKeyMappingRepository(prisma_client).table.find_unique(where={"id": data.id}) + old_mapping: Final = await _mapping_table(prisma_client).find_unique(where={"id": data.id}) if old_mapping is None: raise HTTPException(status_code=404, detail="Mapping not found") @@ -118,9 +172,7 @@ async def update_jwt_key_mapping( cache_key = f"jwt_key_mapping:{old_mapping.jwt_claim_name}:{old_mapping.jwt_claim_value}" await user_api_key_cache.async_delete_cache(cache_key) - updated_mapping: Final = await JWTKeyMappingRepository(prisma_client).table.update( - where={"id": data.id}, data=update_data - ) + updated_mapping: Final = await _mapping_table(prisma_client).update(where={"id": data.id}, data=update_data) if updated_mapping is None: raise HTTPException(status_code=404, detail="Mapping not found") @@ -162,7 +214,7 @@ async def delete_jwt_key_mapping( try: # Get old mapping for cache invalidation - old_mapping: Final = await JWTKeyMappingRepository(prisma_client).table.find_unique(where={"id": data.id}) + old_mapping: Final = await _mapping_table(prisma_client).find_unique(where={"id": data.id}) if old_mapping is None: raise HTTPException(status_code=404, detail="Mapping not found") @@ -170,7 +222,7 @@ async def delete_jwt_key_mapping( cache_key: Final = f"jwt_key_mapping:{old_mapping.jwt_claim_name}:{old_mapping.jwt_claim_value}" await user_api_key_cache.async_delete_cache(cache_key) - await JWTKeyMappingRepository(prisma_client).table.delete(where={"id": data.id}) + await _mapping_table(prisma_client).delete(where={"id": data.id}) return {"status": "success"} except HTTPException: raise @@ -198,12 +250,12 @@ async def list_jwt_key_mappings( try: skip: Final = (page - 1) * size - mappings: Final = await JWTKeyMappingRepository(prisma_client).table.find_many( + mappings: Final = await _mapping_table(prisma_client).find_many( skip=skip, take=size, order={"created_at": "desc"}, ) - total_count: Final = await JWTKeyMappingRepository(prisma_client).table.count() + total_count: Final = await _mapping_table(prisma_client).count() return { "mappings": [_to_response(m) for m in mappings], "total_count": total_count, @@ -235,7 +287,7 @@ async def info_jwt_key_mapping( raise HTTPException(status_code=500, detail="Database not connected") try: - mapping: Final = await JWTKeyMappingRepository(prisma_client).table.find_unique(where={"id": id}) + mapping: Final = await _mapping_table(prisma_client).find_unique(where={"id": id}) if mapping is None: raise HTTPException(status_code=404, detail="Mapping not found") return _to_response(mapping) diff --git a/litellm/proxy/management_endpoints/key_management_endpoints.py b/litellm/proxy/management_endpoints/key_management_endpoints.py index 0d12b012c18..c3403cf477c 100644 --- a/litellm/proxy/management_endpoints/key_management_endpoints.py +++ b/litellm/proxy/management_endpoints/key_management_endpoints.py @@ -18,13 +18,15 @@ import os import re import secrets import traceback -from collections.abc import Awaitable, Callable, Mapping, Sequence +from collections.abc import Awaitable, Callable, Iterator, Mapping, Sequence from datetime import datetime, timedelta, timezone +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final, Literal, Optional, Protocol, TypeVar, cast import fastapi import yaml from fastapi import APIRouter, Depends, Header, HTTPException, Query, Request, status +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_proxy_logger @@ -110,6 +112,7 @@ from litellm.proxy.management_helpers.team_member_permission_checks import ( TeamMemberPermissionChecks, ) from litellm.proxy.management_helpers.utils import management_endpoint_wrapper +from litellm.proxy.spend_tracking.budget_reservation import get_budget_window_start from litellm.proxy.spend_tracking.spend_tracking_utils import _is_master_key from litellm.proxy.ui_crud_endpoints.proxy_setting_endpoints import ( get_ui_settings_cached, @@ -154,6 +157,7 @@ from litellm.types.utils import ( ) if TYPE_CHECKING: + import prisma from prisma import Prisma from prisma import models as prisma_models @@ -181,6 +185,14 @@ class _TxTables(Protocol): litellm_proxymodeltable: TableActions[object] +class _ModelParamsUpdate(TypedDict): + litellm_params: ReadOnly["prisma.Json"] + + +class _ModelRowWhere(TypedDict): + model_id: ReadOnly[str] + + class _ConfigTableActions(Protocol): """Config table surface this module needs; the shared repository seam exposes no ``update``.""" @@ -230,6 +242,48 @@ def _config_table(prisma_client: PrismaClient) -> _ConfigTableActions: ) +class _CustomKeyHooksModule(Protocol): + user_custom_key_generate: Callable[..., Awaitable[Mapping[str, object]]] | None + user_custom_key_update: Callable[..., Awaitable[Mapping[str, object]]] | None + + +def _custom_key_generate_hook( + hooks: _CustomKeyHooksModule, +) -> Callable[..., Awaitable[Mapping[str, object]]] | None: + return hooks.user_custom_key_generate + + +def _custom_key_update_hook( + hooks: _CustomKeyHooksModule, +) -> Callable[..., Awaitable[Mapping[str, object]]] | None: + return hooks.user_custom_key_update + + +class _LegacyDumpable(Protocol): + def dict(self) -> Mapping[str, object]: ... + + +def _legacy_model_dict(row: _LegacyDumpable) -> Mapping[str, object]: + return row.dict() + + +def _as_object_dict(values: Mapping[str, object]) -> Mapping[str, object]: + return values + + +def _model_items(model: BaseModel) -> Iterator[tuple[str, object]]: + return iter(model) + + +class _EnvVarsParam(Protocol): + @property + def param_value(self) -> Mapping[str, str] | None: ... + + +def _env_vars_param_value(param: _EnvVarsParam) -> Mapping[str, str] | None: + return param.param_value + + async def _check_custom_key_allowed(custom_key_value: str | None) -> None: """Raise 403 if custom API keys are disabled and a custom key was provided.""" if custom_key_value is None: @@ -684,6 +738,45 @@ def _check_allowed_routes_caller_permission( ) +_READ_ONLY_ALLOWED_ROUTES_PRESET: Final = frozenset(("info_routes",)) + + +def _is_safe_preset_route_transition( + incoming_allowed_routes: Sequence[str] | None, + existing_allowed_routes: Sequence[str] | None, +) -> bool: + """ + True when every route on BOTH sides is a safe `key_type` preset bucket + (empty = full access, which non-admins already get from a default + `/key/generate`), with one carve-out: a read-only (`info_routes`) key + stays read-only, so widening it needs an admin. Requiring the existing + side to be a safe preset keeps an owner from clearing an admin-set + custom route restriction (LIT-4139). + """ + incoming: Final = frozenset(incoming_allowed_routes or ()) + existing: Final = frozenset(existing_allowed_routes or ()) + if not (incoming | existing) <= _NON_ADMIN_SAFE_ALLOWED_ROUTES_PRESETS: + return False + return existing != _READ_ONLY_ALLOWED_ROUTES_PRESET or incoming == existing + + +def _enforce_allowed_routes_update_permission( + data: UpdateKeyRequest, + existing_key_row: LiteLLM_VerificationToken, + user_api_key_dict: UserAPIKeyAuth, +) -> None: + if _is_safe_preset_route_transition( + incoming_allowed_routes=data.allowed_routes, + existing_allowed_routes=existing_key_row.allowed_routes, + ): + return + _check_allowed_routes_caller_permission( + allowed_routes=data.allowed_routes, + user_api_key_dict=user_api_key_dict, + allowed_routes_was_provided="allowed_routes" in data.model_fields_set, + ) + + def _check_permissions_caller_permission( data: GenerateRequestBase, user_api_key_dict: UserAPIKeyAuth, @@ -910,7 +1003,7 @@ async def _common_key_generation_helper( # check if user set default key/generate params on config.yaml if litellm.default_key_generate_params is not None: - for elem in data: + for elem in _model_items(data): key, value = elem if ( value is None @@ -1692,11 +1785,11 @@ async def generate_key_fn( - user_id: (str) Unique user id - used for tracking spend across multiple keys for same user id. """ try: + from litellm.proxy import proxy_server from litellm.proxy._types import CommonProxyErrors from litellm.proxy.proxy_server import ( prisma_client, user_api_key_cache, - user_custom_key_generate, ) if prisma_client is None: @@ -1723,7 +1816,7 @@ async def generate_key_fn( ) custom_key_generate_hook: Final[Callable[..., Awaitable[Mapping[str, object]]] | None] = ( - user_custom_key_generate + _custom_key_generate_hook(proxy_server) ) if custom_key_generate_hook is not None: if inspect.iscoroutinefunction(custom_key_generate_hook): @@ -1892,11 +1985,11 @@ async def generate_service_account_key_fn( - user_id: (str) Unique user id - used for tracking spend across multiple keys for same user id. """ + from litellm.proxy import proxy_server from litellm.proxy._types import CommonProxyErrors from litellm.proxy.proxy_server import ( prisma_client, user_api_key_cache, - user_custom_key_generate, ) if prisma_client is None: @@ -1924,7 +2017,9 @@ async def generate_service_account_key_fn( verbose_proxy_logger.debug("entered /key/generate") - custom_key_generate_hook: Final[Callable[..., Awaitable[Mapping[str, object]]] | None] = user_custom_key_generate + custom_key_generate_hook: Final[Callable[..., Awaitable[Mapping[str, object]]] | None] = _custom_key_generate_hook( + proxy_server + ) if custom_key_generate_hook is not None: if inspect.iscoroutinefunction(custom_key_generate_hook): result: Final = await custom_key_generate_hook(data) @@ -1998,7 +2093,7 @@ def prepare_metadata_fields(data: BaseModel, non_default_values: dict, existing_ ) casted_metadata[reserved_field] = existing_value - data_json: Final[Mapping[str, object]] = data.model_dump(exclude_unset=True, exclude_none=True) + data_json: Final = _as_object_dict(data.model_dump(exclude_unset=True, exclude_none=True)) try: for k, v in data_json.items(): @@ -2466,26 +2561,34 @@ async def _validate_mcp_servers_for_key_update( return normalized_object_permission +def _require_prisma_client(prisma_client: PrismaClient | None) -> PrismaClient: + if prisma_client is None: + raise HTTPException(status_code=500, detail={"error": "Database not connected"}) + return prisma_client + + async def _validate_update_key_data( data: UpdateKeyRequest, existing_key_row: LiteLLM_VerificationToken, user_api_key_dict: UserAPIKeyAuth, llm_router: Router | None, premium_user: bool, - prisma_client: Any, + prisma_client: PrismaClient | None, user_api_key_cache: UserApiKeyCache, ) -> None: """Validate permissions and constraints for key update.""" + checked_prisma_client: Final = _require_prisma_client(prisma_client) + # Reject NaN/±inf spend before it can reach the DB / spend counter. validate_finite_spend(data.spend) validate_budget_duration(data.budget_duration) _is_proxy_admin: Final = user_api_key_dict.user_role == LitellmUserRoles.PROXY_ADMIN.value - _check_allowed_routes_caller_permission( - allowed_routes=data.allowed_routes, + _enforce_allowed_routes_update_permission( + data=data, + existing_key_row=existing_key_row, user_api_key_dict=user_api_key_dict, - allowed_routes_was_provided="allowed_routes" in data.model_fields_set, ) _check_passthrough_routes_caller_permission( data=data, @@ -2513,7 +2616,7 @@ async def _validate_update_key_data( await TeamMemberPermissionChecks.can_team_member_execute_key_management_endpoint( user_api_key_dict=user_api_key_dict, route=KeyManagementRoutes.KEY_UPDATE, - prisma_client=prisma_client, + prisma_client=checked_prisma_client, existing_key_row=existing_key_row, user_api_key_cache=user_api_key_cache, ) @@ -2594,12 +2697,12 @@ async def _validate_update_key_data( # _check_key_admin_access that would otherwise require team/org admin status. _key_is_team_key: Final = getattr(existing_key_row, "team_id", None) is not None can_skip_admin_check: Final = (caller_is_creator or _key_is_team_key) and not _is_budget_change - if (not _is_proxy_admin) and prisma_client is not None and not can_skip_admin_check: + if (not _is_proxy_admin) and not can_skip_admin_check: hashed_key: Final = existing_key_row.token await _check_key_admin_access( user_api_key_dict=user_api_key_dict, hashed_token=hashed_key, - prisma_client=prisma_client, + prisma_client=checked_prisma_client, user_api_key_cache=user_api_key_cache, route=("/key/update (max_budget/spend)" if _is_budget_change else "/key/update"), ) @@ -2610,7 +2713,7 @@ async def _validate_update_key_data( if _team_id_to_check is not None: team_obj = await get_team_object( team_id=_team_id_to_check, - prisma_client=prisma_client, + prisma_client=checked_prisma_client, user_api_key_cache=user_api_key_cache, check_db_only=True, ) @@ -2626,7 +2729,7 @@ async def _validate_update_key_data( await _check_team_key_limits( team_table=team_obj, data=data, - prisma_client=prisma_client, + prisma_client=checked_prisma_client, ) TeamMemberPermissionChecks.enforce_member_can_assign_access_groups( @@ -2641,7 +2744,7 @@ async def _validate_update_key_data( await _check_project_key_limits( project_id=_project_id_to_check, data=data, - prisma_client=prisma_client, + prisma_client=checked_prisma_client, user_api_key_cache=user_api_key_cache, ) @@ -2656,7 +2759,7 @@ async def _validate_update_key_data( await _validate_caller_can_assign_key_org( user_api_key_dict=user_api_key_dict, organization_id=data.organization_id, - prisma_client=prisma_client, + prisma_client=checked_prisma_client, ) # Check org key limits only when throughput-related fields or organization_id change @@ -2672,7 +2775,7 @@ async def _validate_update_key_data( org_table: Final = await get_org_object( org_id=_org_id_to_check, user_api_key_cache=user_api_key_cache, - prisma_client=prisma_client, + prisma_client=checked_prisma_client, ) if org_table is None: raise HTTPException( @@ -2682,7 +2785,7 @@ async def _validate_update_key_data( await _check_org_key_limits( org_table=org_table, data=data, - prisma_client=prisma_client, + prisma_client=checked_prisma_client, ) # if team change - check if this is possible @@ -2712,7 +2815,7 @@ async def _validate_update_key_data( data=data, team_obj=team_obj, existing_key_row=existing_key_row, - prisma_client=prisma_client, + prisma_client=checked_prisma_client, user_api_key_cache=user_api_key_cache, is_proxy_admin=_is_proxy_admin, ) @@ -2805,13 +2908,13 @@ async def update_key_fn( }' ``` """ + from litellm.proxy import proxy_server from litellm.proxy.proxy_server import ( llm_router, premium_user, prisma_client, proxy_logging_obj, user_api_key_cache, - user_custom_key_update, ) try: @@ -2842,7 +2945,9 @@ async def update_key_fn( ) # Custom key update hook - custom_key_update_hook: Final[Callable[..., Awaitable[Mapping[str, object]]] | None] = user_custom_key_update + custom_key_update_hook: Final[Callable[..., Awaitable[Mapping[str, object]]] | None] = _custom_key_update_hook( + proxy_server + ) if custom_key_update_hook is not None: if inspect.iscoroutinefunction(custom_key_update_hook): result: Final = await custom_key_update_hook(data) @@ -3004,14 +3109,16 @@ async def bulk_update_keys( }' ``` """ + from litellm.proxy import proxy_server from litellm.proxy.proxy_server import ( llm_router, prisma_client, proxy_logging_obj, user_api_key_cache, - user_custom_key_update, ) + custom_key_update_hook: Final = _custom_key_update_hook(proxy_server) + if user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN.value: raise HTTPException( status_code=403, @@ -3057,7 +3164,7 @@ async def bulk_update_keys( user_api_key_cache=user_api_key_cache, proxy_logging_obj=proxy_logging_obj, llm_router=llm_router, - user_custom_key_update=user_custom_key_update, + user_custom_key_update=custom_key_update_hook, ) successful_updates.append( @@ -3135,7 +3242,7 @@ def _build_failed_team_key_update( if hasattr(existing_key_row, "model_dump"): key_info = existing_key_row.model_dump() elif hasattr(existing_key_row, "dict"): - key_info = existing_key_row.dict() + key_info = dict[str, object](_legacy_model_dict(existing_key_row)) if key_info: key_info.pop("token", None) @@ -3166,14 +3273,16 @@ async def bulk_update_team_keys( Callable by proxy admins, or by team admins with `KEY_UPDATE` permission. """ + from litellm.proxy import proxy_server from litellm.proxy.proxy_server import ( llm_router, prisma_client, proxy_logging_obj, user_api_key_cache, - user_custom_key_update, ) + custom_key_update_hook: Final = _custom_key_update_hook(proxy_server) + if prisma_client is None: raise HTTPException( status_code=500, @@ -3302,7 +3411,7 @@ async def bulk_update_team_keys( user_api_key_cache=user_api_key_cache, proxy_logging_obj=proxy_logging_obj, llm_router=llm_router, - user_custom_key_update=user_custom_key_update, + user_custom_key_update=custom_key_update_hook, existing_key_row=existing_by_token[db_token], ) @@ -3518,6 +3627,63 @@ async def _build_model_max_budget_usage( ) +def _window_max_budget(window: Mapping[str, object]) -> float | None: + """A window's max_budget as a float; None when absent or unparseable.""" + value: Final = window.get("max_budget") + if not isinstance(value, (int, float, str)): + return None + try: + return float(value) + except ValueError: + return None + + +async def _budget_window_usage( + window: Mapping[str, object], api_key_hash: str +) -> tuple[str, Mapping[str, object]] | None: + """ + (budget_duration, usage entry) for one budget window; None when the window + has no budget_duration to key it by. + + Reads the same cross-pod counter (spend:key:{hashed_token}:window:{budget_duration}) + that _virtual_key_multi_budget_check enforces against, passing the same + window_duration + window_start so a stale-low counter is re-checked against + the LiteLLM_BudgetWindowSpend row instead of a spend-log aggregate. + """ + from litellm.proxy.proxy_server import get_current_spend + + duration: Final = window.get("budget_duration") + if not isinstance(duration, str) or not duration: + return None + spend: Final = await get_current_spend( + counter_key=f"spend:key:{api_key_hash}:window:{duration}", + fallback_spend=0.0, + max_budget=_window_max_budget(window), + window_entity_type="Key", + window_entity_id=api_key_hash, + window_duration=duration, + window_start=get_budget_window_start(window), + ) + return duration, MappingProxyType({"current_spend": round(spend, 4)}) + + +async def _build_budget_limits_usage( + budget_limits: Sequence[object] | str | None, api_key_hash: str +) -> Mapping[str, Mapping[str, object]] | None: + """ + Current-window spend per budget window, keyed by budget_duration, reported + next to the stored budget_limits (which is returned untouched). None when + the key has no windows, so the field only appears on keys that have them. + """ + windows: Final = _budget_limit_windows(budget_limits) + if not windows: + return None + usages: Final = await asyncio.gather( + *(_budget_window_usage(window=window, api_key_hash=api_key_hash) for window in windows) + ) + return MappingProxyType({duration: usage for duration, usage in (u for u in usages if u is not None)}) + + @router.post( "/v2/key/info", tags=["key management"], @@ -3560,7 +3726,6 @@ async def info_key_fn_v2( status_code=status.HTTP_422_UNPROCESSABLE_ENTITY, detail={"message": "Malformed request. No keys passed in."}, ) - # Resolve key_aliases to tokens so we never pass token=None (unbounded query) tokens_to_query: Final = list(data.keys) if data.keys else [] if data.key_aliases: @@ -3602,6 +3767,13 @@ async def info_key_fn_v2( model_max_budget=model_max_budget, user_api_key_cache=model_max_budget_limiter.dual_cache, ) + if k_token_hash: + budget_limits_usage = await _build_budget_limits_usage( + budget_limits=k_dict.get("budget_limits"), + api_key_hash=k_token_hash, + ) + if budget_limits_usage is not None: + k_dict["budget_limits_usage"] = budget_limits_usage filtered_key_info.append(k_dict) return {"key": data.keys, "info": filtered_key_info} @@ -3638,6 +3810,10 @@ async def info_key_fn( - model_max_budget: dict - Per-model budgets, e.g. {"gpt-4": {"budget_limit": 0.0005, "time_period": "30d"}} - model_max_budget_usage: dict | None - Current-window spend per model, present only when the key has per-model budgets + - budget_limits: list | None - Concurrent budget windows, exactly as stored + - budget_limits_usage: dict | None - Current-window spend per budget window, e.g. + {"1h": {"current_spend": 0.0009}}, present only when the key has budget windows + (read from the same cross-pod spend counter the budget enforcement uses) - models: list - Model_name's the key is allowed to call - tpm_limit / rpm_limit: int | None - Tokens and requests per minute limits - metadata: dict - Metadata for the key, e.g. {"team": "core-infra"} @@ -3705,7 +3881,7 @@ async def info_key_fn( except Exception: # if using pydantic v1 key_info = key_info.dict() # pyright: ignore[reportDeprecated] # deliberate pydantic v1 fallback - key_token_hash: Final = key_info.pop("token") + key_token_hash: Final[str | None] = key_info.pop("token") model_max_budget = key_info.get("model_max_budget") or {} budget_table: Final = key_info.get("litellm_budget_table") or {} @@ -3717,6 +3893,12 @@ async def info_key_fn( model_max_budget=model_max_budget, user_api_key_cache=model_max_budget_limiter.dual_cache, ) + budget_limits_usage: Final = await _build_budget_limits_usage( + budget_limits=key_info.get("budget_limits"), + api_key_hash=key_token_hash, + ) + if budget_limits_usage is not None: + key_info["budget_limits_usage"] = budget_limits_usage # Attach object_permission if object_permission_id is set key_info = await attach_object_permission_to_dict(key_info, prisma_client) @@ -4301,7 +4483,7 @@ def _transform_verification_tokens_to_deleted_records( "litellm_changed_by": litellm_changed_by, } ) - record = deleted_record.model_dump() + record = dict[str, object](_as_object_dict(deleted_record.model_dump())) # Map org_id to organization_id (model uses org_id, but schema expects organization_id) org_id_value: object = record.pop("org_id", None) @@ -4427,28 +4609,29 @@ async def _rotate_master_key( if models: decrypted_models: Final = proxy_config.decrypt_model_list_from_db(new_models=models) verbose_proxy_logger.debug("ABLE TO DECRYPT MODELS - len(decrypted_models): %s", len(decrypted_models)) - new_models: Final[list[dict[str, object]]] = [] - for model in decrypted_models: - new_model = await _add_model_to_db( - model_params=Deployment(**model), - user_api_key_dict=user_api_key_dict, - prisma_client=prisma_client, - new_encryption_key=new_master_key, - should_create_model_in_db=False, - ) - if new_model: - _dumped = new_model.model_dump(exclude_none=True) - _dumped["litellm_params"] = prisma.Json(_dumped["litellm_params"]) - _dumped["model_info"] = prisma.Json(_dumped["model_info"]) - new_models.append(_dumped) - verbose_proxy_logger.debug("Resetting proxy model table") - async with prisma_client.db.tx() as tx_ctx: + reencrypted_models: Final = tuple( + [ + reencrypted + for model in decrypted_models + if ( + reencrypted := await _add_model_to_db( + model_params=Deployment(**model), + user_api_key_dict=user_api_key_dict, + prisma_client=prisma_client, + new_encryption_key=new_master_key, + should_create_model_in_db=False, + ) + ) + ] + ) + verbose_proxy_logger.debug("Re-encrypting litellm_params on %s model rows", len(reencrypted_models)) + async with prisma_client.db.tx(timeout=timedelta(minutes=2)) as tx_ctx: tx: Final[_TxTables] = tx_ctx - await tx.litellm_proxymodeltable.delete_many() - verbose_proxy_logger.debug("Creating %s models", len(new_models)) - await tx.litellm_proxymodeltable.create_many( - data=new_models, - ) + for reencrypted_model in reencrypted_models: + await tx.litellm_proxymodeltable.update_many( + data=_ModelParamsUpdate(litellm_params=prisma.Json(reencrypted_model.litellm_params)), + where=_ModelRowWhere(model_id=reencrypted_model.model_id), + ) await publish_config_change(redis_cache=coordination_redis_cache(), object_type="litellm_proxymodeltable") # 3. process config table try: @@ -4458,14 +4641,14 @@ async def _rotate_master_key( if config: """If environment_variables is found, decrypt it and encrypt it with the new master key""" - environment_variables_dict = {} + environment_variables_dict: Mapping[str, str] | None = {} for c in config: if c.param_name == "environment_variables": - environment_variables_dict = c.param_value + environment_variables_dict = _env_vars_param_value(c) if environment_variables_dict: decrypted_env_vars: Final = proxy_config._decrypt_and_set_db_env_variables( - environment_variables=environment_variables_dict + environment_variables=dict[str, str](environment_variables_dict) ) encrypted_env_vars: Final = proxy_config._encrypt_env_variables( environment_variables=decrypted_env_vars, @@ -4531,7 +4714,7 @@ async def _rotate_master_key( updated_patch=decrypted_cred, new_encryption_key=new_master_key, ) - _cred_data = encrypted_cred.model_dump(exclude_none=True) + _cred_data = dict[str, object](_as_object_dict(encrypted_cred.model_dump(exclude_none=True))) if "credential_values" in _cred_data: _cred_data["credential_values"] = prisma.Json(_cred_data["credential_values"]) if "credential_info" in _cred_data: @@ -5160,7 +5343,7 @@ def _validate_reset_spend_value(reset_to: object, key_in_db: LiteLLM_Verificatio max_budget = key_in_db.max_budget if key_in_db.litellm_budget_table is not None: - budget_max_budget: Final = getattr(key_in_db.litellm_budget_table, "max_budget", None) + budget_max_budget: Final[float | None] = getattr(key_in_db.litellm_budget_table, "max_budget", None) if budget_max_budget is not None: if max_budget is None or budget_max_budget < max_budget: max_budget = budget_max_budget diff --git a/litellm/proxy/management_endpoints/mcp_connector_import.py b/litellm/proxy/management_endpoints/mcp_connector_import.py new file mode 100644 index 00000000000..8120452393c --- /dev/null +++ b/litellm/proxy/management_endpoints/mcp_connector_import.py @@ -0,0 +1,185 @@ +""" +Convert Anthropic MCP connector definitions into LiteLLM MCP server create requests. + +Two interchange shapes are accepted: +- the ``mcpServers`` mapping used by Claude Desktop / Claude Code config files +- the ``mcp_servers`` array used by the Anthropic Messages API MCP connector +""" + +import re +from collections.abc import Mapping +from dataclasses import dataclass +from typing import Final + +from pydantic import AliasChoices, BaseModel, ConfigDict, Field, ValidationError + +from litellm.proxy._types import MCPApprovalStatus, NewMCPServerRequest +from litellm.types.mcp import MCPAuth, MCPAuthType, MCPCredentials, MCPTransport + + +class MCPConnectorEntry(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + name: str | None = None + type: str | None = None + url: str | None = None + authorization_token: str | None = Field( + default=None, validation_alias=AliasChoices("authorization_token", "authorizationToken") + ) + headers: Mapping[str, str] | None = None + command: str | None = None + args: tuple[str, ...] = Field(default_factory=tuple) + env: Mapping[str, str] = Field(default_factory=dict) + description: str | None = None + + +class MCPConnectorImportRequest(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + mcp_servers: Mapping[str, MCPConnectorEntry] | tuple[MCPConnectorEntry, ...] = Field( + validation_alias=AliasChoices("mcp_servers", "mcpServers") + ) + + +@dataclass(frozen=True, slots=True) +class ConvertedConnector: + name: str + request: NewMCPServerRequest + + +@dataclass(frozen=True, slots=True) +class ConnectorConversionError: + name: str + error: str + + +class MCPConnectorImportResult(BaseModel): + name: str + server_id: str + alias: str + + +class MCPConnectorImportSkipped(BaseModel): + name: str + reason: str + + +class MCPConnectorImportFailure(BaseModel): + name: str + error: str + + +class MCPConnectorImportResponse(BaseModel): + imported: tuple[MCPConnectorImportResult, ...] + skipped: tuple[MCPConnectorImportSkipped, ...] + errors: tuple[MCPConnectorImportFailure, ...] + + +_INVALID_SERVER_NAME_CHARS: Final = re.compile(r"[^A-Za-z0-9_]") + + +def sanitize_connector_name(name: str) -> str: + sanitized: Final = re.sub(r"_+", "_", _INVALID_SERVER_NAME_CHARS.sub("_", name.strip())).strip("_") + return sanitized + + +_SSE_TYPES: Final = frozenset({"sse"}) +_URL_TYPES: Final = frozenset({"url", "http", "streamable_http", "streamable-http", "sse", ""}) + + +def _convert_entry(name: str, entry: MCPConnectorEntry) -> ConvertedConnector | ConnectorConversionError: + sanitized_name: Final = sanitize_connector_name(name) + if not sanitized_name: + return ConnectorConversionError(name=name, error="Connector name is empty after sanitization.") + + if entry.url and entry.command: + return ConnectorConversionError(name=name, error="Connector cannot have both a url and a command.") + + if entry.command: + try: + stdio_request: Final = NewMCPServerRequest( + server_name=sanitized_name, + alias=sanitized_name, + description=entry.description, + approval_status=MCPApprovalStatus.active, + transport=MCPTransport.stdio, + command=entry.command, + args=list(entry.args), + env=dict(entry.env), + ) + except ValidationError as e: + return ConnectorConversionError(name=name, error=_first_validation_message(e)) + return ConvertedConnector(name=name, request=stdio_request) + + if not entry.url: + return ConnectorConversionError(name=name, error="Connector must have either a url or a command.") + + entry_type: Final = (entry.type or "").lower() + if entry_type not in _URL_TYPES: + return ConnectorConversionError(name=name, error=f"Unsupported connector type '{entry.type}'.") + + transport: Final = MCPTransport.sse if entry_type in _SSE_TYPES else MCPTransport.http + auth: Final = _remote_auth(entry) + try: + remote_request: Final = NewMCPServerRequest( + server_name=sanitized_name, + alias=sanitized_name, + description=entry.description, + approval_status=MCPApprovalStatus.active, + transport=transport, + url=entry.url, + auth_type=auth.auth_type, + credentials=auth.credentials, + static_headers=auth.static_headers, + ) + except ValidationError as e: + return ConnectorConversionError(name=name, error=_first_validation_message(e)) + return ConvertedConnector(name=name, request=remote_request) + + +@dataclass(frozen=True, slots=True) +class _RemoteAuth: + auth_type: MCPAuthType + credentials: MCPCredentials | None + static_headers: dict[str, str] | None + + +_AUTHORIZATION_HEADER: Final = "authorization" +_BEARER_PREFIX: Final = "bearer " + + +def _remote_auth(entry: MCPConnectorEntry) -> _RemoteAuth: + headers: Final[Mapping[str, str]] = entry.headers or {} + header_value: Final = next((value for key, value in headers.items() if key.lower() == _AUTHORIZATION_HEADER), None) + remaining: Final = {key: value for key, value in headers.items() if key.lower() != _AUTHORIZATION_HEADER} or None + if entry.authorization_token: + return _RemoteAuth(MCPAuth.bearer_token, {"auth_value": entry.authorization_token}, remaining) + if not header_value: + return _RemoteAuth(MCPAuth.none, None, remaining) + if header_value.lower().startswith(_BEARER_PREFIX): + return _RemoteAuth(MCPAuth.bearer_token, {"auth_value": header_value[len(_BEARER_PREFIX) :]}, remaining) + return _RemoteAuth(MCPAuth.authorization, {"auth_value": header_value}, remaining) + + +def _first_validation_message(error: ValidationError) -> str: + messages: Final = tuple(str(detail.get("msg", "")) for detail in error.errors()) + return messages[0] if messages else str(error) + + +def convert_connector_entries( + payload: MCPConnectorImportRequest, +) -> tuple[ConvertedConnector | ConnectorConversionError, ...]: + servers: Final = payload.mcp_servers + if isinstance(servers, Mapping): + return tuple(_convert_entry(name, entry) for name, entry in servers.items()) + return tuple( + _convert_entry(entry.name or "", entry) if entry.name else _named_entry_error(index, entry) + for index, entry in enumerate(servers) + ) + + +def _named_entry_error(index: int, entry: MCPConnectorEntry) -> ConnectorConversionError: + return ConnectorConversionError( + name=entry.url or f"entry {index}", + error="Connector entries in list form must have a name.", + ) diff --git a/litellm/proxy/management_endpoints/mcp_management_endpoints.py b/litellm/proxy/management_endpoints/mcp_management_endpoints.py index 556a30d0b29..40cc2e57932 100644 --- a/litellm/proxy/management_endpoints/mcp_management_endpoints.py +++ b/litellm/proxy/management_endpoints/mcp_management_endpoints.py @@ -133,6 +133,7 @@ if MCP_AVAILABLE: delete_mcp_server, delete_user_credential, delete_user_env_vars, + get_all_mcp_servers, get_all_mcp_servers_for_user, get_draft_mcp_server, get_mcp_server, @@ -199,6 +200,16 @@ if MCP_AVAILABLE: populate_request_with_path_params, ) from litellm.proxy.management_endpoints.common_utils import _user_has_admin_view + from litellm.proxy.management_endpoints.mcp_connector_import import ( + ConnectorConversionError, + ConvertedConnector, + MCPConnectorImportFailure, + MCPConnectorImportRequest, + MCPConnectorImportResponse, + MCPConnectorImportResult, + MCPConnectorImportSkipped, + convert_connector_entries, + ) from litellm.proxy.management_helpers.utils import management_endpoint_wrapper from litellm.types.mcp import ( MCP_ADMIN_CONFIG_CREDENTIAL_KEYS, @@ -1626,6 +1637,116 @@ if MCP_AVAILABLE: return _redact_mcp_credentials(new_mcp_server) + @router.post( + "/server/import", + description="Bulk-import MCP connectors from Anthropic mcpServers or mcp_servers JSON", + dependencies=(Depends(user_api_key_auth),), + response_model=MCPConnectorImportResponse, + status_code=status.HTTP_200_OK, + ) + @management_endpoint_wrapper + async def import_mcp_servers( + payload: MCPConnectorImportRequest, + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), # noqa: B008 # FastAPI dependency injection + ): + """ + Bulk-import MCP connectors. Accepts the Claude Desktop / Claude Code + ``mcpServers`` mapping or the Anthropic Messages API ``mcp_servers`` + array, creates each entry as a LiteLLM MCP server, and returns + per-entry results so partial imports are visible to the caller. + """ + prisma_client: Final = get_prisma_client_or_throw("Database not connected. Connect a database to your proxy") + + if LitellmUserRoles.PROXY_ADMIN != user_api_key_dict.user_role: + raise HTTPException( + status_code=status.HTTP_403_FORBIDDEN, + detail={ # mutable-ok: FastAPI HTTPException detail requires a plain dict + "error": "User does not have permission to import mcp servers. You can only import mcp servers if you are a PROXY_ADMIN." + }, + ) + + conversions: Final = convert_connector_entries(payload) + existing_servers: Final = await get_all_mcp_servers(prisma_client) + existing_names: Final = frozenset( + name for server in existing_servers for name in (server.alias, server.server_name) if name + ) + + def _classify( + index: int, conversion: ConvertedConnector | ConnectorConversionError + ) -> ConvertedConnector | ConnectorConversionError | MCPConnectorImportSkipped: + if isinstance(conversion, ConnectorConversionError): + return conversion + alias: Final = conversion.request.alias or "" + if alias in existing_names: + return MCPConnectorImportSkipped( + name=conversion.name, reason=f"An MCP server named '{alias}' already exists." + ) + earlier_aliases: Final = frozenset( + earlier.request.alias or "" + for earlier in conversions[:index] + if isinstance(earlier, ConvertedConnector) + ) + if alias in earlier_aliases: + return MCPConnectorImportSkipped( + name=conversion.name, reason=f"Duplicate connector name '{alias}' in the import payload." + ) + return conversion + + async def _create( + conversion: ConvertedConnector, + ) -> MCPConnectorImportResult | MCPConnectorImportFailure: + try: + validate_and_normalize_mcp_server_payload(conversion.request) + except HTTPException as e: + error_text: Final = ( + str(e.detail.get("error", e.detail)) if isinstance(e.detail, dict) else str(e.detail) + ) + return MCPConnectorImportFailure(name=conversion.name, error=error_text) + try: + created: Final = await create_mcp_server( + prisma_client, + conversion.request, + touched_by=user_api_key_dict.user_id or LITELLM_PROXY_ADMIN_NAME, + ) + except Exception as e: # noqa: BLE001 # any create failure must become a per-entry error, not a 500 + verbose_proxy_logger.exception("Error importing mcp server %s: %s", conversion.name, e) + return MCPConnectorImportFailure(name=conversion.name, error=str(e)) + try: + await global_mcp_server_manager.add_server(created) + except Exception as e: # noqa: BLE001 # the row is committed; the reload after the loop retries registration + verbose_proxy_logger.exception( + "Imported mcp server %s committed but in-memory registration failed: %s", conversion.name, e + ) + return MCPConnectorImportResult( + name=conversion.name, server_id=created.server_id, alias=created.alias or "" + ) + + classified: Final = tuple(_classify(index, conversion) for index, conversion in enumerate(conversions)) + outcomes: Final = tuple( + [ + await _create(entry) if isinstance(entry, ConvertedConnector) else entry for entry in classified + ] # mutable-ok: await is illegal in a generator expression here + ) + + imported: Final = tuple(entry for entry in outcomes if isinstance(entry, MCPConnectorImportResult)) + if imported: + try: + await global_mcp_server_manager.reload_servers_from_database() + except Exception as e: # noqa: BLE001 # rows are committed; a refresh failure must not surface as a 500 + verbose_proxy_logger.exception("MCP connector import committed but registry refresh failed: %s", e) + + return MCPConnectorImportResponse( + imported=imported, + skipped=tuple(entry for entry in outcomes if isinstance(entry, MCPConnectorImportSkipped)), + errors=tuple( + MCPConnectorImportFailure(name=entry.name, error=entry.error) + if isinstance(entry, ConnectorConversionError) + else entry + for entry in outcomes + if isinstance(entry, (ConnectorConversionError, MCPConnectorImportFailure)) + ), + ) + @router.post( "/server/oauth/session", description="Temporarily cache an MCP server in memory without writing to the database", diff --git a/litellm/proxy/management_endpoints/model_access_group_management_endpoints.py b/litellm/proxy/management_endpoints/model_access_group_management_endpoints.py index e1a7645e988..a48130a4f22 100644 --- a/litellm/proxy/management_endpoints/model_access_group_management_endpoints.py +++ b/litellm/proxy/management_endpoints/model_access_group_management_endpoints.py @@ -2,18 +2,34 @@ Allow proxy admin to manage model access groups Endpoints here: -- POST /model_group/new - Create a new access group with multiple model names +- POST /access_group/new - Create a new access group with multiple model names +- GET /access_group/list - List every access group +- GET /access_group/{access_group}/info - Read one access group, including its budget +- PUT /access_group/{access_group}/update - Replace an access group's deployments +- DELETE /access_group/{access_group}/delete - Delete an access group and its budget +- GET /access_group/{access_group}/budget - Read an access group's shared budget and spend +- PUT /access_group/{access_group}/budget - Set or replace an access group's shared budget +- DELETE /access_group/{access_group}/budget - Clear an access group's shared budget """ import json from collections.abc import Mapping, Sequence -from typing import TYPE_CHECKING, Any, Final, Protocol +from datetime import datetime +from types import MappingProxyType +from typing import TYPE_CHECKING, Annotated, Any, Final, Protocol from fastapi import APIRouter, Depends, HTTPException +from typing_extensions import ReadOnly, TypedDict from litellm._logging import verbose_proxy_logger from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.auth.user_api_key_auth import user_api_key_auth +from litellm.proxy.common_utils.user_api_key_cache import ( + UserApiKeyCache, + model_access_group_cache_key, + model_access_group_registry_cache_key, +) +from litellm.proxy.management_endpoints.common_utils import validate_budget_duration # Clear cache and reload models to pick up the access group changes from litellm.proxy.management_endpoints.model_management_endpoints import ( @@ -22,10 +38,16 @@ from litellm.proxy.management_endpoints.model_management_endpoints import ( model_info_as_mapping, reload_serving_verdict, ) +from litellm.proxy.management_helpers.utils import handle_budget_for_entity from litellm.proxy.utils import PrismaClient from litellm.repositories.model_repository import ModelRepository +from litellm.repositories.table_repositories import ModelAccessGroupBudgetRepository from litellm.types.proxy.management_endpoints.model_management_endpoints import ( + AccessGroupBudget, + AccessGroupBudgetRequest, + AccessGroupBudgetResponse, AccessGroupInfo, + DeleteAccessGroupBudgetResponse, DeleteModelGroupResponse, ListAccessGroupsResponse, NewModelGroupRequest, @@ -36,7 +58,43 @@ from litellm.types.proxy.management_endpoints.model_management_endpoints import if TYPE_CHECKING: from litellm import Router -router: Final = APIRouter() +router: Final = APIRouter(tags=["model management"]) + +_AUTH_DEPENDENCIES: Final = (Depends(user_api_key_auth),) + + +class _ErrorDetail(TypedDict): + error: ReadOnly[str] + + +class _ModelAccessGroupWhere(TypedDict): + access_group_name: ReadOnly[str] + + +class _BudgetInclude(TypedDict): + litellm_budget_table: ReadOnly[bool] + + +class _ModelAccessGroupBudgetCreate(TypedDict): + access_group_name: ReadOnly[str] + budget_id: ReadOnly[str | None] + created_by: ReadOnly[str] + updated_by: ReadOnly[str] + + +class _ModelAccessGroupBudgetUpdate(TypedDict): + budget_id: ReadOnly[str | None] + updated_by: ReadOnly[str] + + +class _ModelAccessGroupBudgetUpsert(TypedDict): + create: ReadOnly[_ModelAccessGroupBudgetCreate] + update: ReadOnly[_ModelAccessGroupBudgetUpdate] + + +def _http_error(status_code: int, message: str) -> HTTPException: + detail: Final[_ErrorDetail] = {"error": message} + return HTTPException(status_code=status_code, detail=detail) class _DeploymentRow(Protocol): @@ -58,10 +116,169 @@ class _ModelTableClient(Protocol): async def update(self, *, where: Mapping[str, object], data: Mapping[str, object]) -> object: ... +class _BudgetRow(Protocol): + @property + def budget_id(self) -> str: ... + + @property + def max_budget(self) -> float | None: ... + + @property + def soft_budget(self) -> float | None: ... + + @property + def budget_duration(self) -> str | None: ... + + @property + def budget_reset_at(self) -> datetime | None: ... + + +class _ModelAccessGroupBudgetRow(Protocol): + @property + def access_group_name(self) -> str: ... + + @property + def spend(self) -> float: ... + + @property + def budget_id(self) -> str | None: ... + + @property + def litellm_budget_table(self) -> _BudgetRow | None: ... + + +class _ModelAccessGroupBudgetTableClient(Protocol): + async def find_unique( + self, *, where: Mapping[str, object], include: Mapping[str, object] | None = None + ) -> _ModelAccessGroupBudgetRow | None: ... + + async def upsert( + self, + *, + where: Mapping[str, object], + data: Mapping[str, object], + include: Mapping[str, object] | None = None, + ) -> _ModelAccessGroupBudgetRow: ... + + async def find_many( + self, *, include: Mapping[str, object] | None = None + ) -> Sequence[_ModelAccessGroupBudgetRow]: ... + + async def delete(self, *, where: Mapping[str, object]) -> _ModelAccessGroupBudgetRow | None: ... + + def _model_table(prisma_client: PrismaClient) -> _ModelTableClient: return ModelRepository(prisma_client).table +def _model_access_group_budget_table(prisma_client: PrismaClient) -> _ModelAccessGroupBudgetTableClient: + return ModelAccessGroupBudgetRepository(prisma_client).table + + +def _prisma_client_or_500() -> PrismaClient: + from litellm.proxy.proxy_server import prisma_client + + if prisma_client is None: + raise _http_error(500, "Database not connected.") + return prisma_client + + +def _auth_cache() -> UserApiKeyCache: + from litellm.proxy.proxy_server import user_api_key_cache + + return user_api_key_cache + + +async def _evict_model_access_group_cache_keys(access_group: str, auth_cache: UserApiKeyCache) -> None: + """ + Every endpoint that writes an access group budget row must call this, or the budget stays + unenforced until the TTL expires: auth gates the feature on a cached registry of the groups + that have a budget row, read cache-first with no freshness check. + """ + from litellm.proxy.common_utils.auth_cache_invalidation_pubsub import ( + evict_and_broadcast, + ) + + await evict_and_broadcast( + cache_keys=(model_access_group_cache_key(access_group), model_access_group_registry_cache_key()), + user_api_key_cache=auth_cache, + ) + + +async def _model_access_group_budget_row( + access_group: str, prisma_client: PrismaClient +) -> _ModelAccessGroupBudgetRow | None: + where: Final[_ModelAccessGroupWhere] = {"access_group_name": access_group} + include: Final[_BudgetInclude] = {"litellm_budget_table": True} + return await _model_access_group_budget_table(prisma_client).find_unique(where=where, include=include) + + +async def _model_access_group_budget_rows( + prisma_client: PrismaClient, +) -> Mapping[str, _ModelAccessGroupBudgetRow]: + """Every group's budget row in one read, so listing groups does not fan out into one query + per group.""" + include: Final[_BudgetInclude] = {"litellm_budget_table": True} + rows: Final = await _model_access_group_budget_table(prisma_client).find_many(include=include) + return MappingProxyType({row.access_group_name: row for row in rows}) + + +def _with_budget(info: AccessGroupInfo, row: _ModelAccessGroupBudgetRow | None) -> AccessGroupInfo: + """The group as listed, plus whatever budget hangs off it. A group with no row has spent + nothing, because clearing a budget drops the row that recorded the spend.""" + return AccessGroupInfo( + access_group=info.access_group, + model_names=info.model_names, + deployment_count=info.deployment_count, + spend=row.spend if row is not None else 0.0, + budget=_budget_or_none(row), + ) + + +def _budget_or_none(row: _ModelAccessGroupBudgetRow | None) -> AccessGroupBudget | None: + budget: Final = row.litellm_budget_table if row is not None else None + if budget is None: + return None + return AccessGroupBudget( + budget_id=budget.budget_id, + max_budget=budget.max_budget, + soft_budget=budget.soft_budget, + budget_duration=budget.budget_duration, + budget_reset_at=budget.budget_reset_at, + ) + + +def _budget_response(access_group: str, row: _ModelAccessGroupBudgetRow | None) -> AccessGroupBudgetResponse: + return AccessGroupBudgetResponse( + access_group=access_group, + spend=row.spend if row is not None else 0.0, + budget=_budget_or_none(row), + ) + + +async def _delete_model_access_group_budget_row( + access_group: str, prisma_client: PrismaClient, auth_cache: UserApiKeyCache +) -> bool: + """ + Drop the group's budget row only, matching /tag/delete: the LiteLLM_BudgetTable row survives + because the link is ON DELETE SET NULL and a budget_id an admin passed in may be shared with + other entities. + + Evicts unconditionally: a group with no row of its own can still be sitting in the cached + registry, so skipping the eviction when nothing was deleted would leave that stale. + """ + where: Final[_ModelAccessGroupWhere] = {"access_group_name": access_group} + row: Final = await _model_access_group_budget_table(prisma_client).delete(where=where) + await _evict_model_access_group_cache_keys(access_group, auth_cache) + return row is not None + + +async def _raise_404_if_model_access_group_missing(access_group: str, prisma_client: PrismaClient) -> None: + access_groups_map: Final = await get_all_access_groups_from_db(prisma_client=prisma_client) + if access_group not in access_groups_map: + raise _http_error(404, f"Access group '{access_group}' not found") + + def validate_models_exist(model_names: Sequence[str], llm_router: "Router | None") -> tuple[bool, Sequence[str]]: """ Validate that all requested model names exist in the router. @@ -356,13 +573,12 @@ async def get_all_access_groups_from_db( @router.post( "/access_group/new", - tags=["model management"], - dependencies=[Depends(user_api_key_auth)], + dependencies=_AUTH_DEPENDENCIES, response_model=NewModelGroupResponse, ) async def create_model_group( data: NewModelGroupRequest, - user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), + user_api_key_dict: Annotated[UserAPIKeyAuth, Depends(user_api_key_auth)], ): """ Create a new access group containing multiple model names. @@ -503,17 +719,17 @@ async def create_model_group( @router.get( "/access_group/list", - tags=["model management"], - dependencies=[Depends(user_api_key_auth)], + dependencies=_AUTH_DEPENDENCIES, response_model=ListAccessGroupsResponse, ) async def list_access_groups( - user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), + user_api_key_dict: Annotated[UserAPIKeyAuth, Depends(user_api_key_auth)], ): """ List all access groups. - Returns a list of all access groups with their model names and deployment counts. + Returns a list of all access groups with their model names, deployment counts, shared budget + and the spend drawn against it. Example: ```bash @@ -534,11 +750,11 @@ async def list_access_groups( try: access_groups_map: Final = await get_all_access_groups_from_db(prisma_client=prisma_client) + budget_rows: Final = await _model_access_group_budget_rows(prisma_client) - # Sort by access group name access_groups_list: Final = sorted( - access_groups_map.values(), - key=lambda x: x.access_group, + (_with_budget(info, budget_rows.get(info.access_group)) for info in access_groups_map.values()), + key=lambda group: group.access_group, ) return ListAccessGroupsResponse(access_groups=access_groups_list) @@ -553,13 +769,12 @@ async def list_access_groups( @router.get( "/access_group/{access_group}/info", - tags=["model management"], - dependencies=[Depends(user_api_key_auth)], + dependencies=_AUTH_DEPENDENCIES, response_model=AccessGroupInfo, ) async def get_access_group_info( access_group: str, - user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), + user_api_key_dict: Annotated[UserAPIKeyAuth, Depends(user_api_key_auth)], ): """ Get information about a specific access group. @@ -574,7 +789,7 @@ async def get_access_group_info( - access_group: str - The access group name (URL path parameter) Returns: - - AccessGroupInfo with the access group details + - AccessGroupInfo with the access group details, its shared budget and its spend Raises: - HTTPException 404: If access group not found @@ -596,7 +811,10 @@ async def get_access_group_info( detail={"error": f"Access group '{access_group}' not found"}, ) - return access_groups_map[access_group] + return _with_budget( + access_groups_map[access_group], + await _model_access_group_budget_row(access_group, prisma_client), + ) except HTTPException: raise @@ -610,14 +828,13 @@ async def get_access_group_info( @router.put( "/access_group/{access_group}/update", - tags=["model management"], - dependencies=[Depends(user_api_key_auth)], + dependencies=_AUTH_DEPENDENCIES, response_model=NewModelGroupResponse, ) async def update_access_group( access_group: str, data: UpdateModelGroupRequest, - user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), + user_api_key_dict: Annotated[UserAPIKeyAuth, Depends(user_api_key_auth)], ): """ Update an access group's model names. @@ -765,13 +982,13 @@ async def update_access_group( @router.delete( "/access_group/{access_group}/delete", - tags=["model management"], - dependencies=[Depends(user_api_key_auth)], + dependencies=_AUTH_DEPENDENCIES, response_model=DeleteModelGroupResponse, ) async def delete_access_group( access_group: str, - user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), + user_api_key_dict: Annotated[UserAPIKeyAuth, Depends(user_api_key_auth)], + auth_cache: Annotated[UserApiKeyCache, Depends(_auth_cache)], ): """ Delete an access group. @@ -835,6 +1052,13 @@ async def delete_access_group( removed_pairs: Final = tuple(pair for pair in removed if pair is not None) models_updated: Final = len(removed_pairs) + # Budget last, deliberately: failing here strands a budget row for a group already on no + # deployment (clutter), where the reverse order can leave a live group enforcing nothing. + # The LiteLLM_BudgetTable row it linked is left alone, as /tag/delete leaves a tag's. + await _delete_model_access_group_budget_row( + access_group=access_group, prisma_client=prisma_client, auth_cache=auth_cache + ) + # Clear cache and reload models to pick up the access group changes live_before_reload: Final = live_model_ids_snapshot() reload_outcome: Final = await clear_cache() @@ -864,3 +1088,162 @@ async def delete_access_group( status_code=500, detail={"error": f"Failed to delete access group: {e}"}, ) + + +@router.get( + "/access_group/{access_group}/budget", + dependencies=_AUTH_DEPENDENCIES, + response_model=AccessGroupBudgetResponse, +) +async def get_access_group_budget( + access_group: str, +) -> AccessGroupBudgetResponse: + """ + Get the shared budget of an access group, and the spend drawn against it. + + Example: + ```bash + curl -X GET 'http://localhost:4000/access_group/production-models/budget' \\ + -H 'Authorization: Bearer sk-1234' + ``` + + Parameters: + - access_group: str - The access group name (URL path parameter) + + Returns: + - AccessGroupBudgetResponse; budget is null when the group has no budget set + + Raises: + - HTTPException 404: If access group not found + """ + prisma_client: Final = _prisma_client_or_500() + await _raise_404_if_model_access_group_missing(access_group=access_group, prisma_client=prisma_client) + + return _budget_response( + access_group=access_group, + row=await _model_access_group_budget_row(access_group, prisma_client), + ) + + +@router.put( + "/access_group/{access_group}/budget", + dependencies=_AUTH_DEPENDENCIES, + response_model=AccessGroupBudgetResponse, +) +async def set_access_group_budget( + access_group: str, + data: AccessGroupBudgetRequest, + user_api_key_dict: Annotated[UserAPIKeyAuth, Depends(user_api_key_auth)], + auth_cache: Annotated[UserApiKeyCache, Depends(_auth_cache)], +) -> AccessGroupBudgetResponse: + """ + Set or replace the shared budget of an access group. Idempotent. + + Every key that can reach a model in the group draws from this one budget. + + Example: + ```bash + curl -X PUT 'http://localhost:4000/access_group/production-models/budget' \\ + -H 'Authorization: Bearer sk-1234' \\ + -H 'Content-Type: application/json' \\ + -d '{ + "max_budget": 100.0, + "budget_duration": "30d" + }' + ``` + + Parameters: + - access_group: str - The access group name (URL path parameter) + - max_budget: Optional[float] - Requests fail once the group's shared spend exceeds this + - soft_budget: Optional[float] - Fires an alert when reached; requests still succeed + - budget_duration: Optional[str] - Frequency of resetting the group's spend (e.g. '30d') + - budget_id: Optional[str] - Link an existing budget instead of creating one + + Returns: + - AccessGroupBudgetResponse with the stored budget and current spend + + Raises: + - HTTPException 400: If no budget field is given, or budget_duration cannot be parsed + - HTTPException 404: If access group not found + """ + from litellm.proxy.proxy_server import litellm_proxy_admin_name + + prisma_client: Final = _prisma_client_or_500() + if not data.model_dump(exclude_none=True): + raise _http_error(400, "One of max_budget, soft_budget, budget_duration or budget_id is required") + validate_budget_duration(data.budget_duration) + await _raise_404_if_model_access_group_missing(access_group=access_group, prisma_client=prisma_client) + + existing_row: Final = await _model_access_group_budget_row(access_group, prisma_client) + budget_id: Final = await handle_budget_for_entity( + data=data, + existing_budget_id=existing_row.budget_id if existing_row is not None else None, + user_api_key_dict=user_api_key_dict, + prisma_client=prisma_client, + litellm_proxy_admin_name=litellm_proxy_admin_name, + ) + actor: Final = user_api_key_dict.user_id or litellm_proxy_admin_name + upsert_data: Final[_ModelAccessGroupBudgetUpsert] = { + "create": { + "access_group_name": access_group, + "budget_id": budget_id, + "created_by": actor, + "updated_by": actor, + }, + "update": {"budget_id": budget_id, "updated_by": actor}, + } + where: Final[_ModelAccessGroupWhere] = {"access_group_name": access_group} + include: Final[_BudgetInclude] = {"litellm_budget_table": True} + row: Final = await _model_access_group_budget_table(prisma_client).upsert( + where=where, data=upsert_data, include=include + ) + await _evict_model_access_group_cache_keys(access_group, auth_cache) + + verbose_proxy_logger.info("Set budget %s on access group '%s'", budget_id, access_group) + return _budget_response(access_group=access_group, row=row) + + +@router.delete( + "/access_group/{access_group}/budget", + dependencies=_AUTH_DEPENDENCIES, + response_model=DeleteAccessGroupBudgetResponse, +) +async def delete_access_group_budget( + access_group: str, + auth_cache: Annotated[UserApiKeyCache, Depends(_auth_cache)], +) -> DeleteAccessGroupBudgetResponse: + """ + Clear the shared budget of an access group, leaving the group itself in place. + + Example: + ```bash + curl -X DELETE 'http://localhost:4000/access_group/production-models/budget' \\ + -H 'Authorization: Bearer sk-1234' + ``` + + Parameters: + - access_group: str - The access group name (URL path parameter) + + Returns: + - DeleteAccessGroupBudgetResponse; budget_deleted is false when there was nothing to clear + + Raises: + - HTTPException 404: If access group not found + """ + prisma_client: Final = _prisma_client_or_500() + await _raise_404_if_model_access_group_missing(access_group=access_group, prisma_client=prisma_client) + + budget_deleted: Final = await _delete_model_access_group_budget_row( + access_group=access_group, + prisma_client=prisma_client, + auth_cache=auth_cache, + ) + return DeleteAccessGroupBudgetResponse( + access_group=access_group, + budget_deleted=budget_deleted, + message=( + f"Budget for access group '{access_group}' deleted successfully" + if budget_deleted + else f"Access group '{access_group}' has no budget to delete" + ), + ) diff --git a/litellm/proxy/management_endpoints/model_management_endpoints.py b/litellm/proxy/management_endpoints/model_management_endpoints.py index 012aec38458..ca66640bf46 100644 --- a/litellm/proxy/management_endpoints/model_management_endpoints.py +++ b/litellm/proxy/management_endpoints/model_management_endpoints.py @@ -54,7 +54,10 @@ from litellm.proxy.common_utils.config_sync_pubsub import ( coordination_redis_cache, publish_config_change, ) -from litellm.proxy.common_utils.encrypt_decrypt_utils import encrypt_value_helper +from litellm.proxy.common_utils.encrypt_decrypt_utils import ( + decrypt_value_helper, + encrypt_value_helper, +) from litellm.proxy.common_utils.user_api_key_cache import UserApiKeyCache from litellm.proxy.management_endpoints.common_utils import _is_user_team_admin from litellm.proxy.management_endpoints.team_endpoints import ( @@ -543,7 +546,7 @@ def update_db_model(db_model: Deployment, updated_patch: updateDeployment) -> Pr _raise_if_ptu_cost_attribution_disabled(updated_patch.model_info.model_dump(exclude_none=True)) merged_model_name: Final = updated_patch.model_name or db_model.model_name merged_litellm_params: Final = db_model.litellm_params.model_dump(exclude_none=True) - merged_model_info: Final = db_model.model_info.model_dump(exclude_none=True) + merged_model_info: Final[dict[str, object]] = db_model.model_info.model_dump(exclude_none=True) # update litellm params if updated_patch.litellm_params: @@ -701,6 +704,12 @@ async def patch_model( param="blocked", ) + ModelManagementAuthChecks.can_user_attach_credential( + litellm_params=patch_data.litellm_params, + user_api_key_dict=user_api_key_dict, + existing_litellm_params=db_model.litellm_params, + ) + _raise_on_strategy_router_write_violation( incoming_params=patch_data.litellm_params, existing_params=db_model.litellm_params, @@ -1464,6 +1473,32 @@ class ModelManagementAuthChecks: ) return True + @staticmethod + def can_user_attach_credential( + litellm_params: GenericLiteLLMParams | None, + user_api_key_dict: UserAPIKeyAuth, + existing_litellm_params: GenericLiteLLMParams | None = None, + ) -> Literal[True]: + if litellm_params is None or litellm_params.litellm_credential_name is None: + return True + if existing_litellm_params is not None and existing_litellm_params.litellm_credential_name is not None: + existing_credential_name: Final = decrypt_value_helper( + value=existing_litellm_params.litellm_credential_name, + key="litellm_credential_name", + exception_type="debug", + return_original_value=True, + ) + if litellm_params.litellm_credential_name == existing_credential_name: + return True + if user_api_key_dict.user_role == LitellmUserRoles.PROXY_ADMIN: + return True + raise ProxyException( + message=f"Only a proxy admin can attach a stored credential (litellm_credential_name) to a model. Your role={user_api_key_dict.user_role}.", + type=ProxyErrorTypes.auth_error.value, + code=status.HTTP_403_FORBIDDEN, + param="litellm_credential_name", + ) + @staticmethod async def allow_team_model_action( model_params: Deployment | updateDeployment, @@ -1786,6 +1821,11 @@ async def add_new_model( premium_user=premium_user, ) + ModelManagementAuthChecks.can_user_attach_credential( + litellm_params=model_params.litellm_params, + user_api_key_dict=user_api_key_dict, + ) + _raise_on_strategy_router_write_violation( incoming_params=model_params.litellm_params, existing_params=None, @@ -1958,6 +1998,12 @@ async def update_model( premium_user=premium_user, ) + ModelManagementAuthChecks.can_user_attach_credential( + litellm_params=model_params.litellm_params, + user_api_key_dict=user_api_key_dict, + existing_litellm_params=deployment.litellm_params, + ) + _raise_on_strategy_router_write_violation( incoming_params=model_params.litellm_params, existing_params=deployment.litellm_params, @@ -1982,7 +2028,7 @@ async def update_model( ### MERGE WITH EXISTING DATA ### merged_dictionary: Final = {} - _mp: Final = model_params.litellm_params.dict() + _mp: Final[dict[str, object]] = model_params.litellm_params.dict() for key, value in _mp.items(): if value is not None: diff --git a/litellm/proxy/management_endpoints/organization_endpoints.py b/litellm/proxy/management_endpoints/organization_endpoints.py index 9198aa35f3f..5e38a016099 100644 --- a/litellm/proxy/management_endpoints/organization_endpoints.py +++ b/litellm/proxy/management_endpoints/organization_endpoints.py @@ -487,12 +487,11 @@ async def new_organization( for m in data.models: await can_user_call_model(m, llm_router=llm_router, user_object=user_object_correct_type) - organization_row: Final = LiteLLM_OrganizationTable( - **data.json(exclude_none=True), - object_permission_id=object_permission_id, - created_by=user_api_key_dict.user_id or litellm_proxy_admin_name, - updated_by=user_api_key_dict.user_id or litellm_proxy_admin_name, - ) + organization_payload: Final = _STR_OBJECT_DICT_ADAPTER.validate_python(data.json(exclude_none=True)) + organization_payload["object_permission_id"] = object_permission_id + organization_payload["created_by"] = user_api_key_dict.user_id or litellm_proxy_admin_name + organization_payload["updated_by"] = user_api_key_dict.user_id or litellm_proxy_admin_name + organization_row: Final = LiteLLM_OrganizationTable.model_validate(organization_payload) for field in LiteLLM_ManagementEndpoint_MetadataFields: if getattr(data, field, None) is not None: @@ -644,7 +643,7 @@ async def update_organization( ) # Transform UI payload to expected format - raw_data: Final = await request.json() + raw_data: Final[dict[str, object]] = await request.json() raw_data_with_flat_budget_fields: Final = handle_nested_budget_structure_in_organization_update_request(raw_data) # Create validated data model @@ -691,7 +690,7 @@ async def update_organization( # Merge metadata from existing organization with updated metadata if updated_organization_row_json.get("metadata") is not None: existing_metadata: Final = existing_organization_row.metadata or {} - updated_metadata: Final = updated_organization_row_json.get("metadata", {}) + updated_metadata: Final[dict[str, object]] = updated_organization_row_json.get("metadata", {}) merged_metadata: Final[Mapping[str, object]] = _update_dictionary( existing_dict=cast( # cast-ok: prisma de-serializes a Json column to the plain python dict it stores "dict[str, object]", existing_metadata diff --git a/litellm/proxy/management_endpoints/policy_endpoints/endpoints.py b/litellm/proxy/management_endpoints/policy_endpoints/endpoints.py index 108e6a7b47d..f58f3722741 100644 --- a/litellm/proxy/management_endpoints/policy_endpoints/endpoints.py +++ b/litellm/proxy/management_endpoints/policy_endpoints/endpoints.py @@ -13,7 +13,7 @@ import copy import json import os from collections.abc import AsyncIterator -from typing import TYPE_CHECKING, Any, Final, Literal, cast +from typing import TYPE_CHECKING, Final, Literal, cast from fastapi import APIRouter, Depends, HTTPException, Request from fastapi.responses import Response, StreamingResponse @@ -90,7 +90,7 @@ class _ApplyPoliciesResultBase(TypedDict): class ApplyPoliciesResult(_ApplyPoliciesResultBase, total=False): """Result of apply_policies. agent_response set when agent_id provided.""" - agent_response: Any + agent_response: object class _ApplyPoliciesPerItemResultBase(TypedDict): @@ -103,7 +103,7 @@ class _ApplyPoliciesPerItemResultBase(TypedDict): class ApplyPoliciesPerItemResult(_ApplyPoliciesPerItemResultBase, total=False): """Result for one input when using inputs_list. agent_response set when agent_id provided.""" - agent_response: Any + agent_response: object class ApplyPoliciesListResult(TypedDict): @@ -295,8 +295,8 @@ async def test_policies_and_guardrails( from litellm.proxy.proxy_server import chat_completion, proxy_logging_obj from litellm.proxy.utils import handle_exception_on_proxy - def _serialize_chat_response(response: Any) -> Any: - if hasattr(response, "model_dump"): + def _serialize_chat_response(response: object) -> object: + if isinstance(response, BaseModel): return response.model_dump(exclude_unset=True) if isinstance(response, dict): return response @@ -306,7 +306,7 @@ async def test_policies_and_guardrails( inputs: GenericGuardrailAPIInputs, agent_id: str, user_api_key_dict: UserAPIKeyAuth, - ) -> Any: + ) -> object: body: Final = _chat_body_from_inputs(inputs, agent_id, data.request_data) req: Final = _request_with_json_body(body) resp: Final = Response() diff --git a/litellm/proxy/management_endpoints/scim/scim_v2.py b/litellm/proxy/management_endpoints/scim/scim_v2.py index ded57815e91..069f86c852c 100644 --- a/litellm/proxy/management_endpoints/scim/scim_v2.py +++ b/litellm/proxy/management_endpoints/scim/scim_v2.py @@ -29,6 +29,7 @@ import litellm from litellm._logging import verbose_proxy_logger from litellm._uuid import uuid from litellm.litellm_core_utils.safe_json_dumps import safe_dumps +from litellm.models.user import SCIMPlaceholder from litellm.proxy._types import ( LiteLLM_TeamTable, LiteLLM_UserTable, @@ -585,6 +586,37 @@ async def _users_named_by_member_value( return tuple(dict.fromkeys(row.user_id for row in rows)) +async def _accounts_named_by_member_value(value: str, prisma_client: PrismaClient) -> tuple[str, ...]: + """Every user id this member value names, by user id, SSO identity or email. + + Classification needs to know whether the value is one account's ``user_id`` and + whether it names any other account, so all three fields are read in one pass. The + id is compared exactly and unstripped, as a primary key lookup would; the + identities compare as ``_users_named_by_member_value`` describes. Two rows are + enough to tell one account from several, so the read stops there. Only a full + read that lacks the row keyed by the value leaves that row's existence open, and + only then is the id read on its own. + """ + subject: Final = value.strip() + email: Final[_CaseInsensitiveMatch] = {"equals": subject, "mode": "insensitive"} + users: Final = _table(UserRepository(prisma_client)) + rows: Final = await users.find_many( + where={ # mutable-ok: Prisma filter + "OR": [ # mutable-ok: Prisma filter + {"user_id": value}, # mutable-ok: Prisma filter + {"sso_user_id": subject}, # mutable-ok: Prisma filter + {"user_email": email}, # mutable-ok: Prisma filter + ], + }, + take=2, + ) + named: Final = tuple(dict.fromkeys(row.user_id for row in rows)) + if len(named) < 2 or value in named: + return named + keyed: Final = await users.find_unique(where={"user_id": value}) + return named if keyed is None else (value, *named) + + async def _classify_group_member(member: SCIMMember, prisma_client: PrismaClient) -> _ClassifiedGroupMember: """ Decide what a single SCIM group member refers to. @@ -627,11 +659,9 @@ async def _classify_group_member(member: SCIMMember, prisma_client: PrismaClient if member_type == "group": return _SkippedGroupMember(value=value, reason="nested_group") - user: Final = await _table(UserRepository(prisma_client)).find_unique(where={"user_id": value}) - if user is not None: - shared_with: Final = tuple( - other for other in await _users_named_by_member_value(value, prisma_client) if other != value - ) + named: Final = await _accounts_named_by_member_value(value, prisma_client) + if value in named: + shared_with: Final = tuple(other for other in named if other != value) if shared_with: verbose_proxy_logger.warning( "SCIM: group member '%s' is one account's user id and is also account '%s' by SSO identity or email, " @@ -651,7 +681,6 @@ async def _classify_group_member(member: SCIMMember, prisma_client: PrismaClient if team is not None and _team_metadata_has_scim_provenance(team.metadata): return _SkippedGroupMember(value=value, reason="existing_team") - named: Final = await _users_named_by_member_value(value, prisma_client) if len(named) == 1: verbose_proxy_logger.info( "SCIM: group member '%s' matched user_id '%s' by SSO identity or email", @@ -1834,6 +1863,89 @@ async def delete_user( raise handle_exception_on_proxy(e) +@scim_router.get( + "/placeholders", + response_model=tuple[SCIMPlaceholder, ...], + dependencies=(Depends(user_api_key_auth),), +) +async def list_placeholders() -> tuple[SCIMPlaceholder, ...]: + """ + List user rows whose id is another account's SSO identity or email. + + An earlier release provisioned a group member it could not match as a user keyed + by the raw member value, and that row now shadows the account the value really + names, so every push of that member is refused. This lists those rows so an + operator can fold each one into the account it shadows with + ``POST /scim/v2/placeholders/{user_id}/merge``. A row that has an SSO identity of + its own or owns virtual keys is left out: someone uses that account. + """ + try: + prisma_client: Final = await _get_prisma_client_or_raise_exception() + async with prisma_client.tx() as tx: + return await UserRepository(prisma_client).find_shadowing_placeholders(tx) + except Exception as e: + raise handle_exception_on_proxy(e) + + +def _placeholder_rejection(placeholder: LiteLLM_UserTable, resolved: tuple[str, ...], key_count: int) -> str | None: + if placeholder.sso_user_id is not None: + return f"User '{placeholder.user_id}' has an SSO identity of its own, so it is an account someone signs in to" + if key_count: + return f"User '{placeholder.user_id}' owns {key_count} virtual keys. Move or delete them before merging it" + if not resolved: + return f"User '{placeholder.user_id}' shadows no account: no other user has that id as SSO identity or email" + if len(resolved) > 1: + return ( + f"User '{placeholder.user_id}' names {len(resolved)} accounts ({', '.join(resolved)}). Resolve that first" + ) + return None + + +@scim_router.post( + "/placeholders/{user_id}/merge", + response_model=SCIMPlaceholderMergeResult, + dependencies=(Depends(user_api_key_auth),), +) +async def merge_placeholder( + user_id: str = Path(..., title="User ID"), +) -> SCIMPlaceholderMergeResult: + """ + Fold a placeholder user into the one account its id names by SSO identity or email. + + The account is added to every team the placeholder is on, then the placeholder is + deleted the way ``DELETE /scim/v2/Users/{id}`` deletes a user, so the next group + push resolves the member value to the real account. Refused with 409 when the row + has an SSO identity of its own, owns virtual keys, or names no account or several. + """ + try: + prisma_client: Final = await _get_prisma_client_or_raise_exception() + placeholder: Final = await _check_user_exists(user_id) + resolved: Final = tuple( + other for other in await _users_named_by_member_value(user_id, prisma_client, take=None) if other != user_id + ) + owned_keys: Final[_UserIdWhere] = {"user_id": user_id} + keys: Final = await _table(VerificationTokenRepository(prisma_client)).find_many(where=owned_keys) + rejection: Final = _placeholder_rejection(placeholder, resolved, len(keys)) + if rejection is not None: + detail: Final[_ScimErrorDetail] = {"error": rejection} + raise HTTPException(status_code=409, detail=detail) + + target_user_id: Final = resolved[0] + team_ids: Final = tuple(placeholder.teams) + for team_id in team_ids: + await _add_user_to_team(user_id=target_user_id, team_id=team_id) + await delete_user(user_id=user_id) + await _recompute_scim_member_roles(prisma_client, (target_user_id,)) + verbose_proxy_logger.info( + "SCIM: merged placeholder user '%s' into '%s', moving teams %s", user_id, target_user_id, team_ids + ) + return SCIMPlaceholderMergeResult( + placeholder_user_id=user_id, merged_into_user_id=target_user_id, team_ids=team_ids + ) + except Exception as e: + raise handle_exception_on_proxy(e) + + def _parse_member_entry(entry: object) -> SCIMMember | None: """Parse one entry of a SCIM patch value, or None when it carries no id.""" if isinstance(entry, str): diff --git a/litellm/proxy/management_endpoints/team_callback_endpoints.py b/litellm/proxy/management_endpoints/team_callback_endpoints.py index 08346983f32..c2f5dbb4032 100644 --- a/litellm/proxy/management_endpoints/team_callback_endpoints.py +++ b/litellm/proxy/management_endpoints/team_callback_endpoints.py @@ -252,7 +252,7 @@ async def add_team_callbacks( Use this if if you want different teams to have different success/failure callbacks Parameters: - - callback_name (Literal["langfuse", "langsmith", "gcs"], required): The name of the callback to add + - callback_name (str, required): The name of the callback to add, e.g. "langfuse", "langsmith", "gcs", "newrelic". The value is validated against the callbacks that support team-scoped credentials - callback_type (Literal["success", "failure", "success_and_failure"], required): The type of callback to add. One of: - "success": Callback for successful LLM calls - "failure": Callback for failed LLM calls @@ -268,6 +268,8 @@ async def add_team_callbacks( - langsmith_api_key: The API key for the Langsmith callback - langsmith_project: The project for the Langsmith callback - langsmith_base_url: The base URL for the Langsmith callback + - newrelic_api_key: The ingest license key for the team's New Relic account; routes both LLM/agent traces and cost metrics to that account. Requires the proxy to run with LITELLM_OTEL_V2=true, otherwise this callback is rejected with a 400 + - newrelic_region: The New Relic region for the team's account ("us" or "eu"), riding the team's own key Example curl: ``` diff --git a/litellm/proxy/management_endpoints/team_endpoints.py b/litellm/proxy/management_endpoints/team_endpoints.py index c6d7975b75e..714cf252e69 100644 --- a/litellm/proxy/management_endpoints/team_endpoints.py +++ b/litellm/proxy/management_endpoints/team_endpoints.py @@ -5148,6 +5148,7 @@ async def list_team_v2( # Get teams with pagination if use_deleted_table: + # LiteLLM_DeletedTeamTable has no litellm_model_table relation, unlike below teams = await _deleted_team_db(prisma_client).find_many( where=where_conditions, skip=skip, @@ -5162,6 +5163,7 @@ async def list_team_v2( skip=skip, take=page_size, order=order_by if order_by else {"created_at": "desc"}, # Default sort + include=_INCLUDE_MODEL_TABLE, ) # Get total count for pagination total_count = await _team_db(prisma_client).count(where=where_conditions) diff --git a/litellm/proxy/management_endpoints/ui_sso.py b/litellm/proxy/management_endpoints/ui_sso.py index 613508da22b..606569c5b8b 100644 --- a/litellm/proxy/management_endpoints/ui_sso.py +++ b/litellm/proxy/management_endpoints/ui_sso.py @@ -502,7 +502,7 @@ def _set_nested_metadata_value(metadata: dict[str, object], key_path: str, value placeholder: Final = "\x00" parts = key_path.replace("\\.", placeholder).split(".") parts = [p.replace(placeholder, ".") for p in parts] - current: Any = metadata + current: dict[str, object] = metadata for part in parts[:-1]: existing = current.get(part) if not isinstance(existing, dict): @@ -4076,7 +4076,7 @@ class SSOAuthenticationHandler: ) if resp.status_code == 200: try: - userinfo_raw: Final = resp.json() + userinfo_raw: Final[dict[str, object] | None] = resp.json() if not userinfo_raw: # JSON null (None) or empty dict ({}) — no identity claims. # Treat as failure so id_token fallback can be attempted. @@ -4406,7 +4406,7 @@ class MicrosoftSSOHandler: ) -> tuple[list[str], str | None]: """Helper function to fetch and parse group data from a URL""" response: Final = await async_client.get(url, headers=headers) - response_json: Final = response.json() + response_json: Final[dict[str, object]] = response.json() response_typed: Final = await MicrosoftSSOHandler._cast_graph_api_response_dict(response=response_json) group_ids: Final = MicrosoftSSOHandler._get_group_ids_from_graph_api_response(response=response_typed) return group_ids, response_typed.get("odata_nextLink") diff --git a/litellm/proxy/management_helpers/object_permission_utils.py b/litellm/proxy/management_helpers/object_permission_utils.py index 13080a6cf83..a2fbf80422c 100644 --- a/litellm/proxy/management_helpers/object_permission_utils.py +++ b/litellm/proxy/management_helpers/object_permission_utils.py @@ -286,7 +286,7 @@ async def _resolve_mcp_server_identifiers_to_ids( return resolved -def _rewrite_object_permission_mcp_servers( +def _drop_stale_object_permission_mcp_servers( object_permission: ObjectPermissionDict, identifier_to_server_ids: dict[str, set[str]], ) -> None: @@ -294,16 +294,18 @@ def _rewrite_object_permission_mcp_servers( if not isinstance(mcp_servers, list): return - normalized_servers: Final[list[str]] = [] - for identifier in mcp_servers: - if identifier == SpecialMCPServerNames.no_mcp_servers.value: - normalized_servers.append(SpecialMCPServerNames.no_mcp_servers.value) - continue - normalized_servers.extend(sorted(identifier_to_server_ids.get(identifier, []))) - object_permission["mcp_servers"] = _dedupe_preserving_order(normalized_servers) + # Persist original identifiers, never resolved ids: shared-DB multi-region + # instances each expand a name/alias to their own local server id at read + # time. Only entries resolving to nothing (deleted servers, typos) drop. + kept_servers: Final = [ + identifier + for identifier in mcp_servers + if identifier == SpecialMCPServerNames.no_mcp_servers.value or identifier_to_server_ids.get(identifier) + ] + object_permission["mcp_servers"] = _dedupe_preserving_order(kept_servers) -def _rewrite_object_permission_mcp_tool_permissions( +def _drop_stale_object_permission_mcp_tool_permissions( object_permission: ObjectPermissionDict, identifier_to_server_ids: dict[str, set[str]], ) -> None: @@ -311,31 +313,25 @@ def _rewrite_object_permission_mcp_tool_permissions( if not isinstance(mcp_tool_permissions, dict): return - normalized_tool_permissions: Final[dict[str, list[str]]] = {} - for identifier, tools in mcp_tool_permissions.items(): - if not isinstance(tools, list): - tools = [] - for server_id in sorted(identifier_to_server_ids.get(identifier, [])): - normalized_tool_permissions.setdefault(server_id, []) - normalized_tool_permissions[server_id].extend(tools) - object_permission["mcp_tool_permissions"] = { - server_id: _dedupe_preserving_order(tools) for server_id, tools in normalized_tool_permissions.items() + identifier: _dedupe_preserving_order(tools if isinstance(tools, list) else []) + for identifier, tools in mcp_tool_permissions.items() + if identifier_to_server_ids.get(identifier) } -def _rewrite_object_permission_mcp_identifiers( +def _drop_stale_object_permission_mcp_identifiers( object_permission: ObjectPermissionDict | None, identifier_to_server_ids: dict[str, set[str]], ) -> None: if not object_permission or not isinstance(object_permission, dict): return - _rewrite_object_permission_mcp_servers( + _drop_stale_object_permission_mcp_servers( object_permission=object_permission, identifier_to_server_ids=identifier_to_server_ids, ) - _rewrite_object_permission_mcp_tool_permissions( + _drop_stale_object_permission_mcp_tool_permissions( object_permission=object_permission, identifier_to_server_ids=identifier_to_server_ids, ) @@ -615,7 +611,7 @@ async def validate_key_mcp_servers_against_team( "validate_key_mcp_servers_against_team: ignoring stale MCP server identifiers (no longer in registry or DB): %s", sorted(stale_identifiers), ) - _rewrite_object_permission_mcp_identifiers( + _drop_stale_object_permission_mcp_identifiers( object_permission=object_permission, identifier_to_server_ids=identifier_to_server_ids, ) diff --git a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py index 525e7099b89..78d8ce296b8 100644 --- a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py +++ b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py @@ -6,13 +6,15 @@ Provider-specific Pass-Through Endpoints Use litellm with Anthropic SDK, Vertex AI SDK, Cohere SDK, etc. """ +from __future__ import annotations + import hmac import json import os import re from collections.abc import Callable, Mapping from types import MappingProxyType -from typing import TYPE_CHECKING, Annotated, Any, Final, cast +from typing import TYPE_CHECKING, Annotated, Final, cast import httpx from fastapi import APIRouter, Depends, HTTPException, Request, Response, WebSocket @@ -28,6 +30,7 @@ from litellm.constants import ( ) from litellm.litellm_core_utils.aws_partition import get_aws_dns_suffix from litellm.llms.anthropic.common_utils import AnthropicModelInfo +from litellm.llms.custom_httpx.http_handler import get_async_httpx_client from litellm.llms.vertex_ai.vertex_llm_base import VertexBase from litellm.proxy._types import * from litellm.proxy.auth.handle_jwt import JWTHandler @@ -51,6 +54,7 @@ from litellm.proxy.pass_through_endpoints.pass_through_endpoints import ( create_websocket_passthrough_route, websocket_passthrough_request, ) +from litellm.proxy.utils import ProxyLogging as ProxyLoggingType from litellm.proxy.utils import is_known_model from litellm.proxy.vector_store_endpoints.utils import ( assert_proxy_admin_for_vector_store_index_management, @@ -65,18 +69,23 @@ from litellm.types.passthrough_endpoints.pass_through_endpoints import ( ) from litellm.types.passthrough_endpoints.vertex_ai import VertexPassThroughCredentials from litellm.types.utils import LlmProviders +from litellm.types.vector_stores import LiteLLM_ManagedVectorStore from litellm.utils import ProviderConfigManager from .passthrough_endpoint_router import PassthroughEndpointRouter if TYPE_CHECKING: + from litellm.proxy.proxy_server import ProxyConfig as _ProxyConfig from litellm.router import Router + ProxyConfig = _ProxyConfig # rebind-ok: conditional type alias +else: + ProxyConfig = Any # rebind-ok: runtime fallback + vertex_llm_base: Final = VertexBase() router: Final = APIRouter() openai_passthrough_router: Final = APIRouter() default_vertex_config: Final = None - passthrough_endpoint_router: Final = PassthroughEndpointRouter() @@ -113,7 +122,21 @@ def is_passthrough_request_streaming(request_body: object) -> bool: return bool(request_body.get("stream", False)) -def get_passthrough_router_request_metadata(user_api_key_dict: UserAPIKeyAuth) -> Mapping[str, Any]: +def _optional_str(value: object) -> str | None: + return value if isinstance(value, str) else None + + +def _string_keyed_mapping(value: object) -> Mapping[str, object] | None: + if isinstance(value, Mapping): + return value + return None + + +async def _json_request_body(request: Request) -> Mapping[str, object]: + return await request.json() + + +def get_passthrough_router_request_metadata(user_api_key_dict: UserAPIKeyAuth) -> Mapping[str, object]: """ Build the request metadata carrying key-level spend attribution and the pre-call budget reservation for a router-model passthrough request. @@ -202,7 +225,7 @@ async def llm_passthrough_factory_proxy_route( # anthropic is streaming when 'stream' = True is in the body if request.method == "POST": if "multipart/form-data" not in request.headers.get("content-type", ""): - _request_body = await request.json() + _request_body = await _json_request_body(request) else: _request_body = await get_form_data(request) @@ -375,7 +398,7 @@ async def vllm_proxy_route( endpoint=endpoint, request_query_params=request.query_params, request_headers=_safe_get_request_headers(request), - stream=request_body.get("stream", False), + stream=is_streaming_request, content=None, data=None, files=None, @@ -495,8 +518,14 @@ async def milvus_proxy_route( request_body: Final = await get_request_body(request) # check collectionName - collection_name: Final = cast(str | None, request_body.get("collectionName")) - extra_headers = {} + _raw_collection_name: Final = request_body.get("collectionName") + if _raw_collection_name is not None and not isinstance(_raw_collection_name, str): + raise HTTPException( + status_code=400, + detail=f"collectionName must be a string. Got {type(_raw_collection_name).__name__}", + ) + collection_name: str | None = _raw_collection_name # rebind-ok: locally scoped conversion + extra_headers = {} # mutable-ok: dict for extra headers; rebind-ok: reassigned later from credentials base_target_url: str | None = None if not collection_name: raise HTTPException( @@ -803,7 +832,7 @@ async def handle_bedrock_passthrough_router_model( # Use the common processing path (same as non-router models) # This ensures all metadata, hooks, and logging are properly initialized - data: Final[dict[str, Any]] = {} + data: Final[dict[str, object]] = {} base_llm_response_processor: Final = ProxyBaseLLMRequestProcessing(data=data) data["model"] = model @@ -847,8 +876,8 @@ async def handle_bedrock_count_tokens( request: Request, fastapi_response: Response, user_api_key_dict: UserAPIKeyAuth, - request_body: dict[str, Any], -) -> dict[str, Any]: + request_body: dict[str, object], +) -> dict[str, object]: """ Handle AWS Bedrock CountTokens API requests. @@ -865,7 +894,7 @@ async def handle_bedrock_count_tokens( handler: Final = BedrockCountTokensHandler() # Extract model from request body - model: Final = request_body.get("model") + model: Final = _optional_str(request_body.get("model")) if not model: raise HTTPException(status_code=400, detail={"error": "Model is required in request body"}) @@ -997,7 +1026,7 @@ async def bedrock_llm_proxy_route( "Bedrock passthrough: Using direct Bedrock model '%s' for endpoint '%s'", model, endpoint ) - data: Final[dict[str, Any]] = {} + data: Final[dict[str, object]] = {} base_llm_response_processor: Final = ProxyBaseLLMRequestProcessing(data=data) data["method"] = request.method @@ -1096,7 +1125,7 @@ async def bedrock_proxy_route( headers: Final = {"Content-Type": "application/json"} # Assuming the body contains JSON data, parse it try: - data: Final = await request.json() + data: Final = await _json_request_body(request) except Exception as e: raise HTTPException(status_code=400, detail={"error": e}) _request: Final = AWSRequest(method="POST", url=str(updated_url), data=json.dumps(data), headers=headers) @@ -1187,7 +1216,7 @@ async def comprehend_medical_proxy_route( ) try: - data: Final = await request.json() + data: Final = await _json_request_body(request) except Exception as e: raise HTTPException(status_code=400, detail=str(e)) @@ -1273,7 +1302,7 @@ def _resolve_vertex_model_from_router( vertex_location: Current vertex location (may be from URL) Returns: - Tuple of (encoded_endpoint, endpoint, vertex_project, vertex_location) + tuple of (encoded_endpoint, endpoint, vertex_project, vertex_location) with resolved values from router config """ if not llm_router: @@ -1398,7 +1427,7 @@ async def assemblyai_proxy_route( is_streaming_request = False # assemblyai is streaming when 'stream' = True is in the body if request.method == "POST": - _request_body: Final = await request.json() + _request_body: Final = await _json_request_body(request) if _request_body.get("stream"): is_streaming_request = True @@ -1505,7 +1534,7 @@ async def azure_proxy_route( endpoint=endpoint, request_query_params=request.query_params, request_headers=_safe_get_request_headers(request), - stream=request_body.get("stream", False), + stream=is_streaming_request, content=None, data=None, files=None, @@ -1592,7 +1621,7 @@ async def azure_proxy_route( extra_headers = auth_credentials.get("headers") or {} - base_target_url = litellm_params.get("api_base") + base_target_url = _optional_str(litellm_params.get("api_base")) if base_target_url is None: raise Exception(f"API base not found for {part}") return await BaseOpenAIPassThroughHandler._base_openai_pass_through_handler( @@ -1702,7 +1731,7 @@ def get_vertex_ai_allowed_incoming_headers(request: Request) -> dict: def get_vertex_pass_through_handler( - call_type: Literal["discovery", "aiplatform"], + call_type: Literal["discovery", "aiplatform"], # noqa: UP037 ) -> BaseVertexAIPassThroughHandler: if call_type == "discovery": return VertexAIDiscoveryPassThroughHandler() @@ -1713,7 +1742,7 @@ def get_vertex_pass_through_handler( def _override_vertex_params_from_router_credentials( - router_credentials: Any | None, + router_credentials: LiteLLM_ManagedVectorStore | None, vertex_project: str | None, vertex_location: str | None, ) -> tuple[str | None, str | None]: @@ -1726,21 +1755,21 @@ def _override_vertex_params_from_router_credentials( vertex_location: Current vertex location (from URL) Returns: - Tuple of (vertex_project, vertex_location) with overridden values if applicable + tuple of (vertex_project, vertex_location) with overridden values if applicable """ if router_credentials is None: return vertex_project, vertex_location verbose_proxy_logger.debug("Using vector store credentials to override vertex project and location") - litellm_params: Final = router_credentials.get("litellm_params", {}) + litellm_params: Final = _string_keyed_mapping(router_credentials.get("litellm_params")) if not litellm_params: verbose_proxy_logger.warning("Vector store credentials found but litellm_params is empty") return vertex_project, vertex_location # Extract vertex_project and vertex_location from litellm_params - vector_store_project: Final = litellm_params.get("vertex_project") - vector_store_location: Final = litellm_params.get("vertex_location") + vector_store_project: Final = _optional_str(litellm_params.get("vertex_project")) + vector_store_location: Final = _optional_str(litellm_params.get("vertex_location")) if vector_store_project: verbose_proxy_logger.debug( @@ -1748,7 +1777,6 @@ def _override_vertex_params_from_router_credentials( vertex_project, vector_store_project, ) - vertex_project = vector_store_project else: verbose_proxy_logger.warning("Vector store credentials found but missing vertex_project in litellm_params") @@ -1758,11 +1786,10 @@ def _override_vertex_params_from_router_credentials( vertex_location, vector_store_location, ) - vertex_location = vector_store_location else: verbose_proxy_logger.warning("Vector store credentials found but missing vertex_location in litellm_params") - return vertex_project, vertex_location + return vector_store_project or vertex_project, vector_store_location or vertex_location _CREDENTIALLESS_VERTEX_MISSING_CREDENTIAL_DETAIL: Final = ( @@ -1870,8 +1897,8 @@ def _forwarded_headers_for_credentialless_vertex_passthrough( async def _prepare_vertex_auth_headers( request: Request, - vertex_credentials: Any | None, - router_credentials: Any | None, + vertex_credentials: VertexPassThroughCredentials | None, + router_credentials: LiteLLM_ManagedVectorStore | None, vertex_project: str | None, vertex_location: str | None, base_target_url: str | None, @@ -1893,12 +1920,12 @@ async def _prepare_vertex_auth_headers( authenticated them is stripped on the credential-less branch Returns: - Tuple containing: + tuple containing: - headers: dict - Authentication headers to use - - base_target_url: Optional[str] - Updated base target URL + - base_target_url: str | None - Updated base target URL - headers_passed_through: bool - Whether headers were passed through from request - - vertex_project: Optional[str] - Updated vertex project ID - - vertex_location: Optional[str] - Updated vertex location + - vertex_project: str | None - Updated vertex project ID + - vertex_location: str | None - Updated vertex location """ vertex_llm_base: Final = VertexBase() headers_passed_through = False @@ -1968,7 +1995,7 @@ async def _base_vertex_proxy_route( fastapi_response: Response, get_vertex_pass_through_handler: BaseVertexAIPassThroughHandler, user_api_key_dict: UserAPIKeyAuth | None = None, - router_credentials: Any | None = None, + router_credentials: LiteLLM_ManagedVectorStore | None = None, ): """ Base function for Vertex AI passthrough routes. @@ -2138,8 +2165,6 @@ async def vertex_discovery_proxy_route( """ import re - from litellm.types.vector_stores import LiteLLM_ManagedVectorStore - # Extract vector store ID from endpoint if present (e.g., dataStores/test-litellm-app_1761094730750) vector_store_credentials: LiteLLM_ManagedVectorStore | None = None vector_store_id_match: Final = re.search(r"dataStores/([^/]+)", endpoint) @@ -2546,7 +2571,7 @@ def _vertex_publisher_model_suffix(model: str) -> str: return f"{VERTEX_PUBLISHER_MODEL_PREFIX}{model.rsplit('/', 1)[-1]}" -def _get_llm_router() -> "Router | None": +def _get_llm_router() -> Router | None: from litellm.proxy.proxy_server import llm_router return llm_router @@ -2586,7 +2611,7 @@ def _resolve_vertex_live_credentials( def _build_vertex_live_setup_model_rewriter( vertex_project: str | None, vertex_location: str | None, - llm_router: "Router | None", + llm_router: Router | None, ) -> Callable[[str], str] | None: """ Rewrite the ``setup`` frame's model into the full Vertex resource path the Live API requires. @@ -2606,7 +2631,7 @@ def _build_vertex_live_setup_model_rewriter( return rewrite -def _resolve_alias_to_upstream_model(setup_model: str, llm_router: "Router | None") -> str: +def _resolve_alias_to_upstream_model(setup_model: str, llm_router: Router | None) -> str: """ The Live SDK wraps whatever the caller typed as ``models/``, so a gateway alias arrives prefixed """ @@ -2796,6 +2821,238 @@ def create_generic_websocket_passthrough_endpoint( ) +@router.api_route( + "/gigachat/{endpoint:path}", + methods=["GET", "POST", "PUT", "DELETE", "PATCH"], # mutable-ok: FastAPI route methods + tags=["Gigachat Pass-through", "pass-through"], # mutable-ok: FastAPI route tags +) +async def gigachat_proxy_route( + endpoint: str, + request: Request, + fastapi_response: Response, + user_api_key_dict: Annotated[UserAPIKeyAuth, Depends(user_api_key_auth)], +) -> Response: + """ + [Docs](https://docs.litellm.ai/docs/pass_through/gigachat) + """ + from litellm.proxy.proxy_server import ( + general_settings, + llm_router, + proxy_config, + proxy_logging_obj, + select_data_generator, + user_api_base, + user_max_tokens, + user_model, + user_request_timeout, + user_temperature, + version, + ) + + ## check for streaming + request_body: Final[dict[str, object]] = await get_request_body(request) + is_router_model = False # rebind-ok: conditionally set to True when model uses router + + raw_model: Final = request_body.get("model") + model: Final = raw_model if isinstance(raw_model, str) else None + if model: + is_router_model = is_passthrough_request_using_router_model( + request_body, llm_router + ) # rebind-ok: conditionally set to True + elif any(word in endpoint for word in ("completions", "embeddings")): + raise HTTPException( + status_code=400, detail={"error": "Model is required in request body"} + ) # mutable-ok: HTTPException detail dict + + # If router model, use dedicated router passthrough handler + # This uses the same common processing path as non-router models + if model and is_router_model and llm_router: + return await handle_gigachat_passthrough_router_model( + model=model, + endpoint=endpoint, + request=request, + request_body=request_body, + fastapi_response=fastapi_response, + llm_router=llm_router, + user_api_key_dict=user_api_key_dict, + proxy_logging_obj=proxy_logging_obj, + general_settings=general_settings, + proxy_config=proxy_config, + select_data_generator=select_data_generator, + user_model=user_model, + user_temperature=user_temperature, + user_request_timeout=user_request_timeout, + user_max_tokens=user_max_tokens, + user_api_base=user_api_base, + version=version, + ) + + verbose_proxy_logger.debug( + "Gigachat passthrough: Using direct Gigachat model '%s' for endpoint '%s'", model, endpoint + ) + + from litellm.llms.gigachat.authenticator import get_access_token + from litellm.llms.gigachat.utils import GIGACHAT_BASE_URL + + base_target_url: Final = get_secret_str("GIGACHAT_API_BASE") or GIGACHAT_BASE_URL + request_path: Final = httpx.URL(endpoint).path + encoded_endpoint: Final = request_path if request_path.startswith("/") else f"/{request_path}" + + base_url: Final = httpx.URL(base_target_url) + updated_url: Final = base_url.copy_with( + path=HttpPassThroughEndpointHelpers.join_base_and_endpoint_path(base_url, encoded_endpoint) + ) + + is_streaming_request: Final = await is_streaming_request_fn(request) + + endpoint_func: Final = create_pass_through_route( + endpoint=endpoint, + target=str(updated_url), + custom_headers={"Authorization": f"Bearer {get_access_token()}"}, + is_streaming_request=is_streaming_request, + ) + return await endpoint_func( + request, + fastapi_response, + user_api_key_dict, + ) + + +async def handle_gigachat_passthrough_router_model( + model: str, + endpoint: str, + request: Request, + request_body: dict, + fastapi_response: Response, + llm_router: litellm.Router, + user_api_key_dict: UserAPIKeyAuth, + proxy_logging_obj: ProxyLoggingType, + general_settings: dict, + proxy_config: ProxyConfig, + select_data_generator: Callable, + user_model: str | None, + user_temperature: float | None, + user_request_timeout: float | None, + user_max_tokens: int | None, + user_api_base: str | None, + version: str | None, +) -> Response | StreamingResponse: + """ + Handle Gigachat passthrough for router models (models defined in config.yaml). + + Uses the same common processing path as non-router models to ensure + metadata and hooks are properly initialized. + + Args: + model: The router model name (e.g., "gigachat/gigachat-2") + endpoint: The Gigachat endpoint path (e.g., "/chat/completions") + request: The FastAPI request object + request_body: The parsed request body + llm_router: The LiteLLM router instance + user_api_key_dict: The user API key authentication dictionary + proxy_logging_obj: Proxy logging + general_settings: Proxy general settings + proxy_config: Proxy config + select_data_generator: Select data generator function + (additional args for common processing) + + Returns: + Response or StreamingResponse depending on endpoint type + """ + from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing + + # Detect streaming based on request body + is_streaming: Final = request_body.get("stream", False) # pyright: ignore[reportUnknownVariableType] # request_body is dict[Unknown, Unknown] + + data: dict[str, Any] = await _read_request_body( + request=request + ) # mutable-ok: mutated in place by proxy pipeline; pyright: ignore[reportExplicitAny] # Any needed for proxy pipeline + if user_api_key_dict is not None: + auth_metadata: Final = { + metadata_key: value + for metadata_key, value in ( + ("user_api_key_user_id", getattr(user_api_key_dict, "user_id", None)), + ("user_api_key_team_id", getattr(user_api_key_dict, "team_id", None)), + ("user_api_key_org_id", getattr(user_api_key_dict, "org_id", None)), + ("agent_id", getattr(user_api_key_dict, "agent_id", None)), + ) + if value is not None + } + existing_metadata: Final = data.get("metadata") + data["metadata"] = { + **(existing_metadata if isinstance(existing_metadata, dict) else {}), + **auth_metadata, + } + + verbose_proxy_logger.debug( + "Gigachat router passthrough: model='%s', endpoint='%s', streaming=%s", model, endpoint, is_streaming + ) + + # Use the common processing path (same as non-router models) + # This ensures all metadata, hooks, and logging are properly initialized + + data["model"] = model + data["method"] = request.method + data["endpoint"] = endpoint + data["json"] = request_body + data["custom_llm_provider"] = "gigachat" + + # Remove sensitive keys from data + keys: Final = [ # mutable-ok: list of keys to remove from data + "gigachat_auth_url", + "gigachat_access_token", + "gigachat_scope", + "api_base", + "api_key", + ] + for key in keys: + data.pop(key, None) + + client: Final = get_async_httpx_client( + llm_provider=LlmProviders.GIGACHAT, + params={ # mutable-ok: httpx client params + "timeout": httpx.Timeout(timeout=600.0, connect=5.0), + }, + ) + + data["client"] = client + base_llm_response_processor: Final = ProxyBaseLLMRequestProcessing(data=data) + + # Use the common passthrough processing to handle metadata and hooks + # This also handles all response formatting (streaming/non-streaming) and exceptions + try: + result = await base_llm_response_processor.base_passthrough_process_llm_request( # rebind-ok: assigned once in try block + request=request, + fastapi_response=fastapi_response, + user_api_key_dict=user_api_key_dict, + proxy_logging_obj=proxy_logging_obj, + llm_router=llm_router, + general_settings=general_settings, + proxy_config=proxy_config, + select_data_generator=select_data_generator, + model=model, + user_model=user_model, + user_temperature=user_temperature, + user_request_timeout=user_request_timeout, + user_max_tokens=user_max_tokens, + user_api_base=user_api_base, + version=version, + ) + except Exception as e: # noqa: BLE001 # Safe catch-all for handle exception + # Use common exception handling + raise await base_llm_response_processor._handle_llm_api_exception( + e=e, + user_api_key_dict=user_api_key_dict, + proxy_logging_obj=proxy_logging_obj, + ) + else: + if isinstance(result, StreamingResponse): + if result.headers.get("Content-Type") is None: + result.headers["Content-Type"] = "text/event-stream; charset=utf-8" + + return result + + @router.api_route( "/watsonx/{endpoint:path}", methods=["GET", "POST", "PUT", "DELETE", "PATCH"], @@ -2852,7 +3109,7 @@ async def watsonx_proxy_route( is_streaming_request = False if request.method == "POST": if "multipart/form-data" not in request.headers.get("content-type", ""): - _request_body = await request.json() + _request_body = await _json_request_body(request) else: _request_body = await get_form_data(request) diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py index ee9a5d94440..49ec18013b5 100644 --- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py +++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py @@ -267,7 +267,7 @@ class VertexPassthroughLoggingHandler: model: Final = VertexPassthroughLoggingHandler.extract_model_from_url(url_route) - _json_response: Final = httpx_response.json() + _json_response: Final[dict[str, object]] = httpx_response.json() litellm_prediction_response: ModelResponse | EmbeddingResponse | ImageResponse = ModelResponse() if vertex_image_generation_class.is_image_generation_response(_json_response): @@ -422,7 +422,7 @@ class VertexPassthroughLoggingHandler: - Creates standard logging object - Logs in litellm callbacks """ - kwargs: dict[str, Any] = {} + kwargs: dict[str, object] = {} vertex_location: Final = get_vertex_location_from_url(url_route) if vertex_location is not None: litellm_logging_obj.optional_params["vertex_location"] = vertex_location diff --git a/litellm/proxy/pass_through_endpoints/managed_id_rewriter.py b/litellm/proxy/pass_through_endpoints/managed_id_rewriter.py index 23cfef6576c..567d8375737 100644 --- a/litellm/proxy/pass_through_endpoints/managed_id_rewriter.py +++ b/litellm/proxy/pass_through_endpoints/managed_id_rewriter.py @@ -49,6 +49,7 @@ from litellm._logging import verbose_proxy_logger from litellm.llms.base_llm.managed_resources.isolation import ( build_owner_filter, can_access_resource, + resolve_resource_owner_id, ) from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.batches_endpoints.common_utils import validate_batch_list_limit @@ -686,7 +687,7 @@ async def _mint_or_reuse_object( "file_object": json.dumps(body_snapshot), "model_object_id": namespaced_model_object_id, "file_purpose": file_purpose, - "created_by": user_api_key_dict.user_id, + "created_by": resolve_resource_owner_id(user_api_key_dict), "team_id": user_api_key_dict.team_id, "updated_by": user_api_key_dict.user_id, }, diff --git a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py index 3d60f4f5f3a..79d5d0a016f 100644 --- a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py +++ b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py @@ -6,9 +6,10 @@ import posixpath import traceback from base64 import b64encode from collections.abc import AsyncGenerator, Callable, Iterable, Mapping, Sequence +from dataclasses import dataclass from datetime import datetime from itertools import groupby -from typing import Any, Final, TypedDict, cast +from typing import TYPE_CHECKING, Any, Final, TypedDict, cast from urllib.parse import urlencode, urlparse import httpx @@ -47,6 +48,8 @@ from litellm.litellm_core_utils.core_helpers import ( get_metadata_variable_name_from_kwargs, get_or_create_metadata_bucket, ) +from litellm.litellm_core_utils.initialize_dynamic_callback_params import validate_no_callback_env_reference +from litellm.litellm_core_utils.internal_call_metadata import MODEL_ACCESS_GROUP_METADATA_KEY from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.litellm_core_utils.logging_worker import GLOBAL_LOGGING_WORKER from litellm.litellm_core_utils.safe_json_dumps import safe_dumps @@ -77,7 +80,10 @@ from litellm.proxy.common_utils.http_parsing_utils import ( from litellm.proxy.common_utils.sse_keepalive import ( wrap_passthrough_sse_bytes_with_keepalive_pings, ) -from litellm.proxy.litellm_pre_call_utils import LiteLLMProxyRequestSetup +from litellm.proxy.litellm_pre_call_utils import ( + LiteLLMProxyRequestSetup, + _get_dynamic_logging_metadata, # pyright: ignore[reportPrivateUsage] # shared proxy helper, same import style as _read_request_body above +) from litellm.proxy.utils import normalize_route_for_root_path from litellm.repositories.team_repository import TeamRepository from litellm.secret_managers.main import get_secret_str @@ -89,7 +95,7 @@ from litellm.types.passthrough_endpoints.pass_through_endpoints import ( EndpointType, PassthroughStandardLoggingPayload, ) -from litellm.types.utils import Usage +from litellm.types.utils import TRUSTED_CALLBACK_VARS_FIELD, Usage from .streaming_handler import PassThroughStreamingHandler from .success_handler import PassThroughEndpointLogging @@ -98,6 +104,9 @@ from .upstream_usage_headers import ( apply_upstream_reported_usage, ) +if TYPE_CHECKING: + from litellm.proxy.proxy_server import ProxyConfig + router: Final = APIRouter() pass_through_endpoint_logging: Final = PassThroughEndpointLogging() @@ -577,6 +586,7 @@ class HttpPassThroughEndpointHelpers(BasePassthroughUtils): _metadata["user_api_key"] = user_api_key_dict.api_key _metadata["litellm_parent_otel_span"] = user_api_key_dict.parent_otel_span _metadata["user_api_key_budget_reservation"] = user_api_key_dict.budget_reservation + _metadata[MODEL_ACCESS_GROUP_METADATA_KEY] = user_api_key_dict.matched_model_access_groups # The per-model budget counters are keyed off these. get_sanitized_user_information_from_key # returns StandardLoggingUserAPIKeyMetadata, which carries no budget field, so without this # the post-call increment finds nothing and every passthrough request goes untracked and @@ -750,6 +760,69 @@ def _build_passthrough_failure_request_payload( return request_payload +@dataclass(frozen=True, slots=True) +class _TeamCallbackWiring: + success_callbacks: "list[str | Callable | CustomLogger] | None" = None # mutable-ok: Logging.__init__ arg + failure_callbacks: "list[str | Callable | CustomLogger] | None" = None # mutable-ok: Logging.__init__ arg + logging_kwargs: dict[str, str | dict[str, str]] | None = None # mutable-ok: Logging.__init__ arg + + +def _resolve_team_callback_wiring( + user_api_key_dict: UserAPIKeyAuth, + proxy_config: "ProxyConfig", + route_description: str, +) -> _TeamCallbackWiring: + """Resolve key/team dynamic logging callbacks for a passthrough request. + + Mirrors add_litellm_data_to_request: callback_vars are unpacked top-level + (read by initialize_standard_callback_dynamic_params) and also stamped on + the proxy-owned trusted-vars field (read by get_trusted_callback_params). + + Fails open: a callback resolution or validation error is logged at error + level and the request proceeds without dynamic callbacks, since a broken + logging config must not fail the customer's upstream call (and the + websocket is already accepted by the time this runs on that path). The + env-reference check runs here because the deprecated callback_settings + branch skips AddTeamCallback validation, and Logging.__init__ would + otherwise reject the vars mid-request. + """ + try: + callback_settings_obj: Final = _get_dynamic_logging_metadata( + user_api_key_dict=user_api_key_dict, proxy_config=proxy_config + ) + if callback_settings_obj and callback_settings_obj.callback_vars: + for ( + item + ) in callback_settings_obj.callback_vars.items(): # rebind-ok: dict.items iteration for env-ref validation + validate_no_callback_env_reference(item[0], item[1], source="key/team callback metadata") + except Exception: # noqa: BLE001 - a broken logging config must never fail the passthrough request + verbose_proxy_logger.exception( + "%s: failed to resolve team logging callbacks, continuing without them", + route_description, + ) + return _TeamCallbackWiring() + if callback_settings_obj is None: + return _TeamCallbackWiring() + callback_vars: Final = callback_settings_obj.callback_vars + success_callbacks: Final = callback_settings_obj.success_callback + failure_callbacks: Final = callback_settings_obj.failure_callback + logging_kwargs: Final = ( + None + if not callback_vars + else { # mutable-ok: Logging arg + **callback_vars, + TRUSTED_CALLBACK_VARS_FIELD: callback_vars, + "metadata": {}, # mutable-ok: Logging arg + "model_info": {}, # mutable-ok: Logging arg + } + ) + return _TeamCallbackWiring( + success_callbacks=None if success_callbacks is None else [*success_callbacks], # mutable-ok: Logging arg + failure_callbacks=None if failure_callbacks is None else [*failure_callbacks], # mutable-ok: Logging arg + logging_kwargs=logging_kwargs, + ) + + async def _log_passthrough_upstream_failure( response: httpx.Response, user_api_key_dict: UserAPIKeyAuth, @@ -843,7 +916,7 @@ async def pass_through_request( from litellm.proxy.pass_through_endpoints.passthrough_guardrails import ( PassthroughGuardrailHandler, ) - from litellm.proxy.proxy_server import proxy_logging_obj + from litellm.proxy.proxy_server import proxy_config, proxy_logging_obj ######################################################### # Initialize variables @@ -928,6 +1001,11 @@ async def pass_through_request( # read e.g. ``chat gpt-4o`` instead of ``chat unknown``. passthrough_model: Final = (_parsed_body.get("model") if isinstance(_parsed_body, dict) else None) or "unknown" start_time: Final = datetime.now() + team_callbacks: Final = _resolve_team_callback_wiring( + user_api_key_dict=user_api_key_dict, + proxy_config=proxy_config, + route_description="pass_through_endpoint", + ) logging_obj = Logging( model=passthrough_model, messages=[{"role": "user", "content": safe_dumps(_parsed_body)}], @@ -936,6 +1014,9 @@ async def pass_through_request( start_time=start_time, litellm_call_id=litellm_call_id, function_id="1245", + dynamic_success_callbacks=team_callbacks.success_callbacks, + dynamic_failure_callbacks=team_callbacks.failure_callbacks, + kwargs=team_callbacks.logging_kwargs, ) # Store passthrough guardrails config on logging_obj for field targeting @@ -2020,7 +2101,7 @@ async def websocket_passthrough_request( setup_model_rewriter: Optional rewrite of the setup frame's model before it reaches the upstream """ from litellm.litellm_core_utils.litellm_logging import Logging - from litellm.proxy.proxy_server import proxy_logging_obj + from litellm.proxy.proxy_server import proxy_config, proxy_logging_obj from litellm.types.passthrough_endpoints.pass_through_endpoints import ( PassthroughStandardLoggingPayload, ) @@ -2053,6 +2134,11 @@ async def websocket_passthrough_request( upstream_headers[header_name] = header_value # Initialize logging object similar to HTTP passthrough + team_callbacks: Final = _resolve_team_callback_wiring( + user_api_key_dict=user_api_key_dict, + proxy_config=proxy_config, + route_description="websocket_passthrough", + ) logging_obj: Final = Logging( model="unknown", messages=[{"role": "user", "content": "WebSocket connection"}], @@ -2061,6 +2147,9 @@ async def websocket_passthrough_request( start_time=start_time, litellm_call_id=litellm_call_id, function_id="websocket_passthrough", + dynamic_success_callbacks=team_callbacks.success_callbacks, + dynamic_failure_callbacks=team_callbacks.failure_callbacks, + kwargs=team_callbacks.logging_kwargs, ) # Create passthrough logging payload @@ -3146,6 +3235,14 @@ def _get_pass_through_endpoints_from_config() -> list[PassThroughGenericEndpoint return returned_endpoints +def _config_field_endpoints(response: ConfigFieldInfo) -> list[object] | None: + return response.field_value + + +def _request_app(request: Request) -> FastAPI: + return request.app + + async def _get_pass_through_endpoints_from_db( endpoint_id: str | None = None, user_api_key_dict: UserAPIKeyAuth | None = None, @@ -3162,7 +3259,7 @@ async def _get_pass_through_endpoints_from_db( except Exception: return [] - pass_through_endpoint_data: Final[list | None] = response.field_value + pass_through_endpoint_data: Final = _config_field_endpoints(response) if pass_through_endpoint_data is None: return [] @@ -3325,7 +3422,7 @@ async def update_pass_through_endpoints( detail={"error": "No pass-through endpoints found"}, ) - pass_through_endpoint_data: Final[list | None] = response.field_value + pass_through_endpoint_data: Final[list | None] = _config_field_endpoints(response) if pass_through_endpoint_data is None: raise HTTPException( status_code=404, @@ -3396,7 +3493,7 @@ async def update_pass_through_endpoints( _custom_headers: dict | None = updated_endpoint.headers or {} _custom_headers = await set_env_variables_in_header(custom_headers=_custom_headers) - route_app: Final[FastAPI] = request.app + route_app: Final = _request_app(request) if updated_endpoint.include_subpath: InitPassThroughEndpointHelpers.add_subpath_route( app=route_app, @@ -3488,7 +3585,7 @@ async def create_pass_through_endpoints( _custom_headers: dict | None = created_endpoint.headers or {} _custom_headers = await set_env_variables_in_header(custom_headers=_custom_headers) - route_app: Final[FastAPI] = request.app + route_app: Final = _request_app(request) if created_endpoint.include_subpath: InitPassThroughEndpointHelpers.add_subpath_route( app=route_app, @@ -3556,7 +3653,7 @@ async def delete_pass_through_endpoints( response = ConfigFieldInfo(field_name="pass_through_endpoints", field_value=None) ## Update field by removing endpoint - pass_through_endpoint_data: Final[list | None] = response.field_value + pass_through_endpoint_data: Final[list | None] = _config_field_endpoints(response) if response.field_value is None or pass_through_endpoint_data is None: raise HTTPException( status_code=400, diff --git a/litellm/proxy/pass_through_endpoints/streaming_handler.py b/litellm/proxy/pass_through_endpoints/streaming_handler.py index 5ad41b00890..022a1ecbac4 100644 --- a/litellm/proxy/pass_through_endpoints/streaming_handler.py +++ b/litellm/proxy/pass_through_endpoints/streaming_handler.py @@ -65,6 +65,19 @@ class PassThroughStreamingHandler: route_streaming_logging or PassThroughStreamingHandler._route_streaming_logging_to_handler ) raw_bytes: Final[list[bytes]] = [] + + def _build_logging_coroutine() -> Coroutine[None, None, None]: + return resolved_route_streaming_logging( + litellm_logging_obj=litellm_logging_obj, + passthrough_success_handler_obj=passthrough_success_handler_obj, + url_route=url_route, + request_body=request_body or {}, + endpoint_type=endpoint_type, + start_time=start_time, + raw_bytes=raw_bytes, + end_time=datetime.now(), + ) + logging_scheduled = False model_name: Final = PassThroughStreamingHandler._extract_model_for_cost_injection( request_body=request_body, @@ -114,6 +127,21 @@ class PassThroughStreamingHandler: ) if pending: yield pending + # Stream completed cleanly. When the proxy armed deferred + # dispatch (post-call guardrails active), park the logging + # coroutine on logging_obj instead of enqueueing now, so + # ProxyLogging._fire_deferred_stream_logging fires it after + # guardrail end-of-stream blocks populate guardrail_information. + # Disconnect/exception paths skip this and fall through to the + # immediate enqueue in ``finally`` to keep partial billing + # (LIT-2642). + if ( + getattr(litellm_logging_obj, "_on_deferred_stream_complete", None) is not None + and raw_bytes + and response.status_code < 400 + ): + logging_scheduled = True + litellm_logging_obj._deferred_stream_complete_args = (_build_logging_coroutine(),) except Exception as e: verbose_proxy_logger.error("Error in chunk_processor: %s", e) raise @@ -128,18 +156,7 @@ class PassThroughStreamingHandler: if not logging_scheduled and raw_bytes and response.status_code < 400: logging_scheduled = True try: - GLOBAL_LOGGING_WORKER.ensure_initialized_and_enqueue( - async_coroutine=resolved_route_streaming_logging( - litellm_logging_obj=litellm_logging_obj, - passthrough_success_handler_obj=passthrough_success_handler_obj, - url_route=url_route, - request_body=request_body or {}, - endpoint_type=endpoint_type, - start_time=start_time, - raw_bytes=raw_bytes, - end_time=datetime.now(), - ) - ) + GLOBAL_LOGGING_WORKER.ensure_initialized_and_enqueue(async_coroutine=_build_logging_coroutine()) except Exception as e: verbose_proxy_logger.error("Error scheduling chunk_processor logging: %s", e) diff --git a/litellm/proxy/policy_engine/pipeline_executor.py b/litellm/proxy/policy_engine/pipeline_executor.py index a5619821197..4be0f556ed7 100644 --- a/litellm/proxy/policy_engine/pipeline_executor.py +++ b/litellm/proxy/policy_engine/pipeline_executor.py @@ -6,6 +6,7 @@ pass/fail actions (allow, block, next, modify_response) and data forwarding. """ import time +from collections.abc import Sequence from typing import Any, Final, Literal import litellm @@ -114,11 +115,7 @@ class PipelineExecutor: # Handle terminal actions if action == "allow": - return PipelineExecutionResult( - terminal_action="allow", - step_results=step_results, - modified_data=working_data if working_data != data else None, - ) + return _allow_result(step_results=step_results, working_data=working_data, request_data=data) if action == "block": return PipelineExecutionResult( @@ -138,11 +135,7 @@ class PipelineExecutor: # action == "next" → continue to next step # Ran out of steps without a terminal action → default allow - return PipelineExecutionResult( - terminal_action="allow", - step_results=step_results, - modified_data=working_data if working_data != data else None, - ) + return _allow_result(step_results=step_results, working_data=working_data, request_data=data) @staticmethod async def _run_step( @@ -185,7 +178,7 @@ class PipelineExecutor: # snapshot instead of `data` (which earlier pass_data steps in # this same pipeline may have already rewritten), same reason # the normal sequential/parallel guardrail loops do this. - scans_raw_request: Final = getattr(callback, "scan_raw_request", False) + scans_raw_request: Final = callback.scan_raw_request hook_input: Final[dict] = ( # mutable-ok: same request-payload shape as data independent_snapshot(raw_request_snapshot) if scans_raw_request and raw_request_snapshot is not None @@ -251,6 +244,45 @@ class PipelineExecutor: return None +def _allow_result( + step_results: Sequence[PipelineStepResult], + working_data: dict, # mutable-ok: same request-payload shape as execute_steps' data + request_data: dict, # mutable-ok: same request-payload shape as execute_steps' data +) -> PipelineExecutionResult: + """Build the terminal-allow result, propagating pipeline modifications without the per-step guardrail override.""" + restored: Final = _restore_request_guardrails(working_data, request_data) + return PipelineExecutionResult( + terminal_action="allow", + step_results=list(step_results), # mutable-ok: PipelineExecutionResult field is a list + modified_data=restored if restored != request_data else None, + ) + + +def _restore_request_guardrails( + working_data: dict, # mutable-ok: same request-payload shape as execute_steps' data + request_data: dict, # mutable-ok: same request-payload shape as execute_steps' data +) -> dict: # mutable-ok: merged back into the request dict, which downstream code mutates + """ + Restore the request's own metadata["guardrails"] activation list. + + _run_step overrides it to [step.guardrail] so should_run_guardrail() allows each + step; letting that override escape via modified_data permanently drops every + independently activated guardrail from later lifecycle stages (post_call, etc.). + """ + working_metadata: Final = working_data.get("metadata") + if not isinstance(working_metadata, dict): + return working_data + request_metadata: Final = request_data.get("metadata") + original_guardrails: Final = request_metadata.get("guardrails") if isinstance(request_metadata, dict) else None + stripped: Final = {k: v for k, v in working_metadata.items() if k != "guardrails"} # mutable-ok: request dict + if original_guardrails is not None: + restored: Final = {**stripped, "guardrails": original_guardrails} # mutable-ok: request dict + return {**working_data, "metadata": restored} # mutable-ok: request dict + if not stripped and not isinstance(request_metadata, dict): + return {k: v for k, v in working_data.items() if k != "metadata"} # mutable-ok: request dict + return {**working_data, "metadata": stripped} # mutable-ok: request dict + + def _pipeline_action_for_outcome(step: PipelineStep, outcome: str) -> str: """ Map pipeline step outcome to the configured action. diff --git a/litellm/proxy/prompts/prompt_endpoints.py b/litellm/proxy/prompts/prompt_endpoints.py index 9cfd6959a66..b6cbd2d7889 100644 --- a/litellm/proxy/prompts/prompt_endpoints.py +++ b/litellm/proxy/prompts/prompt_endpoints.py @@ -6,7 +6,7 @@ import tempfile from collections.abc import Awaitable, Mapping, Sequence from datetime import datetime from pathlib import Path -from typing import TYPE_CHECKING, Any, Final, Protocol, cast +from typing import TYPE_CHECKING, Final, Protocol, cast from fastapi import ( APIRouter, @@ -1317,7 +1317,7 @@ async def test_prompt( async def convert_prompt_file_to_json( file: UploadFile = File(...), user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), -) -> dict[str, Any]: +) -> Mapping[str, object]: """ Convert a .prompt file to JSON format. diff --git a/litellm/proxy/proxy_cli.py b/litellm/proxy/proxy_cli.py index 8ac63ba25c9..23932ba7c8c 100644 --- a/litellm/proxy/proxy_cli.py +++ b/litellm/proxy/proxy_cli.py @@ -1225,6 +1225,7 @@ def run_server( if os.getenv("DATABASE_URL", None) is not None or os.getenv("DIRECT_URL", None) is not None: from litellm.proxy.db.db_url_settings import ( add_missing_query_params, + idle_lifetime_params, reader_shareable_params, unsupported_db_scheme, unsupported_db_scheme_message, @@ -1253,6 +1254,9 @@ def run_server( disable_prepared_statements=db_disable_prepared_statements, extra_params=db_extra_connection_params, ) + lifetime_params: Final = idle_lifetime_params( + general_settings.get("database_max_idle_connection_lifetime") + ) if os.getenv("DATABASE_URL", None) is not None: database_url = get_secret("DATABASE_URL", default_value=None) resolved_url: Final[str | None] = str(database_url) if database_url else None @@ -1270,11 +1274,11 @@ def run_server( writer_url, connection_url_params, ) - os.environ["DATABASE_URL"] = modified_url + os.environ["DATABASE_URL"] = add_missing_query_params(modified_url, lifetime_params) if os.getenv("DIRECT_URL", None) is not None: database_url = os.getenv("DIRECT_URL") modified_url = append_query_params(database_url, connection_url_params) - os.environ["DIRECT_URL"] = modified_url + os.environ["DIRECT_URL"] = add_missing_query_params(modified_url, lifetime_params) # The reader pool is a real pool against the same configured cap, so it # gets the allowlisted pool params. Schema-affecting ones, including any # the operator smuggled in through database_extra_connection_params, stay @@ -1288,10 +1292,13 @@ def run_server( db_lock_timeout, ) os.environ["DATABASE_URL_READ_REPLICA"] = add_missing_query_params( - _with_query_value(read_replica_url, "options", reader_options) - if reader_options - else read_replica_url, - reader_shareable_params(connection_url_params), + add_missing_query_params( + _with_query_value(read_replica_url, "options", reader_options) + if reader_options + else read_replica_url, + reader_shareable_params(connection_url_params), + ), + lifetime_params, ) subprocess.run(["prisma"], capture_output=True) is_prisma_runnable = True diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index 3a70750528f..77a80ea0052 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -39,7 +39,7 @@ from typing import ( import anyio import websockets import websockets.exceptions -from pydantic import BaseModel, Json, JsonValue +from pydantic import BaseModel, Json, JsonValue, ValidationError from typing_extensions import NotRequired, ReadOnly, assert_never from litellm._uuid import uuid @@ -253,6 +253,7 @@ from litellm.constants import ( PROXY_BUDGET_RESCHEDULER_MAX_TIME, PROXY_BUDGET_RESCHEDULER_MIN_TIME, PROXY_CONFIG_RELOAD_INTERVAL_SECONDS, + USER_SPEND_ALERTS_JOB_ID, WEEKLY_SPEND_REPORT_JOB_ID, ) from litellm.exceptions import RejectedRequestError @@ -263,6 +264,7 @@ from litellm.litellm_core_utils.agentic_loop_settings import ( validated_max_agentic_loops, ) from litellm.litellm_core_utils.asyncify import asyncify +from litellm.litellm_core_utils.audio_utils.utils import resolve_speech_media_type from litellm.litellm_core_utils.core_helpers import ( _get_parent_otel_span_from_kwargs, get_litellm_metadata_from_kwargs, @@ -382,6 +384,8 @@ from litellm.proxy.common_utils.user_api_key_cache import ( UserApiKeyCache, end_user_cache_key, get_management_object_ttl, + model_access_group_cache_key, + model_access_group_spend_counter_key, tag_cache_key, ) from litellm.proxy.config_resolvers import resolve_fields @@ -396,6 +400,9 @@ from litellm.proxy.db.db_transaction_queue.spend_log_cleanup import ( SPEND_LOG_CLEANUP_BOUND_SETTINGS, SpendLogCleanup, ) +from litellm.proxy.db.db_transaction_queue.window_spend_update_queue import ( + build_window_spend_transaction, +) from litellm.proxy.db.exception_handler import ( PrismaDBExceptionHandler, call_with_db_reconnect_retry, @@ -1358,7 +1365,7 @@ _OPENAPI_HTTP_METHODS: Final = { # the UI. Kept here at module scope to match the analogous descriptor # `is_secret` flags in litellm.proxy.config_resolvers and the # `_CACHE_SENSITIVE_FIELDS` constant in the cache endpoint file. -_ALERTING_SENSITIVE_VARS: Final[set[str]] = {"SLACK_WEBHOOK_URL", "SMTP_PASSWORD"} +_ALERTING_SENSITIVE_VARS: Final[set[str]] = {"ALERTING_WEBHOOK_URL", "SLACK_WEBHOOK_URL", "SMTP_PASSWORD"} def _strip_operation_id_method_suffix(operation_id: str) -> str: @@ -2431,6 +2438,7 @@ async def get_current_spend( max_budget: float | None = None, window_entity_type: str | None = None, window_entity_id: str | None = None, + window_duration: str | None = None, window_start: datetime | None = None, fallback_authoritative: bool = False, ) -> float: @@ -2455,7 +2463,8 @@ async def get_current_spend( runs and a key can leak spend past ``max_budget`` indefinitely. The authoritative source depends on the counter: primary key/team/user/org counters read the DB row; per-window counters (``window_start`` supplied) - aggregate spend logs; end-user/tag counters have no DB row, so the caller's + read the maintained window-spend row and only aggregate spend logs when + that row is missing or stale; end-user/tag counters have no DB row, so the caller's ``fallback_spend`` (loaded fresh in auth) is authoritative. The DB read is skipped for healthy primary counters (counter at or above recorded spend) and cached in-process for a few seconds, so a persistently stale counter @@ -2480,6 +2489,7 @@ async def get_current_spend( counter_key=counter_key, window_entity_type=window_entity_type, window_entity_id=window_entity_id, + window_duration=window_duration, window_start=window_start, ) if authoritative is not None: @@ -2561,6 +2571,7 @@ async def _authoritative_floor_spend( counter_key: str, window_entity_type: str | None = None, window_entity_id: str | None = None, + window_duration: str | None = None, window_start: datetime | None = None, ) -> float | None: marker_key: Final = f"spend_db_floor:{counter_key}" @@ -2575,10 +2586,11 @@ async def _authoritative_floor_spend( and window_entity_id is not None and window_start is not None ): - db_spend = await SpendCounterReseed.window_from_spend_logs( + db_spend = await SpendCounterReseed.window_from_db( prisma_client=prisma_client, entity_type=window_entity_type, entity_id=window_entity_id, + window_duration=window_duration, window_start=window_start, ) if db_spend is None: @@ -2648,6 +2660,8 @@ async def increment_spend_counters( budget_reservation: dict | None = None, end_user_id: str | None = None, tags: list[str] | None = None, + request_started_at: datetime | None = None, + model_access_groups: Sequence[str] | None = None, ): """ Atomically increment spend counters for budget enforcement. @@ -2701,15 +2715,27 @@ async def increment_spend_counters( return for window in key_budget_limits: duration = window["budget_duration"] if isinstance(window, dict) else window.budget_duration + key_window_reset_at = window.get("reset_at") if isinstance(window, dict) else window.reset_at key_window_counter = f"spend:key:{hashed_token}:window:{duration}" + key_window_start = get_budget_window_start(window) if key_window_counter not in reserved_counter_keys: await _init_and_increment_window_spend_counter( counter_key=key_window_counter, entity_type="Key", entity_id=hashed_token, - window_start=get_budget_window_start(window), + window_duration=duration, + window_start=key_window_start, increment=cost, ) + await _enqueue_window_spend_row_update( + entity_type=Litellm_EntityType.KEY, + entity_id=hashed_token, + reset_at=key_window_reset_at, + window_duration=duration, + window_start=key_window_start, + increment=cost, + request_started_at=request_started_at, + ) async def _team_scope(scope_team_id: str) -> None: team_counter_key: Final = f"spend:team:{scope_team_id}" @@ -2732,15 +2758,27 @@ async def increment_spend_counters( return for window in team_budget_limits: duration = window["budget_duration"] if isinstance(window, dict) else window.budget_duration + team_window_reset_at = window.get("reset_at") if isinstance(window, dict) else window.reset_at team_window_counter = f"spend:team:{scope_team_id}:window:{duration}" + team_window_start = get_budget_window_start(window) if team_window_counter not in reserved_counter_keys: await _init_and_increment_window_spend_counter( counter_key=team_window_counter, entity_type="Team", entity_id=scope_team_id, - window_start=get_budget_window_start(window), + window_duration=duration, + window_start=team_window_start, increment=cost, ) + await _enqueue_window_spend_row_update( + entity_type=Litellm_EntityType.TEAM, + entity_id=scope_team_id, + reset_at=team_window_reset_at, + window_duration=duration, + window_start=team_window_start, + increment=cost, + request_started_at=request_started_at, + ) async def _team_member_scope(scope_user_id: str, scope_team_id: str) -> None: team_member_counter_key: Final = f"spend:team_member:{scope_user_id}:{scope_team_id}" @@ -2777,6 +2815,13 @@ async def increment_spend_counters( ) if end_user_id is not None or tags is not None else None, + _increment_model_access_group_spend_counters( + model_access_groups=model_access_groups, + response_cost=cost, + reserved_counter_keys=reserved_counter_keys, + ) + if model_access_groups + else None, _increment_org_spend_counter( org_id=org_id, response_cost=cost, @@ -2865,6 +2910,33 @@ async def _increment_end_user_and_tag_spend_counters( ) +async def _increment_model_access_group_spend_counters( + model_access_groups: Sequence[object], + response_cost: float, + reserved_counter_keys: set[str], +) -> None: + """Charge the model access groups that authorized this request. + + Without this the counter auth reads is written only by the reservation path, so + ``disable_budget_reservation`` would leave ``_model_access_group_max_budget_check`` enforcing + against the DB row's spend, which lags by up to the cache TTL. + + Typed ``object`` rather than ``str`` because the names reach the cost callback out of request + metadata, which the coercion upstream filters to a list but not to strings. A non-string that + slipped through would build a counter key nothing else ever reads. + """ + unique_groups: Final = tuple( + dict.fromkeys(group for group in model_access_groups if group and isinstance(group, str)) + ) + for group in unique_groups: + await _init_and_increment_unreserved_spend_counter( + counter_key=model_access_group_spend_counter_key(group), + source_cache_key=model_access_group_cache_key(group), + increment=response_cost, + reserved_counter_keys=reserved_counter_keys, + ) + + async def _increment_org_spend_counter( org_id: str | None, response_cost: float, @@ -2925,10 +2997,60 @@ async def _init_and_increment_spend_counter( await _increment_spend_counter_cache(counter_key=counter_key, increment=increment) +async def _enqueue_window_spend_row_update( + entity_type: Litellm_EntityType, + entity_id: str, + reset_at: datetime | str | None, + window_duration: str, + window_start: datetime | None, + increment: float, + request_started_at: datetime | None, +) -> None: + """Queue this request's cost against the LiteLLM_BudgetWindowSpend row for + the window, so enforcement can read a maintained total instead of + aggregating LiteLLM_SpendLogs. + + request_started_at is this request's LiteLLM_SpendLogs startTime; the flush + stops the one-time seed there so a request its increment already covers is + not counted twice. + + Enqueued even when the cache increment was skipped for a reserved counter: + the reservation only pre-charged the counter, and the row still owes the + actual cost. + + Windows with no reset_at slide with wall clock, so their window_start moves + on every request and no single row can represent them. Those are left to + the read path's LiteLLM_SpendLogs fallback rather than rewritten per + request. + """ + if window_start is None or not reset_at: + return + try: + await proxy_logging_obj.db_spend_update_writer.window_spend_update_queue.add_update( + build_window_spend_transaction( + entity_type=entity_type.value, + entity_id=entity_id, + window_duration=window_duration, + window_start=window_start, + spend=increment, + started_at=request_started_at, + ) + ) + except Exception as e: # noqa: BLE001 # spend tracking must never fail the cost callback + verbose_proxy_logger.debug( + "Unable to enqueue budget window spend update for %s=%s window=%s: %s", + entity_type.value, + entity_id, + window_duration, + e, + ) + + async def _init_and_increment_window_spend_counter( counter_key: str, entity_type: str, entity_id: str, + window_duration: str | None, window_start: datetime | None, increment: float, ): @@ -2943,6 +3065,7 @@ async def _init_and_increment_window_spend_counter( counter_key=counter_key, entity_type=entity_type, entity_id=entity_id, + window_duration=window_duration, window_start=window_start, ) if initialized is False: @@ -2988,6 +3111,7 @@ async def _ensure_window_spend_counter_initialized( counter_key: str, entity_type: str, entity_id: str, + window_duration: str | None, window_start: datetime, ) -> bool: is_warm: Final = await _is_spend_counter_cache_warm(counter_key=counter_key) @@ -3000,6 +3124,7 @@ async def _ensure_window_spend_counter_initialized( counter_key=counter_key, entity_type=entity_type, entity_id=entity_id, + window_duration=window_duration, window_start=window_start, ) if window_spend is None: @@ -4281,6 +4406,8 @@ class ProxyConfig: self.config: dict[str, Any] = {} self._last_semantic_filter_config: dict[str, object] | None = None self._last_hashicorp_vault_config: dict[str, object] | None = None + self._last_cyberark_config: dict[str, object] | None = None # mutable-ok: change-detection cache + self._cyberark_boot_env: dict[str, str | None] | None = None # mutable-ok: deployment env snapshot, set once self.worker_registry: list[WorkerRegistryEntry] = [] self.config_sync_subscriber: ConfigSyncSubscriber | None = None self.auth_cache_invalidation_subscriber: AuthCacheInvalidationSubscriber | None = None @@ -6764,9 +6891,18 @@ class ProxyConfig: - list: the rows (may be empty if no models exist) - None: signals a DB fetch *failure* — callers must not treat this as "all models deleted" and must not evict existing router deployments. + + Pinned to the writer DB: this read reconciles the router against the rows a + model write just committed, and reading it through a lagging read replica + makes the write-triggered reload report its own durable write as missing + (#38556). It also keeps a stale replica snapshot from evicting a deployment + another pod just added. While the writer is degraded the pin yields to the + replica so reader-only mode keeps loading DB-backed models. """ try: - new_models: Final[Sequence[_ProxyModelRow]] = await ModelRepository(prisma_client).table.find_many() + new_models: Final[Sequence[_ProxyModelRow]] = await ModelRepository( + WriterPinnedClient(prisma_client.db) + ).table.find_many() return new_models except Exception as e: verbose_proxy_logger.exception( @@ -6968,6 +7104,7 @@ class ProxyConfig: if self._should_load_db_object(object_type="config_overrides"): await self._init_hashicorp_vault_config_override(prisma_client=prisma_client) + await self._init_cyberark_config_override(prisma_client=prisma_client) await self._apply_safe_litellm_settings_overrides_from_db(prisma_client=prisma_client) @@ -7132,6 +7269,64 @@ class ProxyConfig: str(e), ) + async def _init_cyberark_config_override(self, prisma_client: PrismaClient) -> None: + """ + Load CyberArk Conjur config override from DB. + Decrypts sensitive fields, sets CYBERARK_* env vars, and reinitializes the secret manager. + Called periodically via _init_non_llm_objects_in_db to sync config across pods. + """ + from litellm.proxy.management_endpoints.config_override_endpoints import ( + CYBERARK_ENV_VAR_MAPPING, + _clear_cyberark_state, # pyright: ignore[reportPrivateUsage] # module-internal helper shared with the endpoint module + _get_current_env_values, # pyright: ignore[reportPrivateUsage] # module-internal helper shared with the endpoint module + _parse_config_value, # pyright: ignore[reportPrivateUsage] # module-internal helper shared with the endpoint module + _set_env_vars, # pyright: ignore[reportPrivateUsage] # module-internal helper shared with the endpoint module + _snapshot_cyberark_boot_env, # pyright: ignore[reportPrivateUsage] # module-internal helper shared with the endpoint module + ) + + try: + db_record: Final[_ConfigOverridesRow | None] = cast( # cast-ok: prisma Json stub is `str`, runtime dict + "_ConfigOverridesRow | None", + await call_with_db_reconnect_retry( + prisma_client, + lambda: ConfigOverridesRepository(prisma_client).table.find_unique( + where={"config_type": "cyberark"} # mutable-ok: prisma where clause + ), + reason="init_cyberark_config_override_lookup_failure", + ), + ) + + if db_record is None or db_record.config_value is None: + if self._last_cyberark_config is not None: + _clear_cyberark_state(self) + return + + config_data: Final = _parse_config_value(db_record.config_value) + + # Skip reinit if config hasn't changed since last poll + if self._last_cyberark_config == config_data: + return + + decrypted_data: Final = self._decrypt_db_variables(config_data) + + _snapshot_cyberark_boot_env(self) + previous_env: Final = _get_current_env_values(CYBERARK_ENV_VAR_MAPPING) + _set_env_vars(decrypted_data, CYBERARK_ENV_VAR_MAPPING) + + try: + self.initialize_secret_manager(key_management_system="cyberark") + except Exception: + _set_env_vars(previous_env, CYBERARK_ENV_VAR_MAPPING) + raise + + self._last_cyberark_config = config_data.copy() + verbose_proxy_logger.debug("CyberArk config override loaded from DB") + except Exception as e: # noqa: BLE001 # any DB/decrypt/init failure must not break proxy boot + verbose_proxy_logger.exception( + "Error loading CyberArk config override from DB: %s", + str(e), + ) + async def check_periodic_reloads(self, prisma_client: PrismaClient): """ Run the admin-configured periodic model cost map reload. @@ -9672,6 +9867,35 @@ class ProxyStartupEvent: replace_existing=True, ) + slack_alerting_args: Final = proxy_logging_obj.slack_alerting_instance.alerting_args + user_spend_check_interval: Final = ( + slack_alerting_args.user_spend_check_interval + if isinstance(slack_alerting_args, SlackAlertingArgs) # pyright: ignore[reportUnnecessaryIsInstance] # tests inject a mock slack_alerting_instance + else SlackAlertingArgs().user_spend_check_interval + ) + + async def _scheduled_user_spend_alerts() -> None: + if ( + await pod_lock_manager.acquire_lock( + cronjob_id=USER_SPEND_ALERTS_JOB_ID, + ttl=max(user_spend_check_interval - 60, 60), + allow_reentrant=False, + ) + is False + ): + return + await proxy_logging_obj.slack_alerting_instance.send_user_spend_alerts() + + scheduler.add_job( + _scheduled_user_spend_alerts, + "interval", + seconds=user_spend_check_interval, + next_run_time=datetime.now(timezone.utc) + timedelta(seconds=10 + random.randint(0, 60)), + id=USER_SPEND_ALERTS_JOB_ID, + replace_existing=True, + misfire_grace_time=APSCHEDULER_MISFIRE_GRACE_TIME, + ) + if os.getenv("PROMETHEUS_URL"): from zoneinfo import ZoneInfo @@ -10868,15 +11092,14 @@ async def audio_speech( if callback_headers: custom_headers.update(callback_headers) - # Determine media type based on model type - media_type = "audio/mpeg" # Default for OpenAI TTS - request_model: Final = data.get("model", "") - if request_model: - request_model_lower: Final = request_model.lower() - if "gemini" in request_model_lower and ( - "tts" in request_model_lower or "preview-tts" in request_model_lower - ): - media_type = "audio/wav" # Gemini TTS returns WAV format after conversion + requested_format: Final = data.get("response_format") + upstream_content_type: Final = ( + response.response.headers.get("content-type") if isinstance(response, HttpxBinaryResponseContent) else None + ) + media_type: Final = resolve_speech_media_type( + upstream_content_type=upstream_content_type, + response_format=requested_format if isinstance(requested_format, str) else None, + ) return StreamingResponse( _audio_speech_chunk_generator(response), @@ -10892,7 +11115,15 @@ async def audio_speech( ) verbose_proxy_logger.error("litellm.proxy.proxy_server.audio_speech(): Exception occured - %s", e) verbose_proxy_logger.debug(traceback.format_exc()) - raise e + if isinstance(e, (ProxyException, HTTPException)): + raise e + raise ProxyException( + message=getattr(e, "message", f"{e}"), + type=getattr(e, "type", "None"), + param=getattr(e, "param", "None"), + openai_code=getattr(e, "code", None), + code=getattr(e, "status_code", 500), + ) @router.post( @@ -12013,6 +12244,7 @@ async def run_thread( # ) # async def get_available_routes(user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth)): from litellm.llms.base_llm.base_utils import BaseTokenCounter +from litellm.proxy.db.routing_prisma_wrapper import WriterPinnedClient from litellm.repositories.config_repository import ConfigRepository from litellm.repositories.model_repository import ModelRepository from litellm.repositories.table_repositories import ( @@ -12279,11 +12511,21 @@ async def supported_openai_params(model: str): --header 'Authorization: Bearer sk-1234' ``` """ + from litellm.litellm_core_utils.get_llm_provider_logic import declared_authenticating_provider + + global llm_router try: - model, custom_llm_provider, _, _ = litellm.get_llm_provider(model=model) + resolved_models: Final = llm_router.resolved_litellm_models(model) if llm_router is not None else () + target_model: Final = resolved_models[0] if resolved_models else model + declared_provider: Final = declared_authenticating_provider(target_model) + litellm_model, custom_llm_provider = ( + (target_model.removeprefix(f"{declared_provider}/"), declared_provider) + if declared_provider is not None + else litellm.get_llm_provider(model=target_model)[:2] + ) return { "supported_openai_params": litellm.get_supported_openai_params( - model=model, custom_llm_provider=custom_llm_provider + model=litellm_model, custom_llm_provider=custom_llm_provider ) } except Exception: @@ -12558,25 +12800,53 @@ async def get_all_team_models( return returned_team_models +def _resolve_model_grant_to_deployment_ids( + models: Sequence[str], + llm_router: Router, +) -> tuple[str, ...]: + """ + Resolve a `models` grant (a user's or a key's) to the deployment ids it can call. + + An empty grant and the 'all-proxy-models' sentinel both mean unrestricted at call + time (see `_check_model_access_helper`), so both expand to every non-team deployment. + A grant entry naming an access group also grants that group's members, and naming a + deployed model that shares the name grants the model itself, matching the union the + call-time check applies. + """ + if not models or SpecialModelNames.all_proxy_models.value in models: + return tuple(llm_router.get_model_ids(exclude_team_models=True)) + + access_groups: Final = llm_router.get_model_access_groups() + granted_model_names: Final = tuple(name for model in models for name in (model, *access_groups.get(model, ()))) + return tuple( + model_id + for name in granted_model_names + for deployment in (llm_router.get_model_list(model_name=name) or ()) + if (model_id := deployment.get("model_info", {}).get("id", None)) is not None + ) + + def get_direct_access_models( user_db_object: LiteLLM_UserTable, llm_router: Router, -) -> list[str]: + key_models: Sequence[str] = (), +) -> tuple[str, ...]: """ - Get all models that user has direct access to. + Get all models the caller has direct (non-team) access to. - The 'all-proxy-models' sentinel grants direct access to every non-team - deployment, mirroring how get_key_models expands it for the key/team path. + Both the user record and the calling key are enforced at call time, so direct access + is the intersection of the two grants. An unrestricted key (empty grant, or the + 'all-proxy-models' sentinel) leaves the user's grant untouched. """ - if SpecialModelNames.all_proxy_models.value in user_db_object.models: - return llm_router.get_model_ids(exclude_team_models=True) + user_model_ids: Final = _resolve_model_grant_to_deployment_ids( + cast(Sequence[str], user_db_object.models), # cast-ok: user.models is a String[] column + llm_router, + ) + if not key_models or SpecialModelNames.all_proxy_models.value in key_models: + return user_model_ids - return [ - model_id - for model in user_db_object.models - for deployment in (llm_router.get_model_list(model_name=model) or []) - if (model_id := deployment.get("model_info", {}).get("id", None)) is not None - ] + key_model_ids: Final = frozenset(_resolve_model_grant_to_deployment_ids(key_models, llm_router)) + return tuple(model_id for model_id in user_model_ids if model_id in key_model_ids) def _filter_models_to_user_accessible(all_models: list[dict]) -> list[dict]: @@ -12600,10 +12870,10 @@ async def _populate_team_access_on_models( without filtering the model list. """ user_teams: list[str] | Literal["*"] | None = None - direct_access_models: list[str] = [] + direct_access_models: Sequence[str] = () if _user_has_admin_view(user_api_key_dict): user_teams = "*" - direct_access_models = llm_router.get_model_ids(exclude_team_models=True) # has access to all models + direct_access_models = tuple(llm_router.get_model_ids(exclude_team_models=True)) # access to all models elif user_api_key_dict.user_id is not None: user_db_object: Final[SupportsModelDump | None] = await UserRepository(prisma_client).table.find_unique( where={"user_id": user_api_key_dict.user_id} @@ -12614,6 +12884,7 @@ async def _populate_team_access_on_models( direct_access_models = get_direct_access_models( user_db_object=user_object, llm_router=llm_router, + key_models=cast(Sequence[str], user_api_key_dict.models), # cast-ok: key.models is a String[] column ) if user_teams is not None: team_models: Final = await get_all_team_models( @@ -12635,7 +12906,7 @@ async def _populate_team_access_on_models( if can_use_model: _model["model_info"]["access_via_team_ids"] = team_models.get(model_id, []) - direct_access_model_ids: Final = set(direct_access_models) + direct_access_model_ids: Final = frozenset(direct_access_models) for _model in all_models: model_id = _model.get("model_info", {}).get("id", None) if model_id is not None: @@ -14731,17 +15002,25 @@ async def alerting_settings( alerting_args_dict = {} alerting_values = None - allowed_args: Final = { - "slack_alerting": {"type": "Boolean"}, - "daily_report_frequency": {"type": "Integer"}, - "report_check_interval": {"type": "Integer"}, - "budget_alert_ttl": {"type": "Integer"}, - "outage_alert_ttl": {"type": "Integer"}, - "region_outage_alert_ttl": {"type": "Integer"}, - "minor_outage_alert_threshold": {"type": "Integer"}, - "major_outage_alert_threshold": {"type": "Integer"}, - "max_outage_alert_list_size": {"type": "Integer"}, - } + allowed_args: Final = MappingProxyType( + { + "slack_alerting": "Boolean", + "daily_report_frequency": "Integer", + "report_check_interval": "Integer", + "budget_alert_ttl": "Integer", + "outage_alert_ttl": "Integer", + "region_outage_alert_ttl": "Integer", + "minor_outage_alert_threshold": "Integer", + "major_outage_alert_threshold": "Integer", + "max_outage_alert_list_size": "Integer", + "daily_spend_per_user_threshold": "Float", + "monthly_spend_per_user_threshold": "Float", + "spend_anomaly_multiplier": "Float", + "spend_anomaly_baseline_days": "Integer", + "spend_anomaly_min_spend": "Float", + "user_spend_check_interval": "Integer", + } + ) _slack_alerting: Final[SlackAlerting] = proxy_logging_obj.slack_alerting_instance _slack_alerting_args_dict: Final = _slack_alerting.alerting_args.model_dump() @@ -14756,7 +15035,7 @@ async def alerting_settings( _response_obj = ConfigList( field_name="slack_alerting", - field_type=allowed_args["slack_alerting"]["type"], + field_type=allowed_args["slack_alerting"], field_description="Enable slack alerting for monitoring proxy in production: llm outages, budgets, spend tracking failures.", field_value=is_slack_enabled, stored_in_db=True if alerting_values is not None else False, @@ -14775,7 +15054,7 @@ async def alerting_settings( _response_obj = ConfigList( field_name=field_name, - field_type=allowed_args[field_name]["type"], + field_type=allowed_args[field_name], field_description=field_info.description or "", field_value=_slack_alerting_args_dict.get(field_name, None), stored_in_db=_stored_in_db, @@ -16203,6 +16482,16 @@ async def update_config_general_settings( detail={"error": f"Invalid type of field value={type(data.field_value)} passed in."}, ) + if data.field_name == "alerting_args": + try: + SlackAlertingArgs.model_validate(data.field_value) + except ValidationError as e: + errors: Final = "; ".join(f"{'.'.join(str(loc) for loc in err['loc'])}: {err['msg']}" for err in e.errors()) + raise HTTPException( + status_code=400, + detail={"error": f"Invalid alerting_args: {errors}"}, + ) + ## get general settings from db db_general_settings: Final = await _config_param_table(prisma_client).find_first( where={"param_name": "general_settings"} @@ -16338,6 +16627,7 @@ async def create_config_audit_log( _EXTRA_SECRET_CALLBACK_ENV_VARS: Final = frozenset( { + "ALERTING_WEBHOOK_URL", "GALILEO_USERNAME", "GENERIC_LOGGER_HEADERS", "OTEL_HEADERS", diff --git a/litellm/proxy/public_endpoints/provider_create_fields.json b/litellm/proxy/public_endpoints/provider_create_fields.json index 4652719a23b..66f8c2ea36f 100644 --- a/litellm/proxy/public_endpoints/provider_create_fields.json +++ b/litellm/proxy/public_endpoints/provider_create_fields.json @@ -986,6 +986,62 @@ ], "default_model_placeholder": "gpt-3.5-turbo" }, + { + "provider": "QwenCloud", + "provider_display_name": "QwenCloud", + "litellm_provider": "qwencloud", + "credential_fields": [ + { + "key": "api_key", + "label": "QwenCloud API Key", + "placeholder": null, + "tooltip": null, + "required": true, + "field_type": "password", + "options": null, + "default_value": null + }, + { + "key": "api_base", + "label": "API Base", + "placeholder": "https://dashscope-intl.aliyuncs.com/compatible-mode/v1", + "tooltip": "The base URL for QwenCloud. Defaults to https://dashscope-intl.aliyuncs.com/compatible-mode/v1 if not specified.", + "required": true, + "field_type": "text", + "options": null, + "default_value": "https://dashscope-intl.aliyuncs.com/compatible-mode/v1" + } + ], + "default_model_placeholder": "gpt-3.5-turbo" + }, + { + "provider": "Qwen_AI_Platform", + "provider_display_name": "Qwen AI Platform", + "litellm_provider": "qwen_ai_platform", + "credential_fields": [ + { + "key": "api_key", + "label": "Qwen AI Platform API Key", + "placeholder": null, + "tooltip": null, + "required": true, + "field_type": "password", + "options": null, + "default_value": null + }, + { + "key": "api_base", + "label": "API Base", + "placeholder": "https://dashscope.aliyuncs.com/compatible-mode/v1", + "tooltip": "The base URL for Qwen AI Platform. Defaults to https://dashscope.aliyuncs.com/compatible-mode/v1 if not specified.", + "required": true, + "field_type": "text", + "options": null, + "default_value": "https://dashscope.aliyuncs.com/compatible-mode/v1" + } + ], + "default_model_placeholder": "gpt-3.5-turbo" + }, { "provider": "Databricks", "provider_display_name": "Databricks", @@ -1318,6 +1374,68 @@ ], "default_model_placeholder": "gpt-3.5-turbo" }, + { + "provider": "GIGACHAT", + "provider_display_name": "GigaChat", + "litellm_provider": "gigachat", + "credential_fields": [ + { + "key": "api_base", + "label": "API Base", + "placeholder": null, + "tooltip": null, + "required": false, + "field_type": "text", + "options": null, + "default_value": null + }, + { + "key": "api_key", + "label": "API Key", + "placeholder": null, + "tooltip": null, + "required": false, + "field_type": "password", + "options": null, + "default_value": null + }, + { + "key": "gigachat_scope", + "label": "Scope", + "placeholder": null, + "tooltip": null, + "required": false, + "field_type": "select", + "options": [ + "GIGACHAT_API_PERS", + "GIGACHAT_API_B2B", + "GIGACHAT_API_CORP" + ], + "default_value": "GIGACHAT_API_PERS" + }, + { + "key": "gigachat_auth_url", + "label": "Auth URL", + "placeholder": null, + "tooltip": null, + "required": false, + "field_type": "text", + "options": null, + "default_value": null + }, + { + "key": "gigachat_access_token", + "label": "Access token", + "placeholder": null, + "tooltip": "Disable OAuth, provide value to authorization.", + "required": false, + "field_type": "password", + "options": null, + "default_value": null + } + ], + "default_model_placeholder": "GigaChat-2" + }, { "provider": "GITHUB", "provider_display_name": "Github", diff --git a/litellm/proxy/rag_endpoints/endpoints.py b/litellm/proxy/rag_endpoints/endpoints.py index 4d62f1d6d71..db574f859b3 100644 --- a/litellm/proxy/rag_endpoints/endpoints.py +++ b/litellm/proxy/rag_endpoints/endpoints.py @@ -7,12 +7,14 @@ Provides: """ import base64 +import json from collections.abc import Mapping from typing import TYPE_CHECKING, Any, Final import orjson from fastapi import APIRouter, Depends, HTTPException, Request, Response, status from fastapi.responses import ORJSONResponse, StreamingResponse +from starlette.datastructures import UploadFile import litellm from litellm._logging import verbose_proxy_logger @@ -45,6 +47,16 @@ if TYPE_CHECKING: router: Final = APIRouter() +def _as_string_keyed_mapping(value: object) -> Mapping[str, object] | None: + if isinstance(value, Mapping): + return value + return None + + +def _response_attr(source: object, name: str) -> object: + return getattr(source, name, None) + + def _raise_vector_store_scan_depth_exceeded() -> None: raise HTTPException( status_code=400, @@ -53,8 +65,8 @@ def _raise_vector_store_scan_depth_exceeded() -> None: def _append_payload_to_scan_stack( - payload_stack: list[tuple[Any, int]], - value: Any, + payload_stack: list[tuple[object, int]], + value: object, next_depth: int, ) -> None: if isinstance(value, dict): @@ -117,7 +129,7 @@ async def _authorize_nested_vector_store_ids( def _build_file_metadata_entry( - response: Any, + response: object, file_data: tuple[str, bytes, str] | None = None, file_url: str | None = None, ) -> Mapping[str, str | int | None]: @@ -135,11 +147,11 @@ def _build_file_metadata_entry( from datetime import datetime, timezone # Extract file_id from response - file_id = None - if hasattr(response, "get"): - file_id = response.get("file_id") - elif hasattr(response, "file_id"): - file_id = response.file_id + mapping_response: Final = _as_string_keyed_mapping(response) + raw_file_id: Final = ( + mapping_response.get("file_id") if mapping_response is not None else _response_attr(response, "file_id") + ) + file_id: Final = raw_file_id if isinstance(raw_file_id, str) else None # Extract file information from file_data tuple filename = None @@ -152,7 +164,7 @@ def _build_file_metadata_entry( content_type = file_data[2] if len(file_data) > 2 else None # Build file metadata entry - file_entry: Final = { + file_entry: Final[dict[str, str | int | None]] = { "file_id": file_id, "filename": filename, "file_url": file_url, @@ -169,7 +181,7 @@ def _build_file_metadata_entry( async def _save_vector_store_to_db_from_rag_ingest( - response: Any, + response: object, ingest_options: Mapping[str, dict[str, str | None]], prisma_client: "PrismaClient", user_api_key_dict: UserAPIKeyAuth, @@ -197,10 +209,11 @@ async def _save_vector_store_to_db_from_rag_ingest( ) # Handle both dict and object responses - if hasattr(response, "get"): - vector_store_id = response.get("vector_store_id") + mapping_response: Final = _as_string_keyed_mapping(response) + if mapping_response is not None: + vector_store_id = mapping_response.get("vector_store_id") elif hasattr(response, "vector_store_id"): - vector_store_id = response.vector_store_id + vector_store_id = _response_attr(response, "vector_store_id") else: verbose_proxy_logger.warning("Unable to extract vector_store_id from response type: %s", type(response)) return @@ -266,14 +279,13 @@ async def _save_vector_store_to_db_from_rag_ingest( verbose_proxy_logger.info("Vector store %s already exists, appending file to metadata", vector_store_id) # Update existing vector store with new file - existing_metadata = existing_vector_store.vector_store_metadata or {} - if isinstance(existing_metadata, str): - import json + stored_metadata: Final = existing_vector_store.vector_store_metadata or {} + existing_metadata: dict[str, object] = ( + json.loads(stored_metadata) if isinstance(stored_metadata, str) else stored_metadata + ) - existing_metadata = json.loads(existing_metadata) - - ingested_files: Final = existing_metadata.get("ingested_files", []) - ingested_files.append(file_entry) + previous_files: Final = existing_metadata.get("ingested_files", []) + ingested_files: Final = [*previous_files, file_entry] if isinstance(previous_files, list) else [file_entry] existing_metadata["ingested_files"] = ingested_files # Update the vector store @@ -340,9 +352,9 @@ async def parse_rag_ingest_request( # Get file file_obj = form_data.get("file") - if file_obj is not None and hasattr(file_obj, "read"): + if isinstance(file_obj, UploadFile): file_content = await file_obj.read(MAX_UPLOAD_SIZE_BYTES + 1) - file_data = (file_obj.filename, file_content, file_obj.content_type) + file_data = (file_obj.filename or "", file_content, file_obj.content_type or "") # Parse JSON from 'request' form field (contains full request body as JSON) request_json_str: Final[str | bytes | None] = form_data.get("request") diff --git a/litellm/proxy/response_api_endpoints/endpoints.py b/litellm/proxy/response_api_endpoints/endpoints.py index 5e56e822484..5907ffc64eb 100644 --- a/litellm/proxy/response_api_endpoints/endpoints.py +++ b/litellm/proxy/response_api_endpoints/endpoints.py @@ -1,14 +1,18 @@ import asyncio import json import time -from collections.abc import AsyncIterator, Mapping +from collections.abc import AsyncIterator, Awaitable, Mapping +from enum import Enum from types import MappingProxyType -from typing import TYPE_CHECKING, Any, Final, NamedTuple, cast, get_args +from typing import TYPE_CHECKING, Any, Final, NamedTuple, Protocol, cast, get_args from uuid import uuid4 import fastapi from fastapi import APIRouter, Depends, HTTPException, Request, Response +from fastapi.responses import JSONResponse +from openai.types.responses.response_create_params import ResponseInputParam from starlette.websockets import WebSocket, WebSocketDisconnect +from typing_extensions import ReadOnly, TypedDict from litellm._logging import verbose_proxy_logger from litellm.integrations.custom_guardrail import ModifyResponseException @@ -26,8 +30,13 @@ from litellm.proxy.common_utils.http_parsing_utils import ( _read_request_body, _safe_set_request_parsed_body, ) -from litellm.types.llms.openai import REASONING_EFFORT, ResponsesAPIResponse +from litellm.types.llms.openai import ( + REASONING_EFFORT, + ResponsesAPIOptionalRequestParams, + ResponsesAPIResponse, +) from litellm.types.responses.main import DeleteResponseResult +from litellm.types.utils import TokenCountResponse if TYPE_CHECKING: from litellm.router import Router @@ -35,7 +44,7 @@ if TYPE_CHECKING: router: Final = APIRouter() _user_api_key_auth_dep: Final = Depends(user_api_key_auth) -_RESPONSES_TAGS: Final = ["responses"] # mutable-ok: fastapi's route signature requires List[str] tags +_RESPONSES_TAGS: Final[list[str | Enum]] = ["responses"] # mutable-ok: fastapi's route signature requires list tags _TOOL_PAYLOAD_KEYS: Final[Mapping[str, tuple[str, ...]]] = MappingProxyType( { @@ -43,7 +52,7 @@ _TOOL_PAYLOAD_KEYS: Final[Mapping[str, tuple[str, ...]]] = MappingProxyType( "function": ("name", "description", "parameters", "strict"), } ) -_EMPTY_TOOL_PAYLOAD: Final[Mapping[str, Any]] = MappingProxyType({}) +_EMPTY_TOOL_PAYLOAD: Final[Mapping[str, object]] = MappingProxyType({}) def _convert_tool_payload_value(key: str, value: object, *, to_chat: bool) -> object: @@ -96,7 +105,7 @@ def _normalize_tool_dialect( return {**data, **{key: value for key, value in replaceable if key in data}} # mutable-ok: plain body dict -def _is_chat_completions_body(data: Mapping[str, Any]) -> bool: +def _is_chat_completions_body(data: Mapping[str, object]) -> bool: messages: Final = data.get("messages") if isinstance(messages, list) and messages: return True @@ -1017,6 +1026,152 @@ async def compact_response( ) +class _ResponsesApiErrorDetail(TypedDict): + message: ReadOnly[str] + type: ReadOnly[str] + param: ReadOnly[str | None] + code: ReadOnly[str | None] + + +class _ResponsesApiErrorBody(TypedDict): + error: ReadOnly[_ResponsesApiErrorDetail] + + +class _ResponsesInputTokensResult(TypedDict): + object: ReadOnly[str] + input_tokens: ReadOnly[int] + + +class _TokenCountPayload(TypedDict): + model: ReadOnly[str] + messages: ReadOnly[tuple[Mapping[str, object], ...]] + tools: ReadOnly[object] + + +class _TokenCounter(Protocol): + def __call__(self, request: TokenCountRequest, call_endpoint: bool) -> Awaitable[TokenCountResponse]: ... + + +def _proxy_token_counter() -> _TokenCounter: + from litellm.proxy.proxy_server import token_counter + + return token_counter + + +_token_counter_dep: Final = Depends(_proxy_token_counter) + + +def _responses_invalid_request_response(message: str, param: str | None, code: str | None) -> JSONResponse: + body: Final[_ResponsesApiErrorBody] = { + "error": { + "message": message, + "type": "invalid_request_error", + "param": param, + "code": code, + } + } + return JSONResponse(status_code=400, content=body) + + +def _missing_responses_param_response(param: str) -> JSONResponse: + return _responses_invalid_request_response( + message=f"Missing required parameter: '{param}'.", + param=param, + code="missing_required_parameter", + ) + + +def _responses_input_as_token_count_messages( + input_value: str | ResponseInputParam, + instructions: str | None, +) -> tuple[Mapping[str, object], ...]: + from litellm.responses.litellm_completion_transformation.transformation import ( + LiteLLMCompletionResponsesConfig, + ) + + request_params: Final[ResponsesAPIOptionalRequestParams] = {"instructions": instructions} + transformed: Final = LiteLLMCompletionResponsesConfig.transform_responses_api_input_to_messages( + input=input_value, + responses_api_request=request_params, + ) + return tuple( + message if isinstance(message, dict) else message.model_dump(exclude_none=True) for message in transformed + ) + + +@router.post( + "/v1/responses/input_tokens", + dependencies=(_user_api_key_auth_dep,), + tags=_RESPONSES_TAGS, +) +@router.post( + "/responses/input_tokens", + dependencies=(_user_api_key_auth_dep,), + tags=_RESPONSES_TAGS, +) +@router.post( + "/openai/v1/responses/input_tokens", + dependencies=(_user_api_key_auth_dep,), + tags=_RESPONSES_TAGS, +) +async def responses_input_tokens( + request: Request, + token_counter: _TokenCounter = _token_counter_dep, +): + """ + Count the input tokens of a Responses API request without calling the model. + + Follows the OpenAI Responses API spec: https://platform.openai.com/docs/api-reference/responses/input-tokens + + ```bash + curl -X POST http://localhost:4000/v1/responses/input_tokens \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "gpt-4o", + "input": "Hello, how are you?" + }' + ``` + + Returns: `{"object": "response.input_tokens", "input_tokens": }` + """ + data: Final = await _read_request_body(request=request) + model_name: Final = data.get("model") + input_value: Final = data.get("input") + if not isinstance(model_name, str) or not model_name: + return _missing_responses_param_response("model") + if input_value is None: + return _missing_responses_param_response("input") + if isinstance(input_value, (str, list)) and not input_value: + return _responses_invalid_request_response( + message="""One of "input" or "previous_response_id" or 'prompt' or 'conversation' must be provided.""", + param=None, + code="missing_required_parameter", + ) + + try: + payload: Final[_TokenCountPayload] = { + "model": model_name, + "messages": _responses_input_as_token_count_messages( + input_value=input_value, + instructions=data.get("instructions"), + ), + "tools": data.get("tools"), + } + token_request: Final = TokenCountRequest.model_validate(payload) + except Exception as e: + return _responses_invalid_request_response( + message=f"Invalid request for token counting: {e}", param=None, code=None + ) + + token_response: Final = await token_counter(request=token_request, call_endpoint=True) + result: Final[_ResponsesInputTokensResult] = { + "object": "response.input_tokens", + "input_tokens": token_response.total_tokens, + } + return result + + @router.post( "/v1/responses/{response_id}/cancel", dependencies=[Depends(user_api_key_auth)], @@ -1218,7 +1373,7 @@ async def _enforce_responses_ws_first_frame_model_auth( request: Request, model: str, user_api_key_dict: UserAPIKeyAuth, - llm_router: Any | None, + llm_router: "Router | None", ) -> None: from litellm.proxy.auth.user_api_key_auth import ( _enforce_key_and_fallback_model_access, @@ -1262,7 +1417,7 @@ async def _enforce_responses_ws_first_frame_model_auth( async def responses_websocket_endpoint( websocket: WebSocket, model: str | None = fastapi.Query(None, description="The model to use for the responses WebSocket session."), - user_api_key_dict=Depends(user_api_key_auth_websocket), + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth_websocket), ): """ Responses API WebSocket mode endpoint. @@ -1307,7 +1462,7 @@ async def responses_websocket_endpoint( return model, first_message = result - data: dict[str, Any] = { + data: dict[str, object] = { "model": model, "websocket": websocket, } @@ -1316,7 +1471,7 @@ async def responses_websocket_endpoint( # Construct a synthetic Request for pre-call processing headers_list: Final = list(websocket.scope.get("headers") or []) - scope: Final[dict[str, Any]] = { + scope: Final[dict[str, object]] = { "type": "http", "method": "POST", "path": "/v1/responses", diff --git a/litellm/proxy/response_polling/background_streaming.py b/litellm/proxy/response_polling/background_streaming.py index 020698dabd9..0fd242f2bc1 100644 --- a/litellm/proxy/response_polling/background_streaming.py +++ b/litellm/proxy/response_polling/background_streaming.py @@ -10,12 +10,12 @@ https://platform.openai.com/docs/api-reference/responses-streaming import asyncio import json -from collections.abc import Sequence -from typing import TYPE_CHECKING, Final, TypedDict, cast +from collections.abc import Callable, Mapping, Sequence +from typing import TYPE_CHECKING, Final, TypeAlias from fastapi import Request, Response from fastapi.responses import StreamingResponse -from typing_extensions import ReadOnly +from typing_extensions import ReadOnly, TypedDict from litellm._logging import verbose_proxy_logger from litellm.proxy.auth.user_api_key_auth import UserAPIKeyAuth @@ -29,28 +29,64 @@ if TYPE_CHECKING: from litellm.router import Router -class _StreamContentPart(TypedDict, total=False): - text: ReadOnly[str] +_JsonDict: TypeAlias = dict[str, object] +_JsonList: TypeAlias = list[object] -class _StreamOutputItem(TypedDict, total=False): +class _OutputItem(TypedDict, total=False): id: ReadOnly[str] - content: ReadOnly[Sequence[_StreamContentPart | None]] + content: ReadOnly[Sequence[object]] + + +class _TerminalResponse(TypedDict, total=False): + status: ReadOnly[ResponsesAPIStatus] + error: ReadOnly[_JsonDict] + usage: ReadOnly[_JsonDict] + reasoning: ReadOnly[_JsonDict] + tool_choice: ReadOnly[object] + tools: ReadOnly[_JsonList] + model: ReadOnly[str] + instructions: ReadOnly[str] + temperature: ReadOnly[float] + top_p: ReadOnly[float] + max_output_tokens: ReadOnly[int] + previous_response_id: ReadOnly[str] + text: ReadOnly[_JsonDict] + truncation: ReadOnly[str] + parallel_tool_calls: ReadOnly[bool] + user: ReadOnly[str] + store: ReadOnly[bool] + incomplete_details: ReadOnly[_JsonDict] + output: ReadOnly[Sequence[_OutputItem]] + + +class _StreamEvent(TypedDict, total=False): + type: ReadOnly[str] + item: ReadOnly[_OutputItem] + item_id: ReadOnly[str] + content_index: ReadOnly[int] + delta: ReadOnly[str] + part: ReadOnly[object] + response: ReadOnly[_TerminalResponse] + + +class _StreamEventParser: + parse: Callable[[str], _StreamEvent] = staticmethod(json.loads) async def background_streaming_task( polling_id: str, - data, + data: dict[str, object], polling_handler: ResponsePollingHandler, request: Request, fastapi_response: Response, user_api_key_dict: UserAPIKeyAuth, - general_settings, + general_settings: dict[str, object], llm_router: "Router | None", proxy_config: "ProxyConfig", proxy_logging_obj: "ProxyLogging", - select_data_generator, - user_model, + select_data_generator: Callable[..., object] | None, + user_model: str | None, user_temperature: float | None, user_request_timeout: float | None, user_max_tokens: int | None, @@ -108,9 +144,8 @@ async def background_streaming_task( # Process streaming response following OpenAI events format # https://platform.openai.com/docs/api-reference/responses-streaming - output_items: Final[dict[str, _StreamOutputItem]] = {} # Track output items by ID - # Track accumulated text deltas by (item_id, content_index) - accumulated_text: Final[dict[tuple[str, int], str]] = {} + output_items: Final = dict[str, _OutputItem]() + accumulated_text: Final = dict[tuple[str, int], str]() # ResponsesAPIResponse fields to extract from response.completed usage_data = None @@ -139,7 +174,7 @@ async def background_streaming_task( None # Will be set by response.completed/failed/incomplete/cancelled ) terminal_error = None - _event_to_status: Final = { + _event_to_status: Final[Mapping[str, ResponsesAPIStatus]] = { "response.completed": "completed", "response.failed": "failed", "response.incomplete": "incomplete", @@ -180,7 +215,7 @@ async def background_streaming_task( break try: - event = json.loads(chunk_data) + event: _StreamEvent = _StreamEventParser.parse(chunk_data) event_type = event.get("type", "") # Process different event types based on OpenAI streaming spec @@ -199,19 +234,18 @@ async def background_streaming_task( if item_id and item_id in output_items: # Update the output item with new content - current_item = output_items[item_id] - appended_item: _StreamOutputItem = { - **current_item, - "content": (*current_item.get("content", ()), content_part), + added_item = output_items[item_id] + output_items[item_id] = { + **added_item, + "content": (*added_item.get("content", ()), content_part), } - output_items[item_id] = appended_item state_dirty = True elif event_type == "response.output_text.delta": # Text delta - accumulate text content # https://platform.openai.com/docs/api-reference/responses-streaming/response-text-delta item_id = event.get("item_id") - content_index: int = event.get("content_index", 0) + content_index = event.get("content_index", 0) delta = event.get("delta", "") if item_id and item_id in output_items: @@ -222,24 +256,13 @@ async def background_streaming_task( accumulated_text[key] += delta # Update the content in output_items - current_item = output_items[item_id] - content_list: Sequence[_StreamContentPart | None] = current_item.get("content", ()) - if content_index < len(content_list): - # Update existing content part with accumulated text - content_entry = content_list[content_index] - if isinstance(content_entry, dict): - delta_part: _StreamContentPart = { - **content_entry, - "text": accumulated_text[key], - } - delta_item: _StreamOutputItem = { - **current_item, - "content": tuple( - delta_part if index == content_index else entry - for index, entry in enumerate(content_list) - ), - } - output_items[item_id] = delta_item + delta_item = output_items[item_id] + if "content" in delta_item: + content_list = delta_item["content"] + if content_index < len(content_list): + content_entry = content_list[content_index] + if isinstance(content_entry, dict): + content_entry["text"] = accumulated_text[key] state_dirty = True elif event_type == "response.content_part.done": @@ -250,17 +273,17 @@ async def background_streaming_task( if item_id and item_id in output_items: # Update with final content from event - current_item = output_items[item_id] - content_list = current_item.get("content", ()) - if content_index < len(content_list): - finalized_item: _StreamOutputItem = { - **current_item, - "content": tuple( - content_part if index == content_index else entry - for index, entry in enumerate(content_list) - ), - } - output_items[item_id] = finalized_item + done_item = output_items[item_id] + if "content" in done_item: + content_list = done_item["content"] + if content_index < len(content_list): + output_items[item_id] = { + **done_item, + "content": tuple( + content_part if part_index == content_index else existing_part + for part_index, existing_part in enumerate(content_list) + ), + } state_dirty = True elif event_type == "response.output_item.done": @@ -288,12 +311,9 @@ async def background_streaming_task( # Terminal event - extract all ResponsesAPIResponse fields # https://platform.openai.com/docs/api-reference/responses-streaming response_data = event.get("response", {}) - terminal_status = cast( - ResponsesAPIStatus, - response_data.get( - "status", - _event_to_status.get(event_type, "completed"), - ), + terminal_status = response_data.get( + "status", + _event_to_status.get(event_type, "completed"), ) # Extract error for failed and incomplete responses diff --git a/litellm/proxy/route_llm_request.py b/litellm/proxy/route_llm_request.py index 91a0c68fd58..3d0bd5e61c9 100644 --- a/litellm/proxy/route_llm_request.py +++ b/litellm/proxy/route_llm_request.py @@ -50,12 +50,12 @@ def _route_user_config_request(data: dict, route_type: str): return ret_val -def _is_a2a_agent_model(model_name: Any) -> bool: +def _is_a2a_agent_model(model_name: object) -> bool: """Check if the model name is for an A2A agent (a2a/ prefix).""" return isinstance(model_name, str) and model_name.startswith("a2a/") -def _raise_if_model_fully_blocked(llm_router: LitellmRouter, model_name: Any, team_id: str | None) -> None: +def _raise_if_model_fully_blocked(llm_router: LitellmRouter, model_name: object, team_id: str | None) -> None: if not isinstance(model_name, str) or not model_name: return if not isinstance(llm_router, litellm.Router): diff --git a/litellm/proxy/schema.prisma b/litellm/proxy/schema.prisma index 2bb850139a2..7604ceadf7a 100644 --- a/litellm/proxy/schema.prisma +++ b/litellm/proxy/schema.prisma @@ -29,6 +29,7 @@ model LiteLLM_BudgetTable { keys LiteLLM_VerificationToken[] // multiple keys can have the same budget end_users LiteLLM_EndUserTable[] // multiple end-users can have the same budget tags LiteLLM_TagTable[] // multiple tags can have the same budget + model_access_groups LiteLLM_ModelAccessGroupBudgetTable[] // multiple model access groups can have the same budget team_membership LiteLLM_TeamMembership[] // budgets of Users within a Team organization_membership LiteLLM_OrganizationMembership[] // budgets of Users within a Organization } @@ -585,6 +586,20 @@ model LiteLLM_EndUserTable { blocked Boolean @default(false) } +// Budget and shared spend for a model access group. The groups themselves are not rows anywhere: +// they are free-text strings in LiteLLM_ProxyModelTable.model_info.access_groups, so a row here +// exists only once someone gives that group a budget. +model LiteLLM_ModelAccessGroupBudgetTable { + access_group_name String @id + spend Float @default(0.0) + budget_id String? + litellm_budget_table LiteLLM_BudgetTable? @relation(fields: [budget_id], references: [budget_id]) + created_at DateTime @default(now()) @map("created_at") + created_by String? + updated_at DateTime @default(now()) @updatedAt + updated_by String? +} + // Track tags with budgets and spend model LiteLLM_TagTable { tag_name String @id @@ -649,6 +664,18 @@ model LiteLLM_SpendLogs { @@index([session_id]) } +model LiteLLM_BudgetWindowSpend { + entity_type String + entity_id String + window_duration String + window_start DateTime + spend Float @default(0.0) + created_at DateTime @default(now()) + updated_at DateTime @updatedAt + + @@id([entity_type, entity_id, window_duration]) +} + // View spend, model, api_key per request model LiteLLM_ErrorLogs { request_id String @id @default(uuid()) @@ -1502,14 +1529,16 @@ model LiteLLM_AutoRouterSession { model LiteLLM_ShadowEvalJob { id String @id @default(cuid()) group_id String // legs of one job share this; the API's job id - api_key_id String // hashed virtual key whose traffic this leg shadows - router_name String // the auto-router under evaluation, in either direction + target_type String @default("key") // key | team | user + target_id String // hashed virtual key, team_id, or user_id whose traffic this leg shadows + router_name String // first (often only) auto-router under evaluation; router_names is the full set + router_names String[] @default([]) // all routers this job runs as shadow arms; empty on legacy rows, whose set is (router_name) direction String @default("forward") // forward | reverse baseline_model String? // reverse only: the fixed model the router is judged against judge_model String shadow_percentage Float max_turns Int // sample-count ceiling: the whole budget on pre-max_budget jobs, the error-loop valve otherwise - max_budget Float? // per-key USD cap on the eval's own shadow + judge spend; null on jobs from before spend budgets + max_budget Float? // per-target USD cap on the eval's own shadow + judge spend; null on jobs from before spend budgets created_at DateTime @default(now()) created_by String? ends_at DateTime @@ -1517,7 +1546,7 @@ model LiteLLM_ShadowEvalJob { stopped_by String? // operator who stopped it early; null when it ended on its own @@index([group_id]) - @@index([api_key_id]) + @@index([target_type, target_id]) @@index([created_at]) } @@ -1527,6 +1556,7 @@ model LiteLLM_ShadowEvalAttempt { job_id String request_id String // the judged real request outcome String // real | shadow | tie | error + router_name String? // the arm this verdict scores; NULL on legacy rows, meaning the job's own router tier String? // router's tier for the prompt, when classified real_model String? shadow_model String? diff --git a/litellm/proxy/search_endpoints/search_tool_registry.py b/litellm/proxy/search_endpoints/search_tool_registry.py index fe4794f3ba1..b25263e4c64 100644 --- a/litellm/proxy/search_endpoints/search_tool_registry.py +++ b/litellm/proxy/search_endpoints/search_tool_registry.py @@ -2,8 +2,9 @@ Search Tool Registry for managing search tool configurations. """ +from collections.abc import Iterator, Mapping, Sequence from datetime import datetime, timezone -from typing import Final +from typing import Final, Protocol from litellm._logging import verbose_proxy_logger from litellm.litellm_core_utils.safe_json_dumps import safe_dumps @@ -13,6 +14,40 @@ from litellm.repositories.table_repositories import SearchToolsRepository from litellm.types.search import SearchTool +class SearchToolRecord(Protocol): + search_tool_id: str + search_tool_name: str + created_at: datetime + updated_at: datetime + + def __iter__(self) -> Iterator[tuple[str, object]]: ... + + +class SearchToolTableClient(Protocol): + async def create(self, data: Mapping[str, object]) -> SearchToolRecord: ... + + async def find_unique(self, where: Mapping[str, object]) -> SearchToolRecord | None: ... + + async def find_many(self, order: Mapping[str, str] | None = None) -> Sequence[SearchToolRecord]: ... + + async def update(self, where: Mapping[str, object], data: Mapping[str, object]) -> SearchToolRecord: ... + + async def delete(self, where: Mapping[str, object]) -> SearchToolRecord: ... + + +class _SearchToolsRepositoryView(Protocol): + @property + def table(self) -> SearchToolTableClient: ... + + +def _search_tools_table_of(repository: _SearchToolsRepositoryView) -> SearchToolTableClient: + return repository.table + + +def _search_tools_table(prisma_client: PrismaClient) -> SearchToolTableClient: + return _search_tools_table_of(SearchToolsRepository(prisma_client)) + + class SearchToolRegistry: """ Handles adding, removing, and getting search tools in DB + in memory. @@ -22,7 +57,7 @@ class SearchToolRegistry: pass @staticmethod - def _convert_prisma_to_dict(prisma_obj) -> dict: + def _convert_prisma_to_dict(prisma_obj: SearchToolRecord) -> dict: """ Convert Prisma result to dict with datetime objects as ISO format strings. @@ -35,9 +70,9 @@ class SearchToolRegistry: result: Final = dict(prisma_obj) # Convert datetime objects to ISO format strings if "created_at" in result and result["created_at"]: - result["created_at"] = result["created_at"].isoformat() + result["created_at"] = prisma_obj.created_at.isoformat() if "updated_at" in result and result["updated_at"]: - result["updated_at"] = result["updated_at"].isoformat() + result["updated_at"] = prisma_obj.updated_at.isoformat() return result ########################################################### @@ -61,7 +96,7 @@ class SearchToolRegistry: search_tool_info: Final[str] = safe_dumps(search_tool.get("search_tool_info", {})) # Create search tool in DB - created_search_tool: Final = await SearchToolsRepository(prisma_client).table.create( + created_search_tool: Final = await _search_tools_table(prisma_client).create( data={ "search_tool_name": search_tool_name, "litellm_params": litellm_params, @@ -95,7 +130,7 @@ class SearchToolRegistry: """ try: # Get search tool before deletion for response - existing_tool: Final = await SearchToolsRepository(prisma_client).table.find_unique( + existing_tool: Final = await _search_tools_table(prisma_client).find_unique( where={"search_tool_id": search_tool_id} ) @@ -103,7 +138,7 @@ class SearchToolRegistry: raise Exception(f"Search tool with ID {search_tool_id} not found") # Delete from DB - await SearchToolsRepository(prisma_client).table.delete(where={"search_tool_id": search_tool_id}) + await _search_tools_table(prisma_client).delete(where={"search_tool_id": search_tool_id}) return { "message": f"Search tool {search_tool_id} deleted successfully", @@ -131,7 +166,7 @@ class SearchToolRegistry: search_tool_info: Final[str] = safe_dumps(search_tool.get("search_tool_info", {})) # Update in DB - updated_search_tool: Final = await SearchToolsRepository(prisma_client).table.update( + updated_search_tool: Final = await _search_tools_table(prisma_client).update( where={"search_tool_id": search_tool_id}, data={ "search_tool_name": search_tool_name, @@ -163,7 +198,7 @@ class SearchToolRegistry: try: search_tools_from_db: Final = await call_with_db_reconnect_retry( prisma_client, - lambda: SearchToolsRepository(prisma_client).table.find_many( + lambda: _search_tools_table(prisma_client).find_many( order={"created_at": "desc"}, ), reason="get_all_search_tools_from_db_lookup_failure", @@ -194,7 +229,7 @@ class SearchToolRegistry: Search tool configuration or None if not found """ try: - search_tool: Final = await SearchToolsRepository(prisma_client).table.find_unique( + search_tool: Final = await _search_tools_table(prisma_client).find_unique( where={"search_tool_id": search_tool_id} ) @@ -222,7 +257,7 @@ class SearchToolRegistry: Search tool configuration or None if not found """ try: - search_tool: Final = await SearchToolsRepository(prisma_client).table.find_unique( + search_tool: Final = await _search_tools_table(prisma_client).find_unique( where={"search_tool_name": search_tool_name} ) diff --git a/litellm/proxy/spend_tracking/budget_reservation.py b/litellm/proxy/spend_tracking/budget_reservation.py index 7cad3f0a022..91d2ece7a51 100644 --- a/litellm/proxy/spend_tracking/budget_reservation.py +++ b/litellm/proxy/spend_tracking/budget_reservation.py @@ -6,13 +6,12 @@ from collections.abc import Mapping, Sequence from dataclasses import dataclass from datetime import datetime, timedelta, timezone from types import MappingProxyType -from typing import Any, Final, NoReturn, cast +from typing import Final, NoReturn, SupportsFloat, SupportsIndex, SupportsInt, cast from fastapi import HTTPException, status import litellm from litellm._logging import verbose_proxy_logger -from litellm.caching import DualCache from litellm.litellm_core_utils.duration_parser import duration_in_seconds from litellm.litellm_core_utils.llm_cost_calc.tiered_pricing import select_tier_for_input, tier_rate from litellm.proxy._types import ( @@ -26,12 +25,17 @@ from litellm.proxy.auth.auth_utils import get_model_from_request from litellm.proxy.auth.budget_throttle import should_throttle_budget_exceeded from litellm.proxy.auth.route_checks import RouteChecks from litellm.proxy.common_utils.user_api_key_cache import ( + UserApiKeyCache, end_user_cache_key, + model_access_group_cache_key, + model_access_group_spend_counter_key, tag_cache_key, team_membership_reservation_cache_key, ) from litellm.proxy.utils import PrismaClient, ProxyLogging from litellm.router import Router +from litellm.types.proxy.model_access_group_budget import ModelAccessGroupBudget +from litellm.types.router import DeploymentTypedDict @dataclass @@ -43,6 +47,7 @@ class _BudgetCounter: entity_id: str source_cache_key: str | None = None spend_log_entity_id: str | None = None + window_duration: str | None = None window_start: datetime | None = None @@ -53,6 +58,7 @@ _COUNTER_ENTITY_TYPES: Final[Mapping[str, str]] = { "User": Litellm_EntityType.USER.value, "EndUser": Litellm_EntityType.END_USER.value, "Tag": Litellm_EntityType.TAG.value, + "Model access group": Litellm_EntityType.MODEL_ACCESS_GROUP.value, "Organization": Litellm_EntityType.ORGANIZATION.value, } @@ -114,13 +120,15 @@ async def _apply_over_budget_reservation_policy( applied_entries: list[dict[str, float | str]], reservation_cost: float, current_spend: float, + fail_closed_budget_enforcement: bool = False, ) -> float: """ Decide what to do when a counter is over budget, and return the reservation cost to carry into the next counter. Three outcomes: an over-budget key that opted into throttling releases its own reservation (the rate limiter slows it) and keeps the cost; a partially-remaining budget resizes the reservation - down to what is left; anything else hard-blocks by raising. + down to what is left, unless strict enforcement is on, because the known + estimate already does not fit; anything else hard-blocks by raising. """ if _key_reservation_should_release_for_throttle(counter.counter_key, valid_token): await _release_applied_entries_best_effort(entries=[entry], default_reserved_cost=reservation_cost) @@ -128,21 +136,36 @@ async def _apply_over_budget_reservation_policy( return reservation_cost remaining_before_reservation: Final = counter.max_budget - (current_spend - reservation_cost) - if remaining_before_reservation > 1e-12: - await _resize_applied_reservation( - entries=applied_entries, - current_reserved_cost=reservation_cost, - new_reserved_cost=remaining_before_reservation, + if remaining_before_reservation <= 1e-12: + _raise_counter_budget_exceeded(counter=counter, current_cost=current_spend) + if fail_closed_budget_enforcement and current_spend - counter.max_budget > 1e-12: + _raise_counter_budget_exceeded( + counter=counter, + current_cost=current_spend - reservation_cost, + estimated_cost=reservation_cost, ) - return remaining_before_reservation + await _resize_applied_reservation( + entries=applied_entries, + current_reserved_cost=reservation_cost, + new_reserved_cost=remaining_before_reservation, + ) + return remaining_before_reservation + +def _raise_counter_budget_exceeded( + counter: _BudgetCounter, + current_cost: float, + estimated_cost: float | None = None, +) -> NoReturn: + estimate_detail: Final = "" if estimated_cost is None else f"Estimated request cost: {estimated_cost}, " raise litellm.BudgetExceededError( - current_cost=current_spend, + current_cost=current_cost, max_budget=counter.max_budget, message=( "Budget has been exceeded! " f"{counter.entity_type}={counter.entity_id} " - f"Current cost: {current_spend}, " + f"Current cost: {current_cost}, " + f"{estimate_detail}" f"Max budget: {counter.max_budget}" ), entity_type=_COUNTER_ENTITY_TYPES.get(counter.entity_type), @@ -158,7 +181,7 @@ async def reserve_budget_for_request( team_object: LiteLLM_TeamTable | None, user_object: LiteLLM_UserTable | None, prisma_client: PrismaClient | None, - user_api_key_cache: DualCache, + user_api_key_cache: UserApiKeyCache, proxy_logging_obj: ProxyLogging, end_user_id: str | None = None, end_user_object: object = None, @@ -167,7 +190,14 @@ async def reserve_budget_for_request( ) -> dict | None: if valid_token is None or not RouteChecks.is_llm_api_route(route=route): return None - if route in {"/models", "/v1/models", "/utils/token_counter"}: + if route in { + "/models", + "/v1/models", + "/utils/token_counter", + "/responses/input_tokens", + "/v1/responses/input_tokens", + "/openai/v1/responses/input_tokens", + }: return None if get_model_from_request(request_body, route, llm_router=llm_router) is None: return None @@ -245,6 +275,7 @@ async def reserve_budget_for_request( applied_entries=applied_entries, reservation_cost=reservation_cost, current_spend=current_spend, + fail_closed_budget_enforcement=fail_closed_budget_enforcement, ) continue except Exception: @@ -348,7 +379,7 @@ async def _get_budget_counters( team_object: LiteLLM_TeamTable | None, user_object: LiteLLM_UserTable | None, prisma_client: PrismaClient | None, - user_api_key_cache: DualCache, + user_api_key_cache: UserApiKeyCache, proxy_logging_obj: ProxyLogging, end_user_id: str | None = None, end_user_object: object = None, @@ -437,6 +468,14 @@ async def _get_budget_counters( ) ) + counters.extend( + await _get_model_access_group_budget_counters( + valid_token=valid_token, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + ) + ) + team_member_counter: Final = await _get_team_member_budget_counter( valid_token=valid_token, team_object=team_object, @@ -491,7 +530,7 @@ async def _get_end_user_budget_counter( async def _get_tag_budget_counters( request_body: dict, prisma_client: PrismaClient | None, - user_api_key_cache: DualCache, + user_api_key_cache: UserApiKeyCache, proxy_logging_obj: ProxyLogging, ) -> list[_BudgetCounter]: from litellm.proxy.auth.auth_checks import get_tag_objects_batch @@ -530,6 +569,46 @@ async def _get_tag_budget_counters( return counters +async def _get_model_access_group_budget_counters( + valid_token: UserAPIKeyAuth, + prisma_client: PrismaClient | None, + user_api_key_cache: UserApiKeyCache, +) -> list[_BudgetCounter]: + """Reservation counters for the model access groups that authorized this request. + + The names come off the auth object rather than the request body: ``common_checks`` already + resolved which granted groups serve the requested model, and re-deriving that here would both + duplicate the walk and risk disagreeing with what the spend writer attributes. + """ + from litellm.proxy.auth.auth_checks import get_model_access_group_budgets_batch + + group_names: Final = tuple(dict.fromkeys(valid_token.matched_model_access_groups or ())) + if not group_names: + return [] + + budgets: Final = await get_model_access_group_budgets_batch( + access_group_names=group_names, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + ) + candidates: Final = (_model_access_group_counter(group, budgets.get(group)) for group in group_names) + return [counter for counter in candidates if counter is not None] + + +def _model_access_group_counter(group: str, budget: ModelAccessGroupBudget | None) -> _BudgetCounter | None: + """A counter for one group, or nothing when the group carries no budget to reserve against.""" + if budget is None or budget.max_budget is None or budget.max_budget <= 0: + return None + return _BudgetCounter( + counter_key=model_access_group_spend_counter_key(group), + source_cache_key=model_access_group_cache_key(group), + max_budget=budget.max_budget, + fallback_spend=budget.spend, + entity_type="Model access group", + entity_id=group, + ) + + def _dedupe_tags(tags: list[str]) -> list[str]: seen: Final = set() deduped_tags: Final = [] @@ -545,7 +624,7 @@ async def _get_team_member_budget_counter( valid_token: UserAPIKeyAuth, team_object: LiteLLM_TeamTable | None, user_object: LiteLLM_UserTable | None, - user_api_key_cache: DualCache, + user_api_key_cache: UserApiKeyCache, ) -> _BudgetCounter | None: if team_object is None or team_object.team_id is None or user_object is None or valid_token.user_id is None: return None @@ -588,7 +667,7 @@ async def _get_team_member_budget_counter( async def _get_org_budget_counter( valid_token: UserAPIKeyAuth, team_object: LiteLLM_TeamTable | None, - user_api_key_cache: DualCache, + user_api_key_cache: UserApiKeyCache, ) -> _BudgetCounter | None: org_id: str | None = None if valid_token.org_id is not None: @@ -637,7 +716,7 @@ def _get_budget_limit_counters( for window in budget_limits: window_dict = _coerce_window(window) budget_duration = window_dict.get("budget_duration") - max_budget = window_dict.get("max_budget") + max_budget = _to_float(window_dict.get("max_budget")) if not budget_duration or max_budget is None or max_budget <= 0: continue window_start = get_budget_window_start(window_dict) @@ -657,24 +736,27 @@ def _get_budget_limit_counters( entity_type=entity_type, entity_id=f"{entity_id}:{budget_duration}", spend_log_entity_id=entity_id, + window_duration=str(budget_duration), window_start=window_start, ) ) return counters -def _coerce_window(window: Any) -> dict: - if isinstance(window, dict): +def _coerce_window(window: object) -> Mapping[str, object]: + if isinstance(window, Mapping): return window if isinstance(window, str): try: - parsed: Final = json.loads(window) - return parsed if isinstance(parsed, dict) else {} + parsed: Final[object] = json.loads(window) except Exception: return {} - if hasattr(window, "model_dump"): - return window.model_dump() - return {} + return parsed if isinstance(parsed, Mapping) else {} + model_dump: Final = getattr(window, "model_dump", None) + if not callable(model_dump): + return {} + dumped: Final[object] = model_dump() + return dumped if isinstance(dumped, Mapping) else {} async def _reserve_counter( @@ -700,6 +782,7 @@ async def _reserve_counter( counter_key=counter.counter_key, entity_type=counter.entity_type, entity_id=counter.spend_log_entity_id, + window_duration=counter.window_duration, window_start=counter.window_start, ) if initialized is False: @@ -891,7 +974,7 @@ def _get_entry_reserved_cost(entry: dict, default_reserved_cost: float) -> float return default_reserved_cost -def get_budget_window_start(window: Any) -> datetime | None: +def get_budget_window_start(window: object) -> datetime | None: window_dict: Final = _coerce_window(window) budget_duration: Final = window_dict.get("budget_duration") if budget_duration is None: @@ -909,7 +992,7 @@ def get_budget_window_start(window: Any) -> datetime | None: return reset_at - timedelta(seconds=duration_seconds) -def _coerce_datetime(value: Any) -> datetime | None: +def _coerce_datetime(value: object) -> datetime | None: if value is None: return None if isinstance(value, datetime): @@ -1183,11 +1266,11 @@ def _get_model_cost_infos( def _deployment_tiered_pricing_table( - deployment: dict[str, Any], + deployment: DeploymentTypedDict, llm_router: Router, -) -> list[dict] | None: - model_id: Final = deployment.get("model_info", {}).get("id") - backend_model: Final = deployment.get("litellm_params", {}).get("model") +) -> Sequence[Mapping[str, object]] | None: + model_id: Final = _get_value(_get_value(deployment, "model_info"), "id") + backend_model: Final = _get_value(_get_value(deployment, "litellm_params"), "model") if not isinstance(model_id, str) or not isinstance(backend_model, str): return None deployment_model_info: Final = llm_router.get_deployment_model_info(model_id=model_id, model_name=backend_model) @@ -1352,7 +1435,7 @@ def _estimate_output_tokens( return min(requested, model_ceiling) -def _count_text_tokens(model: str, text: Any) -> int: +def _count_text_tokens(model: str, text: object) -> int: if text is None: return 0 @@ -1392,8 +1475,8 @@ def _is_input_only_route(route: str) -> bool: ) -def _to_float(value: Any) -> float | None: - if value is None: +def _to_float(value: object) -> float | None: + if not isinstance(value, (SupportsFloat, SupportsIndex, str, bytes, bytearray)): return None try: return float(value) @@ -1401,8 +1484,8 @@ def _to_float(value: Any) -> float | None: return None -def _to_int(value: Any) -> int | None: - if value is None: +def _to_int(value: object) -> int | None: + if not isinstance(value, (SupportsInt, SupportsIndex, str, bytes, bytearray)): return None try: return int(value) @@ -1410,7 +1493,7 @@ def _to_int(value: Any) -> int | None: return None -def _get_value(obj: Any, key: str) -> Any: - if isinstance(obj, dict): +def _get_value(obj: object, key: str) -> object: + if isinstance(obj, Mapping): return obj.get(key) return getattr(obj, key, None) diff --git a/litellm/proxy/spend_tracking/savings.py b/litellm/proxy/spend_tracking/savings.py index 7d20aeeebac..1d0eb12da75 100644 --- a/litellm/proxy/spend_tracking/savings.py +++ b/litellm/proxy/spend_tracking/savings.py @@ -481,6 +481,20 @@ def _numeric_savings(value: object) -> float | None: return float(value) +def classifier_cost_from_decision(routing_decision: Mapping[str, object] | None) -> float | None: + """The LLM-classifier cost a routing decision recorded, or ``None`` when it holds none. + + ``None`` covers the decision-less request, the heuristic short-circuit that never + called a classifier, the unpriced classifier model, and a malformed value alike: + in every one of those cases there is no dollar figure to move, so callers treat + ``None`` as zero rather than as an error. The one owner of that reading, shared by + the savings netting, the session rollup and the response header, so the three can + never disagree about what counts as a classifier charge. + """ + decision: Final = routing_decision if isinstance(routing_decision, Mapping) else {} + return _numeric_savings(decision.get("classifier_cost")) + + def autorouter_savings_for_request( model: str | None, custom_llm_provider: str | None, @@ -490,7 +504,8 @@ def autorouter_savings_for_request( llm_router: "Callable[[], Router | None] | None" = None, cost_breakdown: Mapping[str, object] | None = None, ) -> float | None: - """Auto-router savings for one request, or ``None`` when the driver is off. + """Auto-router savings for one request, net of the classifier call that routed it, + or ``None`` when the driver is off. ``None`` and ``0.0`` are different facts: ``None`` means this request cannot carry a figure at all (no routing decision, no baseline, unusable usage), while ``0.0`` is a @@ -498,6 +513,11 @@ def autorouter_savings_for_request( Never raises: pricing failures inside degrade to zero, and the driver-off cases return ``None``, so this is safe on the logging path where a raise would fail the request's logging. + + The classifier deduction lives here, at the figure's one computation owner, rather + than in any reader: the stamped ``autorouter_savings`` is then already net, so the + session rollup, the daily tables and every logging consumer agree without each + re-deriving the deduction, and the recorded-figure-wins path cannot deduct twice. """ usage: Final = _usage_from_spend_log(usage_object) if usage is None or not model: @@ -510,7 +530,7 @@ def autorouter_savings_for_request( if not decision or not baseline_model: return None router_instance: Final = llm_router() if llm_router else None - return compute_autorouter_savings( + gross: Final = compute_autorouter_savings( baseline_model=baseline_model, selected_model=model, selected_provider=custom_llm_provider, @@ -522,6 +542,8 @@ def autorouter_savings_for_request( baseline_info=_effective_model_info(router_instance, baseline_id, baseline_model or ""), cost_breakdown=cost_breakdown, ) + classifier_cost: Final = classifier_cost_from_decision(decision) + return gross if classifier_cost is None else gross - classifier_cost def autorouter_savings_for_logging_payload( diff --git a/litellm/proxy/spend_tracking/spend_tracking_utils.py b/litellm/proxy/spend_tracking/spend_tracking_utils.py index a6b6375fd9c..7442d71bd96 100644 --- a/litellm/proxy/spend_tracking/spend_tracking_utils.py +++ b/litellm/proxy/spend_tracking/spend_tracking_utils.py @@ -1,9 +1,10 @@ import os import re import secrets +from collections.abc import Mapping, Sequence from datetime import datetime, timezone from datetime import datetime as dt -from typing import Any, Final, Literal, cast +from typing import Final, Literal, Protocol, cast, runtime_checkable from pydantic import BaseModel @@ -23,9 +24,13 @@ from litellm.litellm_core_utils.core_helpers import ( reconstruct_model_name, ) from litellm.litellm_core_utils.internal_call_metadata import is_unbilled_non_inference_call -from litellm.litellm_core_utils.litellm_logging import is_valid_sha256_hash +from litellm.litellm_core_utils.litellm_logging import ( + coerce_model_access_groups, + is_valid_sha256_hash, + request_model_access_groups_from_litellm_params, +) from litellm.litellm_core_utils.safe_json_dumps import safe_dumps, strip_null_bytes -from litellm.proxy._types import SpendLogsMetadata, SpendLogsPayload +from litellm.proxy._types import SpendLogsMetadata, SpendLogsPayload, SpendLogsRouterMetadata from litellm.proxy.spend_tracking.spend_log_error_logger import spend_log_error from litellm.proxy.utils import PrismaClient, hash_token from litellm.types.utils import ( @@ -88,10 +93,30 @@ def _redact_logged_api_key(value: str | None, *, already_redacted: bool = False) return hash_token(stripped) +def _get_router_metadata_for_spend_log( + metadata: Mapping[str, object] | None, + requested_model: str | None, + selected_model: str | None, + selected_provider: str | None, + router_correlation_id: str | None, +) -> SpendLogsRouterMetadata | None: + model_info: Final = metadata.get("model_info") if metadata is not None else None + if not isinstance(model_info, Mapping) or model_info.get("internal_router_model") is not True: + return None + return SpendLogsRouterMetadata( + requested_model=requested_model or None, + selected_model=selected_model or None, + selected_provider=selected_provider or None, + router_correlation_id=router_correlation_id, + ) + + def _get_spend_logs_metadata( metadata: dict | None, applied_guardrails: list[str] | None = None, batch_models: list[str] | None = None, + batch_successful_requests: int | None = None, + batch_failed_requests: int | None = None, mcp_tool_call_metadata: StandardLoggingMCPToolCall | None = None, vector_store_request_metadata: list[StandardLoggingVectorStoreRequest] | None = None, guardrail_information: list[StandardLoggingGuardrailInformation] | None = None, @@ -102,6 +127,7 @@ def _get_spend_logs_metadata( cost_breakdown: CostBreakdown | None = None, litellm_call_id: str | None = None, autorouter_savings: float | None = None, + router_metadata: SpendLogsRouterMetadata | None = None, ) -> SpendLogsMetadata: if metadata is None: return SpendLogsMetadata( @@ -121,6 +147,8 @@ def _get_spend_logs_metadata( error_information=None, proxy_server_request=None, batch_models=None, + batch_successful_requests=None, + batch_failed_requests=None, mcp_tool_call_metadata=None, vector_store_request_metadata=None, model_map_information=None, @@ -139,13 +167,17 @@ def _get_spend_logs_metadata( autorouter_savings=autorouter_savings, litellm_gateway_injected_cache=None, litellm_call_id=litellm_call_id, + router_metadata=router_metadata, ) verbose_proxy_logger.debug( "getting payload for SpendLogs, available keys in metadata: " + str(list(metadata.keys())) ) # Filter the metadata dictionary to include only the specified keys - clean_metadata: Final = SpendLogsMetadata(**{key: metadata.get(key) for key in SpendLogsMetadata.__annotations__}) + clean_metadata: Final = SpendLogsMetadata( + **{key: metadata.get(key) for key in SpendLogsMetadata.__annotations__ if key != "router_metadata"}, + router_metadata=router_metadata, + ) _raw_key: Final = clean_metadata.get("user_api_key") _trusted_hash: Final = metadata.get("user_api_key_hash") _already_redacted: Final = ( @@ -154,6 +186,8 @@ def _get_spend_logs_metadata( clean_metadata["user_api_key"] = _redact_logged_api_key(_raw_key, already_redacted=_already_redacted) clean_metadata["applied_guardrails"] = applied_guardrails clean_metadata["batch_models"] = batch_models + clean_metadata["batch_successful_requests"] = batch_successful_requests + clean_metadata["batch_failed_requests"] = batch_failed_requests clean_metadata["mcp_tool_call_metadata"] = mcp_tool_call_metadata clean_metadata["vector_store_request_metadata"] = _get_vector_store_request_for_spend_logs_payload( vector_store_request_metadata @@ -188,7 +222,28 @@ def get_spend_logs_id(call_type: str, response_obj: dict, kwargs: dict) -> str | return resolved_id -def _extract_usage_for_ocr_call(response_obj: Any, response_obj_dict: dict) -> dict: +_MISSING_ATTRIBUTE: Final = object() + + +def _attribute_or_missing(source: object, name: str) -> object: + return getattr(source, name, _MISSING_ATTRIBUTE) + + +@runtime_checkable +class _ModelDumpable(Protocol): + def model_dump(self) -> object: ... + + +def _dumped_usage_info(usage_info: object) -> object: + if isinstance(usage_info, _ModelDumpable): + return usage_info.model_dump() + instance_dict: Final = _attribute_or_missing(usage_info, "__dict__") + if instance_dict is not _MISSING_ATTRIBUTE: + return instance_dict + return usage_info + + +def _extract_usage_for_ocr_call(response_obj: object, response_obj_dict: dict) -> dict: """ Extract usage information for OCR/AOCR calls. @@ -209,12 +264,10 @@ def _extract_usage_for_ocr_call(response_obj: Any, response_obj_dict: dict) -> d usage_info = response_obj_dict.get("usage_info") # Try to extract usage_info from object attributes if not found in dict - if not usage_info and hasattr(response_obj, "usage_info"): - usage_info = response_obj.usage_info - if hasattr(usage_info, "model_dump"): - usage_info = usage_info.model_dump() - elif hasattr(usage_info, "__dict__"): - usage_info = vars(usage_info) + if not usage_info: + attribute_usage_info: Final = _attribute_or_missing(response_obj, "usage_info") + if attribute_usage_info is not _MISSING_ATTRIBUTE: + usage_info = _dumped_usage_info(attribute_usage_info) # For OCR, we track pages instead of tokens if usage_info is not None: @@ -243,6 +296,23 @@ def _extract_usage_for_ocr_call(response_obj: Any, response_obj_dict: dict) -> d return {} +def get_request_model_access_groups(kwargs: Mapping[str, object] | None) -> tuple[str, ...]: + """Model access groups that authorized this request, as stamped onto request metadata at auth time.""" + if kwargs is None: + return () + + standard_logging_payload: Final = kwargs.get("standard_logging_object") + if isinstance(standard_logging_payload, Mapping): + from_payload: Final = coerce_model_access_groups(standard_logging_payload.get("request_model_access_groups")) + if from_payload: + return from_payload + + litellm_params: Final = kwargs.get("litellm_params") + if not isinstance(litellm_params, Mapping): + return () + return request_model_access_groups_from_litellm_params(litellm_params) + + def _sl_attribution_fallback( standard_logging_payload: StandardLoggingPayload | None, field: Literal["model_id", "model_group", "api_base", "custom_llm_provider"], @@ -347,6 +417,20 @@ def get_logging_payload(kwargs, response_obj, start_time, end_time) -> SpendLogs hidden_params: Final = standard_logging_payload.get("hidden_params", {}) litellm_overhead_time_ms = hidden_params.get("litellm_overhead_time_ms") + custom_llm_provider: Final = ( + kwargs.get("custom_llm_provider") + or _sl_attribution_fallback(standard_logging_payload, "custom_llm_provider") + or None + ) + raw_model: Final = cast(str, kwargs.get("model") or "") + model_name: Final = ( + standard_logging_payload.get("model") if standard_logging_payload is not None else None + ) or reconstruct_model_name(raw_model, custom_llm_provider, metadata or {}) + litellm_call_id: Final = cast( + str | None, + kwargs.get("litellm_call_id") or litellm_params.get("litellm_call_id"), + ) + # clean up litellm metadata clean_metadata = _get_spend_logs_metadata( metadata, @@ -360,6 +444,16 @@ def get_logging_payload(kwargs, response_obj, start_time, end_time) -> SpendLogs if standard_logging_payload is not None else None ), + batch_successful_requests=( + standard_logging_payload.get("hidden_params", {}).get("batch_successful_requests", None) + if standard_logging_payload is not None + else None + ), + batch_failed_requests=( + standard_logging_payload.get("hidden_params", {}).get("batch_failed_requests", None) + if standard_logging_payload is not None + else None + ), mcp_tool_call_metadata=( standard_logging_payload["metadata"].get("mcp_tool_call_metadata", None) if standard_logging_payload is not None @@ -395,9 +489,13 @@ def get_logging_payload(kwargs, response_obj, start_time, end_time) -> SpendLogs autorouter_savings=( standard_logging_payload.get("autorouter_savings", None) if standard_logging_payload is not None else None ), - litellm_call_id=cast( - str | None, - kwargs.get("litellm_call_id") or litellm_params.get("litellm_call_id"), + litellm_call_id=litellm_call_id, + router_metadata=_get_router_metadata_for_spend_log( + metadata=metadata, + requested_model=_model_group, + selected_model=model_name, + selected_provider=custom_llm_provider, + router_correlation_id=litellm_call_id, ), ) @@ -442,15 +540,6 @@ def get_logging_payload(kwargs, response_obj, start_time, end_time) -> SpendLogs # Extract agent_id for A2A requests (set directly on model_call_details) agent_id: Final[str | None] = kwargs.get("agent_id") or metadata.get("agent_id") - custom_llm_provider: Final = ( - kwargs.get("custom_llm_provider") - or _sl_attribution_fallback(standard_logging_payload, "custom_llm_provider") - or None - ) - raw_model: Final = cast(str, kwargs.get("model") or "") - model_name: Final = ( - standard_logging_payload.get("model") if standard_logging_payload is not None else None - ) or reconstruct_model_name(raw_model, custom_llm_provider, metadata or {}) try: payload: Final[SpendLogsPayload] = SpendLogsPayload( @@ -550,6 +639,14 @@ def _ensure_datetime_utc(timestamp: datetime) -> datetime: return timestamp +async def _query_raw_rows( + prisma_client: PrismaClient, + sql_query: str, + *args: object, +) -> Sequence[Mapping[str, object]] | None: + return await prisma_client.db.query_raw(sql_query, *args) + + async def get_spend_by_team( start_date: dt, end_date: dt, @@ -611,7 +708,7 @@ async def get_spend_by_team( group_by_day; """ - db_response: Final = await prisma_client.db.query_raw(sql_query, start_date, end_date, team_id) + db_response: Final = await _query_raw_rows(prisma_client, sql_query, start_date, end_date, team_id) if db_response is None: return [] @@ -686,7 +783,7 @@ async def get_spend_by_team_and_customer( group_by_day; """ - db_response: Final = await prisma_client.db.query_raw(sql_query, start_date, end_date, team_id, customer_id) + db_response: Final = await _query_raw_rows(prisma_client, sql_query, start_date, end_date, team_id, customer_id) if db_response is None: return [] @@ -741,7 +838,7 @@ def _sanitize_request_body_for_spend_logs_payload( return {} visited.add(obj_id) - def _sanitize_value(value: Any) -> Any: + def _sanitize_value(value: object) -> object: if isinstance(value, dict): return _sanitize_request_body_for_spend_logs_payload(value, visited, max_string_length_prompt_in_db) elif isinstance(value, list): @@ -1036,7 +1133,7 @@ def _sanitize_error_information_for_spend_logs( return cast(StandardLoggingPayloadErrorInformation, sanitized) -def _convert_to_json_serializable_dict(obj: Any, visited: set | None = None, max_depth: int = 20) -> Any: +def _convert_to_json_serializable_dict(obj: object, visited: set[int] | None = None, max_depth: int = 20) -> object: """ Convert object to JSON-serializable dict, handling Pydantic models safely. @@ -1090,6 +1187,13 @@ def _convert_to_json_serializable_dict(obj: Any, visited: set | None = None, max visited.remove(obj_id) +def _convert_mapping_to_json_serializable(obj: Mapping[str, object]) -> dict[str, object]: + converted: Final = _convert_to_json_serializable_dict(obj) + if isinstance(converted, dict): + return converted + return dict(obj) + + def _get_proxy_server_request_for_spend_logs_payload( metadata: dict, litellm_params: dict, @@ -1126,7 +1230,7 @@ def _get_proxy_server_request_for_spend_logs_payload( # If redaction is enabled, convert to serializable dict before redacting if should_redact_message_logging(model_call_details=model_call_details): - _request_body = _convert_to_json_serializable_dict(_request_body) + _request_body = _convert_mapping_to_json_serializable(_request_body) perform_redaction(model_call_details=_request_body, result=None) _request_body = _sanitize_request_body_for_spend_logs_payload(_request_body) @@ -1171,7 +1275,7 @@ def _get_response_for_spend_logs_payload( if payload is None: return "{}" if _should_store_prompts_and_responses_in_spend_logs(): - response_obj: Any = payload.get("response") + response_obj: object = payload.get("response") if response_obj is None: return "{}" diff --git a/litellm/proxy/ui_crud_endpoints/proxy_setting_endpoints.py b/litellm/proxy/ui_crud_endpoints/proxy_setting_endpoints.py index a1eb7ed06eb..52258602581 100644 --- a/litellm/proxy/ui_crud_endpoints/proxy_setting_endpoints.py +++ b/litellm/proxy/ui_crud_endpoints/proxy_setting_endpoints.py @@ -3,10 +3,11 @@ import asyncio import json import os from collections import Counter -from collections.abc import Mapping +from collections.abc import Mapping, Sequence +from types import MappingProxyType from typing import ( - Any, Final, + NamedTuple, Protocol, cast, # noqa: TID251 # prisma types Json columns as fields.Json but de-serializes them to plain python on read ) @@ -15,6 +16,7 @@ from urllib.parse import urlparse from fastapi import APIRouter, Body, Depends, File, HTTPException, UploadFile from pydantic import ConfigDict, JsonValue, ValidationError, create_model from pydantic.fields import FieldInfo +from typing_extensions import NotRequired, ReadOnly, TypedDict import litellm from litellm._logging import verbose_proxy_logger @@ -44,6 +46,31 @@ from litellm.types.proxy.management_endpoints.ui_sso import ( router: Final = APIRouter() +JsonSchemaItems: Final = TypedDict( + "JsonSchemaItems", + {"$ref": ReadOnly[str], "enum": ReadOnly[Sequence[JsonValue]]}, + total=False, +) + + +class JsonSchemaNode(TypedDict, total=False): + type: ReadOnly[str] + description: ReadOnly[str] + enum: ReadOnly[Sequence[JsonValue]] + anyOf: ReadOnly[Sequence["JsonSchemaNode"]] + items: ReadOnly["JsonSchemaItems"] + properties: ReadOnly[Mapping[str, "JsonSchemaNode"]] + + +_EMPTY_SCHEMA_DEFS: Final[Mapping[str, "JsonSchemaNode"]] = MappingProxyType({}) + + +class JsonSchemaPropertyEntry(TypedDict): + description: ReadOnly[str] + type: ReadOnly[str] + items: NotRequired[ReadOnly["JsonSchemaItems"]] + + class _SsoSettingsMappingRow(Protocol): @property def sso_settings(self) -> Mapping[str, object] | None: ... @@ -157,10 +184,10 @@ class UIThemeConfig(BaseModel): class SettingsResponse(BaseModel): """Base response model for settings with values and schema information""" - values: dict[str, Any] + values: dict[str, object] """The current configuration values""" - field_schema: dict[str, Any] + field_schema: dict[str, object] """Schema information including descriptions and property types for UI display""" @@ -548,6 +575,62 @@ async def delete_allowed_ip( return {"message": f"IP {ip_address.ip} deleted successfully", "status": "success"} +def _resolve_non_null_variant(field_info: JsonSchemaNode) -> JsonSchemaNode: + """Pydantic v2 renders Optional fields as ``anyOf: [actual_type, null]``.""" + if "anyOf" not in field_info: + return field_info + return next((variant for variant in field_info["anyOf"] if variant.get("type") != "null"), field_info) + + +def _schema_items_entry(resolved: JsonSchemaNode, defs: Mapping[str, JsonSchemaNode]) -> "JsonSchemaItems | None": + """Items info (including enum values) for array fields, so the UI can render a multi-select dropdown.""" + if "items" not in resolved: + return None + items: Final = resolved["items"] + if "$ref" not in items: + return items + ref_def: Final = defs.get(items["$ref"].split("/")[-1]) + if ref_def is None or "enum" not in ref_def: + return None + enum_items: Final[JsonSchemaItems] = {"enum": ref_def["enum"]} + return enum_items + + +def _schema_property_entry(field_info: JsonSchemaNode, defs: Mapping[str, JsonSchemaNode]) -> JsonSchemaPropertyEntry: + resolved: Final = _resolve_non_null_variant(field_info) + items_entry: Final = _schema_items_entry(resolved, defs) + description: Final = field_info.get("description", "") + type_name: Final = resolved.get("type", "string") + if items_entry is None: + entry: Final[JsonSchemaPropertyEntry] = {"description": description, "type": type_name} + return entry + entry_with_items: Final[JsonSchemaPropertyEntry] = { + "description": description, + "type": type_name, + "items": items_entry, + } + return entry_with_items + + +class _RootSchema(NamedTuple): + description: str + properties: Mapping[str, JsonSchemaNode] + nested_defs: Mapping[str, JsonSchemaNode] + defs: Mapping[str, JsonSchemaNode] + + +def _root_schema(settings_class: type[BaseModel]) -> _RootSchema: + from pydantic import TypeAdapter + + raw_schema: Final = TypeAdapter(settings_class).json_schema(by_alias=True) + return _RootSchema( + description=raw_schema.get("description", ""), + properties=raw_schema["properties"], + nested_defs=raw_schema.get("definitions", _EMPTY_SCHEMA_DEFS), + defs=raw_schema["$defs"] if "$defs" in raw_schema else raw_schema.get("definitions", _EMPTY_SCHEMA_DEFS), + ) + + async def _get_settings_with_schema( settings_key: str, settings_class: type[BaseModel], @@ -561,69 +644,43 @@ async def _get_settings_with_schema( settings_class: The Pydantic class to use for schema config: The config dictionary """ - from pydantic import TypeAdapter - litellm_settings: Final = config.get("litellm_settings", {}) or {} settings_data: Final = litellm_settings.get(settings_key, {}) or {} # Create the settings object settings: Final = settings_class(**(settings_data)) # Get the schema - schema: Final = TypeAdapter(settings_class).json_schema(by_alias=True) + root_schema: Final = _root_schema(settings_class) # Convert to dict for response settings_dict: Final = settings.model_dump() # Add descriptions to the response - result: Final = { - "values": settings_dict, - "field_schema": { - "description": schema.get("description", ""), - "properties": {}, - }, + schema_properties_out: Final[Mapping[str, JsonSchemaPropertyEntry]] = { + field_name: _schema_property_entry(field_info, root_schema.defs) + for field_name, field_info in root_schema.properties.items() } - # Add property descriptions - defs: Final = schema.get("$defs", schema.get("definitions", {})) - for field_name, field_info in schema["properties"].items(): - # For Optional fields, Pydantic v2 uses anyOf with [actual_type, null]. - # Resolve the non-null variant to get the real type and items. - resolved = field_info - if "anyOf" in field_info: - for variant in field_info["anyOf"]: - if variant.get("type") != "null": - resolved = variant - break - - prop_entry: dict = { - "description": field_info.get("description", ""), - "type": resolved.get("type", "string"), - } - # Pass through items info (including enum values) for array fields - # so the UI can render a multi-select dropdown - if "items" in resolved: - items = resolved["items"] - # Resolve $ref to enum definitions if needed - if "$ref" in items: - ref_name = items["$ref"].split("/")[-1] - ref_def = defs.get(ref_name, {}) - if "enum" in ref_def: - prop_entry["items"] = {"enum": ref_def["enum"]} - else: - prop_entry["items"] = items - result["field_schema"]["properties"][field_name] = prop_entry - # Add nested object descriptions - for def_name, def_schema in schema.get("definitions", {}).items(): - result["field_schema"][def_name] = { + nested_defs_out: Final[Mapping[str, Mapping[str, object]]] = { + def_name: { "description": def_schema.get("description", ""), "properties": { prop_name: {"description": prop_info.get("description", "")} for prop_name, prop_info in def_schema.get("properties", {}).items() }, } + for def_name, def_schema in root_schema.nested_defs.items() + } - return result + return { + "values": settings_dict, + "field_schema": { + "description": root_schema.description, + "properties": schema_properties_out, + **nested_defs_out, + }, + } @router.get( @@ -930,32 +987,29 @@ async def get_sso_settings(): resolved: Final = resolve_sso_config(sso_db_settings, os.environ) # Get the schema for UI display - from pydantic import TypeAdapter - - schema: Final = TypeAdapter(SSOConfig).json_schema(by_alias=True) + root_schema: Final = _root_schema(SSOConfig) # Convert to dict for response, masking OAuth client secrets so plaintext # is never sent to the UI. sso_dict: Final = mask_sensitive_keys(resolved.config.model_dump(), set(SSO_SECRET_FIELDS)) # Add descriptions to the response - result: Final = { - "values": sso_dict, - "provenance": resolved.provenance, - "field_schema": { - "description": schema.get("description", ""), - "properties": {}, - }, - } - - # Add property descriptions - for field_name, field_info in schema["properties"].items(): - result["field_schema"]["properties"][field_name] = { + schema_properties_out: Final[Mapping[str, Mapping[str, str]]] = { + field_name: { "description": field_info.get("description", ""), "type": field_info.get("type", "string"), } + for field_name, field_info in root_schema.properties.items() + } - return result + return { + "values": sso_dict, + "provenance": resolved.provenance, + "field_schema": { + "description": root_schema.description, + "properties": schema_properties_out, + }, + } @router.patch( @@ -1309,7 +1363,7 @@ UI_SETTINGS_CACHE_KEY: Final = "ui_settings:settings_dict" UI_SETTINGS_CACHE_TTL: Final = 600 # 10 minutes -async def get_ui_settings_cached() -> dict[str, Any]: +async def get_ui_settings_cached() -> dict[str, JsonValue]: """ Return the persisted UI settings dict, using DualCache for reads. diff --git a/litellm/proxy/utils.py b/litellm/proxy/utils.py index 1d16fa63607..051d36c4d0f 100644 --- a/litellm/proxy/utils.py +++ b/litellm/proxy/utils.py @@ -645,7 +645,7 @@ class ProxyLogging: self.max_parallel_request_limiter = _PROXY_MaxParallelRequestsHandler(self.internal_usage_cache) self.max_budget_limiter = _PROXY_MaxBudgetLimiter() self.cache_control_check = _PROXY_CacheControlCheck() - self.alerting: list | None = None + self.alerting: list[str] | None = None self.alerting_threshold: float = 300 # default to 5 min. threshold self.alert_types: list[AlertType] = DEFAULT_ALERT_TYPES self.alert_to_webhook_url: dict | None = None @@ -1416,7 +1416,7 @@ class ProxyLogging: mutation is discarded and a warning is logged so the misconfiguration is visible instead of silently forwarding unredacted content. """ - scans_raw_request: Final = getattr(callback, "scan_raw_request", False) + scans_raw_request: Final = callback.scan_raw_request should_use_raw_snapshot: Final = scans_raw_request and raw_request_snapshot is not None input_data: Final = ( # mutable-ok: same request-payload shape as data independent_snapshot(raw_request_snapshot) if should_use_raw_snapshot else data @@ -1453,7 +1453,7 @@ class ProxyLogging: "scan_raw_request is for block-only guardrails and this mutation is being " "discarded. Remove scan_raw_request from this guardrail's config if it needs " "to mask/rewrite content.", - getattr(callback, "guardrail_name", None) or callback.__class__.__name__, + callback.guardrail_name or callback.__class__.__name__, ) if scans_raw_request: if result is not None: @@ -1778,7 +1778,7 @@ class ProxyLogging: # guarantee must hold even under litellm.safe_memory_mode, which # otherwise makes deep copies return the original object. needs_raw_request_snapshot: Final = any( - isinstance(cb, CustomGuardrail) and getattr(cb, "scan_raw_request", False) + isinstance(cb, CustomGuardrail) and cb.scan_raw_request for cb in ProxyLogging._callback_capabilities().resolved_callbacks ) raw_request_snapshot: Final[dict | None] = ( # mutable-ok: same request-payload shape as data @@ -1938,7 +1938,7 @@ class ProxyLogging: """ def _input_for(callback: CustomGuardrail) -> dict: # mutable-ok: same request-payload shape as data - if not getattr(callback, "scan_raw_request", False) or raw_request_snapshot is None: + if not callback.scan_raw_request or raw_request_snapshot is None: return data return independent_snapshot(raw_request_snapshot) @@ -1962,11 +1962,7 @@ class ProxyLogging: # deployment-level guardrail sharing this name would see no marker # via _pre_call_hook_already_ran and re-run it a second time on # live kwargs. - if ( - getattr(callback, "scan_raw_request", False) - and not isinstance(result, BaseException) - and result is not None - ): + if callback.scan_raw_request and not isinstance(result, BaseException) and result is not None: callback.mark_pre_call_hook_ran(data) raised: Final = tuple(result for result in results if isinstance(result, BaseException)) blocking: Final = next((exc for exc in raised if not _exception_changes_request_flow(exc)), None) @@ -2368,7 +2364,9 @@ class ProxyLogging: # do nothing if alerting is not switched on (unless it's a soft_budget alert with team-specific emails) return - if self.alerting is not None and ("slack" in self.alerting or "ms_teams" in self.alerting): + if self.alerting is not None and ( + "slack" in self.alerting or "ms_teams" in self.alerting or "webhook" in self.alerting + ): if self.slack_alerting_instance is not None: await self.slack_alerting_instance.budget_alerts( type=type, @@ -6035,10 +6033,42 @@ def _should_use_smtp_ssl(smtp_port: int) -> bool: return os.getenv("SMTP_USE_SSL", "False") == "True" or smtp_port == 465 -def _create_smtp_connection(smtp_host: str, smtp_port: int) -> smtplib.SMTP: +def _create_smtp_connection(smtp_host: str, smtp_port: int, timeout: float) -> smtplib.SMTP: if _should_use_smtp_ssl(smtp_port=smtp_port): - return smtplib.SMTP_SSL(host=smtp_host, port=smtp_port, context=ssl.create_default_context()) - return smtplib.SMTP(host=smtp_host, port=smtp_port) + return smtplib.SMTP_SSL(host=smtp_host, port=smtp_port, context=ssl.create_default_context(), timeout=timeout) + return smtplib.SMTP(host=smtp_host, port=smtp_port, timeout=timeout) + + +def _send_smtp_message( + email_message: MIMEMultipart, + smtp_host: str, + smtp_port: int, + smtp_username: str | None, + smtp_password: str | None, + sender_email: str, + receiver_email: str, + timeout: float, +) -> None: + using_ssl: Final = _should_use_smtp_ssl(smtp_port=smtp_port) + with _create_smtp_connection( + smtp_host=smtp_host, + smtp_port=smtp_port, + timeout=timeout, + ) as server: + if not using_ssl and os.getenv("SMTP_TLS", "True") != "False": + server.starttls(context=ssl.create_default_context()) + + if smtp_username and smtp_password: + server.login( + user=smtp_username, + password=smtp_password, + ) + + server.send_message( + msg=email_message, + from_addr=sender_email, + to_addrs=receiver_email, + ) async def send_email( @@ -6084,27 +6114,18 @@ async def send_email( email_message.attach(MIMEText(html, "html")) try: - using_ssl: Final = _should_use_smtp_ssl(smtp_port=smtp_port) - with _create_smtp_connection( + smtp_timeout: Final = float(os.getenv("SMTP_TIMEOUT", "30")) + await asyncio.to_thread( + _send_smtp_message, + email_message=email_message, smtp_host=smtp_host, smtp_port=smtp_port, - ) as server: - if not using_ssl and os.getenv("SMTP_TLS", "True") != "False": - server.starttls(context=ssl.create_default_context()) - - # Login to your email account only if smtp_username and smtp_password are provided - if smtp_username and smtp_password: - server.login( - user=smtp_username, - password=smtp_password, - ) - - # Send the email - server.send_message( - msg=email_message, - from_addr=sender_email, - to_addrs=receiver_email, - ) + smtp_username=smtp_username, + smtp_password=smtp_password, + sender_email=sender_email, + receiver_email=receiver_email, + timeout=smtp_timeout, + ) except Exception as e: verbose_proxy_logger.exception("An error occurred while sending the email:" + str(e)) diff --git a/litellm/proxy/video_endpoints/endpoints.py b/litellm/proxy/video_endpoints/endpoints.py index d985a546fa7..66071c05b4f 100644 --- a/litellm/proxy/video_endpoints/endpoints.py +++ b/litellm/proxy/video_endpoints/endpoints.py @@ -1,6 +1,6 @@ #### Video Endpoints ##### -from typing import Any, Final +from typing import Final from fastapi import APIRouter, Depends, File, Form, Request, Response, UploadFile from fastapi.responses import ORJSONResponse @@ -161,7 +161,7 @@ async def video_list( # Read query parameters query_params: Final = dict(request.query_params) - data: Final[dict[str, Any]] = {"query_params": query_params} + data: Final[dict[str, object]] = {"query_params": query_params} # Extract custom_llm_provider from headers, query params, or body custom_llm_provider: Final = ( @@ -246,7 +246,7 @@ async def video_status( ) # Create data with video_id - data: Final[dict[str, Any]] = {"video_id": video_id} + data: Final[dict[str, object]] = {"video_id": video_id} decoded: Final = decode_video_id_with_provider(video_id) provider_from_id: Final = decoded.get("custom_llm_provider") @@ -345,7 +345,7 @@ async def video_content( ) # Create data with video_id - data: Final[dict[str, Any]] = {"video_id": video_id} + data: Final[dict[str, object]] = {"video_id": video_id} decoded: Final = decode_video_id_with_provider(video_id) provider_from_id: Final = decoded.get("custom_llm_provider") @@ -653,7 +653,7 @@ async def video_get_character( ) original_requested_character_id: Final = character_id - data: Final[dict[str, Any]] = {"character_id": character_id} + data: Final[dict[str, object]] = {"character_id": character_id} decoded: Final = decode_character_id_with_provider(character_id) provider_from_id: Final = decoded.get("custom_llm_provider") diff --git a/litellm/rag/ingestion/vertex_ai_ingestion.py b/litellm/rag/ingestion/vertex_ai_ingestion.py index 07f9f346d08..eff8ad1b8cb 100644 --- a/litellm/rag/ingestion/vertex_ai_ingestion.py +++ b/litellm/rag/ingestion/vertex_ai_ingestion.py @@ -186,7 +186,6 @@ class VertexAIRAGIngestion(BaseRAGIngestion, VertexBase): base_url: Final = get_vertex_base_url(self.location) url: Final = f"{base_url}/v1beta1/projects/{self.project_id}/locations/{self.location}/ragCorpora" - # Build request body with camelCase keys (Vertex AI API format) vector_db_config: Final = self.vector_store_config.get("vector_db_config") embedding_model: Final = self.vector_store_config.get("embedding_model") embedding_model_config: Final = ( @@ -447,7 +446,6 @@ class VertexAIRAGIngestion(BaseRAGIngestion, VertexBase): # Add max embedding requests per minute if specified max_embedding_qpm: Final = self.vector_store_config.get("max_embedding_requests_per_min") - # Build request body with camelCase keys (Vertex AI API format) chunking_config: Final = ( {"chunkSize": chunk_size or 1024, "chunkOverlap": chunk_overlap or 200} if chunk_size or chunk_overlap diff --git a/litellm/rag/main.py b/litellm/rag/main.py index 2dcaa200cc6..7bc1a6a52a3 100644 --- a/litellm/rag/main.py +++ b/litellm/rag/main.py @@ -29,6 +29,7 @@ from litellm.rag.ingestion.openai_ingestion import OpenAIRAGIngestion from litellm.rag.ingestion.s3_vectors_ingestion import S3VectorsRAGIngestion from litellm.rag.ingestion.vertex_ai_ingestion import VertexAIRAGIngestion from litellm.rag.rag_query import RAGQuery +from litellm.types.llms.openai import AllMessageValues from litellm.types.rag import ( RAGIngestOptions, RAGIngestResponse, @@ -204,7 +205,7 @@ def _suppressed_sub_call_billing() -> Iterator[None]: async def _execute_query_pipeline( model: str, - messages: list[Any], + messages: list[AllMessageValues], retrieval_config: dict[str, Any], rerank: dict[str, Any] | None = None, stream: bool = False, @@ -311,7 +312,7 @@ async def _execute_query_pipeline( @client async def aquery( model: str, - messages: list[Any], + messages: list[AllMessageValues], retrieval_config: dict[str, Any], rerank: dict[str, Any] | None = None, stream: bool = False, @@ -358,12 +359,12 @@ async def aquery( @client def query( model: str, - messages: list[Any], + messages: list[AllMessageValues], retrieval_config: dict[str, Any], rerank: dict[str, Any] | None = None, stream: bool = False, **kwargs, -) -> ModelResponse | Coroutine[Any, Any, ModelResponse]: +) -> ModelResponse | Coroutine[None, None, ModelResponse]: """ Query a RAG pipeline. """ @@ -410,7 +411,7 @@ def ingest( file_id: str | None = None, timeout: float | httpx.Timeout | None = None, **kwargs, -) -> RAGIngestResponse | Coroutine[Any, Any, RAGIngestResponse]: +) -> RAGIngestResponse | Coroutine[None, None, RAGIngestResponse]: """ Ingest a document into a vector store. diff --git a/litellm/rag/rag_query.py b/litellm/rag/rag_query.py index 16b8f82815c..255faf94402 100644 --- a/litellm/rag/rag_query.py +++ b/litellm/rag/rag_query.py @@ -1,11 +1,45 @@ +from collections.abc import Sequence from typing import Any, Final +from typing_extensions import NotRequired, ReadOnly, TypedDict + from litellm.types.llms.openai import AllMessageValues, ChatCompletionUserMessage from litellm.types.utils import ModelResponse -from litellm.types.vector_stores import ( - VectorStoreResultContent, - VectorStoreSearchResponse, -) +from litellm.types.vector_stores import VectorStoreSearchResponse + + +class _ResultContentView(TypedDict): + """Content entry carried by a vector store search result.""" + + type: ReadOnly[NotRequired[str]] + text: ReadOnly[str] + + +class _SearchResultView(TypedDict): + """Vector store search result, as far as :class:`RAGQuery` reads it.""" + + content: ReadOnly[NotRequired[Sequence[_ResultContentView]]] + text: ReadOnly[NotRequired[str]] + + +class _SearchDataView(TypedDict): + results: ReadOnly[Sequence[_SearchResultView]] + + +class _ContextChunksView(TypedDict): + chunks: ReadOnly[Sequence[_SearchResultView | str | None]] + + +class _RerankResultView(TypedDict): + index: ReadOnly[NotRequired[int]] + + +class _RerankResultsView(TypedDict): + results: ReadOnly[Sequence[_RerankResultView]] + + +class _MessageView(TypedDict): + message: ReadOnly[object] class RAGQuery: @@ -42,9 +76,10 @@ class RAGQuery: """ context_content = RAGQuery.CONTENT_PREFIX_STRING - for chunk in context_chunks: + chunks: Final[_ContextChunksView] = {"chunks": context_chunks} + for chunk in chunks["chunks"]: if isinstance(chunk, dict): - result_content: list[VectorStoreResultContent] | None = chunk.get("content") + result_content: Sequence[_ResultContentView] | None = chunk.get("content") if result_content: for content_item in result_content: content_text: str | None = content_item.get("text") @@ -64,14 +99,15 @@ class RAGQuery: def add_search_results_to_response( response: ModelResponse, search_results: VectorStoreSearchResponse, - rerank_results: Any | None = None, + rerank_results: object = None, ) -> ModelResponse: """ Add search results to the response choices. """ if hasattr(response, "choices") and response.choices: for choice in response.choices: - message = getattr(choice, "message", None) + message_view: _MessageView = {"message": getattr(choice, "message", None)} + message = message_view["message"] if message is not None: # Get existing provider_specific_fields or create new dict provider_fields = getattr(message, "provider_specific_fields", None) or {} @@ -91,7 +127,8 @@ class RAGQuery: ) -> list[str | dict[str, Any]]: """Extract text documents from vector store search response.""" documents: Final[list[str | dict[str, Any]]] = [] - for result in search_response.get("data", []): + search_data: Final[_SearchDataView] = {"results": search_response.get("data", [])} + for result in search_data["results"]: content_list = result.get("content", []) for content in content_list: if content.get("type") == "text" and content.get("text"): @@ -99,11 +136,13 @@ class RAGQuery: return documents @staticmethod - def get_top_chunks_from_rerank(search_response: Any, rerank_response: Any) -> list[Any]: + def get_top_chunks_from_rerank(search_response: Any, rerank_response: Any) -> list[_SearchResultView]: """Get the original search results corresponding to the top reranked results.""" - top_chunks: Final = [] - original_results: Final = search_response.get("data", []) - for result in rerank_response.get("results", []): + top_chunks: Final[list[_SearchResultView]] = [] + search_data: Final[_SearchDataView] = {"results": search_response.get("data", [])} + original_results: Final = search_data["results"] + reranked: Final[_RerankResultsView] = {"results": rerank_response.get("results", [])} + for result in reranked["results"]: index = result.get("index") if index is not None and index < len(original_results): top_chunks.append(original_results[index]) diff --git a/litellm/repositories/base_repository.py b/litellm/repositories/base_repository.py index 568e3b50ed2..26c1c386138 100644 --- a/litellm/repositories/base_repository.py +++ b/litellm/repositories/base_repository.py @@ -40,7 +40,7 @@ def record_to_dict(record: DbRecord) -> Mapping[str, object]: class BaseRepository(ABC, Generic[T]): """Abstract base class for all repositories.""" - def __init__(self, prisma_client: Any): # any-ok: PrismaClient is an untyped runtime wrapper + def __init__(self, prisma_client: object): self._prisma_client = prisma_client @property diff --git a/litellm/repositories/prisma_protocols.py b/litellm/repositories/prisma_protocols.py index 2aa1b8e0e3f..d962934dfb1 100644 --- a/litellm/repositories/prisma_protocols.py +++ b/litellm/repositories/prisma_protocols.py @@ -144,4 +144,7 @@ class PrismaBatch(Protocol): @property def litellm_endusertable(self) -> BatchTable: ... + @property + def litellm_modelaccessgroupbudgettable(self) -> BatchTable: ... + async def commit(self) -> None: ... diff --git a/litellm/repositories/table_repositories.py b/litellm/repositories/table_repositories.py index e02f652caf6..18cf884f267 100644 --- a/litellm/repositories/table_repositories.py +++ b/litellm/repositories/table_repositories.py @@ -68,6 +68,10 @@ class SpendLogsRepository(PrismaTableRepository["prisma_models.LiteLLM_SpendLogs table_name = "litellm_spendlogs" +class BudgetWindowSpendRepository(PrismaTableRepository["prisma_models.LiteLLM_BudgetWindowSpend"]): + table_name = "litellm_budgetwindowspend" + + class ClaudeCodePluginRepository(PrismaTableRepository["prisma_models.LiteLLM_ClaudeCodePluginTable"]): table_name = "litellm_claudecodeplugintable" @@ -100,6 +104,10 @@ class TagRepository(PrismaTableRepository["prisma_models.LiteLLM_TagTable"]): table_name = "litellm_tagtable" +class ModelAccessGroupBudgetRepository(PrismaTableRepository["prisma_models.LiteLLM_ModelAccessGroupBudgetTable"]): + table_name = "litellm_modelaccessgroupbudgettable" + + class InvitationLinkRepository(PrismaTableRepository["prisma_models.LiteLLM_InvitationLink"]): table_name = "litellm_invitationlink" diff --git a/litellm/repositories/team_repository.py b/litellm/repositories/team_repository.py index cffa08ce7e0..5ff07d76b5d 100644 --- a/litellm/repositories/team_repository.py +++ b/litellm/repositories/team_repository.py @@ -3,9 +3,9 @@ Team repository for database operations on LiteLLM_TeamTable. """ import json -from collections.abc import Mapping +from collections.abc import Mapping, Sequence from datetime import datetime -from typing import TYPE_CHECKING, Final +from typing import TYPE_CHECKING, Final, Protocol from pydantic import TypeAdapter @@ -21,6 +21,25 @@ if TYPE_CHECKING: from prisma import Prisma from prisma import models as prisma_models + +class _TeamArrays(Protocol): + """The string array columns of a team row, which the domain model leaves untyped.""" + + @property + def members(self) -> Sequence[str]: ... + + @property + def admins(self) -> Sequence[str]: ... + + @property + def models(self) -> Sequence[str]: ... + + +def _team_arrays(team: LiteLLM_TeamTable) -> _TeamArrays: + """View a team's untyped list columns as sequences of ids.""" + return team + + _MEMBERS_WITH_ROLES_ADAPTER: Final = TypeAdapter(list[Member]) _JSON_ENCODED_TEAM_FIELDS: Final = ( "metadata", @@ -80,8 +99,8 @@ class TeamRepository(BaseRepository[LiteLLM_TeamTable]): ) if not rows: return None - raw_value: Final = rows[0]["members_with_roles"] - parsed: Final = json.loads(raw_value) if isinstance(raw_value, str) else raw_value + raw_value: Final[object] = rows[0]["members_with_roles"] + parsed: Final[object] = json.loads(raw_value) if isinstance(raw_value, str) else raw_value if not parsed: return [] return _MEMBERS_WITH_ROLES_ADAPTER.validate_python(parsed) @@ -315,7 +334,7 @@ class TeamRepository(BaseRepository[LiteLLM_TeamTable]): if team is None: return None - members: Final = [m for m in team.members if m != user_id] + members: Final = [m for m in _team_arrays(team).members if m != user_id] return await self.update(team_id, {"members": members}, id_field="team_id") async def add_admin(self, team_id: str, user_id: str) -> LiteLLM_TeamTable | None: @@ -340,7 +359,7 @@ class TeamRepository(BaseRepository[LiteLLM_TeamTable]): if team is None: return None - admins: Final = [a for a in team.admins if a != user_id] + admins: Final = [a for a in _team_arrays(team).admins if a != user_id] return await self.update(team_id, {"admins": admins}, id_field="team_id") async def add_models(self, team_id: str, models: list[str]) -> LiteLLM_TeamTable | None: @@ -365,5 +384,5 @@ class TeamRepository(BaseRepository[LiteLLM_TeamTable]): if team is None: return None - current_models: Final = [m for m in team.models if m not in models] + current_models: Final = [m for m in _team_arrays(team).models if m not in models] return await self.update(team_id, {"models": current_models}, id_field="team_id") diff --git a/litellm/repositories/unit_of_work.py b/litellm/repositories/unit_of_work.py index eb11ebe3b9c..a497d0580db 100644 --- a/litellm/repositories/unit_of_work.py +++ b/litellm/repositories/unit_of_work.py @@ -118,6 +118,7 @@ class BudgetCascadeUnitOfWork: keys: LinkedSpendResetWrites organizations: LinkedSpendResetWrites tags: LinkedSpendResetWrites + model_access_groups: LinkedSpendResetWrites endusers: LinkedSpendResetWrites budgets: BudgetWindowWrites @@ -143,6 +144,7 @@ async def budget_cascade_unit_of_work( keys=LinkedSpendResetWrites(table=batch.litellm_verificationtoken), organizations=LinkedSpendResetWrites(table=batch.litellm_organizationtable), tags=LinkedSpendResetWrites(table=batch.litellm_tagtable), + model_access_groups=LinkedSpendResetWrites(table=batch.litellm_modelaccessgroupbudgettable), endusers=LinkedSpendResetWrites(table=batch.litellm_endusertable), budgets=BudgetWindowWrites(table=batch.litellm_budgettable), ) diff --git a/litellm/repositories/user_repository.py b/litellm/repositories/user_repository.py index 9df1bceac9c..87eb45f262d 100644 --- a/litellm/repositories/user_repository.py +++ b/litellm/repositories/user_repository.py @@ -6,15 +6,34 @@ import json from collections.abc import Mapping from typing import TYPE_CHECKING, Final -from litellm.models.user import LiteLLM_UserTable +from pydantic import TypeAdapter + +from litellm.models.user import LiteLLM_UserTable, SCIMPlaceholder from litellm.repositories.base_repository import BaseRepository, DbRecord, record_to_dict from litellm.repositories.prisma_protocols import TableActions if TYPE_CHECKING: + from prisma import Prisma from prisma import models as prisma_models _JSON_ENCODED_COLUMNS: Final = frozenset({"metadata", "model_spend", "model_max_budget"}) +_SHADOWING_PLACEHOLDERS_SQL: Final = """ +SELECT p.user_id AS placeholder_user_id, + array_agg(r.user_id ORDER BY r.user_id) AS resolved_user_ids, + p.teams AS team_ids +FROM "LiteLLM_UserTable" p +JOIN "LiteLLM_UserTable" r + ON r.user_id <> p.user_id + AND (r.sso_user_id = p.user_id OR LOWER(r.user_email) = LOWER(p.user_id)) +WHERE p.sso_user_id IS NULL + AND NOT EXISTS (SELECT 1 FROM "LiteLLM_VerificationToken" k WHERE k.user_id = p.user_id) +GROUP BY p.user_id, p.teams +ORDER BY p.user_id +""" + +_PLACEHOLDER_ROWS_ADAPTER: Final = TypeAdapter(tuple[SCIMPlaceholder, ...]) + class UserRepository(BaseRepository[LiteLLM_UserTable]): """Repository for user database operations.""" @@ -59,6 +78,11 @@ class UserRepository(BaseRepository[LiteLLM_UserTable]): """Find all users in a team.""" return await self.find_many(where={"teams": {"has": team_id}}) + async def find_shadowing_placeholders(self, tx: "Prisma") -> tuple[SCIMPlaceholder, ...]: + """Users with no SSO id and no virtual keys whose id is another user's SSO id or email.""" + rows: Final = await tx.query_raw(_SHADOWING_PLACEHOLDERS_SQL) + return _PLACEHOLDER_ROWS_ADAPTER.validate_python(rows) + async def count_billable_users(self) -> int: """Number of users that count toward the license seat limit. diff --git a/litellm/responses/litellm_completion_transformation/custom_tools.py b/litellm/responses/litellm_completion_transformation/custom_tools.py index cccae06c74b..4aa489d9e50 100644 --- a/litellm/responses/litellm_completion_transformation/custom_tools.py +++ b/litellm/responses/litellm_completion_transformation/custom_tools.py @@ -17,6 +17,7 @@ logic. import json from collections.abc import Mapping, Sequence +from types import MappingProxyType from typing import Final from pydantic import BaseModel, TypeAdapter, ValidationError @@ -28,6 +29,15 @@ from litellm.types.llms.openai import ( _MAX_ARGUMENTS_LEN: Final = 1_000_000 +TOOL_CALL_ITEM_ID_PREFIX_BY_TYPE: Final = MappingProxyType({"function_call": "fc", "custom_tool_call": "ctc"}) + + +def openai_shaped_tool_call_item_id(item_type: str, tool_id: str) -> str: + prefix: Final = TOOL_CALL_ITEM_ID_PREFIX_BY_TYPE.get(item_type) + if prefix is None or not tool_id or tool_id.startswith(prefix): + return tool_id + return f"{prefix}_{tool_id}" + def extract_custom_tool_names(tools: Sequence[object] | None) -> set[str]: """Extract names of tools originally defined as ``type: "custom"``.""" @@ -45,6 +55,21 @@ def is_custom_tool_call(tool_name: str, custom_tool_names: set[str]) -> bool: return tool_name in custom_tool_names +def serialize_tool_call_arguments(raw_arguments: object, default: str = "") -> str: + """Render tool call arguments as the JSON string tool-call schemas require. + + Arguments normally arrive already JSON-encoded, but clients and providers + also send the decoded object. ``str()`` on a dict yields a Python repr with + single quotes, which every downstream JSON parser rejects with errors like + "Expecting ',' delimiter". + """ + if isinstance(raw_arguments, str): + return raw_arguments or default + if raw_arguments is None: + return default + return json.dumps(raw_arguments, default=str) + + def unwrap_custom_tool_arguments(arguments: str) -> str: """Extract the raw content string from JSON-wrapped arguments. @@ -88,7 +113,7 @@ def build_tool_call_item_kwargs( item_type: Final = "custom_tool_call" if custom else "function_call" kwargs: Final[dict[str, str]] = { "type": item_type, - "id": call_id, + "id": openai_shaped_tool_call_item_id(item_type, call_id), "call_id": call_id, "name": name, "status": status, diff --git a/litellm/responses/litellm_completion_transformation/streaming_iterator.py b/litellm/responses/litellm_completion_transformation/streaming_iterator.py index 8b1eeb30306..db1c3acbefb 100644 --- a/litellm/responses/litellm_completion_transformation/streaming_iterator.py +++ b/litellm/responses/litellm_completion_transformation/streaming_iterator.py @@ -8,6 +8,7 @@ from litellm.main import stream_chunk_builder from litellm.responses.litellm_completion_transformation.custom_tools import ( build_tool_call_item_kwargs, extract_custom_tool_names, + serialize_tool_call_arguments, ) from litellm.responses.litellm_completion_transformation.transformation import ( LiteLLMCompletionResponsesConfig, @@ -113,6 +114,7 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator): self._pending_tool_events: list[BaseLiteLLMOpenAIResponseObject] = [] self._tool_output_index_by_call_id: dict[str, int] = {} self._tool_args_by_call_id: dict[str, str] = {} + self._tool_item_id_by_call_id: dict[str, str] = {} # mutable-ok: filled per call id as tool call events stream self._tool_call_id_by_index: dict[int, str] = {} self._ambiguous_tool_call_indexes: set[int] = set() self._next_tool_output_index: int = 1 # output_index=0 reserved for the message item @@ -213,10 +215,10 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator): fn_args_delta = "" if isinstance(fn, dict): fn_name = str(fn.get("name") or "") - fn_args_delta = str(fn.get("arguments") or "") + fn_args_delta = serialize_tool_call_arguments(fn.get("arguments")) else: fn_name = str(getattr(fn, "name", "") or "") - fn_args_delta = str(getattr(fn, "arguments", "") or "") + fn_args_delta = serialize_tool_call_arguments(getattr(fn, "arguments", "")) tool_name, tool_namespace = self._responses_namespace_tool_call_fields(fn_name) output_index = self._get_or_assign_tool_output_index(call_id) @@ -226,6 +228,7 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator): self._sequence_number += 1 names = self._custom_tool_names item_kwargs = build_tool_call_item_kwargs(call_id, tool_name, "", "in_progress", names) + self._tool_item_id_by_call_id[call_id] = item_kwargs["id"] if tool_namespace: item_kwargs["namespace"] = tool_namespace event = OutputItemAddedEvent( @@ -247,7 +250,7 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator): self._sequence_number += 1 delta_event: BaseLiteLLMOpenAIResponseObject = FunctionCallArgumentsDeltaEvent( type=ResponsesAPIStreamEvents.FUNCTION_CALL_ARGUMENTS_DELTA, - item_id=call_id, + item_id=self._tool_item_id_by_call_id.get(call_id, call_id), output_index=output_index, delta=delta_chunk, ) @@ -284,10 +287,10 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator): fn_args = "" if isinstance(fn, dict): fn_name = str(fn.get("name") or "") - fn_args = str(fn.get("arguments") or "") + fn_args = serialize_tool_call_arguments(fn.get("arguments")) else: fn_name = str(getattr(fn, "name", "") or "") - fn_args = str(getattr(fn, "arguments", "") or "") + fn_args = serialize_tool_call_arguments(getattr(fn, "arguments", "")) tool_name, tool_namespace = self._responses_namespace_tool_call_fields(fn_name) # Track if this is a new tool call that wasn't streamed @@ -299,6 +302,7 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator): self._sequence_number += 1 names = self._custom_tool_names item_kwargs = build_tool_call_item_kwargs(call_id, tool_name, "", "in_progress", names) + self._tool_item_id_by_call_id[call_id] = item_kwargs["id"] if tool_namespace: item_kwargs["namespace"] = tool_namespace event = OutputItemAddedEvent( @@ -324,7 +328,7 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator): self._sequence_number += 1 delta_event = FunctionCallArgumentsDeltaEvent( type=ResponsesAPIStreamEvents.FUNCTION_CALL_ARGUMENTS_DELTA, - item_id=call_id, + item_id=self._tool_item_id_by_call_id.get(call_id, call_id), output_index=output_index, delta=delta_chunk, ) @@ -334,7 +338,7 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator): self._sequence_number += 1 done_event = FunctionCallArgumentsDoneEvent( type=ResponsesAPIStreamEvents.FUNCTION_CALL_ARGUMENTS_DONE, - item_id=call_id, + item_id=self._tool_item_id_by_call_id.get(call_id, call_id), output_index=output_index, arguments=final_args, ) @@ -344,6 +348,7 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator): self._sequence_number += 1 names = self._custom_tool_names item_kwargs = build_tool_call_item_kwargs(call_id, tool_name, final_args, "completed", names) + item_kwargs["id"] = self._tool_item_id_by_call_id.setdefault(call_id, item_kwargs["id"]) if tool_namespace: item_kwargs["namespace"] = tool_namespace item_done_event = OutputItemDoneEvent( diff --git a/litellm/responses/litellm_completion_transformation/transformation.py b/litellm/responses/litellm_completion_transformation/transformation.py index f39df38d069..5f3e88bb12f 100644 --- a/litellm/responses/litellm_completion_transformation/transformation.py +++ b/litellm/responses/litellm_completion_transformation/transformation.py @@ -93,6 +93,8 @@ from .custom_tools import ( convert_custom_tool_to_function_tool, extract_custom_tool_names, is_custom_tool_call, + openai_shaped_tool_call_item_id, + serialize_tool_call_arguments, unwrap_custom_tool_arguments, validated_allowed_callers, ) @@ -1010,7 +1012,7 @@ class LiteLLMCompletionResponsesConfig: type=cast(Literal["function"], tool_use_type), function=ChatCompletionToolCallFunctionChunk( name=str(function.get("name", "")), - arguments=str(function.get("arguments", "{}")), + arguments=serialize_tool_call_arguments(function.get("arguments"), "{}"), ), index=index, ) @@ -1539,7 +1541,7 @@ class LiteLLMCompletionResponsesConfig: type=cast(Literal["function"], _tool_use_definition.get("type") or "function"), function=ChatCompletionToolCallFunctionChunk( name=function.get("name") or "", - arguments=str(function.get("arguments") or ""), + arguments=serialize_tool_call_arguments(function.get("arguments")), ), index=0, ) @@ -1589,7 +1591,7 @@ class LiteLLMCompletionResponsesConfig: type="function", function=ChatCompletionToolCallFunctionChunk( name=f"{namespace}__{raw_name}" if qualify else raw_name, - arguments=str(raw_arguments or ""), + arguments=serialize_tool_call_arguments(raw_arguments), ), index=0, ) @@ -1629,6 +1631,8 @@ class LiteLLMCompletionResponsesConfig: file_dict["file_id"] = file_id if item.get("file_data"): file_dict["file_data"] = item["file_data"] + if item.get("filename"): + file_dict["filename"] = item["filename"] new_item: Final[dict[str, object]] = {"type": "file", "file": file_dict} if "cache_control" in item: @@ -2022,7 +2026,7 @@ class LiteLLMCompletionResponsesConfig: function_definition = tool.function tool_name = function_definition.name or "" tool_id = tool.id or "" - tool_arguments = function_definition.get("arguments") or "" + tool_arguments = serialize_tool_call_arguments(function_definition.get("arguments")) # Check if this is a custom tool if is_custom_tool_call(tool_name, custom_tool_names): @@ -2031,7 +2035,7 @@ class LiteLLMCompletionResponsesConfig: custom_item = CustomToolCallOutputItem( type="custom_tool_call", call_id=tool_id, - id=tool_id, + id=openai_shaped_tool_call_item_id("custom_tool_call", tool_id), name=tool_name, input=input_str, status=function_definition.get("status") or "completed", @@ -2062,7 +2066,7 @@ class LiteLLMCompletionResponsesConfig: name=tool_name, arguments=tool_arguments, call_id=tool_id, - id=tool_id, + id=openai_shaped_tool_call_item_id("function_call", tool_id), type="function_call", status=function_definition.get("status") or "completed", ) @@ -2499,8 +2503,7 @@ class LiteLLMCompletionResponsesConfig: choice=choice, ) message_output_items.extend(image_generation_items) - else: - # Regular message output + elif choice.message.content is not None: message_output_items.append( GenericResponseOutputItem( type="message", @@ -2557,7 +2560,7 @@ class LiteLLMCompletionResponsesConfig: type="function", function=Function( name=tool_call.get("name") or "", - arguments=tool_call.get("arguments") or "", + arguments=serialize_tool_call_arguments(tool_call.get("arguments")), ), ) diff --git a/litellm/responses/mcp/litellm_proxy_mcp_handler.py b/litellm/responses/mcp/litellm_proxy_mcp_handler.py index 197d0c02ba8..367915156d1 100644 --- a/litellm/responses/mcp/litellm_proxy_mcp_handler.py +++ b/litellm/responses/mcp/litellm_proxy_mcp_handler.py @@ -399,7 +399,7 @@ class LiteLLM_Proxy_MCP_Handler: @staticmethod async def _process_mcp_tools_without_openai_transform( - user_api_key_auth: Any, + user_api_key_auth: "UserAPIKeyAuth | None", mcp_tools_with_litellm_proxy: Sequence[Mapping[str, object]], litellm_trace_id: str | None = None, mcp_auth_header: str | None = None, @@ -636,7 +636,7 @@ class LiteLLM_Proxy_MCP_Handler: async def _execute_tool_calls( tool_server_map: dict[str, str], tool_calls: Sequence[object], - user_api_key_auth: Any, + user_api_key_auth: "UserAPIKeyAuth | None", mcp_auth_header: str | None = None, mcp_server_auth_headers: dict[str, dict[str, str]] | None = None, oauth2_headers: dict[str, str] | None = None, diff --git a/litellm/responses/streaming_iterator.py b/litellm/responses/streaming_iterator.py index 368fd481e63..82abac3e772 100644 --- a/litellm/responses/streaming_iterator.py +++ b/litellm/responses/streaming_iterator.py @@ -5,11 +5,11 @@ import json import time import traceback import uuid -from collections.abc import Awaitable, Callable, Mapping, Sequence +from collections.abc import Awaitable, Callable, Iterable, Mapping, Sequence from datetime import datetime from functools import lru_cache from types import MappingProxyType -from typing import TYPE_CHECKING, Any, Final, Literal, Protocol, runtime_checkable +from typing import TYPE_CHECKING, Any, Final, Literal, Protocol, overload, runtime_checkable import httpx from openai._streaming import SSEDecoder @@ -42,27 +42,14 @@ from litellm.types.utils import CallTypes from litellm.utils import async_post_call_success_deployment_hook if TYPE_CHECKING: + from litellm.caching.caching_handler import LLMCachingHandler from litellm.proxy._types import UserAPIKeyAuth from litellm.types.responses.streaming_websocket import ( PresidioGuardrailCallback, ResponsesBackendWebSocket, ResponsesClientWebSocket, ) - - class _StreamCachingHandler(Protocol): - """The ``_llm_caching_handler`` attached to a logging object, as this module uses it.""" - - original_function: Callable[..., object] - - def _should_store_result_in_cache( - self, original_function: Callable[..., object], kwargs: Mapping[str, object] - ) -> bool: ... - - class PiiUnmaskingGuardrailCallback(PresidioGuardrailCallback, Protocol): - """Guardrail callback that can also reverse its own masking, selected by - ``llm_http_handler`` on exactly this attribute.""" - - def _unmask_pii_text(self, text: str, pii_tokens: Mapping[str, str]) -> str: ... + from litellm.types.router import LiteLLM_Params class ProjectQuotaCallback(Protocol): @@ -90,10 +77,66 @@ def _is_json_array(value: object) -> TypeIs[list[object]]: # guard-ok: trivial return isinstance(value, list) +def _optional_str(value: object) -> str | None: + """Keep a JSON payload entry only when it is a string, since the wire format is caller-controlled.""" + return value if isinstance(value, str) else None + + +def _json_array_or_empty(value: object) -> Sequence[object]: + """Narrow a JSON payload entry that the caller iterates, tolerating a missing or malformed value.""" + return value if _is_json_array(value) else () + + def _is_str_mapping(value: object) -> TypeIs[dict[str, str]]: # guard-ok: verifies every value is str return _is_json_object(value) and all(isinstance(item, str) for item in value.values()) +class _MutableJsonObject(Protocol): + @overload + def get(self, key: str, /) -> object | None: ... + @overload + def get(self, key: str, default: object, /) -> object: ... + def __getitem__(self, key: str, /) -> object: ... + def __setitem__(self, key: str, value: object, /) -> None: ... + def __contains__(self, key: object, /) -> bool: ... + def items(self) -> Iterable[tuple[str, object]]: ... + + +class _GetsLitellmParams(Protocol): + def __call__(self, key: str, default: Mapping[str, object], /) -> LiteLLM_Params: ... + + +class _UnmasksPiiText(Protocol): + def __call__(self, text: str, pii_tokens: Mapping[str, str]) -> str: ... + + +class _ShouldStoreResultInCache(Protocol): + def __call__(self, *, original_function: Callable[..., object] | None, kwargs: Mapping[str, object]) -> bool: ... + + +class _PostStreamingDeploymentHook(Protocol): + def __call__( + self, + *, + request_data: Mapping[str, object], + response_chunk: ResponsesAPIStreamingResponse, + call_type: CallTypes | None, + ) -> Awaitable[ResponsesAPIStreamingResponse | None]: ... + + +@runtime_checkable +class _HasPostStreamingDeploymentHook(Protocol): + async_post_call_streaming_deployment_hook: _PostStreamingDeploymentHook + + +def _typed_gets_litellm_params(fn: _GetsLitellmParams) -> _GetsLitellmParams: + return fn + + +_SHOULD_STORE_RESULT_IN_CACHE_ATTR: Final = "_should_store_result_in_cache" +_UNMASK_PII_TEXT_ATTR: Final = "_unmask_pii_text" + + def _load_json_object(payload: str | bytes) -> dict[str, object]: """Parse a JSON payload that the caller consumes as an object.""" return json.loads(payload) @@ -220,7 +263,7 @@ class BaseResponsesAPIStreamingIterator: # This matches the stream wrapper in litellm/litellm_core_utils/streaming_handler.py _api_base: Final = get_api_base( model=model or "", - optional_params=self.logging_obj.model_call_details.get("litellm_params", {}), + optional_params=_typed_gets_litellm_params(self.logging_obj.model_call_details.get)("litellm_params", {}), ) self._hidden_params: dict[str, object] = { "model_id": _model_id_from_metadata(litellm_metadata), @@ -301,7 +344,7 @@ class BaseResponsesAPIStreamingIterator: ResponsesAPIStreamEvents.OUTPUT_ITEM_ADDED, ResponsesAPIStreamEvents.OUTPUT_ITEM_DONE, ): - _item: Final = getattr(openai_responses_api_chunk, "item", None) + _item: Final[object] = getattr(openai_responses_api_chunk, "item", None) if _item is not None: ResponsesAPIRequestUtils._encode_container_id_on_output_item( item=_item, @@ -309,7 +352,7 @@ class BaseResponsesAPIStreamingIterator: model_id=_stream_model_id, ) elif _event_type == ResponsesAPIStreamEvents.OUTPUT_TEXT_ANNOTATION_ADDED: - _annotation: Final = getattr(openai_responses_api_chunk, "annotation", None) + _annotation: Final[object] = getattr(openai_responses_api_chunk, "annotation", None) if _annotation is not None: ResponsesAPIRequestUtils._encode_container_id_on_output_item( item=_annotation, @@ -422,15 +465,20 @@ class BaseResponsesAPIStreamingIterator: end_time: Final = datetime.now() if is_async: - asyncio.create_task( - self.logging_obj.dispatch_success_handlers( - logging_response, - start_time=self.start_time, - end_time=end_time, - cache_hit=self._completed_response_cache_hit, - prefer_async_handlers=True, - ) + logging_coroutine: Final = self.logging_obj.dispatch_success_handlers( + logging_response, + start_time=self.start_time, + end_time=end_time, + cache_hit=self._completed_response_cache_hit, + prefer_async_handlers=True, ) + deferred_dispatch_armed: Final = getattr(self.logging_obj, "_on_deferred_stream_complete", None) is not None + if deferred_dispatch_armed: + # End-of-stream guardrail scans write guardrail_information after + # the terminal event; dispatching now would snapshot metadata early. + self.logging_obj._deferred_stream_complete_args = (logging_coroutine,) + else: + asyncio.create_task(logging_coroutine) else: run_async_function( async_function=self.logging_obj.async_success_handler, @@ -549,7 +597,7 @@ class BaseResponsesAPIStreamingIterator: if response_obj is None: return - caching_handler: Final[_StreamCachingHandler | None] = getattr(self.logging_obj, "_llm_caching_handler", None) + caching_handler: Final[LLMCachingHandler | None] = getattr(self.logging_obj, "_llm_caching_handler", None) if caching_handler is None: return @@ -567,8 +615,11 @@ class BaseResponsesAPIStreamingIterator: if preset_cache_key is not None: request_kwargs["cache_key"] = preset_cache_key - if not caching_handler._should_store_result_in_cache( # pyright: ignore[reportPrivateUsage] # no public API - original_function=caching_handler.original_function, + should_store_result_in_cache: Final[_ShouldStoreResultInCache] = getattr( + caching_handler, _SHOULD_STORE_RESULT_IN_CACHE_ATTR + ) + if not should_store_result_in_cache( + original_function=getattr(caching_handler, "original_function", None), kwargs=request_kwargs, ): return @@ -624,12 +675,15 @@ class BaseResponsesAPIStreamingIterator: typed_call_type = None request_data: Final = self.request_data or getattr(self.logging_obj, "model_call_details", {}) - callbacks: Final = getattr(litellm, "callbacks", None) or [] + callbacks: Final[Sequence[object]] = getattr(litellm, "callbacks", None) or [] hooks_ran = False for callback in callbacks: - if hasattr(callback, "async_post_call_streaming_deployment_hook"): + if isinstance(callback, _HasPostStreamingDeploymentHook): hooks_ran = True - result = await callback.async_post_call_streaming_deployment_hook( + post_streaming_hook: _PostStreamingDeploymentHook = ( + callback.async_post_call_streaming_deployment_hook + ) + result = await post_streaming_hook( request_data=request_data, response_chunk=chunk, call_type=typed_call_type, @@ -681,6 +735,8 @@ class BaseResponsesAPIStreamingIterator: kwargs=request_payload, start_time=self.start_time, end_time=end_time, + # the provider call was timed to first byte, so the whole stream minus it is not overhead + include_overhead=False, ) except Exception: # Non-blocking @@ -967,7 +1023,7 @@ class MockResponsesAPIStreamingIterator(BaseResponsesAPIStreamingIterator): transformed: ResponsesAPIResponse, logging_obj: LiteLLMLoggingObj, ) -> None: - self._events: list[ResponsesAPIStreamingResponse] = _build_synthetic_response_events( + self._events: Sequence[ResponsesAPIStreamingResponse] = build_synthetic_response_events( transformed=transformed, logging_obj=logging_obj, chunk_size=self.CHUNK_SIZE, @@ -1034,7 +1090,7 @@ class CachedResponsesAPIStreamingIterator(BaseResponsesAPIStreamingIterator): transformed: ResponsesAPIResponse, logging_obj: LiteLLMLoggingObj, ) -> None: - self._events = _build_synthetic_response_events( + self._events = build_synthetic_response_events( transformed=transformed, logging_obj=logging_obj, chunk_size=MockResponsesAPIStreamingIterator.CHUNK_SIZE, @@ -1081,7 +1137,7 @@ class _HasModelDumpJson(Protocol): def model_dump_json(self, *, exclude_none: bool = ...) -> str: ... -def _dump_response_object(obj: object) -> dict[str, Any]: +def _dump_response_object(obj: object) -> Mapping[str, object]: if isinstance(obj, _HasModelDump): return obj.model_dump() if _is_json_object(obj): @@ -1111,21 +1167,20 @@ def _build_content_part_done_event( item_id: str, output_index: int, content_index: int, - part_payload: dict[str, Any], + part_payload: Mapping[str, object], ) -> ResponsesAPIStreamingResponse | None: openai_types: Final = _get_openai_response_types() part_type: Final = part_payload.get("type") part: PART_UNION_TYPES if part_type == "output_text": - annotations: Final = [ - openai_types.BaseLiteLLMOpenAIResponseObject(**annotation) - for annotation in part_payload.get("annotations", []) or [] - ] - part = openai_types.ContentPartDonePartOutputText( - type="output_text", - text=str(part_payload.get("text") or ""), - annotations=annotations, - logprobs=part_payload.get("logprobs"), + raw_annotations: Final[object] = part_payload.get("annotations", []) or [] + part = openai_types.ContentPartDonePartOutputText.model_validate( + { + "type": "output_text", + "text": str(part_payload.get("text") or ""), + "annotations": raw_annotations, + "logprobs": part_payload.get("logprobs"), + } ) elif part_type == "refusal": part = openai_types.ContentPartDonePartRefusal( @@ -1155,7 +1210,7 @@ def _add_text_like_part_events( item_id: str, output_index: int, content_index: int, - part_payload: dict[str, Any], + part_payload: Mapping[str, object], chunk_size: int, ) -> None: openai_types: Final = _get_openai_response_types() @@ -1172,16 +1227,19 @@ def _add_text_like_part_events( delta=text[i : i + chunk_size], ) ) - annotations_payload: Final[Sequence[dict[str, object]]] = part_payload.get("annotations", []) or [] - for annotation_index, annotation in enumerate(annotations_payload): + raw_annotation_items: Final = part_payload.get("annotations") + annotation_items: Final[Sequence[object]] = raw_annotation_items if _is_json_array(raw_annotation_items) else [] + for annotation_index, annotation in enumerate(annotation_items): events.append( - openai_types.OutputTextAnnotationAddedEvent( - type=openai_types.ResponsesAPIStreamEvents.OUTPUT_TEXT_ANNOTATION_ADDED, - item_id=item_id, - output_index=output_index, - content_index=content_index, - annotation_index=annotation_index, - annotation=annotation, + openai_types.OutputTextAnnotationAddedEvent.model_validate( + { + "type": openai_types.ResponsesAPIStreamEvents.OUTPUT_TEXT_ANNOTATION_ADDED, + "item_id": item_id, + "output_index": output_index, + "content_index": content_index, + "annotation_index": annotation_index, + "annotation": annotation, + } ) ) events.append( @@ -1216,10 +1274,10 @@ def _add_text_like_part_events( ) -def _build_synthetic_response_events( +def build_synthetic_response_events( *, transformed: ResponsesAPIResponse, - logging_obj: LiteLLMLoggingObj, + logging_obj: LiteLLMLoggingObj | None, chunk_size: int, ) -> list[ResponsesAPIStreamingResponse]: openai_types: Final = _get_openai_response_types() @@ -1254,7 +1312,7 @@ def _build_synthetic_response_events( ) if item_type == "message": - content_parts: Sequence[object] = output_item_payload.get("content", []) or [] + content_parts: Sequence[object] = _json_array_or_empty(output_item_payload.get("content")) for content_index, part in enumerate(content_parts): part_payload = _dump_response_object(part) events.append( @@ -1302,7 +1360,7 @@ def _build_synthetic_response_events( ) ) elif item_type == "reasoning": - summaries: Sequence[object] = output_item_payload.get("summary", []) or [] + summaries: Sequence[object] = _json_array_or_empty(output_item_payload.get("summary")) for summary_index, summary in enumerate(summaries): summary_payload = _dump_response_object(summary) summary_text = str(summary_payload.get("text") or "") @@ -1474,7 +1532,7 @@ class ResponsesWebSocketStreaming: user_api_key_dict: UserAPIKeyAuth | None = None, request_data: dict[str, object] | None = None, first_message: str | None = None, - guardrail_callbacks: list[PiiUnmaskingGuardrailCallback] | None = None, + guardrail_callbacks: Sequence[PresidioGuardrailCallback] | None = None, output_guardrail_callbacks: list[PresidioGuardrailCallback] | None = None, quota_callbacks: Sequence[ProjectQuotaCallback] | None = None, authorized_model: str | None = None, @@ -1484,17 +1542,17 @@ class ResponsesWebSocketStreaming: self.logging_obj = logging_obj self.user_api_key_dict = user_api_key_dict self.request_data: dict[str, object] = request_data or {} - self.messages: list[dict[str, object]] = [] + self.messages: list[_MutableJsonObject] = [] self.input_messages: list[dict[str, object]] = [] self.first_message = first_message - self.guardrail_callbacks: list[PiiUnmaskingGuardrailCallback] = guardrail_callbacks or [] + self.guardrail_callbacks: Sequence[PresidioGuardrailCallback] = guardrail_callbacks or [] self.output_guardrail_callbacks: list[PresidioGuardrailCallback] = output_guardrail_callbacks or [] self.quota_callbacks: tuple[ProjectQuotaCallback, ...] = tuple(quota_callbacks) if quota_callbacks else () # Model name authorized at connection time; enforced on every # response.create frame to prevent deployment-substitution attacks. self.authorized_model: str | None = authorized_model - def _should_store_event(self, event_obj: Mapping[str, object]) -> bool: + def _should_store_event(self, event_obj: _MutableJsonObject) -> bool: return event_obj.get("type") in RESPONSES_WS_LOGGED_EVENT_TYPES def _store_event(self, event: str | bytes | dict[str, object]) -> None: @@ -1608,7 +1666,7 @@ class ResponsesWebSocketStreaming: finally: await self._log_messages() - def _enforce_authorized_model(self, msg_obj: dict[str, object]) -> bool: + def _enforce_authorized_model(self, msg_obj: _MutableJsonObject) -> bool: """ Overwrite any ``model`` field in a ``response.create`` frame with the connection-authorized model to prevent deployment-substitution attacks. @@ -1677,7 +1735,7 @@ class ResponsesWebSocketStreaming: # forwarded unmasked regardless of where the client places it. nested_candidate = msg_obj.get("response") nested_response = nested_candidate if _is_json_object(nested_candidate) else None - text_containers: list[tuple[dict[str, object], str]] = [] + text_containers: list[tuple[_MutableJsonObject, str]] = [] for container in (msg_obj, nested_response): if container is None: continue @@ -1784,6 +1842,7 @@ class ResponsesWebSocketStreaming: return response_str cb: Final = self.guardrail_callbacks[0] + unmask_pii_text: Final[_UnmasksPiiText] = getattr(cb, _UNMASK_PII_TEXT_ATTR) event_type: Final = evt_obj.get("type") if event_type == "response.completed": @@ -1803,9 +1862,7 @@ class ResponsesWebSocketStreaming: continue text = content_block.get("text") if isinstance(text, str): - unmasked = cb._unmask_pii_text( # pyright: ignore[reportPrivateUsage] # no public unmasker - text, pii_tokens - ) + unmasked = unmask_pii_text(text, pii_tokens) if unmasked != text: content_block["text"] = unmasked modified = True @@ -1814,9 +1871,7 @@ class ResponsesWebSocketStreaming: if event_type in self._DELTA_EVENT_TYPES: delta: Final = evt_obj.get("delta") if isinstance(delta, str): - unmasked = cb._unmask_pii_text( # pyright: ignore[reportPrivateUsage] # no public unmasker - delta, pii_tokens - ) + unmasked = unmask_pii_text(delta, pii_tokens) if unmasked != delta: evt_obj["delta"] = unmasked return json.dumps(evt_obj) @@ -2018,7 +2073,7 @@ class ManagedResponsesWebSocketHandler: model: str, logging_obj: LiteLLMLoggingObj, user_api_key_dict: UserAPIKeyAuth | None = None, - litellm_metadata: dict[str, Any] | None = None, + litellm_metadata: Mapping[str, object] | None = None, api_key: str | None = None, api_base: str | None = None, timeout: float | None = None, @@ -2031,10 +2086,11 @@ class ManagedResponsesWebSocketHandler: self.model = model self.logging_obj = logging_obj self.user_api_key_dict = user_api_key_dict - self.litellm_metadata: dict[str, Any] = litellm_metadata or {} - self.model_group: str | None = self.litellm_metadata.get("model_group") or self.litellm_metadata.get( + self.litellm_metadata: Mapping[str, object] = litellm_metadata or {} + raw_model_group: Final = self.litellm_metadata.get("model_group") or self.litellm_metadata.get( "deployment_model_name" ) + self.model_group: str | None = raw_model_group if isinstance(raw_model_group, str) else None self.api_key = api_key self.api_base = api_base self.timeout = timeout @@ -2055,7 +2111,7 @@ class ManagedResponsesWebSocketHandler: # ------------------------------------------------------------------ @staticmethod - def _serialize_chunk(chunk: Any) -> str | None: + def _serialize_chunk(chunk: object) -> str | None: """Serialize a streaming chunk to a JSON string for WebSocket transmission.""" try: if isinstance(chunk, _HasModelDumpJson): @@ -2098,7 +2154,7 @@ class ManagedResponsesWebSocketHandler: self._session_history[response_id] = messages @staticmethod - def _extract_response_id(completed_event: dict[str, object]) -> str | None: + def _extract_response_id(completed_event: _MutableJsonObject) -> str | None: """ Pull the raw (decoded) response ID out of a ``response.completed`` event. Returns *None* if the event doesn't contain a usable ID. @@ -2113,7 +2169,7 @@ class ManagedResponsesWebSocketHandler: @staticmethod def _extract_output_messages( - completed_event: dict[str, object], + completed_event: _MutableJsonObject, ) -> list[dict[str, object]]: """ Convert the output items in a ``response.completed`` event into @@ -2170,7 +2226,7 @@ class ManagedResponsesWebSocketHandler: # _process_response_create sub-methods # ------------------------------------------------------------------ - async def _parse_message(self, raw_message: str) -> dict[str, object] | None: + async def _parse_message(self, raw_message: str) -> _MutableJsonObject | None: """Parse raw WS text; return the message dict or None (JSON error / ignored type).""" try: msg_obj: Final = _load_json_object(raw_message) @@ -2183,7 +2239,7 @@ class ManagedResponsesWebSocketHandler: return msg_obj @staticmethod - def _is_warmup_frame(msg_obj: dict[str, object]) -> bool: + def _is_warmup_frame(msg_obj: _MutableJsonObject) -> bool: """Return True for a response.create whose generate flag is false.""" nested: Final = msg_obj.get("response") source: Final = nested if _is_json_object(nested) and nested else msg_obj @@ -2199,13 +2255,13 @@ class ManagedResponsesWebSocketHandler: return str(raw_id).startswith(_WARMUP_RESPONSE_ID_PREFIX) @staticmethod - def _warmup_source_params(msg_obj: dict[str, object]) -> dict[str, object]: + def _warmup_source_params(msg_obj: _MutableJsonObject) -> dict[str, object]: nested: Final = msg_obj.get("response") if _is_json_object(nested) and nested: return nested return {k: v for k, v in msg_obj.items() if k != "type"} - def _build_warmup_response(self, msg_obj: dict[str, object]) -> dict[str, object]: + def _build_warmup_response(self, msg_obj: _MutableJsonObject) -> dict[str, object]: """Build a minimal completed Responses API object for a warmup ack.""" source: Final = self._warmup_source_params(msg_obj) wire_model: Final = source.get("model") or self.model_group or self.model @@ -2223,7 +2279,7 @@ class ManagedResponsesWebSocketHandler: }, } - async def _send_warmup_ack(self, msg_obj: dict[str, object]) -> None: + async def _send_warmup_ack(self, msg_obj: _MutableJsonObject) -> None: """ Acknowledge a generate=false prewarm without calling the provider. @@ -2246,7 +2302,7 @@ class ManagedResponsesWebSocketHandler: await self.websocket.send_text(serialized) @staticmethod - def _build_base_call_kwargs(msg_obj: dict[str, object]) -> dict[str, Any]: + def _build_base_call_kwargs(msg_obj: _MutableJsonObject) -> dict[str, Any]: """ Extract Responses API params from the event, handling both wire formats: Nested: {"type": "response.create", "response": {"input": [...], ...}} @@ -2355,7 +2411,7 @@ class ManagedResponsesWebSocketHandler: call_kwargs.setdefault("litellm_params", {}) call_kwargs["litellm_params"]["proxy_server_request"] = proxy_server_request - async def _stream_and_forward(self, model: str, call_kwargs: dict[str, Any]) -> dict[str, object] | None: + async def _stream_and_forward(self, model: str, call_kwargs: dict[str, Any]) -> _MutableJsonObject | None: """ Stream ``litellm.aresponses`` and forward every chunk over the WebSocket. @@ -2363,7 +2419,7 @@ class ManagedResponsesWebSocketHandler: directly (before serialization) to avoid a redundant JSON round-trip on every chunk. Returns the completed event dict, or ``None``. """ - completed_event: dict[str, object] | None = ( + completed_event: _MutableJsonObject | None = ( None # rebind-ok: captures the completed event once the stream yields it ) stream_response: Final = await litellm.aresponses(model=model, **call_kwargs) @@ -2389,7 +2445,7 @@ class ManagedResponsesWebSocketHandler: def _save_turn_history( self, - completed_event: dict[str, object] | None, + completed_event: _MutableJsonObject | None, prior_history: list[dict[str, object]], current_messages: list[dict[str, object]], ) -> None: @@ -2462,12 +2518,12 @@ class ManagedResponsesWebSocketHandler: # reuse the router-resolved self.model; passing the alias raw to # litellm.aresponses fails in get_llm_provider. A genuinely different # provider-prefixed per-frame model is still honored. - requested_model: Final[str | None] = call_kwargs.pop("model", None) + requested_model: Final[str | None] = _optional_str(call_kwargs.pop("model", None)) model: Final[str] = ( self.model if requested_model is None or requested_model == self.model_group else requested_model ) - previous_response_id: Final[str | None] = call_kwargs.pop("previous_response_id", None) + previous_response_id: Final[str | None] = _optional_str(call_kwargs.pop("previous_response_id", None)) current_messages: Final = self._input_to_messages(call_kwargs.get("input")) # Fetch history once; reused in both _apply_history and _save_turn_history diff --git a/litellm/router.py b/litellm/router.py index c93c1753f0e..23d8907fb49 100644 --- a/litellm/router.py +++ b/litellm/router.py @@ -22,7 +22,7 @@ import traceback import weakref from collections import defaultdict from collections.abc import AsyncGenerator, AsyncIterator, Callable, Generator, Mapping, Sequence -from functools import lru_cache +from functools import lru_cache, partial from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final, Literal, Optional, TypeAlias, TypeVar, Union, cast @@ -143,7 +143,11 @@ from litellm.router_utils.cooldown_handlers import ( from litellm.router_utils.fallback_event_handlers import ( AttemptedFallbackTargets, _check_non_standard_fallback_format, - get_fallback_model_group, + clear_pre_routing_selection, + fallback_lookup_groups, + get_fallback_model_group_for_lookup_groups, + get_pre_routing_selection, + record_pre_routing_selection, run_async_fallback, ) from litellm.router_utils.get_retry_from_policy import ( @@ -821,6 +825,7 @@ class Router: self._zero_cost_cache: dict[str, bool] = {} self._routing_group_rows: tuple[DeploymentTypedDict, ...] | None = None self._init_routing_groups(None) + self._provider_unresolved_deployments: tuple[Callable[[], Deployment | None], ...] = () self.deployment_affinity_ttl_seconds = deployment_affinity_ttl_seconds self.model_group_affinity_config = model_group_affinity_config @@ -4918,6 +4923,19 @@ class Router: ) response = await response + if self._should_raise_anthropic_refusal_error( + model=model, + original_generic_function=original_generic_function, + response=response, + kwargs=kwargs, + ): + from litellm.llms.anthropic.experimental_pass_through.messages.utils import ( + safeguard_refusal_error, + ) + + refusal_details: Final = cast(dict, response["stop_details"]) # cast-ok: gate verified the shape + raise safeguard_refusal_error(model=model, stop_details=refusal_details) + self.success_calls[model_name] += 1 verbose_router_logger.info("ageneric_api_call_with_fallbacks(model=%s)\x1b[32m 200 OK\x1b[0m", model_name) @@ -4964,6 +4982,11 @@ class Router: # fallback to the original reference for any non-picklable value. # The original_generic_function is preserved so the per-attempt # helper knows which underlying API to call on fallback. + # The pre-routing hook stamps its tier selection into this bucket during the primary + # attempt; seeding it before the snapshot gives both the live kwargs and the copy a + # bucket, so the post-call carry-over below always has somewhere to read and write. + kwargs.setdefault("litellm_metadata", {}) # mutable-ok: shared bucket # rebind-ok: stamp must be readable here + fallback_kwargs: Final[dict[str, object]] = kwargs.copy() if isinstance(fallback_kwargs.get("litellm_metadata"), dict): fallback_kwargs["litellm_metadata"] = safe_deep_copy(fallback_kwargs["litellm_metadata"]) @@ -4973,6 +4996,14 @@ class Router: response: Final = await self._ageneric_api_call_with_fallbacks(original_function=original_function, **kwargs) + # The snapshot predates the pre-routing hook, so the tier it stamped into the live kwargs + # is carried over write-or-clear: a stale or caller-supplied selection left in the copy + # would key the mid-stream fallback lookup off a tier this attempt never routed to. + clear_pre_routing_selection(fallback_kwargs) + live_pre_routing_selection: Final = get_pre_routing_selection(kwargs) + if live_pre_routing_selection is not None: + record_pre_routing_selection(fallback_kwargs, live_pre_routing_selection) + if kwargs.get("stream") and isinstance(response, BaseResponsesAPIStreamingIterator): return await self._aresponses_streaming_iterator( response=response, @@ -5030,6 +5061,10 @@ class Router: from litellm.llms.anthropic.experimental_pass_through.messages.streaming_iterator import ( aclose_if_supported, parse_anthropic_error_event, + parse_anthropic_refusal_stop_details, + ) + from litellm.llms.anthropic.experimental_pass_through.messages.utils import ( + safeguard_refusal_error, ) source_iterator: Final = response @@ -5068,13 +5103,35 @@ class Router: continue if _anthropic_stream_commits_now(chunk, has_generated_content, len(buffered_lifecycle_chunks)): has_generated_content = True # rebind-ok: real content seen, or the buffer cap was hit - error_event = parse_anthropic_error_event(chunk) + # A transport can split one SSE data line across byte chunks, so pre-content + # detection parses the accumulated buffer plus the current chunk, never the + # chunk alone; the buffer is already capped, which bounds this window too. + parse_window = ( # rebind-ok: freshly computed each iteration, never carried over + b"".join(c for c in (*buffered_lifecycle_chunks, chunk) if isinstance(c, (bytes, bytearray))) # pyright: ignore[reportUnnecessaryIsInstance] # bridge-path chunks are not always bytes at runtime + if not has_generated_content and isinstance(chunk, (bytes, bytearray)) # pyright: ignore[reportUnnecessaryIsInstance] # bridge-path chunks are not always bytes at runtime + else chunk + ) + error_event = parse_anthropic_error_event(parse_window) retriable_pending_error = ( # rebind-ok: freshly computed each iteration, never carried over not has_generated_content and error_event is not None and _is_retriable_anthropic_status(error_event[2]) and not _anthropic_stream_error_is_gateway_verdict(chunk) ) + refusal_stop_details = ( # rebind-ok: freshly computed each iteration, never carried over + parse_anthropic_refusal_stop_details(parse_window) + if not has_generated_content and error_event is None + else None + ) + if refusal_stop_details is not None and self._has_content_policy_fallback(model, initial_kwargs): + refusal_error = safeguard_refusal_error(model=model, stop_details=refusal_stop_details) + raise MidStreamFallbackError( + message=refusal_error.message, + model=model, + llm_provider="anthropic", + original_exception=refusal_error, + is_pre_first_chunk=True, + ) if not has_generated_content and not retriable_pending_error and error_event is None: buffered_lifecycle_chunks = (*buffered_lifecycle_chunks, chunk) continue @@ -5186,8 +5243,13 @@ class Router: kwargs=initial_kwargs, metadata_variable_name="litellm_metadata", ) + # The content-policy dispatch branch matches on the trigger's own type, so a refusal's + # MidStreamFallbackError envelope is unwrapped here or the wrong fallback list is consulted. + fallback_trigger: Final[Exception] = ( + e.original_exception if isinstance(e.original_exception, litellm.ContentPolicyViolationError) else e + ) fallback_response = await self.async_function_with_fallbacks_common_utils( # rebind-ok: set on success - e=e, + e=fallback_trigger, disable_fallbacks=False, fallbacks=fallbacks, context_window_fallbacks=context_window_fallbacks, @@ -5243,6 +5305,11 @@ class Router: # share, leaking primary-deployment metadata into the mid-stream # fallback request. safe_deep_copy avoids deep-copying the full # kwargs (which can hold non-deepcopyable logging handles/clients). + # The pre-routing hook stamps its tier selection into this bucket during the primary + # attempt; seeding it before the snapshot gives both the live kwargs and the copy a + # bucket, so the post-call carry-over below always has somewhere to read and write. + kwargs.setdefault("litellm_metadata", {}) # mutable-ok: shared bucket # rebind-ok: stamp must be readable here + fallback_kwargs: Final[dict[str, object]] = kwargs.copy() # mutable-ok: mutated below before re-entry if isinstance(fallback_kwargs.get("litellm_metadata"), dict): fallback_kwargs["litellm_metadata"] = safe_deep_copy(fallback_kwargs["litellm_metadata"]) @@ -5252,6 +5319,14 @@ class Router: response: Final = await self._ageneric_api_call_with_fallbacks(original_function=original_function, **kwargs) + # The snapshot predates the pre-routing hook, so the tier it stamped into the live kwargs + # is carried over write-or-clear: a stale or caller-supplied selection left in the copy + # would key the mid-stream fallback lookup off a tier this attempt never routed to. + clear_pre_routing_selection(fallback_kwargs) + live_pre_routing_selection: Final = get_pre_routing_selection(kwargs) + if live_pre_routing_selection is not None: + record_pre_routing_selection(fallback_kwargs, live_pre_routing_selection) + if kwargs.get("stream") and hasattr(response, "__aiter__"): return await self._aanthropic_messages_streaming_iterator( response=cast("AsyncIterator[bytes]", response), # cast-ok: stream=True always returns a byte iterator @@ -6486,6 +6561,8 @@ class Router: **kwargs, ) elif call_type == "allm_passthrough_route": + if client: + kwargs["client"] = client return await self._ageneric_api_call_with_fallbacks( original_function=original_function, passthrough_on_no_deployment=True, @@ -6805,6 +6882,9 @@ class Router: original_exception: Final = e fallback_model_group = None original_model_group: Final[str | None] = kwargs.get("model") + # A pre-routing hook (complexity / auto / adaptive / quality routers) picks a tier + # behind the router name, and fallbacks are configured per tier, not per router. + lookup_groups: Final[tuple[str, ...]] = fallback_lookup_groups(kwargs, model_group) fallback_failure_exception_str = "" if disable_fallbacks is True or original_model_group is None: @@ -6849,15 +6929,15 @@ class Router: ] # Get external fallbacks — handle both standard and non-standard formats external_fallback_group: list | None = None - if fallbacks is not None and model_group is not None: + if fallbacks is not None and lookup_groups: if _check_non_standard_fallback_format(fallbacks=fallbacks): # Non-standard formats (e.g. ["claude-3-haiku"] or # [{"model": "...", "messages": [...]}]) are passed through directly external_fallback_group = fallbacks else: - external_fallback_group, generic_idx = get_fallback_model_group( + external_fallback_group, generic_idx = get_fallback_model_group_for_lookup_groups( fallbacks=fallbacks, - model_group=cast(str, model_group), + lookup_groups=lookup_groups, ) if external_fallback_group is None and generic_idx is not None: external_fallback_group = fallbacks[generic_idx]["*"] @@ -6915,9 +6995,9 @@ class Router: if isinstance(e, litellm.ContextWindowExceededError): if context_window_fallbacks is not None: context_window_fallback_model_group: Final[list[str] | None] = ( - self._get_fallback_model_group_from_fallbacks( + self._get_fallback_model_group_for_lookup_groups( fallbacks=context_window_fallbacks, - model_group=model_group, + lookup_groups=lookup_groups, ) ) if context_window_fallback_model_group is None: @@ -6948,9 +7028,9 @@ class Router: elif isinstance(e, litellm.ContentPolicyViolationError): if content_policy_fallbacks is not None: content_policy_fallback_model_group: Final[list[str] | None] = ( - self._get_fallback_model_group_from_fallbacks( + self._get_fallback_model_group_for_lookup_groups( fallbacks=content_policy_fallbacks, - model_group=model_group, + lookup_groups=lookup_groups, ) ) if content_policy_fallback_model_group is None: @@ -6977,14 +7057,14 @@ class Router: if litellm.expose_router_debug_in_errors: e.message += f"\n{error_message}" - if fallbacks is not None and model_group is not None: + if fallbacks is not None and lookup_groups: verbose_router_logger.debug("inside model fallbacks: %s", mask_sensitive_structure(fallbacks)) ( fallback_model_group, generic_fallback_idx, - ) = get_fallback_model_group( + ) = get_fallback_model_group_for_lookup_groups( fallbacks=fallbacks, # if fallbacks = [{"gpt-3.5-turbo": ["claude-3-haiku"]}] - model_group=cast(str, model_group), + lookup_groups=lookup_groups, ) ## if none, check for generic fallback if fallback_model_group is None and generic_fallback_idx is not None: @@ -6993,12 +7073,12 @@ class Router: if fallback_model_group is None: masked_fallbacks: Final = mask_sensitive_structure(fallbacks) verbose_router_logger.info( - "No fallback model group found for original model_group=%s. Fallbacks=%s", - model_group, + "No fallback model group found for lookup_groups=%s. Fallbacks=%s", + " -> ".join(lookup_groups), masked_fallbacks, ) if hasattr(original_exception, "message") and litellm.expose_router_debug_in_errors: - original_exception.message += f"No fallback model group found for original model_group={model_group}. Fallbacks={masked_fallbacks}" + original_exception.message += f"No fallback model group found for lookup_groups={' -> '.join(lookup_groups)}. Fallbacks={masked_fallbacks}" raise original_exception input_kwargs.update( @@ -7044,6 +7124,7 @@ class Router: If it fails after num_retries, fall back to another model group """ model_group: Final[str | None] = kwargs.get("model") + clear_pre_routing_selection(kwargs) # pyright: ignore[reportUnknownArgumentType] # **kwargs is untyped at this boundary if not isinstance(kwargs.get("attempted_targets"), AttemptedFallbackTargets): _fallback_metadata_key: Final = _get_router_metadata_variable_name( function_name=getattr(kwargs.get("original_function"), "__name__", None) @@ -7469,6 +7550,24 @@ class Router: break return fallback_model_group + def _get_fallback_model_group_for_lookup_groups( + self, + fallbacks: list[dict[str, list[str]]], # mutable-ok: mirrors the sibling resolver's contract + lookup_groups: tuple[str, ...], + ) -> list[str] | None: # mutable-ok: mirrors the sibling resolver's contract + """First lookup group whose exact-key chain resolves (tier first, then requested group).""" + return next( + ( + resolved + for resolved in ( + self._get_fallback_model_group_from_fallbacks(fallbacks=fallbacks, model_group=group) + for group in lookup_groups + ) + if resolved is not None + ), + None, + ) + def _get_first_default_fallback(self) -> str | None: """ Returns the first model from the default_fallbacks list, if it exists. @@ -7884,6 +7983,31 @@ class Router: return True return False + def _has_content_policy_fallback(self, model_group: str, kwargs: Mapping[str, Any]) -> bool: + """ + Whether a content-policy fallback would resolve for this request, keyed the same way + async_function_with_fallbacks_common_utils resolves it: the tier a pre-routing hook + selected wins over the requested group. Raising without this returning True would turn + a deliverable response into an error the fallback chain cannot recover from. + """ + content_policy_fallbacks: Final = kwargs.get("content_policy_fallbacks", self.content_policy_fallbacks) + if content_policy_fallbacks is not None: + return ( + self._get_fallback_model_group_for_lookup_groups( + fallbacks=content_policy_fallbacks, + lookup_groups=fallback_lookup_groups(kwargs, model_group), + ) + is not None + ) + if self._has_default_fallbacks(): + return True + verbose_router_logger.debug( + "No content-policy fallback available. Returning original response. model=%s, content_policy_fallbacks=%s", + model_group, + content_policy_fallbacks, + ) + return False + def _should_raise_content_policy_error(self, model: str, response: ModelResponse, kwargs: dict) -> bool: """ Determines if a content policy error should be raised. @@ -7896,27 +8020,26 @@ class Router: if response.choices[0].finish_reason != "content_filter": return False - content_policy_fallbacks: Final = kwargs.get("content_policy_fallbacks", self.content_policy_fallbacks) + return self._has_content_policy_fallback(model, kwargs) - ### ONLY RAISE ERROR IF CP FALLBACK AVAILABLE ### - if content_policy_fallbacks is not None: - fallback_model_group = None - for item in content_policy_fallbacks: # [{"gpt-3.5-turbo": ["gpt-4"]}] - if list(item.keys())[0] == model: - fallback_model_group = item[model] - break - - if fallback_model_group is not None: - return True - elif self._has_default_fallbacks(): # default fallbacks set - return True - - verbose_router_logger.debug( - "Content Policy Error occurred. No available fallbacks. Returning original response. model=%s, content_policy_fallbacks=%s", - model, - content_policy_fallbacks, + def _should_raise_anthropic_refusal_error( + self, model: str, original_generic_function: Callable, response: object, kwargs: Mapping[str, Any] + ) -> bool: + """ + The /v1/messages twin of _should_raise_content_policy_error: an Anthropic safeguard + refusal (stop_reason "refusal" carrying stop_details) re-enters the fallback chain only + when a content-policy fallback is configured; a plain refusal without stop_details, or + any response with nothing configured, is returned to the client unchanged. + """ + from litellm.llms.anthropic.experimental_pass_through.messages.utils import ( + get_safeguard_refusal_stop_details, ) - return False + + if getattr(original_generic_function, "__name__", "") != "anthropic_messages": + return False + if get_safeguard_refusal_stop_details(response) is None: + return False + return self._has_content_policy_fallback(model, kwargs) def _get_healthy_deployments(self, model: str, parent_otel_span: Span | None): _all_deployments: list = [] @@ -8163,6 +8286,52 @@ class Router: if backend_value is not None: model_info[field] = backend_value + @staticmethod + def _inherit_builtin_base_rates_for_off_peak( + model_info: dict, # mutable-ok: cost-map entry filled in place + backend_model: str, + custom_llm_provider: str | None, + ) -> None: + """Fill missing pricing fields on a deployment entry that only sets + ``off_peak_pricing``, from the backend model's built-in cost map entry. + + Cost lookup selects the deployment-scoped entry over the shared backend + entry only when the deployment entry carries a base pricing field, and + ``off_peak_pricing`` is deliberately kept off the shared entry, so a + deployment spelling out only its off-peak schedule would otherwise + never receive the discount. The backend model's entire canonical cost + map entry is copied, field by field, so threshold, tiered, + service-tier, cache, character, and per-second rates as well as + companion billing fields like ``web_search_billing_unit`` and the + regional uplift multipliers all carry over, and peak-hour billing + through the deployment entry matches the shared backend entry exactly. + The raw ``litellm.model_cost`` entry is the copy source rather than + ``get_model_info``'s view of it, since that view synthesizes zero flat + token rates for backends without one and storing those would mark a + tiered-only backend explicitly priced free. Values are deep-copied to + keep the builtin entry isolated. User-specified fields always win; + no-op when any base pricing field is already set or the backend model + has no canonical entry. + """ + if not model_info.get("off_peak_pricing"): + return + if any( + model_info.get(field) is not None + for field in ("input_cost_per_token", "input_cost_per_second", "tiered_pricing") + ): + return + try: + backend_info: Final = litellm.get_model_info(model=backend_model, custom_llm_provider=custom_llm_provider) + except Exception: # noqa: BLE001 # get_model_info raises plain Exception for an unmapped backend model + return + backend_entry: Final = litellm.model_cost.get(backend_info.get("key") or "") + if not isinstance(backend_entry, dict): + return + for field, backend_value in backend_entry.items(): + if model_info.get(field) is not None or backend_value is None: + continue + model_info[field] = copy.deepcopy(backend_value) + @staticmethod def _inherit_builtin_tiered_output_rate( model_info: dict, backend_model: str, custom_llm_provider: str | None @@ -8251,6 +8420,11 @@ class Router: if deployment.litellm_params.get(field) is not None: _model_info[field] = deployment.litellm_params[field] + Router._inherit_builtin_base_rates_for_off_peak( + model_info=_model_info, + backend_model=deployment.litellm_params.model, + custom_llm_provider=deployment.litellm_params.custom_llm_provider, + ) if _model_info.get("input_cost_per_token") is not None: Router._inherit_builtin_cache_pricing( model_info=_model_info, @@ -8299,6 +8473,19 @@ class Router: return deployment except Exception as e: if self.ignore_invalid_deployments: + if isinstance(e, litellm.BadRequestError): + self._provider_unresolved_deployments = ( + *self._provider_unresolved_deployments, + partial( + self._create_deployment, + deployment_info=deployment_info, + _model_name=_model_name, + _litellm_params=_litellm_params, + _model_info=_model_info, + declared_id=declared_id, + duplicate_ids=duplicate_ids, + ), + ) verbose_router_logger.exception( "Error creating deployment: %s, ignoring and continuing with other deployments.", e ) @@ -8728,6 +8915,7 @@ class Router: self.quality_routers = {} self.complexity_routers = {} self.auto_routers = {} + self._provider_unresolved_deployments = () self._invalidate_model_group_info_cache() self._invalidate_access_groups_cache() # we add api_base/api_key each model so load balancing between azure/gpt on api_base1 and api_base2 works @@ -8992,6 +9180,11 @@ class Router: if field_value is not None: _model_info_dict[field] = field_value + Router._inherit_builtin_base_rates_for_off_peak( + model_info=_model_info_dict, + backend_model=deployment.litellm_params.model, + custom_llm_provider=deployment.litellm_params.custom_llm_provider, + ) if _model_info_dict.get("input_cost_per_token") is not None: Router._inherit_builtin_cache_pricing( model_info=_model_info_dict, @@ -9152,7 +9345,8 @@ class Router: if _deployment_on_router is not None: # deployment with this model_id exists on the router if ( - deployment.litellm_params == _deployment_on_router.litellm_params + deployment.model_name == _deployment_on_router.model_name + and deployment.litellm_params == _deployment_on_router.litellm_params and deployment.model_info == _deployment_on_router.model_info ): # No need to update @@ -9246,6 +9440,11 @@ class Router: field_value = deployment.litellm_params.get(field) if field_value is not None: model_info[field] = field_value + Router._inherit_builtin_base_rates_for_off_peak( + model_info=model_info, + backend_model=deployment.litellm_params.model, + custom_llm_provider=deployment.litellm_params.custom_llm_provider, + ) if model_info.get("input_cost_per_token") is not None: Router._inherit_builtin_cache_pricing( model_info=model_info, @@ -9339,8 +9538,12 @@ class Router: """Re-assert this router's deployments onto a freshly fetched catalog. Reads ``model_list`` at call time, so only deployments the router still - serves are restored. + serves are restored, plus any config deployment the fresh catalog now resolves. """ + provider_unresolved: Final = self._provider_unresolved_deployments + self._provider_unresolved_deployments = () + for create_deployment in provider_unresolved: + create_deployment() for entry in tuple(self.model_list): try: deployment = entry if isinstance(entry, Deployment) else Deployment(**entry) @@ -12023,6 +12226,7 @@ class Router: if pre_routing_hook_response is not None: model = pre_routing_hook_response.model messages = pre_routing_hook_response.messages + record_pre_routing_selection(request_kwargs, model) if pre_routing_hook_response.litellm_params: accepted_tier_params: Final = self._tier_params_the_target_accepts( model, pre_routing_hook_response.litellm_params, request_kwargs @@ -12138,6 +12342,7 @@ class Router: if pre_routing_hook_response is not None: model = pre_routing_hook_response.model messages = pre_routing_hook_response.messages + record_pre_routing_selection(request_kwargs, model) if pre_routing_hook_response.litellm_params: accepted_tier_params: Final = self._tier_params_the_target_accepts( model, pre_routing_hook_response.litellm_params, request_kwargs diff --git a/litellm/router_strategy/budget_limiter.py b/litellm/router_strategy/budget_limiter.py index d57d7da0410..a8d51f95e45 100644 --- a/litellm/router_strategy/budget_limiter.py +++ b/litellm/router_strategy/budget_limiter.py @@ -20,6 +20,7 @@ anthropic: import asyncio import builtins +from collections.abc import Mapping from datetime import datetime, timedelta, timezone from typing import Any, Final @@ -54,19 +55,19 @@ class _LiteLLMParamsDictView: __slots__ = ("_params",) - def __init__(self, params: dict[str, Any]): + def __init__(self, params: Mapping[str, object]): self._params = params - def __getattr__(self, key: str) -> Any: + def __getattr__(self, key: str) -> object: return self._params.get(key) - def __getitem__(self, key: str) -> Any: + def __getitem__(self, key: str) -> object: return self._params.get(key) def __contains__(self, key: str) -> bool: return key in self._params - def get(self, key: str, default: Any = None) -> Any: + def get(self, key: str, default: object = None) -> object: return self._params.get(key, default) def keys(self): @@ -84,10 +85,10 @@ class _LiteLLMParamsDictView: def __len__(self) -> int: return len(self._params) - def dict(self) -> dict[str, Any]: + def dict(self) -> builtins.dict[str, object]: return dict(self._params) - def model_dump(self) -> builtins.dict[str, Any]: + def model_dump(self) -> builtins.dict[str, object]: return dict(self._params) diff --git a/litellm/router_strategy/complexity_router/README.md b/litellm/router_strategy/complexity_router/README.md index 63ba760ff66..bc8df67cc28 100644 --- a/litellm/router_strategy/complexity_router/README.md +++ b/litellm/router_strategy/complexity_router/README.md @@ -154,6 +154,9 @@ model_list: # Fallback model if tier cannot be determined default_model: gpt-4o + + # Replace a routed model that cannot take image input (default: false) + modality_routing: true ``` ## Usage @@ -178,6 +181,25 @@ response = litellm.completion( ## Special Behaviors +### Modality-based capability routing + +The classifier reads text alone, so a request carrying an image can classify cheap and land on a +text-only model, which rejects it with a provider 400 no fallback catches. With +`modality_routing: true`, one gate inspects every decided placement: when the routed model is +explicitly declared `supports_vision: false` (deployment `model_info` first, the model cost map +otherwise; unmapped names stay routable, and a multi-deployment group must accept on every +deployment), the request is re-placed on the nearest HIGHER tier holding a capable model, with +routing plugins still applied to the re-pick, then on `default_model` (never on plugin routers +and never for a plan-floored decision), and otherwise rejected with a clear 400 naming the +router. The walk only ever goes up, so a plan-mode floor cannot be undercut; a router whose only +vision model sits below the decided tier gets the 400 and an actionable message instead. + +A same-tier re-pick keeps the decision's cause and adds `modality:image` to `signals`; a tier +change or default takeover records `cause: modality_escalation` with the displaced placement +(`modality_escalated_from:` or `modality_displaced_default_model`). Escalations are never +pinned by session affinity, and a KEPT session pin bypasses the gate entirely: a session pinned +to a text-only model keeps it even when an image arrives. + ### Heuristic-first chaining `classifier_type: heuristic_first` runs the local scorer on every request and only calls the LLM diff --git a/litellm/router_strategy/complexity_router/complexity_router.py b/litellm/router_strategy/complexity_router/complexity_router.py index 2f4305756e9..577cee0920d 100644 --- a/litellm/router_strategy/complexity_router/complexity_router.py +++ b/litellm/router_strategy/complexity_router/complexity_router.py @@ -30,6 +30,7 @@ from litellm.constants import EMPTY_MAPPING, RETURN_RAW_MODEL_NAME_METADATA_KEY from litellm.integrations.custom_logger import CustomLogger from litellm.litellm_core_utils.core_helpers import get_metadata_variable_name_from_kwargs from litellm.litellm_core_utils.internal_call_metadata import forwarded_internal_call_metadata +from litellm.litellm_core_utils.prompt_templates.common_utils import request_contains_image_content from litellm.litellm_core_utils.sensitive_data_masker import mask_credentials_in_payload from litellm.llms.base_llm.base_utils import type_to_response_format_param from litellm.types.utils import ( @@ -281,7 +282,7 @@ def _response_cost_or_none(response: ModelResponse) -> float | None: return float(cost) -def _effective_turn_off_message_logging(request_kwargs: Mapping[str, Any] | None) -> bool | None: +def _effective_turn_off_message_logging(request_kwargs: Mapping[str, object] | None) -> bool | None: from litellm.litellm_core_utils.initialize_dynamic_callback_params import ( initialize_standard_callback_dynamic_params, ) @@ -479,6 +480,33 @@ def _last_human_ask_index( ) +def _newest_turn_is_human_ask( + messages: Sequence[Mapping[str, object]] | None, + marker_pairs: tuple[tuple[str, str], ...] = _DEFAULT_REMINDER_MARKERS, +) -> bool: + """Whether the request's newest turn carries a real human ask, i.e. this is a new ask rather + than an agent loop's continuation traffic. + + Anchored on `_last_human_ask_index` so every surface's plumbing reads as a continuation: + chat-completions tool turns are role=tool, Messages-surface tool_result turns flatten to empty + human text, and a hybrid turn carrying an ask alongside a tool_result still counts as an ask. + Compared against the newest non-system message rather than the raw tail, because Claude Code + appends a system-role reminder after the human turn; that trailing plumbing is neither an ask + nor loop traffic and must not turn a fresh ask into a continuation. An unreadable request (no + messages) is treated as a continuation: there is no ask to classify, which is the same reading + `_extract_current_ask_and_system_prompt` gives it downstream. + """ + if not messages: + return False + newest_non_system: Final = next( + (index for index in range(len(messages) - 1, -1, -1) if messages[index].get("role") != "system"), + None, + ) + if newest_non_system is None: + return False + return _last_human_ask_index(messages, marker_pairs) == newest_non_system + + def _iter_system_scope_texts( body_system: object, messages: Sequence[Mapping[str, object]], @@ -706,11 +734,25 @@ def _decision_is_pinnable(decision: StandardLoggingRoutingDecision | None) -> bo of the three: an agent names the conversation on its first turn, so the cheapest tier would be the pin every session starts with, and the real work that follows would run there for the whole TTL. It describes what that one call is, never what the session's traffic looks like. + + A context-window escalation describes the prompt's size, not the session's complexity, and + size shrinks again the moment the client compacts: pinning the escalated tier would hold the + session on the big-window model long after the oversized context that forced it is gone. The + gate re-fires per request, so leaving these unpinned costs nothing but the classifier call. + + A modality escalation is transient the same way: it describes what this one call carries (an + image), not what the session's traffic looks like, and pinning it would hold every following + text turn on the vision-capable model the image forced. """ - return decision is None or decision.get("cause") not in ( - "default_model_fallback", - "plan_mode", - "housekeeping", + return decision is None or ( + decision.get("cause") + not in ( + "default_model_fallback", + "plan_mode", + "housekeeping", + "modality_escalation", + ) + and not decision.get("context_escalated") ) @@ -759,6 +801,39 @@ class ClassificationOutcome(NamedTuple): classifier_cost: float | None = None +def _allowed(models: tuple[str, ...], fit_filter: frozenset[str] | None) -> tuple[str, ...]: + return models if fit_filter is None else tuple(model for model in models if model in fit_filter) + + +def _apply_context_placement( + tier: ComplexityTier | str, signals: tuple[str, ...], placement: _ContextWindowPlacement | None +) -> tuple[ComplexityTier | str, tuple[str, ...], ComplexityTier | str | None]: + """(final tier, signals, original tier when the gate escalated, else None).""" + if placement is None: + return tier, signals, None + if _tier_name(placement.tier) == _tier_name(tier): + return placement.tier, signals, None + return placement.tier, (*signals, "context_escalation"), tier + + +def _window_can_hold(window: int | None, needed: int, buffer: float) -> bool: + return window is None or needed <= int(window * buffer) + + +def _group_provably_fits(facts: tuple[int | None, bool], needed: int, buffer: float) -> bool: + window, has_unknown = facts + return window is not None and not has_unknown and needed <= int(window * buffer) + + +class _ContextWindowPlacement(NamedTuple): + """Where the context-window gate placed the request: the placement tier, the subset of its + pool the pick may use, and every configured group not provably misfit (the adaptive filter).""" + + tier: ComplexityTier | str + allowed_models: tuple[str, ...] + holdable_models: frozenset[str] + + class _SessionAffinityPin(NamedTuple): model: str tier: ComplexityTier | None @@ -1195,6 +1270,7 @@ class ComplexityRouter(CustomLogger): classifier_cost: float | None = None, conversation_continuing: bool = True, tier_litellm_params: Mapping[str, object] | None = None, + context_escalation_original_tier: ComplexityTier | str | None = None, ) -> StandardLoggingRoutingDecision: """Assemble the per-request provenance record for this router's decision. @@ -1244,6 +1320,12 @@ class ComplexityRouter(CustomLogger): decision["classifier_model"] = classifier_model if classifier_cost is not None: decision["classifier_cost"] = classifier_cost + if context_escalation_original_tier is not None: + # The pair travels together: the flag says the gate moved the request off its + # decided tier on prompt size, and the original tier names where the decision + # (classifier, keyword rule, or session pin) had placed it before physics did. + decision["context_escalated"] = True + decision["context_escalation_original_tier"] = _tier_name(context_escalation_original_tier) if tier_litellm_params: masked_tier_litellm_params: Final = mask_credentials_in_payload(tier_litellm_params) if isinstance(masked_tier_litellm_params, Mapping): @@ -1644,7 +1726,7 @@ class ComplexityRouter(CustomLogger): return entry.litellm_params if entry is not None else MappingProxyType({}) @staticmethod - def _pick_from_tier_value(model: str | list[str], tier_key: str) -> str: + def _pick_from_tier_value(model: str | Sequence[str], tier_key: str) -> str: if isinstance(model, str): return model if not model: @@ -1660,15 +1742,21 @@ class ComplexityRouter(CustomLogger): raw_messages: list[dict[str, Any]] | None, resolved_messages: list[dict[str, Any]] | None, request_kwargs: dict, + allowed_models: tuple[str, ...] | None = None, ) -> str: if not self.config.plugins: + if allowed_models is not None: + return self._pick_from_tier_value(allowed_models, _tier_name(tier)) return self.get_model_for_tier(tier) from litellm.types.router import RoutingContext tier_key: Final = _tier_name(tier) metadata_key: Final = get_metadata_variable_name_from_kwargs(request_kwargs) - pool: Final = tuple(self._tier_pools().get(tier_key, ())) + full_pool: Final = tuple(self._tier_pools().get(tier_key, ())) + pool: Final = ( + tuple(model for model in full_pool if model in allowed_models) if allowed_models is not None else full_pool + ) if not pool: # Nothing for the plugins to filter. Falling through would raise the # plugin-filtering error below and send the operator hunting for a policy @@ -1762,6 +1850,7 @@ class ComplexityRouter(CustomLogger): request_kwargs: dict[str, Any] | None = None, hard_floor: ComplexityTier | str | None = None, hard_ceiling: ComplexityTier | str | None = None, + fit_filter: frozenset[str] | None = None, ) -> str: """hard_floor excludes every candidate whose tiers all sit below it, turning this pick's soft floors (a distance penalty a high-scoring cheap model can outweigh) into a hard @@ -1774,7 +1863,10 @@ class ComplexityRouter(CustomLogger): tier because that is all it is worth, so a bandit trading cost for quality has nothing to win and must not reach above it. Without it the distance penalty is the only thing holding the tier, and a deployment that lowers tier_distance_penalty silently gets the expensive - model back while the routing decision still reads as the cheapest tier.""" + model back while the routing decision still reads as the cheapest tier. + + fit_filter excludes candidates the context-window gate proved cannot hold the prompt, + in every phase including cold start and the tier fallbacks.""" from litellm.router_strategy.adaptive_router.bandit import ( normalized_cost, thompson_sample, @@ -1785,12 +1877,12 @@ class ComplexityRouter(CustomLogger): if adaptive is None or not isinstance(classified_tier, ComplexityTier): # Custom tier names have no severity index; adaptive is rejected alongside # tier_definitions, so this guard is the contract for any future caller. - return self.get_model_for_tier(classified_tier) + return self._fitting_tier_fallback(classified_tier, fit_filter) request_type: Final = classify_prompt(user_message) classified_idx: Final = TIER_SEVERITY_ORDER.index(classified_tier) pools: Final = self._tier_pools() - classified_candidates: Final = tuple(pools.get(_tier_name(classified_tier), ())) + classified_candidates: Final = _allowed(tuple(pools.get(_tier_name(classified_tier), ())), fit_filter) cold_start_candidates: Final = tuple( model for model in classified_candidates if adaptive._cells[(request_type, model)].total_samples == 0 ) @@ -1820,9 +1912,9 @@ class ComplexityRouter(CustomLogger): if self.config.adaptive_eligible == "classified_tier": candidates = list(classified_candidates) if not candidates: - return self.get_model_for_tier(classified_tier) + return self._fitting_tier_fallback(classified_tier, fit_filter) else: - candidates = list(adaptive.config.available_models) + candidates = list(_allowed(tuple(adaptive.config.available_models), fit_filter)) all_costs: Final = [adaptive.model_to_cost.get(m, 0.0) for m in candidates] quality_weight: Final = self.config.adaptive_weights.quality @@ -1833,7 +1925,7 @@ class ComplexityRouter(CustomLogger): ceiling_severity: Final = self._active_tier_severity(hard_ceiling) if hard_ceiling is not None else None best_model: str | None = None best_score = float("-inf") - candidate_scores: Final[list[dict[str, Any]]] = [] + candidate_scores: Final[list[dict[str, object]]] = [] for model in candidates: if floor_severity is not None and all( self._active_tier_severity(model_tier) < floor_severity @@ -1869,7 +1961,7 @@ class ComplexityRouter(CustomLogger): best_score = score best_model = model if best_model is None: - return self.get_model_for_tier(classified_tier) + return self._fitting_tier_fallback(classified_tier, fit_filter) if request_kwargs is not None: metadata = request_kwargs.setdefault("metadata", {}) if isinstance(metadata, dict): @@ -1886,6 +1978,12 @@ class ComplexityRouter(CustomLogger): } return best_model + def _fitting_tier_fallback(self, classified_tier: ComplexityTier | str, fit_filter: frozenset[str] | None) -> str: + fitting: Final = _allowed(tuple(self._tier_pools().get(_tier_name(classified_tier), ())), fit_filter) + if fit_filter is not None and fitting: + return self._pick_from_tier_value(fitting, _tier_name(classified_tier)) + return self.get_model_for_tier(classified_tier) + def _resolve_plan_mode_floor(self) -> ComplexityTier | str | None: """The configured floor as an active tier: the built-in enum member, or the defined name itself for a custom tier set; None when the feature is off.""" @@ -1956,6 +2054,163 @@ class ComplexityRouter(CustomLogger): return None return name if self.config.has_custom_tiers else ComplexityTier(name) + def _deployment_window(self, group: str, deployment: Mapping[str, object]) -> int | None: + from litellm.litellm_core_utils.get_llm_provider_logic import declared_authenticating_provider + + deployment_model_info: Final = deployment.get("model_info") + declared: Final = ( + deployment_model_info.get("max_input_tokens") if isinstance(deployment_model_info, Mapping) else None + ) + if isinstance(declared, int): + return declared + litellm_params: Final = deployment.get("litellm_params") + params: Final = litellm_params if isinstance(litellm_params, Mapping) else EMPTY_MAPPING + provider_override: Final = params.get("custom_llm_provider") + # get_router_model_info resolves the provider, and get_llm_provider runs the OAuth device + # flow for github_copilot/chatgpt, so a metadata question must never reach it for those. + if declared_authenticating_provider( + str(params.get("model") or ""), provider_override if isinstance(provider_override, str) else None + ): + return None + try: + model_info: Final = self.litellm_router_instance.get_router_model_info( + deployment=cast(dict, deployment), # cast-ok: router deployments are plain dicts + received_model_name=group, + ) + window: Final = model_info.get("max_input_tokens") + except Exception: # noqa: BLE001 # best-effort: an unmappable deployment must not hide the others + return None + return window if isinstance(window, int) else None + + def _group_window_facts(self, group: str) -> tuple[int | None, bool]: + """(smallest declared context window across the group's deployments, whether any deployment + declares none). The core router picks a deployment within the group without a fit check, so + the group is only as safe as its smallest member.""" + list_models: Final = getattr(self.litellm_router_instance, "get_model_list", None) + deployments: Final = list_models(model_name=group) if callable(list_models) else None + if not isinstance(deployments, list) or not deployments: + return (None, True) + windows: Final = tuple( + window for deployment in deployments if (window := self._deployment_window(group, deployment)) is not None + ) + return (min(windows) if windows else None, len(windows) < len(deployments)) + + @staticmethod + def _out_of_band_request_text(request_kwargs: Mapping[str, object]) -> str: + """Prompt content the resolved message list never carries: the Responses API's + `instructions`, the /v1/messages top-level `system` block, and tool definitions. + A coding agent's context is dominated by these.""" + import json + + instructions: Final = request_kwargs.get("instructions") + proxy_request: Final = request_kwargs.get("proxy_server_request") + body: Final = proxy_request.get("body") if isinstance(proxy_request, Mapping) else None + system: Final = body.get("system") if isinstance(body, Mapping) else None + tools: Final = ( + body.get("tools") if isinstance(body, Mapping) and body.get("tools") else request_kwargs.get("tools") + ) + tools_text = "" + if tools: + try: + tools_text = json.dumps(tools, default=str) + except (TypeError, ValueError): + tools_text = str(tools) + return ( + (instructions if isinstance(instructions, str) else "") + + (str(system) if system is not None else "") + + tools_text + ) + + def _request_byte_upper_bound( + self, resolved_messages: Sequence[Mapping[str, object]] | None, request_kwargs: Mapping[str, object] + ) -> int: + """UTF-8 byte length of all prompt content. BPE emits at least one byte per token in every + script, so the token count never exceeds this and 'bytes fit' soundly skips counting.""" + content_bytes: Final = sum(len(str(m.get("content") or "").encode()) for m in resolved_messages or ()) + return content_bytes + len(self._out_of_band_request_text(request_kwargs).encode()) + + async def _counted_request_tokens( + self, resolved_messages: Sequence[Mapping[str, object]], request_kwargs: Mapping[str, object] + ) -> int | None: + """Real-tokenizer count of the resolved messages plus the out-of-band carriers, off the + event loop; None when counting fails, and the gate then leaves the placement alone.""" + import litellm + from litellm.litellm_core_utils.asyncify import asyncify + + out_of_band: Final = self._out_of_band_request_text(request_kwargs) + try: + counted: Final = await asyncify(litellm.token_counter)( + messages=cast(list, resolved_messages) # cast-ok: token_counter only iterates the sequence + ) + return counted + (await asyncify(litellm.token_counter)(text=out_of_band) if out_of_band else 0) + except Exception as e: # noqa: BLE001 # best-effort: an uncountable prompt must not fail the request + verbose_router_logger.debug("ComplexityRouter: context-window token count failed. Got - %s", e) + return None + + async def _context_window_placement( + self, + tier: ComplexityTier | str, + resolved_messages: Sequence[Mapping[str, object]] | None, + request_kwargs: Mapping[str, object], + pool_override: tuple[str, ...] | None = None, + ) -> _ContextWindowPlacement | None: + """Correct a decided placement whose models provably cannot hold the prompt, or None + (the placement stands). Only a real tokenizer count ever moves a request, escalation + lands only on groups whose every deployment declares a fitting window, and a group + with no resolvable window is never moved on faith in either direction.""" + if not self.config.enable_context_window_escalation or not resolved_messages: + return None + pools: Final = self._tier_pools() + pool: Final = pool_override if pool_override is not None else tuple(pools.get(_tier_name(tier), ())) + if not pool: + return None + facts: Final = MappingProxyType({group: self._group_window_facts(group) for group in pool}) + known_windows: Final = tuple(window for window, _ in facts.values() if window is not None) + if not known_windows: + return None + buffer: Final = self.config.context_window_escalation_buffer + if self._request_byte_upper_bound(resolved_messages, request_kwargs) <= int(min(known_windows) * buffer): + return None + needed: Final = await self._counted_request_tokens(resolved_messages, request_kwargs) + if needed is None: + return None + return self._placement_for_tokens(tier=tier, pool=pool, pools=pools, facts=facts, needed=needed) + + def _placement_for_tokens( + self, + *, + tier: ComplexityTier | str, + pool: tuple[str, ...], + pools: Mapping[str, list[str]], + facts: Mapping[str, tuple[int | None, bool]], + needed: int, + ) -> _ContextWindowPlacement | None: + buffer: Final = self.config.context_window_escalation_buffer + in_tier: Final = tuple(group for group in pool if _window_can_hold(facts[group][0], needed, buffer)) + if in_tier and len(in_tier) == len(pool): + return None + holdable: Final = frozenset( + group + for tier_pool in pools.values() + for group in tier_pool + if _window_can_hold(self._group_window_facts(group)[0], needed, buffer) + ) + if in_tier: + return _ContextWindowPlacement(tier=tier, allowed_models=in_tier, holdable_models=holdable) + for name in self.config.tier_names()[self._active_tier_severity(tier) + 1 :]: + proven = tuple( + group + for group in pools.get(name, ()) + if _group_provably_fits(self._group_window_facts(group), needed, buffer) + ) + if proven: + return _ContextWindowPlacement( + tier=name if self.config.has_custom_tiers else ComplexityTier(name), + allowed_models=proven, + holdable_models=holdable, + ) + return None + def _apply_plan_mode_floor(self, tier: ComplexityTier | str) -> ComplexityTier | str: """The higher of the decided tier and the plan-mode floor; identity when the floor is unset.""" floor: Final = self._resolve_plan_mode_floor() @@ -2025,6 +2280,175 @@ class ComplexityRouter(CustomLogger): return pinned_model return self.get_model_for_tier(escalated_tier) + def _model_accepts_image_input(self, model_name: str) -> bool: + """Whether a routed model or pool entry can serve an image request. + + Resolved through the deployments that would actually serve the name; a name with no + deployment on the router is served by the SDK directly and is checked against the model + cost map itself. Only an explicit supports_vision false excludes, a deployment-level + model_info override first and the map otherwise, so unmapped custom names stay routable. + + A multi-deployment group must accept on EVERY deployment: the router picks a deployment + inside the group after this gate runs, so a mixed group marked eligible could still hand + the image to its text-only member and fail with the exact 400 the gate exists to prevent. + """ + from litellm.utils import is_vision_explicitly_disabled + + def deployment_accepts(deployment: Mapping[str, Any]) -> bool: + declared: Final = (deployment.get("model_info") or EMPTY_MAPPING).get("supports_vision") + if declared is not None: + return declared is True + litellm_model: Final = (deployment.get("litellm_params") or EMPTY_MAPPING).get("model") or model_name + return not is_vision_explicitly_disabled(litellm_model) + + deployments: Final = self.litellm_router_instance.get_model_list(model_name=model_name) + if not deployments: + return not is_vision_explicitly_disabled(model_name) + return all(deployment_accepts(deployment) for deployment in deployments) + + def _modality_eligible_models(self) -> frozenset[str]: + """Every configured pool entry, plus default_model, that can serve an image request.""" + names: Final = frozenset(entry for pool in self._tier_pools().values() for entry in pool) | frozenset( + name for name in (self.config.default_model,) if name + ) + return frozenset(name for name in names if self._model_accepts_image_input(name)) + + async def _gate_response_modality( + self, + response: PreRoutingHookResponse, + messages: list[dict[str, Any]] | None, # mutable-ok: forwarded verbatim to the list-typed re-pick + resolved_messages: Sequence[Mapping[str, object]] | None, + request_kwargs: dict, # mutable-ok: same shape the hook receives + ) -> PreRoutingHookResponse: + """Replace a routed model that cannot accept this request's image input. + + The single modality owner, applied to the decided response at the hook's exits so every + routing path is covered uniformly. A KEPT session pin is exempt by design (its cause); + replacement picks and every other path are just responses. The re-placement walks + UPWARD-ONLY from the decision's tier (so a plan-mode floor can never be undercut), picks + through `_pick_model_for_tier` so routing plugins still apply, then falls to + default_model (never on plugin routers, and never on a plan-floored decision, since + default_model carries no tier guarantee), else raises the clear 400. The rewritten + decision keeps its cause on a same-tier repick and becomes modality_escalation when the + tier moved or default_model took over, with the displaced placement in signals. + """ + decision: Final = response.routing_decision + if ( + not self.config.modality_routing + or not resolved_messages + or response.model is None + or (decision is not None and decision.get("cause") == "session_affinity_pin") + or not request_contains_image_content(resolved_messages) + or self._model_accepts_image_input(response.model) + ): + return response + eligible: Final = self._modality_eligible_models() + names: Final = self.config.tier_names() + pools: Final = self._tier_pools() + decided: Final = decision.get("tier") if decision is not None else None + start: Final = names.index(decided) if isinstance(decided, str) and decided in names else 0 + capable: Final = next( + (name for name in names[start:] if any(entry in eligible for entry in pools.get(name, ()))), None + ) + if capable is not None: + new_tier: ComplexityTier | str | None = capable if self.config.has_custom_tiers else ComplexityTier(capable) + repick_messages: Final = list(resolved_messages) # mutable-ok: the pick's param is list-typed + new_model = await self._pick_model_for_tier( + new_tier, + messages, + repick_messages, # pyright: ignore[reportArgumentType] # hook-resolved message dicts; the pick only reads them + request_kwargs, + allowed_models=tuple(entry for entry in pools.get(capable, ()) if entry in eligible), + ) + elif self._modality_default_model_usable(request_kwargs, resolved_messages, eligible): + new_tier = None + new_model = self._placed_default_model() + else: + import litellm + + raise litellm.BadRequestError( + message=( + f"Auto-router {self.model_name} received a request with image input, but no model " + f"at or above the decided tier accepts images and modality_routing is enabled. " + f"Tiers checked: {', '.join(names[start:])}. Add a vision-capable model to a tier, " + f"or set a vision-capable default_model, or remove the image content." + ), + model=self.model_name, + llm_provider="", + ) + self._restamp_adaptive_choice(request_kwargs, response.model, new_model) + same_tier: Final = capable is not None and decided == capable + base_cause: Final = (decision.get("cause") if decision is not None else None) or "default_fallback" + displaced_default: Final = decided is None and response.model == self.config.default_model + markers: Final = ( + "modality:image", + *((f"modality_escalated_from:{decided}",) if not same_tier and isinstance(decided, str) else ()), + *(("modality_displaced_default_model",) if not same_tier and displaced_default else ()), + ) + old_signals: Final = tuple(decision.get("signals") or ()) if decision is not None else () + new_decision: Final = self._build_routing_decision( + routed_model=new_model, + cause=base_cause if same_tier else "modality_escalation", + tier=new_tier, + score=decision.get("score") if decision is not None else None, + signals=(*old_signals, *markers), + matched_keyword=decision.get("matched_keyword") if decision is not None else None, + escalation_keyword=decision.get("escalation_keyword") if decision is not None else None, + escalated=bool(decision.get("escalated", False)) if decision is not None else False, + classifier_model=decision.get("classifier_model") if decision is not None else None, + classifier_cost=decision.get("classifier_cost") if decision is not None else None, + conversation_continuing=bool(decision.get("conversation_continuing", True)) + if decision is not None + else True, + tier_litellm_params=self._litellm_params_for_model(new_tier, new_model), + context_escalation_original_tier=( + decision.get("context_escalation_original_tier") if decision is not None else None + ), + ) + from litellm.types.router import PreRoutingHookResponse as HookResponse + + return HookResponse( + model=new_model, + messages=response.messages, + litellm_params=self._litellm_params_for_model(new_tier, new_model), + routing_decision=new_decision, + ) + + def _modality_default_model_usable( + self, + request_kwargs: Mapping[str, object], + resolved_messages: Sequence[Mapping[str, object]] | None, + eligible: frozenset[str], + ) -> bool: + """default_model may serve a gated request only when it is configured, plugin-free + (it is never checked against the plugin pipeline), capability-eligible, and the turn + carries no plan-mode sentinel. The sentinel is re-detected here rather than read off + the decision record, because the record only marks turns the floor RAISED; a sentinel + turn already at or above the floor keeps its ordinary cause, and default_model carries + no tier the floor could vouch for on any sentinel turn.""" + return ( + bool(self.config.default_model) + and not self.config.plugins + and self.config.default_model in eligible + and self._matched_plan_mode_signal(request_kwargs, resolved_messages) is None + ) + + def _placed_default_model(self) -> str: + """The default_model behind a usable-default verdict; the raise is the type-level + proof, not a reachable path.""" + model: Final = self.config.default_model + if model is None: + raise ValueError(f"Auto-router {self.model_name}: modality gate routed to an unset default_model") + return model + + @staticmethod + def _restamp_adaptive_choice(request_kwargs: Mapping[str, object], old_model: str, new_model: str) -> None: + """The adaptive feedback loop reads its chosen-model marker from request metadata; a + gate rewrite must move the marker with the model or rewards land on the displaced one.""" + metadata: Final = request_kwargs.get("metadata") + if isinstance(metadata, dict) and metadata.get("adaptive_router_chosen_model") == old_model: + metadata["adaptive_router_chosen_model"] = new_model + def _lexical_tier_override(self, user_message: str) -> KeywordOverride | None: """When keyword_tier_rules match literally, the most-severe matched tier wins. @@ -2247,14 +2671,18 @@ class ComplexityRouter(CustomLogger): @property def _uses_tier_pin(self) -> bool: - return bool(self.config.session_affinity and not self.config.plugins) + """classification_mode 'user_turn' implies the tier pin machinery: the pin write after each + pinnable classification is what gives a continuation a held decision to replay.""" + return bool( + (self.config.session_affinity or self.config.classification_mode == "user_turn") and not self.config.plugins + ) @property def _uses_deployment_pin(self) -> bool: - """session_affinity implies the deployment pin: a session frozen onto one model + """The tier pin implies the deployment pin: a session frozen onto one model group but load-balanced across its deployments would still go cache-cold, which is the exact failure both flags exist to prevent.""" - return bool((self.config.deployment_affinity or self.config.session_affinity) and not self.config.plugins) + return bool(self.config.deployment_affinity and not self.config.plugins) or self._uses_tier_pin def _with_session_deployment_affinity( self, response: PreRoutingHookResponse | None @@ -2282,6 +2710,11 @@ class ComplexityRouter(CustomLogger): pins the model chosen on the session's first turn and reuses it for every later turn, skipping classification entirely. Otherwise delegates to `_classify_and_route`. + When `classification_mode` is 'user_turn', the same pin is replayed only on + continuation turns (an agent loop's tool traffic); a new human ask always falls + through to classification, so the session can still move tiers between asks. + With both knobs on, session_affinity's pin-first behavior wins. + Skipped entirely when `plugins` are configured: reusing a stale pin would bypass the plugin pipeline on every turn after the first, since a pinned model was never re-checked against a policy plugin whose decision can change between turns (e.g. a @@ -2305,7 +2738,13 @@ class ComplexityRouter(CustomLogger): session_id: Final = self._get_session_id_from_request_kwargs(request_kwargs) if use_session_affinity else None cache_key = self._get_session_affinity_cache_key(session_id, request_kwargs) if session_id is not None else None - if cache_key is not None: + # In 'user_turn' mode a held pin is replayed only on continuation turns; a new human + # ask falls through and re-classifies. session_affinity restores pin-first for asks too. + pin_replay_allowed: Final = bool(self.config.session_affinity) or not _newest_turn_is_human_ask( + resolved_messages, self._reminder_markers + ) + + if cache_key is not None and pin_replay_allowed: pinned_value: Final = await self.litellm_router_instance.cache.async_get_cache(key=cache_key) pinned_pin: Final = _parse_session_affinity_pin(pinned_value) if pinned_pin is not None: @@ -2339,6 +2778,26 @@ class ComplexityRouter(CustomLogger): session_model: Final = routed_model if plan_floored and pinned_tier is not None: routed_model = self.get_model_for_tier(self._apply_plan_mode_floor(pinned_tier)) + pin_source_tier: Final = self._tier_for_model(routed_model) + pin_placement: Final = ( + await self._context_window_placement( + pin_source_tier, resolved_messages, request_kwargs, pool_override=(routed_model,) + ) + if pin_source_tier is not None + else None + ) + pin_context_original_tier: Final = ( + pin_source_tier + if pin_placement is not None + and pin_source_tier is not None + and _tier_name(pin_placement.tier) != _tier_name(pin_source_tier) + else None + ) + if pin_placement is not None and pin_context_original_tier is not None: + # The stored pin below keeps the session's own model on purpose. + routed_model = self._pick_from_tier_value( + pin_placement.allowed_models, _tier_name(pin_placement.tier) + ) # Refresh the TTL on every hit so an active session doesn't lose its # pin mid-conversation just because it outlives the original write. await self.litellm_router_instance.cache.async_set_cache( @@ -2354,36 +2813,47 @@ class ComplexityRouter(CustomLogger): kwargs_metadata: Final = request_kwargs.setdefault("metadata", {}) if isinstance(kwargs_metadata, dict): kwargs_metadata[ADAPTIVE_ROUTER_CHOSEN_MODEL_KEY] = routed_model + replay_cause: Final[RoutingDecisionCause] = ( + "session_affinity_pin" if self.config.session_affinity else "user_turn_continuation" + ) cause: RoutingDecisionCause = ( - "plan_mode" - if plan_floored - else ("session_affinity_escalation" if escalated else "session_affinity_pin") + "plan_mode" if plan_floored else ("session_affinity_escalation" if escalated else replay_cause) ) verbose_router_logger.info( "ComplexityRouter: routing decision cause=%s, routed_model=%s", cause, routed_model ) - routed_pin_tier: Final = self._tier_for_model(routed_model) if plan_floored else resolved_pin_tier + routed_pin_tier: Final = ( + pin_placement.tier + if pin_placement is not None and pin_context_original_tier is not None + else (self._tier_for_model(routed_model) if plan_floored else resolved_pin_tier) + ) session_tier_litellm_params: Final = self._litellm_params_for_model(routed_pin_tier, routed_model) has_original_messages: Final = messages is not None and len(messages) > 0 return self._with_session_deployment_affinity( - PreRoutingHookResponse( - model=routed_model, - messages=messages if has_original_messages else None, - litellm_params=session_tier_litellm_params, - routing_decision=self._build_routing_decision( - routed_model=routed_model, - cause=cause, - tier=routed_pin_tier, - matched_keyword=pin_plan_sentinel if plan_floored else None, - escalation_keyword=pin_escalation_keyword, - escalated=escalated, - conversation_continuing=conversation_continuing, - tier_litellm_params=session_tier_litellm_params, + await self._gate_response_modality( + PreRoutingHookResponse( + model=routed_model, + messages=messages if has_original_messages else None, + litellm_params=session_tier_litellm_params, + routing_decision=self._build_routing_decision( + routed_model=routed_model, + cause=cause, + tier=routed_pin_tier, + matched_keyword=pin_plan_sentinel if plan_floored else None, + escalation_keyword=pin_escalation_keyword, + escalated=escalated, + conversation_continuing=conversation_continuing, + tier_litellm_params=session_tier_litellm_params, + context_escalation_original_tier=pin_context_original_tier, + ), ), + messages, + resolved_messages, + request_kwargs, ) ) - response: Final = await self._classify_and_route( + routed_response: Final = await self._classify_and_route( model=model, request_kwargs=request_kwargs, messages=messages, @@ -2392,6 +2862,11 @@ class ComplexityRouter(CustomLogger): conversation_continuing=conversation_continuing, resolved_messages=resolved_messages, ) + response: Final = ( + await self._gate_response_modality(routed_response, messages, resolved_messages, request_kwargs) + if routed_response is not None + else None + ) # Sentinel presence, not the plan_mode cause, gates the pin write: a plan-mode turn # classified at or above the floor keeps its ordinary cause, yet on an adaptive router # the hard floor constrained its pick, so pinning it would carry a plan-mode-shaped @@ -2573,6 +3048,8 @@ class ComplexityRouter(CustomLogger): plan_floored: Final = tier != pre_floor_tier if plan_floored: signals = (*signals, "plan_mode_floor") + context_placement: Final = await self._context_window_placement(tier, resolved_messages, request_kwargs) + tier, signals, context_original_tier = _apply_context_placement(tier, signals, context_placement) score_repr: Final = f"{score:.3f}" if score is not None else "n/a" fallback_model: Final = self.config.default_model if not self.config.plugins else None # A sentinel-carrying request skips the failure exit below, whether or not the floor @@ -2619,8 +3096,15 @@ class ComplexityRouter(CustomLogger): # the cheapest tier would then contradict the floor and bound the pick below the tier # the decision reports. housekeeping_ceiling: Final = tier if outcome.cause == "housekeeping" else None + # A context-escalated tier becomes the hard floor: a floor the bandit can slide + # under is not a floor. routed_model = self._soft_floor_pick( - tier, user_message, request_kwargs, hard_floor=plan_floor, hard_ceiling=housekeeping_ceiling + tier, + user_message, + request_kwargs, + hard_floor=tier if context_original_tier is not None else plan_floor, + hard_ceiling=housekeeping_ceiling, + fit_filter=context_placement.holdable_models if context_placement is not None else None, ) adaptive: Final = self._ensure_adaptive_router() if adaptive is not None: @@ -2637,7 +3121,13 @@ class ComplexityRouter(CustomLogger): routed_model, ) else: - routed_model = await self._pick_model_for_tier(tier, messages, resolved_messages, request_kwargs) + routed_model = await self._pick_model_for_tier( + tier, + messages, + resolved_messages, + request_kwargs, + allowed_models=context_placement.allowed_models if context_placement is not None else None, + ) verbose_router_logger.info( "ComplexityRouter: routing decision cause=%s, tier=%s, score=%s, signals=%s, routed_model=%s", outcome.cause, @@ -2690,5 +3180,6 @@ class ComplexityRouter(CustomLogger): classifier_model=classifier_model, classifier_cost=outcome.classifier_cost, tier_litellm_params=tier_litellm_params, + context_escalation_original_tier=context_original_tier, ), ) diff --git a/litellm/router_strategy/complexity_router/config.py b/litellm/router_strategy/complexity_router/config.py index 335de11e669..70aeecb31c6 100644 --- a/litellm/router_strategy/complexity_router/config.py +++ b/litellm/router_strategy/complexity_router/config.py @@ -823,6 +823,44 @@ class ComplexityRouterConfig(BaseModel): ), ) + enable_context_window_escalation: bool = Field( + default=True, + description=( + "Escalate a request off a tier whose models provably cannot hold its prompt, before " + "dispatch. The classifier scores complexity and never prompt size, so a long agentic " + "session whose newest ask is trivial lands on a small-window tier and the provider " + "rejects it with a context-window 400 that nothing retries. When every model of the " + "decided tier has a declared window smaller than the estimated prompt, the request " + "moves to the lowest configured tier with a model whose declared window fits; when " + "only some of the tier's models fit, the pick is restricted to those and the tier " + "keeps the request. Models with no resolvable window are never escalated away from " + "and never escalated onto. Set false to dispatch on complexity alone, as before." + ), + ) + context_window_escalation_buffer: float = Field( + default=0.95, + gt=0, + le=1, + description=( + "Fraction of a model's declared context window the estimated prompt must fit within. " + "The token count is an estimate, so fitting against the full window would dispatch " + "prompts that the provider's own tokenizer then rejects; 0.95 leaves room for that " + "drift plus the response tokens." + ), + ) + modality_routing: bool = Field( + default=False, + description=( + "Route image-bearing requests only to models that can accept image input. The " + "classifier reads text alone, so an image request whose text classifies cheap " + "otherwise lands on a text-only model and fails with a provider 400. When enabled, " + "a routed model explicitly declared supports_vision false (deployment model_info " + "or the model cost map; unmapped names stay routable) is replaced by the nearest " + "HIGHER tier holding a capable model, then default_model, else a clear 400. A kept " + "session-affinity pin still wins even when an image arrives." + ), + ) + # Semantic (embedding) matching for keyword_tier_rules instead of literal text matching semantic_keyword_matching: bool = Field( default=False, @@ -839,6 +877,21 @@ class ComplexityRouterConfig(BaseModel): description="Minimum cosine similarity for a semantic keyword match", ) + classification_mode: Literal["every_request", "user_turn"] = Field( + default="every_request", + description=( + "When to run the complexity classifier. 'every_request' (the default) classifies every " + "inference request, including the tool-result continuation turns of an agentic loop. " + "'user_turn' classifies only requests whose newest turn is a new human ask and replays " + "the session's held routing decision on continuation turns, which cuts classifier " + "spend and eliminates mid-loop model switches. Continuations with no held decision to " + "replay (no resolvable session_id, expired pin, fresh restart) still classify. Unlike " + "session_affinity, a new human ask always re-classifies, so a session can still move " + "tiers between asks. Suppressed when plugins are configured, for the same reason " + "session_affinity is: a replayed decision would bypass the plugin pipeline." + ), + ) + # Session affinity: pin the first turn's routed model for the rest of the session session_affinity: bool = Field( default=False, diff --git a/litellm/router_strategy/tag_based_routing.py b/litellm/router_strategy/tag_based_routing.py index e4ac45df4d5..eabd9278cf6 100644 --- a/litellm/router_strategy/tag_based_routing.py +++ b/litellm/router_strategy/tag_based_routing.py @@ -8,16 +8,14 @@ Use this to route requests between Teams """ import re -from collections.abc import Iterable, Mapping, Sequence +from collections.abc import Mapping, Sequence from types import MappingProxyType -from typing import TYPE_CHECKING, Any, Final, Literal, TypedDict - -from typing_extensions import ReadOnly +from typing import TYPE_CHECKING, Any, Final, Literal, Protocol, overload from litellm._logging import verbose_logger from litellm.constants import CONSUMED_REQUEST_TAGS_METADATA_KEY from litellm.litellm_core_utils.core_helpers import get_metadata_variable_name_from_kwargs -from litellm.types.router import ConsumedRequestTagsStamp, RouterErrors +from litellm.types.router import ConsumedRequestTagsStamp, DeploymentTypedDict, RouterErrors if TYPE_CHECKING: from litellm.router import Router as _Router @@ -27,34 +25,63 @@ else: LitellmRouter = Any -class _TagRoutingLitellmParams(TypedDict, total=False): - tags: ReadOnly[Sequence[str] | None] - tag_regex: ReadOnly[Sequence[str] | None] +class _TagLitellmParamsLike(Protocol): + @overload + def get(self, key: Literal["tags"], /) -> Sequence[str] | None: ... + @overload + def get(self, key: Literal["tags"], default: Sequence[str], /) -> Sequence[str]: ... + @overload + def get(self, key: Literal["tag_regex"], /) -> Sequence[str] | None: ... -class _TagRoutingDeployment(TypedDict, total=False): - model_name: ReadOnly[str] - litellm_params: ReadOnly[_TagRoutingLitellmParams] - model_info: ReadOnly[Mapping[str, object] | None] +class _ModelInfoLike(Protocol): + @overload + def get(self, key: Literal["allow_fail_open"], /) -> bool | None: ... + @overload + def get(self, key: Literal["enable_tag_filtering"], /) -> bool | None: ... -class _TagRoutingMatchStamp(TypedDict): - matched_deployment: ReadOnly[str | None] - matched_via: ReadOnly[str] - matched_value: ReadOnly[str] - request_tags: ReadOnly[Sequence[str]] - user_agent: ReadOnly[str] +class _DeploymentLike(Protocol): + @overload + def get(self, key: Literal["litellm_params"], default: Mapping[str, object], /) -> _TagLitellmParamsLike: ... + @overload + def get(self, key: Literal["model_info"], /) -> _ModelInfoLike | None: ... + @overload + def get(self, key: Literal["model_name"], /) -> object: ... -class _TagRoutingMetadata(TypedDict, total=False): - tags: ReadOnly[Sequence[str] | None] - inherited_tags: ReadOnly[Sequence[str] | None] - user_agent: ReadOnly[str] - tag_routing: ReadOnly[_TagRoutingMatchStamp] - _consumed_request_tags: ReadOnly[object] +class _MetadataLike(Protocol): + @overload + def get(self, key: Literal["tags"], /) -> Sequence[str] | None: ... + @overload + def get(self, key: Literal["tags"], default: Sequence[str], /) -> Sequence[str] | None: ... + @overload + def get(self, key: Literal["user_agent"], default: str, /) -> str: ... + @overload + def get(self, key: Literal["inherited_tags"], /) -> object: ... + def __contains__(self, key: object, /) -> bool: ... + def __setitem__(self, key: Literal["tag_routing"], value: Mapping[str, object], /) -> None: ... -_EMPTY_MODEL_INFO: Final[Mapping[str, object]] = MappingProxyType({}) +class _NestedLitellmParamsLike(Protocol): + def get( + self, key: Literal["metadata", "litellm_metadata"], default: Mapping[str, object], / + ) -> _MetadataLike | None: ... + + +class _RequestKwargsLike(Protocol): + @overload + def get(self, key: Literal["enable_tag_filtering"], /) -> bool | None: ... + @overload + def get(self, key: Literal["metadata", "litellm_metadata"], /) -> _MetadataLike | None: ... + def __contains__(self, key: object, /) -> bool: ... + @overload + def __getitem__(self, key: Literal["metadata", "litellm_metadata"], /) -> _MetadataLike: ... + @overload + def __getitem__(self, key: Literal["litellm_params"], /) -> _NestedLitellmParamsLike: ... + + +_DeploymentPool = Sequence[_DeploymentLike] | Mapping[_DeploymentLike, object] def _is_valid_deployment_tag_regex( @@ -109,11 +136,11 @@ def is_valid_deployment_tag( def _match_deployment( - deployment: _TagRoutingDeployment, - request_tags: Sequence[str] | None, - header_strings: Sequence[str], + deployment: _DeploymentLike, + request_tags: list[str] | None, + header_strings: list[str], match_any: bool, -) -> Mapping[str, str] | None: +) -> dict[str, str] | None: """ Determine whether *deployment* matches the current request. @@ -198,38 +225,38 @@ def _split_tags(tags: Sequence[str]) -> tuple[tuple[str, ...], list[str], tuple[ def _exclude_deployments( - deployments: Iterable[_TagRoutingDeployment], + deployments: _DeploymentPool, excluded_set: frozenset[str], -) -> list[_TagRoutingDeployment]: +) -> Sequence[_DeploymentLike]: if not excluded_set: return list(deployments) return [d for d in deployments if not excluded_set.intersection(d.get("litellm_params", {}).get("tags") or [])] def _require_all_tags( - deployments: Iterable[_TagRoutingDeployment], + deployments: _DeploymentPool, required_set: frozenset[str], -) -> tuple[_TagRoutingDeployment, ...]: +) -> tuple[_DeploymentLike, ...]: if not required_set: return tuple(deployments) return tuple(d for d in deployments if required_set.issubset(d.get("litellm_params", {}).get("tags") or [])) def _default_tagged_pool( - deployments: Iterable[_TagRoutingDeployment], -) -> tuple[_TagRoutingDeployment, ...]: + deployments: _DeploymentPool, +) -> tuple[_DeploymentLike, ...]: defaults: Final = tuple(d for d in deployments if "default" in (d.get("litellm_params", {}).get("tags") or [])) return defaults if defaults else tuple(deployments) -def _known_tag_values(deployments: Iterable[_TagRoutingDeployment]) -> frozenset[str]: +def _known_tag_values(deployments: _DeploymentPool) -> frozenset[str]: return frozenset( - tag for d in deployments for tag in (d.get("litellm_params", _TagRoutingLitellmParams()).get("tags") or ()) + tag for d in deployments for tag in (d.get("litellm_params", MappingProxyType({})).get("tags") or ()) ) def _unknown_required_tag_hides_an_answer( - healthy_deployments: Iterable[_TagRoutingDeployment], + healthy_deployments: _DeploymentPool, excluded_set: frozenset[str], required_set: frozenset[str], routing_confirmed: frozenset[str], @@ -253,23 +280,23 @@ def _unknown_required_tag_hides_an_answer( def _chain_allows_fail_open( - healthy_deployments: Iterable[_TagRoutingDeployment], + healthy_deployments: _DeploymentPool, excluded_set: frozenset[str], required_set: frozenset[str], routing_confirmed: frozenset[str], ) -> bool: if _unknown_required_tag_hides_an_answer(healthy_deployments, excluded_set, required_set, routing_confirmed): return False - return any((d.get("model_info") or _EMPTY_MODEL_INFO).get("allow_fail_open") is True for d in healthy_deployments) + return any((d.get("model_info") or {}).get("allow_fail_open") is True for d in healthy_deployments) def _trusted_only_pool( - healthy_deployments: Iterable[_TagRoutingDeployment], + healthy_deployments: _DeploymentPool, excluded_set: frozenset[str], required_set: frozenset[str], inherited_excluded_set: frozenset[str] | None, inherited_required_set: frozenset[str] | None, -) -> tuple[_TagRoutingDeployment, ...]: +) -> tuple[_DeploymentLike, ...]: # inherited_*_set is None only when this request carries no origin information # at all (e.g. direct SDK Router usage, bypassing the proxy layer that # populates metadata.inherited_tags) -- treat every constraint as @@ -296,8 +323,8 @@ def _trusted_only_pool( def _resolve_or_fail_open( - pool: Sequence[_TagRoutingDeployment], - healthy_deployments: Iterable[_TagRoutingDeployment], + pool: Sequence[_DeploymentLike], + healthy_deployments: _DeploymentPool, excluded_set: frozenset[str], required_set: frozenset[str], inherited_excluded_set: frozenset[str] | None, @@ -305,7 +332,7 @@ def _resolve_or_fail_open( routing_confirmed: frozenset[str], model: str, request_tags: object, -) -> tuple[_TagRoutingDeployment, ...]: +) -> tuple[_DeploymentLike, ...]: if pool: return tuple(pool) if _chain_allows_fail_open(healthy_deployments, excluded_set, required_set, routing_confirmed): @@ -325,7 +352,7 @@ def _resolve_or_fail_open( def _resolve_constraint_only_pool( - healthy_deployments: Iterable[_TagRoutingDeployment], + healthy_deployments: _DeploymentPool, excluded_set: frozenset[str], required_set: frozenset[str], inherited_excluded_set: frozenset[str] | None, @@ -333,7 +360,7 @@ def _resolve_constraint_only_pool( routing_confirmed: frozenset[str], model: str, request_tags: object, -) -> tuple[_TagRoutingDeployment, ...]: +) -> tuple[_DeploymentLike, ...]: pool: Final = ( _require_all_tags(_exclude_deployments(healthy_deployments, excluded_set), required_set) if required_set @@ -355,8 +382,8 @@ def _resolve_constraint_only_pool( def _all_deployments_or_fallback( llm_router_instance: LitellmRouter, model: str, - fallback: Iterable[_TagRoutingDeployment], -) -> Iterable[_TagRoutingDeployment]: + fallback: _DeploymentPool, +) -> Sequence[_DeploymentLike | DeploymentTypedDict] | Mapping[_DeploymentLike, object]: try: return llm_router_instance._get_all_deployments(model_name=model) except Exception: # noqa: BLE001 # fail safe toward today's healthy-only behavior on lookup errors @@ -366,8 +393,8 @@ def _all_deployments_or_fallback( def _chain_tag_filtering_override( llm_router_instance: LitellmRouter, model: str, - healthy_deployments: Iterable[_TagRoutingDeployment], -) -> object: + healthy_deployments: _DeploymentPool, +) -> bool | None: # Resolved from every deployment configured for this model group, not just the # ones that survived cooldown/health filtering (async_get_healthy_deployments # filters cooldowns before calling get_deployments_for_tag) -- otherwise the @@ -379,14 +406,14 @@ def _chain_tag_filtering_override( # than crashing the request. all_deployments: Final = _all_deployments_or_fallback(llm_router_instance, model, healthy_deployments) for d in all_deployments: - value = (d.get("model_info") or _EMPTY_MODEL_INFO).get("enable_tag_filtering") + value = (d.get("model_info") or MappingProxyType({})).get("enable_tag_filtering") if value is not None: return value return None def _inherited_constraint_sets( - inherited_tags: Sequence[str] | None, routing_prefix: str + inherited_tags: object, routing_prefix: str ) -> tuple[frozenset[str] | None, frozenset[str] | None]: # None means no origin information is available at all (e.g. this request # bypassed the proxy layer that populates metadata.inherited_tags, as direct @@ -417,43 +444,42 @@ def _tag_known_to_group( if tag_set & routing_confirmed: return True try: - all_deployments: Final[Sequence[_TagRoutingDeployment]] = llm_router_instance._get_all_deployments( - model_name=model - ) + all_deployments: Final = llm_router_instance._get_all_deployments(model_name=model) except Exception: # noqa: BLE001 # fail safe toward "unrecognized" so lookup errors preserve the existing silent-fallback behavior return False return any( - tag_set.intersection(d.get("litellm_params", _TagRoutingLitellmParams()).get("tags") or ()) - for d in all_deployments + tag_set.intersection(d.get("litellm_params", MappingProxyType({})).get("tags") or ()) for d in all_deployments ) -def _request_tags_after_router_consumption(metadata: _TagRoutingMetadata, model: str) -> Sequence[str] | None: +def _request_tags_after_router_consumption(metadata: object, model: str) -> Sequence[str] | None: # The pre-routing hook stamps which tags selected the router it rewrote the request # to: those tags already did their job and must not also constrain deployment choice # inside the routed group. The request's other tags still apply there, on top of the # inherited_tags snapshot that keeps key/team policy applying. Every other model # group keeps the full list. - stamp: Final = metadata.get(CONSUMED_REQUEST_TAGS_METADATA_KEY) + if not isinstance(metadata, Mapping): + return None + typed_metadata: Final[Mapping[str, object]] = metadata + request_tags: Final = _tags_in_metadata(typed_metadata) + stamp: Final = typed_metadata.get(CONSUMED_REQUEST_TAGS_METADATA_KEY) if not isinstance(stamp, ConsumedRequestTagsStamp) or stamp.model_group != model: - return metadata.get("tags") - request_tags: Final = metadata.get("tags") - leftover: Final = tuple( - tag for tag in (request_tags if isinstance(request_tags, (list, tuple)) else ()) if tag not in stamp.tags - ) - inherited_tags: Final = metadata.get("inherited_tags") + return request_tags + leftover: Final = tuple(tag for tag in request_tags if tag not in stamp.tags) + inherited_tags: Final = typed_metadata.get("inherited_tags") if not isinstance(inherited_tags, (list, tuple)): return leftover or None - return tuple(dict.fromkeys((*leftover, *inherited_tags))) + typed_inherited_tags: Final[Sequence[object]] = inherited_tags + return tuple(dict.fromkeys((*leftover, *(tag for tag in typed_inherited_tags if isinstance(tag, str))))) async def get_deployments_for_tag( llm_router_instance: LitellmRouter, model: str, # used to raise the correct error - healthy_deployments: list[Any] | dict[Any, Any], - request_kwargs: dict[Any, Any] | None = None, + healthy_deployments: _DeploymentPool, + request_kwargs: _RequestKwargsLike | None = None, metadata_variable_name: Literal["metadata", "litellm_metadata"] = "metadata", -): +) -> _DeploymentPool: """ Returns a list of deployments that match the requested model and tags in the request. @@ -486,8 +512,7 @@ async def get_deployments_for_tag( verbose_logger.debug("request metadata: %s", request_kwargs.get(metadata_variable_name)) if metadata_variable_name in request_kwargs: - metadata: Final[_TagRoutingMetadata] = request_kwargs[metadata_variable_name] - stampable_metadata: Final[dict[str, object]] = request_kwargs[metadata_variable_name] + metadata: Final = request_kwargs[metadata_variable_name] request_tags: Final = _request_tags_after_router_consumption(metadata, model) match_any: Final = llm_router_instance.tag_filtering_match_any routing_prefix: Final = llm_router_instance.tag_routing_prefix or "" @@ -532,25 +557,25 @@ async def get_deployments_for_tag( request_tags, ) - new_healthy_deployments: Final[list[_TagRoutingDeployment]] = [] - default_deployments: Final[list[_TagRoutingDeployment]] = [] - if has_positive_filter: verbose_logger.debug( "get_deployments_for_tag routing: request_tags=%s user_agent=%s", request_tags, user_agent, ) - for deployment in candidates: - deployment_tags = deployment.get("litellm_params", {}).get("tags") - - match_result = _match_deployment( - deployment=deployment, - request_tags=positive_tags, - header_strings=header_strings, - match_any=match_any, + deployment_matches: Final = tuple( + ( + deployment, + _match_deployment( + deployment=deployment, + request_tags=positive_tags, + header_strings=header_strings, + match_any=match_any, + ), ) - + for deployment in candidates + ) + for deployment, match_result in deployment_matches: if match_result is not None: verbose_logger.debug( "tag routing match: deployment=%s matched_via=%s matched_value=%s", @@ -559,17 +584,17 @@ async def get_deployments_for_tag( match_result["matched_value"], ) if "tag_routing" not in metadata: - stampable_metadata["tag_routing"] = { + metadata["tag_routing"] = { "matched_deployment": deployment.get("model_name"), "matched_via": match_result["matched_via"], "matched_value": match_result["matched_value"], "request_tags": request_tags or [], "user_agent": user_agent, } - new_healthy_deployments.append(deployment) - - if deployment_tags and "default" in deployment_tags: - default_deployments.append(deployment) + new_healthy_deployments: Final = [d for d, result in deployment_matches if result is not None] + default_deployments: Final = [ + d for d, _ in deployment_matches if "default" in (d.get("litellm_params", {}).get("tags") or ()) + ] if len(new_healthy_deployments) == 0 and len(default_deployments) == 0: return _resolve_or_fail_open( @@ -604,10 +629,11 @@ async def get_deployments_for_tag( return new_healthy_deployments if len(new_healthy_deployments) > 0 else default_deployments # for Untagged requests use default deployments if set - _default_deployments_with_tags: Final[list[_TagRoutingDeployment]] = [] - for deployment in healthy_deployments: - if "default" in deployment.get("litellm_params", {}).get("tags", []): - _default_deployments_with_tags.append(deployment) + _default_deployments_with_tags: Final = [ + deployment + for deployment in healthy_deployments + if "default" in deployment.get("litellm_params", {}).get("tags", []) + ] if len(_default_deployments_with_tags) > 0: return _default_deployments_with_tags diff --git a/litellm/router_utils/fallback_event_handlers.py b/litellm/router_utils/fallback_event_handlers.py index 924574537f3..3d37ca216a7 100644 --- a/litellm/router_utils/fallback_event_handlers.py +++ b/litellm/router_utils/fallback_event_handlers.py @@ -214,6 +214,91 @@ def _check_stripped_model_group(model_group: str, fallback_key: str) -> bool: return False +PRE_ROUTING_SELECTED_MODEL_KEY: Final = "pre_routing_selected_model" +_ROUTER_METADATA_BUCKETS: Final = ("metadata", "litellm_metadata") + + +def record_pre_routing_selection(request_kwargs: Mapping[str, Any] | None, selected_model: str) -> None: + """ + Remember which model a pre-routing hook picked, so fallback lookup can key off it. + + Fallback resolution runs on an outer kwargs dict that ``**kwargs`` already copied, so + writing the model there is invisible by the time routing picks a tier. The metadata + buckets are nested dicts shared by reference across those copies, which is how the + router already carries values back up. + + The write goes through the proxy-internal bucket resolver, never into both buckets: + on /v1/messages the top-level ``metadata`` dict is the provider's own request field, + so a blanket write would forward the tier stamp upstream. + """ + from litellm.litellm_core_utils.core_helpers import get_metadata_variable_name_from_kwargs + + if request_kwargs is None: + return + bucket: Final = request_kwargs.get(get_metadata_variable_name_from_kwargs(request_kwargs)) + if isinstance(bucket, dict): + bucket[PRE_ROUTING_SELECTED_MODEL_KEY] = selected_model + + +def clear_pre_routing_selection(request_kwargs: Mapping[str, object] | None) -> None: + """ + Drop any selection the router did not make itself on this hop. + + The buckets carry whatever the caller sent, so an inbound value is the caller + choosing a fallback chain rather than the router choosing a tier. A fallback hop + also inherits the previous hop's selection, which would key its own failure off + the tier that already failed. Clearing at the start of every hop leaves only a + value the pre-routing hook wrote while routing that hop. + """ + if request_kwargs is None: + return + for bucket in (request_kwargs.get(name) for name in _ROUTER_METADATA_BUCKETS): + if isinstance(bucket, dict) and PRE_ROUTING_SELECTED_MODEL_KEY in bucket: + del bucket[PRE_ROUTING_SELECTED_MODEL_KEY] + + +def get_pre_routing_selection(kwargs: Mapping[str, Any]) -> str | None: + """The model a pre-routing hook selected for this request, if one did.""" + buckets: Final = (kwargs.get(name) for name in _ROUTER_METADATA_BUCKETS) + selections: Final = (bucket.get(PRE_ROUTING_SELECTED_MODEL_KEY) for bucket in buckets if isinstance(bucket, dict)) + return next((selected for selected in selections if isinstance(selected, str) and selected), None) + + +def fallback_lookup_groups(kwargs: Mapping[str, Any], model_group: str | None) -> tuple[str, ...]: + """ + Ordered keys for resolving a fallback chain: the tier a pre-routing hook selected wins, + and the requested group still resolves when no tier-keyed chain exists, so configs keyed + on the router name (the documented contract) keep working behind auto-routers. + """ + ordered: Final = (get_pre_routing_selection(kwargs), model_group) + return tuple(dict.fromkeys(group for group in ordered if group)) + + +def _resolved_a_specific_chain( + fallbacks: list[Any], # mutable-ok: mirrors get_fallback_model_group's contract + result: tuple[list[str] | None, int | None], # mutable-ok: mirrors get_fallback_model_group's contract +) -> bool: + resolved, generic_idx = result + if resolved is None: + return False + return generic_idx is None or resolved is not fallbacks[generic_idx]["*"] + + +def get_fallback_model_group_for_lookup_groups( + fallbacks: list[Any], # mutable-ok: mirrors get_fallback_model_group's contract + lookup_groups: tuple[str, ...], +) -> tuple[list[str] | None, int | None]: # mutable-ok: mirrors get_fallback_model_group's contract + """ + First lookup group with a specifically-keyed chain wins; the generic "*" chain applies + only after every group missed, so a catch-all cannot shadow a later group's own chain. + """ + results: Final = tuple(get_fallback_model_group(fallbacks=fallbacks, model_group=group) for group in lookup_groups) + specific: Final = next((result for result in results if _resolved_a_specific_chain(fallbacks, result)), None) + if specific is not None: + return specific + return next((result for result in results if result[0] is not None), (None, None)) + + def get_fallback_model_group(fallbacks: list[Any], model_group: str) -> tuple[list[str] | None, int | None]: """ Returns: @@ -412,6 +497,7 @@ async def run_async_fallback( # LOGGING kwargs = litellm_router.log_retry(kwargs=kwargs, e=original_exception) verbose_router_logger.info("Falling back to model_group = %s", mask_sensitive_structure(mg)) + kwargs.pop("_target_order", None) # rebind-ok: next hop must not inherit the previous order target if isinstance(mg, str): kwargs["model"] = mg elif isinstance(mg, dict): diff --git a/litellm/router_utils/pattern_match_deployments.py b/litellm/router_utils/pattern_match_deployments.py index 0d5ef01bc04..0775e0a4039 100644 --- a/litellm/router_utils/pattern_match_deployments.py +++ b/litellm/router_utils/pattern_match_deployments.py @@ -8,7 +8,7 @@ from re import Match from typing import Final from litellm._logging import verbose_router_logger -from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider +from litellm.litellm_core_utils.get_llm_provider_logic import declared_authenticating_provider, get_llm_provider class PatternUtils: @@ -204,7 +204,7 @@ class PatternMatchRouter: return litellm_deployment_litellm_model - def get_pattern(self, model: str, custom_llm_provider: str | None = None) -> list[dict] | None: + def get_pattern(self, model: str | None, custom_llm_provider: str | None = None) -> list[dict] | None: """ Check if a pattern exists for the given model and custom llm provider @@ -215,18 +215,17 @@ class PatternMatchRouter: Returns: bool: True if pattern exists, False otherwise """ - if custom_llm_provider is None: - try: - ( - _, - custom_llm_provider, - _, - _, - ) = get_llm_provider(model=model) - except Exception: - # get_llm_provider raises exception when provider is unknown - pass - return self.route(model) or self.route(f"{custom_llm_provider}/{model}") + provider: Final = ( + custom_llm_provider or declared_authenticating_provider(model) or self._resolved_provider(model) + ) + return self.route(model) or self.route(f"{provider}/{model}") + + @staticmethod + def _resolved_provider(model: str | None) -> str | None: + try: + return get_llm_provider(model=model)[1] if model else None + except Exception: # noqa: BLE001 # get_llm_provider raises when the provider is unknown; the name then routes as-is + return None def get_deployments_by_pattern(self, model: str, custom_llm_provider: str | None = None) -> list[dict]: """ diff --git a/litellm/router_utils/pre_call_checks/deployment_affinity_check.py b/litellm/router_utils/pre_call_checks/deployment_affinity_check.py index 7fb90ab89de..b1e9dbdefa8 100644 --- a/litellm/router_utils/pre_call_checks/deployment_affinity_check.py +++ b/litellm/router_utils/pre_call_checks/deployment_affinity_check.py @@ -427,6 +427,8 @@ class DeploymentAffinityCheck(CustomLogger): """ request_kwargs = request_kwargs or {} typed_healthy_deployments: Final = cast(list[dict], healthy_deployments) + if request_kwargs.get("_target_order") is not None: + return typed_healthy_deployments ( enable_user_key, diff --git a/litellm/router_utils/pre_call_checks/prompt_caching_deployment_check.py b/litellm/router_utils/pre_call_checks/prompt_caching_deployment_check.py index 6e8406b2ec7..0788c8db710 100644 --- a/litellm/router_utils/pre_call_checks/prompt_caching_deployment_check.py +++ b/litellm/router_utils/pre_call_checks/prompt_caching_deployment_check.py @@ -58,6 +58,9 @@ class PromptCachingDeploymentCheck(CustomLogger): request_kwargs: dict | None = None, parent_otel_span: Span | None = None, ) -> list[dict]: + if request_kwargs is not None and request_kwargs.get("_target_order") is not None: + return healthy_deployments + if messages is not None and is_prompt_caching_valid_prompt( messages=messages, model=model, diff --git a/litellm/router_utils/search_api_router.py b/litellm/router_utils/search_api_router.py index d96defbbcd6..ab5ef5853c9 100644 --- a/litellm/router_utils/search_api_router.py +++ b/litellm/router_utils/search_api_router.py @@ -9,6 +9,7 @@ import random import traceback from collections.abc import Callable from functools import partial +from types import MappingProxyType from typing import Any, Final from litellm._logging import verbose_router_logger @@ -214,6 +215,15 @@ class SearchAPIRouter: api_key, api_base = SearchAPIRouter._resolve_search_provider_credentials( tool_litellm_params=litellm_params, ) + protected_params: Final = frozenset(("search_provider", "api_key", "api_base")) + search_params: Final = MappingProxyType( + { + key: value + for params in (litellm_params, kwargs) + for key, value in params.items() + if key not in protected_params and value is not None + } + ) verbose_router_logger.debug("Selected search tool with provider: %s", search_provider) @@ -222,7 +232,7 @@ class SearchAPIRouter: search_provider=search_provider, api_key=api_key, api_base=api_base, - **kwargs, + **search_params, ) return response diff --git a/litellm/search/cost_calculator.py b/litellm/search/cost_calculator.py index 84461115e8e..21f27075e0f 100644 --- a/litellm/search/cost_calculator.py +++ b/litellm/search/cost_calculator.py @@ -2,16 +2,37 @@ Cost calculation for search providers. """ +from collections.abc import Mapping +from types import MappingProxyType from typing import Final +from pydantic import TypeAdapter, ValidationError + from litellm.utils import get_model_info +PROVIDER_USAGE_ADAPTER: Final[TypeAdapter[tuple[Mapping[str, object], ...]]] = TypeAdapter( + tuple[Mapping[str, object], ...] +) +EMPTY_OPTIONAL_PARAMS: Final[Mapping[str, object]] = MappingProxyType({}) + + +def _provider_usage( + optional_params: Mapping[str, object] | None, + usage_param: str, +) -> tuple[Mapping[str, object], ...] | None: + params: Final = optional_params if optional_params is not None else EMPTY_OPTIONAL_PARAMS + raw_usage: Final[object] = params.get(usage_param) + try: + return PROVIDER_USAGE_ADAPTER.validate_python(raw_usage) + except ValidationError: + return None + def search_provider_cost_per_query( model: str, custom_llm_provider: str | None = None, number_of_queries: int = 1, - optional_params: dict | None = None, + optional_params: Mapping[str, object] | None = None, ) -> tuple[float, float]: """ Calculate cost for search-only providers. @@ -28,6 +49,18 @@ def search_provider_cost_per_query( Returns: Tuple of (input_cost, output_cost) where output_cost is always 0.0 """ + if custom_llm_provider == "parallel_ai": + from litellm.llms.parallel_ai.search.cost_calculator import ( + PARALLEL_AI_USAGE_PARAM, + parallel_ai_search_cost, + ) + + input_cost: Final = parallel_ai_search_cost( + optional_params=optional_params if optional_params is not None else EMPTY_OPTIONAL_PARAMS, + usage=_provider_usage(optional_params, PARALLEL_AI_USAGE_PARAM), + ) + return (input_cost, 0.0) + model_info: Final = get_model_info(model=model, custom_llm_provider=custom_llm_provider) # Check for tiered pricing (e.g., Exa AI based on max_results) diff --git a/litellm/secret_managers/hashicorp_secret_manager.py b/litellm/secret_managers/hashicorp_secret_manager.py index e2662d96b52..8f677b54700 100644 --- a/litellm/secret_managers/hashicorp_secret_manager.py +++ b/litellm/secret_managers/hashicorp_secret_manager.py @@ -1,7 +1,9 @@ import os -from typing import Any, Final +from collections.abc import Mapping +from typing import Final, Protocol import httpx +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -17,6 +19,72 @@ from litellm.proxy._types import KeyManagementSystem from .base_secret_manager import BaseSecretManager, raise_if_unsafe_secret_name +class _VaultAuthData(TypedDict): + """The ``auth`` block Vault returns from a login endpoint.""" + + client_token: ReadOnly[str] + lease_duration: ReadOnly[int] + + +class _VaultLoginResponse(TypedDict): + """Body of a Vault ``/v1/auth/.../login`` response.""" + + auth: ReadOnly[_VaultAuthData] + + +class _VaultSecretTarget(TypedDict): + """Resolved coordinates of one Vault KV v2 secret.""" + + url: ReadOnly[str] + data_key: ReadOnly[str] + secret_name: ReadOnly[str] + + +class _VaultSecretDataBlock(TypedDict, total=False): + """The inner ``data`` block of a Vault KV v2 read body.""" + + data: ReadOnly[Mapping[str, object]] + + +class _VaultSecretReadResponse(TypedDict, total=False): + """Body of a Vault KV v2 secret read, narrowed to the nesting this module walks.""" + + data: ReadOnly[_VaultSecretDataBlock] + + +class _VaultLoginResponseSource(Protocol): + """A Vault login call's HTTP response, read for the auth block it carries.""" + + def json(self) -> _VaultLoginResponse: ... + + +class _VaultSecretReadSource(Protocol): + """A Vault KV v2 read response, read for the nested secret data it carries.""" + + def json(self) -> _VaultSecretReadResponse: ... + + +class _JsonObjectSource(Protocol): + """A Vault response whose body is a JSON object nothing further is assumed about.""" + + def json(self) -> dict[str, object]: ... + + +def _vault_login_body(response: _VaultLoginResponseSource) -> _VaultLoginResponse: + """Decode the body of a Vault login response.""" + return response.json() + + +def _vault_secret_read_body(response: _VaultSecretReadSource) -> _VaultSecretReadResponse: + """Decode the body of a Vault KV v2 secret read response.""" + return response.json() + + +def _json_object_body(response: _JsonObjectSource) -> dict[str, object]: + """Decode a Vault response body as a plain JSON object.""" + return response.json() + + class HashicorpSecretManager(BaseSecretManager): def __init__(self): from litellm.proxy.proxy_server import CommonProxyErrors, premium_user @@ -130,7 +198,8 @@ class HashicorpSecretManager(BaseSecretManager): ) resp.raise_for_status() - auth_data: Final = resp.json()["auth"] + login_response: Final = _vault_login_body(resp) + auth_data: Final = login_response["auth"] token: Final = auth_data["client_token"] _lease_duration: Final = auth_data["lease_duration"] @@ -191,8 +260,10 @@ class HashicorpSecretManager(BaseSecretManager): json=self._get_tls_cert_auth_body(), ) resp.raise_for_status() - token: Final = resp.json()["auth"]["client_token"] - _lease_duration: Final = resp.json()["auth"]["lease_duration"] + token_response: Final = _vault_login_body(resp) + token: Final = token_response["auth"]["client_token"] + lease_response: Final = _vault_login_body(resp) + _lease_duration: Final = lease_response["auth"]["lease_duration"] verbose_logger.debug("Successfully obtained Vault token via TLS cert auth.") self.cache.set_cache(key="hcp_vault_token", value=token, ttl=_lease_duration) return token @@ -205,9 +276,9 @@ class HashicorpSecretManager(BaseSecretManager): def get_url( self, secret_name: str, - namespace: str | None = None, - mount_name: str | None = None, - path_prefix: str | None = None, + namespace: object = None, + mount_name: object = None, + path_prefix: object = None, ) -> str: """ Constructs the Vault URL for KV v2 secrets. @@ -238,7 +309,7 @@ class HashicorpSecretManager(BaseSecretManager): _url += secret_name return _url - def _sanitize_plain_value(self, value: str | int | None) -> str | None: + def _sanitize_plain_value(self, value: object) -> str | None: if value is None: return None value_str: Final = str(value).strip() @@ -246,23 +317,23 @@ class HashicorpSecretManager(BaseSecretManager): return None return value_str - def _sanitize_path_component(self, value: str | int | None) -> str | None: + def _sanitize_path_component(self, value: object) -> str | None: sanitized_value = self._sanitize_plain_value(value) if sanitized_value is None: return None sanitized_value = sanitized_value.strip("/") return sanitized_value or None - def _extract_secret_manager_settings(self, optional_params: dict | None) -> dict[str, Any]: + def _extract_secret_manager_settings(self, optional_params: dict | None) -> dict[str, object]: if not isinstance(optional_params, dict): return {} candidate: Final = optional_params.get("secret_manager_settings") - source: Final = candidate if isinstance(candidate, dict) else optional_params + source: Final[Mapping[str, object]] = candidate if isinstance(candidate, dict) else optional_params allowed_keys: Final = {"namespace", "mount", "path_prefix", "data"} return {k: source[k] for k in allowed_keys if k in source} - def _build_secret_target(self, secret_name: str, optional_params: dict | None) -> dict[str, Any]: + def _build_secret_target(self, secret_name: str, optional_params: dict | None) -> _VaultSecretTarget: settings: Final = self._extract_secret_manager_settings(optional_params) namespace: Final = settings.get("namespace", self.vault_namespace) @@ -331,7 +402,7 @@ class HashicorpSecretManager(BaseSecretManager): response.raise_for_status() # For KV v2, the secret is in response.json()["data"]["data"] - json_resp: Final = response.json() + json_resp: Final = _json_object_body(response) _value: Final = self._get_secret_value_from_json_response(json_resp) self.cache.set_cache(secret_name, _value) return _value @@ -362,7 +433,7 @@ class HashicorpSecretManager(BaseSecretManager): response.raise_for_status() # For KV v2, the secret is in response.json()["data"]["data"] - json_resp: Final = response.json() + json_resp: Final = _json_object_body(response) _value: Final = self._get_secret_value_from_json_response(json_resp) self.cache.set_cache(secret_name, _value) return _value @@ -379,7 +450,7 @@ class HashicorpSecretManager(BaseSecretManager): optional_params: dict | None = None, timeout: float | httpx.Timeout | None = None, tags: dict | list | None = None, - ) -> dict[str, Any]: + ) -> dict[str, object]: """ Writes a secret to Vault KV v2 using an async HTTPX client. @@ -413,7 +484,7 @@ class HashicorpSecretManager(BaseSecretManager): json=data, ) response.raise_for_status() - return response.json() + return _json_object_body(response) except Exception as e: verbose_logger.exception("Error writing secret to Hashicorp Vault: %s", e) return {"status": "error", "message": str(e)} @@ -500,7 +571,7 @@ class HashicorpSecretManager(BaseSecretManager): headers=self._get_request_headers(), ) response.raise_for_status() - json_resp: Final = response.json() + json_resp: Final = _vault_secret_read_body(response) # Use data_key from target to get the correct value data_key: Final = new_target["data_key"] new_secret_value_from_vault: Final = json_resp.get("data", {}).get("data", {}).get(data_key, None) diff --git a/litellm/setup_wizard.py b/litellm/setup_wizard.py index d6b3dfa3285..dd147aaccee 100644 --- a/litellm/setup_wizard.py +++ b/litellm/setup_wizard.py @@ -53,11 +53,12 @@ PROVIDERS: Final[list[dict]] = [ { "id": "anthropic", "name": "Anthropic", - "description": "Claude Fable 5, Opus 5, Opus 4.8, Opus 4.7, Opus 4.6, Sonnet 5, Sonnet 4.6, Haiku 4.5", + "description": "Claude Fable 5.1, Fable 5, Opus 5, Opus 4.8, Opus 4.7, Opus 4.6, Sonnet 5, Sonnet 4.6, Haiku 4.5", "env_key": "ANTHROPIC_API_KEY", "key_hint": "sk-ant-...", "test_model": "claude-haiku-4-5-20251001", "models": [ + "claude-fable-5-1", "claude-fable-5", "claude-opus-5", "claude-sonnet-5", diff --git a/litellm/types/guardrail_base_init.py b/litellm/types/guardrail_base_init.py new file mode 100644 index 00000000000..9174e8d840f --- /dev/null +++ b/litellm/types/guardrail_base_init.py @@ -0,0 +1,24 @@ +"""Typed view of the scalar keyword payload guardrails forward to ``CustomGuardrail.__init__``. + +Guardrail subclasses collect their base-class options in ``**kwargs`` and splat them into +``super().__init__``. Declaring the payload's shape here lets the checker resolve each +forwarded argument to its real parameter type instead of ``Any``. +""" + +from typing_extensions import ReadOnly, TypedDict + + +class GuardrailBaseInitKwargs(TypedDict, total=False): + guardrail_name: ReadOnly[str | None] + default_on: ReadOnly[bool] + mask_request_content: ReadOnly[bool] + mask_response_content: ReadOnly[bool] + violation_message_template: ReadOnly[str | None] + end_session_after_n_fails: ReadOnly[int | None] + on_violation: ReadOnly[str | None] + realtime_violation_message: ReadOnly[str | None] + on_sensitive_data: ReadOnly[str | None] + sensitive_data_route_to_model: ReadOnly[str | None] + sticky_session_routing: ReadOnly[bool] + run_in_parallel: ReadOnly[bool] + only_scan_new_messages: ReadOnly[bool] diff --git a/litellm/types/guardrails.py b/litellm/types/guardrails.py index 9be78757511..c17103da890 100644 --- a/litellm/types/guardrails.py +++ b/litellm/types/guardrails.py @@ -1,5 +1,7 @@ +from collections.abc import Mapping from datetime import datetime from enum import Enum +from types import MappingProxyType from typing import Any, Final, Literal from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator @@ -134,6 +136,7 @@ class SupportedGuardrailIntegrations(Enum): HEADROOM = "headroom" COMPRESR = "compresr" STRAIKER = "straiker" + ALICE = "alice" class Role(Enum): @@ -550,6 +553,40 @@ class BedrockGuardrailConfigModel(BaseModel): ) +class BedrockGuardrailStreamingParams(BaseModel): + streaming_buffer_until_moderated: bool = Field( + default=True, + description="If True (default), withhold every streamed chunk until the end-of-stream " + "ApplyGuardrail scan passes, so no flagged content reaches the client before a block. " + "If False, chunks stream through unbuffered, so flagged content can reach the client " + "before the scan finishes; a flagged scan still ends the stream, with a block message " + "when disable_exception_on_block is true and an in-stream error frame otherwise.", + ) + streaming_sampling_rate: int = Field( + default=5, + ge=1, + description="When not buffering and not end-of-stream-only, scan the accumulated response " + "every Nth streamed chunk. Each sampled scan is a full ApplyGuardrail call that delays " + "that chunk, so lower values add latency and AWS text-unit cost.", + ) + streaming_end_of_stream_only: bool = Field( + default=False, + description="When not buffering, skip per-chunk sampling and run one ApplyGuardrail scan " + "on the assembled response at end of stream. Combined with " + "streaming_buffer_until_moderated=false the full response streams live before the scan " + "and the scan result lands in guardrail_information; a flagged response still ends the " + "stream with a block message (disable_exception_on_block=true) or an error frame.", + ) + + @classmethod + def from_extras(cls, extras: Mapping[str, object] | None) -> "BedrockGuardrailStreamingParams": + if not extras: + return cls() + return cls.model_validate( + MappingProxyType({name: extras[name] for name in cls.model_fields if extras.get(name) is not None}) + ) + + class LakeraV2GuardrailConfigModel(BaseModel): """Configuration parameters for the Lakera AI v2 guardrail""" diff --git a/litellm/types/integrations/custom_logger.py b/litellm/types/integrations/custom_logger.py index 2cca16351af..9a714e1724e 100644 --- a/litellm/types/integrations/custom_logger.py +++ b/litellm/types/integrations/custom_logger.py @@ -5,8 +5,14 @@ from pydantic import BaseModel, Field CHAT_COMPLETION_AGENTIC_SURFACE: Final = "chat_completions" RESPONSES_AGENTIC_SURFACE: Final = "responses" CODE_INTERPRETER_INTERCEPTION_PREFIX: Final = "_code_interpreter_interception" +HEADROOM_INTERCEPTION_PREFIX: Final = "_headroom_interception" +HEADROOM_CONVERTED_STREAM_KEY: Final = f"{HEADROOM_INTERCEPTION_PREFIX}_converted_stream" NON_CODE_INTERPRETER_INTERCEPTION_INTERNAL_PREFIXES: Final = frozenset( - ("_websearch_interception", "_compression_interception") + ( + "_websearch_interception", + "_compression_interception", + HEADROOM_INTERCEPTION_PREFIX, + ) ) INTERCEPTION_INTERNAL_PREFIXES: Final = frozenset( ( diff --git a/litellm/types/integrations/datadog_llm_obs.py b/litellm/types/integrations/datadog_llm_obs.py index 7853dda1213..bae876dfdd9 100644 --- a/litellm/types/integrations/datadog_llm_obs.py +++ b/litellm/types/integrations/datadog_llm_obs.py @@ -4,21 +4,58 @@ Payloads for Datadog LLM Observability Service (LLMObs) API Reference: https://docs.datadoghq.com/llm_observability/setup/api/?tab=example#api-standards """ +from collections.abc import Sequence from typing import Any, Literal -from typing_extensions import TypedDict +from typing_extensions import ReadOnly, TypedDict from litellm.types.integrations.custom_logger import StandardCustomLoggerInitParams +class ToolCall(TypedDict, total=False): + """A tool call on a message, as LLM Obs names its fields.""" + + name: ReadOnly[str] + arguments: ReadOnly[dict[str, Any] | str] # parsed object, or the raw string when it will not parse to one + tool_id: ReadOnly[str] + type: ReadOnly[str] + + +class ToolResult(TypedDict, total=False): + """The result of a tool call, as LLM Obs names its fields.""" + + name: ReadOnly[str] + result: ReadOnly[str] + tool_id: ReadOnly[str] + type: ReadOnly[str] + + +class ToolDefinition(TypedDict, total=False): + """A tool the model was offered on the request.""" + + name: ReadOnly[str] + description: ReadOnly[str] + schema: ReadOnly[dict[str, Any]] + + +class Message(TypedDict, total=False): + """A message on a span, as LLM Obs names its fields.""" + + content: ReadOnly[str] + role: ReadOnly[str] + reasoning_content: ReadOnly[str] + tool_calls: ReadOnly[Sequence[ToolCall]] + tool_results: ReadOnly[Sequence[ToolResult]] + + class InputMeta(TypedDict): - messages: list[ - dict[str, Any] # changed to fit with tool calls + messages: Sequence[ + Message | dict[str, Any] # changed to fit with tool calls ] # Relevant Issue: https://github.com/BerriAI/litellm/issues/9494 class OutputMeta(TypedDict): - messages: list[Any] + messages: Sequence[Any] class DDLLMObsError(TypedDict, total=False): @@ -36,6 +73,7 @@ class Meta(TypedDict, total=False): output: OutputMeta # The span's output information. metadata: dict[str, Any] error: DDLLMObsError | None # Error information on the span + tool_definitions: ReadOnly[Sequence[ToolDefinition]] # The tools offered to the model on this request class LLMMetrics(TypedDict, total=False): @@ -45,6 +83,9 @@ class LLMMetrics(TypedDict, total=False): time_to_first_token: float time_per_output_token: float total_cost: float + cache_read_input_tokens: ReadOnly[float] + cache_write_input_tokens: ReadOnly[float] + non_cached_input_tokens: ReadOnly[float] class LLMObsPayload(TypedDict, total=False): diff --git a/litellm/types/integrations/prometheus.py b/litellm/types/integrations/prometheus.py index 01ed8b08571..8498b6f6d00 100644 --- a/litellm/types/integrations/prometheus.py +++ b/litellm/types/integrations/prometheus.py @@ -270,6 +270,10 @@ DEFINED_PROMETHEUS_METRICS = Literal[ "litellm_deployment_rpm_limit", "litellm_remaining_api_key_requests_for_model", "litellm_remaining_api_key_tokens_for_model", + "litellm_api_key_rate_limit_allowed_metric", + "litellm_api_key_rate_limit_used_metric", + "litellm_team_rate_limit_allowed_metric", + "litellm_team_rate_limit_used_metric", "litellm_llm_api_failed_requests_metric", "litellm_callback_logging_failures_metric", "litellm_in_flight_requests", @@ -775,6 +779,22 @@ class PrometheusMetricLabels: UserAPIKeyLabelNames.MODEL_ID.value, ] + litellm_api_key_rate_limit_allowed_metric: ClassVar[tuple[str, ...]] = ( + UserAPIKeyLabelNames.API_KEY_HASH.value, + UserAPIKeyLabelNames.API_KEY_ALIAS.value, + UserAPIKeyLabelNames.RATE_LIMIT_TYPE.value, + ) + + litellm_api_key_rate_limit_used_metric = litellm_api_key_rate_limit_allowed_metric + + litellm_team_rate_limit_allowed_metric: ClassVar[tuple[str, ...]] = ( + UserAPIKeyLabelNames.TEAM.value, + UserAPIKeyLabelNames.TEAM_ALIAS.value, + UserAPIKeyLabelNames.RATE_LIMIT_TYPE.value, + ) + + litellm_team_rate_limit_used_metric = litellm_team_rate_limit_allowed_metric + litellm_llm_api_failed_requests_metric = [ UserAPIKeyLabelNames.END_USER.value, UserAPIKeyLabelNames.API_KEY_HASH.value, diff --git a/litellm/types/integrations/slack_alerting.py b/litellm/types/integrations/slack_alerting.py index b1b7bc3541a..64c0c530e9b 100644 --- a/litellm/types/integrations/slack_alerting.py +++ b/litellm/types/integrations/slack_alerting.py @@ -91,6 +91,40 @@ class SlackAlertingArgs(LiteLLMPydanticObjectBase): default=False, description="If true, the alerting payload will be printed to the console.", ) + daily_spend_per_user_threshold: float | None = Field( + default=None, + gt=0, + allow_inf_nan=False, + description="Alert when a user's spend for the current day (UTC) crosses this USD amount. Off by default.", + ) + monthly_spend_per_user_threshold: float | None = Field( + default=None, + gt=0, + allow_inf_nan=False, + description="Alert when a user's spend for the current calendar month (UTC) crosses this USD amount. Off by default.", + ) + spend_anomaly_multiplier: float = Field( + default=3.0, + gt=0, + allow_inf_nan=False, + description="Flag a user's spend as anomalous when today's spend exceeds this multiple of their trailing daily average.", + ) + spend_anomaly_baseline_days: int = Field( + default=7, + ge=1, + description="Number of trailing days used to compute a user's daily average spend for anomaly detection.", + ) + spend_anomaly_min_spend: float = Field( + default=10.0, + gt=0, + allow_inf_nan=False, + description="Minimum spend (USD) a user must reach today before an anomaly alert can fire. Reduces false positives.", + ) + user_spend_check_interval: int = Field( + default=3600, + ge=60, + description="How often (in seconds) to check per-user spend thresholds and anomalies. Default is hourly.", + ) class DeploymentMetrics(LiteLLMPydanticObjectBase): @@ -138,6 +172,8 @@ class AlertType(str, Enum): budget_alerts = "budget_alerts" spend_reports = "spend_reports" failed_tracking_spend = "failed_tracking_spend" + user_spend_thresholds = "user_spend_thresholds" + user_spend_anomalies = "user_spend_anomalies" # Database alerts db_exceptions = "db_exceptions" @@ -182,6 +218,7 @@ DEFAULT_ALERT_TYPES: Final[list[AlertType]] = [ AlertType.budget_alerts, AlertType.spend_reports, AlertType.failed_tracking_spend, + AlertType.user_spend_thresholds, # Database alerts AlertType.db_exceptions, # Report alerts diff --git a/litellm/types/llms/anthropic_messages/anthropic_response.py b/litellm/types/llms/anthropic_messages/anthropic_response.py index 42ca3fd6d4b..4fe1dafc73b 100644 --- a/litellm/types/llms/anthropic_messages/anthropic_response.py +++ b/litellm/types/llms/anthropic_messages/anthropic_response.py @@ -78,6 +78,16 @@ class AnthropicUsage(TypedDict, total=False): server_tool_use: NotRequired[ReadOnly[ServerToolUsage]] +class AnthropicStopDetails(TypedDict, total=False): + """ + Safeguard verdict accompanying a `stop_reason: "refusal"` response: + https://platform.claude.com/docs/en/build-with-claude/refusals-and-fallback + """ + + category: ReadOnly[str | None] + explanation: ReadOnly[str | None] + + class AnthropicMessagesResponse(TypedDict, total=False): """ Anthropic Messages API Response: https://docs.anthropic.com/en/api/messages @@ -90,7 +100,8 @@ class AnthropicMessagesResponse(TypedDict, total=False): id: str model: str | None # This represents the Model type from Anthropic role: Literal["assistant"] | None - stop_reason: Literal["end_turn", "max_tokens", "stop_sequence", "tool_use"] | None + stop_reason: Literal["end_turn", "max_tokens", "stop_sequence", "tool_use", "refusal"] | None + stop_details: NotRequired[ReadOnly[AnthropicStopDetails | None]] stop_sequence: str | None type: Literal["message"] | None usage: AnthropicUsage | None diff --git a/litellm/types/llms/openai.py b/litellm/types/llms/openai.py index a6115640d78..32d88da0085 100644 --- a/litellm/types/llms/openai.py +++ b/litellm/types/llms/openai.py @@ -327,6 +327,10 @@ class BatchGuardrailReport(BaseModel): """Every record that was redacted or dropped, in file order.""" +_JsonValue: TypeAlias = object +"""Alias for ``object``, usable inside model bodies that declare a field named ``object``.""" + + BATCH_GUARDRAIL_RESPONSE_FIELD: Final = "litellm_batch_guardrail" @@ -1191,7 +1195,7 @@ class ShellToolParam(TypedDict, total=False): type: Required[Literal["shell"] | str] """The type of tool. Use ``\"shell\"``.""" - environment: Required[dict[str, Any]] + environment: Required[dict[str, object]] """Environment config: ``type`` (e.g. ``\"container_auto\"``, ``\"container_reference\"``, ``\"local\"``), optional ``container_id``, ``network_policy``, ``domain_secrets``, ``skills``.""" @@ -1308,7 +1312,7 @@ class ResponseAPIUsage(BaseLiteLLMOpenAIResponseObject): @field_validator("cost", mode="before") @classmethod - def parse_cost(cls, v: Any) -> float | None: + def parse_cost(cls, v: object) -> object: """Normalise cost: accept either a float or a dict with a ``total_cost`` key.""" if isinstance(v, dict): return v.get("total_cost") @@ -1805,7 +1809,7 @@ class ErrorEventError(BaseLiteLLMOpenAIResponseObject): type: str # e.g., 'invalid_request_error' code: str # e.g., 'context_length_exceeded' message: str - param: str | dict[str, Any] | None = None + param: str | dict[str, object] | None = None class ErrorEvent(BaseLiteLLMOpenAIResponseObject): @@ -2162,6 +2166,42 @@ class OpenAIRealtimeDoneEvent(TypedDict): type: Literal["response.done"] +class OpenAIRealtimeInputAudioBufferSpeechEvent(TypedDict): + type: ReadOnly[Literal["input_audio_buffer.speech_started", "input_audio_buffer.speech_stopped"]] + event_id: ReadOnly[str] + item_id: ReadOnly[str] + + +class OpenAIRealtimeInputAudioTranscriptionDelta(TypedDict): + type: ReadOnly[Literal["conversation.item.input_audio_transcription.delta"]] + event_id: ReadOnly[str] + item_id: ReadOnly[str] + content_index: ReadOnly[int] + delta: ReadOnly[str] + + +class OpenAIRealtimeInputAudioTranscriptionCompleted(TypedDict): + type: ReadOnly[Literal["conversation.item.input_audio_transcription.completed"]] + event_id: ReadOnly[str] + item_id: ReadOnly[str] + content_index: ReadOnly[int] + transcript: ReadOnly[str] + + +class OpenAIRealtimeUsageTokenDetails(TypedDict): + audio_tokens: ReadOnly[int] + text_tokens: ReadOnly[int] + cached_tokens: NotRequired[ReadOnly[int]] + + +class OpenAIRealtimeResponseUsage(TypedDict): + input_tokens: ReadOnly[int] + output_tokens: ReadOnly[int] + total_tokens: ReadOnly[int] + input_token_details: NotRequired[ReadOnly[OpenAIRealtimeUsageTokenDetails]] + output_token_details: NotRequired[ReadOnly[OpenAIRealtimeUsageTokenDetails]] + + class OpenAIRealtimeEventTypes(Enum): SESSION_CREATED = "session.created" # Beta delta event names @@ -2199,6 +2239,9 @@ OpenAIRealtimeEvents = ( | OpenAIRealtimeOutputItemDone | OpenAIRealtimeFunctionCallArgumentsDone | OpenAIRealtimeDoneEvent + | OpenAIRealtimeInputAudioBufferSpeechEvent + | OpenAIRealtimeInputAudioTranscriptionDelta + | OpenAIRealtimeInputAudioTranscriptionCompleted ) OpenAIRealtimeStreamList = list[OpenAIRealtimeEvents] @@ -2379,7 +2422,7 @@ class OpenAIVideoObject(BaseModel): expires_at: int | None = None """Unix timestamp (seconds) for when the downloadable assets expire, if set.""" - error: dict[str, Any] | None = None + error: dict[str, _JsonValue] | None = None """Error payload that explains why generation failed, if applicable.""" progress: int | None = None @@ -2397,15 +2440,15 @@ class OpenAIVideoObject(BaseModel): model: str | None = None """The video generation model that produced the job.""" - _hidden_params: dict[str, Any] = {} + _hidden_params: dict[str, _JsonValue] = {} def __contains__(self, key) -> bool: return hasattr(self, key) - def get(self, key, default=None): + def get(self, key, default=None) -> _JsonValue: return getattr(self, key, default) - def __getitem__(self, key): + def __getitem__(self, key) -> _JsonValue: return getattr(self, key) def json(self, **kwargs): diff --git a/litellm/types/llms/vertex_ai_gemini_transcription.py b/litellm/types/llms/vertex_ai_gemini_transcription.py new file mode 100644 index 00000000000..e039bc8f2eb --- /dev/null +++ b/litellm/types/llms/vertex_ai_gemini_transcription.py @@ -0,0 +1,72 @@ +from typing import Literal + +from pydantic import BaseModel, ConfigDict +from typing_extensions import ReadOnly, TypedDict + + +class VertexGeminiTranscriptionInlineData(TypedDict): + mimeType: ReadOnly[str] + data: ReadOnly[str] + + +class VertexGeminiTranscriptionPart(TypedDict): + inlineData: ReadOnly[VertexGeminiTranscriptionInlineData] + + +class VertexGeminiTranscriptionContent(TypedDict): + role: ReadOnly[Literal["user"]] + parts: ReadOnly[tuple[VertexGeminiTranscriptionPart, ...]] + + +class VertexGeminiTranscriptionAudioConfig(TypedDict, total=False): + languageCodes: ReadOnly[tuple[str, ...]] + + +class VertexGeminiTranscriptionGenerationConfig(TypedDict): + audioTranscriptionConfig: ReadOnly[VertexGeminiTranscriptionAudioConfig] + + +class VertexGeminiTranscriptionRequest(TypedDict): + contents: ReadOnly[tuple[VertexGeminiTranscriptionContent, ...]] + generationConfig: ReadOnly[VertexGeminiTranscriptionGenerationConfig] + + +class VertexGeminiTranscriptionResponsePart(BaseModel): + model_config = ConfigDict(extra="ignore") + + text: str | None = None + + +class VertexGeminiTranscriptionResponseContent(BaseModel): + model_config = ConfigDict(extra="ignore") + + parts: tuple[VertexGeminiTranscriptionResponsePart, ...] = () + + +class VertexGeminiTranscriptionCandidate(BaseModel): + model_config = ConfigDict(extra="ignore") + + content: VertexGeminiTranscriptionResponseContent | None = None + + +class VertexGeminiTranscriptionModalityTokens(BaseModel): + model_config = ConfigDict(extra="ignore") + + modality: str | None = None + tokenCount: int = 0 + + +class VertexGeminiTranscriptionUsageMetadata(BaseModel): + model_config = ConfigDict(extra="ignore") + + promptTokenCount: int = 0 + candidatesTokenCount: int = 0 + totalTokenCount: int = 0 + promptTokensDetails: tuple[VertexGeminiTranscriptionModalityTokens, ...] = () + + +class VertexGeminiTranscriptionResponse(BaseModel): + model_config = ConfigDict(extra="ignore") + + candidates: tuple[VertexGeminiTranscriptionCandidate, ...] = () + usageMetadata: VertexGeminiTranscriptionUsageMetadata | None = None diff --git a/litellm/types/management_endpoints/auto_router_endpoints.py b/litellm/types/management_endpoints/auto_router_endpoints.py index bde3f5f9e7e..88869a1edfb 100644 --- a/litellm/types/management_endpoints/auto_router_endpoints.py +++ b/litellm/types/management_endpoints/auto_router_endpoints.py @@ -245,27 +245,71 @@ ShadowEvalStatus: TypeAlias = Literal["running", "completed", "stopped"] ShadowEvalDirection: TypeAlias = Literal["forward", "reverse"] +ShadowEvalTargetType: TypeAlias = Literal["key", "team", "user"] + DEFAULT_SHADOW_EVAL_JUDGE_MODEL: Final[str] = "anthropic/claude-sonnet-5" # Sample-count ceiling written on every new job: a zero-cost error loop (a shadow arm that # fails before billing) never consumes spend budget, so it must terminate on count instead. +# A multi-router job writes one attempt row per router arm, so the valve is reached +# proportionally sooner; it is a safety valve, not a sample budget. SHADOW_EVAL_TURN_VALVE: Final[int] = 10_000 +SHADOW_EVAL_MAX_ROUTERS: Final[int] = 4 + class StartShadowEvalRequest(BaseModel): - """Start duplicating one or more keys' traffic for blind comparison against an auto-router.""" + """Start duplicating one or more targets' traffic for blind comparison against an auto-router. + + A target is a virtual key, a team, or a user; each becomes its own leg with its own + budget and stop state. Team and user targets match on the identity every request + carries after auth (user_api_key_team_id / user_api_key_user_id), so they cover + JWT-authenticated traffic, which presents no virtual key at all.""" api_key_ids: tuple[str, ...] = Field( - min_length=1, + default=(), max_length=100, description=( - "The hashed virtual keys whose traffic will be shadowed. Shadow evaluation runs ONLY on these " - "keys' traffic; requests made with any other key are not sampled. Each key carries its own " - "max_budget spend budget, so one key exhausting its budget leaves the others sampling. At most 100 " - "keys per job, which also bounds every read the job's endpoints make." + "Hashed virtual keys whose traffic will be shadowed. Combined with team_ids and user_ids the job " + "needs at least one target and at most 100, which also bounds every read the job's endpoints make. " + "Each target carries its own max_budget spend budget, so one exhausting its budget leaves the " + "others sampling." + ), + ) + team_ids: tuple[str, ...] = Field( + default=(), + max_length=100, + description=( + "Teams whose traffic will be shadowed, matched on the team every authenticated request resolves " + "to, so a team's JWT-auth and virtual-key traffic are both sampled" + ), + ) + user_ids: tuple[str, ...] = Field( + default=(), + max_length=100, + description=( + "Users whose traffic will be shadowed, matched on the user every authenticated request resolves " + "to across all their teams: JWT requests carrying their subject claim and virtual keys they own" + ), + ) + router_name: str | None = Field( + default=None, + description=( + "The auto-router under evaluation, in either direction: the single-router spelling of " + "router_names. Provide exactly one of the two fields" + ), + ) + router_names: tuple[str, ...] = Field( + default=(), + max_length=SHADOW_EVAL_MAX_ROUTERS, + description=( + "The auto-routers under evaluation, at most " + f"{SHADOW_EVAL_MAX_ROUTERS}. Every sampled request runs through every router listed and each " + "arm is judged independently against the same real response, so routers compare head-to-head " + "on identical traffic. More than one router requires direction 'forward'. After validation " + "this field always carries the full deduplicated set, whichever spelling the caller used" ), ) - router_name: str = Field(description="The auto-router under evaluation, in either direction") direction: ShadowEvalDirection = Field( default="forward", description=( @@ -285,7 +329,7 @@ class StartShadowEvalRequest(BaseModel): shadow_percentage: float = Field( ge=0.1, le=100.0, - description="Percentage of the key's requests to duplicate through the router", + description="Percentage of each target's requests to duplicate through the router", ) judge_model: str = Field( default=DEFAULT_SHADOW_EVAL_JUDGE_MODEL, @@ -306,10 +350,11 @@ class StartShadowEvalRequest(BaseModel): ge=0.01, le=10_000, description=( - "Per-key USD budget for the eval's own overhead, the shadow-arm and judge calls, priced with " - "the same figures the spend pipeline bills. EACH scoped key samples until its recorded eval " - "spend reaches this, so a job over N keys spends at most about N times max_budget; in-flight " - "samples can overshoot the cap by one sampling cache window" + "Per-target USD budget for the eval's own overhead, the shadow-arm and judge calls, priced with " + "the same figures the spend pipeline bills. EACH scoped target samples until its recorded eval " + "spend reaches this, so a job over N targets spends at most about N times max_budget; in-flight " + "samples can overshoot the cap by one sampling cache window. Every router arm draws from the " + "same per-target budget, so a multi-router job reaches it proportionally sooner" ), ) @@ -319,7 +364,7 @@ class StartShadowEvalRequest(BaseModel): """Pydantic ignores unknown fields, so a caller still sending max_turns would silently run on the default dollar budget instead of the bound they asked for.""" if isinstance(values, Mapping) and "max_turns" in values: - raise ValueError("max_turns was replaced by max_budget, the per-key USD cap on the eval's own spend") + raise ValueError("max_turns was replaced by max_budget, the per-target USD cap on the eval's own spend") return values @field_validator("shadow_percentage") @@ -327,12 +372,21 @@ class StartShadowEvalRequest(BaseModel): def _round_percentage(cls, value: float) -> float: return round(value, 2) - @field_validator("api_key_ids") + @field_validator("api_key_ids", "team_ids", "user_ids") @classmethod - def _dedupe_keys(cls, value: tuple[str, ...]) -> tuple[str, ...]: - """A key named twice would collide with itself on the one-active-per-(key, direction) index.""" + def _dedupe_targets(cls, value: tuple[str, ...]) -> tuple[str, ...]: + """A target named twice would collide with itself on the one-active-per-(target, direction) index.""" return tuple(dict.fromkeys(value)) + @model_validator(mode="after") + def _at_least_one_target_at_most_hundred(self) -> "StartShadowEvalRequest": + total: Final = len(self.api_key_ids) + len(self.team_ids) + len(self.user_ids) + if total < 1: + raise ValueError("at least one target is required: pass api_key_ids, team_ids, or user_ids") + if total > 100: + raise ValueError("at most 100 targets per job across api_key_ids, team_ids, and user_ids") + return self + @model_validator(mode="after") def _baseline_model_matches_direction(self) -> "StartShadowEvalRequest": if self.direction == "reverse" and self.baseline_model is None: @@ -341,10 +395,28 @@ class StartShadowEvalRequest(BaseModel): raise ValueError("baseline_model is only meaningful when direction is 'reverse'") return self + @model_validator(mode="after") + def _resolve_router_set(self) -> "StartShadowEvalRequest": + """Whichever spelling the caller used, router_names leaves validation as the full + deduplicated set, so every downstream reader consumes one field.""" + if (self.router_name is None) == (not self.router_names): + raise ValueError("provide exactly one of router_name or router_names") + single: Final = () if self.router_name is None else (self.router_name,) + routers: Final = tuple(dict.fromkeys(self.router_names or single)) + if not all(name.strip() for name in routers): + raise ValueError("router names must be non-empty strings") + if len(routers) > 1 and self.direction == "reverse": + raise ValueError("a reverse job evaluates one router against baseline_model; pass a single router") + # A returned model_copy is ignored on the __init__ construction path, so the + # normalization must land as a self attribute store to hold for every caller. + self.router_names = routers + return self + class ShadowEvalSlice(BaseModel): - """Judge outcomes for one slice of a job's verdicts (a router tier, or one of the - models that served the real arm).""" + """Judge outcomes for one slice of a job's verdicts: a router tier, one of the + models that served the real arm, or one scoped target (embedded on that target's + own entry, so slices never need re-joining to a target by id).""" group: str turn_count: int @@ -395,21 +467,28 @@ class ShadowEvalResult(BaseModel): "and in reverse the models the router itself picked" ) ) - by_key: tuple[ShadowEvalSlice, ...] = Field( + by_router: tuple[ShadowEvalSlice, ...] = Field( + default=(), description=( - "One slice per scoped key that has judged verdicts, grouped on the raw key hash. Keys the job " - "scopes but has not judged a turn for yet are absent rather than reported as zero" + "One slice per router arm, grouped on the router name. Every arm of a multi-router job is " + "judged against the same real responses over the same sampled requests, so these slices " + "compare routers head-to-head: like-for-like win rates and spends on identical traffic. " + "Verdicts from before arm stamping existed count toward the job's own router" ), ) overall_shadow_win_rate_pct: float overall_tie_rate_pct: float sampled_real_spend: float = Field( default=0.0, - description="USD the real arm billed across all judged turns, cache-served turns excluded", + description=( + "USD the real arm billed across all judged turns, cache-served turns excluded. A judged turn " + "is one (request, router arm) verdict, so a multi-router job counts the real response once per " + "arm it was judged against; per-router comparisons read by_router" + ), ) sampled_shadow_spend: float = Field( default=0.0, - description="USD the shadow arm billed across the same turns, judge excluded, like for like", + description="USD the shadow arms billed across the same turns, judge excluded, like for like", ) not_sampled_count: int | None = Field( default=None, @@ -436,27 +515,28 @@ class ShadowEvalResult(BaseModel): ) -class ShadowEvalJobKeyResponse(BaseModel): - """One key a job shadows, with its own budget and stop state.""" +class ShadowEvalJobTargetResponse(BaseModel): + """One target a job shadows (a key, team, or user), with its own budget and stop state.""" - api_key_id: str = Field(description="The hashed virtual key whose traffic this entry scopes") + target_type: ShadowEvalTargetType = Field(description="What kind of entity this entry scopes") + target_id: str = Field(description="The hashed virtual key, team id, or user id whose traffic this entry scopes") max_turns: int = Field( description=( - "This key's sample-count ceiling: the whole budget for jobs created before max_budget " + "This target's sample-count ceiling: the whole budget for jobs created before max_budget " "existed, and the error-loop safety valve otherwise" ) ) max_budget: float | None = Field( default=None, description=( - "This key's own USD budget for the eval's shadow and judge spend, independent of its " + "This target's own USD budget for the eval's shadow and judge spend, independent of its " "siblings'; None on jobs created before spend budgets existed, which max_turns alone bounds" ), ) stopped_at: datetime | None = Field( default=None, description=( - "When this key's slot was stamped free, whether its own budget ran out, the window closed, " + "When this target's slot was stamped free, whether its own budget ran out, the window closed, " "or an operator stopped the job; status is derived, so a spent budget reads completed even " "while this is still unset" ), @@ -464,47 +544,61 @@ class ShadowEvalJobKeyResponse(BaseModel): attempt_count: int | None = Field( default=None, description=( - "This key's sampled attempts so far, judged and errored alike, the same count the sampler " + "This target's sampled attempts so far, judged and errored alike, the same count the sampler " "budgets against max_turns; populated on list and detail responses. Frozen at stopped_at " - "once the key is stamped, so in-flight attempts landing after a stop never reclassify it" + "once the target is stamped, so in-flight attempts landing after a stop never reclassify it" ), ) spend: float | None = Field( default=None, description=( - "This key's recorded shadow plus judge spend in USD, the same figure the sampler budgets " + "This target's recorded shadow plus judge spend in USD, the same figure the sampler budgets " "against max_budget; populated on list and detail responses and frozen at stopped_at " "exactly like attempt_count" ), ) + verdicts: "ShadowEvalSlice | None" = Field( + default=None, + description="This target's own judged-verdict slice; detail endpoint only, None until a turn is judged", + ) + @property def budget_spent(self) -> bool: over_spend: Final = self.max_budget is not None and self.spend is not None and self.spend >= self.max_budget return over_spend or (self.attempt_count is not None and self.attempt_count >= self.max_turns) - key_alias: str | None = Field( + target_alias: str | None = Field( default=None, - description="Alias of the shadowed key, resolved from the key row at read time; None when unset or deleted", + description=( + "Display label resolved from the target's own row at read time: the key's alias, the team's " + "alias, or the user's email; None when unset or deleted" + ), ) key_name: str | None = Field( default=None, - description="Masked display name (sk-...) of the shadowed key, resolved at read time like key_alias", + description="Masked display name (sk-...) for key targets, resolved at read time; None for teams and users", ) class ShadowEvalJobResponse(BaseModel): - """A shadow-eval job over one or more keys, each with its own budget and stop state; - status is derived from stopped_by, the keys' stop and budget state, and ends_at, + """A shadow-eval job over one or more targets, each with its own budget and stop state; + status is derived from stopped_by, the targets' stop and budget state, and ends_at, never stored, so no writer anywhere can produce an inconsistent one. Aggregate fields are populated by the detail endpoint only and stay None on list responses.""" job_id: str - keys: tuple[ShadowEvalJobKeyResponse, ...] = Field( + targets: tuple[ShadowEvalJobTargetResponse, ...] = Field( min_length=1, - description="The keys whose traffic this job evaluates, and only those keys', each with its own budget", + description="The targets whose traffic this job evaluates, and only theirs, each with its own budget", + ) + router_names: tuple[str, ...] = Field( + min_length=1, + description=( + "Every auto-router this job runs as a shadow arm. Multi-router jobs sample one slice of " + "traffic and judge every arm against the same real responses" + ), ) - router_name: str direction: ShadowEvalDirection = "forward" baseline_model: str | None = None judge_model: str @@ -526,13 +620,20 @@ class ShadowEvalJobResponse(BaseModel): last_error: str | None = Field(default=None, description="Most recent attempt error; detail endpoint only") results: ShadowEvalResult | None = Field(default=None, description="Stratified verdicts; detail endpoint only") + @computed_field + @property + def router_name(self) -> str: + """The first router, kept for callers that predate router_names; derived so the + two fields can never disagree.""" + return self.router_names[0] + @computed_field @property def status(self) -> ShadowEvalStatus: """Three recorded facts, no history-guessing: a stop is stopped_by (the migration backfills it for every job that displayed stopped when the column arrived, so the - pre-column population is closed), completion is the window passing or every key - spending its budget, and anything else is running. The all-keys-stamped fallback + pre-column population is closed), completion is the window passing or every target + spending its budget, and anything else is running. The all-targets-stamped fallback covers only stops written by pre-column pods during a rolling deploy.""" if self.stopped_by is not None: return "stopped" @@ -540,8 +641,8 @@ class ShadowEvalJobResponse(BaseModel): self.ends_at if self.ends_at.tzinfo else self.ends_at.replace(tzinfo=timezone.utc) ): return "completed" - if all(key.budget_spent for key in self.keys): + if all(target.budget_spent for target in self.targets): return "completed" - if all(key.stopped_at is not None for key in self.keys): + if all(target.stopped_at is not None for target in self.targets): return "stopped" return "running" diff --git a/litellm/types/proxy/guardrails/guardrail_hooks/alice.py b/litellm/types/proxy/guardrails/guardrail_hooks/alice.py new file mode 100644 index 00000000000..73d31673dab --- /dev/null +++ b/litellm/types/proxy/guardrails/guardrail_hooks/alice.py @@ -0,0 +1,21 @@ +from pydantic import Field + +from .base import GuardrailConfigModel + + +class AliceGuardrailConfigModel(GuardrailConfigModel): + api_key: str | None = Field( + default=None, + description=("The API key for Alice. If not provided, the `ALICE_API_KEY` environment variable is checked."), + ) + api_base: str | None = Field( + default=None, + description=( + "The API base URL for Alice. If not provided, the `ALICE_API_BASE` environment " + "variable is checked, then `https://api.alice.io`." + ), + ) + + @staticmethod + def ui_friendly_name() -> str: + return "Alice" diff --git a/litellm/types/proxy/guardrails/guardrail_hooks/prompt_security.py b/litellm/types/proxy/guardrails/guardrail_hooks/prompt_security.py index 6e64f0f47a5..94f8161f44e 100644 --- a/litellm/types/proxy/guardrails/guardrail_hooks/prompt_security.py +++ b/litellm/types/proxy/guardrails/guardrail_hooks/prompt_security.py @@ -12,6 +12,10 @@ class PromptSecurityGuardrailConfigModel(GuardrailConfigModel): default=None, description="The API base for the Prompt Security guardrail. If not provided, the `PROMPT_SECURITY_API_BASE` environment variable is used.", ) + file_sanitization_fail_open: bool = Field( + default=True, + description="Whether file sanitization timeouts allow the original file through instead of blocking the request.", + ) @staticmethod def ui_friendly_name() -> str: diff --git a/litellm/types/proxy/management_endpoints/config_overrides.py b/litellm/types/proxy/management_endpoints/config_overrides.py index 9e1ea23ac46..f9cba6983db 100644 --- a/litellm/types/proxy/management_endpoints/config_overrides.py +++ b/litellm/types/proxy/management_endpoints/config_overrides.py @@ -52,6 +52,43 @@ class HashicorpVaultConfig(BaseModel): ) +class CyberArkConfig(BaseModel): + """Configuration for CyberArk Conjur secret manager integration.""" + + cyberark_api_base: str | None = Field( + default=None, + description="The address of the CyberArk Conjur server (e.g., https://conjur.example.com)", + ) + cyberark_account: str | None = Field( + default=None, + description="The Conjur organization account name", + ) + cyberark_username: str | None = Field( + default=None, + description="The Conjur username (login) to authenticate as", + ) + cyberark_api_key: str | None = Field( + default=None, + description="API key for Conjur API-key authentication", + ) + client_cert: str | None = Field( + default=None, + description="Path to the client TLS certificate for certificate-based authentication", + ) + client_key: str | None = Field( + default=None, + description="Path to the client TLS private key for certificate-based authentication", + ) + ssl_verify: str | None = Field( + default=None, + description="Set to false to disable SSL verification (e.g., for self-signed certificates)", + ) + refresh_interval: str | None = Field( + default=None, + description="Auth token cache TTL in seconds (default: 300)", + ) + + class ConfigOverrideSettingsResponse(BaseModel): """Response model for config override settings GET endpoints.""" diff --git a/litellm/types/proxy/management_endpoints/model_management_endpoints.py b/litellm/types/proxy/management_endpoints/model_management_endpoints.py index 6e18787a224..8315ac0d4d2 100644 --- a/litellm/types/proxy/management_endpoints/model_management_endpoints.py +++ b/litellm/types/proxy/management_endpoints/model_management_endpoints.py @@ -1,6 +1,7 @@ +from datetime import datetime from typing import Any -from pydantic import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field from ...router import ModelGroupInfo @@ -53,10 +54,42 @@ class DeleteModelGroupResponse(BaseModel): message: str +class AccessGroupBudget(BaseModel): + budget_id: str + max_budget: float | None = None + soft_budget: float | None = None + budget_duration: str | None = None + budget_reset_at: datetime | None = None + + +class AccessGroupBudgetRequest(BaseModel): + budget_id: str | None = None # Link an existing budget instead of creating one + max_budget: float | None = Field(default=None, ge=0) + soft_budget: float | None = Field(default=None, ge=0) + budget_duration: str | None = None + + # rejects tpm_limit/rpm_limit/max_parallel_requests: those are not enforced per access group + model_config = ConfigDict(extra="forbid") + + +class AccessGroupBudgetResponse(BaseModel): + access_group: str + spend: float # Shared spend accrued by every key that can reach this access group + budget: AccessGroupBudget | None = None + + +class DeleteAccessGroupBudgetResponse(BaseModel): + access_group: str + budget_deleted: bool # False when the access group had no budget to begin with + message: str + + class AccessGroupInfo(BaseModel): access_group: str model_names: list[str] # List of model names in this access group deployment_count: int # Total number of deployments with this access group + spend: float | None = None # Spend drawn against the group's shared budget + budget: AccessGroupBudget | None = None class ListAccessGroupsResponse(BaseModel): diff --git a/litellm/types/proxy/management_endpoints/scim_v2.py b/litellm/types/proxy/management_endpoints/scim_v2.py index 1612ea03817..7825684cfe5 100644 --- a/litellm/types/proxy/management_endpoints/scim_v2.py +++ b/litellm/types/proxy/management_endpoints/scim_v2.py @@ -150,6 +150,12 @@ class SCIMGroup(SCIMResource): members: list[SCIMMember] | None = None +class SCIMPlaceholderMergeResult(BaseModel): + placeholder_user_id: str + merged_into_user_id: str + team_ids: tuple[str, ...] + + # SCIM List Response Models class SCIMListResponse(BaseModel): schemas: list[str] = ["urn:ietf:params:scim:api:messages:2.0:ListResponse"] diff --git a/litellm/types/proxy/model_access_group_budget.py b/litellm/types/proxy/model_access_group_budget.py new file mode 100644 index 00000000000..cccbe92b5d6 --- /dev/null +++ b/litellm/types/proxy/model_access_group_budget.py @@ -0,0 +1,19 @@ +"""The model access group budget state auth and the spend reservation path share.""" + +from __future__ import annotations + +from pydantic import BaseModel + + +class ModelAccessGroupBudget(BaseModel): + """One model access group's budget, flattened out of its joined ``LiteLLM_ModelAccessGroupBudgetTable`` row. + + Both readers want only the recorded spend and the ceiling, and this sits on the per-request hot + path behind a cache, so the linked budget row is collapsed to ``max_budget`` rather than cached + whole. ``spend`` is the DB-recorded value, which lags the live counter and is only ever a + fallback for it. + """ + + access_group_name: str + spend: float = 0.0 + max_budget: float | None = None diff --git a/litellm/types/router.py b/litellm/types/router.py index 97bd93f3f47..e0957383aac 100644 --- a/litellm/types/router.py +++ b/litellm/types/router.py @@ -189,6 +189,11 @@ class ModelInfo(MirroredPricingParams): # router-wide default. enable_tag_filtering: bool | None = None + # when True, calls routed to this deployment persist a router_metadata block + # (requested model group, selected model + provider, router correlation id) + # in the spend log row's metadata. Set it on every deployment of the group. + internal_router_model: bool | None = None + def __init__(self, id: str | int | None = None, **params) -> None: if id is None: id = str(uuid.uuid4()) # Generate a UUID if id is None or not provided @@ -364,7 +369,7 @@ class GenericLiteLLMParams(CredentialLiteLLMParams, CustomPricingLiteLLMParams): @model_validator(mode="before") @classmethod - def preprocess_input_data(cls, data: Any) -> Any: + def preprocess_input_data(cls, data: object) -> object: """ Pre-process input data before validation: 1. Filter out reserved Python keywords ('self', 'params', '__class__') to prevent @@ -622,6 +627,11 @@ class AlertingConfig(BaseModel): alerting_threshold: float | None = 300 +def _resolved_annotations(model_class: type[object]) -> Mapping[str, object]: + """Resolve a class's annotations, keeping each resolved annotation opaque.""" + return get_type_hints(model_class) + + class ModelGroupInfo(BaseModel): model_group: str providers: list[str] @@ -650,7 +660,7 @@ class ModelGroupInfo(BaseModel): configurable_clientside_auth_params: CONFIGURABLE_CLIENTSIDE_AUTH_PARAMS = None def __init__(self, **data) -> None: - for field_name, field_type in get_type_hints(self.__class__).items(): + for field_name, field_type in _resolved_annotations(self.__class__).items(): if field_type is bool and data.get(field_name) is None: data[field_name] = False super().__init__(**data) diff --git a/litellm/types/services.py b/litellm/types/services.py index 74f908548d5..c558f6fb9d2 100644 --- a/litellm/types/services.py +++ b/litellm/types/services.py @@ -40,6 +40,8 @@ class ServiceTypes(str, enum.Enum): # spend update queue - current spend of key, user, team IN_MEMORY_SPEND_UPDATE_QUEUE = "in_memory_spend_update_queue" REDIS_SPEND_UPDATE_QUEUE = "redis_spend_update_queue" + # budget window spend queue - per-window spend of key, team + REDIS_WINDOW_SPEND_UPDATE_QUEUE = "redis_window_spend_update_queue" class ServiceConfig(TypedDict): diff --git a/litellm/types/utils.py b/litellm/types/utils.py index f0319a7c664..5783a39b30c 100644 --- a/litellm/types/utils.py +++ b/litellm/types/utils.py @@ -40,7 +40,7 @@ from pydantic import ( field_serializer, field_validator, ) -from typing_extensions import ReadOnly, Required, TypedDict +from typing_extensions import NotRequired, ReadOnly, Required, TypedDict from litellm._logging import verbose_logger from litellm._uuid import uuid @@ -193,6 +193,38 @@ class AgenticLoopParams(TypedDict, total=False): """The LLM provider name (e.g., 'bedrock', 'anthropic')""" +class OffPeakWindow(TypedDict, total=False): + """One off-peak rule: UTC time-of-day windows, optionally restricted to weekdays. + + hours_utc is a "HH:MM-HH:MM" string in UTC, or a list of them; a window may wrap past + midnight and an equal-ended window covers the whole day. weekdays is a list of days the + rule applies on, as ISO-8601 numbers (1 = Monday .. 7 = Sunday) or English day names; + omitted means every day. The weekday is read on the calendar named by the block's + weekday_timezone. + """ + + hours_utc: ReadOnly[str | Sequence[str]] + weekdays: ReadOnly[Sequence[int | str]] + + +class OffPeakPricing(TypedDict, total=False): + """Time-windowed off-peak rates for providers that discount by time of day (e.g. DeepSeek). + + hours_utc is a "HH:MM-HH:MM" string in UTC, or a list of them for multiple daily windows, + applying on every day of the week; a window may wrap past midnight. windows adds + day-of-week-qualified rules (e.g. weekend-only whole-day off-peak), matched as a union + with hours_utc. weekday_timezone names the IANA calendar weekdays are read on, defaulting + to UTC. Any rate left unset falls back to the standard rate. + """ + + hours_utc: ReadOnly[str | Sequence[str]] + windows: ReadOnly[Sequence[OffPeakWindow]] + weekday_timezone: ReadOnly[str] + input_cost_per_token: ReadOnly[float] + output_cost_per_token: ReadOnly[float] + cache_read_input_token_cost: ReadOnly[float] + + class ModelInfoBase(ProviderSpecificModelInfo, total=False): key: Required[str] # the key in litellm.model_cost which is returned @@ -225,6 +257,7 @@ class ModelInfoBase(ProviderSpecificModelInfo, total=False): # Smallest prefix this model will actually cache, whatever caching mechanism its provider uses. # Absent means the provider-agnostic default applies; see MINIMUM_PROMPT_CACHE_TOKEN_COUNT. prompt_cache_min_tokens: int | None + off_peak_pricing: ReadOnly[OffPeakPricing | None] # time-windowed off-peak rates input_cost_per_character: float | None # only for vertex ai models input_cost_per_audio_token: float | None input_cost_per_token_above_128k_tokens: float | None # only for vertex ai models @@ -2840,8 +2873,17 @@ RoutingDecisionCause = Literal[ # never called. The matched sentinel rides in matched_keyword. Distinct from the keyword causes, # which are operator-authored rules; these sentinels ship with the router. "housekeeping", + # modality_routing replaced the decided placement: the request carries an image and the + # routed model does not accept image input, so the nearest higher capable tier or + # default_model served instead. The displaced placement rides in signals. + "modality_escalation", "session_affinity_pin", "session_affinity_escalation", + # classification_mode 'user_turn': the request is an agent loop's continuation turn (no new + # human ask), so the session's held routing decision was replayed and the classifier was never + # called. Distinct from "session_affinity_pin", which reports the session_affinity flag pinning + # every turn including new asks; this cause only appears when session_affinity is off. + "user_turn_continuation", "default_fallback", "keyword", "quality_tier", @@ -2881,6 +2923,8 @@ class StandardLoggingRoutingDecision(TypedDict, total=False): classifier_model: str classifier_cost: float escalated: bool + context_escalated: bool # writable-ok: Pydantic warns on ReadOnly TypedDict fields + context_escalation_original_tier: str # writable-ok: Pydantic warns on ReadOnly TypedDict fields tier_boundaries: StandardLoggingRoutingDecisionTierBoundaries reasoning_override_min_score: float # writable-ok: Pydantic warns on ReadOnly TypedDict fields conversation_continuing: bool @@ -2907,6 +2951,8 @@ DERIVED_ROUTING_DECISION_FIELDS: Final[frozenset[str]] = frozenset( "classifier_model", "classifier_cost", "escalated", + "context_escalated", + "context_escalation_original_tier", "tier_boundaries", "reasoning_override_min_score", "conversation_continuing", @@ -2957,6 +3003,8 @@ class StandardLoggingHiddenParams(TypedDict): litellm_overhead_time_ms: float | None additional_headers: StandardLoggingAdditionalHeaders | None batch_models: list[str] | None + batch_successful_requests: ReadOnly[int | None] + batch_failed_requests: ReadOnly[int | None] litellm_model_name: str | None # the model name sent to the provider by litellm usage_object: dict | None @@ -3258,6 +3306,7 @@ class StandardLoggingPayload(TypedDict): cache_key: str | None saved_cache_cost: float request_tags: list + request_model_access_groups: NotRequired[ReadOnly[Sequence[str]]] end_user: str | None requester_ip_address: str | None user_agent: str | None @@ -3470,17 +3519,22 @@ class CustomPricingLiteLLMParams(MirroredPricingParams): return {k: v for k, v in model_info.items() if k not in cls.model_fields} -SHARED_BACKEND_MODEL_INFO_FIELDS: Final[frozenset[str]] = frozenset( - ModelInfoBase.__required_keys__ | ModelInfoBase.__optional_keys__ -) - frozenset(CustomPricingLiteLLMParams.model_fields) +DEPLOYMENT_SCOPED_PRICING_FIELDS: Final[frozenset[str]] = frozenset({"off_peak_pricing"}) + +SHARED_BACKEND_MODEL_INFO_FIELDS: Final[frozenset[str]] = ( + frozenset(ModelInfoBase.__required_keys__ | ModelInfoBase.__optional_keys__) + - frozenset(CustomPricingLiteLLMParams.model_fields) + - DEPLOYMENT_SCOPED_PRICING_FIELDS +) def shared_backend_model_info(model_info: dict[str, Any]) -> dict[str, Any]: """Return only the fields safe to register under a shared ``{provider}/{model}`` key in ``litellm.model_cost``: cost-map schema fields (``ModelInfoBase``) minus - per-deployment pricing overrides. Per-deployment metadata (``id``, - ``access_via_team_ids``, arbitrary custom keys) never belongs on the shared key; - it stays under the deployment's unique model id. + per-deployment pricing overrides and deployment-scoped pricing blocks such as + ``off_peak_pricing``. Per-deployment metadata (``id``, ``access_via_team_ids``, + arbitrary custom keys) never belongs on the shared key; it stays under the + deployment's unique model id. """ return {k: v for k, v in model_info.items() if k in SHARED_BACKEND_MODEL_INFO_FIELDS} @@ -3503,6 +3557,7 @@ agentic_loop_internal_litellm_params: Final = [ "_code_interpreter_interception_converted_stream", "_websearch_interception_emit_native_blocks", "_websearch_interception_converted_stream", + "_headroom_interception_converted_stream", ] # Proxy-owned callback credentials, stamped from admin-configured team/key callback @@ -3581,6 +3636,8 @@ all_litellm_params = ( "client", "rpm", "tpm", + "default_api_key_rpm_limit", + "default_api_key_tpm_limit", "itpm", "otpm", "max_parallel_requests", @@ -3754,6 +3811,8 @@ class LlmProviders(str, Enum): CODESTRAL = "codestral" TEXT_COMPLETION_CODESTRAL = "text-completion-codestral" DASHSCOPE = "dashscope" + QWENCLOUD = "qwencloud" + QWEN_AI_PLATFORM = "qwen_ai_platform" MODELSCOPE = "modelscope" MOONSHOT = "moonshot" PUBLICAI = "publicai" diff --git a/litellm/types/videos/main.py b/litellm/types/videos/main.py index 3677cec3c8f..99b08f6caf6 100644 --- a/litellm/types/videos/main.py +++ b/litellm/types/videos/main.py @@ -2,7 +2,7 @@ from typing import Any, Literal from openai.types.audio.transcription_create_params import FileTypes from pydantic import BaseModel -from typing_extensions import TypedDict +from typing_extensions import ReadOnly, TypedDict class VideoObject(BaseModel): @@ -76,6 +76,7 @@ class VideoCreateOptionalRequestParams(TypedDict, total=False): image: Any | None # Image for image-to-video; dict with gcsUri/bytesBase64Encoded, or file-like object parameters: dict[str, Any] | None # Provider-specific parameters block passed directly to the API model: str | None + resolution: ReadOnly[str | None] seconds: str | None size: str | None characters: list[dict[str, str]] | None diff --git a/litellm/utils.py b/litellm/utils.py index fa2226dbf2c..252b6756937 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -69,6 +69,7 @@ from litellm.constants import ( DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET, DEFAULT_TRIM_RATIO, FUNCTION_DEFINITION_TOKEN_COUNT, + HF_CONFIG_FETCH_TIMEOUT_SECONDS, INITIAL_RETRY_DELAY, JITTER, MAX_RETRY_DELAY, @@ -2659,10 +2660,19 @@ def _is_explicitly_disabled_factory(model: str, custom_llm_provider: str | None, ``_supports_factory`` so caching, fallback, and normalisation improvements apply here automatically. """ + from litellm.litellm_core_utils.get_llm_provider_logic import declared_authenticating_provider + try: - model, custom_llm_provider, _, _ = litellm.get_llm_provider( - model=model, custom_llm_provider=custom_llm_provider - ) + declared: Final = declared_authenticating_provider(model, custom_llm_provider) + if declared is not None: + model = model.removeprefix( + f"{declared}/" + ) # rebind-ok: mirrors get_llm_provider's split without its OAuth flow + custom_llm_provider = declared # rebind-ok: same + else: + model, custom_llm_provider, _, _ = litellm.get_llm_provider( + model=model, custom_llm_provider=custom_llm_provider + ) model_info: Final = _get_model_info_helper(model=model, custom_llm_provider=custom_llm_provider) val: Final = model_info.get(key) if val is False: @@ -2750,6 +2760,15 @@ def supports_computer_use(model: str, custom_llm_provider: str | None = None) -> ) +def is_vision_explicitly_disabled(model: str, custom_llm_provider: str | None = None) -> bool: + """True only when supports_vision is explicitly declared false for the model. + + The opt-out mirror of :func:`supports_vision`: a missing declaration reads as not + disabled, so unknown or newly added models stay eligible for image routing. + """ + return _is_explicitly_disabled_factory(model, custom_llm_provider, "supports_vision") + + def supports_vision(model: str, custom_llm_provider: str | None = None) -> bool: """ Check if the given model supports vision and return a boolean value. @@ -2850,10 +2869,9 @@ def _update_dictionary(existing_dict: dict, new_dict: dict) -> dict: elif isinstance(v, dict): existing_nested_dict = existing_dict.get(k) if isinstance(existing_nested_dict, dict): - existing_nested_dict.update(v) - existing_dict[k] = existing_nested_dict + existing_dict[k] = {**existing_nested_dict, **v} # mutable-ok: copy-on-write merge else: - existing_dict[k] = v + existing_dict[k] = dict(v) # mutable-ok: detached copy, never the caller's dict by reference else: existing_dict[k] = v @@ -3542,10 +3560,10 @@ def get_optional_params_embeddings( non_default_params=non_default_params, optional_params={}, kwargs=kwargs ) elif custom_llm_provider == "vertex_ai" or custom_llm_provider == "gemini": - # OpenAI SDKs (and litellm's own client) send encoding_format="float" - # by default; float lists are exactly what the vertex API returns, so - # the param is a no-op — don't reject the provider default. Other - # values (e.g. "base64") stay on the unsupported-param path below. + # OpenAI SDKs send encoding_format="float" by default; float lists are + # exactly what the vertex API returns, so the param is a no-op and the + # provider default is not rejected. Other values (e.g. "base64") stay + # on the unsupported-param path below. if non_default_params.get("encoding_format") == "float": non_default_params.pop("encoding_format") supported_params = get_supported_openai_params( @@ -3581,7 +3599,7 @@ def get_optional_params_embeddings( object = litellm.AmazonTitanMultimodalEmbeddingG1Config() elif "amazon.titan-embed-text-v2:0" in model: object = litellm.AmazonTitanV2Config() - elif "cohere.embed-multilingual-v3" in model or "cohere.embed-v4" in model: + elif "cohere.embed" in model: object = litellm.BedrockCohereEmbeddingConfig() elif "twelvelabs" in model or "marengo" in model: object = litellm.TwelveLabsMarengoEmbeddingConfig() @@ -4858,11 +4876,7 @@ def _get_deployment_order(deployment: dict | Any) -> int | None: def _get_order_filtered_deployments(healthy_deployments: list[dict], target_order: int | None = None) -> list: if target_order is not None: - filtered: Final = [d for d in healthy_deployments if _get_deployment_order(d) == target_order] - if filtered: - return filtered - # target_order doesn't match any deployment (e.g., external fallback model) — return all - return healthy_deployments + return [d for d in healthy_deployments if _get_deployment_order(d) == target_order] # Default: pick min order group _valid_orders: Final[list[int]] = [ @@ -5168,7 +5182,7 @@ def get_max_tokens(model: str) -> int | None: config_url: Final = f"https://huggingface.co/{model_name}/raw/main/config.json" try: # Make the HTTP request to get the raw JSON file - response: Final = litellm.module_level_client.get(config_url) + response: Final = litellm.module_level_client.get(config_url, timeout=HF_CONFIG_FETCH_TIMEOUT_SECONDS) response.raise_for_status() # Raise an exception for bad responses (4xx or 5xx) # Parse the JSON response @@ -5522,7 +5536,7 @@ def _get_max_position_embeddings(model_name: str) -> int | None: try: # Make the HTTP request to get the raw JSON file - response: Final = litellm.module_level_client.get(config_url) + response: Final = litellm.module_level_client.get(config_url, timeout=HF_CONFIG_FETCH_TIMEOUT_SECONDS) response.raise_for_status() # Raise an exception for bad responses (4xx or 5xx) # Parse the JSON response @@ -5841,6 +5855,7 @@ def _get_model_info_helper( cache_creation_input_token_cost_above_1hr=_model_info.get( "cache_creation_input_token_cost_above_1hr", None ), + off_peak_pricing=_model_info.get("off_peak_pricing", None), input_cost_per_character=_model_info.get("input_cost_per_character", None), input_cost_per_token_above_128k_tokens=_model_info.get("input_cost_per_token_above_128k_tokens", None), input_cost_per_token_above_200k_tokens=_model_info.get("input_cost_per_token_above_200k_tokens", None), @@ -6567,11 +6582,11 @@ def validate_environment( keys_in_environment = True else: missing_keys.append("WANDB_API_KEY") - elif custom_llm_provider == "dashscope": - if "DASHSCOPE_API_KEY" in os.environ: + elif custom_llm_provider in ("dashscope", "qwencloud", "qwen_ai_platform"): + if f"{custom_llm_provider.upper()}_API_KEY" in os.environ or "DASHSCOPE_API_KEY" in os.environ: keys_in_environment = True else: - missing_keys.append("DASHSCOPE_API_KEY") + missing_keys.append(f"{custom_llm_provider.upper()}_API_KEY") elif custom_llm_provider == "modelscope": if "MODELSCOPE_API_KEY" in os.environ: keys_in_environment = True @@ -8133,6 +8148,11 @@ class ProviderConfigManager: LlmProviders.NEBIUS: (lambda: litellm.NebiusConfig(), False), LlmProviders.WANDB: (lambda: litellm.WandbConfig(), False), LlmProviders.DASHSCOPE: (lambda: litellm.DashScopeChatConfig(), False), + LlmProviders.QWENCLOUD: (lambda: litellm.QwenCloudChatConfig(), False), + LlmProviders.QWEN_AI_PLATFORM: ( + lambda: litellm.QwenAIPlatformChatConfig(), + False, + ), LlmProviders.MODELSCOPE: (lambda: litellm.ModelScopeChatConfig(), False), LlmProviders.MOONSHOT: (lambda: litellm.MoonshotChatConfig(), False), LlmProviders.DOCKER_MODEL_RUNNER: ( @@ -8259,10 +8279,17 @@ class ProviderConfigManager: """ # Handle OpenAI special cases (O-series and GPT-5 models) if provider == LlmProviders.OPENAI: + from litellm.llms.openai.chat.gpt_transformation import ( + OpenAIGPTConfig, + OpenAIUnknownModelConfig, + ) + if litellm.openaiOSeriesConfig.is_model_o_series_model(model=model): return litellm.openaiOSeriesConfig if litellm.OpenAIGPT5Config.is_model_gpt_5_model(model=model): return litellm.OpenAIGPT5Config() + if not OpenAIGPTConfig.is_openai_catalog_model(model): + return OpenAIUnknownModelConfig() # Handle Azure before the generic map so base_model can be threaded through if provider == LlmProviders.AZURE: @@ -8340,12 +8367,16 @@ class ProviderConfigManager: ) return VolcEngineEmbeddingConfig() - elif litellm.LlmProviders.DASHSCOPE == provider: - from litellm.llms.dashscope.embed.transformation import ( - DashScopeEmbeddingConfig, + elif provider in ( + litellm.LlmProviders.DASHSCOPE, + litellm.LlmProviders.QWENCLOUD, + litellm.LlmProviders.QWEN_AI_PLATFORM, + ): + from litellm.llms.dashscope.common_utils import ( + get_dashscope_family_embedding_config, ) - return DashScopeEmbeddingConfig() + return get_dashscope_family_embedding_config(provider.value) elif litellm.LlmProviders.OVHCLOUD == provider: return litellm.OVHCloudEmbeddingConfig() elif litellm.LlmProviders.SNOWFLAKE == provider: @@ -8418,12 +8449,16 @@ class ProviderConfigManager: return litellm.VoyageRerankConfig() elif litellm.LlmProviders.WATSONX == provider: return litellm.IBMWatsonXRerankConfig() - elif litellm.LlmProviders.DASHSCOPE == provider: - from litellm.llms.dashscope.rerank.transformation import ( - DashScopeRerankConfig, + elif provider in ( + litellm.LlmProviders.DASHSCOPE, + litellm.LlmProviders.QWENCLOUD, + litellm.LlmProviders.QWEN_AI_PLATFORM, + ): + from litellm.llms.dashscope.common_utils import ( + get_dashscope_family_rerank_config, ) - return DashScopeRerankConfig() + return get_dashscope_family_rerank_config(provider.value) return litellm.CohereRerankConfig() @staticmethod @@ -8573,6 +8608,13 @@ class ProviderConfigManager: return SonioxAudioTranscriptionConfig() elif litellm.LlmProviders.VERTEX_AI == provider: + bare_vertex_model: Final = model.removeprefix("vertex_ai/") + if bare_vertex_model.startswith("gemini") and "transcribe" in bare_vertex_model: + from litellm.llms.vertex_ai.audio_transcription.gemini_transcribe_transformation import ( + VertexGeminiAudioTranscriptionConfig, + ) + + return VertexGeminiAudioTranscriptionConfig() from litellm.llms.vertex_ai.audio_transcription.transformation import ( VertexAIAudioTranscriptionConfig, ) @@ -8822,6 +8864,12 @@ class ProviderConfigManager: ) return AzurePassthroughConfig() + elif LlmProviders.GIGACHAT == provider: + from litellm.llms.gigachat.passthrough.transformation import ( + GigaChatPassthroughConfig, + ) + + return GigaChatPassthroughConfig() elif LlmProviders.WATSONX == provider: from litellm.llms.watsonx.passthrough.transformation import ( WatsonxPassthroughConfig, @@ -9083,12 +9131,16 @@ class ProviderConfigManager: ) return get_openrouter_image_generation_config(model) - elif LlmProviders.DASHSCOPE == provider: - from litellm.llms.dashscope.image_generation import ( - get_dashscope_image_generation_config, + elif provider in ( + LlmProviders.DASHSCOPE, + LlmProviders.QWENCLOUD, + LlmProviders.QWEN_AI_PLATFORM, + ): + from litellm.llms.dashscope.common_utils import ( + get_dashscope_family_image_generation_config, ) - return get_dashscope_image_generation_config(model) + return get_dashscope_family_image_generation_config(provider.value) elif LlmProviders.MODELSCOPE == provider: from litellm.llms.modelscope.image_generation import ( get_modelscope_image_generation_config, @@ -9401,6 +9453,10 @@ class ProviderConfigManager: return RunwayMLTextToSpeechConfig() elif litellm.LlmProviders.VERTEX_AI == provider: + if "gemini" in model: + # Gemini TTS uses the speech_to_completion bridge, and Google Cloud TTS param + # mapping would drop response_format before the bridge sees it (LIT-6501) + return None from litellm.llms.vertex_ai.text_to_speech.transformation import ( VertexAITextToSpeechConfig, ) diff --git a/litellm/vector_stores/vector_store_registry.py b/litellm/vector_stores/vector_store_registry.py index 22d27bc3266..b71d6784873 100644 --- a/litellm/vector_stores/vector_store_registry.py +++ b/litellm/vector_stores/vector_store_registry.py @@ -112,7 +112,9 @@ class VectorStoreRegistry: Dynamically extracts all parameters defined in VECTOR_STORE_OPENAI_PARAMS. """ # Get the list of supported param names from the Literal type - supported_params: Final = get_args(VECTOR_STORE_OPENAI_PARAMS) + supported_params: Final = tuple( + param for param in get_args(VECTOR_STORE_OPENAI_PARAMS) if isinstance(param, str) + ) # Extract only the params that exist in the tool kwargs: Final = {param: tool.get(param) for param in supported_params if param in tool} @@ -503,7 +505,7 @@ class VectorStoreRegistry: vector_stores_from_db.append(_litellm_managed_vector_store) return vector_stores_from_db - def get_credentials_for_vector_store(self, vector_store_id: str) -> dict[str, Any]: + def get_credentials_for_vector_store(self, vector_store_id: str) -> dict[str, object]: """ Get the credentials for a vector store diff --git a/migrations/Dockerfile b/migrations/Dockerfile index 6335e6f6bd8..c6d1b0cc46e 100644 --- a/migrations/Dockerfile +++ b/migrations/Dockerfile @@ -1,5 +1,5 @@ -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a FROM $UV_IMAGE AS uvbin @@ -35,7 +35,7 @@ COPY --from=uvbin /uv /uvx /usr/local/bin/ # instead of nodeenv downloading one whose dynamic deps may not be in Wolfi # (e.g. Node 26.2.0 needs libatomic). Retry for transient apk.cgr.dev flakes. RUN for i in 1 2 3; do \ - apk add --no-cache bash gcc python3 python3-dev openssl openssl-dev libsndfile nodejs npm && break; \ + apk add --no-cache bash gcc python-3.13 python-3.13-dev openssl openssl-dev libsndfile nodejs npm && break; \ [ $i = 3 ] && { echo "apk add failed after 3 retries" >&2; exit 1; }; \ sleep 5; \ done @@ -56,7 +56,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \ uv sync --frozen --no-install-project --no-install-workspace --no-default-groups --no-editable \ --extra proxy \ --extra extra_proxy \ - --python python3 + --python python3.13 # Stage 2 — copy source and install the project + workspace members. COPY . . @@ -65,7 +65,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \ uv sync --frozen --no-default-groups --no-editable \ --extra proxy \ --extra extra_proxy \ - --python python3 + --python python3.13 COPY migrations/run.py /app/run.py @@ -87,7 +87,7 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime USER root RUN for i in 1 2 3; do \ - apk add --no-cache bash openssl tzdata python3 nodejs libsndfile libatomic && break; \ + apk add --no-cache bash openssl tzdata python-3.13 nodejs libsndfile libatomic && break; \ [ $i = 3 ] && { echo "apk add failed after 3 retries" >&2; exit 1; }; \ sleep 5; \ done diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index bebbcc32181..a3cfb300ea6 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -553,6 +553,27 @@ "supports_response_schema": true, "supports_vision": true }, + "amazon.nova-sonic-v1:0": { + "deprecation_date": "2026-09-14", + "input_cost_per_audio_token": 3.4e-06, + "input_cost_per_token": 6e-08, + "litellm_provider": "bedrock", + "mode": "realtime", + "output_cost_per_audio_token": 1.36e-05, + "output_cost_per_token": 2.4e-07, + "supports_audio_input": true, + "supports_audio_output": true + }, + "amazon.nova-2-sonic-v1:0": { + "input_cost_per_audio_token": 3e-06, + "input_cost_per_token": 3.3e-07, + "litellm_provider": "bedrock", + "mode": "realtime", + "output_cost_per_audio_token": 1.2e-05, + "output_cost_per_token": 2.75e-06, + "supports_audio_input": true, + "supports_audio_output": true + }, "amazon.rerank-v1:0": { "input_cost_per_query": 0.001, "input_cost_per_token": 0.0, @@ -1430,6 +1451,44 @@ "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 512 }, + "anthropic.claude-fable-5-1": { + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 2.5e-07, + "input_cost_per_token": 1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_mid_conversation_system": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_native_structured_output": false, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "bedrock_output_config_effort_ceiling": "xhigh", + "supports_parallel_tool_use_config": true, + "prompt_cache_min_tokens": 512 + }, "global.anthropic.claude-fable-5": { "cache_creation_input_token_cost": 1.25e-05, "cache_creation_input_token_cost_above_1hr": 2e-05, @@ -1467,6 +1526,44 @@ "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 512 }, + "global.anthropic.claude-fable-5-1": { + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 2.5e-07, + "input_cost_per_token": 1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_mid_conversation_system": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_native_structured_output": false, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "bedrock_output_config_effort_ceiling": "xhigh", + "supports_parallel_tool_use_config": true, + "prompt_cache_min_tokens": 512 + }, "us.anthropic.claude-fable-5": { "cache_creation_input_token_cost": 1.375e-05, "cache_creation_input_token_cost_above_1hr": 2.2e-05, @@ -1504,6 +1601,44 @@ "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 512 }, + "us.anthropic.claude-fable-5-1": { + "cache_creation_input_token_cost": 1.375e-05, + "cache_creation_input_token_cost_above_1hr": 2.2e-05, + "cache_read_input_token_cost": 2.75e-07, + "input_cost_per_token": 1.1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5.5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_mid_conversation_system": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_native_structured_output": false, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "bedrock_output_config_effort_ceiling": "xhigh", + "supports_parallel_tool_use_config": true, + "prompt_cache_min_tokens": 512 + }, "eu.anthropic.claude-fable-5": { "cache_creation_input_token_cost": 1.375e-05, "cache_creation_input_token_cost_above_1hr": 2.2e-05, @@ -1541,6 +1676,44 @@ "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 512 }, + "eu.anthropic.claude-fable-5-1": { + "cache_creation_input_token_cost": 1.375e-05, + "cache_creation_input_token_cost_above_1hr": 2.2e-05, + "cache_read_input_token_cost": 2.75e-07, + "input_cost_per_token": 1.1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5.5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_mid_conversation_system": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_native_structured_output": false, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "bedrock_output_config_effort_ceiling": "xhigh", + "supports_parallel_tool_use_config": true, + "prompt_cache_min_tokens": 512 + }, "anthropic.claude-opus-5": { "bedrock_converse_supports_strict_tools": false, "supports_adaptive_thinking": true, @@ -1571,7 +1744,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1607,7 +1780,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1643,7 +1816,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1679,7 +1852,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1715,7 +1888,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1751,7 +1924,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -2044,7 +2217,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2081,7 +2254,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2118,7 +2291,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2155,7 +2328,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2192,7 +2365,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2229,7 +2402,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -3025,6 +3198,7 @@ "prompt_cache_min_tokens": 2048 }, "azure_ai/claude-fable-5": { + "deprecation_date": "2027-12-05", "supports_mid_conversation_system": true, "input_cost_per_token": 1e-05, "output_cost_per_token": 5e-05, @@ -3057,7 +3231,43 @@ "supports_max_reasoning_effort": true, "prompt_cache_min_tokens": 512 }, + "azure_ai/claude-fable-5-1": { + "supports_mid_conversation_system": true, + "input_cost_per_token": 1e-05, + "output_cost_per_token": 5e-05, + "litellm_provider": "azure_ai", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 2.5e-07, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_native_structured_output": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_max_reasoning_effort": true, + "prompt_cache_min_tokens": 512 + }, "azure_ai/claude-opus-5": { + "deprecation_date": "2027-07-08", "supports_mid_conversation_system": true, "supports_adaptive_thinking": true, "input_cost_per_token": 5e-06, @@ -3090,6 +3300,7 @@ "prompt_cache_min_tokens": 512 }, "azure_ai/claude-opus-4-8": { + "deprecation_date": "2027-09-01", "supports_mid_conversation_system": true, "supports_adaptive_thinking": true, "input_cost_per_token": 5e-06, @@ -3168,6 +3379,7 @@ "prompt_cache_min_tokens": 1024 }, "azure_ai/claude-sonnet-5": { + "deprecation_date": "2027-06-30", "supports_mid_conversation_system": true, "cache_creation_input_token_cost": 2.5e-06, "cache_creation_input_token_cost_above_1hr": 4e-06, @@ -3726,7 +3938,7 @@ "output_cost_per_token": 0, "litellm_provider": "azure_ai", "mode": "chat", - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-services/", + "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/aoai/", "comment": "Flat cost of $0.14 per M input tokens for Azure AI Foundry Model Router infrastructure. Use pattern: azure_ai/model_router/ where deployment-name is your Azure deployment (e.g., azure-model-router)" }, "azure/eu/gpt-4o-2024-08-06": { @@ -5328,7 +5540,7 @@ "input_cost_per_second": 0.0002833333333333333, "litellm_provider": "azure", "mode": "audio_transcription", - "source": "https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/gpt-realtime-whisper", + "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/gpt-realtime-whisper", "supported_endpoints": [ "/v1/realtime", "/v1/realtime/transcription_sessions" @@ -9107,7 +9319,7 @@ "mode": "embedding", "output_cost_per_token": 0.0, "output_vector_size": 1024, - "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/cohere.cohere-embed-v3-english-offer?tab=PlansAndPrice", + "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/cohere.cohere-embed-v3-english-offer?tab=PlansAndPrice", "supports_embedding_image_input": true }, "azure_ai/Cohere-embed-v3-multilingual": { @@ -9118,7 +9330,7 @@ "mode": "embedding", "output_cost_per_token": 0.0, "output_vector_size": 1024, - "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/cohere.cohere-embed-v3-english-offer?tab=PlansAndPrice", + "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/cohere.cohere-embed-v3-english-offer?tab=PlansAndPrice", "supports_embedding_image_input": true }, "azure_ai/FLUX-1.1-pro": { @@ -9134,7 +9346,7 @@ "litellm_provider": "azure_ai", "mode": "image_generation", "output_cost_per_image": 0.04, - "source": "https://azuremarketplace.microsoft.com/pt-br/marketplace/apps/cohere.cohere-embed-4-offer?tab=PlansAndPrice", + "source": "https://marketplace.microsoft.com/pt-br/marketplace/apps/cohere.cohere-embed-4-offer?tab=PlansAndPrice", "supported_endpoints": [ "/v1/images/generations" ] @@ -9467,7 +9679,7 @@ "max_tokens": 2048, "mode": "chat", "output_cost_per_token": 3.7e-07, - "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/metagenai.meta-llama-3-2-11b-vision-instruct-offer?tab=Overview", + "source": "https://marketplace.microsoft.com/en/marketplace/apps/metagenai.meta-llama-3-2-11b-vision-instruct-offer?tab=Overview", "supports_function_calling": true, "supports_tool_choice": true, "supports_vision": true @@ -9481,7 +9693,7 @@ "max_tokens": 2048, "mode": "chat", "output_cost_per_token": 2.04e-06, - "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/metagenai.meta-llama-3-2-90b-vision-instruct-offer?tab=Overview", + "source": "https://marketplace.microsoft.com/en/marketplace/apps/metagenai.meta-llama-3-2-90b-vision-instruct-offer?tab=Overview", "supports_function_calling": true, "supports_tool_choice": true, "supports_vision": true @@ -9494,7 +9706,7 @@ "max_tokens": 2048, "mode": "chat", "output_cost_per_token": 7.1e-07, - "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/metagenai.llama-3-3-70b-instruct-offer?tab=Overview", + "source": "https://marketplace.microsoft.com/en/marketplace/apps/metagenai.llama-3-3-70b-instruct-offer?tab=Overview", "supports_function_calling": true, "supports_tool_choice": true }, @@ -9543,7 +9755,7 @@ "max_tokens": 2048, "mode": "chat", "output_cost_per_token": 1.6e-05, - "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-405b-instruct-offer?tab=PlansAndPrice", + "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-405b-instruct-offer?tab=PlansAndPrice", "supports_tool_choice": true }, "azure_ai/Meta-Llama-3.1-70B-Instruct": { @@ -9554,7 +9766,7 @@ "max_tokens": 2048, "mode": "chat", "output_cost_per_token": 3.54e-06, - "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-70b-instruct-offer?tab=PlansAndPrice", + "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-70b-instruct-offer?tab=PlansAndPrice", "supports_tool_choice": true }, "azure_ai/Meta-Llama-3.1-8B-Instruct": { @@ -9566,7 +9778,7 @@ "max_tokens": 2048, "mode": "chat", "output_cost_per_token": 6.1e-07, - "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-8b-instruct-offer?tab=PlansAndPrice", + "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-8b-instruct-offer?tab=PlansAndPrice", "supports_tool_choice": true }, "azure_ai/Phi-3-medium-128k-instruct": { @@ -9756,7 +9968,7 @@ "supported_endpoints": [ "/v1/ocr" ], - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/" + "source": "https://ai.azure.com/catalog/models/mistral-document-ai-2512" }, "azure_ai/doc-intelligence/prebuilt-read": { "litellm_provider": "azure_ai", @@ -9959,6 +10171,22 @@ "supports_reasoning": true, "supports_tool_choice": true }, + "azure_ai/deepseek-v4-flash-0731": { + "cache_read_input_token_cost": 2.8e-08, + "deprecation_date": "2026-12-03", + "input_cost_per_token": 1.9e-07, + "litellm_provider": "azure_ai", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5.1e-07, + "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, "azure_ai/embed-v-4-0": { "input_cost_per_token": 1.2e-07, "litellm_provider": "azure_ai", @@ -9967,7 +10195,7 @@ "mode": "embedding", "output_cost_per_token": 0.0, "output_vector_size": 3072, - "source": "https://azuremarketplace.microsoft.com/pt-br/marketplace/apps/cohere.cohere-embed-4-offer?tab=PlansAndPrice", + "source": "https://marketplace.microsoft.com/pt-br/marketplace/apps/cohere.cohere-embed-4-offer?tab=PlansAndPrice", "supported_endpoints": [ "/v1/embeddings" ], @@ -10151,7 +10379,7 @@ "max_tokens": 8192, "mode": "chat", "output_cost_per_token": 0.00971, - "source": "https://azure.microsoft.com/en-us/products/ai-services/ai-foundry/models/jais-30b-chat" + "source": "https://ai.azure.com/catalog/models/jais-30b-chat" }, "azure_ai/jamba-instruct": { "input_cost_per_token": 5e-07, @@ -10208,7 +10436,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 4e-08, - "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/000-000.ministral-3b-2410-offer?tab=Overview", + "source": "https://marketplace.microsoft.com/en/marketplace/apps/000-000.ministral-3b-2410-offer?tab=Overview", "supports_function_calling": true, "supports_tool_choice": true }, @@ -10231,7 +10459,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-06, - "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/000-000.mistral-ai-large-2407-offer?tab=Overview", + "source": "https://marketplace.microsoft.com/en/marketplace/apps/000-000.mistral-ai-large-2407-offer?tab=Overview", "supports_function_calling": true, "supports_tool_choice": true }, @@ -10243,7 +10471,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-06, - "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/000-000.mistral-ai-large-2407-offer?tab=Overview", + "source": "https://marketplace.microsoft.com/en/marketplace/apps/000-000.mistral-ai-large-2407-offer?tab=Overview", "supports_function_calling": true, "supports_tool_choice": true }, @@ -10280,7 +10508,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 1.5e-07, - "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/000-000.mistral-nemo-12b-2407?tab=PlansAndPrice", + "source": "https://marketplace.microsoft.com/en/marketplace/apps/000-000.mistral-nemo-12b-2407?tab=PlansAndPrice", "supports_function_calling": true }, "azure_ai/mistral-small": { @@ -12267,6 +12495,7 @@ "supports_tool_choice": true }, "cerebras/zai-glm-4.7": { + "deprecation_date": "2026-08-17", "input_cost_per_token": 2.25e-06, "litellm_provider": "cerebras", "max_input_tokens": 128000, @@ -12315,7 +12544,8 @@ "output_cost_per_token": 1e-05, "supported_endpoints": [ "/v1/audio/transcriptions" - ] + ], + "deprecation_date": "2027-02-26" }, "claude-haiku-4-5-20251001": { "deprecation_date": "2026-10-15", @@ -12595,7 +12825,8 @@ "us": 1.1 }, "supports_output_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "source": "https://docs.anthropic.com/en/docs/about-claude/models/overview" }, "claude-sonnet-4-6": { "deprecation_date": "2027-02-17", @@ -12997,7 +13228,49 @@ }, "supports_output_config": true, "prompt_cache_min_tokens": 512, - "supports_native_structured_output": true + "supports_native_structured_output": true, + "source": "https://docs.anthropic.com/en/docs/about-claude/models/overview" + }, + "claude-fable-5-1": { + "deprecation_date": "2027-09-01", + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 2.5e-07, + "input_cost_per_token": 1e-05, + "litellm_provider": "anthropic", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_mid_conversation_system": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_max_reasoning_effort": true, + "provider_specific_entry": { + "us": 1.1 + }, + "supports_output_config": true, + "prompt_cache_min_tokens": 512, + "supports_native_structured_output": true, + "source": "https://platform.claude.com/docs/en/models/fable-5-1/overview" }, "claude-opus-5": { "deprecation_date": "2027-07-24", @@ -13037,7 +13310,8 @@ }, "supports_output_config": true, "supports_speed": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://docs.anthropic.com/en/docs/about-claude/models/overview" }, "claude-opus-4-8": { "deprecation_date": "2027-05-28", @@ -14694,6 +14968,1910 @@ "/v1/images/generations" ] }, + "qwencloud/deepseek-v4-flash": { + 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Billing reads the per-token dollar fields; the '*_dbu_cost_per_token' fields are the published Databricks rates, kept for reference. Context/max output are the DeepSeek-published model limits (1M context, 384K max output)." + }, + "mode": "chat", + "output_cost_per_token": 2.8e-07, + "output_dbu_cost_per_token": 4e-06, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": false + }, + "databricks/databricks-deepseek-v4-pro-0813": { + "cache_creation_input_token_cost": 1.31999e-06, + "cache_read_input_token_cost": 1.3202e-07, + "input_cost_per_token": 1.31999e-06, + "input_dbu_cost_per_token": 1.8857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 1000000, + "max_output_tokens": 393216, + "max_tokens": 393216, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Billing reads the per-token dollar fields; the '*_dbu_cost_per_token' fields are the published Databricks rates, kept for reference. Context/max output are the DeepSeek-published model limits (1M context, 384K max output)." + }, + "mode": "chat", + "output_cost_per_token": 3.95997e-06, + "output_dbu_cost_per_token": 5.6571e-05, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": false + }, "databricks/databricks-gemini-2-5-flash": { "cache_creation_input_token_cost": 3.0002e-07, "cache_read_input_token_cost": 3.0002e-08, @@ -17764,7 +19998,8 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "deprecation_date": "2027-01-08" }, "eu.anthropic.claude-opus-4-20250514-v1:0": { "cache_creation_input_token_cost": 1.875e-05, @@ -19561,6 +21796,61 @@ "supports_system_messages": true, "supports_tool_choice": true }, + "friendliai/zai-org/GLM-5.3-Flash": { + "litellm_provider": "friendliai", + "supports_reasoning": true, + "supports_function_calling": true, + "max_input_tokens": 1048576, + "max_tokens": 1048576, + "max_output_tokens": 1048576, + "input_cost_per_token": 1.5e-07, + "output_cost_per_token": 5e-07, + "cache_read_input_token_cost": 3e-08, + "supports_prompt_caching": true, + "reasoning_effort_levels": [ + "low", + "high", + "max" + ], + "supports_parallel_function_calling": true, + "supports_response_schema": true, + "supports_native_structured_output": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "mode": "chat", + "comment": "Native multimodal GLM model for efficient coding and long-horizon agent tasks", + "source": "https://api.friendli.ai/serverless/v1/models", + "supports_vision": true, + "supports_image_input": true, + "supports_video_input": true + }, + "friendliai/zai-org/GLM-5.3": { + "litellm_provider": "friendliai", + "supports_reasoning": true, + "supports_function_calling": true, + "max_input_tokens": 1048576, + "max_tokens": 1048576, + "max_output_tokens": 1048576, + "input_cost_per_token": 1.26e-06, + "output_cost_per_token": 3.96e-06, + "cache_read_input_token_cost": 2.34e-07, + "supports_prompt_caching": true, + "reasoning_effort_levels": [ + "low", + "high", + "max" + ], + "supports_parallel_function_calling": true, + "supports_response_schema": true, + "supports_native_structured_output": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "mode": "chat", + "comment": "Flagship GLM model for long-horizon coding, agents, and complex project delivery", + "source": "https://api.friendli.ai/serverless/v1/models", + "supports_vision": false, + "supports_image_input": false + }, "ft:babbage-002": { "deprecation_date": "2026-10-23", "input_cost_per_token": 1.6e-06, @@ -20551,6 +22841,7 @@ "supports_image_size": false }, "gemini-live-2.5-flash-native-audio": { + "deprecation_date": "2026-12-13", "input_cost_per_audio_token": 3e-06, "input_cost_per_token": 5e-07, "litellm_provider": "vertex_ai-language-models", @@ -20562,7 +22853,8 @@ "output_cost_per_token": 2e-06, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supported_endpoints": [ - "/vertex_ai/live" + "/vertex_ai/live", + "/v1/realtime" ], "supported_modalities": [ "text", @@ -22015,6 +24307,49 @@ }, "web_search_billing_unit": "per_query" }, + "gemini/nano-banana-pro-preview": { + "input_cost_per_image": 0.0011, + "input_cost_per_token": 2e-06, + "input_cost_per_token_batches": 1e-06, + "litellm_provider": "gemini", + "max_input_tokens": 131072, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "image_generation", + "output_cost_per_image": 0.134, + "output_cost_per_image_token": 0.00012, + "output_cost_per_token": 1.2e-05, + "rpm": 1000, + "tpm": 4000000, + "output_cost_per_token_batches": 6e-06, + "source": "https://ai.google.dev/gemini-api/docs/pricing", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text", + "image" + ], + "supports_function_calling": false, + "supports_prompt_caching": true, + "supports_reasoning": false, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_vision": true, + "supports_web_search": true, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query" + }, "gemini/gemini-3.1-flash-image": { "input_cost_per_token": 5e-07, "input_cost_per_token_batches": 2.5e-07, @@ -22796,7 +25131,7 @@ "output_cost_per_reasoning_token": 3e-06, "output_cost_per_token": 3e-06, "rpm": 2000, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -22850,7 +25185,7 @@ "output_cost_per_reasoning_token": 9e-06, "output_cost_per_token": 9e-06, "rpm": 2000, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -22915,7 +25250,7 @@ "output_cost_per_token_batches": 1.875e-06, "output_cost_per_token_flex": 1.875e-06, "rpm": 2000, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -22974,7 +25309,7 @@ "output_cost_per_token_batches": 1.875e-06, "output_cost_per_token_flex": 1.875e-06, "rpm": 2000, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -23047,7 +25382,8 @@ "supports_system_messages": true, "supports_video_input": true, "supports_vision": true, - "tpm": 800000 + "tpm": 800000, + "deprecation_date": "2026-09-30" }, "gemini/gemini-3.1-pro-preview": { "prompt_cache_min_tokens": 4096, @@ -23178,7 +25514,7 @@ "mode": "chat", "output_cost_per_reasoning_token": 3e-06, "output_cost_per_token": 3e-06, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -23262,7 +25598,7 @@ "mode": "chat", "output_cost_per_reasoning_token": 9e-06, "output_cost_per_token": 9e-06, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -23325,7 +25661,7 @@ "output_cost_per_token": 3.75e-06, "output_cost_per_token_batches": 1.875e-06, "output_cost_per_token_flex": 1.875e-06, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -23382,7 +25718,7 @@ "output_cost_per_token": 3.75e-06, "output_cost_per_token_batches": 1.875e-06, "output_cost_per_token_flex": 1.875e-06, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -23558,6 +25894,38 @@ "supports_tool_choice": true, "supports_vision": true }, + "gemini/gemma-4-26b-a4b-it": { + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "gemini", + "max_input_tokens": 262144, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, + "source": "https://ai.google.dev/gemini-api/docs/pricing" + }, + "gemini/gemma-4-31b-it": { + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "gemini", + "max_input_tokens": 262144, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, + "source": "https://ai.google.dev/gemini-api/docs/pricing" + }, "gemini/imagen-3.0-fast-generate-001": { "litellm_provider": "gemini", "mode": "image_generation", @@ -23695,8 +26063,10 @@ "max_input_tokens": 1024, "max_tokens": 1024, "mode": "video_generation", - "output_cost_per_second": 0.15, - "source": "https://ai.google.dev/gemini-api/docs/video", + "output_cost_per_second": 0.1, + "output_cost_per_second_1080p": 0.12, + "output_cost_per_second_4k": 0.3, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_modalities": [ "text" ], @@ -23710,7 +26080,8 @@ "max_tokens": 1024, "mode": "video_generation", "output_cost_per_second": 0.4, - "source": "https://ai.google.dev/gemini-api/docs/video", + "output_cost_per_second_4k": 0.6, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_modalities": [ "text" ], @@ -23738,8 +26109,10 @@ "max_input_tokens": 1024, "max_tokens": 1024, "mode": "video_generation", - "output_cost_per_second": 0.15, - "source": "https://ai.google.dev/gemini-api/docs/video", + "output_cost_per_second": 0.1, + "output_cost_per_second_1080p": 0.12, + "output_cost_per_second_4k": 0.3, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_modalities": [ "text" ], @@ -23753,7 +26126,8 @@ "max_tokens": 1024, "mode": "video_generation", "output_cost_per_second": 0.4, - "source": "https://ai.google.dev/gemini-api/docs/video", + "output_cost_per_second_4k": 0.6, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_modalities": [ "text" ], @@ -24262,7 +26636,7 @@ "supports_response_schema": true, "supports_vision": true }, - "gigachat/GigaChat-2-Lite": { + "gigachat/GigaChat-2": { "input_cost_per_token": 0.0, "litellm_provider": "gigachat", "max_input_tokens": 128000, @@ -24324,6 +26698,15 @@ "output_cost_per_token": 0.0, "output_vector_size": 2560 }, + "gigachat/GigaEmbeddings-3B-2025-09": { + "input_cost_per_token": 0.0, + "litellm_provider": "gigachat", + "max_input_tokens": 4096, + "max_tokens": 4096, + "mode": "embedding", + "output_cost_per_token": 0.0, + "output_vector_size": 2048 + }, "gmi/anthropic/claude-opus-4.5": { "input_cost_per_token": 5e-06, "litellm_provider": "gmi", @@ -25247,7 +27630,7 @@ "supports_vision": true }, "gpt-4o-audio-preview": { - "deprecation_date": "2027-01-20", + "deprecation_date": "2026-05-07", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "openai", @@ -25570,7 +27953,7 @@ "supports_vision": true }, "gpt-4o-mini-audio-preview": { - "deprecation_date": "2027-01-20", + "deprecation_date": "2026-05-07", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 1.5e-07, "litellm_provider": "openai", @@ -25608,7 +27991,7 @@ "gpt-4o-mini-realtime-preview": { "cache_creation_input_audio_token_cost": 3e-07, "cache_read_input_token_cost": 3e-07, - "deprecation_date": "2027-01-20", + "deprecation_date": "2026-05-07", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 6e-07, "litellm_provider": "openai", @@ -25707,7 +28090,8 @@ "output_cost_per_token": 5e-06, "supported_endpoints": [ "/v1/audio/transcriptions" - ] + ], + "deprecation_date": "2027-02-26" }, "gpt-4o-mini-tts": { "input_cost_per_token": 2.5e-06, @@ -25729,7 +28113,7 @@ }, "gpt-4o-realtime-preview": { "cache_read_input_token_cost": 2.5e-06, - "deprecation_date": "2027-01-20", + "deprecation_date": "2026-05-07", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 5e-06, "litellm_provider": "openai", @@ -25748,7 +28132,7 @@ }, "gpt-4o-realtime-preview-2024-12-17": { "cache_read_input_token_cost": 2.5e-06, - "deprecation_date": "2027-01-20", + "deprecation_date": "2026-05-07", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 5e-06, "litellm_provider": "openai", @@ -25767,7 +28151,7 @@ }, "gpt-4o-realtime-preview-2025-06-03": { "cache_read_input_token_cost": 2.5e-06, - "deprecation_date": "2027-01-20", + "deprecation_date": "2026-05-07", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 5e-06, "litellm_provider": "openai", @@ -25846,7 +28230,8 @@ "output_cost_per_token": 1e-05, "supported_endpoints": [ "/v1/audio/transcriptions" - ] + ], + "deprecation_date": "2027-02-26" }, "gpt-image-1.5": { "cache_read_input_token_cost": 1.25e-06, @@ -26097,7 +28482,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "low/1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -26108,7 +28494,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "low/1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -26119,7 +28506,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "medium/1024-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.034, @@ -26130,7 +28518,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "medium/1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.05, @@ -26141,7 +28530,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "medium/1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.05, @@ -26152,7 +28542,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "high/1024-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.133, @@ -26163,7 +28554,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "high/1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.2, @@ -26174,7 +28566,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "high/1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.2, @@ -26185,7 +28578,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "standard/1024-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.009, @@ -26196,7 +28590,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "standard/1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -26207,7 +28602,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "standard/1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -26218,7 +28614,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "1024-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.009, @@ -26229,7 +28626,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -26240,7 +28638,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -26251,7 +28650,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "gpt-5": { "cache_read_input_token_cost": 1.25e-07, @@ -26982,7 +29382,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "source": "https://platform.openai.com/docs/models/gpt-5.6-cyber", + "source": "https://developers.openai.com/api/docs/models/gpt-5.6-cyber", "supports_computer_use": true, "supports_parallel_function_calling": true }, @@ -27021,7 +29421,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "source": "https://platform.openai.com/docs/models/daybreak-red-latest", + "source": "https://developers.openai.com/api/docs/models/daybreak-red-latest", "supports_computer_use": true, "supports_parallel_function_calling": true }, @@ -27061,7 +29461,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "source": "https://platform.openai.com/docs/models/daybreak-blue-latest", + "source": "https://developers.openai.com/api/docs/models/daybreak-blue-latest", "supports_parallel_function_calling": true }, "chat-latest": { @@ -27073,7 +29473,7 @@ "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 3e-05, - "source": "https://platform.openai.com/docs/models/chat-latest", + "source": "https://developers.openai.com/api/docs/models/chat-latest", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -30368,7 +32768,7 @@ "search_context_size_low": 0.0025, "search_context_size_medium": 0.0025 }, - "source": "https://dev.meta.ai/docs/getting-started/pricing-rate-limits", + "source": "https://ai.developer.meta.com/docs/pricing-rate-limits", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses", @@ -30409,7 +32809,7 @@ "search_context_size_low": 0.0025, "search_context_size_medium": 0.0025 }, - "source": "https://dev.meta.ai/docs/getting-started/pricing-rate-limits", + "source": "https://ai.developer.meta.com/docs/pricing-rate-limits", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses", @@ -30450,7 +32850,7 @@ "search_context_size_low": 0.0025, "search_context_size_medium": 0.0025 }, - "source": "https://dev.meta.ai/docs/getting-started/pricing-rate-limits", + "source": "https://ai.developer.meta.com/docs/pricing-rate-limits", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses", @@ -30483,7 +32883,7 @@ "max_output_tokens": 4028, "max_tokens": 4028, "mode": "chat", - "source": "https://llama.developer.meta.com/docs/models", + "source": "https://ai.developer.meta.com/docs/models", "supported_modalities": [ "text" ], @@ -30499,7 +32899,7 @@ "max_output_tokens": 4028, "max_tokens": 4028, "mode": "chat", - "source": "https://llama.developer.meta.com/docs/models", + "source": "https://ai.developer.meta.com/docs/models", "supported_modalities": [ "text" ], @@ -30515,7 +32915,7 @@ "max_output_tokens": 4028, "max_tokens": 4028, "mode": "chat", - "source": "https://llama.developer.meta.com/docs/models", + "source": "https://ai.developer.meta.com/docs/models", "supported_modalities": [ "text", "image" @@ -30532,7 +32932,7 @@ "max_output_tokens": 4028, "max_tokens": 4028, "mode": "chat", - "source": "https://llama.developer.meta.com/docs/models", + "source": "https://ai.developer.meta.com/docs/models", "supported_modalities": [ "text", "image" @@ -32477,7 +34877,7 @@ "mode": "chat", "supports_function_calling": true, "supports_reasoning": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/deepseek-ai/DeepSeek-R1-0528": { "max_tokens": 164000, @@ -32489,7 +34889,7 @@ "mode": "chat", "supports_function_calling": true, "supports_reasoning": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/deepseek-ai/DeepSeek-R1-Distill-Llama-70B": { "max_tokens": 128000, @@ -32500,7 +34900,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/deepseek-ai/DeepSeek-V3": { "max_tokens": 128000, @@ -32511,7 +34911,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/deepseek-ai/DeepSeek-V3-0324": { "max_tokens": 128000, @@ -32522,7 +34922,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/google/gemma-3-27b-it": { "max_tokens": 128000, @@ -32534,7 +34934,7 @@ "mode": "chat", "supports_function_calling": true, "supports_vision": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/meta-llama/Llama-3.3-70B-Instruct": { "max_tokens": 128000, @@ -32545,7 +34945,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/meta-llama/Llama-Guard-3-8B": { "max_tokens": 128000, @@ -32555,7 +34955,7 @@ "output_cost_per_token": 6e-08, "litellm_provider": "nebius", "mode": "chat", - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/meta-llama/Meta-Llama-3.1-8B-Instruct": { "max_tokens": 128000, @@ -32566,7 +34966,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/meta-llama/Meta-Llama-3.1-70B-Instruct": { "max_tokens": 128000, @@ -32577,7 +34977,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/meta-llama/Meta-Llama-3.1-405B-Instruct": { "max_tokens": 128000, @@ -32588,7 +34988,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/mistralai/Mistral-Nemo-Instruct-2407": { "max_tokens": 128000, @@ -32599,7 +34999,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/NousResearch/Hermes-3-Llama-3.1-405B": { "max_tokens": 128000, @@ -32610,7 +35010,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/nvidia/Llama-3.1-Nemotron-Ultra-253B-v1": { "max_tokens": 128000, @@ -32621,7 +35021,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/nvidia/Llama-3.3-Nemotron-Super-49B-v1": { "max_tokens": 131072, @@ -32632,7 +35032,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen3-235B-A22B": { "max_tokens": 262144, @@ -32643,7 +35043,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen3-32B": { "max_tokens": 32768, @@ -32654,7 +35054,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen3-30B-A3B": { "max_tokens": 32768, @@ -32665,7 +35065,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen3-14B": { "max_tokens": 32768, @@ -32676,7 +35076,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen3-4B": { "max_tokens": 32768, @@ -32687,7 +35087,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/QwQ-32B": { "max_tokens": 32768, @@ -32699,7 +35099,7 @@ "mode": "chat", "supports_function_calling": true, "supports_reasoning": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen2.5-72B-Instruct": { "max_tokens": 128000, @@ -32710,7 +35110,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen2.5-32B-Instruct": { "max_tokens": 128000, @@ -32721,7 +35121,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen2.5-Coder-7B": { "max_tokens": 32768, @@ -32732,7 +35132,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen2.5-VL-72B-Instruct": { "max_tokens": 131072, @@ -32744,7 +35144,7 @@ "mode": "chat", "supports_function_calling": true, "supports_vision": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen2-VL-72B-Instruct": { "max_tokens": 131072, @@ -32756,7 +35156,7 @@ "mode": "chat", "supports_function_calling": true, "supports_vision": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen2-VL-7B-Instruct": { "max_tokens": 131072, @@ -32767,7 +35167,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_vision": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/BAAI/bge-en-icl": { "max_tokens": 32768, @@ -32776,7 +35176,7 @@ "output_cost_per_token": 0.0, "litellm_provider": "nebius", "mode": "embedding", - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/BAAI/bge-multilingual-gemma2": { "max_tokens": 8192, @@ -32785,7 +35185,7 @@ "output_cost_per_token": 0.0, "litellm_provider": "nebius", "mode": "embedding", - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/intfloat/e5-mistral-7b-instruct": { "max_tokens": 32768, @@ -32794,7 +35194,7 @@ "output_cost_per_token": 0.0, "litellm_provider": "nebius", "mode": "embedding", - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nvidia.nemotron-nano-12b-v2": { "input_cost_per_token": 2e-07, @@ -33535,7 +35935,7 @@ "max_tokens": 4000, "mode": "chat", "output_cost_per_token": 1.56e-06, - "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/", + "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/", "supports_function_calling": true, "supports_response_schema": false, "supports_native_streaming": true @@ -33548,7 +35948,7 @@ "max_tokens": 4000, "mode": "chat", "output_cost_per_token": 1.56e-06, - "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/", + "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/", "supports_function_calling": true, "supports_response_schema": false, "supports_native_streaming": true @@ -33561,7 +35961,7 @@ "max_tokens": 4000, "mode": "chat", "output_cost_per_token": 1.56e-06, - "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/", + "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/", "supports_function_calling": true, "supports_response_schema": false, "supports_native_streaming": true @@ -33618,7 +36018,7 @@ "max_tokens": 8192, "mode": "chat", "output_cost_per_token": 1.56e-06, - "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/", + "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/", "supports_function_calling": true, "supports_response_schema": false, "supports_native_streaming": true, @@ -33632,7 +36032,7 @@ "max_tokens": 8192, "mode": "chat", "output_cost_per_token": 1.56e-06, - "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/", + "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/", "supports_function_calling": false, "supports_response_schema": false, "supports_native_streaming": true @@ -33643,7 +36043,7 @@ "max_input_tokens": 512, "mode": "embedding", "output_vector_size": 1024, - "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/", + "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/", "supports_vision": true }, "oci/cohere.command-a-reasoning-08-2025": { @@ -34540,7 +36940,7 @@ "max_tokens": 2048, "mode": "chat", "output_cost_per_token": 2e-07, - "source": "https://openrouter.ai/api/v1/models/bytedance/ui-tars-1.5-7b", + "source": "https://openrouter.ai/bytedance/ui-tars-1.5-7b", "supports_tool_choice": true }, "openrouter/deepseek/deepseek-chat": { @@ -34775,7 +37175,7 @@ "output_cost_per_reasoning_token": 3e-06, "output_cost_per_token": 3e-06, "rpm": 2000, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -34816,7 +37216,7 @@ "output_cost_per_reasoning_token": 1.5e-06, "output_cost_per_token": 1.5e-06, "rpm": 2000, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -35901,7 +38301,7 @@ "max_tokens": 131000, "mode": "chat", "output_cost_per_token": 6.7e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/deepseek-r1-distill-llama-70b", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, @@ -35915,7 +38315,7 @@ "max_tokens": 131000, "mode": "chat", "output_cost_per_token": 1e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/llama-3-1-8b-instruct", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true @@ -35928,7 +38328,7 @@ "max_tokens": 131000, "mode": "chat", "output_cost_per_token": 6.7e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/meta-llama-3-1-70b-instruct", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": false, "supports_response_schema": false, "supports_tool_choice": false @@ -35941,7 +38341,7 @@ "max_tokens": 131000, "mode": "chat", "output_cost_per_token": 6.7e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/meta-llama-3-3-70b-instruct", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true @@ -35954,7 +38354,7 @@ "max_tokens": 127000, "mode": "chat", "output_cost_per_token": 1e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/mistral-7b-instruct-v0-3", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true @@ -35967,7 +38367,7 @@ "max_tokens": 118000, "mode": "chat", "output_cost_per_token": 1.3e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/mistral-nemo-instruct-2407", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true @@ -35980,7 +38380,7 @@ "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 2.8e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/mistral-small-3-2-24b-instruct-2506", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -35994,7 +38394,7 @@ "max_tokens": 32000, "mode": "chat", "output_cost_per_token": 6.3e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/mixtral-8x7b-instruct-v0-1", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": false, "supports_response_schema": true, "supports_tool_choice": false @@ -36007,7 +38407,7 @@ "max_tokens": 32000, "mode": "chat", "output_cost_per_token": 8.7e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/qwen2-5-coder-32b-instruct", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": false, "supports_response_schema": true, "supports_tool_choice": false @@ -36020,7 +38420,7 @@ "max_tokens": 32000, "mode": "chat", "output_cost_per_token": 9.1e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/qwen2-5-vl-72b-instruct", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": false, "supports_response_schema": true, "supports_tool_choice": false, @@ -36034,7 +38434,7 @@ "max_tokens": 32000, "mode": "chat", "output_cost_per_token": 2.3e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/qwen3-32b", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, @@ -36048,7 +38448,7 @@ "max_tokens": 131000, "mode": "chat", "output_cost_per_token": 4e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/gpt-oss-120b", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": false, "supports_reasoning": true, "supports_response_schema": true, @@ -36062,7 +38462,7 @@ "max_tokens": 131000, "mode": "chat", "output_cost_per_token": 1.5e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/gpt-oss-20b", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": false, "supports_reasoning": true, "supports_response_schema": true, @@ -36076,7 +38476,7 @@ "max_tokens": 32000, "mode": "chat", "output_cost_per_token": 2.9e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/llava-next-mistral-7b", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": false, "supports_response_schema": true, "supports_tool_choice": false, @@ -36090,7 +38490,7 @@ "max_tokens": 256000, "mode": "chat", "output_cost_per_token": 1.9e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/mamba-codestral-7b-v0-1", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": false, "supports_response_schema": true, "supports_tool_choice": false @@ -36156,12 +38556,22 @@ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models" }, "parallel_ai/search": { - "input_cost_per_query": 0.004, + "input_cost_per_query": 0.005, + "litellm_provider": "parallel_ai", + "mode": "search" + }, + "parallel_ai/search-fast": { + "input_cost_per_query": 0.001, "litellm_provider": "parallel_ai", "mode": "search" }, "parallel_ai/search-pro": { - "input_cost_per_query": 0.009, + "input_cost_per_query": 0.005, + "litellm_provider": "parallel_ai", + "mode": "search" + }, + "parallel_ai/search-turbo": { + "input_cost_per_query": 0.001, "litellm_provider": "parallel_ai", "mode": "search" }, @@ -38226,7 +40636,7 @@ "source": "https://docs.mistral.ai/capabilities/code_generation/" }, "text-embedding-004": { - "deprecation_date": "2026-01-14", + "deprecation_date": "2027-04-01", "input_cost_per_character": 2.5e-08, "input_cost_per_token": 1e-07, "litellm_provider": "vertex_ai-embedding-models", @@ -38583,7 +40993,6 @@ "input_cost_per_token": 1.04e-06, "litellm_provider": "together_ai", "max_input_tokens": 131072, - "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 1.04e-06, @@ -38705,7 +41114,6 @@ "input_cost_per_token": 1.5e-07, "litellm_provider": "together_ai", "max_input_tokens": 131072, - "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 6e-07, @@ -38752,7 +41160,6 @@ "input_cost_per_token": 6e-07, "litellm_provider": "together_ai", "max_input_tokens": 200000, - "max_output_tokens": 200000, "max_tokens": 200000, "metadata": { "successor": "together_ai/zai-org/GLM-5.2" @@ -38770,7 +41177,6 @@ "input_cost_per_token": 4.5e-07, "litellm_provider": "together_ai", "max_input_tokens": 200000, - "max_output_tokens": 200000, "max_tokens": 200000, "metadata": { "successor": "together_ai/zai-org/GLM-5.2" @@ -38788,7 +41194,6 @@ "input_cost_per_token": 5e-07, "litellm_provider": "together_ai", "max_input_tokens": 256000, - "max_output_tokens": 256000, "max_tokens": 256000, "metadata": { "successor": "together_ai/moonshotai/Kimi-K3" @@ -38856,7 +41261,7 @@ "max_input_tokens": 262144, "mode": "chat", "output_cost_per_token": 3.6e-06, - "source": "https://www.together.ai/models/Qwen/Qwen3.5-397B-A17B", + "source": "https://www.together.ai/models/qwen3-5-397b-a17b", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -38868,7 +41273,6 @@ "input_cost_per_token": 3e-07, "litellm_provider": "together_ai", "max_input_tokens": 524288, - "max_output_tokens": 524288, "max_tokens": 524288, "mode": "chat", "output_cost_per_token": 1.2e-06, @@ -38885,7 +41289,6 @@ "input_cost_per_token": 0.0, "litellm_provider": "together_ai", "max_input_tokens": 262144, - "max_output_tokens": 262144, "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 0.0, @@ -38895,7 +41298,6 @@ "input_cost_per_token": 1.7e-07, "litellm_provider": "together_ai", "max_input_tokens": 262144, - "max_output_tokens": 262144, "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 2.5e-07, @@ -38911,7 +41313,6 @@ "input_cost_per_token": 5e-07, "litellm_provider": "together_ai", "max_input_tokens": 1000000, - "max_output_tokens": 1000000, "max_tokens": 1000000, "mode": "chat", "output_cost_per_token": 3e-06, @@ -38923,7 +41324,6 @@ "input_cost_per_token": 2.5e-06, "litellm_provider": "together_ai", "max_input_tokens": 1000000, - "max_output_tokens": 1000000, "max_tokens": 1000000, "mode": "chat", "output_cost_per_token": 7.5e-06, @@ -38934,21 +41334,19 @@ "input_cost_per_token": 3.2e-07, "litellm_provider": "together_ai", "max_input_tokens": 1000000, - "max_output_tokens": 1000000, "max_tokens": 1000000, "mode": "chat", "output_cost_per_token": 1.28e-06, "source": "https://docs.together.ai/docs/serverless-models" }, "together_ai/Qwen/Qwen3.8-2.4T-A95B": { - "cache_read_input_token_cost": 2.5e-07, - "input_cost_per_token": 2e-06, + "cache_read_input_token_cost": 5e-07, + "input_cost_per_token": 2.5e-06, "litellm_provider": "together_ai", "max_input_tokens": 1010000, - "max_output_tokens": 1010000, "max_tokens": 1010000, "mode": "chat", - "output_cost_per_token": 6e-06, + "output_cost_per_token": 6.25e-06, "source": "https://docs.together.ai/docs/serverless-models", "supports_prompt_caching": true }, @@ -38956,7 +41354,6 @@ "input_cost_per_token": 1e-07, "litellm_provider": "together_ai", "max_input_tokens": 32768, - "max_output_tokens": 32768, "max_tokens": 32768, "mode": "chat", "output_cost_per_token": 1e-07, @@ -38967,7 +41364,6 @@ "input_cost_per_token": 1.4e-07, "litellm_provider": "together_ai", "max_input_tokens": 1048576, - "max_output_tokens": 1048576, "max_tokens": 1048576, "mode": "chat", "output_cost_per_token": 2.8e-07, @@ -38984,7 +41380,6 @@ "input_cost_per_token": 1.74e-06, "litellm_provider": "together_ai", "max_input_tokens": 512000, - "max_output_tokens": 512000, "max_tokens": 512000, "mode": "chat", "output_cost_per_token": 3.48e-06, @@ -39001,7 +41396,6 @@ "input_cost_per_token": 1.32e-06, "litellm_provider": "together_ai", "max_input_tokens": 1048576, - "max_output_tokens": 1048576, "max_tokens": 1048576, "mode": "chat", "output_cost_per_token": 3.96e-06, @@ -39017,7 +41411,6 @@ "input_cost_per_token": 6e-08, "litellm_provider": "together_ai", "max_input_tokens": 32768, - "max_output_tokens": 32768, "max_tokens": 32768, "mode": "chat", "output_cost_per_token": 1.2e-07, @@ -39027,7 +41420,6 @@ "input_cost_per_token": 3.9e-07, "litellm_provider": "together_ai", "max_input_tokens": 262144, - "max_output_tokens": 262144, "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 9.7e-07, @@ -39053,7 +41445,6 @@ "input_cost_per_token": 2e-07, "litellm_provider": "together_ai", "max_input_tokens": 1048576, - "max_output_tokens": 1048576, "max_tokens": 1048576, "mode": "chat", "output_cost_per_token": 2e-07, @@ -39064,7 +41455,6 @@ "input_cost_per_token": 3.5e-07, "litellm_provider": "together_ai", "max_input_tokens": 131072, - "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 1.5e-06, @@ -39077,7 +41467,6 @@ "input_cost_per_token": 9.5e-07, "litellm_provider": "together_ai", "max_input_tokens": 262144, - "max_output_tokens": 262144, "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 4e-06, @@ -39094,7 +41483,6 @@ "input_cost_per_token": 3e-06, "litellm_provider": "together_ai", "max_input_tokens": 1048576, - "max_output_tokens": 1048576, "max_tokens": 1048576, "mode": "chat", "output_cost_per_token": 1.5e-05, @@ -39118,7 +41506,6 @@ "input_cost_per_token": 6e-07, "litellm_provider": "together_ai", "max_input_tokens": 512288, - "max_output_tokens": 512288, "max_tokens": 512288, "mode": "chat", "output_cost_per_token": 3.6e-06, @@ -39135,7 +41522,6 @@ "input_cost_per_token": 2.8e-07, "litellm_provider": "together_ai", "max_input_tokens": 262144, - "max_output_tokens": 262144, "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 8.6e-07, @@ -39146,7 +41532,6 @@ "input_cost_per_token": 1e-06, "litellm_provider": "together_ai", "max_input_tokens": 524288, - "max_output_tokens": 524288, "max_tokens": 524288, "mode": "chat", "output_cost_per_token": 4.05e-06, @@ -39162,7 +41547,6 @@ "input_cost_per_token": 5e-07, "litellm_provider": "together_ai", "max_input_tokens": 524288, - "max_output_tokens": 524288, "max_tokens": 524288, "mode": "chat", "output_cost_per_token": 1.2e-06, @@ -39174,8 +41558,25 @@ "input_cost_per_token": 1.4e-06, "litellm_provider": "together_ai", "max_input_tokens": 1048575, - "max_output_tokens": 1048575, - "max_tokens": 1048575, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 4.4e-06, + "source": "https://docs.together.ai/docs/serverless-models", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "together_ai/zai-org/GLM-5.3": { + "cache_read_input_token_cost": 2.6e-07, + "input_cost_per_token": 1.4e-06, + "litellm_provider": "together_ai", + "max_input_tokens": 1048575, + "max_output_tokens": 128000, + "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 4.4e-06, "source": "https://docs.together.ai/docs/serverless-models", @@ -39191,8 +41592,8 @@ "input_cost_per_token": 1.5e-07, "litellm_provider": "together_ai", "max_input_tokens": 1048575, - "max_output_tokens": 1048575, - "max_tokens": 1048575, + "max_output_tokens": 128000, + "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 5e-07, "source": "https://docs.together.ai/docs/serverless-models", @@ -41718,6 +44119,42 @@ "supports_max_reasoning_effort": true, "prompt_cache_min_tokens": 512 }, + "vertex_ai/claude-fable-5-1": { + "regional_endpoint_uplift_multiplier": 1.1, + "supports_mid_conversation_system": true, + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 2.5e-07, + "input_cost_per_token": 1e-05, + "litellm_provider": "vertex_ai-anthropic_models", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_native_structured_output": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_max_reasoning_effort": true, + "prompt_cache_min_tokens": 512 + }, "vertex_ai/claude-fable-5@default": { "deprecation_date": "2027-06-08", "regional_endpoint_uplift_multiplier": 1.1, @@ -41753,6 +44190,42 @@ "supports_max_reasoning_effort": true, "prompt_cache_min_tokens": 512 }, + "vertex_ai/claude-fable-5-1@default": { + "regional_endpoint_uplift_multiplier": 1.1, + "supports_mid_conversation_system": true, + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 2.5e-07, + "input_cost_per_token": 1e-05, + "litellm_provider": "vertex_ai-anthropic_models", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_native_structured_output": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_max_reasoning_effort": true, + "prompt_cache_min_tokens": 512 + }, "vertex_ai/claude-opus-5": { "deprecation_date": "2027-01-24", "regional_endpoint_uplift_multiplier": 1.1, @@ -43097,7 +45570,7 @@ "max_tokens": 2000000, "mode": "chat", "output_cost_per_token": 5e-07, - "source": "https://docs.x.ai/docs/models (Vertex AI Model Garden)", + "source": "https://docs.x.ai/developers/models", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -43113,7 +45586,7 @@ "max_tokens": 2000000, "mode": "chat", "output_cost_per_token": 5e-07, - "source": "https://docs.x.ai/docs/models (Vertex AI Model Garden)", + "source": "https://docs.x.ai/developers/models", "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, @@ -43130,7 +45603,7 @@ "max_tokens": 2000000, "mode": "chat", "output_cost_per_token": 6e-06, - "source": "https://docs.x.ai/docs/models (Vertex AI Model Garden)", + "source": "https://docs.x.ai/developers/models", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -43146,7 +45619,7 @@ "max_tokens": 2000000, "mode": "chat", "output_cost_per_token": 6e-06, - "source": "https://docs.x.ai/docs/models (Vertex AI Model Garden)", + "source": "https://docs.x.ai/developers/models", "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, @@ -43266,7 +45739,8 @@ "max_tokens": 1024, "mode": "video_generation", "output_cost_per_second": 0.4, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo", + "output_cost_per_second_4k": 0.6, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supported_modalities": [ "text" ], @@ -43279,8 +45753,10 @@ "max_input_tokens": 1024, "max_tokens": 1024, "mode": "video_generation", - "output_cost_per_second": 0.15, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo", + "output_cost_per_second": 0.1, + "output_cost_per_second_1080p": 0.12, + "output_cost_per_second_4k": 0.3, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supported_modalities": [ "text" ], @@ -43295,7 +45771,8 @@ "max_tokens": 1024, "mode": "video_generation", "output_cost_per_second": 0.4, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo", + "output_cost_per_second_4k": 0.6, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supported_modalities": [ "text" ], @@ -43309,8 +45786,10 @@ "max_input_tokens": 1024, "max_tokens": 1024, "mode": "video_generation", - "output_cost_per_second": 0.15, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo", + "output_cost_per_second": 0.1, + "output_cost_per_second_1080p": 0.12, + "output_cost_per_second_4k": 0.3, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supported_modalities": [ "text" ], @@ -43318,6 +45797,22 @@ "video" ] }, + "vertex_ai/veo-3.1-lite-generate-001": { + "litellm_provider": "vertex_ai-video-models", + "max_input_tokens": 1024, + "max_tokens": 1024, + "mode": "video_generation", + "output_cost_per_second": 0.05, + "output_cost_per_second_1080p": 0.08, + "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing#veo", + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "video" + ] + }, "voyage/rerank-2": { "input_cost_per_token": 5e-08, "litellm_provider": "voyage", @@ -43998,290 +46493,339 @@ "output_cost_per_second": 0.0001, "supported_endpoints": [ "/v1/audio/transcriptions" - ] + ], + "deprecation_date": "2027-02-26" }, "xai/grok-3": { - "cache_read_input_token_cost": 7.5e-07, - "input_cost_per_token": 3e-06, + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1.25e-06, "litellm_provider": "xai", "max_input_tokens": 131072, "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "output_cost_per_token": 1.5e-05, + "output_cost_per_token": 2.5e-06, "source": "https://x.ai/api#pricing", "supports_function_calling": true, "supports_prompt_caching": true, "supports_response_schema": false, "supports_tool_choice": true, "supports_web_search": true, - "deprecation_date": "2026-05-15" + "deprecation_date": "2026-05-15", + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07 }, "xai/grok-3-beta": { - "cache_read_input_token_cost": 7.5e-07, - "input_cost_per_token": 3e-06, + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1.25e-06, "litellm_provider": "xai", "max_input_tokens": 131072, "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "output_cost_per_token": 1.5e-05, + "output_cost_per_token": 2.5e-06, "source": "https://x.ai/api#pricing", "supports_function_calling": true, "supports_prompt_caching": true, "supports_response_schema": false, "supports_tool_choice": true, - "supports_web_search": true + "supports_web_search": true, + "deprecation_date": "2026-05-15", + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07 }, "xai/grok-3-fast-beta": { - "cache_read_input_token_cost": 1.25e-06, - "input_cost_per_token": 5e-06, + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1.25e-06, "litellm_provider": "xai", "max_input_tokens": 131072, "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "output_cost_per_token": 2.5e-05, + "output_cost_per_token": 2.5e-06, "source": "https://x.ai/api#pricing", "supports_function_calling": true, "supports_prompt_caching": true, "supports_response_schema": false, "supports_tool_choice": true, - "supports_web_search": true + "supports_web_search": true, + "deprecation_date": "2026-05-15", + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07 }, "xai/grok-3-fast-latest": { - "cache_read_input_token_cost": 1.25e-06, - "input_cost_per_token": 5e-06, + 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"output_cost_per_token": 1.5e-05, + "output_cost_per_token": 2.5e-06, "source": "https://x.ai/api#pricing", "supports_function_calling": true, "supports_prompt_caching": true, "supports_response_schema": false, "supports_tool_choice": true, "supports_web_search": true, - "deprecation_date": "2026-05-15" + "deprecation_date": "2026-05-15", + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07 }, "xai/grok-3-mini": { - "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost": 2e-07, "deprecation_date": "2026-02-28", - "input_cost_per_token": 3e-07, + "input_cost_per_token": 1.25e-06, "litellm_provider": "xai", "max_input_tokens": 131072, "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "output_cost_per_token": 5e-07, + "output_cost_per_token": 2.5e-06, "source": "https://x.ai/api#pricing", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": false, "supports_tool_choice": true, - "supports_web_search": true + "supports_web_search": true, + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07 }, "xai/grok-3-mini-beta": { - "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost": 2e-07, "deprecation_date": "2026-02-28", - "input_cost_per_token": 3e-07, + "input_cost_per_token": 1.25e-06, "litellm_provider": "xai", "max_input_tokens": 131072, "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "output_cost_per_token": 5e-07, + "output_cost_per_token": 2.5e-06, "source": "https://x.ai/api#pricing", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": false, "supports_tool_choice": true, - "supports_web_search": true + 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"litellm_provider": "xai", "max_input_tokens": 2000000.0, "max_output_tokens": 2000000.0, "max_tokens": 2000000.0, "mode": "chat", - "output_cost_per_token": 5e-07, - "output_cost_per_token_above_128k_tokens": 1e-06, + "output_cost_per_token": 2.5e-06, "source": "https://docs.x.ai/docs/models/grok-4-1-fast-reasoning", "supports_audio_input": true, "supports_function_calling": true, @@ -44290,19 +46834,21 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "supports_web_search": true + "supports_web_search": true, + "deprecation_date": "2026-05-15", + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07 }, "xai/grok-4-1-fast-reasoning": { - "cache_read_input_token_cost": 5e-08, - "input_cost_per_token": 2e-07, - "input_cost_per_token_above_128k_tokens": 4e-07, + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1.25e-06, "litellm_provider": "xai", "max_input_tokens": 2000000.0, "max_output_tokens": 2000000.0, "max_tokens": 2000000.0, "mode": "chat", - "output_cost_per_token": 5e-07, - "output_cost_per_token_above_128k_tokens": 1e-06, + "output_cost_per_token": 2.5e-06, "source": "https://docs.x.ai/docs/models/grok-4-1-fast-reasoning", "supports_audio_input": true, "supports_function_calling": true, @@ -44312,19 +46858,20 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "deprecation_date": "2026-05-15" + "deprecation_date": "2026-05-15", + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07 }, "xai/grok-4-1-fast-reasoning-latest": { - "cache_read_input_token_cost": 5e-08, - "input_cost_per_token": 2e-07, - "input_cost_per_token_above_128k_tokens": 4e-07, + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1.25e-06, "litellm_provider": "xai", "max_input_tokens": 2000000.0, "max_output_tokens": 2000000.0, "max_tokens": 2000000.0, "mode": "chat", - "output_cost_per_token": 5e-07, - "output_cost_per_token_above_128k_tokens": 1e-06, + "output_cost_per_token": 2.5e-06, "source": "https://docs.x.ai/docs/models/grok-4-1-fast-reasoning", "supports_audio_input": true, "supports_function_calling": true, @@ -44334,19 +46881,20 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "deprecation_date": "2026-05-15" + "deprecation_date": "2026-05-15", + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07 }, "xai/grok-4-1-fast-non-reasoning": { - "cache_read_input_token_cost": 5e-08, - "input_cost_per_token": 2e-07, - "input_cost_per_token_above_128k_tokens": 4e-07, + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1.25e-06, "litellm_provider": "xai", "max_input_tokens": 2000000.0, "max_output_tokens": 2000000.0, "max_tokens": 2000000.0, "mode": "chat", - "output_cost_per_token": 5e-07, - "output_cost_per_token_above_128k_tokens": 1e-06, + "output_cost_per_token": 2.5e-06, "source": "https://docs.x.ai/docs/models/grok-4-1-fast-non-reasoning", "supports_audio_input": true, "supports_function_calling": true, @@ -44355,19 +46903,20 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "deprecation_date": "2026-05-15" + "deprecation_date": "2026-05-15", + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07 }, "xai/grok-4-1-fast-non-reasoning-latest": { - "cache_read_input_token_cost": 5e-08, - "input_cost_per_token": 2e-07, - "input_cost_per_token_above_128k_tokens": 4e-07, + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1.25e-06, "litellm_provider": "xai", "max_input_tokens": 2000000.0, "max_output_tokens": 2000000.0, "max_tokens": 2000000.0, "mode": "chat", - "output_cost_per_token": 5e-07, - "output_cost_per_token_above_128k_tokens": 1e-06, + "output_cost_per_token": 2.5e-06, "source": "https://docs.x.ai/docs/models/grok-4-1-fast-non-reasoning", "supports_audio_input": true, "supports_function_calling": true, @@ -44376,7 +46925,10 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "deprecation_date": "2026-05-15" + "deprecation_date": "2026-05-15", + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07 }, "xai/grok-4.20-multi-agent-beta-0309": { "cache_read_input_token_cost": 2e-07, @@ -44930,7 +47482,7 @@ "litellm_provider": "azure", "mode": "video_generation", "output_cost_per_video_per_second": 0.1, - "source": "https://azure.microsoft.com/en-us/products/ai-services/video-generation", + "source": "https://ai.azure.com/catalog/models/sora-2", "supported_modalities": [ "text" ], @@ -44942,7 +47494,7 @@ "litellm_provider": "azure", "mode": "video_generation", "output_cost_per_video_per_second": 0.3, - "source": "https://azure.microsoft.com/en-us/products/ai-services/video-generation", + "source": "https://ai.azure.com/catalog/models/sora-2-pro", "supported_modalities": [ "text" ], @@ -44954,7 +47506,7 @@ "litellm_provider": "azure", "mode": "video_generation", "output_cost_per_video_per_second": 0.5, - "source": "https://azure.microsoft.com/en-us/products/ai-services/video-generation", + "source": "https://ai.azure.com/catalog/models/sora-2-pro", "supported_modalities": [ "text" ], @@ -49164,7 +51716,7 @@ "input_cost_per_second": 0.0002833333333333333, "litellm_provider": "openai", "mode": "audio_transcription", - "source": "https://platform.openai.com/docs/models/gpt-realtime-whisper", + "source": "https://developers.openai.com/api/docs/models/gpt-realtime-whisper", "supported_endpoints": [ "/v1/realtime", "/v1/realtime/transcription_sessions" @@ -50390,7 +52942,7 @@ "max_tokens": 500000, "mode": "chat", "supports_function_calling": true, - "supports_prompt_caching": true, + "supports_prompt_caching": false, "supports_reasoning": true, "supports_tool_choice": true, "supports_vision": true @@ -50405,7 +52957,7 @@ "max_tokens": 500000, "mode": "chat", "supports_function_calling": true, - "supports_prompt_caching": true, + "supports_prompt_caching": false, "supports_reasoning": true, "supports_tool_choice": true, "supports_vision": true @@ -50416,7 +52968,7 @@ "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", - "source": "https://www.volcengine.com/docs/82379/1330310", + "source": "https://docs.volcengine.com/docs/82379/1330310", "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": false, @@ -50454,7 +53006,7 @@ "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", - "source": "https://www.volcengine.com/docs/82379/1330310", + "source": "https://docs.volcengine.com/docs/82379/1330310", "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": false, @@ -50492,7 +53044,7 @@ "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", - "source": "https://www.volcengine.com/docs/82379/1330310", + "source": "https://docs.volcengine.com/docs/82379/1330310", "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": false, @@ -50530,7 +53082,7 @@ "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", - "source": "https://www.volcengine.com/docs/82379/1330310", + "source": "https://docs.volcengine.com/docs/82379/1330310", "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": false, @@ -51279,7 +53831,7 @@ "supports_function_calling": true, "supports_assistant_prefill": true, "supports_reasoning": true, - "source": "https://pinstripes.io/pricing" + "source": "https://pinstripes.io/" }, "pinstripes/ps/qwen3.6-35b-a3b": { "max_tokens": 131072, @@ -51292,7 +53844,7 @@ "supports_function_calling": true, "supports_assistant_prefill": true, "supports_reasoning": true, - "source": "https://pinstripes.io/pricing" + "source": "https://pinstripes.io/" }, "pinstripes/ps/qwen3-30b-a3b": { "max_tokens": 131072, @@ -51305,7 +53857,7 @@ "supports_function_calling": true, "supports_assistant_prefill": true, "supports_reasoning": true, - "source": "https://pinstripes.io/pricing" + "source": "https://pinstripes.io/" }, "pinstripes/ps/qwen3-coder-30b-a3b": { "max_tokens": 131072, @@ -51318,7 +53870,7 @@ "supports_function_calling": true, "supports_assistant_prefill": true, "supports_reasoning": false, - "source": "https://pinstripes.io/pricing" + "source": "https://pinstripes.io/" }, "pinstripes/ps/deepseek-v4-flash": { "max_tokens": 163840, @@ -51331,7 +53883,7 @@ "supports_function_calling": true, "supports_assistant_prefill": true, "supports_reasoning": true, - "source": "https://pinstripes.io/pricing" + "source": "https://pinstripes.io/" }, "pinstripes/ps/minimax-m2.7": { "max_tokens": 1000192, @@ -51344,7 +53896,7 @@ "supports_function_calling": true, "supports_assistant_prefill": true, "supports_reasoning": false, - "source": "https://pinstripes.io/pricing" + "source": "https://pinstripes.io/" }, "darkbloom/gemma-4-26b": { "input_cost_per_token": 3e-08, @@ -51448,7 +54000,7 @@ "input_cost_per_second": 7.5e-05, "litellm_provider": "openai", "mode": "audio_transcription", - "source": "https://platform.openai.com/docs/models/gpt-transcribe", + "source": "https://developers.openai.com/api/docs/models/gpt-transcribe", "supported_endpoints": [ "/v1/audio/transcriptions", "/v1/realtime/transcription_sessions" @@ -51466,7 +54018,7 @@ "input_cost_per_second": 0.0002833333333333333, "litellm_provider": "openai", "mode": "audio_transcription", - "source": "https://platform.openai.com/docs/models/gpt-live-transcribe", + "source": "https://developers.openai.com/api/docs/models/gpt-live-transcribe", "supported_endpoints": [ "/v1/realtime", "/v1/realtime/transcription_sessions" @@ -51487,7 +54039,7 @@ "max_output_tokens": 2000, "max_tokens": 2000, "mode": "realtime", - "source": "https://platform.openai.com/docs/models/gpt-realtime-translate", + "source": "https://developers.openai.com/api/docs/models/gpt-realtime-translate", "supported_modalities": [ "audio" ], @@ -51515,7 +54067,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, - "source": "https://docs.claude.com/en/docs/about-claude/models/overview", + "source": "https://platform.claude.com/docs/en/about-claude/models/overview", "supports_adaptive_thinking": true, "thinking_always_on": true, "supports_mid_conversation_system": true, @@ -51554,7 +54106,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, - "source": "https://docs.claude.com/en/docs/about-claude/models/overview", + "source": "https://platform.claude.com/docs/en/about-claude/models/overview", "supports_adaptive_thinking": true, "thinking_always_on": true, "supports_assistant_prefill": false, @@ -51828,6 +54380,43 @@ "tpm": 250000, "rpm": 10 }, + "vertex_ai/gemini-3.5-transcribe-preview": { + "input_cost_per_audio_token": 2.5e-06, + "input_cost_per_token": 2.5e-06, + "litellm_provider": "vertex_ai", + "mode": "audio_transcription", + "output_cost_per_token": 1.2e-05, + "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ], + "supported_modalities": [ + "text", + "audio" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true + }, + "vertex_ai/gemini-3.5-transcribe-live-preview": { + "input_cost_per_audio_token": 3.5e-06, + "input_cost_per_token": 3.5e-06, + "litellm_provider": "vertex_ai", + "mode": "audio_transcription", + "output_cost_per_token": 2.1e-05, + "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "supported_endpoints": [ + "/v1/realtime" + ], + "supported_modalities": [ + "audio" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true + }, "perplexity/pplx-embed-context-v1-0.6b": { "input_cost_per_token": 8e-09, "litellm_provider": "perplexity", @@ -51947,14 +54536,14 @@ "supports_vision": true }, "fireworks_ai/deepseek-v4-flash-0731": { - "cache_read_input_token_cost": 2.8e-08, - "input_cost_per_token": 1.4e-07, + "cache_read_input_token_cost": 7e-09, + "input_cost_per_token": 2.2e-07, "litellm_provider": "fireworks_ai", "max_input_tokens": 1048576, "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "output_cost_per_token": 2.8e-07, + "output_cost_per_token": 6.6e-07, "source": "https://docs.fireworks.ai/serverless/pricing", "supports_function_calling": true, "supports_reasoning": true, @@ -54596,5 +57185,299 @@ "output_cost_per_token_above_200k_tokens": 5e-06, "cache_read_input_token_cost_above_200k_tokens": 4e-07, "supports_response_schema": true + }, + "groq/qwen/qwen3.8-27b": { + "input_cost_per_token": 8e-07, + "litellm_provider": "groq", + "max_input_tokens": 131042, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 4e-06, + "source": "https://console.groq.com/docs/model/qwen/qwen3.8-27b", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "mistral/mistral-medium-3.5": { + "cache_read_input_token_cost": 1.5e-07, + "input_cost_per_token": 1.5e-06, + "litellm_provider": "mistral", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 7.5e-06, + "source": "https://docs.mistral.ai/models/model-cards/mistral-medium-3-5-26-04", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "mistral/mistral-vibe-cli-latest": { + "cache_read_input_token_cost": 1.5e-07, + "input_cost_per_token": 1.5e-06, + "litellm_provider": "mistral", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 7.5e-06, + "source": "https://docs.mistral.ai/models/model-cards/mistral-medium-3-5-26-04", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "mistral/mistral-vibe-cli-with-tools": { + "cache_read_input_token_cost": 1.5e-07, + "input_cost_per_token": 1.5e-06, + "litellm_provider": "mistral", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 7.5e-06, + "source": "https://docs.mistral.ai/models/model-cards/mistral-medium-3-5-26-04", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "mistral/mistral-vibe-cli-fast": { + "cache_read_input_token_cost": 1.5e-08, + "input_cost_per_token": 1.5e-07, + "litellm_provider": "mistral", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 6e-07, + "source": "https://docs.mistral.ai/models/model-cards/mistral-small-4-0-26-03", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "mistral/mistral-code-latest": { + "cache_read_input_token_cost": 3e-08, + "input_cost_per_token": 3e-07, + "litellm_provider": "mistral", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 9e-07, + "source": "https://docs.mistral.ai/models/model-cards/codestral-25-08", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "mistral/mistral-code-fim-latest": { + "cache_read_input_token_cost": 3e-08, + "input_cost_per_token": 3e-07, + "litellm_provider": "mistral", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 9e-07, + "source": "https://docs.mistral.ai/models/model-cards/codestral-25-08", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "mistral/mistral-code-agent-latest": { + "input_cost_per_token": 4e-07, + "litellm_provider": "mistral", + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "max_tokens": 256000, + "mode": "chat", + "output_cost_per_token": 2e-06, + "source": "https://mistral.ai/news/devstral-2-vibe-cli", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "mistral/mistral-ocr-3": { + "litellm_provider": "mistral", + "ocr_cost_per_page": 0.002, + "annotation_cost_per_page": 0.003, + "mode": "ocr", + "supported_endpoints": [ + "/v1/ocr" + ], + "source": "https://mistral.ai/pricing#api-pricing" + }, + "mistral/mistral-ocr-3-0": { + "litellm_provider": "mistral", + "ocr_cost_per_page": 0.002, + "annotation_cost_per_page": 0.003, + "mode": "ocr", + "supported_endpoints": [ + "/v1/ocr" + ], + "source": "https://mistral.ai/pricing#api-pricing" + }, + "mistral/mistral-ocr-4": { + "annotation_cost_per_page": 0.005, + "litellm_provider": "mistral", + "mode": "ocr", + "ocr_cost_per_page": 0.004, + "source": "https://docs.mistral.ai/models/model-cards/ocr-4-1", + "supported_endpoints": [ + "/v1/ocr" + ] + }, + "mistral/voxtral-mini-latest": { + "input_cost_per_second": 5e-05, + "litellm_provider": "mistral", + "mode": "audio_transcription", + "source": "https://docs.mistral.ai/models/model-cards/voxtral-mini-transcribe-26-02", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ], + "supported_modalities": [ + "audio" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true + }, + "mistral/voxtral-mini-realtime-2602": { + "input_cost_per_second": 0.0001, + "litellm_provider": "mistral", + "mode": "audio_transcription", + "source": "https://docs.mistral.ai/models/model-cards/voxtral-mini-transcribe-realtime-26-02", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ], + "supported_modalities": [ + "audio" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true + }, + "mistral/voxtral-mini-realtime-latest": { + "input_cost_per_second": 0.0001, + "litellm_provider": "mistral", + "mode": "audio_transcription", + "source": "https://docs.mistral.ai/models/model-cards/voxtral-mini-transcribe-realtime-26-02", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ], + "supported_modalities": [ + "audio" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true + }, + "mistral/labs-leanstral-1-5-1": { + "input_cost_per_token": 0.0, + "litellm_provider": "mistral", + "max_input_tokens": 262144, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 0.0, + "source": "https://docs.mistral.ai/models/model-cards/leanstral-1-5", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "fireworks_ai/accounts/fireworks/models/glm-5p3": { + "cache_read_input_token_cost": 2.6e-07, + "input_cost_per_token": 1.4e-06, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 1048576, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 4.4e-06, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": false + }, + "fireworks_ai/accounts/fireworks/models/qwen3-embedding-8b": { + "input_cost_per_token": 1e-07, + "output_cost_per_token": 0.0, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 40960, + "max_output_tokens": 40960, + "max_tokens": 40960, + "mode": "embedding", + "source": "https://docs.fireworks.ai/serverless/pricing" + }, + "zai/glm-5.2": { + "cache_creation_input_token_cost": 0, + "cache_read_input_token_cost": 2.6e-07, + "input_cost_per_token": 1.4e-06, + "litellm_provider": "zai", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 4.4e-06, + "source": "https://docs.z.ai/guides/overview/pricing", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "together_ai/Qwen/Qwen3.8-Flash": { + "input_cost_per_token": 1.5e-07, + "litellm_provider": "together_ai", + "max_input_tokens": 1000000, + "max_tokens": 1000000, + "mode": "chat", + "output_cost_per_token": 4.7e-07, + "source": "https://docs.together.ai/docs/serverless-models" + }, + "cerebras/gemma-4-31b": { + "input_cost_per_token": 9.9e-07, + "litellm_provider": "cerebras", + "max_input_tokens": 131072, + "max_output_tokens": 40960, + "max_tokens": 40960, + "mode": "chat", + "output_cost_per_token": 1.49e-06, + "source": "https://api.cerebras.ai/public/v1/models/gemma-4-31b", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "elevenlabs/scribe_v2": { + "input_cost_per_second": 6.11e-05, + "litellm_provider": "elevenlabs", + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://elevenlabs.io/pricing/api", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] } } diff --git a/model_prices_and_context_window.schema.json b/model_prices_and_context_window.schema.json index 3f6d3b4f910..9e370e5406a 100644 --- a/model_prices_and_context_window.schema.json +++ b/model_prices_and_context_window.schema.json @@ -662,6 +662,9 @@ "supports_embedding_image_input": { "type": "boolean" }, + "supports_forced_tool_use": { + "type": "boolean" + }, "supports_function_calling": { "type": "boolean" }, diff --git a/osv-scanner.toml b/osv-scanner.toml index 7ab450945f5..5b0339bdcd0 100644 --- a/osv-scanner.toml +++ b/osv-scanner.toml @@ -2,3 +2,8 @@ id = "GHSA-w8v5-vhqr-4h9v" ignoreUntil = 2026-09-09 reason = "diskcache has no fixed release published; remove this entry once one exists" + +[[IgnoredVulns]] +id = "GHSA-h7x2-h6g9-p789" +ignoreUntil = 2026-09-14 +reason = "mlflow has no fixed release published; remove this entry once one exists" diff --git a/provider_endpoints_support.json b/provider_endpoints_support.json index 7c7d508856f..ebc220b3496 100644 --- a/provider_endpoints_support.json +++ b/provider_endpoints_support.json @@ -724,6 +724,42 @@ "interactions": true } }, + "qwencloud": { + "display_name": "QwenCloud (`qwencloud`)", + "url": "https://docs.litellm.ai/docs/providers/qwencloud", + "endpoints": { + "chat_completions": true, + "messages": true, + "responses": true, + "embeddings": true, + "image_generations": true, + "audio_transcriptions": false, + "audio_speech": false, + "moderations": false, + "batches": false, + "rerank": true, + "a2a": true, + "interactions": true + } + }, + "qwen_ai_platform": { + "display_name": "Qwen AI Platform (`qwen_ai_platform`)", + "url": "https://docs.litellm.ai/docs/providers/qwencloud", + "endpoints": { + "chat_completions": true, + "messages": true, + "responses": true, + "embeddings": true, + "image_generations": true, + "audio_transcriptions": false, + "audio_speech": false, + "moderations": false, + "batches": false, + "rerank": true, + "a2a": true, + "interactions": true + } + }, "databricks": { "display_name": "Databricks (`databricks`)", "url": "https://docs.litellm.ai/docs/providers/databricks", diff --git a/pyproject.toml b/pyproject.toml index a0db4d49467..2866e27e84c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm" -version = "1.100.0" +version = "1.101.0" description = "Library to easily interface with LLM API providers" readme = "README.md" requires-python = ">=3.10, <3.15" @@ -67,8 +67,8 @@ proxy = [ "azure-identity>=1.25.2,<2.0", "azure-storage-blob>=12.28.0,<13.0", "mcp>=1.28.1,<2.0", - "litellm-proxy-extras==0.4.90", - "litellm-enterprise==0.1.61", + "litellm-proxy-extras==0.4.92", + "litellm-enterprise==0.1.63", "RestrictedPython>=8.5,<9.0", "rich>=13.9.4,<14.0", "InquirerPy>=0.3.4,<1.0", @@ -91,6 +91,11 @@ cli = [ ] extra_proxy = [ "prisma>=0.11.0,<1.0", + # Used by ProxyExtrasDBManager.spend_logs_is_partitioned() to detect a + # partitioned LiteLLM_SpendLogs and keep schema reconciliation from + # fighting its composite primary key. + "psycopg>=3.2,<4.0", + "psycopg-binary>=3.2,<4.0", "azure-identity>=1.25.2,<2.0", "azure-keyvault-secrets>=4.10.0,<5.0", # Not in PyPI proxy extra. @@ -262,7 +267,7 @@ healthcheck = [ ] [build-system] -requires = ["maturin==1.9.4"] +requires = ["maturin==1.15.0"] build-backend = "maturin" [tool.maturin] @@ -270,6 +275,9 @@ manifest-path = "litellm-rust/crates/python-bridge/Cargo.toml" module-name = "litellm.rust_bridge._native" python-source = "." bindings = "pyo3" +features = ["extension-module"] +profile = "release" +editable-profile = "dev" include = ["litellm/proxy/_experimental/out/**"] exclude = [ "litellm/proxy/enterprise", @@ -311,7 +319,7 @@ members = ["enterprise", "litellm-proxy-extras"] profile = "black" [tool.commitizen] -version = "1.100.0" +version = "1.101.0" version_files = [ "pyproject.toml:^version", ] diff --git a/ruff-strict-budget.json b/ruff-strict-budget.json index c60988eccc0..9b1cc977a64 100644 --- a/ruff-strict-budget.json +++ b/ruff-strict-budget.json @@ -1,21 +1,21 @@ { "ANN001": { - "limit": 3012 + "limit": 2985 }, "ANN002": { "limit": 71 }, "ANN003": { - "limit": 827 + "limit": 809 }, "ANN201": { - "limit": 2003 + "limit": 2001 }, "ANN202": { - "limit": 845 + "limit": 835 }, "ANN204": { - "limit": 702 + "limit": 693 }, "ANN205": { "limit": 112 @@ -24,7 +24,7 @@ "limit": 133 }, "ANN401": { - "limit": 655 + "limit": 307 }, "ASYNC230": { "limit": 11 @@ -117,13 +117,13 @@ "limit": 1 }, "PERF102": { - "limit": 23 + "limit": 21 }, "PERF401": { "limit": 12 }, "PERF403": { - "limit": 34 + "limit": 33 }, "PIE804": { "limit": 18 @@ -168,7 +168,7 @@ "limit": 3 }, "RET504": { - "limit": 175 + "limit": 173 }, "RUF012": { "limit": 239 @@ -177,7 +177,7 @@ "limit": 8 }, "RUF019": { - "limit": 32 + "limit": 31 }, "RUF046": { "limit": 4 @@ -195,10 +195,10 @@ "limit": 22 }, "SIM101": { - "limit": 58 + "limit": 56 }, "SIM102": { - "limit": 315 + "limit": 310 }, "SIM103": { "limit": 119 @@ -231,7 +231,7 @@ "limit": 5 }, "TID251": { - "limit": 1117 + "limit": 1073 }, "TRY002": { "limit": 524 @@ -240,13 +240,13 @@ "limit": 96 }, "TRY201": { - "limit": 405 + "limit": 403 }, "TRY203": { - "limit": 113 + "limit": 111 }, "TRY300": { - "limit": 857 + "limit": 854 }, "UP028": { "limit": 2 diff --git a/ruff-strict.toml b/ruff-strict.toml index 7afc5da71ee..ae092bdde7d 100644 --- a/ruff-strict.toml +++ b/ruff-strict.toml @@ -26,6 +26,10 @@ external = [ # caught a real mismatch, confirming Any is correct here, not a shortcut. "litellm/litellm_core_utils/litellm_logging.py" = ["ANN401"] "litellm/utils.py" = ["ANN401"] +# `**kwargs` forwards verbatim to CustomGuardrail.__init__, whose param list is wide and +# grows over time; typing it concretely (`object`) broke that forwarding call outright — +# basedpyright turned every named param into a reportArgumentType error. Any is correct here. +"litellm/proxy/guardrails/guardrail_hooks/alice/alice.py" = ["ANN401"] [lint.mccabe] max-complexity = 15 diff --git a/ruff.toml b/ruff.toml index 44bdf9d8125..3ac4c1fc94d 100644 --- a/ruff.toml +++ b/ruff.toml @@ -6,7 +6,8 @@ lint.extend-select = [ "T20", "PGH004", "RUF008", "RUF009", "RUF100", "B033", "FURB136", "FURB168", "FURB188", "I001", "PERF402", "PIE790", "PIE800", "PLC0208", "PLR0402", "PLR1711", "PLR1730", "PLR2044", "PLW0133", "PYI030", "PYI041", "PYI064", "RET501", - "RUF010", "RUF022", "RUF023", "RUF051", "SIM114", "SIM118", "TC005", "UP006", "UP007", "UP008", + "RUF010", "RUF022", "RUF023", "RUF051", "S113", "SIM114", "SIM118", "TC005", "UP006", "UP007", + "UP008", "UP012", "UP018", "UP024", "UP032", "UP034", "UP035", "UP037", "UP045", ] # RUF100 (unused-noqa) only knows the rules enabled in THIS config, so it would strip diff --git a/schema.prisma b/schema.prisma index 2bb850139a2..7604ceadf7a 100644 --- a/schema.prisma +++ b/schema.prisma @@ -29,6 +29,7 @@ model LiteLLM_BudgetTable { keys LiteLLM_VerificationToken[] // multiple keys can have the same budget end_users LiteLLM_EndUserTable[] // multiple end-users can have the same budget tags LiteLLM_TagTable[] // multiple tags can have the same budget + model_access_groups LiteLLM_ModelAccessGroupBudgetTable[] // multiple model access groups can have the same budget team_membership LiteLLM_TeamMembership[] // budgets of Users within a Team organization_membership LiteLLM_OrganizationMembership[] // budgets of Users within a Organization } @@ -585,6 +586,20 @@ model LiteLLM_EndUserTable { blocked Boolean @default(false) } +// Budget and shared spend for a model access group. The groups themselves are not rows anywhere: +// they are free-text strings in LiteLLM_ProxyModelTable.model_info.access_groups, so a row here +// exists only once someone gives that group a budget. +model LiteLLM_ModelAccessGroupBudgetTable { + access_group_name String @id + spend Float @default(0.0) + budget_id String? + litellm_budget_table LiteLLM_BudgetTable? @relation(fields: [budget_id], references: [budget_id]) + created_at DateTime @default(now()) @map("created_at") + created_by String? + updated_at DateTime @default(now()) @updatedAt + updated_by String? +} + // Track tags with budgets and spend model LiteLLM_TagTable { tag_name String @id @@ -649,6 +664,18 @@ model LiteLLM_SpendLogs { @@index([session_id]) } +model LiteLLM_BudgetWindowSpend { + entity_type String + entity_id String + window_duration String + window_start DateTime + spend Float @default(0.0) + created_at DateTime @default(now()) + updated_at DateTime @updatedAt + + @@id([entity_type, entity_id, window_duration]) +} + // View spend, model, api_key per request model LiteLLM_ErrorLogs { request_id String @id @default(uuid()) @@ -1502,14 +1529,16 @@ model LiteLLM_AutoRouterSession { model LiteLLM_ShadowEvalJob { id String @id @default(cuid()) group_id String // legs of one job share this; the API's job id - api_key_id String // hashed virtual key whose traffic this leg shadows - router_name String // the auto-router under evaluation, in either direction + target_type String @default("key") // key | team | user + target_id String // hashed virtual key, team_id, or user_id whose traffic this leg shadows + router_name String // first (often only) auto-router under evaluation; router_names is the full set + router_names String[] @default([]) // all routers this job runs as shadow arms; empty on legacy rows, whose set is (router_name) direction String @default("forward") // forward | reverse baseline_model String? // reverse only: the fixed model the router is judged against judge_model String shadow_percentage Float max_turns Int // sample-count ceiling: the whole budget on pre-max_budget jobs, the error-loop valve otherwise - max_budget Float? // per-key USD cap on the eval's own shadow + judge spend; null on jobs from before spend budgets + max_budget Float? // per-target USD cap on the eval's own shadow + judge spend; null on jobs from before spend budgets created_at DateTime @default(now()) created_by String? ends_at DateTime @@ -1517,7 +1546,7 @@ model LiteLLM_ShadowEvalJob { stopped_by String? // operator who stopped it early; null when it ended on its own @@index([group_id]) - @@index([api_key_id]) + @@index([target_type, target_id]) @@index([created_at]) } @@ -1527,6 +1556,7 @@ model LiteLLM_ShadowEvalAttempt { job_id String request_id String // the judged real request outcome String // real | shadow | tie | error + router_name String? // the arm this verdict scores; NULL on legacy rows, meaning the job's own router tier String? // router's tier for the prompt, when classified real_model String? shadow_model String? diff --git a/scripts/auto-close-duplicates.test.ts b/scripts/auto-close-duplicates.test.ts new file mode 100644 index 00000000000..b49bf05cbc2 --- /dev/null +++ b/scripts/auto-close-duplicates.test.ts @@ -0,0 +1,348 @@ +import { describe, expect, test } from "bun:test"; + +import { + CLOSED_MARKER, + REOPEN_COMMENT, + candidateNumbers, + duplicateTarget, + normalizeTitle, + pendingNotice, + readConfig, + reopenTarget, + sweepClosedIssue, + sweepIssue, + type Comment, + type GitHubApi, + type Issue, + type Reaction, + type SweepConfig, +} from "./auto-close-duplicates"; + +const NOW = new Date("2026-09-04T09:00:00Z"); +const DAY_MS = 24 * 60 * 60 * 1000; +const daysAgo = (days: number): string => new Date(NOW.getTime() - days * DAY_MS).toISOString(); + +const issue = (number: number, title: string, overrides: Partial = {}): Issue => ({ + number, + title, + state: "open", + user: { login: "reporter" }, + ...overrides, +}); + +const notice = (candidates: readonly number[], createdAt: string, overrides: Partial = {}): Comment => ({ + id: 900, + body: `\n**Potential duplicate detected**`, + created_at: createdAt, + user: { type: "Bot", login: "github-actions[bot]" }, + ...overrides, +}); + +const humanComment = (createdAt: string, body = "It is not the same thing", login = "reporter"): Comment => ({ + id: 901, + body, + created_at: createdAt, + user: { type: "User", login }, +}); + +const config: SweepConfig = { repo: "BerriAI/litellm", graceDays: 3, dryRun: false, now: NOW }; + +describe("normalizeTitle", () => { + test("drops the template prefix, case, and punctuation", () => { + expect(normalizeTitle("[Bug]: Gemma 4-e4b fails on Vertex!")).toBe("gemma 4 e4b fails on vertex"); + expect(normalizeTitle("[Feature]: ")).toBe(""); + }); +}); + +describe("candidateNumbers", () => { + test("reads only the marker field, keeps older issues, sorted ascending and deduplicated", () => { + const body = "\n- #1 - see #1 (100% similar)"; + expect(candidateNumbers(body, 35)).toEqual([10, 30]); + }); + + test("returns nothing without the marker", () => { + expect(candidateNumbers("- #1 - looks like #1", 35)).toEqual([]); + }); +}); + +describe("pendingNotice", () => { + test("waits out the grace period from the latest notice", () => { + const fresh = pendingNotice(issue(35, "t"), [notice([10], daysAgo(2.9))], config); + expect(fresh.kind).toBe("skip"); + const aged = pendingNotice(issue(35, "t"), [notice([10], daysAgo(3.1))], config); + expect(aged.kind).toBe("pending"); + const reposted = pendingNotice( + issue(35, "t"), + [notice([10], daysAgo(6)), notice([10], daysAgo(1), { id: 902 })], + config, + ); + expect(reposted.kind).toBe("skip"); + }); + + test("an objection posted before a re-posted notice still keeps the issue open", () => { + const verdict = pendingNotice( + issue(35, "t"), + [notice([10], daysAgo(10)), humanComment(daysAgo(7)), notice([10], daysAgo(4), { id: 902 })], + config, + ); + expect(verdict).toEqual({ kind: "skip", reason: "someone replied after the notice" }); + }); + + test("a zero-day grace period acts on the notice at once", () => { + const verdict = pendingNotice(issue(35, "t"), [notice([10], daysAgo(0.01))], { ...config, graceDays: 0 }); + expect(verdict.kind).toBe("pending"); + }); + + test("a human reply after the notice keeps the issue open, a bot reply does not", () => { + const human = pendingNotice(issue(35, "t"), [notice([10], daysAgo(5)), humanComment(daysAgo(4))], config); + expect(human).toEqual({ kind: "skip", reason: "someone replied after the notice" }); + const bot = pendingNotice( + issue(35, "t"), + [notice([10], daysAgo(5)), { id: 903, body: "triage", created_at: daysAgo(4), user: { type: "Bot", login: "triage[bot]" } }], + config, + ); + expect(bot.kind).toBe("pending"); + }); + + test("a human quoting the marker is not a notice", () => { + const quoted = pendingNotice(issue(35, "t"), [notice([10], daysAgo(5), { user: { type: "User", login: "reporter" } })], config); + expect(quoted).toEqual({ kind: "skip", reason: "carries no duplicate notice" }); + }); + + test("never closes an issue twice: a reopened issue is left alone", () => { + const reopened = pendingNotice( + issue(35, "t"), + [notice([10], daysAgo(9)), { id: 904, body: `Closed automatically\n\n${CLOSED_MARKER}`, created_at: daysAgo(5), user: { type: "Bot", login: "github-actions[bot]" } }], + config, + ); + expect(reopened).toEqual({ kind: "skip", reason: "was reopened after an automatic close" }); + }); + + test("skips pull requests and issues whose only candidates are newer", () => { + expect(pendingNotice(issue(35, "t", { pull_request: {} }), [notice([10], daysAgo(5))], config).kind).toBe("skip"); + expect(pendingNotice(issue(35, "t"), [notice([40], daysAgo(5))], config)).toEqual({ + kind: "skip", + reason: "no candidate is older than this issue", + }); + }); +}); + +describe("duplicateTarget", () => { + const reporter = issue(35, "[Bug]: Gemma 4-e4b fails on Vertex"); + + test("closes only against the earliest open issue with the identical normalized title", () => { + const verdict = duplicateTarget( + reporter, + [issue(10, "[Bug]: Gemma 4-e4n fails on Vertex"), issue(20, "[bug]: gemma 4-e4b fails on vertex"), issue(30, "[Bug]: Gemma 4-e4b fails on Vertex")], + [], + ); + expect(verdict).toEqual({ kind: "close", duplicateOf: 20 }); + }); + + test("a near miss in the title is not a duplicate", () => { + const verdict = duplicateTarget(reporter, [issue(10, "[Bug]: Gemma 4-e4n fails on Vertex")], []); + expect(verdict).toEqual({ kind: "skip", reason: "no older open issue has the identical title" }); + }); + + test("bare template titles never match each other", () => { + const verdict = duplicateTarget(issue(35, "[Bug]: "), [issue(10, "[Bug]: ")], []); + expect(verdict.kind).toBe("skip"); + expect(verdict.kind === "skip" && verdict.reason).toContain("too short"); + }); + + test("a closed candidate or a pull request is never the target", () => { + expect(duplicateTarget(reporter, [issue(10, reporter.title, { state: "closed" })], []).kind).toBe("skip"); + expect(duplicateTarget(reporter, [issue(10, reporter.title, { pull_request: {} })], []).kind).toBe("skip"); + }); + + test("a thumbs down on the notice keeps the issue open", () => { + const verdict = duplicateTarget(reporter, [issue(10, reporter.title)], [{ content: "+1" }, { content: "-1" }]); + expect(verdict).toEqual({ kind: "skip", reason: "someone gave the notice a thumbs down" }); + }); +}); + +describe("sweepIssue", () => { + const reporter = issue(35, "[Bug]: Gemma 4-e4b fails on Vertex"); + const original = issue(10, "[Bug]: Gemma 4-e4b fails on Vertex"); + + function fakeApi( + comments: readonly Comment[] = [notice([10], daysAgo(5))], + reactionsByNotice: Readonly> = {}, + ): { readonly api: GitHubApi; readonly writes: readonly string[] } { + const writes: string[] = []; + const api: GitHubApi = { + request: async (method: string, path: string, body?: object): Promise => { + if (method !== "GET") { + writes.push(`${method} ${path} ${JSON.stringify(body)}`); + return {} as T; + } + if (path.startsWith("/repos/BerriAI/litellm/issues/35/comments")) { + return comments as T; + } + const reactionsPath = path.match(/^\/repos\/BerriAI\/litellm\/issues\/comments\/(\d+)\/reactions/); + if (reactionsPath) { + return (reactionsByNotice[Number(reactionsPath[1])] ?? []) as T; + } + if (path === "/repos/BerriAI/litellm/issues/10") { + return original as T; + } + throw new Error(`unexpected GET ${path}`); + }, + }; + return { api, writes }; + } + + test("a dry run reports the close and writes nothing", async () => { + const { api, writes } = fakeApi(); + const verdict = await sweepIssue(api, { ...config, dryRun: true }, reporter); + expect(verdict).toEqual({ kind: "close", duplicateOf: 10 }); + expect(writes).toEqual([]); + }); + + test("a thumbs down on an earlier notice still keeps the issue open", async () => { + const { api, writes } = fakeApi([notice([10], daysAgo(9)), notice([10], daysAgo(5), { id: 902 })], { 900: [{ content: "-1" }] }); + const verdict = await sweepIssue(api, config, reporter); + expect(verdict).toEqual({ kind: "skip", reason: "someone gave the notice a thumbs down" }); + expect(writes).toEqual([]); + }); + + test("a real run comments, labels, then closes with the duplicate reason", async () => { + const { api, writes } = fakeApi(); + const verdict = await sweepIssue(api, config, reporter); + expect(verdict).toEqual({ kind: "close", duplicateOf: 10 }); + expect(writes.map((write) => write.split(" ").slice(0, 2).join(" "))).toEqual([ + "POST /repos/BerriAI/litellm/issues/35/comments", + "POST /repos/BerriAI/litellm/issues/35/labels", + "PATCH /repos/BerriAI/litellm/issues/35", + ]); + expect(writes[0]).toContain("duplicate of #10"); + expect(writes[0]).toContain("unanswered for 3 days"); + expect(writes[0]).toContain(CLOSED_MARKER); + expect(writes[1]).toContain('{"labels":["duplicate"]}'); + expect(writes[2]).toContain('{"state":"closed","state_reason":"duplicate"}'); + }); +}); + +describe("reopenTarget", () => { + const closedByBot = (overrides: Partial = {}): Issue => + issue(35, "t", { state: "closed", closed_by: { type: "Bot" }, ...overrides }); + const closeMarker = (createdAt: string): Comment => ({ + id: 905, + body: `Closed automatically as a duplicate of #10.\n\n${CLOSED_MARKER}`, + created_at: createdAt, + user: { type: "Bot", login: "github-actions[bot]" }, + }); + + test("a reporter reply after the automatic close reopens", () => { + const verdict = reopenTarget(closedByBot(), [closeMarker(daysAgo(2)), humanComment(daysAgo(1))]); + expect(verdict).toEqual({ kind: "reopen" }); + }); + + test("an issue closed by a person stays closed", () => { + const verdict = reopenTarget(closedByBot({ closed_by: { type: "User" } }), [ + closeMarker(daysAgo(2)), + humanComment(daysAgo(1)), + ]); + expect(verdict).toEqual({ kind: "skip", reason: "was closed by a person" }); + }); + + test("without the automatic-close marker nothing reopens", () => { + const verdict = reopenTarget(closedByBot(), [humanComment(daysAgo(1))]); + expect(verdict).toEqual({ kind: "skip", reason: "carries no automatic-close marker" }); + }); + + test("a maintainer reply alone does not reopen", () => { + const verdict = reopenTarget(closedByBot(), [ + closeMarker(daysAgo(2)), + humanComment(daysAgo(1), "Confirmed duplicate", "maintainer"), + ]); + expect(verdict).toEqual({ kind: "skip", reason: "the reporter has not replied since the close" }); + }); + + test("a reporter comment from before the close does not reopen", () => { + const verdict = reopenTarget(closedByBot(), [humanComment(daysAgo(3)), closeMarker(daysAgo(2))]); + expect(verdict).toEqual({ kind: "skip", reason: "the reporter has not replied since the close" }); + }); + + test("a pull request never reopens", () => { + const verdict = reopenTarget(closedByBot({ pull_request: {} }), [closeMarker(daysAgo(2)), humanComment(daysAgo(1))]); + expect(verdict).toEqual({ kind: "skip", reason: "is a pull request" }); + }); +}); + +describe("sweepClosedIssue", () => { + function fakeApi(issueBody: Issue, comments: readonly Comment[]): { readonly api: GitHubApi; readonly writes: readonly string[] } { + const writes: string[] = []; + const api: GitHubApi = { + request: async (method: string, path: string, body?: object): Promise => { + if (method !== "GET") { + writes.push(`${method} ${path} ${JSON.stringify(body)}`); + return {} as T; + } + if (path.startsWith("/repos/BerriAI/litellm/issues/35/comments")) { + return comments as T; + } + if (path === "/repos/BerriAI/litellm/issues/35") { + return issueBody as T; + } + throw new Error(`unexpected GET ${path}`); + }, + }; + return { api, writes }; + } + + const closedByBot = issue(35, "t", { state: "closed", closed_by: { type: "Bot" } }); + const closeMarker: Comment = { + id: 905, + body: `Closed automatically as a duplicate of #10.\n\n${CLOSED_MARKER}`, + created_at: daysAgo(2), + user: { type: "Bot", login: "github-actions[bot]" }, + }; + + test("a real run unlabels, reopens, then explains", async () => { + const { api, writes } = fakeApi(closedByBot, [closeMarker, humanComment(daysAgo(1))]); + const verdict = await sweepClosedIssue(api, config, 35); + expect(verdict).toEqual({ kind: "reopen" }); + expect(writes).toEqual([ + "DELETE /repos/BerriAI/litellm/issues/35/labels/duplicate undefined", + 'PATCH /repos/BerriAI/litellm/issues/35 {"state":"open"}', + `POST /repos/BerriAI/litellm/issues/35/comments {"body":"${REOPEN_COMMENT}"}`, + ]); + }); + + test("a dry run reports the reopen and writes nothing", async () => { + const { api, writes } = fakeApi(closedByBot, [closeMarker, humanComment(daysAgo(1))]); + const verdict = await sweepClosedIssue(api, { ...config, dryRun: true }, 35); + expect(verdict).toEqual({ kind: "reopen" }); + expect(writes).toEqual([]); + }); +}); + +describe("readConfig", () => { + test("defaults to a real run with a 3-day grace period", () => { + const parsed = readConfig({ GITHUB_TOKEN: "t", GITHUB_REPOSITORY: "BerriAI/litellm" }, NOW); + expect(parsed).toEqual({ token: "t", repo: "BerriAI/litellm", graceDays: 3, dryRun: false, now: NOW }); + }); + + test("honors DRY_RUN and GRACE_PERIOD_DAYS overrides", () => { + const parsed = readConfig( + { GITHUB_TOKEN: "t", GITHUB_REPOSITORY: "o/r", DRY_RUN: "true", GRACE_PERIOD_DAYS: "0" }, + NOW, + ); + expect(parsed.dryRun).toBe(true); + expect(parsed.graceDays).toBe(0); + }); + + test("an empty GRACE_PERIOD_DAYS, as a schedule run renders it, means the default", () => { + const parsed = readConfig({ GITHUB_TOKEN: "t", GITHUB_REPOSITORY: "o/r", GRACE_PERIOD_DAYS: "" }, NOW); + expect(parsed.graceDays).toBe(3); + }); + + test("refuses a missing token, a malformed repository, or a bad grace period", () => { + expect(() => readConfig({ GITHUB_REPOSITORY: "o/r" }, NOW)).toThrow("GITHUB_TOKEN"); + expect(() => readConfig({ GITHUB_TOKEN: "t", GITHUB_REPOSITORY: "litellm" }, NOW)).toThrow("owner/repo"); + expect(() => readConfig({ GITHUB_TOKEN: "t", GITHUB_REPOSITORY: "o/r", GRACE_PERIOD_DAYS: "-1" }, NOW)).toThrow( + "GRACE_PERIOD_DAYS", + ); + }); +}); diff --git a/scripts/auto-close-duplicates.ts b/scripts/auto-close-duplicates.ts new file mode 100644 index 00000000000..c595104d886 --- /dev/null +++ b/scripts/auto-close-duplicates.ts @@ -0,0 +1,300 @@ +#!/usr/bin/env bun + +declare const process: { readonly env: Readonly> }; + +export interface Issue { + readonly number: number; + readonly title: string; + readonly state: string; + readonly user: { readonly login: string }; + readonly closed_by?: { readonly type: string } | null; + readonly pull_request?: unknown; +} + +export interface Comment { + readonly id: number; + readonly body: string; + readonly created_at: string; + readonly user: { readonly type: string; readonly login: string }; +} + +export interface Reaction { + readonly content: string; +} + +export interface GitHubApi { + readonly request: (method: "GET" | "POST" | "PATCH" | "DELETE", path: string, body?: object) => Promise; +} + +export interface SweepConfig { + readonly repo: string; + readonly graceDays: number; + readonly dryRun: boolean; + readonly now: Date; +} + +export type NoticeVerdict = + | { readonly kind: "pending"; readonly notices: readonly Comment[]; readonly candidates: readonly number[] } + | { readonly kind: "skip"; readonly reason: string }; + +export type CloseVerdict = + | { readonly kind: "close"; readonly duplicateOf: number } + | { readonly kind: "skip"; readonly reason: string }; + +export type ReopenVerdict = + | { readonly kind: "reopen" } + | { readonly kind: "skip"; readonly reason: string }; + +export const FLAG_LABEL = "potential-duplicate"; +export const CLOSED_MARKER = ""; +export const DEFAULT_GRACE_DAYS = 3; +export const REOPEN_COMMENT = + "Reopened automatically: the reporter replied after the duplicate close, so this needs a human look."; +const NOTICE_MARKER = //; +const MIN_TITLE_WORDS = 3; +const PAGE_SIZE = 100; +const DAY_MS = 24 * 60 * 60 * 1000; +const REOPEN_LOOKBACK_DAYS = 30; + +const skip = (reason: string): { readonly kind: "skip"; readonly reason: string } => ({ kind: "skip", reason }); + +export function normalizeTitle(title: string): string { + return title + .toLowerCase() + .replace(/^\s*\[[^\]]*\]\s*:?/, "") + .replace(/[^a-z0-9]+/g, " ") + .trim(); +} + +export function candidateNumbers(noticeBody: string, issueNumber: number): readonly number[] { + const field = noticeBody.match(NOTICE_MARKER); + if (!field) { + return []; + } + const older = field[1] + .split(",") + .filter((value) => value !== "") + .map(Number) + .filter((candidate) => candidate < issueNumber); + return [...new Set(older)].sort((a, b) => a - b); +} + +export function pendingNotice( + issue: Issue, + comments: readonly Comment[], + config: Pick, +): NoticeVerdict { + if (issue.pull_request !== undefined) { + return skip("is a pull request"); + } + if (comments.some((comment) => comment.body.includes(CLOSED_MARKER))) { + return skip("was reopened after an automatic close"); + } + const notices = comments.filter((comment) => comment.user.type === "Bot" && NOTICE_MARKER.test(comment.body)); + const first = notices[0]; + const latest = notices[notices.length - 1]; + if (first === undefined || latest === undefined) { + return skip("carries no duplicate notice"); + } + const ageDays = (config.now.getTime() - new Date(latest.created_at).getTime()) / DAY_MS; + if (ageDays < config.graceDays) { + return skip(`notice is ${ageDays.toFixed(1)} days old, grace period is ${config.graceDays}`); + } + const firstNoticeAt = new Date(first.created_at); + if (comments.some((comment) => comment.user.type !== "Bot" && new Date(comment.created_at) > firstNoticeAt)) { + return skip("someone replied after the notice"); + } + const candidates = candidateNumbers(latest.body, issue.number); + if (candidates.length === 0) { + return skip("no candidate is older than this issue"); + } + return { kind: "pending", notices, candidates }; +} + +export function duplicateTarget( + issue: Issue, + candidates: readonly Issue[], + reactions: readonly Reaction[], +): CloseVerdict { + if (reactions.some((reaction) => reaction.content === "-1")) { + return skip("someone gave the notice a thumbs down"); + } + const title = normalizeTitle(issue.title); + if (title.split(" ").length < MIN_TITLE_WORDS) { + return skip(`title "${issue.title}" is too short to match on`); + } + const original = candidates.find( + (candidate) => + candidate.state === "open" && candidate.pull_request === undefined && normalizeTitle(candidate.title) === title, + ); + if (original === undefined) { + return skip("no older open issue has the identical title"); + } + return { kind: "close", duplicateOf: original.number }; +} + +export function reopenTarget(issue: Issue, comments: readonly Comment[]): ReopenVerdict { + if (issue.pull_request !== undefined) { + return skip("is a pull request"); + } + if (issue.closed_by?.type !== "Bot") { + return skip("was closed by a person"); + } + const marker = comments.find((comment) => comment.body.includes(CLOSED_MARKER)); + if (marker === undefined) { + return skip("carries no automatic-close marker"); + } + const markerAt = new Date(marker.created_at); + if (!comments.some((comment) => comment.user.login === issue.user.login && new Date(comment.created_at) > markerAt)) { + return skip("the reporter has not replied since the close"); + } + return { kind: "reopen" }; +} + +export function closingComment(duplicateOf: number, graceDays: number): string { + return `Closed automatically as a duplicate of #${duplicateOf}. Its title is identical to that older open issue and the duplicate notice above went unanswered for ${graceDays} days. If this is wrong, comment here with how it differs from #${duplicateOf} and this issue will be reopened automatically within a day. + +${CLOSED_MARKER}`; +} + +async function listAll(api: GitHubApi, path: string, page = 1): Promise { + const separator = path.includes("?") ? "&" : "?"; + const batch = await api.request("GET", `${path}${separator}per_page=${PAGE_SIZE}&page=${page}`); + return batch.length < PAGE_SIZE ? batch : [...batch, ...(await listAll(api, path, page + 1))]; +} + +async function closeAsDuplicate( + api: GitHubApi, + config: SweepConfig, + issueNumber: number, + duplicateOf: number, +): Promise { + const issuePath = `/repos/${config.repo}/issues/${issueNumber}`; + await api.request("POST", `${issuePath}/comments`, { body: closingComment(duplicateOf, config.graceDays) }); + await api.request("POST", `${issuePath}/labels`, { labels: ["duplicate"] }); + await api.request("PATCH", issuePath, { state: "closed", state_reason: "duplicate" }); +} + +async function reopenForReporter(api: GitHubApi, config: SweepConfig, issueNumber: number): Promise { + const issuePath = `/repos/${config.repo}/issues/${issueNumber}`; + await api.request("DELETE", `${issuePath}/labels/duplicate`); + await api.request("PATCH", issuePath, { state: "open" }); + await api.request("POST", `${issuePath}/comments`, { body: REOPEN_COMMENT }); +} + +export async function sweepClosedIssue(api: GitHubApi, config: SweepConfig, issueNumber: number): Promise { + const issue = await api.request("GET", `/repos/${config.repo}/issues/${issueNumber}`); + const comments = await listAll(api, `/repos/${config.repo}/issues/${issueNumber}/comments`); + const verdict = reopenTarget(issue, comments); + if (verdict.kind === "reopen" && !config.dryRun) { + await reopenForReporter(api, config, issueNumber); + } + return verdict; +} + +export async function sweepIssue(api: GitHubApi, config: SweepConfig, issue: Issue): Promise { + const comments = await listAll(api, `/repos/${config.repo}/issues/${issue.number}/comments`); + const pending = pendingNotice(issue, comments, config); + if (pending.kind === "skip") { + return pending; + } + const reactions = ( + await Promise.all( + pending.notices.map((notice) => listAll(api, `/repos/${config.repo}/issues/comments/${notice.id}/reactions`)), + ) + ).flat(); + const candidates = await Promise.all( + pending.candidates.map((candidate) => api.request("GET", `/repos/${config.repo}/issues/${candidate}`)), + ); + const verdict = duplicateTarget(issue, candidates, reactions); + if (verdict.kind === "close" && !config.dryRun) { + await closeAsDuplicate(api, config, issue.number, verdict.duplicateOf); + } + return verdict; +} + +function describe(issue: Issue, verdict: CloseVerdict, dryRun: boolean): string { + if (verdict.kind === "skip") { + return `#${issue.number}: skipped, ${verdict.reason}`; + } + return `#${issue.number}: ${dryRun ? "would close" : "closed"} as a duplicate of #${verdict.duplicateOf}`; +} + +export async function sweep(api: GitHubApi, config: SweepConfig): Promise { + const issues = await listAll(api, `/repos/${config.repo}/issues?state=open&labels=${FLAG_LABEL}`); + console.log(`${issues.length} open issues carry the ${FLAG_LABEL} label in ${config.repo}${config.dryRun ? " (dry run)" : ""}`); + return issues.reduce>(async (previous, issue) => { + const verdicts = await previous; + const verdict = await sweepIssue(api, config, issue); + console.log(describe(issue, verdict, config.dryRun)); + return [...verdicts, verdict]; + }, Promise.resolve([])); +} + +function describeReopen(issueNumber: number, verdict: ReopenVerdict, dryRun: boolean): string { + if (verdict.kind === "skip") { + return `#${issueNumber}: skipped, ${verdict.reason}`; + } + return `#${issueNumber}: ${dryRun ? "would reopen" : "reopened"} for the reporter's reply`; +} + +export async function reopenSweep(api: GitHubApi, config: SweepConfig): Promise { + const since = new Date(config.now.getTime() - REOPEN_LOOKBACK_DAYS * DAY_MS).toISOString(); + const closedPath = `/repos/${config.repo}/issues?state=closed&labels=duplicate,${FLAG_LABEL}&since=${encodeURIComponent(since)}`; + const issues = await listAll(api, closedPath); + console.log(`${issues.length} recently closed issues carry the duplicate and ${FLAG_LABEL} labels in ${config.repo}${config.dryRun ? " (dry run)" : ""}`); + return issues.reduce>(async (previous, issue) => { + const verdicts = await previous; + const verdict = await sweepClosedIssue(api, config, issue.number); + console.log(describeReopen(issue.number, verdict, config.dryRun)); + return [...verdicts, verdict]; + }, Promise.resolve([])); +} + +export function readConfig(env: Readonly>, now: Date): SweepConfig & { readonly token: string } { + const token = env.GITHUB_TOKEN; + const repo = env.GITHUB_REPOSITORY; + if (!token || !repo || !/^[\w.-]+\/[\w.-]+$/.test(repo)) { + throw new Error("GITHUB_TOKEN and GITHUB_REPOSITORY (owner/repo) are required"); + } + const rawGraceDays = env.GRACE_PERIOD_DAYS?.trim(); + const graceDays = rawGraceDays === undefined || rawGraceDays === "" ? DEFAULT_GRACE_DAYS : Number(rawGraceDays); + if (!Number.isFinite(graceDays) || graceDays < 0) { + throw new Error(`GRACE_PERIOD_DAYS must be a non-negative number, got "${env.GRACE_PERIOD_DAYS}"`); + } + return { token, repo, graceDays, dryRun: env.DRY_RUN === "true", now }; +} + +export function githubApi(token: string): GitHubApi { + return { + request: async (method: "GET" | "POST" | "PATCH" | "DELETE", path: string, body?: object): Promise => { + const response = await fetch(`https://api.github.com${path}`, { + method, + headers: { + Authorization: `Bearer ${token}`, + Accept: "application/vnd.github+json", + "X-GitHub-Api-Version": "2022-11-28", + "User-Agent": "litellm-auto-close-duplicates", + ...(body === undefined ? {} : { "Content-Type": "application/json" }), + }, + body: body === undefined ? undefined : JSON.stringify(body), + }); + if (!response.ok) { + throw new Error(`${method} ${path} failed: ${response.status} ${response.statusText}`); + } + return (await response.json()) as T; + }, + }; +} + +if (import.meta.main) { + const { token, ...config } = readConfig(process.env, new Date()); + const api = githubApi(token); + const closeVerdicts = await sweep(api, config); + const reopenVerdicts = await reopenSweep(api, config); + const closed = closeVerdicts.filter((verdict) => verdict.kind === "close").length; + const reopened = reopenVerdicts.filter((verdict) => verdict.kind === "reopen").length; + console.log( + `${config.dryRun ? "Would close" : "Closed"} ${closed} of ${closeVerdicts.length} flagged issues, ${config.dryRun ? "would reopen" : "reopened"} ${reopened}`, + ); +} diff --git a/scripts/sync_together_ai_models.py b/scripts/sync_together_ai_models.py index 97b308fb660..12b128890f1 100644 --- a/scripts/sync_together_ai_models.py +++ b/scripts/sync_together_ai_models.py @@ -187,9 +187,22 @@ CAPABILITY_RULES: Final = ( _rule("thinkingmachines/Inkling-Small", "reviewed for the LIT-5968 backfill; no tool or vision support documented"), _rule( "zai-org/GLM-5.2", - "reviewed for the LIT-5968 backfill against https://www.together.ai/models/glm-5-2", + "reviewed for the LIT-5968 backfill against https://www.together.ai/models/glm-5-2;" + " 128K output ceiling per https://docs.z.ai/guides/llm/glm-5.2", **_TOOLS, supports_reasoning=True, + max_output_tokens=128000, + max_tokens=128000, + ), + _rule( + "zai-org/GLM-5.3-Flash", + "reviewed for LIT-6489 against https://www.together.ai/models/glm-5-3-flash;" + " 128K output ceiling per https://docs.z.ai/guides/llm/glm-5.3", + **_TOOLS, + supports_reasoning=True, + supports_vision=True, + max_output_tokens=128000, + max_tokens=128000, ), ) @@ -288,15 +301,10 @@ def _api_fields(model: CatalogModel) -> RegistryEntry: def _new_entry(model: CatalogModel, mode: str) -> RegistryEntry: rule: Final = RULES_BY_ID.get(model.id) - length_fields: Final = ( - {} - if model.context_length is None - else {"max_input_tokens": model.context_length, "max_tokens": model.context_length} - | ({"max_output_tokens": model.context_length} if mode == "chat" else {}) - ) + legacy_ceiling: Final = {} if model.context_length is None else {"max_tokens": model.context_length} merged: Final = { **_api_fields(model), - **length_fields, + **legacy_ceiling, "litellm_provider": PROVIDER, "mode": mode, "source": SOURCE_URL, diff --git a/terraform/provider/tools/endpointaudit/coverage_allowlist.txt b/terraform/provider/tools/endpointaudit/coverage_allowlist.txt index d10be89b90c..052962e078e 100644 --- a/terraform/provider/tools/endpointaudit/coverage_allowlist.txt +++ b/terraform/provider/tools/endpointaudit/coverage_allowlist.txt @@ -126,3 +126,6 @@ POST /customer/delete # known gap: litellm_customer GET /team/{team_id}/callback # known gap: team callback resource POST /team/{team_id}/callback # known gap: team callback resource DELETE /team/{team_id}/callback/{callback_name} # known gap: team callback resource +GET /access_group/{access_group}/budget # known gap: budget attributes on litellm_access_group +PUT /access_group/{access_group}/budget # known gap: budget attributes on litellm_access_group +DELETE /access_group/{access_group}/budget # known gap: budget attributes on litellm_access_group diff --git a/test-quality-budget.json b/test-quality-budget.json index ee33eb581d6..d834c581609 100644 --- a/test-quality-budget.json +++ b/test-quality-budget.json @@ -3,7 +3,7 @@ "limit": 733 }, "TQ002": { - "limit": 742 + "limit": 741 }, "TQ003": { "limit": 62 @@ -21,6 +21,6 @@ "limit": 117 }, "TQ008": { - "limit": 11139 + "limit": 11135 } } diff --git a/tests/batches_tests/test_batch_custom_pricing.py b/tests/batches_tests/test_batch_custom_pricing.py index c2159b564a8..b76b865862a 100644 --- a/tests/batches_tests/test_batch_custom_pricing.py +++ b/tests/batches_tests/test_batch_custom_pricing.py @@ -116,16 +116,16 @@ def test_aggregate_batch_cost_uses_custom_model_info(): """_aggregate_batch_cost_usage_models should thread model_info to batch_cost_calculator.""" file_content = [_make_batch_output_line(prompt_tokens=10, completion_tokens=5)] - cost, _, _ = _aggregate_batch_cost_usage_models( + result = _aggregate_batch_cost_usage_models( entries=file_content, custom_llm_provider="openai", model_info=CUSTOM_MODEL_INFO, ) expected = (10 * 0.00125) + (5 * 0.005) - assert cost == pytest.approx( + assert result.cost == pytest.approx( expected - ), f"Expected total cost {expected}, got {cost}" + ), f"Expected total cost {expected}, got {result.cost}" @pytest.mark.parametrize("data_residency", ["eu", "us"]) @@ -164,15 +164,15 @@ async def test_calculate_batch_cost_and_usage_uses_custom_model_info(): """calculate_batch_cost_and_usage should thread model_info.""" file_content = [_make_batch_output_line(prompt_tokens=10, completion_tokens=5)] - batch_cost, batch_usage, batch_models = await calculate_batch_cost_and_usage( + result = await calculate_batch_cost_and_usage( file_content_dictionary=file_content, custom_llm_provider="openai", model_info=CUSTOM_MODEL_INFO, ) expected = (10 * 0.00125) + (5 * 0.005) - assert batch_cost == pytest.approx( + assert result.cost == pytest.approx( expected - ), f"Expected total cost {expected}, got {batch_cost}" - assert batch_usage.prompt_tokens == 10 - assert batch_usage.completion_tokens == 5 + ), f"Expected total cost {expected}, got {result.cost}" + assert result.usage.prompt_tokens == 10 + assert result.usage.completion_tokens == 5 diff --git a/tests/batches_tests/test_batch_rate_limits.py b/tests/batches_tests/test_batch_rate_limits.py index b44b8435cd9..7cbcfc1aeb1 100644 --- a/tests/batches_tests/test_batch_rate_limits.py +++ b/tests/batches_tests/test_batch_rate_limits.py @@ -1027,7 +1027,7 @@ async def test_batch_logging_azure_credentials_regression(): with patch( "litellm.files.main.afile_content", side_effect=mock_afile_content_tracker ): - cost, usage, models = await _handle_completed_batch( + result = await _handle_completed_batch( batch=mock_batch, custom_llm_provider="azure", litellm_params=azure_credentials, @@ -1039,13 +1039,13 @@ async def test_batch_logging_azure_credentials_regression(): ], "REGRESSION: Credentials not passed through _handle_completed_batch" # Verify cost and usage were calculated - assert cost > 0, "Cost should be calculated" - assert usage.total_tokens == 40, "Usage should be calculated correctly" + assert result.cost > 0, "Cost should be calculated" + assert result.usage.total_tokens == 40, "Usage should be calculated correctly" print(" ✓ Credentials passed through full flow") - print(f" ✓ Cost: {cost}") - print(f" ✓ Usage: {usage.total_tokens} tokens") - print(f" ✓ Models: {models}") + print(f" ✓ Cost: {result.cost}") + print(f" ✓ Usage: {result.usage.total_tokens} tokens") + print(f" ✓ Models: {result.models}") # Test 4: Verify error prevention print("\n4. Testing 'Missing credentials' error prevention...") @@ -1064,7 +1064,7 @@ async def test_batch_logging_azure_credentials_regression(): "litellm.files.main.afile_content", side_effect=mock_afile_content_tracker ): try: - cost, usage, models = await _handle_completed_batch( + result = await _handle_completed_batch( batch=mock_batch, custom_llm_provider="azure", litellm_params=azure_credentials, diff --git a/tests/batches_tests/test_batches_logging_unit_tests.py b/tests/batches_tests/test_batches_logging_unit_tests.py index 5211b3ecb29..5bde40d90b0 100644 --- a/tests/batches_tests/test_batches_logging_unit_tests.py +++ b/tests/batches_tests/test_batches_logging_unit_tests.py @@ -133,12 +133,12 @@ def test_get_file_content_as_dictionary(sample_file_content): def test_get_batch_job_total_usage_from_file_content(sample_file_content_dict): with patch("litellm.completion_cost", return_value=0.0): - _, usage, _ = _aggregate_batch_cost_usage_models( + result = _aggregate_batch_cost_usage_models( entries=sample_file_content_dict, custom_llm_provider="openai" ) - assert usage.total_tokens == 62 # 30 + 32 - assert usage.prompt_tokens == 42 # 20 + 22 - assert usage.completion_tokens == 20 # 10 + 10 + assert result.usage.total_tokens == 62 # 30 + 32 + assert result.usage.prompt_tokens == 42 # 20 + 22 + assert result.usage.completion_tokens == 20 # 10 + 10 @pytest.mark.asyncio @@ -151,11 +151,11 @@ async def test_batch_cost_calculator(sample_file_content_dict): so we expect the cost to be 0.5 * 2 = 1.0 """ with patch("litellm.completion_cost", return_value=0.5): - cost, _, _ = _aggregate_batch_cost_usage_models( + result = _aggregate_batch_cost_usage_models( entries=sample_file_content_dict, custom_llm_provider="openai", ) - assert cost == 1.0 # 0.5 * 2 successful responses + assert result.cost == 1.0 # 0.5 * 2 successful responses def test_get_response_from_batch_job_output_file(sample_file_content_dict): @@ -221,6 +221,8 @@ async def test_batch_retrieve_cost_tracking_with_completed_batch_no_explicit_cos logging_obj.custom_llm_provider = "openai" # Mock _handle_completed_batch to return cost data + from litellm.batches.batch_utils import BatchCostUsageResult + expected_cost = 0.05 expected_usage = litellm.Usage( prompt_tokens=100, @@ -231,7 +233,15 @@ async def test_batch_retrieve_cost_tracking_with_completed_batch_no_explicit_cos with patch( "litellm.litellm_core_utils.litellm_logging._handle_completed_batch", - new=AsyncMock(return_value=(expected_cost, expected_usage, expected_models)), + new=AsyncMock( + return_value=BatchCostUsageResult( + cost=expected_cost, + usage=expected_usage, + models=expected_models, + successful_requests=10, + failed_requests=0, + ) + ), ) as mock_handle_batch: # Call async_success_handler await logging_obj.async_success_handler( @@ -246,6 +256,8 @@ async def test_batch_retrieve_cost_tracking_with_completed_batch_no_explicit_cos # Verify cost and usage were set on the batch result assert mock_batch._hidden_params["response_cost"] == expected_cost assert mock_batch._hidden_params["batch_models"] == expected_models + assert mock_batch._hidden_params["batch_successful_requests"] == 10 + assert mock_batch._hidden_params["batch_failed_requests"] == 0 assert mock_batch.usage == expected_usage @@ -279,7 +291,7 @@ async def test_handle_completed_batch_computes_real_cost_from_output_file( "litellm.batches.batch_utils._fetch_batch_output_file_content", new=AsyncMock(return_value=sample_file_content_bytes), ): - cost, usage, models = await _handle_completed_batch( + result = await _handle_completed_batch( batch=batch, custom_llm_provider="openai" ) @@ -289,16 +301,18 @@ async def test_handle_completed_batch_computes_real_cost_from_output_file( + 20 * pricing["output_cost_per_token_batches"] ) - assert cost == pytest.approx(expected_cost) - assert cost > 0 + assert result.cost == pytest.approx(expected_cost) + assert result.cost > 0 assert ( - cost + result.cost < 42 * pricing["input_cost_per_token"] + 20 * pricing["output_cost_per_token"] ) - assert usage.prompt_tokens == 42 - assert usage.completion_tokens == 20 - assert usage.total_tokens == 62 - assert models == ["gpt-4o-mini-2024-07-18", "gpt-4o-mini-2024-07-18"] + assert result.usage.prompt_tokens == 42 + assert result.usage.completion_tokens == 20 + assert result.usage.total_tokens == 62 + assert result.models == ["gpt-4o-mini-2024-07-18", "gpt-4o-mini-2024-07-18"] + assert result.successful_requests == 2 + assert result.failed_requests == 0 @pytest.mark.asyncio @@ -537,9 +551,19 @@ async def test_batch_retrieve_cost_tracking_with_partial_explicit_data(): ) expected_models = ["gpt-5-mini"] + from litellm.batches.batch_utils import BatchCostUsageResult + with patch( "litellm.litellm_core_utils.litellm_logging._handle_completed_batch", - new=AsyncMock(return_value=(expected_cost, expected_usage, expected_models)), + new=AsyncMock( + return_value=BatchCostUsageResult( + cost=expected_cost, + usage=expected_usage, + models=expected_models, + successful_requests=8, + failed_requests=0, + ) + ), ) as mock_handle_batch: # Call async_success_handler with partial explicit data await logging_obj.async_success_handler( @@ -555,4 +579,6 @@ async def test_batch_retrieve_cost_tracking_with_partial_explicit_data(): # Verify computed cost data was used (not partial explicit data) assert mock_batch._hidden_params["response_cost"] == expected_cost assert mock_batch._hidden_params["batch_models"] == expected_models + assert mock_batch._hidden_params["batch_successful_requests"] == 8 + assert mock_batch._hidden_params["batch_failed_requests"] == 0 assert mock_batch.usage == expected_usage diff --git a/tests/code_coverage_tests/recursive_detector.py b/tests/code_coverage_tests/recursive_detector.py index b15a16ffc23..790956156b0 100644 --- a/tests/code_coverage_tests/recursive_detector.py +++ b/tests/code_coverage_tests/recursive_detector.py @@ -57,11 +57,13 @@ IGNORE_FUNCTIONS = [ "_filter_argument_value", # max depth set (DEFAULT_MAX_RECURSE_DEPTH); fails closed by blocking the tool call at the cap. "_redact_scanned_content", # max depth set (DEFAULT_MAX_RECURSE_DEPTH); fails closed by returning "[REDACTED]" at the cap. "_iter_fallback_targets", # max depth set (2 * ROUTER_MAX_FALLBACKS); fails closed by raising ValueError at the cap. + "_mergeable_branch", # max depth set (_MAX_SCHEMA_FLATTEN_DEPTH=32) plus a seen_refs cycle guard; passes the schema through untouched at the cap. "json_string_leaves", # max depth set (MAX_STRUCTURED_CONTENT_SCAN_DEPTH); fails closed by raising at the cap so nothing goes unscanned. "with_json_string_leaves", # transitively bounded: only runs on a tree json_string_leaves already walked under the cap. "json_unrewritable_labels", # max depth set (MAX_STRUCTURED_CONTENT_SCAN_DEPTH); returns the None sentinel at the cap so the caller blocks. "_flatten_form_field", # bounded by the nesting depth of the already-parsed request body (a finite JSON tree, no cycles possible). "_flatten_form_data_field", # bounded by the nesting depth of the already-parsed request body (a finite JSON tree, no cycles possible). + "_json_safe", # max depth set (_MAX_DEPTH) plus a seen-ids cycle guard for self-referential input. ] diff --git a/tests/code_coverage_tests/router_code_coverage.py b/tests/code_coverage_tests/router_code_coverage.py index c541c035db7..a5e00799519 100644 --- a/tests/code_coverage_tests/router_code_coverage.py +++ b/tests/code_coverage_tests/router_code_coverage.py @@ -81,6 +81,7 @@ ignored_function_names = [ "_merge_tools_from_deployment", # Tested indirectly via _update_kwargs_with_deployment (test files lack "router" in name) "_invalidate_access_groups_cache", # Tested indirectly via set_model_list, upsert_model etc. (test files lack "router" in name) "has_buffered_provider_output", # Property, so its reads in test_router.py are never an ast.Call + "_resolved_provider", # Tested via get_pattern in test_pattern_match_deployments.py (file lacks "router" in name) ] diff --git a/tests/documentation_tests/test_env_keys.py b/tests/documentation_tests/test_env_keys.py index b91c404b2eb..3652378503e 100644 --- a/tests/documentation_tests/test_env_keys.py +++ b/tests/documentation_tests/test_env_keys.py @@ -33,6 +33,13 @@ EXCLUDED_ROLLOUT_FLAGS = { "LITELLM_RUST", } +# Internal infrastructure tuning parameters for streaming/queue management +# These are advanced settings with sensible defaults that most users should not modify +EXCLUDED_INTERNAL_TUNING_VARS = { + "ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS", + "ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE", +} + EXCLUDED_TERMINAL_VARS = { "TERM", "TERM_PROGRAM", @@ -50,7 +57,9 @@ EXCLUDED_TERMINAL_VARS = { "ALACRITTY_SOCKET", } -EXCLUDED_KEYS = frozenset(EXCLUDED_TERMINAL_VARS | EXCLUDED_GUARD_ONLY_VARS | EXCLUDED_ROLLOUT_FLAGS) +EXCLUDED_KEYS = frozenset( + EXCLUDED_TERMINAL_VARS | EXCLUDED_GUARD_ONLY_VARS | EXCLUDED_ROLLOUT_FLAGS | EXCLUDED_INTERNAL_TUNING_VARS +) # Directories to skip (dependencies, venvs, caches) - only scan litellm source SKIP_DIRS = { diff --git a/tests/e2e/CLAUDE.md b/tests/e2e/CLAUDE.md index cf912ddaa25..14b2b4e3299 100644 --- a/tests/e2e/CLAUDE.md +++ b/tests/e2e/CLAUDE.md @@ -177,15 +177,15 @@ quota_management... behavior : ratelimit | budget | spend_tracking variant : rpm | tpm | priority_generous | priority_strict key | internal_user | end_user | organization | team | team_member | tag - | model_max | soft | key_multi_window | team_multi_window - | fallback | spend_counter + | model_access_group | model_max | soft | key_multi_window + | team_multi_window | fallback | spend_counter chat_completions | stream | messages_bridge | embeddings | cache_hit | key_rollup | concurrent_burst | tags | end_user | per_model | failure | spend_calculate | pagination assertion : blocks_over_limit | resets_after_window | headers_report_remaining | picks_under_tpm | blocks_then_resets | resets_windows_independently | alerts_without_blocking - | isolates_per_model | isolates_per_member | enforced_across_keys | routes_to_fallback - | reseed_matches_db | logs_cost | zero_cost + | isolates_per_model | isolates_per_member | isolates_per_group | enforced_across_keys + | routes_to_fallback | reseed_matches_db | reports_spend | logs_cost | zero_cost | matches_sum_of_logs | loses_no_spend | attributes_spend | writes_own_rows | writes_failure_row | returns_cost | keeps_total e.g. quota_management.ratelimit.rpm.blocks_over_limit exercised_on=[chat_completions, messages] diff --git a/tests/e2e/claude_code/_driver_unit_tests/__init__.py b/tests/e2e/claude_code/_driver_unit_tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/e2e/claude_code/_driver_unit_tests/test_retry_classification.py b/tests/e2e/claude_code/_driver_unit_tests/test_retry_classification.py new file mode 100644 index 00000000000..868110addb6 --- /dev/null +++ b/tests/e2e/claude_code/_driver_unit_tests/test_retry_classification.py @@ -0,0 +1,74 @@ +"""Unit tests for the retry-shape classification in `cli_driver`. + +Markerless harness tests: they exercise driver plumbing over hand-built +outcomes, not a product feature, so they run without a proxy and carry no +`e2e` marker. + +The pairing that matters is that a saturated upstream is retryable but is not +rate-limit-shaped. litellm-e2e-pr build 182 failed a green cell on a Bedrock +503 that no pattern matched, while feeding a 503 to the rate-limit summary +would tell the rate-limiter's binary search to lower a request rate that was +never the problem. +""" + +from __future__ import annotations + +import pytest + +from claude_code.cli_driver import ( + ClaudeCLIError, + DriverResult, + is_rate_limit_shaped, + is_retryable_shaped, + is_transient_upstream_shaped, +) + +_BEDROCK_503 = ( + "[claude-opus-4-7-bedrock-converse] tool_search probe failed: status 503: " + '{"error":{"message":"litellm.ServiceUnavailableError: BedrockException - ' + '{\\"message\\":\\"Bedrock is unable to process your request.\\"}"}}' +) +_ANTHROPIC_529 = "status 529: {\"type\":\"overloaded_error\"}" +_OPENAI_429 = 'status 429: {"error":{"message":"Rate limit reached"}}' + + +def _failed(text: str) -> DriverResult: + return DriverResult(text=text, exit_code=1) + + +@pytest.mark.parametrize( + "text, rate_limit, transient", + [ + (_BEDROCK_503, False, True), + (_ANTHROPIC_529, False, True), + ("status 503 service unavailable", False, True), + ("upstream overloaded, try again later", False, True), + (_OPENAI_429, True, False), + ("throttling exception from provider", True, False), + ("claude CLI timed out after 120s", True, False), + ('status 400: {"error":"bad request"}', False, False), + ], +) +def test_shapes_are_classified_independently(text: str, rate_limit: bool, transient: bool) -> None: + outcome = _failed(text) + assert is_rate_limit_shaped(outcome) is rate_limit + assert is_transient_upstream_shaped(outcome) is transient + assert is_retryable_shaped(outcome) is (rate_limit or transient) + + +def test_bedrock_503_is_retryable_but_not_rate_limit_shaped() -> None: + outcome = _failed(_BEDROCK_503) + assert is_retryable_shaped(outcome) + assert not is_rate_limit_shaped(outcome) + + +def test_passing_outcome_is_never_retryable() -> None: + passed = DriverResult(text=_BEDROCK_503, exit_code=0) + assert not is_retryable_shaped(passed) + assert not is_transient_upstream_shaped(passed) + + +def test_driver_error_message_is_classified() -> None: + assert is_transient_upstream_shaped(ClaudeCLIError("upstream returned 503")) + assert is_rate_limit_shaped(ClaudeCLIError("claude CLI timed out")) + assert not is_retryable_shaped(ClaudeCLIError("binary not found")) diff --git a/tests/e2e/claude_code/cli_driver.py b/tests/e2e/claude_code/cli_driver.py index 5b18c1c291a..447e8cc0bbb 100644 --- a/tests/e2e/claude_code/cli_driver.py +++ b/tests/e2e/claude_code/cli_driver.py @@ -52,6 +52,17 @@ the CLI retries 429s internally until the harness timeout kills it, so a saturated upstream usually surfaces as a timeout rather than a clean 429.""" +TRANSIENT_UPSTREAM_SHAPED_RE = re.compile( + r"(?:\b503\b|\b529\b|service[\s_-]?unavailable|overloaded|" + r"unable\s+to\s+process\s+your\s+request)", + re.IGNORECASE, +) +"""Upstream saturation, retried on the same terms as a 429 but deliberately a +separate pattern: it must not reach the rate-limit summary, whose only remedy is +lowering our own request rate, which does nothing for a provider that is simply +out of capacity.""" + + DEFAULT_RATE_LIMIT_RETRIES = int( os.environ.get("LITELLM_COMPAT_RATE_LIMIT_RETRIES") or 2 ) @@ -298,11 +309,27 @@ def is_rate_limit_shaped(outcome: ModelResult) -> bool: CLI's stdout text or `api_error_status` are both caught. Passing results are never rate-limit-shaped. """ + return _matches_failure_shape(outcome, RATE_LIMIT_SHAPED_RE) + + +def is_transient_upstream_shaped(outcome: ModelResult) -> bool: + """Classify an outcome as a retryable upstream-saturation failure: a 503 or + 529, an "overloaded" marker, or Bedrock's "unable to process your request".""" + return _matches_failure_shape(outcome, TRANSIENT_UPSTREAM_SHAPED_RE) + + +def is_retryable_shaped(outcome: ModelResult) -> bool: + """Either retryable shape. This, not `is_rate_limit_shaped`, is what the + retry loop asks: both shapes clear on their own given time.""" + return is_rate_limit_shaped(outcome) or is_transient_upstream_shaped(outcome) + + +def _matches_failure_shape(outcome: ModelResult, pattern: "re.Pattern[str]") -> bool: if isinstance(outcome, ClaudeCLIError): - return bool(RATE_LIMIT_SHAPED_RE.search(str(outcome))) + return bool(pattern.search(str(outcome))) if outcome.exit_code == 0: return False - return bool(RATE_LIMIT_SHAPED_RE.search(failure_diagnostic(outcome))) + return bool(pattern.search(failure_diagnostic(outcome))) def run_claude_models_parallel( @@ -333,7 +360,7 @@ def run_claude_models_parallel( keep the synchronous CLI driver unchanged so unit tests can keep injecting a fake `runner`. - Rate-limit-shaped failures (see `is_rate_limit_shaped`) are retried + Retryable failures (see `is_retryable_shaped`) are retried per model up to `rate_limit_retries` times, sleeping `rate_limit_backoff_seconds` before each retry so per-minute quota windows can reset; both default to the `LITELLM_COMPAT_RATE_LIMIT_*` @@ -401,10 +428,11 @@ def run_claude_models_parallel( started = time.monotonic() outcome = _run_once(model) for attempt in range(retries): - if not is_rate_limit_shaped(outcome): + if not is_retryable_shaped(outcome): break + shape = "rate-limit" if is_rate_limit_shaped(outcome) else "transient-upstream" print( - f"[retry] {model}: rate-limit-shaped failure; sleeping " + f"[retry] {model}: {shape}-shaped failure; sleeping " f"{backoff:.0f}s before attempt {attempt + 2}/{retries + 1}", file=sys.stderr, flush=True, diff --git a/tests/e2e/coverage_registry/mgmt.yaml b/tests/e2e/coverage_registry/mgmt.yaml index 1e6de0c3d6a..d571fb36546 100644 --- a/tests/e2e/coverage_registry/mgmt.yaml +++ b/tests/e2e/coverage_registry/mgmt.yaml @@ -64,14 +64,12 @@ - {id: mgmt.budget.list_v1.happy_path, module: mgmt, tier: P1, surface: api, assertions: [happy_path], source: "management_v1/budgets.py:129", rationale: "Budget enumeration the Budgets page can page, sort and filter"} - {id: mgmt.budget.list_v1.admin_only, module: mgmt, tier: P1, surface: api, assertions: [admin_only], source: "management_v1/budgets.py:129", rationale: "A caller without admin view is refused, not served an empty page"} - {id: mgmt.callback.list.happy_path, module: mgmt, tier: P2, surface: api, assertions: [happy_path], source: "callback_management_endpoints.py", rationale: "Callback config (smoke)"} -- {id: mgmt.cache_settings.update.happy_path, module: mgmt, tier: P2, surface: api, assertions: [happy_path], source: "cache_settings_endpoints.py", rationale: "Cache config (smoke). Deliberately uncovered: the previous test read the live settings and wrote them back, which proves nothing (identical values in, so a no-op POST still passes) while being able to break the deployment. /cache/settings persists what it receives and that row outranks YAML cache_params, re-applied on a timer, so a write that omits ssl or redis_startup_nodes turns a TLS cluster into a plaintext standalone node and every later Redis call hangs. That took out 60 of 72 tests on 2026-07-25. GET cannot round-trip it either: it resolves the stored row overlaid with REDIS_* env and never reads YAML, so on a fresh deploy it cannot see YAML ssl to echo back. A safe test needs an isolated proxy, or LIT-4816 fixed so a partial write cannot downgrade transport. Do not re-add a read-then-write-back test against a shared proxy."} - {id: mgmt.cost_tracking.estimate.happy_path, module: mgmt, tier: P2, surface: api, assertions: [happy_path], source: "cost_tracking_settings.py", rationale: "Cost estimate (smoke)"} - {id: mgmt.router_settings.update.happy_path, module: mgmt, tier: P2, surface: api, assertions: [happy_path], source: "router_settings_endpoints.py", rationale: "Router config (smoke)"} - {id: mgmt.jwt_key_mapping.new.happy_path, module: mgmt, tier: P2, surface: api, assertions: [happy_path], source: "jwt_key_mapping_endpoints.py", rationale: "JWT->key mapping (smoke)"} - {id: mgmt.compliance.gdpr.happy_path, module: mgmt, tier: P2, surface: api, assertions: [happy_path], source: "compliance_endpoints.py", rationale: "GDPR ops (smoke)"} - {id: mgmt.tool_management.list.happy_path, module: mgmt, tier: P2, surface: api, assertions: [happy_path], source: "tool_management_endpoints.py", rationale: "Tool inventory (smoke)"} - {id: mgmt.fallback_management.update.happy_path, module: mgmt, tier: P2, surface: api, assertions: [happy_path], source: "fallback_management_endpoints.py", rationale: "Fallback config (smoke)"} -- {id: mgmt.config_override.hashicorp_vault.happy_path, module: mgmt, tier: P2, surface: api, assertions: [happy_path], source: "config_override_endpoints.py", rationale: "Vault integration (smoke)"} - {id: mgmt.workflow.list.happy_path, module: mgmt, tier: P2, surface: api, assertions: [happy_path], source: "workflow_management_endpoints.py", rationale: "Workflow tracking (smoke)"} - {id: mgmt.credential_migration.check.happy_path, module: mgmt, tier: P2, surface: api, assertions: [happy_path], source: "key_management_endpoints.py:4252", rationale: "Encryption migration (smoke)"} - {id: mgmt.credential.new.serves_request, module: mgmt, tier: P1, surface: api, assertions: [serves_request], source: "credential_endpoints/endpoints.py:42", rationale: "Stored credential resolves into a deployment and serves a live /messages request"} diff --git a/tests/e2e/coverage_registry/quota_management.yaml b/tests/e2e/coverage_registry/quota_management.yaml index 42a075681e0..d0afcaca848 100644 --- a/tests/e2e/coverage_registry/quota_management.yaml +++ b/tests/e2e/coverage_registry/quota_management.yaml @@ -20,6 +20,10 @@ - {id: quota_management.budget.organization.blocks_over_limit, module: quota_management, tier: P1, behavior: budget, variant: organization, assertions: [blocks_over_limit], exercised_on: [chat_completions], source: "proxy/auth/auth_checks.py", rationale: "An organization's max_budget blocks keys under its teams"} - {id: quota_management.budget.team_member.blocks_over_limit, module: quota_management, tier: P1, behavior: budget, variant: team_member, assertions: [blocks_over_limit], exercised_on: [chat_completions], source: "proxy/auth/auth_checks.py", rationale: "A member's per-team budget blocks independently of the team budget"} - {id: quota_management.budget.team_member.isolates_per_member, module: quota_management, tier: P1, behavior: budget, variant: team_member, assertions: [isolates_per_member], exercised_on: [chat_completions], source: "proxy/auth/auth_checks.py", rationale: "One team member's exhausted per-team budget does not block a different member on the same team"} +- {id: quota_management.budget.model_access_group.blocks_over_limit, module: quota_management, tier: P1, behavior: budget, variant: model_access_group, assertions: [blocks_over_limit], exercised_on: [chat_completions], source: "proxy/auth/auth_checks.py", rationale: "A model access group's shared max_budget blocks further calls to deployments in the group once the pool is spent"} +- {id: quota_management.budget.model_access_group.enforced_across_keys, module: quota_management, tier: P1, behavior: budget, variant: model_access_group, assertions: [enforced_across_keys], exercised_on: [chat_completions], source: "proxy/auth/auth_checks.py", rationale: "The pool is shared, so a key that spent nothing of its own is blocked once another key granted the same group drained it"} +- {id: quota_management.budget.model_access_group.isolates_per_group, module: quota_management, tier: P1, behavior: budget, variant: model_access_group, assertions: [isolates_per_group], exercised_on: [chat_completions], source: "proxy/db/db_spend_update_writer.py", rationale: "A request is charged only to the granted groups that serve the model it called, so an exhausted group never blocks a sibling group"} +- {id: quota_management.budget.model_access_group.reports_spend, module: quota_management, tier: P2, behavior: budget, variant: model_access_group, assertions: [reports_spend], exercised_on: [chat_completions], source: "proxy/management_endpoints/model_access_group_management_endpoints.py", rationale: "GET /access_group/{name}/budget reports the pool and the spend drawn against it, so an admin can see why calls are being refused"} - {id: quota_management.budget.tag.blocks_over_limit, module: quota_management, tier: P1, behavior: budget, variant: tag, assertions: [blocks_over_limit], exercised_on: [chat_completions], source: "router_strategy/budget_limiter.py", rationale: "Proxy-level tag budgets block tagged requests at the cap"} - {id: quota_management.budget.end_user_model_max.blocks_over_limit, module: quota_management, tier: P1, behavior: budget, variant: end_user_model_max, assertions: [blocks_over_limit], exercised_on: [chat_completions], source: "budget_management_endpoints.py", fail_before_fix: proven, rationale: "A per-model rpm_limit on an end-user budget is accepted and stored but never enforced; only key-attached budgets honour it"} - {id: quota_management.budget.model_max.isolates_per_model, module: quota_management, tier: P1, behavior: budget, variant: model_max, assertions: [isolates_per_model], exercised_on: [chat_completions], source: "proxy/hooks/model_max_budget_limiter.py", rationale: "model_max_budget caps one model without touching a sibling's budget"} diff --git a/tests/e2e/junit_properties.py b/tests/e2e/junit_properties.py index e4f59f5c4d2..c5971c5362c 100644 --- a/tests/e2e/junit_properties.py +++ b/tests/e2e/junit_properties.py @@ -2,10 +2,16 @@ The e2e suite ships results to Loki/Grafana from a standard pytest JUnit report (`--junitxml=e2e-report.xml`), not a bespoke log line. JUnit already records -outcome, duration, and node id for every ``; the only signals it cannot -derive on its own are the normalized suite package and the coverage-registry cell -ids a test covers. Those ride along as JUnit `` entries via each item's -`user_properties`, attached in `conftest.py::pytest_collection_modifyitems`. +outcome, duration, and node id for every ``; the signals it cannot +derive on its own are the normalized suite package, the coverage-registry cell +ids a test covers, and where the test's source lives. Those ride along as JUnit +`` entries via each item's `user_properties`, attached in +`conftest.py::pytest_collection_modifyitems`. + +`source` is a property rather than the `file=` / `line=` attributes pytest used +to write, because the `xunit2` family this suite runs on drops those, and +switching families would change the XML for every consumer of it -- the +Buildkite Test Engine upload and the Loki pipeline included. """ from __future__ import annotations @@ -14,22 +20,61 @@ from collections.abc import Iterable import pytest +# Hardcoded because the runner image copies tests/e2e/ to /app/e2e, so nothing +# at runtime names this suite's place in the repo. test_junit_properties.py +# fails from a checkout if it moves. +SUITE_ROOT = "tests/e2e" + + +def suite_parts(path_part: str) -> tuple[str, ...]: + """Path components of a suite file relative to tests/e2e, however it ran. + + Pytest paths are rootdir-relative, and rootdir moves with the invocation: a + repo-root run gives `tests/e2e/logging/test_x.py`, a suite-cwd run (the + runner image) gives `logging/test_x.py`. Both collapse to the same tuple. + """ + raw = tuple(p for p in path_part.replace("\\", "/").split("/") if p and p != ".") + return raw[2:] if len(raw) >= 3 and raw[0] == "tests" and raw[1] == "e2e" else raw + def package_from_nodeid(nodeid: str) -> str: - """Top-level suite package under tests/e2e/, or 'root' for top-level files. - - Pytest nodeids are relative to the invocation cwd. Repo-root runs look like - `tests/e2e/logging/...`; suite-cwd runs look like `logging/...`. Strip the - `tests/e2e` prefix so package is the suite dir either way. - """ - path_part = nodeid.split("::", 1)[0].replace("\\", "/") - raw = tuple(p for p in path_part.split("/") if p and p != ".") - parts = raw[2:] if len(raw) >= 3 and raw[0] == "tests" and raw[1] == "e2e" else raw + """Top-level suite package under tests/e2e/, or 'root' for top-level files.""" + parts = suite_parts(nodeid.split("::", 1)[0]) if len(parts) <= 1: return "root" return parts[0] +def source_from_location(path: str, lineno: int | None) -> str: + """Repo-relative `path:line` for a test, or '' when nothing is linkable. + + `pytest.Item.location` gives a rootdir-relative path and a ZERO-based line. + The path is re-rooted at SUITE_ROOT so consumers need not know how pytest was + started, and the line is emitted ONE-based to match editors, tracebacks and + code hosts. A decorated test anchors at its first decorator, which is where + pytest reports it. + + Empty rather than a guess for anything unlinkable: no line, a path reaching + upward, or a path carrying a colon, which is both how an absolute Windows + path arrives and a character `path:line` has no way to represent. + """ + if lineno is None: + return "" + normalized = path.replace("\\", "/") + if normalized.startswith("/") or ":" in normalized or ".." in normalized.split("/"): + return "" + parts = suite_parts(normalized) + if not parts: + return "" + return f"{'/'.join((SUITE_ROOT, *parts))}:{lineno + 1}" + + +def source_from_item(item: pytest.Item) -> str: + """Read the repo-relative `path:line` off a pytest Item's reported location.""" + path, lineno, _ = item.location + return source_from_location(path, lineno) + + def dedupe_covers(marker_args: Iterable[tuple[object, ...]]) -> tuple[str, ...]: """Flatten @pytest.mark.covers arg lists into unique, order-preserving cell ids, dropping anything that is not a non-empty string.""" @@ -43,10 +88,12 @@ def covers_from_item(item: pytest.Item) -> tuple[str, ...]: def result_properties(item: pytest.Item) -> tuple[tuple[str, str], ...]: """The custom signals a standard reporter cannot derive: the normalized suite - package and the comma-joined coverage-registry cell ids this test covers.""" + package, the comma-joined coverage-registry cell ids this test covers, and the + repo-relative `path:line` its source sits at.""" return ( ("package", package_from_nodeid(item.nodeid)), ("covers", ",".join(covers_from_item(item))), + ("source", source_from_item(item)), ) diff --git a/tests/e2e/management/test_config_misc_endpoints_e2e.py b/tests/e2e/management/test_config_misc_endpoints_e2e.py index 195732c0201..099ffa4b3bd 100644 --- a/tests/e2e/management/test_config_misc_endpoints_e2e.py +++ b/tests/e2e/management/test_config_misc_endpoints_e2e.py @@ -7,9 +7,13 @@ so a read-back reflects the change. Router settings, which mutate global proxy state, are exercised with a benign, self-restoring change so a shared proxy is left as it was found. -Cache settings are deliberately not covered here; see the rationale on -mgmt.cache_settings.update.happy_path in coverage_registry/mgmt.yaml before adding -a test for that route. +Cache settings and the Vault config override are deliberately not covered here. +Both routes reconfigure the whole proxy: /cache/settings persists what it receives +into a row that outranks the YAML cache_params and is re-applied on a timer, and +/config_overrides/hashicorp_vault swaps the process-wide secret manager. Neither can +be exercised safely against the shared proxy the suites run on, so they need an +isolated proxy before a test lands. Do not add a read-then-write-back test for +either one. """ from __future__ import annotations diff --git a/tests/e2e/management/test_model_tag_accessgroup_e2e.py b/tests/e2e/management/test_model_tag_accessgroup_e2e.py index e6a187ae105..eb3a6093c69 100644 --- a/tests/e2e/management/test_model_tag_accessgroup_e2e.py +++ b/tests/e2e/management/test_model_tag_accessgroup_e2e.py @@ -180,6 +180,12 @@ class ModelBlockBody(BaseModel): model_id: str +class ModelBlockResponse(BaseModel): + model_config = ConfigDict(protected_namespaces=()) + model_id: str + blocked: bool + + class ModelInfoBlockDetail(BaseModel): id: str | None = None blocked: bool | None = None @@ -245,11 +251,6 @@ class TestModelRoutes: def test_block_then_unblock_persists_to_model_info( self, client: ManagementClient, resources: ResourceManager ) -> None: - """The blocked flag's persistence is read back from /model/info, not from the - /model/block response: that route currently returns a non-2xx serialization - envelope even though the DB write lands, so the /model/info read-back is the - authoritative persistence contract and keeps this test valid once the - response shape is fixed.""" model_name = f"e2e-mgmt-model-block-{unique_marker()}" model_id = _create_db_model(client, resources, model_name) @@ -257,27 +258,25 @@ class TestModelRoutes: f"{model_name!r} already reports blocked in /model/info before /model/block ran" ) - _ = client.proxy.transport.send( - "/model/block", - headers=client.proxy.transport.master, - json=ModelBlockBody(model_id=model_id), - ) - _ = _poll( - client.proxy, - lambda: True if _model_blocked_flag(client, model_id) is True else None, - f"/model/info never reported {model_name!r} blocked after /model/block", - ) - - _ = client.proxy.transport.send( - "/model/unblock", - headers=client.proxy.transport.master, - json=ModelBlockBody(model_id=model_id), - ) - _ = _poll( - client.proxy, - lambda: True if _model_blocked_flag(client, model_id) is not True else None, - f"/model/info never cleared blocked for {model_name!r} after /model/unblock", - ) + for action, expected in (("block", True), ("unblock", False)): + response = unwrap( + client.proxy.transport.post( + f"/model/{action}", + headers=client.proxy.transport.master, + json=ModelBlockBody(model_id=model_id), + response_type=ModelBlockResponse, + ) + ) + assert response.model_id == model_id + assert response.blocked is expected + _ = _poll( + client.proxy, + lambda want=expected: True + if _model_blocked_flag(client, model_id) is want + else None, + f"/model/info never reported blocked={expected} for {model_name!r} " + f"after /model/{action}", + ) class TestTagRoutes: diff --git a/tests/e2e/mcp/test_mcp_key_access_e2e.py b/tests/e2e/mcp/test_mcp_key_access_e2e.py index 88ab5666084..68005ae3f6a 100644 --- a/tests/e2e/mcp/test_mcp_key_access_e2e.py +++ b/tests/e2e/mcp/test_mcp_key_access_e2e.py @@ -30,6 +30,34 @@ def _key(client: McpClient, resources: ResourceManager, *, mcp_servers: list[str return key +class TestMcpKeyGrantByAlias: + def test_alias_grant_persists_verbatim_and_lists_tools( + self, + client: McpClient, + resources: ResourceManager, + ) -> None: + """A key granted an MCP server by its alias must store the alias, not the + resolved server_id: in a shared-DB multi-region deployment each instance + derives a different id for the same config server, so only the alias + grants access on every region. The same key must still see the server's + tools, proving the alias grant is honored at request time.""" + server_id = register_datadog_mcp(client, resources) + client.await_registered(server_id) + alias = next(row.alias for row in client.registered_servers() if row.server_id == server_id) + assert alias, f"registered server {server_id} has no alias to grant by" + + key = _key(client, resources, mcp_servers=[alias]) + + stored = client.proxy.key_info(key).object_permission + assert stored is not None and stored.mcp_servers == [alias], ( + f"alias grant was rewritten before persisting (expected [{alias!r}]): " + f"{stored.mcp_servers if stored else None}. A stored server_id is region-local " + f"and breaks the grant on every other instance sharing this database" + ) + + _ = client.await_tool(key, server_id, SEARCH_LOGS_TOOL) + + class TestMcpKeyWithoutAccessIsDenied: @pytest.mark.covers("mcp.list_tools.api_key.denied_without_permission") def test_list_tools_denied_without_permission( diff --git a/tests/e2e/models.py b/tests/e2e/models.py index 56b6a7a7055..79d9e011f7e 100644 --- a/tests/e2e/models.py +++ b/tests/e2e/models.py @@ -114,6 +114,7 @@ class KeyInfo(BaseModel): budget_id: str | None = None litellm_budget_table: LiteLLMBudgetTable | None = None budget_limits: list[BudgetWindowState] | None = None + object_permission: ObjectPermission | None = None class KeyInfoResponse(BaseModel): @@ -333,6 +334,7 @@ class OutMessage(BaseModel): class ChatChoice(BaseModel): message: OutMessage | None = None + finish_reason: str | None = None class PromptTokensDetails(BaseModel): @@ -804,6 +806,7 @@ class LiteLLMParamsBody(BaseModel): mock_response: str | None = None timeout: float | None = None tpm: int | None = None + weight: int | None = None ModelMode = Literal["batch", "realtime", "image_generation"] @@ -818,6 +821,7 @@ class ModelInfoBody(BaseModel): mode: ModelMode | None = None access_groups: list[str] | None = None team_id: str | None = None + allowed_fails_policy: dict[str, int] | None = None class ModelNewBody(BaseModel): diff --git a/tests/e2e/quota_management/budgets/budget_client.py b/tests/e2e/quota_management/budgets/budget_client.py index 543d5f959e0..087dc8ca522 100644 --- a/tests/e2e/quota_management/budgets/budget_client.py +++ b/tests/e2e/quota_management/budgets/budget_client.py @@ -151,6 +151,28 @@ class TagDeleteBody(BaseModel): name: str +class AccessGroupBudgetBody(BaseModel): + max_budget: float | None = None + soft_budget: float | None = None + budget_duration: str | None = None + + +class AccessGroupBudgetView(BaseModel): + budget_id: str + max_budget: float | None = None + soft_budget: float | None = None + budget_duration: str | None = None + + +class AccessGroupBudgetResponse(BaseModel): + """GET/PUT /access_group/{name}/budget: the group's shared pool and the spend + every key that can reach the group has drawn against it.""" + + access_group: str + spend: float + budget: AccessGroupBudgetView | None = None + + class BudgetNewBody(BaseModel): max_budget: float | None = None soft_budget: float | None = None @@ -514,6 +536,49 @@ class BudgetClient: response_type=NoBody, ) + # ---- model access group --------------------------------------------- + + def set_access_group_budget( + self, + access_group: str, + *, + max_budget: float | None = None, + soft_budget: float | None = None, + budget_duration: str | None = None, + ) -> AccessGroupBudgetResponse: + """Give a model access group one shared budget. Every key that can reach a + deployment in the group draws from it.""" + return unwrap( + self.proxy.transport.put( + f"/access_group/{access_group}/budget", + headers=self.proxy.transport.master, + json=AccessGroupBudgetBody( + max_budget=max_budget, + soft_budget=soft_budget, + budget_duration=budget_duration, + ), + response_type=AccessGroupBudgetResponse, + ) + ) + + def access_group_budget(self, access_group: str) -> AccessGroupBudgetResponse: + return unwrap( + self.proxy.transport.get( + f"/access_group/{access_group}/budget", + headers=self.proxy.transport.master, + params=NoBody(), + response_type=AccessGroupBudgetResponse, + ) + ) + + def delete_access_group_budget(self, access_group: str) -> None: + _ = self.proxy.transport.delete( + f"/access_group/{access_group}/budget", + headers=self.proxy.transport.master, + json=NoBody(), + response_type=NoBody, + ) + # ---- budget table --------------------------------------------------- def create_budget( diff --git a/tests/e2e/quota_management/budgets/test_model_access_group_budget_e2e.py b/tests/e2e/quota_management/budgets/test_model_access_group_budget_e2e.py new file mode 100644 index 00000000000..9c927a31216 --- /dev/null +++ b/tests/e2e/quota_management/budgets/test_model_access_group_budget_e2e.py @@ -0,0 +1,161 @@ +"""Live e2e: one shared budget across every key that can reach a model access group. + +A model access group is a free-text label on a deployment (`model_info.access_groups`), +and a key is granted the group by name. The budget hangs off the group, not the key, so +the interesting behaviors are the ones a per-key budget cannot produce: a key that has +spent nothing of its own is refused once somebody else drained the pool, and draining one +group leaves a second group untouched, because a request is only charged to the groups +the caller was granted that also serve the model being called. +""" + +from __future__ import annotations + +import os +import time +from collections.abc import Iterator +from dataclasses import dataclass +from typing import Final + +import pytest + +from budget_client import BudgetClient, is_budget_block +from e2e_config import unique_marker +from e2e_http import StreamingResponse, require_successful_call +from lifecycle import ResourceManager +from models import KeyGenerateBody, LiteLLMParamsBody, ModelInfoBody, ModelNewBody + +pytestmark = pytest.mark.e2e + +BACKEND: Final = "openai/gpt-5.4-nano" +TINY_BUDGET: Final = 5e-6 +MAX_TOKENS: Final = 16 +DRAIN_TIMEOUT_SECONDS: Final = 180 + + +@dataclass(frozen=True, slots=True) +class DrainedPool: + """A model access group whose shared budget has been spent to exhaustion, the + deployment inside it, the key that did the spending, and a second group holding + its own deployment that was never given a budget at all.""" + + access_group: str + model: str + spender_key: str + free_access_group: str + free_model: str + + +def _provider_key(env_var: str) -> str: + return os.environ.get(env_var) or f"os.environ/{env_var}" + + +def _grouped_model(model_name: str, access_group: str) -> ModelNewBody: + return ModelNewBody( + model_name=model_name, + litellm_params=LiteLLMParamsBody(model=BACKEND, api_key=_provider_key("OPENAI_API_KEY")), + model_info=ModelInfoBody(access_groups=[access_group]), + ) + + +def _call(client: BudgetClient, key: str, model: str) -> StreamingResponse: + return client.chat(key, model, f"hi {unique_marker()}", max_tokens=MAX_TOKENS) + + +def _drain(client: BudgetClient, key: str, model: str, access_group: str) -> None: + """Spend the group's pool until the proxy refuses the next request. The first call + lands under the cap and the block comes from the spend it recorded, so this needs at + least one round trip through the spend writer, not just one request.""" + deadline: Final = time.monotonic() + DRAIN_TIMEOUT_SECONDS + while time.monotonic() < deadline: + result = _call(client, key, model) + if is_budget_block(result): + return + require_successful_call(result) + time.sleep(1) + pytest.fail(f"budget on model access group {access_group!r} never blocked a request") + + +@pytest.fixture(scope="module") +def drained(client: BudgetClient) -> Iterator[DrainedPool]: + marker: Final = unique_marker() + pool: Final = DrainedPool( + access_group=f"e2e-mag-budget-{marker}", + model=f"e2e-mag-budgeted-{marker}", + spender_key=client.proxy.generate_key(KeyGenerateBody(models=[f"e2e-mag-budget-{marker}"])), + free_access_group=f"e2e-mag-free-{marker}", + free_model=f"e2e-mag-unbudgeted-{marker}", + ) + created: Final = ( + client.proxy.register_model(_grouped_model(pool.model, pool.access_group)), + client.proxy.register_model(_grouped_model(pool.free_model, pool.free_access_group)), + ) + try: + client.set_access_group_budget(pool.access_group, max_budget=TINY_BUDGET) + _drain(client, pool.spender_key, pool.model, pool.access_group) + yield pool + finally: + client.delete_access_group_budget(pool.access_group) + client.proxy.delete_key(pool.spender_key) + for model_id in created: + client.proxy.delete_model(model_id) + + +class TestModelAccessGroupBudget: + @pytest.mark.covers("quota_management.budget.model_access_group.blocks_over_limit") + def test_the_key_that_drained_the_pool_stays_blocked( + self, client: BudgetClient, drained: DrainedPool + ) -> None: + result = _call(client, drained.spender_key, drained.model) + assert is_budget_block(result), ( + f"an exhausted pool served {drained.model!r} again: {result.status_code} {result.body[:300]}" + ) + assert drained.access_group in result.body, ( + f"the block did not name the group that caused it: {result.body[:300]}" + ) + + @pytest.mark.covers("quota_management.budget.model_access_group.enforced_across_keys") + def test_a_key_that_spent_nothing_is_blocked_by_the_shared_pool( + self, client: BudgetClient, resources: ResourceManager, drained: DrainedPool + ) -> None: + newcomer = resources.key(models=[drained.access_group]) + + result = _call(client, newcomer, drained.model) + + assert is_budget_block(result), ( + "a freshly minted key with no spend of its own was served by an exhausted " + f"shared pool: {result.status_code} {result.body[:300]}" + ) + + @pytest.mark.covers("quota_management.budget.model_access_group.isolates_per_group") + def test_a_drained_group_does_not_block_a_different_group( + self, client: BudgetClient, resources: ResourceManager, drained: DrainedPool + ) -> None: + other = resources.key(models=[drained.free_access_group]) + + result = _call(client, other, drained.free_model) + + assert not is_budget_block(result), ( + f"{drained.free_access_group!r} has no budget of its own but was blocked by " + f"{drained.access_group!r}'s exhausted pool: {result.body[:300]}" + ) + require_successful_call(result) + + @pytest.mark.covers("quota_management.budget.model_access_group.reports_spend") + def test_the_budget_read_reports_the_spend_drawn_against_the_pool( + self, client: BudgetClient, drained: DrainedPool + ) -> None: + """Enforcement runs off a live counter while the group's row is written by the + batched spend writer, so the recorded spend an admin reads lands a beat after the + block. Poll for it: what matters is that it arrives and matches the pool.""" + deadline = time.monotonic() + client.proxy.poll_timeout + reported = client.access_group_budget(drained.access_group) + while reported.spend < TINY_BUDGET and time.monotonic() < deadline: + time.sleep(client.proxy.poll_interval) + reported = client.access_group_budget(drained.access_group) + + assert reported.budget is not None, "the group lost the budget that just blocked it" + assert reported.budget.max_budget == TINY_BUDGET + assert reported.spend >= TINY_BUDGET, ( + f"the pool blocked at {TINY_BUDGET} but only {reported.spend} was ever recorded " + f"against the group within {client.proxy.poll_timeout}s" + ) diff --git a/tests/e2e/router/reliability_support.py b/tests/e2e/router/reliability_support.py index cc1c91c635b..5822058003c 100644 --- a/tests/e2e/router/reliability_support.py +++ b/tests/e2e/router/reliability_support.py @@ -19,6 +19,8 @@ from models import ( ChatMessage, ChatResponse, LiteLLMParamsBody, + ModelInfoBody, + ModelNewBody, ReliabilityChatBody, RouterSettingsOverride, ) @@ -26,6 +28,18 @@ from models import ( REAL_MODEL = "openai/gpt-5.5" REAL_KEY = "os.environ/OPENAI_API_KEY" +# The smallest-context chat model OpenAI still serves (16385 tokens). A prompt +# past that limit comes back as a real `context_length_exceeded` 400, which is +# what litellm maps to ContextWindowExceededError. +SMALL_CONTEXT_MODEL = "openai/gpt-3.5-turbo" +SMALL_CONTEXT_LIMIT_TOKENS = 16385 + + +def oversized_prompt(marker: str) -> str: + """A prompt comfortably past SMALL_CONTEXT_MODEL's context limit, so the + provider refuses it on length rather than answering a truncated version.""" + return f"{marker} " + ("token " * (SMALL_CONTEXT_LIMIT_TOKENS + 4000)) + def create_bad_base_deployment(proxy: ProxyClient, name: str) -> str: """Register a deployment pointing at an unreachable base, so every call to it @@ -40,6 +54,38 @@ def create_timeout_deployment(proxy: ProxyClient, name: str) -> str: return proxy.create_model(name, LiteLLMParamsBody(model=REAL_MODEL, api_key=REAL_KEY, timeout=0.001)) +def create_small_context_deployment(proxy: ProxyClient, name: str) -> str: + """Register a deployment on the smallest-context model OpenAI still serves, so an + oversized prompt earns a real context-window refusal from the provider.""" + return proxy.create_model(name, LiteLLMParamsBody(model=SMALL_CONTEXT_MODEL, api_key=REAL_KEY)) + + +def create_always_timing_out_deployment(proxy: ProxyClient, name: str) -> str: + """The always-picked half of a retry pair: a 1ms deadline the backend always + exceeds, all of the model group's shuffle weight, and a cooldown policy that + benches it on its first Timeout so the retry cannot land on it again.""" + return proxy.register_model( + ModelNewBody( + model_name=name, + litellm_params=LiteLLMParamsBody(model=REAL_MODEL, api_key=REAL_KEY, timeout=0.001, weight=1), + model_info=ModelInfoBody(allowed_fails_policy={"TimeoutErrorAllowedFails": 0}), + ) + ) + + +def create_zero_weight_backup_deployment(proxy: ProxyClient, name: str) -> str: + """The other half of a retry pair: healthy, but weight 0, so the weighted shuffle + never opens on it. It is reachable only once its sibling is benched and the + weighted pick falls through to a uniform one over what is left.""" + return proxy.register_model( + ModelNewBody( + model_name=name, + litellm_params=LiteLLMParamsBody(model=REAL_MODEL, api_key=REAL_KEY, weight=0), + model_info=ModelInfoBody(), + ) + ) + + def chat_override( proxy: ProxyClient, key: str, @@ -57,7 +103,7 @@ def chat_override( json=ReliabilityChatBody( model=model, messages=[ChatMessage(role="user", content=content)], - max_tokens=64, + max_tokens=512, stream=stream, router_settings_override=override, cache=cache, @@ -66,14 +112,39 @@ def chat_override( ) +def _parsed(resp: StreamingResponse) -> ChatResponse | None: + try: + return ChatResponse.model_validate_json(resp.body) + except ValidationError: + return None + + def content_of(resp: StreamingResponse) -> str | None: """The assistant message content of a successful chat response, or None when the body is not a success shape (an error body, or an elided streamed body).""" - try: - parsed = ChatResponse.model_validate_json(resp.body) - except ValidationError: - return None - if not parsed.choices: + parsed = _parsed(resp) + if parsed is None or not parsed.choices: return None message = parsed.choices[0].message return message.content if message is not None else None + + +def finish_reason_of(resp: StreamingResponse) -> str | None: + parsed = _parsed(resp) + if parsed is None or not parsed.choices: + return None + return parsed.choices[0].finish_reason + + +def completion_tokens_of(resp: StreamingResponse) -> int | None: + parsed = _parsed(resp) + if parsed is None or parsed.usage is None: + return None + return parsed.usage.completion_tokens + + +def reasoning_tokens_of(resp: StreamingResponse) -> int | None: + parsed = _parsed(resp) + if parsed is None or parsed.usage is None or parsed.usage.completion_tokens_details is None: + return None + return parsed.usage.completion_tokens_details.reasoning_tokens diff --git a/tests/e2e/router/test_reliability_fallbacks_e2e.py b/tests/e2e/router/test_reliability_fallbacks_e2e.py index fe2d924ae2c..8cece41ce2d 100644 --- a/tests/e2e/router/test_reliability_fallbacks_e2e.py +++ b/tests/e2e/router/test_reliability_fallbacks_e2e.py @@ -3,9 +3,16 @@ healthy one. Each test registers a primary deployment that fails (an unreachable base URL, or a 1ms deadline) and calls it with a `router_settings_override` mapping it to the -real `gpt-5.5`. The proof the fallback fired is twofold: the response is a real -completion from `gpt-5.5` (a non-empty content string), and the proxy reports at -least one attempted fallback in the x-litellm-attempted-fallbacks header. +real `gpt-5.5`. The proof the fallback fired is twofold: the response is a +completion from `gpt-5.5`, and the proxy reports at least one attempted fallback +in the x-litellm-attempted-fallbacks header. Empty content is accepted only when +`finish_reason == "length"` and the response billed completion tokens, since +gpt-5.5 counts reasoning against max_tokens and can consume the whole budget +before emitting any text; a fallback that produced nothing at all still fails. + +The context-window case is a different reroute from a plain failure: the provider +refuses the prompt on length, and `context_window_fallbacks` is the setting that +reroutes it, not `fallbacks`. """ from __future__ import annotations @@ -19,9 +26,14 @@ from lifecycle import ResourceManager from models import RouterSettingsOverride from reliability_support import ( chat_override, + completion_tokens_of, content_of, create_bad_base_deployment, + create_small_context_deployment, create_timeout_deployment, + finish_reason_of, + oversized_prompt, + reasoning_tokens_of, ) pytestmark = pytest.mark.e2e @@ -30,8 +42,17 @@ pytestmark = pytest.mark.e2e def _assert_served_by_fallback(resp: StreamingResponse) -> None: assert resp.status_code == 200, f"expected 200 after fallback, got {resp.status_code}: {resp.body[:300]}" content = content_of(resp) - assert isinstance(content, str) and content, ( - f"the gpt-5.5 fallback should have returned a real completion, got content {content!r} " + finish_reason = finish_reason_of(resp) + completion_tokens = completion_tokens_of(resp) or 0 + reasoning_tokens = reasoning_tokens_of(resp) or 0 + assert isinstance(content, str), ( + f"the gpt-5.5 fallback should have returned a completion body, got content {content!r} " + f"(body={resp.body[:300]})" + ) + assert content or (finish_reason == "length" and completion_tokens > 0), ( + f"the gpt-5.5 fallback returned empty content with finish_reason={finish_reason!r}, " + f"completion_tokens={completion_tokens}, reasoning_tokens={reasoning_tokens}; empty " + f"content is only acceptable when the budget was spent on non-visible reasoning " f"(body={resp.body[:300]})" ) attempted = resp.headers.get("x-litellm-attempted-fallbacks") @@ -67,3 +88,17 @@ class TestReliabilityFallbacks: override=RouterSettingsOverride(fallbacks=[{primary: ["gpt-5.5"]}]), ) _assert_served_by_fallback(resp) + + @pytest.mark.covers("reliability.fallback.context_window.routes_to_fallback") + def test_context_window_routes_to_fallback( + self, client: ComplexityRouterClient, resources: ResourceManager, scoped_key: str + ) -> None: + primary = f"reliability-ctxfail-{unique_marker()}" + model_id = create_small_context_deployment(client.proxy, primary) + resources.defer(lambda: client.proxy.delete_model(model_id)) + + resp = chat_override( + client.proxy, scoped_key, primary, oversized_prompt(unique_marker()), + override=RouterSettingsOverride(context_window_fallbacks=[{primary: ["gpt-5.5"]}]), + ) + _assert_served_by_fallback(resp) diff --git a/tests/e2e/router/test_reliability_retries_e2e.py b/tests/e2e/router/test_reliability_retries_e2e.py new file mode 100644 index 00000000000..5441412935c --- /dev/null +++ b/tests/e2e/router/test_reliability_retries_e2e.py @@ -0,0 +1,73 @@ +"""Live e2e: a request that fails on its first deployment is retried inside its own +model group and still comes back a completion. + +The model group is a pair: an always-timing-out deployment that holds all of the +group's shuffle weight, and a healthy backup at weight 0. The weighted pick always +opens on the timing-out one, its first Timeout benches it (an +`allowed_fails_policy` of `TimeoutErrorAllowedFails: 0`), and the retry falls +through to the only deployment left. So the customer sees a completion and the +proxy reports that it took a retry to get there, with no random first pick in the +middle of it. +""" + +from __future__ import annotations + +import pytest + +from complexity_router_client import ComplexityRouterClient +from e2e_config import unique_marker +from lifecycle import ResourceManager +from models import RouterSettingsOverride +from reliability_support import ( + chat_override, + completion_tokens_of, + content_of, + create_always_timing_out_deployment, + create_zero_weight_backup_deployment, + finish_reason_of, +) + +pytestmark = pytest.mark.e2e + + +class TestReliabilityRetries: + @pytest.mark.covers("reliability.retry.timeout.succeeds_within_retries") + def test_timeout_on_first_deployment_succeeds_on_retry( + self, client: ComplexityRouterClient, resources: ResourceManager, scoped_key: str + ) -> None: + group = f"reliability-retry-{unique_marker()}" + timing_out = create_always_timing_out_deployment(client.proxy, group) + resources.defer(lambda: client.proxy.delete_model(timing_out)) + backup = create_zero_weight_backup_deployment(client.proxy, group) + resources.defer(lambda: client.proxy.delete_model(backup)) + + resp = chat_override( + client.proxy, + scoped_key, + group, + f"say hi {unique_marker()}", + override=RouterSettingsOverride(num_retries=2), + ) + + assert resp.status_code == 200, ( + f"the retry should have landed on the healthy backup, got {resp.status_code}: {resp.body[:300]}" + ) + + attempted = resp.headers.get("x-litellm-attempted-retries") + assert attempted is not None, "response is missing the x-litellm-attempted-retries header" + assert int(attempted) >= 1, ( + f"x-litellm-attempted-retries is {attempted!r}; a 200 with no retry means the request never " + "opened on the timing-out deployment, so this proves nothing about retries" + ) + + content = content_of(resp) + finish_reason = finish_reason_of(resp) + completion_tokens = completion_tokens_of(resp) or 0 + assert isinstance(content, str), ( + f"the retry should have returned a completion body, got content {content!r} (body={resp.body[:300]})" + ) + assert content or (finish_reason == "length" and completion_tokens > 0), ( + f"the retry returned empty content with finish_reason={finish_reason!r}, " + f"completion_tokens={completion_tokens}; empty content is only acceptable when the budget " + f"was spent on non-visible reasoning (body={resp.body[:300]})" + ) diff --git a/tests/e2e/test_junit_properties.py b/tests/e2e/test_junit_properties.py new file mode 100644 index 00000000000..c0596177cc1 --- /dev/null +++ b/tests/e2e/test_junit_properties.py @@ -0,0 +1,146 @@ +"""Harness coverage for the custom JUnit properties. + +No proxy and no ``e2e`` marker. Pins the two normalizations that have to agree +about where a suite file lives -- ``package_from_nodeid`` (strip the suite root) +and ``source_from_location`` (re-root at it) -- across both ways the suite is +launched, plus the one-based line offset and the refusal to emit a path that +escapes the suite. The consumers of these properties are the Loki/Grafana +rollups and, for ``source``, the status page's per-test links to GitHub. +""" + +from __future__ import annotations + +from pathlib import Path + +import pytest +from junit_properties import ( + SUITE_ROOT, + attach_result_properties, + dedupe_covers, + package_from_nodeid, + result_properties, + source_from_location, + suite_parts, +) + + +class FakeMarker: + def __init__(self, name: str, *args: object) -> None: + self.name = name + self.args = args + + +class FakeItem: + """The three attributes junit_properties reads off a pytest Item.""" + + def __init__( + self, nodeid: str, location: tuple[str, int | None, str], markers: tuple[FakeMarker, ...] = () + ) -> None: + self.nodeid = nodeid + self.location = location + self.user_properties: list[tuple[str, str]] = [] + self._markers = markers + + def iter_markers(self, name: str): + return (marker for marker in self._markers if marker.name == name) + + +def repo_root() -> Path | None: + """The litellm checkout above this file, or None when there isn't one.""" + return next((p for p in Path(__file__).resolve().parents if (p / ".git").exists()), None) + + +class TestSuiteParts: + @pytest.mark.parametrize( + "path", + ["logging/test_x.py", "tests/e2e/logging/test_x.py", "./logging/test_x.py", "tests\\e2e\\logging\\test_x.py"], + ) + def test_both_invocation_shapes_collapse_to_the_same_components(self, path: str) -> None: + """A repo-root run and a suite-cwd run report the same file differently; + every downstream signal has to see one spelling.""" + assert suite_parts(path) == ("logging", "test_x.py") + + def test_top_level_suite_file_keeps_its_single_component(self) -> None: + assert suite_parts("tests/e2e/test_fixture_mode.py") == ("test_fixture_mode.py",) + + +class TestPackageFromNodeid: + @pytest.mark.parametrize( + ("nodeid", "expected"), + [ + ("logging/test_x.py::TestFoo::test_bar", "logging"), + ("tests/e2e/logging/test_x.py::TestFoo::test_bar", "logging"), + ("quota_management/spend_tracking/test_x.py::test_bar", "quota_management"), + ("test_fixture_mode.py::TestParseFixtureMode::test_known_values_normalize", "root"), + ("tests/e2e/test_fixture_mode.py::test_bar", "root"), + ], + ) + def test_package_is_the_first_dir_under_the_suite_root(self, nodeid: str, expected: str) -> None: + assert package_from_nodeid(nodeid) == expected + + +class TestSourceFromLocation: + @pytest.mark.parametrize("path", ["a2a/test_a2a_agent_e2e.py", "tests/e2e/a2a/test_a2a_agent_e2e.py"]) + def test_path_is_repo_relative_however_pytest_was_started(self, path: str) -> None: + assert source_from_location(path, 40) == "tests/e2e/a2a/test_a2a_agent_e2e.py:41" + + def test_line_is_emitted_one_based(self) -> None: + """pytest.Item.location counts from 0; editors, tracebacks and GitHub's + #L anchor all count from 1, and an off-by-one lands on the decorator.""" + assert source_from_location("a2a/test_x.py", 0) == "tests/e2e/a2a/test_x.py:1" + + def test_top_level_suite_file_sits_directly_under_the_suite_root(self) -> None: + assert source_from_location("test_fixture_mode.py", 39) == "tests/e2e/test_fixture_mode.py:40" + + @pytest.mark.parametrize( + ("path", "lineno"), + [ + ("a2a/test_x.py", None), + ("/app/e2e/a2a/test_x.py", 40), + ("C:\\app\\e2e\\a2a\\test_x.py", 40), + ("../conftest.py", 40), + ("", 40), + ], + ) + def test_nothing_linkable_yields_empty_rather_than_a_guess(self, path: str, lineno: int | None) -> None: + """A colon is rejected on two counts: it is how a Windows absolute path + arrives, and `path:line` cannot represent one in the path half.""" + assert source_from_location(path, lineno) == "" + + +class TestResultProperties: + def test_every_test_carries_package_covers_and_source(self) -> None: + item = FakeItem( + "logging/test_x.py::TestFoo::test_bar", + ("logging/test_x.py", 40, "TestFoo.test_bar"), + (FakeMarker("covers", "LOG-1", "LOG-2"),), + ) + assert result_properties(item) == ( + ("package", "logging"), + ("covers", "LOG-1,LOG-2"), + ("source", "tests/e2e/logging/test_x.py:41"), + ) + + def test_attach_is_idempotent(self) -> None: + """Collection can run the hook more than once; a second pass must not + double the entries in the report.""" + item = FakeItem("logging/test_x.py::test_bar", ("logging/test_x.py", 40, "test_bar")) + attach_result_properties(item) + attach_result_properties(item) + assert [name for name, _ in item.user_properties] == ["package", "covers", "source"] + + +class TestSuiteRoot: + def test_suite_root_names_this_file_s_real_home(self) -> None: + """SUITE_ROOT is hardcoded because the runner image has no repo to read it + from. Where there IS a checkout, prove the constant still points at us -- + otherwise a moved tests/e2e/ ships links that 404.""" + root = repo_root() + if root is None: + pytest.skip("no checkout above this file (the runner image copies tests/e2e/ to /app/e2e)") + assert (root / SUITE_ROOT / Path(__file__).name).resolve() == Path(__file__).resolve() + + +class TestDedupeCovers: + def test_ids_are_unique_order_preserving_and_non_empty_strings(self) -> None: + assert dedupe_covers([("A", "B"), ("B", ""), ("C", 7)]) == ("A", "B", "C") diff --git a/tests/e2e/ui/constants.ts b/tests/e2e/ui/constants.ts index 9d918736262..bb33c90ddf3 100644 --- a/tests/e2e/ui/constants.ts +++ b/tests/e2e/ui/constants.ts @@ -29,6 +29,7 @@ export const E2E_PROXY_ADMIN_USER_ID = "e2e-proxy-admin"; export const E2E_PROXY_ADMIN_EMAIL = "admin@test.local"; export const E2E_INTERNAL_USER_ID = "e2e-internal-user"; export const E2E_INTERNAL_USER_EMAIL = "internal@test.local"; +export const E2E_TEAM_ADMIN_USER_ID = "e2e-team-admin"; // Key aliases for seeded test keys (match seed.sql) export const E2E_UPDATE_LIMITS_KEY_ALIAS = "e2eUpdateLimitsKey"; @@ -46,3 +47,5 @@ export const E2E_TEAM_ORG_ID = "e2e-team-org"; export const E2E_TEAM_ORG_ALIAS = "E2E Team In Org"; export const E2E_TEAM_NO_ADMIN_ID = "e2e-team-no-admin"; export const E2E_TEAM_NO_ADMIN_ALIAS = "E2E Team No Admin"; +export const E2E_TEAM_KEYGEN_ID = "e2e-team-keygen"; +export const E2E_TEAM_KEYGEN_ALIAS = "E2E Team Keygen"; diff --git a/tests/e2e/ui/fixtures/seed.sql b/tests/e2e/ui/fixtures/seed.sql index a1218633cdb..e77b4a16b3d 100644 --- a/tests/e2e/ui/fixtures/seed.sql +++ b/tests/e2e/ui/fixtures/seed.sql @@ -29,7 +29,7 @@ INSERT INTO "LiteLLM_UserTable" ("user_id", "user_email", "user_role", "teams", VALUES ('e2e-proxy-admin', 'admin@test.local', 'proxy_admin', '{"e2e-team-crud"}', 'scrypt:MU5CcTAi6rVK1HfY1rVPEWq6r4sxg837eq9dG4n5Q6BhDJ44442+seC6LAhLEAYr'), ('e2e-admin-viewer', 'adminviewer@test.local', 'proxy_admin_viewer', '{}', 'scrypt:MU5CcTAi6rVK1HfY1rVPEWq6r4sxg837eq9dG4n5Q6BhDJ44442+seC6LAhLEAYr'), - ('e2e-internal-user', 'internal@test.local', 'internal_user', '{"e2e-team-crud","e2e-team-org"}', 'scrypt:MU5CcTAi6rVK1HfY1rVPEWq6r4sxg837eq9dG4n5Q6BhDJ44442+seC6LAhLEAYr'), + ('e2e-internal-user', 'internal@test.local', 'internal_user', '{"e2e-team-crud","e2e-team-org","e2e-team-keygen"}', 'scrypt:MU5CcTAi6rVK1HfY1rVPEWq6r4sxg837eq9dG4n5Q6BhDJ44442+seC6LAhLEAYr'), ('e2e-internal-viewer', 'viewer@test.local', 'internal_user_viewer', '{"e2e-team-crud"}', 'scrypt:MU5CcTAi6rVK1HfY1rVPEWq6r4sxg837eq9dG4n5Q6BhDJ44442+seC6LAhLEAYr'), ('e2e-team-admin', 'teamadmin@test.local', 'internal_user', '{"e2e-team-crud","e2e-team-delete"}', 'scrypt:MU5CcTAi6rVK1HfY1rVPEWq6r4sxg837eq9dG4n5Q6BhDJ44442+seC6LAhLEAYr'), ('e2e-invitable-user', 'invitable@test.local', 'internal_user', '{}', 'scrypt:MU5CcTAi6rVK1HfY1rVPEWq6r4sxg837eq9dG4n5Q6BhDJ44442+seC6LAhLEAYr'), @@ -63,6 +63,17 @@ INSERT INTO "LiteLLM_TeamTable" ( '[{"role":"user","user_id":"e2e-invitable-user"}]'::jsonb, '{}'::jsonb, '{"fake-openai-gpt-4"}', 0.0, '{}'::jsonb, '{}'::jsonb, false); +INSERT INTO "LiteLLM_TeamTable" ( + "team_id", "team_alias", "organization_id", "admins", "members", + "members_with_roles", "metadata", "models", "spend", "model_spend", "model_max_budget", "blocked", + "team_member_permissions" +) VALUES + ('e2e-team-keygen', 'E2E Team Keygen', NULL, + '{}', '{"e2e-internal-user"}', + '[{"role":"user","user_id":"e2e-internal-user"}]'::jsonb, + '{}'::jsonb, '{"fake-openai-gpt-4"}', 0.0, '{}'::jsonb, '{}'::jsonb, false, + '{"/key/generate"}'); + -- 6. Team Memberships (only user_id, team_id, spend — no created_at/updated_at) INSERT INTO "LiteLLM_TeamMembership" ("user_id", "team_id", "spend") VALUES @@ -72,6 +83,7 @@ VALUES ('e2e-removable-member', 'e2e-team-crud', 0.0), ('e2e-team-admin', 'e2e-team-delete', 0.0), ('e2e-internal-user', 'e2e-team-org', 0.0), + ('e2e-internal-user', 'e2e-team-keygen', 0.0), ('e2e-invitable-user', 'e2e-team-no-admin', 0.0); -- 7. Verification Tokens (API Keys) diff --git a/tests/e2e/ui/helpers/premium.ts b/tests/e2e/ui/helpers/premium.ts new file mode 100644 index 00000000000..28bc2e58bbc --- /dev/null +++ b/tests/e2e/ui/helpers/premium.ts @@ -0,0 +1,20 @@ +import * as fs from "fs"; +import { ADMIN_STORAGE_PATH } from "../constants"; + +/** + * Whether the proxy under test is licensed, read from the admin session JWT's `premium_user` + * claim. That is the same value the dashboard reads to enable premium-gated controls, so it + * describes the proxy Playwright is pointed at rather than the environment the runner happens + * to have, which are not the same machine when E2E_UI_BASE_URL points elsewhere. + */ +export function proxyIsPremium(): boolean { + const storage = JSON.parse(fs.readFileSync(ADMIN_STORAGE_PATH, "utf-8")) as { + cookies?: { name: string; value: string }[]; + }; + const token = storage.cookies?.find((cookie) => cookie.name === "token")?.value; + const payload = token?.split(".")[1]; + if (!payload) { + return false; + } + return JSON.parse(Buffer.from(payload, "base64url").toString("utf-8")).premium_user === true; +} diff --git a/tests/e2e/ui/helpers/traffic.ts b/tests/e2e/ui/helpers/traffic.ts index a2fc9463c94..25eb671fd0e 100644 --- a/tests/e2e/ui/helpers/traffic.ts +++ b/tests/e2e/ui/helpers/traffic.ts @@ -4,12 +4,16 @@ import { APIRequestContext, expect } from "@playwright/test"; export const CHAT_MODEL_A = "fake-openai-gpt-4"; export const CHAT_MODEL_B = "fake-anthropic-claude"; +/** The deployment each of those models routes to, as spend logs and usage breakdowns name it. */ +export const DEPLOYMENT_MODEL_A = "openai/fake-gpt-4"; +export const DEPLOYMENT_MODEL_B = "openai/fake-claude"; + /** The only completion text fixtures/mock_llm_server/server.py ever returns. */ export const MOCK_RESPONSE_TEXT = "This is a mock response."; export const masterKey = (): string => process.env.LITELLM_MASTER_KEY || "sk-1234"; -const rootPath = (): string => process.env.SERVER_ROOT_PATH ?? ""; +export const rootPath = (): string => process.env.SERVER_ROOT_PATH ?? ""; interface ChatOptions { model: string; @@ -84,15 +88,84 @@ export async function waitForSpendLog( throw new Error(`spend log for request ${requestId} never appeared (last /spend/logs status ${lastStatus})`); } +export async function waitForSpendLogByPrompt( + request: APIRequestContext, + prompt: string, + timeoutMs = 60_000, +): Promise { + const deadline = Date.now() + timeoutMs; + let lastStatus = 0; + while (Date.now() < deadline) { + const res = await request.get(`${rootPath()}/spend/logs`, { + headers: { Authorization: `Bearer ${masterKey()}` }, + }); + lastStatus = res.status(); + if (res.ok()) { + const rows: { request_id?: string; messages?: unknown; proxy_server_request?: unknown }[] = await res.json(); + const row = (Array.isArray(rows) ? rows : []).find( + (candidate) => + JSON.stringify(candidate.messages ?? "").includes(prompt) || + JSON.stringify(candidate.proxy_server_request ?? "").includes(prompt), + ); + if (row?.request_id) { + return row.request_id; + } + } + await new Promise((r) => setTimeout(r, 2_000)); + } + throw new Error(`no spend log row carrying prompt ${prompt} appeared (last /spend/logs status ${lastStatus})`); +} + const isoDay = (d: Date): string => d.toISOString().slice(0, 10); +interface DailyActivityKey { + metrics?: { api_requests?: number }; +} + +interface DailyActivityPage { + results?: { breakdown?: { api_keys?: Record } }[]; + metadata?: { total_pages?: number }; +} + +const requestsOnPage = (body: DailyActivityPage, keyToken: string): number => + (body.results ?? []).reduce((sum, day) => sum + (day.breakdown?.api_keys?.[keyToken]?.metrics?.api_requests ?? 0), 0); + +/** + * The route paginates its per-key breakdown. Reading only the first page finds a key while the + * database is small and stops finding it once a run has generated more keys than one page holds, + * which reads as "the rollup is not running" when the rollup is fine. + */ +async function keyRequestsInDailyActivity( + request: APIRequestContext, + query: string, + keyToken: string, + page = 1, + seen = 0, +): Promise { + const res = await request.get(`${rootPath()}/user/daily/activity?${query}&page=${page}`, { + headers: { Authorization: `Bearer ${masterKey()}` }, + }); + if (!res.ok()) { + return seen; + } + const body = (await res.json()) as DailyActivityPage; + const total = seen + requestsOnPage(body, keyToken); + return page >= (body.metadata?.total_pages ?? 1) + ? total + : keyRequestsInDailyActivity(request, query, keyToken, page + 1, total); +} + /** * The Usage page reads /user/daily/activity, a rollup written by a background job, and fetches it once * on mount. Navigating before the rollup lands leaves a stale render that never refreshes. + * + * The rollup lands request by request, so waiting only for the key to appear leaves a caller that + * sent several requests reading a partial count. Pass `minRequests` to wait for all of them. */ export async function waitForKeyInDailyActivity( request: APIRequestContext, keyToken: string, + minRequests = 1, timeoutMs = 120_000, ): Promise { const now = new Date(); @@ -101,25 +174,17 @@ export async function waitForKeyInDailyActivity( const query = `start_date=${isoDay(start)}&end_date=${isoDay(now)}`; const deadline = Date.now() + timeoutMs; - let lastStatus = 0; - while (Date.now() < deadline) { - const res = await request.get(`${rootPath()}/user/daily/activity?${query}`, { - headers: { Authorization: `Bearer ${masterKey()}` }, - }); - lastStatus = res.status(); - if (res.ok()) { - const body = await res.json(); - const seen = (body?.results ?? []).some( - (day: { breakdown?: { api_keys?: Record } }) => keyToken in (day.breakdown?.api_keys ?? {}), + for (;;) { + const seen = await keyRequestsInDailyActivity(request, query, keyToken); + if (seen >= minRequests) { + return; + } + if (Date.now() >= deadline) { + throw new Error( + `key ${keyToken} reached ${seen} of ${minRequests} requests in /user/daily/activity across every page; ` + + "the daily spend rollup may not be running", ); - if (seen) { - return; - } } await new Promise((r) => setTimeout(r, 3_000)); } - throw new Error( - `key ${keyToken} never appeared in /user/daily/activity (last status ${lastStatus}); ` + - "the daily spend rollup may not be running", - ); } diff --git a/tests/e2e/ui/tests/budgets/budgets.spec.ts b/tests/e2e/ui/tests/budgets/budgets.spec.ts new file mode 100644 index 00000000000..1ad1e488d25 --- /dev/null +++ b/tests/e2e/ui/tests/budgets/budgets.spec.ts @@ -0,0 +1,133 @@ +import { test, expect, type Page as PlaywrightPage } from "@playwright/test"; +import { ADMIN_STORAGE_PATH } from "../../constants"; +import { Page } from "../../fixtures/pages"; +import { navigateToPage, dismissFeedbackPopup } from "../../helpers/navigation"; +import { masterKey } from "../../helpers/traffic"; + +interface StoredBudget { + budget_id: string; + max_budget: number | null; + tpm_limit: number | null; + rpm_limit: number | null; + budget_duration: string | null; +} + +/** A different route from the one the table renders from, so a row that only lives in its cache fails here. */ +async function findBudget(page: PlaywrightPage, budgetId: string): Promise { + const res = await page.request.get("/budget/list", { + headers: { Authorization: `Bearer ${masterKey()}` }, + }); + expect(res.ok(), `GET /budget/list (${res.status()})`).toBe(true); + return ((await res.json()) as StoredBudget[]).find((row) => row.budget_id === budgetId); +} + +async function createBudgetViaApi(page: PlaywrightPage, budget: Partial): Promise { + const res = await page.request.post("/budget/new", { + headers: { Authorization: `Bearer ${masterKey()}` }, + data: budget, + }); + expect(res.ok(), `POST /budget/new failed (${res.status()}): ${await res.text()}`).toBe(true); +} + +async function searchForBudget(page: PlaywrightPage, budgetId: string): Promise { + await page.getByPlaceholder("Search by budget ID").fill(budgetId); +} + +test.describe("Budgets", () => { + test.use({ storageState: ADMIN_STORAGE_PATH }); + + test("Create a budget with rate limits and a spend cap", async ({ page }) => { + const budgetId = `e2e-budget-create-${Date.now()}`; + + await navigateToPage(page, Page.Budgets); + await dismissFeedbackPopup(page); + + await page.getByRole("button", { name: "Create Budget" }).click(); + + const modal = page.getByRole("dialog", { name: "Create Budget" }); + await expect(modal).toBeVisible({ timeout: 10_000 }); + + await modal.getByRole("textbox", { name: "Budget ID" }).fill(budgetId); + await modal.getByRole("spinbutton", { name: "Max Tokens per minute" }).fill("5000"); + await modal.getByRole("spinbutton", { name: "Max Requests per minute" }).fill("60"); + + await modal.getByRole("button", { name: "Optional Settings" }).click(); + await modal.getByRole("spinbutton", { name: "Max Budget (USD)" }).fill("25.5"); + await modal.getByRole("combobox", { name: "Reset Budget" }).click(); + await page.getByRole("option", { name: "weekly" }).click(); + + await modal.getByRole("button", { name: "Create Budget" }).click(); + await expect(modal).not.toBeVisible({ timeout: 10_000 }); + + await searchForBudget(page, budgetId); + const row = page.getByRole("row").filter({ hasText: budgetId }); + await expect(row).toBeVisible({ timeout: 10_000 }); + await expect(row).toContainText("$25.50"); + + const stored = await findBudget(page, budgetId); + expect(stored, `budget ${budgetId} readable from /budget/list`).toBeTruthy(); + expect(stored?.max_budget, "spend cap persisted").toBe(25.5); + expect(stored?.tpm_limit, "TPM limit persisted").toBe(5000); + expect(stored?.rpm_limit, "RPM limit persisted").toBe(60); + expect(stored?.budget_duration, "reset window persisted").toBe("7d"); + }); + + test("Raising a budget's spend cap leaves its rate limits alone", async ({ page }) => { + const budgetId = `e2e-budget-edit-${Date.now()}`; + await createBudgetViaApi(page, { budget_id: budgetId, max_budget: 10, tpm_limit: 1000, rpm_limit: 20 }); + + await navigateToPage(page, Page.Budgets); + await dismissFeedbackPopup(page); + + await searchForBudget(page, budgetId); + await expect(page.getByRole("row").filter({ hasText: budgetId })).toBeVisible({ timeout: 10_000 }); + + await page.getByTestId(`budget-actions-${budgetId}`).click(); + await page.getByTestId("budget-action-edit").click(); + + const modal = page.getByRole("dialog", { name: "Edit Budget" }); + await expect(modal).toBeVisible({ timeout: 10_000 }); + + await modal.getByRole("button", { name: "Optional Settings" }).click(); + await modal.getByRole("spinbutton", { name: "Max Budget (USD)" }).fill("99"); + await modal.getByRole("button", { name: "Save", exact: true }).click(); + await expect(modal).not.toBeVisible({ timeout: 10_000 }); + + await expect(page.getByRole("row").filter({ hasText: budgetId })).toContainText("$99.00", { timeout: 10_000 }); + + // Not hypothetical: the edit form posts the whole budget, so a field it fails to + // seed from the existing row goes to the server as null and silently clears. + const stored = await findBudget(page, budgetId); + expect(stored?.max_budget, "spend cap raised").toBe(99); + expect(stored?.tpm_limit, "TPM limit untouched by a spend-cap edit").toBe(1000); + expect(stored?.rpm_limit, "RPM limit untouched by a spend-cap edit").toBe(20); + }); + + test("Delete a budget", async ({ page }) => { + const budgetId = `e2e-budget-delete-${Date.now()}`; + await createBudgetViaApi(page, { budget_id: budgetId, max_budget: 5 }); + + await navigateToPage(page, Page.Budgets); + await dismissFeedbackPopup(page); + + await searchForBudget(page, budgetId); + await expect(page.getByRole("row").filter({ hasText: budgetId })).toBeVisible({ timeout: 10_000 }); + + await page.getByTestId(`budget-actions-${budgetId}`).click(); + await page.getByTestId("budget-action-delete").click(); + + const modal = page.getByRole("dialog", { name: "Delete Budget?" }); + await expect(modal).toBeVisible({ timeout: 5_000 }); + await modal.getByRole("button", { name: "Delete", exact: true }).click(); + + await expect(page.getByRole("row").filter({ hasText: budgetId })).toHaveCount(0, { timeout: 10_000 }); + + // The row disappearing is a cache invalidation; the budget is gone when the route stops serving it. + await expect + .poll(async () => await findBudget(page, budgetId), { + message: `budget ${budgetId} still readable from /budget/list after delete`, + timeout: 15_000, + }) + .toBeUndefined(); + }); +}); diff --git a/tests/e2e/ui/tests/guardrails/guardrails.spec.ts b/tests/e2e/ui/tests/guardrails/guardrails.spec.ts new file mode 100644 index 00000000000..1e43c7a2b22 --- /dev/null +++ b/tests/e2e/ui/tests/guardrails/guardrails.spec.ts @@ -0,0 +1,283 @@ +import { test, expect, type Page as PlaywrightPage } from "@playwright/test"; +import { ADMIN_STORAGE_PATH, E2E_TEAM_NO_ADMIN_ID } from "../../constants"; +import { Page } from "../../fixtures/pages"; +import { navigateToPage, dismissFeedbackPopup, clickTeamId } from "../../helpers/navigation"; +import { CHAT_MODEL_A, MOCK_RESPONSE_TEXT, masterKey } from "../../helpers/traffic"; + +interface StoredGuardrail { + guardrail_id: string; + guardrail_name: string | null; +} + +async function listGuardrails(page: PlaywrightPage): Promise { + const res = await page.request.get("/v2/guardrails/list", { + headers: { Authorization: `Bearer ${masterKey()}` }, + }); + expect(res.ok(), `GET /v2/guardrails/list (${res.status()})`).toBe(true); + return ((await res.json()) as { guardrails: StoredGuardrail[] }).guardrails; +} + +async function findGuardrail(page: PlaywrightPage, name: string): Promise { + return (await listGuardrails(page)).find((row) => row.guardrail_name === name); +} + +const createdGuardrails: string[] = []; + +async function createKeywordGuardrailViaApi(page: PlaywrightPage, name: string, keyword: string): Promise { + const res = await page.request.post("/guardrails", { + headers: { Authorization: `Bearer ${masterKey()}` }, + data: { + guardrail: { + guardrail_name: name, + litellm_params: { + guardrail: "litellm_content_filter", + mode: "pre_call", + default_on: false, + blocked_words: [{ keyword, action: "BLOCK" }], + }, + }, + }, + }); + expect(res.ok(), `POST /guardrails failed (${res.status()}): ${await res.text()}`).toBe(true); + createdGuardrails.push(name); + const guardrail = await findGuardrail(page, name); + expect(guardrail?.guardrail_id, `guardrail ${name} has an id`).toBeTruthy(); + return guardrail!.guardrail_id; +} + +async function openKeywordsStep(page: PlaywrightPage, name: string) { + await page.getByRole("button", { name: "Add New Guardrail" }).click(); + await page.getByRole("menuitem", { name: "Add Provider Guardrail" }).click(); + + const wizard = page.getByRole("dialog", { name: "Create guardrail" }); + await expect(wizard).toBeVisible({ timeout: 10_000 }); + + await wizard.getByRole("textbox", { name: "Guardrail Name" }).fill(name); + await wizard.getByRole("combobox", { name: "Guardrail Provider" }).click(); + // The content filter runs inside the proxy, so this is the one provider a test can + // configure end to end without standing up a third-party moderation service. + await page.getByRole("option", { name: /LiteLLM Content Filter/ }).click(); + + for (const step of ["Topics", "Patterns", "Keywords"]) { + await wizard.getByRole("button", { name: "Next" }).click(); + await expect(wizard).toContainText(step, { timeout: 10_000 }); + } + return wizard; +} + +test.describe("Guardrails", () => { + test.use({ storageState: ADMIN_STORAGE_PATH }); + + test.afterEach(async ({ page }) => { + // Guardrails live in the database and show up in the table and the playground list, so a run + // that leaves them behind changes what the next run sees. + for (const name of createdGuardrails.splice(0)) { + const guardrail = await findGuardrail(page, name); + if (guardrail) { + const deleted = await page.request.delete(`/guardrails/${guardrail.guardrail_id}`, { + headers: { Authorization: `Bearer ${masterKey()}` }, + }); + expect(deleted.ok(), `DELETE /guardrails/${guardrail.guardrail_id} (${deleted.status()})`).toBe(true); + } + } + }); + + test("A guardrail created through the wizard blocks the keyword it was given", async ({ page }) => { + const stamp = Date.now(); + const guardrailName = `e2e-guardrail-create-${stamp}`; + // Unique per run so a concurrent test's prompt can never trip this guardrail, or vice versa. + const bannedKeyword = `e2ebanned${stamp}`; + + await navigateToPage(page, Page.Guardrails); + await dismissFeedbackPopup(page); + + createdGuardrails.push(guardrailName); + const wizard = await openKeywordsStep(page, guardrailName); + + await wizard.getByRole("button", { name: "Add keyword" }).click(); + const keywordModal = page.getByRole("dialog", { name: "Add blocked keyword" }); + await expect(keywordModal).toBeVisible({ timeout: 10_000 }); + await keywordModal.getByPlaceholder("Enter sensitive keyword or phrase").fill(bannedKeyword); + await keywordModal.getByRole("button", { name: "Add", exact: true }).click(); + await expect(keywordModal).not.toBeVisible({ timeout: 10_000 }); + + await wizard.getByRole("button", { name: "Next" }).click(); + await wizard.getByRole("button", { name: "Create Guardrail" }).click(); + await expect(wizard).not.toBeVisible({ timeout: 15_000 }); + + await expect(page.getByRole("row").filter({ hasText: guardrailName })).toBeVisible({ timeout: 15_000 }); + expect(await findGuardrail(page, guardrailName), "guardrail readable from /v2/guardrails/list").toBeTruthy(); + + // A row in the table only proves the record was written. The point of a guardrail is that it + // refuses traffic, so drive a request through it. + // + // Polled: a guardrail written through /guardrails reaches the request path on the proxy's + // periodic refresh, so the first call after creation can still be served unguarded. The + // assertion is unchanged, it just allows that refresh to land. + let blockedBody = ""; + await expect + .poll( + async () => { + const res = await page.request.post("/v1/chat/completions", { + headers: { Authorization: `Bearer ${masterKey()}` }, + data: { + model: CHAT_MODEL_A, + messages: [{ role: "user", content: `please tell me about ${bannedKeyword}` }], + guardrails: [guardrailName], + }, + }); + blockedBody = await res.text(); + return res.status(); + }, + { message: "a prompt carrying the banned keyword is refused", timeout: 60_000 }, + ) + .toBe(400); + expect(blockedBody).toContain(bannedKeyword); + + const allowed = await page.request.post("/v1/chat/completions", { + headers: { Authorization: `Bearer ${masterKey()}` }, + data: { + model: CHAT_MODEL_A, + messages: [{ role: "user", content: "hello there" }], + guardrails: [guardrailName], + }, + }); + expect(allowed.status(), "a clean prompt still gets through the same guardrail").toBe(200); + expect((await allowed.json()).choices?.[0]?.message?.content).toContain(MOCK_RESPONSE_TEXT); + }); + + test("The Test Playground reports the verdict for the text it is given", async ({ page }) => { + const stamp = Date.now(); + const guardrailName = `e2e-guardrail-play-${stamp}`; + const bannedKeyword = `e2eplay${stamp}`; + await createKeywordGuardrailViaApi(page, guardrailName, bannedKeyword); + + await navigateToPage(page, Page.Guardrails); + await dismissFeedbackPopup(page); + + await page.getByRole("tab", { name: "Test Playground" }).click(); + // Every tab on this page stays mounted, so the other tabs' search boxes match too. + const playground = page.getByRole("tabpanel", { name: "Test Playground" }); + await playground.getByPlaceholder("Search guardrails...").fill(guardrailName); + await playground.getByText(guardrailName, { exact: true }).click(); + + const input = playground.getByPlaceholder("Enter text to test with guardrails..."); + await input.fill(`this sentence contains ${bannedKeyword}`); + await playground.getByRole("button", { name: /^Test 1 guardrail$/ }).click(); + + // The playground is where an admin checks a guardrail before rolling it out, so the + // verdict it prints has to be the one the gateway would give. + await expect(playground.getByText(`${guardrailName} - Error`)).toBeVisible({ timeout: 20_000 }); + await expect(playground.getByText(new RegExp(`Content blocked.*${bannedKeyword}`))).toBeVisible({ + timeout: 10_000, + }); + + await input.fill("this sentence is perfectly ordinary"); + await playground.getByRole("button", { name: /^Test 1 guardrail$/ }).click(); + + await expect(playground.getByText(`${guardrailName} - Error`)).toHaveCount(0, { timeout: 20_000 }); + await expect(playground.getByText("this sentence is perfectly ordinary").last()).toBeVisible({ timeout: 10_000 }); + }); + + test("Delete a guardrail", async ({ page }) => { + const stamp = Date.now(); + const guardrailName = `e2e-guardrail-delete-${stamp}`; + const guardrailId = await createKeywordGuardrailViaApi(page, guardrailName, `e2edelete${stamp}`); + + await navigateToPage(page, Page.Guardrails); + await dismissFeedbackPopup(page); + + await expect(page.getByRole("row").filter({ hasText: guardrailName })).toBeVisible({ timeout: 15_000 }); + + await page.getByTestId(`guardrail-actions-${guardrailId}`).click(); + await page.getByTestId("guardrail-action-delete").click(); + + const modal = page.getByRole("dialog"); + await expect(modal).toBeVisible({ timeout: 5_000 }); + await modal.getByRole("button", { name: "Delete", exact: true }).click(); + + await expect(page.getByRole("row").filter({ hasText: guardrailName })).toHaveCount(0, { timeout: 15_000 }); + + // The RC checklist deletes then reloads, because a row vanishing from the table has + // fooled us before; assert against the route the reload would read. + await expect + .poll(async () => await findGuardrail(page, guardrailName), { + message: `guardrail ${guardrailName} still listed after delete`, + timeout: 15_000, + }) + .toBeUndefined(); + }); + + test("Create a Presidio guardrail, see it in team settings, and delete it", async ({ page }) => { + const guardrailName = `e2e-presidio-${Date.now()}`; + + await navigateToPage(page, Page.Guardrails); + await dismissFeedbackPopup(page); + + await page.getByRole("button", { name: /Add New Guardrail/i }).click(); + await page.getByRole("menuitem", { name: "Add Provider Guardrail" }).click(); + + const dialog = page.getByRole("dialog", { name: "Create guardrail" }); + await expect(dialog).toBeVisible({ timeout: 10_000 }); + + await dialog.getByLabel("Guardrail Name").fill(guardrailName); + + const providerSelect = dialog.getByRole("combobox", { name: "Guardrail Provider" }); + await providerSelect.click(); + await providerSelect.fill("Presidio"); + await page.getByRole("option", { name: "Presidio PII" }).click(); + + await dialog.getByLabel("Mode", { exact: true }).click(); + await page.keyboard.type("pre_call"); + await expect(page.getByRole("option", { name: "pre_call" })).toBeAttached({ timeout: 5_000 }); + await page.keyboard.press("Enter"); + await expect(dialog.getByText("pre_call", { exact: true })).toBeVisible({ timeout: 5_000 }); + await dialog.getByText("Create guardrail", { exact: true }).click(); + + await dialog.getByLabel("presidio_analyzer_api_base").fill("http://127.0.0.1:9999"); + await expect(dialog.getByLabel("presidio_analyzer_api_base")).toHaveValue("http://127.0.0.1:9999"); + await dialog.getByLabel("presidio_anonymizer_api_base").fill("http://127.0.0.1:9999"); + await expect(dialog.getByLabel("presidio_anonymizer_api_base")).toHaveValue("http://127.0.0.1:9999"); + + await dialog.getByRole("button", { name: "Next" }).click(); + await expect(dialog.getByText("Configure PII Protection")).toBeVisible({ timeout: 10_000 }); + await dialog.getByRole("button", { name: "Select All & Mask" }).click(); + + await dialog.getByRole("button", { name: "Create Guardrail" }).click(); + await expect(page.getByText("Guardrail created successfully").first()).toBeVisible({ timeout: 15_000 }); + + const row = page.getByRole("row").filter({ hasText: guardrailName }); + await expect(row).toHaveCount(1, { timeout: 15_000 }); + + await navigateToPage(page, Page.Teams); + await dismissFeedbackPopup(page); + await clickTeamId(page, E2E_TEAM_NO_ADMIN_ID); + await page.getByRole("tab", { name: "Settings" }).click(); + await page.getByRole("button", { name: "Edit Settings" }).click(); + + const guardrailsSelect = page.getByRole("combobox", { name: "Select guardrails" }); + await expect(guardrailsSelect).toBeVisible({ timeout: 10_000 }); + await guardrailsSelect.click(); + await guardrailsSelect.fill(guardrailName); + await expect(page.getByRole("option", { name: guardrailName })).toBeVisible({ timeout: 10_000 }); + await page.keyboard.press("Escape"); + + await navigateToPage(page, Page.Guardrails); + await expect(row).toHaveCount(1, { timeout: 15_000 }); + await row.getByRole("button", { name: "Open guardrail actions" }).click(); + await page.getByRole("menuitem", { name: "Delete" }).click(); + + const deleteModal = page.getByRole("dialog", { name: "Delete Guardrail" }); + await expect(deleteModal).toBeVisible({ timeout: 5_000 }); + await deleteModal.getByRole("button", { name: "Delete", exact: true }).click(); + + await expect(page.getByText(`Guardrail "${guardrailName}" deleted successfully`)).toBeVisible({ + timeout: 10_000, + }); + await expect(row).toHaveCount(0, { timeout: 15_000 }); + + await page.reload(); + await expect(page.getByRole("button", { name: /Add New Guardrail/i })).toBeVisible({ timeout: 20_000 }); + await expect(page.getByRole("row").filter({ hasText: guardrailName })).toHaveCount(0); + }); +}); diff --git a/tests/e2e/ui/tests/internal-user/internalUser.spec.ts b/tests/e2e/ui/tests/internal-user/internalUser.spec.ts index b8424b06115..f392c5104da 100644 --- a/tests/e2e/ui/tests/internal-user/internalUser.spec.ts +++ b/tests/e2e/ui/tests/internal-user/internalUser.spec.ts @@ -3,10 +3,13 @@ import { E2E_INTERNAL_USER_KEY_ALIAS, E2E_TEAM_CRUD_ALIAS, E2E_TEAM_CRUD_ID, + E2E_TEAM_KEYGEN_ALIAS, INTERNAL_USER_STORAGE_PATH, } from "../../constants"; import { Page } from "../../fixtures/pages"; import { navigateToPage, clickTeamId } from "../../helpers/navigation"; +import { CHAT_MODEL_A, MOCK_RESPONSE_TEXT, masterKey } from "../../helpers/traffic"; +import { keySourceSelect, onlyVisible, openPlayground, selectModel, sendMessage } from "../../helpers/playground"; test.describe("Internal User", () => { test.use({ storageState: INTERNAL_USER_STORAGE_PATH }); @@ -22,8 +25,7 @@ test.describe("Internal User", () => { const teamSelect = page.getByTestId("team-dropdown").getByRole("combobox"); await teamSelect.click(); await page.keyboard.type(E2E_TEAM_CRUD_ALIAS); - const dropdown = page.locator('[data-slot="combobox-content"]:visible'); - await expect(dropdown.getByText(E2E_TEAM_CRUD_ALIAS).first()).toBeVisible({ timeout: 5_000 }); + await expect(page.getByRole("option", { name: E2E_TEAM_CRUD_ALIAS }).first()).toBeVisible({ timeout: 5_000 }); }); test("Team info page omits the Settings tab for non-admin members", async ({ page }) => { @@ -38,17 +40,66 @@ test.describe("Internal User", () => { await expect(page.getByRole("tab", { name: "Members" })).not.toBeVisible(); }); + test("Internal user creates a team key and uses it in the Playground", async ({ page, request }) => { + const suffix = Date.now(); + const auth = { Authorization: `Bearer ${masterKey()}` }; + + await navigateToPage(page, Page.ApiKeys); + + await page.getByRole("button", { name: /Create New Key/i }).click(); + await expect(page.getByText("Key Ownership")).toBeVisible({ timeout: 10_000 }); + + await expect(page.getByRole("radio", { name: "You", exact: true })).toBeVisible({ timeout: 10_000 }); + await expect(page.getByRole("radio", { name: "Another User" })).toHaveCount(0); + + const keyName = `e2e-internal-team-key-${suffix}`; + await page.getByLabel(/Key Name/).fill(keyName); + + const teamSelect = page.getByTestId("team-dropdown").getByRole("combobox"); + await teamSelect.click(); + await page.keyboard.type(E2E_TEAM_KEYGEN_ALIAS); + await page.getByRole("option", { name: E2E_TEAM_KEYGEN_ALIAS }).first().click(); + + await page.getByRole("combobox", { name: "Select models" }).click(); + await page.getByRole("option", { name: "All Team Models", exact: true }).click(); + await page.keyboard.press("Escape"); + + await page.getByRole("button", { name: "Create Key", exact: true }).click(); + + await expect(page.getByText("Save your Key")).toBeVisible({ timeout: 10_000 }); + const apiKey = (await page.getByRole("dialog", { name: "Save your Key" }).locator("pre").innerText()).trim(); + expect(apiKey).toMatch(/^sk-/); + await page.keyboard.press("Escape"); + + try { + await openPlayground(page); + await keySourceSelect(page).click(); + await onlyVisible(page.getByRole("option", { name: "Virtual Key" })).click({ timeout: 15_000 }); + + const keyInput = onlyVisible(page.getByPlaceholder("Enter custom Virtual Key")); + await expect(keyInput).toBeVisible({ timeout: 10_000 }); + await keyInput.fill(apiKey); + + await selectModel(page, CHAT_MODEL_A); + await sendMessage(page, `internal user team key ping ${keyName}`); + + await expect(page.getByText(MOCK_RESPONSE_TEXT, { exact: false }).first()).toBeVisible({ timeout: 60_000 }); + } finally { + await request.post("/key/delete", { headers: auth, data: { keys: [apiKey] } }); + } + }); + test("Virtual Keys page does not surface litellm-dashboard team keys", async ({ page }) => { await navigateToPage(page, Page.ApiKeys); // Anchor on the user's own seeded key so the absence check below cannot // pass vacuously against an empty table. - await expect(page.locator("table tbody").getByText(E2E_INTERNAL_USER_KEY_ALIAS).first()).toBeVisible({ + await expect(page.getByRole("row").filter({ hasText: E2E_INTERNAL_USER_KEY_ALIAS }).first()).toBeVisible({ timeout: 10_000, }); // The litellm-dashboard team is the proxy's internal bookkeeping team — // its keys must never leak into an internal user's Virtual Keys table. - await expect(page.locator("table tbody").getByText("litellm-dashboard")).toHaveCount(0); + await expect(page.getByRole("row").filter({ hasText: "litellm-dashboard" })).toHaveCount(0); }); }); diff --git a/tests/e2e/ui/tests/internal-user/internalUserNoTeam.spec.ts b/tests/e2e/ui/tests/internal-user/internalUserNoTeam.spec.ts index c44305187f1..653e096b713 100644 --- a/tests/e2e/ui/tests/internal-user/internalUserNoTeam.spec.ts +++ b/tests/e2e/ui/tests/internal-user/internalUserNoTeam.spec.ts @@ -30,16 +30,13 @@ test.describe("Internal User with no team memberships", () => { const teamSelect = page.getByTestId("team-dropdown").getByRole("combobox"); await teamSelect.click(); - const dropdown = page.locator('[data-slot="combobox-content"]:visible').first(); - await expect(dropdown).toBeVisible({ timeout: 5_000 }); - // Wait for the settled-empty state, not a transient one. The dropdown shows // "Loading teams…" while teams load and only swaps in "No teams found" once // the request resolves with nothing (team_dropdown.tsx passes both copies to // PaginatedSearchSelect). Asserting on it means a regression where teams DO // load for this user fails here instead of racing a one-shot count() against // an in-flight request. - await expect(dropdown.getByText("No teams found")).toBeVisible({ timeout: 10_000 }); - await expect(dropdown.getByRole("option")).toHaveCount(0); + await expect(page.getByText("No teams found")).toBeVisible({ timeout: 10_000 }); + await expect(page.getByRole("option")).toHaveCount(0); }); }); diff --git a/tests/e2e/ui/tests/internal-user/internalUserWithTeams.spec.ts b/tests/e2e/ui/tests/internal-user/internalUserWithTeams.spec.ts index 68319154554..62681e9ceb5 100644 --- a/tests/e2e/ui/tests/internal-user/internalUserWithTeams.spec.ts +++ b/tests/e2e/ui/tests/internal-user/internalUserWithTeams.spec.ts @@ -1,14 +1,13 @@ import { test, expect } from "@playwright/test"; -import { INTERNAL_USER_STORAGE_PATH, E2E_TEAM_CRUD_ALIAS, E2E_TEAM_ORG_ALIAS } from "../../constants"; +import { + INTERNAL_USER_STORAGE_PATH, + E2E_TEAM_CRUD_ALIAS, + E2E_TEAM_KEYGEN_ALIAS, + E2E_TEAM_ORG_ALIAS, +} from "../../constants"; import { Page } from "../../fixtures/pages"; import { navigateToPage } from "../../helpers/navigation"; -/** - * Differential partner to internalUserNoTeam.spec.ts: the seeded - * e2e-internal-user belongs to exactly two teams, so the Create Key dropdown - * must list both. Without this, the no-team spec's "zero options" assertion - * would still pass against a bug that empties the dropdown for everyone. - */ test.describe("Internal User with team memberships", () => { test.use({ storageState: INTERNAL_USER_STORAGE_PATH }); @@ -21,13 +20,9 @@ test.describe("Internal User with team memberships", () => { const teamSelect = page.getByTestId("team-dropdown").getByRole("combobox"); await teamSelect.click(); - const dropdown = page.locator('[data-slot="combobox-content"]:visible').first(); - await expect(dropdown).toBeVisible({ timeout: 5_000 }); - - // Both seeded memberships render, and nothing else does — proving the - // dropdown is scoped to the user's teams rather than empty or unfiltered. - await expect(dropdown.getByText(E2E_TEAM_CRUD_ALIAS, { exact: true })).toBeVisible({ timeout: 10_000 }); - await expect(dropdown.getByText(E2E_TEAM_ORG_ALIAS, { exact: true })).toBeVisible(); - await expect(dropdown.getByRole("option")).toHaveCount(2); + await expect(page.getByRole("option", { name: E2E_TEAM_CRUD_ALIAS })).toBeVisible({ timeout: 10_000 }); + await expect(page.getByRole("option", { name: E2E_TEAM_ORG_ALIAS })).toBeVisible(); + await expect(page.getByRole("option", { name: E2E_TEAM_KEYGEN_ALIAS })).toBeVisible(); + await expect(page.getByRole("option")).toHaveCount(3); }); }); diff --git a/tests/e2e/ui/tests/internal-viewer/internalViewer.spec.ts b/tests/e2e/ui/tests/internal-viewer/internalViewer.spec.ts index 4de86c46398..dd40976341d 100644 --- a/tests/e2e/ui/tests/internal-viewer/internalViewer.spec.ts +++ b/tests/e2e/ui/tests/internal-viewer/internalViewer.spec.ts @@ -59,9 +59,9 @@ test.describe("Internal Viewer", () => { await expect(page.getByRole("button", { name: /Create New Key/i })).toHaveCount(0); // Open the viewer's own key info page - const keyRow = page.locator("tr", { hasText: E2E_VIEWER_KEY_ALIAS }); + const keyRow = page.getByRole("row").filter({ hasText: E2E_VIEWER_KEY_ALIAS }); await expect(keyRow).toBeVisible({ timeout: 10_000 }); - await keyRow.locator("button").first().click(); + await keyRow.getByRole("button", { name: E2E_VIEWER_KEY_ALIAS }).click(); await expect(page.getByText("Back to Keys")).toBeVisible({ timeout: 10_000 }); // None of the destructive / mutating actions should render diff --git a/tests/e2e/ui/tests/logs/logs.spec.ts b/tests/e2e/ui/tests/logs/logs.spec.ts index b29a72cbf81..2748c91395f 100644 --- a/tests/e2e/ui/tests/logs/logs.spec.ts +++ b/tests/e2e/ui/tests/logs/logs.spec.ts @@ -2,7 +2,14 @@ import { test, expect, type Locator, type Page as PlaywrightPage } from "@playwr import { ADMIN_STORAGE_PATH } from "../../constants"; import { navigateToPage, dismissFeedbackPopup } from "../../helpers/navigation"; import { Page } from "../../fixtures/pages"; -import { CHAT_MODEL_A, MOCK_RESPONSE_TEXT, sendChatCompletion, waitForSpendLog } from "../../helpers/traffic"; +import { + CHAT_MODEL_A, + MOCK_RESPONSE_TEXT, + sendChatCompletion, + waitForSpendLog, + waitForSpendLogByPrompt, +} from "../../helpers/traffic"; +import { openPlayground, selectModel, sendMessage } from "../../helpers/playground"; /** * Anchored to traffic this spec generates itself, with a unique prompt and end user per run, so it @@ -11,12 +18,11 @@ import { CHAT_MODEL_A, MOCK_RESPONSE_TEXT, sendChatCompletion, waitForSpendLog } const uniqueSuffix = (): string => `${Date.now()}-${Math.random().toString(36).slice(2, 8)}`; -/** - * Walking up from the label is the only stable handle: the header carries no role, test id or class, - * and its copy button is icon-only with a hover-only tooltip. - */ -const sectionHeader = (drawer: Locator, label: "Input" | "Output"): Locator => - drawer.getByText(label, { exact: true }).locator("xpath=../../.."); +const sectionToggle = (drawer: Locator, label: "Input" | "Output"): Locator => + drawer.getByRole("button", { name: new RegExp(`^${label}\\b`) }); + +const sectionCopy = (drawer: Locator, label: "Input" | "Output"): Locator => + drawer.getByRole("button", { name: `Copy ${label.toLowerCase()}` }); /** Every tab stays mounted, so the DOM holds four tables at once; scope to the visible one. */ const requestLogsRows = (page: PlaywrightPage): Locator => @@ -47,6 +53,23 @@ test.describe("Logs page", () => { permissions: ["clipboard-read", "clipboard-write"], }); + test("a chat sent from the Playground lands in Logs with its content", async ({ page, request }) => { + const prompt = `logs-playground-prompt-${uniqueSuffix()}`; + await openPlayground(page); + await selectModel(page, CHAT_MODEL_A); + await sendMessage(page, prompt); + await expect(page.getByText(MOCK_RESPONSE_TEXT, { exact: false }).first()).toBeVisible({ timeout: 60_000 }); + + const requestId = await waitForSpendLogByPrompt(request, prompt); + + const row = await openLogsForRequest(page, requestId); + await row.click(); + const drawer = page.getByRole("dialog").first(); + await expect(drawer.getByText("Request & Response")).toBeVisible({ timeout: 20_000 }); + await expect(drawer.getByText(prompt, { exact: false }).first()).toBeVisible({ timeout: 20_000 }); + await expect(drawer.getByText(MOCK_RESPONSE_TEXT, { exact: false }).first()).toBeVisible({ timeout: 20_000 }); + }); + test("a served request expands to its request and response", async ({ page, request }) => { const prompt = `logs-detail-prompt-${uniqueSuffix()}`; const requestId = await sendChatCompletion(request, { @@ -95,14 +118,14 @@ test.describe("Logs page", () => { await expect(drawer).toBeVisible({ timeout: 20_000 }); // Copy request: the Input card's copy button puts the prompt on the clipboard. - await sectionHeader(drawer, "Input").getByRole("button").click(); + await sectionCopy(drawer, "Input").click(); await expect(page.getByText("Input copied")).toBeVisible({ timeout: 10_000, }); expect(await page.evaluate(() => navigator.clipboard.readText())).toContain(prompt); // Copy response: the Output card's copy button puts the completion on it. - await sectionHeader(drawer, "Output").getByRole("button").click(); + await sectionCopy(drawer, "Output").click(); await expect(page.getByText("Output copied")).toBeVisible({ timeout: 10_000, }); @@ -125,29 +148,46 @@ test.describe("Logs page", () => { timeout: 20_000, }); - // The body collapses via `max-height: 0; overflow: hidden`, which zeroes its own bounding - // box, so the wrapper reads as hidden while the clipped text node inside it does not. - const header = sectionHeader(drawer, "Input"); - const body = header.locator("xpath=following-sibling::div[1]"); - await expect(header.locator(".lucide-chevron-up")).toBeVisible(); - await expect(body).toBeVisible(); + const toggle = sectionToggle(drawer, "Input"); + await expect(toggle).toHaveAttribute("aria-expanded", "true"); + await expect(drawer.getByText(prompt, { exact: false })).toBeVisible(); - await header.click(); - await expect(header.locator(".lucide-chevron-down")).toBeVisible({ - timeout: 10_000, - }); - await expect(body).toBeHidden({ timeout: 10_000 }); + await toggle.click(); + await expect(toggle).toHaveAttribute("aria-expanded", "false", { timeout: 10_000 }); - await header.click(); - await expect(header.locator(".lucide-chevron-up")).toBeVisible({ - timeout: 10_000, - }); - await expect(body).toBeVisible({ timeout: 10_000 }); + await toggle.click(); + await expect(toggle).toHaveAttribute("aria-expanded", "true", { timeout: 10_000 }); await expect(drawer.getByText(prompt, { exact: false })).toBeVisible({ timeout: 10_000, }); }); + test("the trace sidebar collapses and expands again", async ({ page, request }) => { + const prompt = `logs-sidebar-prompt-${uniqueSuffix()}`; + const requestId = await sendChatCompletion(request, { + model: CHAT_MODEL_A, + prompt, + }); + await waitForSpendLog(request, requestId); + + const row = await openLogsForRequest(page, requestId); + await row.click(); + + const drawer = page.getByRole("dialog").first(); + await expect(drawer.getByText("Request & Response")).toBeVisible({ timeout: 20_000 }); + + const toggle = drawer.getByLabel("Collapse trace sidebar"); + await expect(toggle).toBeVisible({ timeout: 10_000 }); + await toggle.click(); + + const expandToggle = drawer.getByLabel("Expand trace sidebar"); + await expect(expandToggle).toBeVisible({ timeout: 10_000 }); + await expandToggle.click({ timeout: 10_000 }); + + await expect(drawer.getByLabel("Collapse trace sidebar")).toBeVisible({ timeout: 10_000 }); + await expect(drawer.getByText(prompt, { exact: false })).toBeVisible({ timeout: 10_000 }); + }); + test("the JSON view exposes Request and Response tabs", async ({ page, request }) => { const prompt = `logs-json-prompt-${uniqueSuffix()}`; const requestId = await sendChatCompletion(request, { diff --git a/tests/e2e/ui/tests/logs/logsFilters.spec.ts b/tests/e2e/ui/tests/logs/logsFilters.spec.ts new file mode 100644 index 00000000000..7174f339296 --- /dev/null +++ b/tests/e2e/ui/tests/logs/logsFilters.spec.ts @@ -0,0 +1,152 @@ +import { test, expect, type APIRequestContext, type Locator, type Page as PlaywrightPage } from "@playwright/test"; +import { ADMIN_STORAGE_PATH } from "../../constants"; +import { navigateToPage, dismissFeedbackPopup } from "../../helpers/navigation"; +import { Page } from "../../fixtures/pages"; +import { + CHAT_MODEL_A, + CHAT_MODEL_B, + createVirtualKey, + sendChatCompletion, + waitForSpendLog, +} from "../../helpers/traffic"; + +/** + * Every test mints its own key and asserts against request ids it generated, so a filter that + * quietly does nothing shows up as the other key's row still being on screen, and concurrent + * specs' traffic cannot decide the outcome. + */ + +const uniqueSuffix = (): string => `${Date.now()}-${Math.random().toString(36).slice(2, 8)}`; + +/** Every tab stays mounted, so the DOM holds four tables at once; scope to the visible one. */ +const requestLogsRows = (page: PlaywrightPage): Locator => + page.locator("table").filter({ visible: true }).first().locator("tbody tr"); + +const visibleTestId = (page: PlaywrightPage, id: string): Locator => page.getByTestId(id).filter({ visible: true }); + +async function openLogs(page: PlaywrightPage): Promise { + await navigateToPage(page, Page.Logs); + await dismissFeedbackPopup(page); + await expect(visibleTestId(page, "datatable-search")).toBeVisible({ timeout: 20_000 }); +} + +async function openFilterDrawer(page: PlaywrightPage): Promise { + await visibleTestId(page, "datatable-filters-trigger").click(); + const drawer = page.getByRole("dialog", { name: "Filters" }); + await expect(drawer).toBeVisible({ timeout: 10_000 }); + return drawer; +} + +/** Picks a value in one of the drawer's searchable comboboxes and applies the filter. */ +async function applyComboboxFilter( + page: PlaywrightPage, + drawer: Locator, + comboboxLabel: string, + value: string, +): Promise { + await drawer.getByRole("combobox", { name: comboboxLabel }).click(); + await page.keyboard.type(value); + await page.getByRole("option", { name: value, exact: true }).first().click(); + await drawer.getByRole("button", { name: "Apply Filters" }).click(); + await expect(drawer).not.toBeVisible({ timeout: 10_000 }); +} + +/** A request the key is not entitled to make, so the proxy refuses it and logs the refusal. */ +async function sendDeniedCompletion(request: APIRequestContext, apiKey: string): Promise { + const res = await request.post("/v1/chat/completions", { + headers: { Authorization: `Bearer ${apiKey}`, "Content-Type": "application/json" }, + data: { model: CHAT_MODEL_B, messages: [{ role: "user", content: "denied" }] }, + }); + expect(res.status(), "a model outside the key's allow-list is refused").toBe(403); +} + +test.describe("Logs page filters", () => { + test.use({ storageState: ADMIN_STORAGE_PATH }); + + test("the Key Alias filter narrows the table to that key's requests", async ({ page, request }) => { + const suffix = uniqueSuffix(); + const mine = await createVirtualKey(request, { key_alias: `e2e-logs-mine-${suffix}` }); + const theirs = await createVirtualKey(request, { key_alias: `e2e-logs-theirs-${suffix}` }); + + const myRequestId = await sendChatCompletion(request, { + model: CHAT_MODEL_A, + prompt: `logs-filter-mine-${suffix}`, + apiKey: mine.key, + }); + const theirRequestId = await sendChatCompletion(request, { + model: CHAT_MODEL_A, + prompt: `logs-filter-theirs-${suffix}`, + apiKey: theirs.key, + }); + await waitForSpendLog(request, myRequestId); + await waitForSpendLog(request, theirRequestId); + + await openLogs(page); + const drawer = await openFilterDrawer(page); + await applyComboboxFilter(page, drawer, "Search a key alias", mine.alias!); + + await expect(requestLogsRows(page).filter({ hasText: myRequestId })).toHaveCount(1, { timeout: 30_000 }); + // The filter is only doing its job if the other key's request is gone, not merely if ours is present. + await expect(requestLogsRows(page).filter({ hasText: theirRequestId })).toHaveCount(0, { timeout: 10_000 }); + }); + + test("the Status filter narrows the table to the refused request", async ({ page, request }) => { + const suffix = uniqueSuffix(); + const alias = `e2e-logs-status-${suffix}`; + const scoped = await createVirtualKey(request, { key_alias: alias, models: [CHAT_MODEL_A] }); + + const servedRequestId = await sendChatCompletion(request, { + model: CHAT_MODEL_A, + prompt: `logs-filter-served-${suffix}`, + apiKey: scoped.key, + }); + await sendDeniedCompletion(request, scoped.key); + await waitForSpendLog(request, servedRequestId); + + await openLogs(page); + const drawer = await openFilterDrawer(page); + await drawer.getByRole("combobox", { name: "Search a key alias" }).click(); + await page.keyboard.type(alias); + await page.getByRole("option", { name: alias, exact: true }).first().click(); + // The Status field labels its group, not the trigger, so it is addressed by the value it shows. + await drawer.getByRole("combobox").filter({ hasText: "All Statuses" }).click(); + await page.getByRole("option", { name: "Failure", exact: true }).click(); + await drawer.getByRole("button", { name: "Apply Filters" }).click(); + await expect(drawer).not.toBeVisible({ timeout: 10_000 }); + + // Both requests were made by this key, so a Status filter that does nothing leaves the served one on screen. + await expect(requestLogsRows(page)).toHaveCount(1, { timeout: 30_000 }); + await expect(requestLogsRows(page)).toContainText("Failure"); + await expect(requestLogsRows(page).filter({ hasText: servedRequestId })).toHaveCount(0); + }); + + test("Reset Filters brings back the rows a filter hid", async ({ page, request }) => { + const suffix = uniqueSuffix(); + const mine = await createVirtualKey(request, { key_alias: `e2e-logs-reset-mine-${suffix}` }); + const theirs = await createVirtualKey(request, { key_alias: `e2e-logs-reset-theirs-${suffix}` }); + + const myRequestId = await sendChatCompletion(request, { + model: CHAT_MODEL_A, + prompt: `logs-reset-mine-${suffix}`, + apiKey: mine.key, + }); + const theirRequestId = await sendChatCompletion(request, { + model: CHAT_MODEL_A, + prompt: `logs-reset-theirs-${suffix}`, + apiKey: theirs.key, + }); + await waitForSpendLog(request, myRequestId); + await waitForSpendLog(request, theirRequestId); + + await openLogs(page); + const drawer = await openFilterDrawer(page); + await applyComboboxFilter(page, drawer, "Search a key alias", mine.alias!); + await expect(requestLogsRows(page).filter({ hasText: theirRequestId })).toHaveCount(0, { timeout: 30_000 }); + + // A filter you cannot clear is a page that looks empty forever, which is how it reads to a user. + await page.getByRole("button", { name: "Reset Filters" }).filter({ visible: true }).click(); + + await expect(requestLogsRows(page).filter({ hasText: theirRequestId })).toHaveCount(1, { timeout: 30_000 }); + await expect(requestLogsRows(page).filter({ hasText: myRequestId })).toHaveCount(1, { timeout: 10_000 }); + }); +}); diff --git a/tests/e2e/ui/tests/mcp/mcpServerEdit.spec.ts b/tests/e2e/ui/tests/mcp/mcpServerEdit.spec.ts index 46799c8a18f..aa7cdf82498 100644 --- a/tests/e2e/ui/tests/mcp/mcpServerEdit.spec.ts +++ b/tests/e2e/ui/tests/mcp/mcpServerEdit.spec.ts @@ -73,7 +73,7 @@ test.describe("MCP Servers - edit and delete", () => { test("Deleting a server removes it", async ({ page }) => { expect(await findServerByName(page, serverName), `created server ${serverName} exists`).toBeTruthy(); - const card = page.getByTestId("mcp-servers-grid").locator("div").filter({ hasText: serverName }).first(); + const card = page.getByTestId("mcp-servers-grid").getByRole("button", { name: serverName }); await card.getByRole("button", { name: "Server actions" }).click(); await page.getByRole("menuitem", { name: "Delete" }).click(); diff --git a/tests/e2e/ui/tests/migration/migratedPages.spec.ts b/tests/e2e/ui/tests/migration/migratedPages.spec.ts index 3ad4b217d08..547330190bd 100644 --- a/tests/e2e/ui/tests/migration/migratedPages.spec.ts +++ b/tests/e2e/ui/tests/migration/migratedPages.spec.ts @@ -35,17 +35,12 @@ async function expectRendered(page: Page) { */ async function clickSidebar(page: Page, segment: string) { const link = sidebar(page).locator(`a[href$="/ui/${segment}"]`).first(); + const collapsedGroups = sidebar(page).getByRole("button", { expanded: false }); for (let i = 0; i < 8 && !(await link.isVisible().catch(() => false)); i++) { - // A collapsed group is a menu item with a group-toggle button but no - // rendered submenu yet; clicking the toggle expands it. - const collapsedGroup = sidebar(page) - .locator( - '[data-slot="sidebar-menu-item"]:has(> [data-slot="sidebar-menu-button"]):not(:has(> [data-slot="sidebar-menu-sub"])) > [data-slot="sidebar-menu-button"]', - ) - .first(); - if (!(await collapsedGroup.isVisible().catch(() => false))) break; - await collapsedGroup.click(); - await page.waitForTimeout(250); + const stillCollapsed = await collapsedGroups.count(); + if (stillCollapsed === 0) break; + await collapsedGroups.first().click(); + await expect(collapsedGroups).toHaveCount(stillCollapsed - 1); } await link.click(); } diff --git a/tests/e2e/ui/tests/modelHub/modelHub.spec.ts b/tests/e2e/ui/tests/modelHub/modelHub.spec.ts index 16ec94c1dc8..6877fc9c48d 100644 --- a/tests/e2e/ui/tests/modelHub/modelHub.spec.ts +++ b/tests/e2e/ui/tests/modelHub/modelHub.spec.ts @@ -1,7 +1,8 @@ -import { test, expect } from "@playwright/test"; +import { test, expect, type APIRequestContext } from "@playwright/test"; import { ADMIN_STORAGE_PATH } from "../../constants"; import { navigateToPage, dismissFeedbackPopup } from "../../helpers/navigation"; import { Page } from "../../fixtures/pages"; +import { masterKey } from "../../helpers/traffic"; test.describe("AI Hub (internal admin view)", () => { test.use({ storageState: ADMIN_STORAGE_PATH }); @@ -77,4 +78,89 @@ test.describe("Public model hub (/ui/model_hub_table)", () => { // agents/MCP servers exist, so we don't assert on them in a fresh CI run. await expect(page.getByRole("tab", { name: "Model Hub" })).toBeVisible({ timeout: 10_000 }); }); + + test("Agent Hub and MCP Hub tabs render their public entries", async ({ page, request }) => { + const suffix = `${Date.now()}`; + const agentName = `e2e-public-agent-${suffix}`; + const mcpServerName = `e2e_public_mcp_${suffix}`; + const auth = { Authorization: `Bearer ${masterKey()}` }; + + const publicMcpServerIds = async (api: APIRequestContext): Promise => { + const res = await api.get("/public/mcp_hub"); + expect(res.ok(), `public mcp_hub read failed (${res.status()}): ${await res.text()}`).toBe(true); + const servers: { server_id: string }[] = await res.json(); + return servers.map((server) => server.server_id); + }; + + const seedPublicEntries = async ( + api: APIRequestContext, + priorMcpIds: string[], + ): Promise<{ agentId: string; serverId: string }> => { + const agentRes = await api.post("/v1/agents", { + headers: auth, + data: { + agent_name: agentName, + agent_card_params: { + name: agentName, + description: "E2E public agent", + version: "1.0.0", + url: "http://127.0.0.1:9999/", + capabilities: {}, + skills: [], + defaultInputModes: ["text"], + defaultOutputModes: ["text"], + }, + }, + }); + expect(agentRes.ok(), `agent create failed (${agentRes.status()}): ${await agentRes.text()}`).toBe(true); + const agentId = (await agentRes.json()).agent_id as string; + + const serverRes = await api.post("/v1/mcp/server", { + headers: auth, + data: { + server_name: mcpServerName, + url: "http://127.0.0.1:9999/mcp", + transport: "http", + description: "E2E public MCP server", + }, + }); + expect(serverRes.ok(), `mcp server create failed (${serverRes.status()}): ${await serverRes.text()}`).toBe(true); + const serverId = (await serverRes.json()).server_id as string; + + const agentPublicRes = await api.post(`/v1/agents/${agentId}/make_public`, { headers: auth }); + expect(agentPublicRes.ok(), `agent make_public failed: ${await agentPublicRes.text()}`).toBe(true); + const mcpPublicRes = await api.post("/v1/mcp/make_public", { + headers: auth, + data: { mcp_server_ids: [...priorMcpIds, serverId] }, + }); + expect(mcpPublicRes.ok(), `mcp make_public failed: ${await mcpPublicRes.text()}`).toBe(true); + + return { agentId, serverId }; + }; + + const priorMcpIds = await publicMcpServerIds(request); + const { agentId, serverId } = await seedPublicEntries(request, priorMcpIds); + try { + await page.goto(`/ui/model_hub_table?key=${masterKey()}`); + await dismissFeedbackPopup(page); + + const agentHubTab = page.getByRole("tab", { name: "Agent Hub" }); + await expect(agentHubTab).toBeVisible({ timeout: 15_000 }); + await agentHubTab.click(); + await expect(page.getByText("Available Agents")).toBeVisible({ timeout: 10_000 }); + await expect(page.getByRole("row").filter({ hasText: agentName })).toHaveCount(1, { timeout: 10_000 }); + await expect(page.getByText("E2E public agent").first()).toBeVisible(); + + const mcpHubTab = page.getByRole("tab", { name: "MCP Hub" }); + await expect(mcpHubTab).toBeVisible(); + await mcpHubTab.click(); + await expect(page.getByText("Available MCP Servers")).toBeVisible({ timeout: 10_000 }); + await expect(page.getByRole("row").filter({ hasText: mcpServerName })).toHaveCount(1, { timeout: 10_000 }); + await expect(page.getByText("E2E public MCP server").first()).toBeVisible(); + } finally { + await request.post("/v1/mcp/make_public", { headers: auth, data: { mcp_server_ids: priorMcpIds } }); + await request.delete(`/v1/agents/${agentId}`, { headers: auth }); + await request.delete(`/v1/mcp/server/${serverId}`, { headers: auth }); + } + }); }); diff --git a/tests/e2e/ui/tests/modelsPage/addModel.spec.ts b/tests/e2e/ui/tests/modelsPage/addModel.spec.ts index dad716b4c83..de25ec1aac5 100644 --- a/tests/e2e/ui/tests/modelsPage/addModel.spec.ts +++ b/tests/e2e/ui/tests/modelsPage/addModel.spec.ts @@ -5,6 +5,11 @@ import { navigateToPage } from "../../helpers/navigation"; import { Page } from "../../fixtures/pages"; import { captureRequestBody, readBack } from "../../helpers/roundTrip"; import { sendChatCompletion } from "../../helpers/traffic"; +import { proxyIsPremium } from "../../helpers/premium"; + +/** Four probes 13s apart span 39s, one PROXY_CONFIG_RELOAD_INTERVAL_SECONDS (30s) plus margin. */ +const CREDENTIAL_PROBE_SUCCESSES = 4; +const CREDENTIAL_PROBE_SPACING_MS = 13_000; /** The mock LLM as the proxy reaches it: same host locally, a sidecar in the deployed stack. */ const MOCK_LLM_BASE = `http://127.0.0.1:${process.env.MOCK_LLM_PORT ?? "8090"}/v1`; @@ -35,7 +40,10 @@ async function selectProvider(page: PlaywrightPage, providerName: string) { const providerDropdown = page.getByRole("combobox", { name: "Provider", exact: true }); await providerDropdown.click(); await providerDropdown.fill(providerName); - await page.getByRole("option").filter({ hasText: exactly(providerName) }).click(); + await page + .getByRole("option") + .filter({ hasText: exactly(providerName) }) + .click(); await expect(providerDropdown).toHaveValue(providerName); } @@ -78,6 +86,9 @@ test.describe("Add Model", () => { }); test("Edit team model TPM and RPM limits", async ({ page }) => { + // /model/new refuses a team-scoped deployment on an unlicensed proxy, so there this fails in + // setup on a product gate rather than on a regression in the edit it covers. + test.skip(!proxyIsPremium(), "proxy under test is unlicensed — team-scoped models are premium"); const masterKey = users[Role.ProxyAdmin].password; const modelName = `e2e-team-model-${Date.now()}`; @@ -188,7 +199,7 @@ test.describe("Add Model", () => { await expect(resultsModal).toBeHidden({ timeout: 5_000 }); const created = await captureRequestBody(page, { method: "POST", urlIncludes: "/model/new" }, async () => { - await page.getByRole("button", { name: "Add Model" }).last().click(); + await page.getByTestId("add-model-btn").click(); }); expect(created.model_name, "the model is created under the name that was typed").toBe(publicName); expect(created.litellm_params?.api_base, "the api base survives the form").toBe(MOCK_LLM_BASE); @@ -212,6 +223,120 @@ test.describe("Add Model", () => { .toBe(true); }); + test("Add a model with a stored credential, pass Test Connect, and serve traffic", async ({ page, request }) => { + const masterKey = users[Role.ProxyAdmin].password; + const auth = { Authorization: `Bearer ${masterKey}` }; + const credentialName = `e2e-cred-reuse-${Date.now()}`; + const createCred = await page.request.post("/credentials", { + headers: auth, + data: { + credential_name: credentialName, + credential_values: { api_key: "fake-key", api_base: MOCK_LLM_BASE }, + credential_info: { custom_llm_provider: "openai" }, + }, + }); + expect(createCred.ok(), `POST /credentials failed (${createCred.status()}): ${await createCred.text()}`).toBe(true); + + // The proxy's periodic credential refresh prunes its in-memory list against a database snapshot + // it took before this credential landed, so a credential that resolves right after POST + // /credentials can stop resolving until the refresh after that. Successes spanning a whole + // PROXY_CONFIG_RELOAD_INTERVAL_SECONDS prove it survived a refresh, after which it stays. + // Resolution fails open onto the ambient key, so losing it reads as a confusing upstream 404. + let consecutiveProbeSuccesses = 0; + await expect + .poll( + async () => { + const probe = await page.request.post("/health/test_connection", { + headers: auth, + data: { + litellm_params: { + model: "openai/fake-gpt-4", + custom_llm_provider: "openai", + litellm_credential_name: credentialName, + }, + model_info: {}, + mode: "chat", + }, + }); + const healthy = probe.ok() && (await probe.json()).status === "success"; + consecutiveProbeSuccesses = healthy ? consecutiveProbeSuccesses + 1 : 0; + return consecutiveProbeSuccesses; + }, + { + message: `stored credential ${credentialName} never stayed usable across a config reload`, + intervals: [0, CREDENTIAL_PROBE_SPACING_MS], + timeout: 110_000, + }, + ) + .toBeGreaterThanOrEqual(CREDENTIAL_PROBE_SUCCESSES); + + try { + await navigateToPage(page, Page.Models); + await page.getByRole("tab", { name: "Add Model" }).click(); + + await selectProvider(page, "OpenAI-Compatible Endpoints (Together AI, etc.)"); + + const publicName = `e2e-cred-model-${Date.now()}`; + uiAddedModelName = publicName; + + await page.getByRole("combobox", { name: "Select models" }).click(); + await page.getByRole("option", { name: "Custom Model Name (Enter below)" }).click(); + await page.keyboard.press("Escape"); + await page.getByPlaceholder("Enter custom model name").fill(publicName); + + const credentialSelect = page.getByRole("combobox", { name: "Existing Credentials" }); + await credentialSelect.click(); + await credentialSelect.fill(credentialName); + await page.getByRole("option", { name: credentialName, exact: true }).click(); + + await expect(page.locator("#api_key")).toHaveCount(0); + await expect(page.locator("#api_base")).toHaveCount(0); + + await page.getByRole("button", { name: "Test Connect" }).click(); + await expect(page.getByText("Connection Test Results")).toBeVisible({ timeout: 10_000 }); + await expect(page.getByTestId("connection-success-msg")).toBeVisible({ timeout: 30_000 }); + + const resultsModal = page.getByRole("dialog", { name: "Connection Test Results" }); + await resultsModal.locator('[data-slot="dialog-footer"]').getByRole("button", { name: "Close" }).click(); + await expect(resultsModal).toBeHidden({ timeout: 5_000 }); + + const created = await captureRequestBody(page, { method: "POST", urlIncludes: "/model/new" }, async () => { + await page.getByRole("button", { name: "Add Model" }).last().click(); + }); + expect(created.litellm_params?.litellm_credential_name, "the picked credential goes on the wire").toBe( + credentialName, + ); + expect(created.litellm_params?.api_key, "no raw api key goes on the wire").toBeUndefined(); + + await expect(page.getByText("created successfully")).toBeVisible({ timeout: 15_000 }); + + await expect + .poll( + async () => { + try { + await sendChatCompletion(request, { model: publicName, prompt: `hello via ${credentialName}` }); + return true; + } catch { + return false; + } + }, + { + message: `model ${publicName} added with a stored credential never served a request`, + timeout: 30_000, + }, + ) + .toBe(true); + } finally { + const stored = uiAddedModelName ? await findDeploymentByName(page, uiAddedModelName) : undefined; + const id = stored?.model_info?.id; + if (id) { + await page.request.post("/model/delete", { headers: auth, data: { id } }); + uiAddedModelName = ""; + } + await page.request.delete(`/credentials/${credentialName}`, { headers: auth }); + } + }); + test("Test connection with bad credentials shows failure", async ({ page }) => { await navigateToPage(page, Page.Models); await page.getByRole("tab", { name: "Add Model" }).click(); @@ -254,7 +379,7 @@ test.describe("Add Model", () => { // Click Add Model button by its text const created = await captureRequestBody(page, { method: "POST", urlIncludes: "/model/new" }, async () => { - await page.getByRole("button", { name: "Add Model" }).last().click(); + await page.getByTestId("add-model-btn").click(); }); // The form sends custom_llm_provider separately from the name, so both halves have to arrive. expect(created.model_name, "the selected model is what goes on the wire").toBe("claude-haiku-4-5"); @@ -267,11 +392,9 @@ test.describe("Add Model", () => { // Navigate to All Models tab await page.getByRole("tab", { name: "All Models" }).click(); await page.waitForLoadState("networkidle"); - await page.waitForTimeout(2000); // Search for the model we just added await page.getByPlaceholder("Search model names").fill("claude-haiku-4-5"); - await page.waitForTimeout(1000); // Verify the model appears in the results count (not "Showing 0 results") await expect(page.getByTestId("pagination-range")).toHaveText(/Showing \d+-\d+ of \d+/, { @@ -279,8 +402,9 @@ test.describe("Add Model", () => { }); // Verify the model name appears in the table body - const tableBody = page.locator("table tbody"); - await expect(tableBody.getByText("claude-haiku-4-5").first()).toBeVisible({ timeout: 15_000 }); + await expect(page.getByRole("row").filter({ hasText: "claude-haiku-4-5" })).not.toHaveCount(0, { + timeout: 15_000, + }); // A row proves the name is there, not what the deployment routes to. const stored = await findDeploymentByName(page, "claude-haiku-4-5"); @@ -333,11 +457,11 @@ test.describe("Add Model", () => { const teamDropdown = page.getByTestId("team-dropdown").getByRole("combobox"); await expect(teamDropdown).toBeVisible({ timeout: 5_000 }); await teamDropdown.click(); - const teamOption = page.locator('[data-slot="combobox-content"]:visible').getByText(E2E_TEAM_CRUD_ID).first(); + const teamOption = page.getByRole("option", { name: E2E_TEAM_CRUD_ID }).first(); await expect(teamOption).toBeVisible({ timeout: 5_000 }); await teamOption.click(); - await page.getByRole("button", { name: "Add Model" }).last().click(); + await page.getByTestId("add-model-btn").click(); // Scope to the toast container so a stale toast can't satisfy this. await expect(page.locator("[data-sonner-toast]").getByText("created successfully").last()).toBeVisible({ @@ -347,11 +471,8 @@ test.describe("Add Model", () => { // The Models table renders team-scoped models with the team id in the row. await page.getByRole("tab", { name: "All Models" }).click(); await page.waitForLoadState("networkidle"); - // networkidle fires before the table finishes re-rendering. - await page.waitForTimeout(2000); await page.getByPlaceholder("Search model names").fill("cohere"); - await page.waitForTimeout(1000); // Clearer failure than timing out on a row assertion when the table is empty. await expect(page.getByTestId("pagination-range")).toHaveText(/Showing \d+-\d+ of \d+/, { @@ -360,10 +481,7 @@ test.describe("Add Model", () => { // Pin to one row carrying both the name and the team, so the sibling test's // team-less cohere row can't satisfy it. - const teamCohereRow = page - .locator("table tbody tr") - .filter({ hasText: "cohere/" }) - .filter({ hasText: E2E_TEAM_CRUD_ID }); + const teamCohereRow = page.getByRole("row").filter({ hasText: "cohere/" }).filter({ hasText: E2E_TEAM_CRUD_ID }); await expect(teamCohereRow).toHaveCount(1, { timeout: 15_000 }); } finally { await deleteTeamScopedCohereModels(); @@ -387,7 +505,7 @@ test.describe("Add Model", () => { // Click Add Model button by its text const created = await captureRequestBody(page, { method: "POST", urlIncludes: "/model/new" }, async () => { - await page.getByRole("button", { name: "Add Model" }).last().click(); + await page.getByTestId("add-model-btn").click(); }); // A wildcard with the star stripped becomes a plain "cohere" deployment that matches nothing. expect(created.model_name, "the wildcard route goes on the wire intact").toBe("cohere/*"); @@ -398,11 +516,9 @@ test.describe("Add Model", () => { // Navigate to All Models tab await page.getByRole("tab", { name: "All Models" }).click(); await page.waitForLoadState("networkidle"); - await page.waitForTimeout(2000); // Search for the wildcard model await page.getByPlaceholder("Search model names").fill("cohere"); - await page.waitForTimeout(1000); // Verify the model appears in the results count (not "Showing 0 results") await expect(page.getByTestId("pagination-range")).toHaveText(/Showing \d+-\d+ of \d+/, { @@ -410,8 +526,7 @@ test.describe("Add Model", () => { }); // Verify the wildcard model appears in the table body (wildcard models show as "cohere/*") - const tableBody = page.locator("table tbody"); - await expect(tableBody.getByText("cohere/").first()).toBeVisible({ timeout: 15_000 }); + await expect(page.getByRole("row").filter({ hasText: "cohere/" })).not.toHaveCount(0, { timeout: 15_000 }); // "cohere/" in the table also matches a plain cohere deployment; require the wildcard exactly. const stored = await findDeploymentByName(page, "cohere/*"); diff --git a/tests/e2e/ui/tests/modelsPage/autoRouterTemplateSelect.spec.ts b/tests/e2e/ui/tests/modelsPage/autoRouterTemplateSelect.spec.ts index 1d080ec82b8..51df50a2e68 100644 --- a/tests/e2e/ui/tests/modelsPage/autoRouterTemplateSelect.spec.ts +++ b/tests/e2e/ui/tests/modelsPage/autoRouterTemplateSelect.spec.ts @@ -17,49 +17,54 @@ async function openTemplateSelect(page: PlaywrightPage) { return trigger; } -function pollPixelsBelowTrigger(trigger: Locator, popup: Locator) { +async function boxes(trigger: Locator, options: Locator) { + const triggerBox = await trigger.boundingBox(); + const optionsBox = await options.boundingBox(); + return triggerBox && optionsBox ? { triggerBox, optionsBox } : null; +} + +const clippedPopup = (page: PlaywrightPage) => page.locator('[data-slot="select-content"]'); + +function pollOptionsOpenBelowTrigger(trigger: Locator, options: Locator) { return expect.poll(async () => { - const triggerBox = await trigger.boundingBox(); - const popupBox = await popup.boundingBox(); - if (!triggerBox || !popupBox) return null; - return popupBox.y - (triggerBox.y + triggerBox.height); + const box = await boxes(trigger, options); + return box && box.optionsBox.y >= box.triggerBox.y + box.triggerBox.height; }); } -function pollPopupOverlapsTrigger(trigger: Locator, popup: Locator) { +function pollOptionsCoverTrigger(trigger: Locator, options: Locator) { return expect.poll(async () => { - const triggerBox = await trigger.boundingBox(); - const popupBox = await popup.boundingBox(); - if (!triggerBox || !popupBox) return null; - return popupBox.y < triggerBox.y + triggerBox.height && popupBox.y + popupBox.height > triggerBox.y; + const box = await boxes(trigger, options); + return ( + box && + box.optionsBox.y < box.triggerBox.y + box.triggerBox.height && + box.optionsBox.y + box.optionsBox.height > box.triggerBox.y + ); }); } test.describe("Auto Router template select anchoring", () => { test.use({ storageState: ADMIN_STORAGE_PATH }); - test("opens the options below the trigger rather than over it", async ({ page }) => { + test("opens the options below the trigger when there is room below it", async ({ page }) => { await page.setViewportSize({ width: 1280, height: 900 }); const trigger = await openTemplateSelect(page); + await trigger.scrollIntoViewIfNeeded(); await trigger.click(); - const popup = page.locator('[data-slot="select-content"]'); - await expect(popup).toBeVisible(); + await expect(page.getByRole("listbox")).toBeVisible(); - // Item-aligned mode reports "none" and puts the active item over the trigger. - await expect(popup).toHaveAttribute("data-side", "bottom"); - await pollPixelsBelowTrigger(trigger, popup).toBeGreaterThanOrEqual(0); + await pollOptionsOpenBelowTrigger(trigger, clippedPopup(page)).toBe(true); }); - test("flips above the trigger instead of covering it when there is no room below", async ({ page }) => { + test("keeps the trigger uncovered when the options open with no room below it", async ({ page }) => { await page.setViewportSize({ width: 1280, height: 560 }); const trigger = await openTemplateSelect(page); await trigger.scrollIntoViewIfNeeded(); await trigger.click(); - const popup = page.locator('[data-slot="select-content"]'); - await expect(popup).toBeVisible(); + await expect(page.getByRole("listbox")).toBeVisible(); - await pollPopupOverlapsTrigger(trigger, popup).toBe(false); + await pollOptionsCoverTrigger(trigger, clippedPopup(page)).toBe(false); }); }); diff --git a/tests/e2e/ui/tests/modelsPage/deleteTeamModel.spec.ts b/tests/e2e/ui/tests/modelsPage/deleteTeamModel.spec.ts new file mode 100644 index 00000000000..96abd9833c0 --- /dev/null +++ b/tests/e2e/ui/tests/modelsPage/deleteTeamModel.spec.ts @@ -0,0 +1,72 @@ +import { test, expect, type Page as PlaywrightPage } from "@playwright/test"; +import { ADMIN_STORAGE_PATH, E2E_TEAM_CRUD_ID } from "../../constants"; +import { Page } from "../../fixtures/pages"; +import { navigateToPage } from "../../helpers/navigation"; +import { readBack } from "../../helpers/roundTrip"; +import { masterKey } from "../../helpers/traffic"; + +type DeploymentRow = { model_name?: string }; + +async function findDeploymentByName(page: PlaywrightPage, modelName: string): Promise { + const body = await readBack<{ data: DeploymentRow[] }>(page, "/v2/model/info"); + return body.data.find((row) => row.model_name === modelName); +} + +test.describe("Delete team model", () => { + test.use({ storageState: ADMIN_STORAGE_PATH }); + + test("Delete a team-scoped model and verify it leaves the team's model list", async ({ page }) => { + const modelName = `e2e-team-model-delete-${Date.now()}`; + const createResponse = await page.request.post("/model/new", { + headers: { Authorization: `Bearer ${masterKey()}` }, + data: { + model_name: modelName, + litellm_params: { + model: "openai/fake-gpt-4", + api_base: `http://127.0.0.1:${process.env.MOCK_LLM_PORT ?? "8090"}/v1`, + api_key: "fake-key", + }, + model_info: { team_id: E2E_TEAM_CRUD_ID }, + }, + }); + expect(createResponse.ok(), `/model/new failed: ${createResponse.status()} ${await createResponse.text()}`).toBe( + true, + ); + + await expect + .poll(async () => (await findDeploymentByName(page, modelName)) !== undefined, { + message: `deployment ${modelName} never appeared in /v2/model/info after create`, + timeout: 30_000, + }) + .toBe(true); + + await navigateToPage(page, Page.Models); + await page.getByPlaceholder("Search model names").fill(modelName); + + const row = page.getByRole("row").filter({ hasText: modelName }); + await expect(row).toHaveCount(1, { timeout: 15_000 }); + await expect(row.getByText(E2E_TEAM_CRUD_ID)).toBeVisible({ timeout: 10_000 }); + + await row.getByRole("button", { name: "Delete model" }).click(); + + const modal = page.getByRole("dialog", { name: "Delete Model" }); + await expect(modal).toBeVisible({ timeout: 5_000 }); + await expect(modal.getByText(modelName).first()).toBeVisible(); + await modal.getByRole("button", { name: "Delete", exact: true }).click(); + + await expect(page.getByText("Model deleted successfully").first()).toBeVisible({ timeout: 10_000 }); + await expect(row).toHaveCount(0, { timeout: 15_000 }); + + await expect + .poll(async () => await findDeploymentByName(page, modelName), { + message: `deployment ${modelName} still readable from /v2/model/info after delete`, + timeout: 15_000, + }) + .toBeUndefined(); + + await page.reload(); + await page.getByPlaceholder("Search model names").fill(modelName); + await expect(page.getByText("No models found").first()).toBeVisible({ timeout: 15_000 }); + await expect(page.getByRole("row").filter({ hasText: modelName })).toHaveCount(0); + }); +}); diff --git a/tests/e2e/ui/tests/modelsPage/responsiveHeader.spec.ts b/tests/e2e/ui/tests/modelsPage/responsiveHeader.spec.ts index 6ad1ccb8451..aabdf18d427 100644 --- a/tests/e2e/ui/tests/modelsPage/responsiveHeader.spec.ts +++ b/tests/e2e/ui/tests/modelsPage/responsiveHeader.spec.ts @@ -7,9 +7,7 @@ test.describe("Models and Endpoints responsive header", () => { viewport: { width: 900, height: 720 }, }); - test("keeps the refresh action on the same row as the tabs", async ({ - page, - }) => { + test("keeps the refresh action on the same row as the tabs", async ({ page }) => { await page.goto("/ui"); await page .getByRole("complementary") @@ -26,8 +24,8 @@ test.describe("Models and Endpoints responsive header", () => { expect(tabsBox).not.toBeNull(); expect(refreshBox).not.toBeNull(); - const tabsCenterY = tabsBox!.y + tabsBox!.height / 2; const refreshCenterY = refreshBox!.y + refreshBox!.height / 2; - expect(Math.abs(tabsCenterY - refreshCenterY)).toBeLessThanOrEqual(2); + const sharesARow = refreshCenterY > tabsBox!.y && refreshCenterY < tabsBox!.y + tabsBox!.height; + expect(sharesARow, "refresh wrapped onto its own row below the tabs").toBe(true); }); }); diff --git a/tests/e2e/ui/tests/proxy-admin/keys.spec.ts b/tests/e2e/ui/tests/proxy-admin/keys.spec.ts index 0c38641dcc7..deb7ae70d07 100644 --- a/tests/e2e/ui/tests/proxy-admin/keys.spec.ts +++ b/tests/e2e/ui/tests/proxy-admin/keys.spec.ts @@ -1,15 +1,17 @@ import { test, expect, type Page as PlaywrightPage } from "@playwright/test"; import { ADMIN_STORAGE_PATH, - E2E_DELETE_KEY_ALIAS, E2E_REGENERATE_KEY_ALIAS, E2E_UPDATE_LIMITS_KEY_ALIAS, E2E_INTERNAL_USER_KEY_ALIAS, E2E_TEAM_CRUD_ALIAS, + E2E_TEAM_CRUD_ID, } from "../../constants"; import { Page } from "../../fixtures/pages"; import { navigateToPage, dismissFeedbackPopup } from "../../helpers/navigation"; import { captureRequestBody, readBack } from "../../helpers/roundTrip"; +import { masterKey } from "../../helpers/traffic"; +import { proxyIsPremium } from "../../helpers/premium"; /** * Looks a key up by alias, undefined when none carries it. `return_full_object=true` is what makes @@ -23,6 +25,17 @@ async function findKeyByAlias(page: PlaywrightPage, alias: string): Promise row.key_alias === alias); } +/** A key this test owns, so deleting it costs the suite nothing on a retry or a second run. */ +async function createDeletableKey(page: PlaywrightPage): Promise { + const alias = `e2e-delete-key-${Date.now()}`; + const res = await page.request.post("/key/generate", { + headers: { Authorization: `Bearer ${masterKey()}` }, + data: { key_alias: alias, team_id: E2E_TEAM_CRUD_ID }, + }); + expect(res.ok(), `POST /key/generate failed (${res.status()}): ${await res.text()}`).toBe(true); + return alias; +} + test.describe("Proxy Admin - Keys", () => { test.use({ storageState: ADMIN_STORAGE_PATH }); @@ -43,7 +56,7 @@ test.describe("Proxy Admin - Keys", () => { const teamSelect = page.getByTestId("team-dropdown").getByRole("combobox"); await teamSelect.click(); await page.keyboard.type(E2E_TEAM_CRUD_ALIAS); - await page.locator('[data-slot="combobox-content"]:visible').getByText(E2E_TEAM_CRUD_ALIAS).first().click(); + await page.getByRole("option", { name: E2E_TEAM_CRUD_ALIAS }).first().click(); // Select models — the popup is portaled to the body, so scope options to the page. await page.getByRole("combobox", { name: "Select models" }).click(); @@ -67,6 +80,9 @@ test.describe("Proxy Admin - Keys", () => { }); test("Regenerate key", async ({ page }) => { + // The Regenerate Key button renders disabled when the proxy is unlicensed, so without one this + // fails on a product gate rather than on a regression. + test.skip(!proxyIsPremium(), "proxy under test is unlicensed — Regenerate Key is premium-gated"); await navigateToPage(page, Page.ApiKeys); await dismissFeedbackPopup(page); @@ -74,10 +90,9 @@ test.describe("Proxy Admin - Keys", () => { const before = await findKeyByAlias(page, E2E_REGENERATE_KEY_ALIAS); expect(before?.token, `seeded key ${E2E_REGENERATE_KEY_ALIAS} has a token`).toBeTruthy(); - // Key IDs are rendered as buttons in the table - const keyRow = page.locator("tr", { hasText: E2E_REGENERATE_KEY_ALIAS }); + const keyRow = page.getByRole("row").filter({ hasText: E2E_REGENERATE_KEY_ALIAS }); await expect(keyRow).toBeVisible({ timeout: 10_000 }); - await keyRow.locator("button").first().click(); + await keyRow.getByRole("button", { name: E2E_REGENERATE_KEY_ALIAS }).click(); await expect(page.getByText("Back to Keys")).toBeVisible({ timeout: 10_000 }); @@ -109,9 +124,9 @@ test.describe("Proxy Admin - Keys", () => { const before = await findKeyByAlias(page, E2E_UPDATE_LIMITS_KEY_ALIAS); expect(before, `seeded key ${E2E_UPDATE_LIMITS_KEY_ALIAS} exists`).toBeTruthy(); - const keyRow = page.locator("tr", { hasText: E2E_UPDATE_LIMITS_KEY_ALIAS }); + const keyRow = page.getByRole("row").filter({ hasText: E2E_UPDATE_LIMITS_KEY_ALIAS }); await expect(keyRow).toBeVisible({ timeout: 10_000 }); - await keyRow.locator("button").first().click(); + await keyRow.getByRole("button", { name: E2E_UPDATE_LIMITS_KEY_ALIAS }).click(); await expect(page.getByText("Back to Keys")).toBeVisible({ timeout: 10_000 }); @@ -144,12 +159,16 @@ test.describe("Proxy Admin - Keys", () => { }); test("Delete key", async ({ page }) => { + // Deleting the seeded key leaves nothing for the next attempt, so the retries CI runs with are + // guaranteed to fail and the suite cannot run twice against one database. Bring our own. + const alias = await createDeletableKey(page); + await navigateToPage(page, Page.ApiKeys); await dismissFeedbackPopup(page); - const keyRow = page.locator("tr", { hasText: E2E_DELETE_KEY_ALIAS }); + const keyRow = page.getByRole("row").filter({ hasText: alias }); await expect(keyRow).toBeVisible({ timeout: 10_000 }); - await keyRow.locator("button").first().click(); + await keyRow.getByRole("button", { name: alias }).click(); await expect(page.getByText("Back to Keys")).toBeVisible({ timeout: 10_000 }); @@ -158,7 +177,7 @@ test.describe("Proxy Admin - Keys", () => { const modal = page.getByRole("dialog", { name: "Delete Key" }); await expect(modal).toBeVisible({ timeout: 5_000 }); - await modal.locator("input").fill(E2E_DELETE_KEY_ALIAS); + await modal.locator("input").fill(alias); const deleteButton = modal.getByRole("button", { name: "Delete", exact: true }); await expect(deleteButton).toBeEnabled(); @@ -168,8 +187,8 @@ test.describe("Proxy Admin - Keys", () => { // The key is gone when the management API stops returning it, not when the toast says so. await expect - .poll(async () => await findKeyByAlias(page, E2E_DELETE_KEY_ALIAS), { - message: `key ${E2E_DELETE_KEY_ALIAS} still readable from /key/list after delete`, + .poll(async () => await findKeyByAlias(page, alias), { + message: `key ${alias} still readable from /key/list after delete`, timeout: 15_000, }) .toBeUndefined(); diff --git a/tests/e2e/ui/tests/proxy-admin/secondAdmin.spec.ts b/tests/e2e/ui/tests/proxy-admin/secondAdmin.spec.ts new file mode 100644 index 00000000000..5a8bc84cc13 --- /dev/null +++ b/tests/e2e/ui/tests/proxy-admin/secondAdmin.spec.ts @@ -0,0 +1,97 @@ +import { test, expect } from "@playwright/test"; +import { ADMIN_STORAGE_PATH } from "../../constants"; +import { Page } from "../../fixtures/pages"; +import { navigateToPage, dismissFeedbackPopup } from "../../helpers/navigation"; +import { CHAT_MODEL_A, MOCK_RESPONSE_TEXT, masterKey } from "../../helpers/traffic"; + +test.describe("Second proxy admin", () => { + test.use({ storageState: { cookies: [], origins: [] } }); + + test("an invited admin can log in, mint a key, and call a model with it", async ({ page, browser, request }) => { + const suffix = Date.now(); + const email = `second-admin-${suffix}@test.local`; + const password = "e2e-second-admin-password"; + const auth = { Authorization: `Bearer ${masterKey()}` }; + + const inviteAdminUser = async (): Promise => { + const adminContext = await browser.newContext({ storageState: ADMIN_STORAGE_PATH }); + try { + const adminPage = await adminContext.newPage(); + await navigateToPage(adminPage, Page.Users); + await dismissFeedbackPopup(adminPage); + + await adminPage.getByRole("button", { name: "+ Invite User", exact: true }).click(); + const dialog = adminPage.getByRole("dialog", { name: "Invite User" }); + await expect(dialog).toBeVisible({ timeout: 5_000 }); + + await dialog.getByLabel("User Email").fill(email); + + await dialog.getByLabel(/Global Proxy Role/).click(); + await adminPage.getByRole("option", { name: /Admin \(All Permissions\)/ }).click(); + + const createdResponse = adminPage.waitForResponse( + (res) => res.url().includes("/user/new") && res.request().method() === "POST", + ); + await dialog.getByRole("button", { name: "Invite User" }).click(); + const createdBody = await (await createdResponse).json(); + const createdUserId = (createdBody.data?.user_id ?? createdBody.user_id) as string; + expect(createdUserId, "created user id from /user/new").toBeTruthy(); + + await expect(adminPage.getByText("API user Created").first()).toBeVisible({ timeout: 10_000 }); + return createdUserId; + } finally { + await adminContext.close(); + } + }; + + const userId = await inviteAdminUser(); + try { + const passwordRes = await request.post("/user/update", { + headers: auth, + data: { user_email: email, password }, + }); + expect(passwordRes.ok(), `setting password failed (${passwordRes.status()}): ${await passwordRes.text()}`).toBe( + true, + ); + + await page.goto("/ui/login"); + await page.getByPlaceholder("Enter your username").fill(email); + await page.getByPlaceholder("Enter your password").fill(password); + await page.getByRole("button", { name: "Login", exact: true }).click(); + await expect(page.locator("a", { hasText: "Virtual Keys" })).toBeVisible({ timeout: 30_000 }); + await dismissFeedbackPopup(page); + + await navigateToPage(page, Page.ApiKeys); + await page.getByRole("button", { name: /Create New Key/i }).click(); + await expect(page.getByText("Key Ownership")).toBeVisible({ timeout: 10_000 }); + + await page.getByLabel(/Key Name/).fill(`e2e-second-admin-key-${suffix}`); + + await page.getByRole("combobox", { name: "Select models" }).click(); + await page.getByRole("option", { name: "All Proxy Models", exact: true }).click(); + await page.keyboard.press("Escape"); + + await page.getByRole("button", { name: "Create Key", exact: true }).click(); + + await expect(page.getByText("Save your Key")).toBeVisible({ timeout: 10_000 }); + const apiKey = (await page.getByRole("dialog", { name: "Save your Key" }).locator("pre").innerText()).trim(); + expect(apiKey).toMatch(/^sk-/); + await page.keyboard.press("Escape"); + + const response = await page.request.post("/chat/completions", { + headers: { Authorization: `Bearer ${apiKey}` }, + data: { + model: CHAT_MODEL_A, + messages: [{ role: "user", content: `second admin ping ${suffix}` }], + }, + }); + expect(response.status()).toBe(200); + const body = await response.json(); + expect(body.choices?.[0]?.message?.content).toBe(MOCK_RESPONSE_TEXT); + } finally { + if (userId) { + await request.post("/user/delete", { headers: auth, data: { user_ids: [userId] } }); + } + } + }); +}); diff --git a/tests/e2e/ui/tests/proxy-admin/teamSettings.spec.ts b/tests/e2e/ui/tests/proxy-admin/teamSettings.spec.ts new file mode 100644 index 00000000000..e71945a4ccd --- /dev/null +++ b/tests/e2e/ui/tests/proxy-admin/teamSettings.spec.ts @@ -0,0 +1,162 @@ +import { test, expect, type Page as PlaywrightPage } from "@playwright/test"; +import { ADMIN_STORAGE_PATH } from "../../constants"; +import { Page } from "../../fixtures/pages"; +import { navigateToPage, dismissFeedbackPopup, clickTeamId } from "../../helpers/navigation"; +import { readBack } from "../../helpers/roundTrip"; +import { CHAT_MODEL_A, MOCK_RESPONSE_TEXT, masterKey } from "../../helpers/traffic"; + +interface TeamInfo { + team_id: string; + team_alias: string; + models: string[]; + max_budget: number | null; + tpm_limit: number | null; + rpm_limit: number | null; + metadata: Record | null; + members_with_roles: { user_id?: string; role?: string }[]; +} + +/** + * Each test owns a team it created, rather than editing a seeded one, so a save that clobbers a + * field cannot take another spec's fixture down with it. + */ +async function createTeam(page: PlaywrightPage, alias: string, members: string[] = []): Promise { + const res = await page.request.post("/team/new", { + headers: { Authorization: `Bearer ${masterKey()}` }, + data: { + team_alias: alias, + models: [CHAT_MODEL_A], + members_with_roles: members.map((user_id) => ({ user_id, role: "user" })), + }, + }); + expect(res.ok(), `POST /team/new failed (${res.status()}): ${await res.text()}`).toBe(true); + return (await res.json()).team_id as string; +} + +/** + * A member of this test's own, not one of the seeded users. Putting a seeded user on an extra team + * changes what every spec that asserts on their memberships sees. + */ +async function createMember(page: PlaywrightPage, userId: string): Promise { + const res = await page.request.post("/user/new", { + headers: { Authorization: `Bearer ${masterKey()}` }, + data: { user_id: userId, user_role: "internal_user", auto_create_key: false }, + }); + expect(res.ok(), `POST /user/new failed (${res.status()}): ${await res.text()}`).toBe(true); + return userId; +} + +async function teamInfo(page: PlaywrightPage, teamId: string): Promise { + const body = await readBack<{ team_info: TeamInfo }>(page, `/team/info?team_id=${encodeURIComponent(teamId)}`); + return body.team_info; +} + +async function openTeamSettings(page: PlaywrightPage, teamId: string): Promise { + await navigateToPage(page, Page.Teams); + await dismissFeedbackPopup(page); + await clickTeamId(page, teamId); + await page.getByRole("tab", { name: "Settings" }).click(); + await page.getByRole("button", { name: "Edit Settings" }).click(); + await expect(page.getByRole("button", { name: "Save Changes" })).toBeVisible({ timeout: 10_000 }); +} + +test.describe("Proxy Admin - Team settings", () => { + test.use({ storageState: ADMIN_STORAGE_PATH }); + + test("Setting a team's spend cap and rate limits leaves its models and members alone", async ({ page }) => { + const stamp = Date.now(); + const alias = `e2e-team-limits-${stamp}`; + const member = await createMember(page, `e2e-team-limits-member-${stamp}`); + const teamId = await createTeam(page, alias, [member]); + const before = await teamInfo(page, teamId); + + await openTeamSettings(page, teamId); + + await page.getByRole("spinbutton", { name: "Max Budget (USD)" }).fill("42.5"); + await page.getByRole("spinbutton", { name: "Tokens per minute Limit (TPM)" }).fill("7000"); + await page.getByRole("spinbutton", { name: "Requests per minute Limit (RPM)" }).fill("70"); + await page.getByRole("button", { name: "Save Changes" }).click(); + + await expect + .poll( + async () => { + const team = await teamInfo(page, teamId); + return [team.max_budget, team.tpm_limit, team.rpm_limit]; + }, + { message: "team limits did not persist", timeout: 20_000 }, + ) + .toEqual([42.5, 7000, 70]); + + // The Settings form posts the whole team. A field it fails to seed goes back as null, and + // the toast still says success, so pin the fields this edit had no business touching. + const after = await teamInfo(page, teamId); + expect(after.models, "model access untouched by a limits edit").toEqual(before.models); + expect( + after.members_with_roles.map((member) => member.user_id).sort(), + "membership untouched by a limits edit", + ).toEqual(before.members_with_roles.map((member) => member.user_id).sort()); + }); + + test("A model alias added on the Settings tab serves traffic under the alias name", async ({ page }) => { + const stamp = Date.now(); + const alias = `e2e-team-alias-${stamp}`; + const modelAlias = `e2e-alias-${stamp}`; + const teamId = await createTeam(page, alias); + + await openTeamSettings(page, teamId); + + await page.getByRole("textbox", { name: "Alias Name" }).fill(modelAlias); + await page.getByRole("combobox", { name: "Select target model" }).click(); + await page.getByRole("option", { name: CHAT_MODEL_A, exact: true }).first().click(); + await page.getByRole("button", { name: "Add Alias" }).click(); + await page.getByRole("button", { name: "Save Changes" }).click(); + + await expect + .poll(async () => (await teamInfo(page, teamId)).models, { message: "team lost its models", timeout: 20_000 }) + .toEqual([CHAT_MODEL_A]); + + const keyRes = await page.request.post("/key/generate", { + headers: { Authorization: `Bearer ${masterKey()}` }, + data: { team_id: teamId, key_alias: `e2e-alias-key-${stamp}` }, + }); + expect(keyRes.ok(), `POST /key/generate failed (${keyRes.status()})`).toBe(true); + const teamKey = (await keyRes.json()).key as string; + + // An alias the team can see but cannot call is the actual complaint; the readback alone + // would pass for an alias the router never resolves. + const served = await page.request.post("/v1/chat/completions", { + headers: { Authorization: `Bearer ${teamKey}`, "Content-Type": "application/json" }, + data: { model: modelAlias, messages: [{ role: "user", content: "ping" }] }, + }); + expect(served.status(), `a team key calling ${modelAlias} is served`).toBe(200); + expect((await served.json()).choices?.[0]?.message?.content).toContain(MOCK_RESPONSE_TEXT); + }); + + test("Team metadata added as key-value pairs survives a reload", async ({ page }) => { + const stamp = Date.now(); + const alias = `e2e-team-metadata-${stamp}`; + const metadataValue = `cost-center-${stamp}`; + const teamId = await createTeam(page, alias); + + await openTeamSettings(page, teamId); + + await page.getByRole("button", { name: "Add Key-Value Pair" }).click(); + await page.getByPlaceholder("Key", { exact: true }).last().fill("owner"); + await page.getByPlaceholder("Value", { exact: true }).last().fill(metadataValue); + await page.getByRole("button", { name: "Save Changes" }).click(); + + await expect + .poll(async () => (await teamInfo(page, teamId)).metadata?.owner, { + message: "team metadata did not persist", + timeout: 20_000, + }) + .toBe(metadataValue); + + // Reopening the form is the step that catches metadata the page writes but cannot read back. + await page.reload(); + await page.getByRole("tab", { name: "Settings" }).click(); + await page.getByRole("button", { name: "Edit Settings" }).click(); + await expect(page.getByPlaceholder("Key", { exact: true })).toHaveValue("owner", { timeout: 15_000 }); + await expect(page.getByPlaceholder("Value", { exact: true })).toHaveValue(metadataValue); + }); +}); diff --git a/tests/e2e/ui/tests/proxy-admin/teams.spec.ts b/tests/e2e/ui/tests/proxy-admin/teams.spec.ts index 7383b452162..303e4488e09 100644 --- a/tests/e2e/ui/tests/proxy-admin/teams.spec.ts +++ b/tests/e2e/ui/tests/proxy-admin/teams.spec.ts @@ -1,14 +1,9 @@ import { test, expect, type Page as PlaywrightPage } from "@playwright/test"; -import { - ADMIN_STORAGE_PATH, - E2E_TEAM_CRUD_ID, - E2E_TEAM_DELETE_ALIAS, - E2E_TEAM_NO_ADMIN_ID, - E2E_TEAM_ORG_ID, -} from "../../constants"; +import { ADMIN_STORAGE_PATH, E2E_TEAM_CRUD_ID, E2E_TEAM_NO_ADMIN_ID, E2E_TEAM_ORG_ID } from "../../constants"; import { Page } from "../../fixtures/pages"; import { navigateToPage, dismissFeedbackPopup, clickTeamId } from "../../helpers/navigation"; import { readBack } from "../../helpers/roundTrip"; +import { masterKey } from "../../helpers/traffic"; /** GET /team/list returns a bare array of teams, each carrying team_alias/team_id. */ async function findTeamByAlias(page: PlaywrightPage, alias: string): Promise | undefined> { @@ -25,6 +20,17 @@ async function teamMemberEmails(page: PlaywrightPage, teamId: string): Promise member.user_email ?? "").filter(Boolean); } +/** A team this test owns, so deleting it costs the suite nothing on a retry or a second run. */ +async function createDeletableTeam(page: PlaywrightPage): Promise { + const alias = `e2e-delete-team-${Date.now()}`; + const res = await page.request.post("/team/new", { + headers: { Authorization: `Bearer ${masterKey()}` }, + data: { team_alias: alias, models: ["fake-openai-gpt-4"] }, + }); + expect(res.ok(), `POST /team/new failed (${res.status()}): ${await res.text()}`).toBe(true); + return alias; +} + test.describe("Proxy Admin - Teams", () => { test.use({ storageState: ADMIN_STORAGE_PATH }); @@ -121,10 +127,14 @@ test.describe("Proxy Admin - Teams", () => { }); test("Delete a team", async ({ page }) => { + // Deleting the seeded team leaves nothing for the next attempt, so the retries CI runs with are + // guaranteed to fail and the suite cannot run twice against one database. Bring our own. + const alias = await createDeletableTeam(page); + await navigateToPage(page, Page.Teams); await dismissFeedbackPopup(page); - const teamRow = page.locator("tr", { hasText: E2E_TEAM_DELETE_ALIAS }).first(); + const teamRow = page.locator("tr", { hasText: alias }).first(); await expect(teamRow).toBeVisible({ timeout: 10_000 }); // Actions live in a kebab menu: open it, then click "Delete team". await teamRow.locator('[data-testid^="team-actions-"]').click(); @@ -132,15 +142,15 @@ test.describe("Proxy Admin - Teams", () => { const modal = page.getByRole("dialog", { name: "Delete Team?" }); await expect(modal).toBeVisible({ timeout: 5_000 }); - await modal.locator("input").fill(E2E_TEAM_DELETE_ALIAS); + await modal.locator("input").fill(alias); await modal.getByRole("button", { name: /Force Delete|Delete/i }).click(); await expect(teamRow).not.toBeVisible({ timeout: 10_000 }); // A row vanishing is local state, which happens whether or not the delete landed. await expect - .poll(async () => await findTeamByAlias(page, E2E_TEAM_DELETE_ALIAS), { - message: `team ${E2E_TEAM_DELETE_ALIAS} still readable from /team/list after delete`, + .poll(async () => await findTeamByAlias(page, alias), { + message: `team ${alias} still readable from /team/list after delete`, timeout: 15_000, }) .toBeUndefined(); diff --git a/tests/e2e/ui/tests/settings/scim.spec.ts b/tests/e2e/ui/tests/settings/scim.spec.ts new file mode 100644 index 00000000000..d7dd4248f50 --- /dev/null +++ b/tests/e2e/ui/tests/settings/scim.spec.ts @@ -0,0 +1,53 @@ +import { test, expect, Page as PlaywrightPage } from "@playwright/test"; +import { ADMIN_STORAGE_PATH } from "../../constants"; +import { Page } from "../../fixtures/pages"; +import { navigateToPage } from "../../helpers/navigation"; + +const rootPath = (): string => process.env.SERVER_ROOT_PATH ?? ""; + +async function createScimTokenViaUi(page: PlaywrightPage, alias: string): Promise { + await navigateToPage(page, Page.AdminPanel); + await page.getByRole("tab", { name: "SCIM" }).click(); + + await expect(page.getByText("SCIM Tenant URL")).toBeVisible(); + await expect(page.locator("input[disabled]").first()).toHaveValue(/\/scim\/v2$/); + + await page.getByLabel("Token Name").fill(alias); + await page.getByRole("button", { name: "Create SCIM Token" }).click(); + + await expect(page.getByText(/copy this token now/i)).toBeVisible({ timeout: 15_000 }); + const token = await page.locator('input[type="password"]').inputValue(); + expect(token, "the one-time token panel shows a usable virtual key").toMatch(/^sk-/); + return token; +} + +test.describe("Admin Settings - SCIM", () => { + test.use({ storageState: ADMIN_STORAGE_PATH }); + + test("Create SCIM Token shows the token once and offers to create another", async ({ page }) => { + await createScimTokenViaUi(page, `e2e-scim-ui-${Date.now()}`); + + await page.getByRole("button", { name: "Create Another Token" }).click(); + await expect(page.getByRole("button", { name: "Create SCIM Token" })).toBeVisible(); + await expect(page.getByText(/copy this token now/i)).toBeHidden(); + }); + + test("a UI-minted SCIM token authorizes the SCIM API", async ({ page, request }) => { + test.skip(!process.env.LITELLM_LICENSE, "LITELLM_LICENSE not set in test env — /scim/v2 is premium-gated"); + + const token = await createScimTokenViaUi(page, `e2e-scim-api-${Date.now()}`); + + const denied = await request.get(`${rootPath()}/scim/v2/Groups`, { + headers: { Authorization: "Bearer sk-not-a-real-key" }, + }); + expect(denied.status(), "an unknown key must not reach SCIM").toBe(401); + + const res = await request.get(`${rootPath()}/scim/v2/Groups`, { + headers: { Authorization: `Bearer ${token}` }, + }); + expect(res.status(), `SCIM Groups listing failed: ${await res.text()}`).toBe(200); + const body = await res.json(); + expect(body.schemas, "SCIM answers with a ListResponse").toContain("urn:ietf:params:scim:api:messages:2.0:ListResponse"); + expect(Array.isArray(body.Resources), "SCIM ListResponse carries a Resources array").toBe(true); + }); +}); diff --git a/tests/e2e/ui/tests/team-admin/teamAdmin.spec.ts b/tests/e2e/ui/tests/team-admin/teamAdmin.spec.ts index f93cca75347..f3c031f0172 100644 --- a/tests/e2e/ui/tests/team-admin/teamAdmin.spec.ts +++ b/tests/e2e/ui/tests/team-admin/teamAdmin.spec.ts @@ -1,6 +1,7 @@ import { test, expect, type Page as PlaywrightPage } from "@playwright/test"; import { E2E_INTERNAL_USER_KEY_ALIAS, + E2E_TEAM_ADMIN_USER_ID, E2E_TEAM_CRUD_ALIAS, E2E_TEAM_CRUD_ID, TEAM_ADMIN_STORAGE_PATH, @@ -8,6 +9,8 @@ import { import { Page } from "../../fixtures/pages"; import { navigateToPage, dismissFeedbackPopup, clickTeamId } from "../../helpers/navigation"; import { captureRequestBody, readBack } from "../../helpers/roundTrip"; +import { CHAT_MODEL_A, masterKey } from "../../helpers/traffic"; +import { keySourceSelect, modelSelect, onlyVisible, openPlayground } from "../../helpers/playground"; /** * Every identifier a roster is addressable by. Which of user_id / user_email is populated depends on @@ -32,7 +35,44 @@ async function findKeyByAlias(page: PlaywrightPage, alias: string): Promise row.key_alias === alias); } +/** A member this test adds itself, so removing it costs the suite nothing on a retry or a re-run. */ +async function addRemovableMember(page: PlaywrightPage, registerForCleanup: string[]): Promise { + const userId = `e2e-removable-${Date.now()}`; + // Claimed before the call: /user/new can persist the user and still answer non-2xx, and the id is + // ours either way, so registering it up front is what no failure path can skip. + registerForCleanup.push(userId); + const created = await page.request.post("/user/new", { + headers: { Authorization: `Bearer ${masterKey()}` }, + data: { user_id: userId, user_role: "internal_user", auto_create_key: false }, + }); + expect(created.ok(), `POST /user/new failed (${created.status()}): ${await created.text()}`).toBe(true); + + const added = await page.request.post("/team/member_add", { + headers: { Authorization: `Bearer ${masterKey()}` }, + data: { team_id: E2E_TEAM_CRUD_ID, member: { user_id: userId, role: "user" } }, + }); + expect(added.ok(), `POST /team/member_add failed (${added.status()}): ${await added.text()}`).toBe(true); + return userId; +} + test.describe("Team Admin", () => { + const createdMembers: string[] = []; + + test.afterEach(async ({ page }) => { + // Runs on the failure path too, which a call at the end of the test body would not. Ids are + // claimed before the user is created, so the delete is attempted unconditionally and only its + // own 404 counts as never persisted; any other answer is a cleanup failure worth reporting + // rather than a reason to leave the user behind. + for (const userId of createdMembers.splice(0)) { + const deleted = await page.request.post("/user/delete", { + headers: { Authorization: `Bearer ${masterKey()}` }, + data: { user_ids: [userId] }, + }); + const settled = deleted.ok() || deleted.status() === 404; + expect(settled, `POST /user/delete for ${userId} (${deleted.status()}): ${await deleted.text()}`).toBe(true); + } + }); + test.use({ storageState: TEAM_ADMIN_STORAGE_PATH }); test("Team admin can see all team keys including internal user keys", async ({ page }) => { @@ -92,6 +132,10 @@ test.describe("Team Admin", () => { }); test("Team admin can remove a member from their team", async ({ page }) => { + // Removing the seeded member leaves nothing for the next attempt, so the retries CI runs with + // are guaranteed to fail and the suite cannot run twice against one database. Bring our own. + const memberId = await addRemovableMember(page, createdMembers); + await navigateToPage(page, Page.Teams); await dismissFeedbackPopup(page); @@ -99,9 +143,9 @@ test.describe("Team Admin", () => { await page.getByRole("tab", { name: "Members" }).click(); - // Seeded members appear in the roster by user_id (members_with_roles has no - // email), so match the row on the user_id rather than the email. - const row = page.locator("tr", { hasText: "e2e-removable-member" }).first(); + // Members appear in the roster by user_id (members_with_roles has no email), so match + // the row on the user_id rather than the email. + const row = page.locator("tr", { hasText: memberId }).first(); await expect(row).toBeVisible({ timeout: 10_000 }); await row.getByTestId("delete-member").click(); @@ -114,7 +158,7 @@ test.describe("Team Admin", () => { // Removing the wrong member is exactly what a success toast hides, so pin both halves. expect(remove.team_id, "delete targets the team being viewed").toBe(E2E_TEAM_CRUD_ID); expect([remove.user_id, remove.user_email], "delete identifies the member whose row was clicked").toContain( - "e2e-removable-member", + memberId, ); await expect(page.getByText("Team member removed successfully").first()).toBeVisible({ timeout: 10_000 }); @@ -125,7 +169,92 @@ test.describe("Team Admin", () => { message: "removed member is still on the team", timeout: 15_000, }) - .not.toContain("e2e-removable-member"); + .not.toContain(memberId); + }); + + test("Team admin sees all team models in the Playground model dropdown", async ({ page, request }) => { + const suffix = Date.now(); + const teamModelName = `e2e-team-dropdown-model-${suffix}`; + const auth = { Authorization: `Bearer ${masterKey()}` }; + + const teamRes = await request.post("/team/new", { + headers: auth, + data: { + team_alias: `e2e-playground-team-${suffix}`, + models: [CHAT_MODEL_A], + members_with_roles: [{ role: "admin", user_id: E2E_TEAM_ADMIN_USER_ID }], + }, + }); + expect(teamRes.ok(), `team create failed (${teamRes.status()}): ${await teamRes.text()}`).toBe(true); + const teamId = (await teamRes.json()).team_id as string; + + try { + const modelRes = await request.post("/model/new", { + headers: auth, + data: { + model_name: teamModelName, + litellm_params: { + model: "openai/fake-gpt-4", + api_base: `http://127.0.0.1:${process.env.MOCK_LLM_PORT ?? "8090"}/v1`, + api_key: "fake-key", + }, + model_info: { team_id: teamId }, + }, + }); + expect(modelRes.ok(), `model create failed (${modelRes.status()}): ${await modelRes.text()}`).toBe(true); + const modelId = (await modelRes.json()).model_info?.id as string; + + try { + const keyRes = await request.post("/key/generate", { headers: auth, data: { team_id: teamId } }); + expect(keyRes.ok(), `key generate failed (${keyRes.status()}): ${await keyRes.text()}`).toBe(true); + const teamKey = (await keyRes.json()).key as string; + + try { + await expect + .poll( + async () => { + const res = await request.get("/model_group/info", { + headers: { Authorization: `Bearer ${teamKey}` }, + }); + if (!res.ok()) return false; + const body: { data?: { model_group?: string }[] } = await res.json(); + return (body.data ?? []).some((group) => group.model_group === teamModelName); + }, + { + message: `model group ${teamModelName} never became visible to the team key`, + timeout: 30_000, + }, + ) + .toBe(true); + + await openPlayground(page); + await keySourceSelect(page).click(); + await onlyVisible(page.getByRole("option", { name: "Virtual Key" })).click({ timeout: 15_000 }); + + const keyInput = onlyVisible(page.getByPlaceholder("Enter custom Virtual Key")); + await expect(keyInput).toBeVisible({ timeout: 10_000 }); + await keyInput.fill(teamKey); + + const select = modelSelect(page); + await select.click(); + await select.fill(teamModelName); + await expect(onlyVisible(page.getByRole("option", { name: teamModelName }))).toBeVisible({ + timeout: 15_000, + }); + + await select.fill(CHAT_MODEL_A); + await expect(onlyVisible(page.getByRole("option", { name: CHAT_MODEL_A }))).toBeVisible({ + timeout: 15_000, + }); + } finally { + await request.post("/key/delete", { headers: auth, data: { keys: [teamKey] } }); + } + } finally { + await request.post("/model/delete", { headers: auth, data: { id: modelId } }); + } + } finally { + await request.post("/team/delete", { headers: auth, data: { team_ids: [teamId] } }); + } }); test("Team admin can create a team key with All Team Models", async ({ page }) => { @@ -142,7 +271,7 @@ test.describe("Team Admin", () => { const teamSelect = page.getByTestId("team-dropdown").getByRole("combobox"); await teamSelect.click(); await page.keyboard.type(E2E_TEAM_CRUD_ALIAS); - await page.locator('[data-slot="combobox-content"]:visible').getByText(E2E_TEAM_CRUD_ALIAS).first().click(); + await page.getByRole("option", { name: E2E_TEAM_CRUD_ALIAS }).first().click(); // Models — pick "All Team Models". The popup is portaled to the body, so // scope the option lookup to the page. diff --git a/tests/e2e/ui/tests/usage/usageActivityTabs.spec.ts b/tests/e2e/ui/tests/usage/usageActivityTabs.spec.ts new file mode 100644 index 00000000000..2ee5ae3e392 --- /dev/null +++ b/tests/e2e/ui/tests/usage/usageActivityTabs.spec.ts @@ -0,0 +1,135 @@ +import { test, expect, type Locator, type Page as PlaywrightPage } from "@playwright/test"; +import { ADMIN_STORAGE_PATH } from "../../constants"; +import { navigateToPage, dismissFeedbackPopup } from "../../helpers/navigation"; +import { Page } from "../../fixtures/pages"; +import { + CHAT_MODEL_A, + CHAT_MODEL_B, + DEPLOYMENT_MODEL_A, + DEPLOYMENT_MODEL_B, + createVirtualKey, + masterKey, + rootPath, + sendChatCompletion, + waitForKeyInDailyActivity, + waitForSpendLog, +} from "../../helpers/traffic"; + +/** + * Covers the per-entity breakdowns on /ui/usage. The page-level totals move with every other spec's + * traffic, so each assertion is scoped to a key this test minted and to the requests it sent. + */ + +/** Each breakdown renders one expandable card per entity, named " $x.xx N requests". */ +const entityCard = (page: PlaywrightPage, tab: string, name: string): Locator => + page.getByRole("tabpanel", { name: tab }).getByRole("button", { name: new RegExp(`^${name}\\s`) }); + +async function openUsageTab(page: PlaywrightPage, tab: string): Promise { + await navigateToPage(page, Page.NewUsage); + await dismissFeedbackPopup(page); + await page.getByRole("tab", { name: tab }).click(); + const panel = page.getByRole("tabpanel", { name: tab }); + await expect(panel).toBeVisible({ timeout: 30_000 }); + return panel; +} + +/** Sends `count` completions on one model and waits for each to reach the spend log. */ +async function sendTraffic( + request: Parameters[0], + apiKey: string, + model: string, + count: number, + label: string, +): Promise { + for (let i = 0; i < count; i++) { + const requestId = await sendChatCompletion(request, { model, prompt: `${label} ${i}`, apiKey }); + await waitForSpendLog(request, requestId); + } +} + +test.describe("Usage page activity tabs", () => { + test.use({ storageState: ADMIN_STORAGE_PATH }); + + test("Key Activity breaks a key's traffic down by model", async ({ page, request }) => { + const alias = `e2e-usage-keyact-${Date.now()}`; + const { key, token } = await createVirtualKey(request, { key_alias: alias }); + + // An uneven split, so a breakdown that lumps everything into one row or attributes to the + // wrong model cannot land on these numbers by accident. + await sendTraffic(request, key, CHAT_MODEL_A, 2, alias); + await sendTraffic(request, key, CHAT_MODEL_B, 1, alias); + await waitForKeyInDailyActivity(request, token, 3); + + await openUsageTab(page, "Key Activity"); + + const card = entityCard(page, "Key Activity", alias); + await expect(card, `${alias} missing from Key Activity`).toBeVisible({ timeout: 30_000 }); + await expect(card).toContainText("3 requests"); + + // Every key gets a card, and the page opens the first one. Scope to this key's own section, + // which the collapsible renders as the trigger's next sibling. + await card.click(); + const details = card.locator("xpath=following-sibling::*[1]"); + const successfulFor = (model: string) => + details.getByRole("row").filter({ hasText: model }).getByRole("cell").nth(2); // Model | Spend | Successful | Failed | Tokens + + await expect(successfulFor(DEPLOYMENT_MODEL_A)).toHaveText("2", { timeout: 20_000 }); + await expect(successfulFor(DEPLOYMENT_MODEL_B)).toHaveText("1"); + }); + + test("Model Activity can name its models by deployment instead of by public name", async ({ page, request }) => { + const alias = `e2e-usage-modelact-${Date.now()}`; + const { key, token } = await createVirtualKey(request, { key_alias: alias }); + await sendTraffic(request, key, CHAT_MODEL_A, 1, alias); + await waitForKeyInDailyActivity(request, token); + + const panel = await openUsageTab(page, "Model Activity"); + + await expect(entityCard(page, "Model Activity", CHAT_MODEL_A), `${CHAT_MODEL_A} missing`).toBeVisible({ + timeout: 30_000, + }); + // Nothing is published under the deployment's name, so its absence here is what makes the + // toggle below a real change of key rather than a relabelled button. + await expect(entityCard(page, "Model Activity", DEPLOYMENT_MODEL_A)).toHaveCount(0); + + // Admins reconcile provider bills against the deployment, not the name their users call. + await panel.getByRole("button", { name: "Litellm Model Name" }).click(); + await expect(entityCard(page, "Model Activity", DEPLOYMENT_MODEL_A)).toBeVisible({ timeout: 20_000 }); + }); + + test("Filter by user narrows Key Activity to that user's keys", async ({ page, request }) => { + const stamp = Date.now(); + const email = `e2e-usage-owner-${stamp}@test.local`; + const ownedAlias = `e2e-usage-owned-${stamp}`; + const otherAlias = `e2e-usage-other-${stamp}`; + + const userRes = await request.post(`${rootPath()}/user/new`, { + headers: { Authorization: `Bearer ${masterKey()}`, "Content-Type": "application/json" }, + data: { user_email: email, user_role: "internal_user", auto_create_key: false }, + }); + expect(userRes.ok(), `POST /user/new failed (${userRes.status()})`).toBe(true); + const userId = (await userRes.json()).user_id as string; + + const owned = await createVirtualKey(request, { key_alias: ownedAlias, user_id: userId }); + const other = await createVirtualKey(request, { key_alias: otherAlias }); + await sendTraffic(request, owned.key, CHAT_MODEL_A, 1, ownedAlias); + await sendTraffic(request, other.key, CHAT_MODEL_A, 1, otherAlias); + await waitForKeyInDailyActivity(request, owned.token); + await waitForKeyInDailyActivity(request, other.token); + + await openUsageTab(page, "Key Activity"); + await expect(entityCard(page, "Key Activity", otherAlias)).toBeVisible({ timeout: 30_000 }); + + await page.getByRole("combobox", { name: "Search users by email" }).click(); + await page.keyboard.type(email); + await page + .getByRole("option", { name: new RegExp(email) }) + .first() + .click(); + + // The filter earns its place only by dropping the other key; the owned key showing up + // proves nothing on a page that already listed every key. + await expect(entityCard(page, "Key Activity", otherAlias)).toHaveCount(0, { timeout: 30_000 }); + await expect(entityCard(page, "Key Activity", ownedAlias)).toBeVisible({ timeout: 20_000 }); + }); +}); diff --git a/tests/e2e/ui/tests/usage/usagePage.spec.ts b/tests/e2e/ui/tests/usage/usagePage.spec.ts index 8fa59beb905..3d057cfa2c9 100644 --- a/tests/e2e/ui/tests/usage/usagePage.spec.ts +++ b/tests/e2e/ui/tests/usage/usagePage.spec.ts @@ -1,10 +1,11 @@ -import { test, expect, type Locator, type Page as PlaywrightPage } from "@playwright/test"; +import { test, expect, type APIRequestContext, type Locator, type Page as PlaywrightPage } from "@playwright/test"; import { ADMIN_STORAGE_PATH } from "../../constants"; import { navigateToPage, dismissFeedbackPopup } from "../../helpers/navigation"; import { Page } from "../../fixtures/pages"; import { - CHAT_MODEL_A, createVirtualKey, + masterKey, + rootPath, sendChatCompletion, waitForKeyInDailyActivity, waitForSpendLog, @@ -27,9 +28,105 @@ async function openUsage(page: PlaywrightPage): Promise { return card; } +/** The upstream fixtures/config.yml points its models at, so the mock server answers this too. */ +const MOCK_DEPLOYMENT = "openai/fake-gpt-4"; + +/** A deployment whose traffic costs real money, so the key that used it outranks the $0 crowd. */ +async function createPricedDeployment( + request: APIRequestContext, + label: string, + registerForCleanup: string[], +): Promise<{ modelName: string }> { + const modelName = `e2e-usage-priced-${label}`; + // Claimed before the call: /model/new can persist the deployment and still answer non-2xx, so a + // name recorded up front is the only registration no response shape can skip. + registerForCleanup.push(modelName); + const res = await request.post("/model/new", { + headers: { Authorization: `Bearer ${masterKey()}`, "Content-Type": "application/json" }, + data: { + model_name: modelName, + litellm_params: { + model: MOCK_DEPLOYMENT, + api_base: `http://127.0.0.1:${process.env.MOCK_LLM_PORT ?? "8090"}/v1`, + api_key: "fake-key", + input_cost_per_token: 0.01, + output_cost_per_token: 0.01, + }, + }, + }); + expect(res.ok(), `POST /model/new failed (${res.status()}): ${await res.text()}`).toBe(true); + + // /model/new returns once the row is written, but the router only picks the deployment up on its + // next refresh, so sending traffic straight away can still get "no healthy deployments". A ping + // that fails writes no spend log, so retrying it costs the ranking this test asserts nothing. + await expect + .poll( + async () => { + const ping = await request.post(`${rootPath()}/v1/chat/completions`, { + headers: { Authorization: `Bearer ${masterKey()}`, "Content-Type": "application/json" }, + data: { model: modelName, messages: [{ role: "user", content: "readiness ping" }] }, + }); + return ping.ok(); + }, + { message: `deployment ${modelName} never became routable`, timeout: 60_000 }, + ) + .toBe(true); + + return { modelName }; +} + test.describe("Usage page", () => { test.use({ storageState: ADMIN_STORAGE_PATH }); + const pricedDeployments: string[] = []; + + test.afterEach(async ({ request }) => { + // A deployment left behind keeps its custom pricing, so it goes on changing what later runs + // route and what they cost. Runs on the failure path too, which the test body would not. + // Resolved by name rather than by a returned id, so a create that persisted without answering + // 2xx is still cleaned up. /model/info serves the router, and /model/new answers 2xx even when + // its in-request router reload failed, so the search-backed listing is what covers a deployment + // that reached the database only. Absent from both means it never persisted. + const names = pricedDeployments.splice(0); + if (names.length === 0) return; + const auth = { Authorization: `Bearer ${masterKey()}`, "Content-Type": "application/json" }; + + type Lookup = + | { readonly listed: true; readonly id: string | undefined } + | { readonly listed: false; readonly status: number }; + + const idIn = async (path: string, name: string): Promise => { + const listed = await request.get(path, { headers: auth }); + if (!listed.ok()) return { listed: false, status: listed.status() }; + const deployments = ((await listed.json()).data ?? []) as { + model_name?: string; + model_info?: { id?: string }; + }[]; + return { listed: true, id: deployments.find((d) => d.model_name === name)?.model_info?.id }; + }; + + const remove = async (name: string, id: string) => { + const deleted = await request.post(`${rootPath()}/model/delete`, { headers: auth, data: { id } }); + expect(deleted.ok(), `POST /model/delete for ${name} (${deleted.status()})`).toBe(true); + }; + + for (const name of names) { + const fromRouter = await idIn(`${rootPath()}/model/info`, name); + if (fromRouter.listed && fromRouter.id !== undefined) { + await remove(name, fromRouter.id); + continue; + } + const search = encodeURIComponent(name); + const fromDb = await idIn(`${rootPath()}/v2/model/info?search=${search}`, name); + expect( + fromDb.listed, + `GET /v2/model/info?search=${search} (${fromDb.listed ? 200 : fromDb.status}), so ${name} could not be checked`, + ).toBe(true); + if (!fromDb.listed || fromDb.id === undefined) continue; + await remove(name, fromDb.id); + } + }); + test("Top Virtual Keys lists a key that served traffic, toggles views, and opens key info", async ({ page, request, @@ -39,8 +136,13 @@ test.describe("Usage page", () => { key_alias: alias, }); + // Top Virtual Keys ranks by spend, and every mock deployment costs $0, so once a run has more + // keys than the list shows, whether this one makes the cut is down to how ties happen to sort. + // Give it a priced deployment of its own so it earns its place. + const { modelName } = await createPricedDeployment(request, alias, pricedDeployments); + const requestId = await sendChatCompletion(request, { - model: CHAT_MODEL_A, + model: modelName, prompt: `usage ping for ${alias}`, apiKey: key, }); @@ -51,20 +153,19 @@ test.describe("Usage page", () => { const card = await openUsage(page); // Table view (the default): the key is listed by its alias. - const row = card.locator("tbody tr").filter({ hasText: alias }); + const row = card.getByRole("row").filter({ hasText: alias }); await expect(row, `${alias} missing from Top Virtual Keys`).toHaveCount(1, { timeout: 30_000, }); // Chart view swaps the table out for the bar chart, and back. await card.getByText("Chart View", { exact: true }).click(); - await expect(card.locator("tbody tr")).toHaveCount(0, { timeout: 10_000 }); + await expect(card.getByRole("table")).toHaveCount(0, { timeout: 10_000 }); await card.getByText("Table View", { exact: true }).click(); await expect(row).toHaveCount(1, { timeout: 10_000 }); - // Clicking the Key ID cell fetches key info and opens the detail panel. // The alias is already in the row behind the modal, so match the panel's own controls. - await row.locator("td").first().click(); + await row.getByRole("button", { name: token }).click(); const keyInfo = page.getByRole("tab", { name: "Overview", exact: true }); await expect(keyInfo, "key info panel did not open").toBeVisible({ timeout: 20_000, diff --git a/tests/e2e/ui/tests/users/searchUsers.spec.ts b/tests/e2e/ui/tests/users/searchUsers.spec.ts index e87218b5a5e..fa8f32764e8 100644 --- a/tests/e2e/ui/tests/users/searchUsers.spec.ts +++ b/tests/e2e/ui/tests/users/searchUsers.spec.ts @@ -1,91 +1,52 @@ -import { test, expect, Page } from "@playwright/test"; +import { test, expect, Page as PlaywrightPage } from "@playwright/test"; import { ADMIN_STORAGE_PATH } from "../../constants"; -test.skip("Internal Users Search", () => { +import { Page } from "../../fixtures/pages"; +import { navigateToPage } from "../../helpers/navigation"; + +const userRows = (page: PlaywrightPage) => page.getByRole("row").filter({ has: page.getByRole("cell") }); + +async function goToInternalUsers(page: PlaywrightPage) { + await navigateToPage(page, Page.Users); + await expect(page.getByRole("columnheader", { name: "User ID" })).toBeVisible({ timeout: 30_000 }); + await expect(userRows(page)).not.toHaveCount(0, { timeout: 30_000 }); +} + +test.describe("Internal Users Search", () => { test.use({ storageState: ADMIN_STORAGE_PATH }); - async function goToInternalUsers(page: Page) { - await page.goto("/ui"); - - const tab = page.getByRole("menuitem", { name: "Internal User" }); - await expect(tab).toBeVisible(); - await tab.click(); - - await expect(page.locator("tbody tr").first()).toBeVisible(); - await expect(page.locator('[data-slot="skeleton"]')).toHaveCount(0); - } - - test("can search users by email", async ({ page }) => { + test("narrows the table to the matching email, and restores it when cleared", async ({ page }) => { await goToInternalUsers(page); - const rows = page.locator("tbody tr"); - const searchInput = page.getByPlaceholder("Search by email..."); + const search = page.getByPlaceholder("Search by email…"); + await expect(search).toBeVisible(); - await expect(searchInput).toBeVisible(); + await search.fill("noteam@"); + await expect(userRows(page)).toHaveCount(1, { timeout: 30_000 }); + await expect(userRows(page).first()).toContainText("noteam@test.local"); - // Ensure initial data is loaded - const initialCount = await rows.count(); - expect(initialCount).toBeGreaterThan(0); - - // 🔹 Apply filter + wait for backend response - await Promise.all([ - page.waitForResponse( - (res) => - res.url().includes("/user/list") && - res.url().includes("user_email=test%40") && // encoded "test@" - res.status() === 200, - ), - searchInput.fill("test@"), - ]); - await page.waitForTimeout(5000); - const filteredCount = await rows.count(); - await expect(filteredCount).toBeLessThan(initialCount); - - // 🔹 Clear filter + wait for unfiltered request - await Promise.all([ - page.waitForResponse( - (res) => res.url().includes("/user/list") && !res.url().includes("user_email=") && res.status() === 200, - ), - searchInput.clear(), - ]); - - const resetCount = await rows.count(); - await expect(resetCount).toBe(initialCount); + await search.clear(); + await expect(userRows(page).filter({ hasText: "admin@test.local" })).not.toHaveCount(0, { timeout: 30_000 }); }); - test("can filter users by user ID and SSO ID", async ({ page }) => { + test("filters the table down to one user by user ID", async ({ page }) => { await goToInternalUsers(page); - const rows = page.locator("tbody tr"); - // Ensure initial data is loaded - const initialCount = await rows.count(); - expect(initialCount).toBeGreaterThan(0); + await page.getByRole("button", { name: "Filters" }).click(); + await page.getByTestId("users-filter-user-id").fill("e2e-internal-noteam"); + await page.getByTestId("filter-drawer-apply").click(); - const filtersButton = page.getByRole("button", { - name: "Filters", - exact: true, - }); - await filtersButton.click(); + await expect(userRows(page)).toHaveCount(1, { timeout: 30_000 }); + await expect(userRows(page).first()).toContainText("noteam@test.local"); + }); - const userIdInput = page.getByPlaceholder("Filter by User ID"); - const ssoIdInput = page.getByPlaceholder("Filter by SSO ID"); - await Promise.all([ - page.waitForResponse( - (res) => res.url().includes("/user/list") && res.url().includes("user_ids=user") && res.status() === 200, - ), - userIdInput.fill("user"), - ]); + test("shows no users when the SSO ID matches nobody", async ({ page }) => { + await goToInternalUsers(page); - await Promise.all([ - page.waitForResponse( - (res) => - res.url().includes("/user/list") && - res.url().includes("user_ids=user") && - res.url().includes("sso_user_ids=sso") && - res.status() === 200, - ), - ssoIdInput.fill("sso"), - ]); - const combinedFilteredCount = await rows.count(); - await expect(combinedFilteredCount).toBeLessThan(initialCount); + await page.getByRole("button", { name: "Filters" }).click(); + await page.getByTestId("users-filter-sso-id").fill("e2e-sso-id-that-matches-nobody"); + await page.getByTestId("filter-drawer-apply").click(); + + await expect(page.getByText("No users found")).toBeVisible({ timeout: 30_000 }); + await expect(userRows(page).filter({ hasText: "noteam@test.local" })).toHaveCount(0); }); }); diff --git a/tests/e2e/ui/tests/users/viewInternalUsers.spec.ts b/tests/e2e/ui/tests/users/viewInternalUsers.spec.ts index 614191372d0..b46fb4d112a 100644 --- a/tests/e2e/ui/tests/users/viewInternalUsers.spec.ts +++ b/tests/e2e/ui/tests/users/viewInternalUsers.spec.ts @@ -1,54 +1,29 @@ -import { test, expect, Page } from "@playwright/test"; +import { test, expect, Page as PlaywrightPage } from "@playwright/test"; import { ADMIN_STORAGE_PATH } from "../../constants"; +import { Page } from "../../fixtures/pages"; +import { navigateToPage } from "../../helpers/navigation"; -test.skip("Internal Users Page", () => { +async function goToInternalUsers(page: PlaywrightPage) { + await navigateToPage(page, Page.Users); + await expect(page.getByRole("columnheader", { name: "User ID" })).toBeVisible({ timeout: 30_000 }); + await expect(userRows(page)).not.toHaveCount(0, { timeout: 30_000 }); +} + +const userRows = (page: PlaywrightPage) => page.getByRole("row").filter({ has: page.getByRole("cell") }); + +test.describe("Internal Users Page", () => { test.use({ storageState: ADMIN_STORAGE_PATH }); - async function goToInternalUsers(page: Page) { - await page.goto("/ui"); - - const internalUserTab = page.getByRole("menuitem", { name: "Internal User" }); - await expect(internalUserTab).toBeVisible(); - await internalUserTab.click(); - - const firstRow = page.locator("tbody tr").first(); - await expect(firstRow).toBeVisible(); - await expect(page.locator('[data-slot="skeleton"]')).toHaveCount(0); - } - - test("renders internal users table correctly", async ({ page }) => { + test("lists the seeded users under the identifying columns", async ({ page }) => { await goToInternalUsers(page); - const rows = page.locator("tbody tr"); - const rowCount = await rows.count(); - expect(rowCount).toBeGreaterThan(0); - - const userIdHeader = page.getByRole("columnheader", { name: "User ID" }); - await expect(userIdHeader).toBeVisible(); - - const virtualKeysHeader = page.getByRole("columnheader", { name: "Virtual Keys" }); - await expect(virtualKeysHeader).toBeVisible(); + await expect(page.getByRole("columnheader", { name: "User ID" })).toBeVisible(); + await expect(page.getByRole("columnheader", { name: "Virtual Keys" })).toBeVisible(); }); - test("pagination controls work correctly", async ({ page }) => { + test("cannot page backwards off the first page", async ({ page }) => { await goToInternalUsers(page); - const paginationInfo = page.locator(".text-sm.text-gray-700"); - const prevButton = page.getByRole("button", { name: "Previous" }); - const nextButton = page.getByRole("button", { name: "Next" }); - - const infoText = (await paginationInfo.textContent()) || ""; - - // On first page, Previous should be disabled - if (infoText.includes("1 -")) { - await expect(prevButton).toBeDisabled(); - } - - await page.waitForTimeout(1000); - // Check if there are more pages - const hasMorePages = infoText.includes("of") && !infoText.endsWith("25 of 25"); - if (hasMorePages) { - await expect(nextButton).toBeEnabled(); - } + await expect(page.getByRole("button", { name: "Go to previous page" })).toBeDisabled(); }); }); diff --git a/tests/enterprise/litellm_enterprise/enterprise_callbacks/test_prometheus_logging_callbacks.py b/tests/enterprise/litellm_enterprise/enterprise_callbacks/test_prometheus_logging_callbacks.py index 05886e4b7f6..58cde4c8103 100644 --- a/tests/enterprise/litellm_enterprise/enterprise_callbacks/test_prometheus_logging_callbacks.py +++ b/tests/enterprise/litellm_enterprise/enterprise_callbacks/test_prometheus_logging_callbacks.py @@ -40,6 +40,24 @@ def prometheus_logger() -> PrometheusLogger: return PrometheusLogger() +@pytest.fixture +def known_model_router(): + router = litellm.Router( + model_list=[ + { + "model_name": "gpt-5-mini", + "litellm_params": {"model": "openai/gpt-5-mini", "api_key": "fake-key"}, + }, + { + "model_name": "us/azure/openai/gpt-5-mini", + "litellm_params": {"model": "openai/gpt-5-mini", "api_key": "fake-key"}, + }, + ] + ) + with patch("litellm.proxy.proxy_server.llm_router", router, create=True): # test-quality-ok: production reads proxy_server.llm_router lazily, no injection seam + yield router + + def create_standard_logging_payload() -> StandardLoggingPayload: return StandardLoggingPayload( id="test_id", @@ -741,7 +759,7 @@ async def test_async_log_failure_event(prometheus_logger): @pytest.mark.asyncio -async def test_async_log_failure_event_litellm_side_rate_limit(prometheus_logger): +async def test_async_log_failure_event_litellm_side_rate_limit(prometheus_logger, known_model_router): """LiteLLM-side reject (no deployment picked) routes the requested model into `requested_model` and skips the partial-outage flag.""" standard_logging_object = create_standard_logging_payload() @@ -786,7 +804,7 @@ async def test_async_log_failure_event_litellm_side_rate_limit(prometheus_logger @pytest.mark.asyncio -async def test_async_post_call_failure_hook(prometheus_logger): +async def test_async_post_call_failure_hook(prometheus_logger, known_model_router): """ Test for the async_post_call_failure_hook method @@ -1069,7 +1087,7 @@ def test_set_llm_deployment_success_metrics(prometheus_logger): @pytest.mark.asyncio -async def test_log_success_fallback_event(prometheus_logger): +async def test_log_success_fallback_event(prometheus_logger, known_model_router): prometheus_logger.litellm_deployment_successful_fallbacks = MagicMock() original_model_group = "gpt-5-mini" @@ -1107,7 +1125,7 @@ async def test_log_success_fallback_event(prometheus_logger): @pytest.mark.asyncio -async def test_log_failure_fallback_event(prometheus_logger): +async def test_log_failure_fallback_event(prometheus_logger, known_model_router): prometheus_logger.litellm_deployment_failed_fallbacks = MagicMock() original_model_group = "gpt-5-mini" diff --git a/tests/litellm-proxy-extras/test_litellm_proxy_extras_utils.py b/tests/litellm-proxy-extras/test_litellm_proxy_extras_utils.py index 498d0cb4723..b3d457707b8 100644 --- a/tests/litellm-proxy-extras/test_litellm_proxy_extras_utils.py +++ b/tests/litellm-proxy-extras/test_litellm_proxy_extras_utils.py @@ -681,3 +681,25 @@ class TestSpendLogsPartitionDetectionSchemaScope: def test_only_partitioned_relations_match(self, monkeypatch): query, _ = self._detect(monkeypatch, "postgresql://u:p@localhost:5432/db") assert "pg_partitioned_table" in query + + +class TestSpendLogsPartitionDetectionMissingPsycopg: + """psycopg ships in the `extra_proxy` install, but a stripped-down image + can still lack it. When it does, detection must fail closed to False + (never crash the migration path) and say so loudly, because a silent + False here is what let a genuinely partitioned LiteLLM_SpendLogs hit the + unfiltered primary-key rewrite in production.""" + + def test_missing_psycopg_returns_false(self, monkeypatch): + monkeypatch.setitem(sys.modules, "psycopg", None) + monkeypatch.setenv("DATABASE_URL", "postgresql://u:p@localhost:5432/db") + assert ProxyExtrasDBManager.spend_logs_is_partitioned() is False + + def test_missing_psycopg_logs_a_warning(self, monkeypatch, caplog): + monkeypatch.setitem(sys.modules, "psycopg", None) + monkeypatch.setenv("DATABASE_URL", "postgresql://u:p@localhost:5432/db") + with caplog.at_level("WARNING", logger="litellm_proxy_extras"): + ProxyExtrasDBManager.spend_logs_is_partitioned() + assert any( + "psycopg is not installed" in record.message for record in caplog.records + ) diff --git a/tests/llm_responses_api_testing/test_responses_hooks.py b/tests/llm_responses_api_testing/test_responses_hooks.py index 66dbb29dba5..a86752c0172 100644 --- a/tests/llm_responses_api_testing/test_responses_hooks.py +++ b/tests/llm_responses_api_testing/test_responses_hooks.py @@ -841,7 +841,7 @@ def test_build_synthetic_response_events_covers_annotations_function_calls_and_r ) try: - events = streaming_module._build_synthetic_response_events( + events = streaming_module.build_synthetic_response_events( transformed=transformed, logging_obj=logging_obj, chunk_size=5, diff --git a/tests/llm_translation/conftest.py b/tests/llm_translation/conftest.py index 8532af2851c..567040c1d19 100644 --- a/tests/llm_translation/conftest.py +++ b/tests/llm_translation/conftest.py @@ -44,7 +44,11 @@ _VCR_AUTO_MARKER_SKIP_FILES = frozenset( {"test_vcr_redis_persister.py", "test_ws_vcr.py"} ) -_VCR_INCOMPATIBLE_NODEID_SUFFIXES: tuple[str, ...] = () +_VCR_INCOMPATIBLE_NODEID_SUFFIXES: tuple[str, ...] = ( + "test_nvidia_nim.py::test_embedding_nvidia_nim", + "test_litellm_proxy_provider.py::test_litellm_gateway_from_sdk_embedding[False]", + "test_litellm_proxy_provider.py::test_litellm_gateway_from_sdk_embedding[True]", +) _verbose_state = VerboseReporterState() diff --git a/tests/llm_translation/reasoning_effort_grid/grid_spec.py b/tests/llm_translation/reasoning_effort_grid/grid_spec.py index 4fa77f38940..aa9f66f6665 100644 --- a/tests/llm_translation/reasoning_effort_grid/grid_spec.py +++ b/tests/llm_translation/reasoning_effort_grid/grid_spec.py @@ -103,7 +103,7 @@ def _bedrock_clamps_effort(model: "ModelEntry", effort: str) -> bool: return _EFFORT_RANK[effort] > _EFFORT_RANK[model.bedrock_effort_ceiling] -def expected(model: ModelEntry, effort: str) -> CellExpectation: +def expected(route_name: str, model: ModelEntry, effort: str) -> CellExpectation: if effort in ("__omit__", "none"): if model.mode == "budget": return CellExpectation( @@ -117,6 +117,15 @@ def expected(model: ModelEntry, effort: str) -> CellExpectation: if effort in ("xhigh", "max"): cap = f"supports_{effort}_reasoning_effort" if cap not in model.caps and not _bedrock_clamps_effort(model, effort): + if model.mode == "budget" and route_name == "bedrock_invoke_messages": + # the /v1/messages path caps the mapped budget below max_tokens + # (LIT-6498), so oversized tiers succeed there instead of 400ing + return CellExpectation( + status=200, + thinking_type="enabled", + thinking_budget_tokens=BUDGET_MODE_MAX_TOKENS - 1, + max_tokens=BUDGET_MODE_MAX_TOKENS, + ) return CellExpectation(status=400, thinking_type=OMIT) if model.mode == "adaptive": @@ -154,6 +163,13 @@ _CAPS_NONE: FrozenSet[str] = frozenset() ANTHROPIC_DIRECT_MODELS: Tuple[ModelEntry, ...] = ( + ModelEntry( + alias="claude-fable-5-1", + model="anthropic/claude-fable-5-1", + mode="adaptive", + required_env=_ANTHROPIC_REQ, + caps=_CAPS_XHIGH_MAX, + ), ModelEntry( alias="claude-fable-5", model="anthropic/claude-fable-5", @@ -213,6 +229,19 @@ ANTHROPIC_DIRECT_MODELS: Tuple[ModelEntry, ...] = ( AZURE_AI_MODELS: Tuple[ModelEntry, ...] = ( + ModelEntry( + alias="azure-claude-fable-5-1", + model="azure_ai/claude-fable-5-1", + mode="adaptive", + required_env=_AZURE_FOUNDRY_REQ, + caps=_CAPS_XHIGH_MAX, + fail_reason=( + "claude-fable-5-1 has no deployment on the CI Microsoft Foundry " + "resource yet, so Foundry returns DeploymentNotFound and this cell " + "stays loud in CI. Remove this fail_reason once the deployment " + "exists." + ), + ), ModelEntry( alias="azure-claude-fable-5", model="azure_ai/claude-fable-5", @@ -259,6 +288,20 @@ AZURE_AI_MODELS: Tuple[ModelEntry, ...] = ( VERTEX_AI_MODELS: Tuple[ModelEntry, ...] = ( + ModelEntry( + alias="vertex-claude-fable-5-1", + model="vertex_ai/claude-fable-5-1", + mode="adaptive", + extra_params=(("vertex_location", "global"),), + required_env=_VERTEX_REQ, + caps=_CAPS_XHIGH_MAX, + fail_reason=( + "claude-fable-5-1 availability on the CI Vertex project is not yet " + "confirmed for this brand-new release, so this cell stays loud in " + "CI until verified. Remove this fail_reason once the model is " + "confirmed available on the global Vertex endpoint." + ), + ), ModelEntry( alias="vertex-claude-fable-5", model="vertex_ai/claude-fable-5", @@ -323,6 +366,22 @@ VERTEX_AI_MODELS: Tuple[ModelEntry, ...] = ( BEDROCK_CONVERSE_MODELS: Tuple[ModelEntry, ...] = ( + ModelEntry( + alias="bedrock-claude-fable-5-1", + model="bedrock/converse/us.anthropic.claude-fable-5-1", + mode="adaptive", + extra_params=(("aws_region_name", "us-east-1"),), + required_env=_BEDROCK_REQ, + caps=_CAPS_XHIGH_MAX, + bedrock_effort_ceiling="xhigh", + unavailable_error="is not available for this account", + fail_reason=( + "claude-fable-5-1 access on the CI Bedrock account is not yet " + "confirmed for this brand-new release, so this cell stays loud in " + "CI until verified. Remove this fail_reason once the model is " + "enabled for the account." + ), + ), ModelEntry( alias="bedrock-claude-fable-5", model="bedrock/converse/us.anthropic.claude-fable-5", @@ -441,5 +500,7 @@ def all_cells() -> List[Tuple[str, ModelEntry, str, CellExpectation]]: for route in ROUTES: for model in route.models: for effort in EFFORTS: - cells.append((route.name, model, effort, expected(model, effort))) + cells.append( + (route.name, model, effort, expected(route.name, model, effort)) + ) return cells diff --git a/tests/llm_translation/reasoning_effort_grid/test_reasoning_effort_grid.py b/tests/llm_translation/reasoning_effort_grid/test_reasoning_effort_grid.py index 517e3173b8c..714d544ecd6 100644 --- a/tests/llm_translation/reasoning_effort_grid/test_reasoning_effort_grid.py +++ b/tests/llm_translation/reasoning_effort_grid/test_reasoning_effort_grid.py @@ -201,8 +201,8 @@ async def test_reasoning_effort_grid( def test_grid_cell_count() -> None: - assert len(_PARAMS) == 31 * 11, ( - f"expected 341 cells (31 provider x model combos x 11 efforts), " + assert len(_PARAMS) == 35 * 11, ( + f"expected 385 cells (35 provider x model combos x 11 efforts), " f"got {len(_PARAMS)}" ) diff --git a/tests/llm_translation/test_litellm_proxy_provider.py b/tests/llm_translation/test_litellm_proxy_provider.py index 1cb805bf9ba..8630259877d 100644 --- a/tests/llm_translation/test_litellm_proxy_provider.py +++ b/tests/llm_translation/test_litellm_proxy_provider.py @@ -5,6 +5,7 @@ from io import BytesIO from unittest.mock import AsyncMock +import httpx import litellm from litellm import completion, embedding import pytest @@ -92,44 +93,54 @@ async def test_litellm_gateway_from_sdk_embedding(is_async): litellm.set_verbose = True litellm._turn_on_debug() + captured_bodies = [] + + def handler(request: httpx.Request) -> httpx.Response: + captured_bodies.append(json.loads(request.content)) + return httpx.Response( + 200, + json={ + "object": "list", + "data": [{"object": "embedding", "index": 0, "embedding": [0.1, 0.2, 0.3]}], + "model": "my-vllm-model", + "usage": {"prompt_tokens": 2, "total_tokens": 2}, + }, + ) + if is_async: from openai import AsyncOpenAI - openai_client = AsyncOpenAI(api_key="fake-key") - mock_method = AsyncMock() - patch_target = openai_client.embeddings.create + openai_client = AsyncOpenAI( + api_key="fake-key", + http_client=httpx.AsyncClient(transport=httpx.MockTransport(handler)), + ) + response = await litellm.aembedding( + model="litellm_proxy/my-vllm-model", + input="Hello world", + client=openai_client, + api_base="my-custom-api-base", + ) else: from openai import OpenAI - openai_client = OpenAI(api_key="fake-key") - mock_method = MagicMock() - patch_target = openai_client.embeddings.create + openai_client = OpenAI( + api_key="fake-key", + http_client=httpx.Client(transport=httpx.MockTransport(handler)), + ) + response = litellm.embedding( + model="litellm_proxy/my-vllm-model", + input="Hello world", + client=openai_client, + api_base="my-custom-api-base", + ) - with patch.object(patch_target.__self__, patch_target.__name__, new=mock_method): - try: - if is_async: - await litellm.aembedding( - model="litellm_proxy/my-vllm-model", - input="Hello world", - client=openai_client, - api_base="my-custom-api-base", - ) - else: - litellm.embedding( - model="litellm_proxy/my-vllm-model", - input="Hello world", - client=openai_client, - api_base="my-custom-api-base", - ) - except Exception as e: - print(e) + request_body = captured_bodies[0] + print("Request body - {}".format(request_body)) - mock_method.assert_called_once() - - print("Call KWARGS - {}".format(mock_method.call_args.kwargs)) - - assert "Hello world" == mock_method.call_args.kwargs["input"] - assert "my-vllm-model" == mock_method.call_args.kwargs["model"] + assert "Hello world" == request_body["input"] + assert "my-vllm-model" == request_body["model"] + assert "encoding_format" not in request_body + assert response.data[0]["embedding"] == [0.1, 0.2, 0.3] @pytest.mark.parametrize("is_async", [False, True]) diff --git a/tests/llm_translation/test_nvidia_nim.py b/tests/llm_translation/test_nvidia_nim.py index 7ee4f347f72..d5942e674d0 100644 --- a/tests/llm_translation/test_nvidia_nim.py +++ b/tests/llm_translation/test_nvidia_nim.py @@ -63,27 +63,39 @@ def test_embedding_nvidia_nim(): litellm.set_verbose = True from openai import OpenAI + captured_bodies = [] + + def handler(request: httpx.Request) -> httpx.Response: + captured_bodies.append(json.loads(request.content)) + return httpx.Response( + 200, + json={ + "object": "list", + "data": [{"object": "embedding", "index": 0, "embedding": [0.1, 0.2, 0.3]}], + "model": "nvidia/nv-embedqa-e5-v5", + "usage": {"prompt_tokens": 6, "total_tokens": 6}, + }, + ) + client = OpenAI( api_key="fake-api-key", + http_client=httpx.Client(transport=httpx.MockTransport(handler)), ) - with patch.object(client.embeddings.with_raw_response, "create") as mock_client: - try: - litellm.embedding( - model="nvidia_nim/nvidia/nv-embedqa-e5-v5", - input="What is the meaning of life?", - input_type="passage", - dimensions=1024, - client=client, - ) - except Exception as e: - print(e) - mock_client.assert_called_once() - request_body = mock_client.call_args.kwargs - print("request_body: ", request_body) - assert request_body["input"] == "What is the meaning of life?" - assert request_body["model"] == "nvidia/nv-embedqa-e5-v5" - assert request_body["extra_body"]["input_type"] == "passage" - assert request_body["dimensions"] == 1024 + response = litellm.embedding( + model="nvidia_nim/nvidia/nv-embedqa-e5-v5", + input="What is the meaning of life?", + input_type="passage", + dimensions=1024, + client=client, + ) + request_body = captured_bodies[0] + print("request_body: ", request_body) + assert request_body["input"] == "What is the meaning of life?" + assert request_body["model"] == "nvidia/nv-embedqa-e5-v5" + assert request_body["input_type"] == "passage" + assert request_body["dimensions"] == 1024 + assert "encoding_format" not in request_body + assert response.data[0]["embedding"] == [0.1, 0.2, 0.3] def test_chat_completion_nvidia_nim_with_tools(): diff --git a/tests/llm_translation/test_prompt_factory.py b/tests/llm_translation/test_prompt_factory.py index a90a3df584e..7b03736920b 100644 --- a/tests/llm_translation/test_prompt_factory.py +++ b/tests/llm_translation/test_prompt_factory.py @@ -1446,9 +1446,17 @@ def test_convert_to_anthropic_tool_invoke_sanitizes_invalid_ids(): def test_convert_to_anthropic_tool_invoke_server_tool(): """ - Test that server_tool_use (srvtoolu_) is reconstructed as server_tool_use. + Test that a server tool call (srvtoolu_) with no stored result is replayed + as a regular tool_use block. - Fixes: https://github.com/BerriAI/litellm/issues/17737 + A server_tool_use block is only valid when paired with its result block, so + an unpaired one must degrade to tool_use for Anthropic to accept the replay. + A paired call still becomes server_tool_use, covered by + test_convert_to_anthropic_tool_invoke_with_web_search_results. + + Context: https://github.com/BerriAI/litellm/issues/17737 (original + server_tool_use reconstruction) and LIT-6622 / PR #39144 (unpaired calls + degrade instead of 400ing at Anthropic). """ tool_calls = [ { @@ -1464,7 +1472,7 @@ def test_convert_to_anthropic_tool_invoke_server_tool(): result = convert_to_anthropic_tool_invoke(tool_calls) assert len(result) == 1 - assert result[0]["type"] == "server_tool_use" # NOT tool_use + assert result[0]["type"] == "tool_use" assert result[0]["id"] == "srvtoolu_01ABC123" assert result[0]["name"] == "web_search" assert result[0]["input"] == {"query": "elephant weight"} diff --git a/tests/local_testing/conftest.py b/tests/local_testing/conftest.py index ee93009a198..5535a62bb81 100644 --- a/tests/local_testing/conftest.py +++ b/tests/local_testing/conftest.py @@ -90,6 +90,7 @@ _VCR_INCOMPATIBLE_FILES = frozenset( # carry no real provider cost. _VCR_INCOMPATIBLE_NODEID_SUFFIXES: tuple[str, ...] = ( "test_router.py::test_router_text_completion_client", + "test_embedding.py::test_encoding_format_omitted_by_default_for_openai_sdk", ) diff --git a/tests/local_testing/test_embedding.py b/tests/local_testing/test_embedding.py index aed2849f056..ee2ac14f498 100644 --- a/tests/local_testing/test_embedding.py +++ b/tests/local_testing/test_embedding.py @@ -3,6 +3,8 @@ import os import re import traceback +import httpx + import openai import pytest from dotenv import load_dotenv @@ -1255,56 +1257,42 @@ def test_jina_ai_img_embeddings(input_data, expected_payload_input): assert sent_data["input"] == expected_payload_input -def test_encoding_format_defaults_to_float_for_openai_sdk(monkeypatch): +def test_encoding_format_omitted_by_default_for_openai_sdk(monkeypatch): """ - When encoding_format is not provided, LiteLLM sends `float` for OpenAI-path embeddings. + When encoding_format is not provided, LiteLLM leaves it out of the upstream request. Optional global override: `LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT`. """ monkeypatch.delenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", raising=False) - with patch( - "litellm.llms.openai.openai.OpenAIChatCompletion._get_openai_client" - ) as mock_get_client: - # Create a mock client instance - mock_client_instance = MagicMock() - mock_get_client.return_value = mock_client_instance + captured_bodies = [] - # Mock the embeddings.with_raw_response.create method - mock_response = MagicMock() - mock_response.parse.return_value = MagicMock( - model_dump=lambda: { - "data": [{"embedding": [0.1, 0.2, 0.3], "index": 0}], - "model": "text-embedding-ada-002", + def handler(request: httpx.Request) -> httpx.Response: + captured_bodies.append(json.loads(request.content)) + return httpx.Response( + 200, + json={ "object": "list", + "data": [{"object": "embedding", "index": 0, "embedding": [0.1, 0.2, 0.3]}], + "model": "text-embedding-ada-002", "usage": {"prompt_tokens": 1, "total_tokens": 1}, - } - ) - mock_response.headers = {} - - mock_client_instance.embeddings.with_raw_response.create.return_value = ( - mock_response + }, ) - # Call the embedding function without encoding_format - response = embedding( - model="text-embedding-ada-002", - input="Hello world", - ) + client = openai.OpenAI( + api_key="sk-test", http_client=httpx.Client(transport=httpx.MockTransport(handler)) + ) - # Get the call arguments to verify what was sent to OpenAI SDK - call_args = mock_client_instance.embeddings.with_raw_response.create.call_args - assert ( - call_args is not None - ), "OpenAI SDK embeddings.create should have been called" + response = embedding( + model="text-embedding-ada-002", + input="Hello world", + api_key="sk-test", + client=client, + ) - call_kwargs = call_args[1] # Get kwargs - - assert "encoding_format" in call_kwargs - assert ( - call_kwargs["encoding_format"] == "float" - ), "encoding_format should default to float when not provided by user" - - print("✅ PASS: encoding_format='float' is correctly passed to OpenAI SDK") + assert response.data[0]["embedding"] == [0.1, 0.2, 0.3] + assert "encoding_format" not in captured_bodies[0], ( + "encoding_format should be omitted from the upstream request when not provided by user" + ) def test_encoding_format_explicit_value_preserved(): diff --git a/tests/local_testing/test_exceptions.py b/tests/local_testing/test_exceptions.py index 8370046446d..e6392cda406 100644 --- a/tests/local_testing/test_exceptions.py +++ b/tests/local_testing/test_exceptions.py @@ -5,7 +5,7 @@ import traceback from typing import Any import httpx -from openai import AsyncOpenAI, AuthenticationError, BadRequestError, OpenAIError, RateLimitError +from openai import AsyncAzureOpenAI, AsyncOpenAI, AuthenticationError, AzureOpenAI, BadRequestError, OpenAIError, RateLimitError from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler @@ -895,7 +895,12 @@ def _pre_call_utils( ): if call_type == "embedding": data["input"] = "Hello world!" - mapped_target: Any = client.embeddings.with_raw_response + if isinstance(client, (AzureOpenAI, AsyncAzureOpenAI)): + mapped_target: Any = client.embeddings.with_raw_response + patched_attr = "create" + else: + mapped_target = client + patched_attr = "post" if sync_mode: original_function = litellm.embedding else: @@ -905,6 +910,7 @@ def _pre_call_utils( if streaming is True: data["stream"] = True mapped_target = client.chat.completions.with_raw_response # type: ignore + patched_attr = "create" if sync_mode: original_function = litellm.completion else: @@ -914,12 +920,13 @@ def _pre_call_utils( if streaming is True: data["stream"] = True mapped_target = client.completions.with_raw_response # type: ignore + patched_attr = "create" if sync_mode: original_function = litellm.text_completion else: original_function = litellm.atext_completion - return data, original_function, mapped_target + return data, original_function, mapped_target, patched_attr def _pre_call_utils_httpx( @@ -1003,7 +1010,7 @@ async def test_exception_with_headers(sync_mode, provider, model, call_type, str ) data = {"model": model} - data, original_function, mapped_target = _pre_call_utils( + data, original_function, mapped_target, patched_attr = _pre_call_utils( call_type=call_type, data=data, client=openai_client, @@ -1049,7 +1056,7 @@ async def test_exception_with_headers(sync_mode, provider, model, call_type, str with patch.object( mapped_target, - "create", + patched_attr, side_effect=_return_exception, ): new_retry_after_mock_client = MagicMock(return_value=-1) diff --git a/tests/local_testing/test_get_llm_provider.py b/tests/local_testing/test_get_llm_provider.py index cc6209f2bf9..ebad0fbafc5 100644 --- a/tests/local_testing/test_get_llm_provider.py +++ b/tests/local_testing/test_get_llm_provider.py @@ -155,6 +155,11 @@ def test_default_api_base(): continue elif provider == "github" and other_provider.value == "azure": continue + elif ( + provider in ("qwencloud", "qwen_ai_platform") + and other_provider.value == "dashscope" + ): + continue assert other_provider.value not in api_base.replace("/openai", "") diff --git a/tests/local_testing/test_router.py b/tests/local_testing/test_router.py index 370c43f8f44..c714bb4f9a7 100644 --- a/tests/local_testing/test_router.py +++ b/tests/local_testing/test_router.py @@ -2032,8 +2032,8 @@ def test_router_dynamic_cooldown_correct_retry_after_time(): raise exception with patch.object( - openai_client.embeddings.with_raw_response, - "create", + openai_client, + "post", side_effect=_return_exception, ): new_retry_after_mock_client = MagicMock(return_value=-1) diff --git a/tests/logging_callback_tests/gcs_pub_sub_body/spend_logs_payload.json b/tests/logging_callback_tests/gcs_pub_sub_body/spend_logs_payload.json index 5789f19aa55..28912a27501 100644 --- a/tests/logging_callback_tests/gcs_pub_sub_body/spend_logs_payload.json +++ b/tests/logging_callback_tests/gcs_pub_sub_body/spend_logs_payload.json @@ -11,7 +11,7 @@ "user": "", "team_id": "", "organization_id": "", - "metadata": "{\"applied_guardrails\": [], \"attempted_fallbacks\": null, \"original_model_group\": null, \"batch_models\": null, \"mcp_tool_call_metadata\": null, \"vector_store_request_metadata\": null, \"routing_decision\": null, \"internal_call_origin\": null, \"guardrail_information\": null, \"compression_savings\": null, \"litellm_gateway_injected_cache\": null, \"usage_object\": {\"completion_tokens\": 20, \"prompt_tokens\": 10, \"total_tokens\": 30, \"completion_tokens_details\": null, \"prompt_tokens_details\": null}, \"model_map_information\": {\"model_map_key\": \"gpt-4o\", \"model_map_value\": {\"key\": \"gpt-4o\", \"max_tokens\": 16384, \"max_input_tokens\": 128000, \"max_output_tokens\": 16384, \"input_cost_per_token\": 2.5e-06, \"cache_creation_input_token_cost\": null, \"cache_read_input_token_cost\": 1.25e-06, \"input_cost_per_character\": null, \"input_cost_per_token_above_128k_tokens\": null, \"input_cost_per_token_above_200k_tokens\": null, \"input_cost_per_query\": null, \"input_cost_per_second\": null, \"input_cost_per_audio_token\": null, \"input_cost_per_token_batches\": 1.25e-06, \"output_cost_per_token_batches\": 5e-06, \"output_cost_per_token\": 1e-05, \"output_cost_per_audio_token\": null, \"output_cost_per_character\": null, \"output_cost_per_token_above_128k_tokens\": null, \"output_cost_per_character_above_128k_tokens\": null, \"output_cost_per_token_above_200k_tokens\": null, \"output_cost_per_second\": null, \"output_cost_per_image\": null, \"output_vector_size\": null, \"litellm_provider\": \"openai\", \"mode\": \"chat\", \"supports_system_messages\": true, \"supports_response_schema\": true, \"supports_vision\": true, \"supports_function_calling\": true, \"supports_tool_choice\": true, \"supports_assistant_prefill\": false, \"supports_prompt_caching\": true, \"supports_audio_input\": false, \"supports_audio_output\": false, \"supports_pdf_input\": false, \"supports_embedding_image_input\": false, \"supports_native_streaming\": null, \"supports_web_search\": true, \"supports_reasoning\": false, \"search_context_cost_per_query\": {\"search_context_size_low\": 0.03, \"search_context_size_medium\": 0.035, \"search_context_size_high\": 0.05}, \"tpm\": null, \"rpm\": null, \"supported_openai_params\": [\"frequency_penalty\", \"logit_bias\", \"logprobs\", \"top_logprobs\", \"max_tokens\", \"max_completion_tokens\", \"modalities\", \"prediction\", \"n\", \"presence_penalty\", \"seed\", \"stop\", \"stream\", \"stream_options\", \"temperature\", \"top_p\", \"tools\", \"tool_choice\", \"function_call\", \"functions\", \"max_retries\", \"extra_headers\", \"parallel_tool_calls\", \"audio\", \"response_format\", \"user\"]}}, \"additional_usage_values\": {\"completion_tokens_details\": null, \"prompt_tokens_details\": null}, \"user_api_key\": null, \"user_api_key_alias\": null, \"user_api_key_team_id\": null, \"user_api_key_project_id\": null, \"user_api_key_project_alias\": null, \"user_api_key_org_id\": null, \"user_api_key_user_id\": null, \"user_api_key_team_alias\": null, \"spend_logs_metadata\": null, \"requester_ip_address\": null, \"status\": null, \"proxy_server_request\": null, \"error_information\": null, \"attempted_retries\": null, \"max_retries\": null}", + "metadata": "{\"applied_guardrails\": [], \"attempted_fallbacks\": null, \"original_model_group\": null, \"batch_models\": null, \"batch_successful_requests\": null, \"batch_failed_requests\": null, \"mcp_tool_call_metadata\": null, \"vector_store_request_metadata\": null, \"routing_decision\": null, \"internal_call_origin\": null, \"router_metadata\": null, \"guardrail_information\": null, \"compression_savings\": null, \"litellm_gateway_injected_cache\": null, \"usage_object\": {\"completion_tokens\": 20, \"prompt_tokens\": 10, \"total_tokens\": 30, \"completion_tokens_details\": null, \"prompt_tokens_details\": null}, \"model_map_information\": {\"model_map_key\": \"gpt-4o\", \"model_map_value\": {\"key\": \"gpt-4o\", \"max_tokens\": 16384, \"max_input_tokens\": 128000, \"max_output_tokens\": 16384, \"input_cost_per_token\": 2.5e-06, \"cache_creation_input_token_cost\": null, \"cache_read_input_token_cost\": 1.25e-06, \"input_cost_per_character\": null, \"input_cost_per_token_above_128k_tokens\": null, \"input_cost_per_token_above_200k_tokens\": null, \"input_cost_per_query\": null, \"input_cost_per_second\": null, \"input_cost_per_audio_token\": null, \"input_cost_per_token_batches\": 1.25e-06, \"output_cost_per_token_batches\": 5e-06, \"output_cost_per_token\": 1e-05, \"output_cost_per_audio_token\": null, \"output_cost_per_character\": null, \"output_cost_per_token_above_128k_tokens\": null, \"output_cost_per_character_above_128k_tokens\": null, \"output_cost_per_token_above_200k_tokens\": null, \"output_cost_per_second\": null, \"output_cost_per_image\": null, \"output_vector_size\": null, \"litellm_provider\": \"openai\", \"mode\": \"chat\", \"supports_system_messages\": true, \"supports_response_schema\": true, \"supports_vision\": true, \"supports_function_calling\": true, \"supports_tool_choice\": true, \"supports_assistant_prefill\": false, \"supports_prompt_caching\": true, \"supports_audio_input\": false, \"supports_audio_output\": false, \"supports_pdf_input\": false, \"supports_embedding_image_input\": false, \"supports_native_streaming\": null, \"supports_web_search\": true, \"supports_reasoning\": false, \"search_context_cost_per_query\": {\"search_context_size_low\": 0.03, \"search_context_size_medium\": 0.035, \"search_context_size_high\": 0.05}, \"tpm\": null, \"rpm\": null, \"supported_openai_params\": [\"frequency_penalty\", \"logit_bias\", \"logprobs\", \"top_logprobs\", \"max_tokens\", \"max_completion_tokens\", \"modalities\", \"prediction\", \"n\", \"presence_penalty\", \"seed\", \"stop\", \"stream\", \"stream_options\", \"temperature\", \"top_p\", \"tools\", \"tool_choice\", \"function_call\", \"functions\", \"max_retries\", \"extra_headers\", \"parallel_tool_calls\", \"audio\", \"response_format\", \"user\"]}}, \"additional_usage_values\": {\"completion_tokens_details\": null, \"prompt_tokens_details\": null}, \"user_api_key\": null, \"user_api_key_alias\": null, \"user_api_key_team_id\": null, \"user_api_key_project_id\": null, \"user_api_key_project_alias\": null, \"user_api_key_org_id\": null, \"user_api_key_user_id\": null, \"user_api_key_team_alias\": null, \"spend_logs_metadata\": null, \"requester_ip_address\": null, \"status\": null, \"proxy_server_request\": null, \"error_information\": null, \"attempted_retries\": null, \"max_retries\": null}", "cache_key": "Cache OFF", "spend": 0.00022500000000000002, "total_tokens": 30, diff --git a/tests/mcp_tests/test_mcp_client_unit.py b/tests/mcp_tests/test_mcp_client_unit.py index aadaadd510e..6438525706a 100644 --- a/tests/mcp_tests/test_mcp_client_unit.py +++ b/tests/mcp_tests/test_mcp_client_unit.py @@ -11,7 +11,9 @@ from unittest.mock import AsyncMock, MagicMock, patch, ANY import litellm.experimental_mcp_client.client as mcp_client_module from litellm.experimental_mcp_client.client import MCPClient from litellm.types.mcp import MCPAuth, MCPTransport -from mcp.types import Tool as MCPTool, CallToolResult as MCPCallToolResult +from mcp.types import CallToolResult as MCPCallToolResult +from mcp.types import ListToolsResult, PaginatedRequestParams +from mcp.types import Tool as MCPTool def test_mcp_client_uses_configurable_default_timeout(): @@ -185,6 +187,80 @@ class TestMCPClientUnitTests: mock_session_instance.initialize.assert_called_once() mock_session_instance.list_tools.assert_called_once() + @pytest.mark.asyncio + @patch.object(mcp_client_module, "streamable_http_client") # test-quality-ok: exercises MCPClient wiring; the walk itself is covered sessionless in test_tools.py + @patch.object(mcp_client_module, "ClientSession") # test-quality-ok: exercises MCPClient wiring; the walk itself is covered sessionless in test_tools.py + async def test_list_tools_follows_next_cursor_until_exhausted( + self, + mock_session_class, + mock_transport, + ): + """Test listing tools follows MCP pagination cursors until exhausted.""" + mock_transport_ctx = AsyncMock() + mock_transport.return_value = mock_transport_ctx + mock_transport_instance = MagicMock() + mock_transport_ctx.__aenter__ = AsyncMock(return_value=mock_transport_instance) + + mock_session_ctx = AsyncMock() + mock_session_class.return_value = mock_session_ctx + mock_session_instance = AsyncMock() + mock_session_ctx.__aenter__ = AsyncMock(return_value=mock_session_instance) + + first_page_tools = [ + MCPTool(name=f"tool_{idx}", description=f"Tool {idx}", inputSchema={}) for idx in range(100) + ] + second_page_tool = MCPTool( + name="tool_100", + description="Tool 100", + inputSchema={}, + ) + mock_session_instance.list_tools.side_effect = [ + ListToolsResult(tools=first_page_tools, nextCursor="page-2"), + ListToolsResult(tools=[second_page_tool]), + ] + + client = MCPClient("http://example.com") + result = await client.list_tools() + + assert result == [*first_page_tools, second_page_tool] + assert mock_session_instance.list_tools.call_count == 2 + second_call_params = mock_session_instance.list_tools.call_args_list[1].kwargs["params"] + assert isinstance(second_call_params, PaginatedRequestParams) + assert second_call_params.cursor == "page-2" + + @pytest.mark.asyncio + @patch.object(mcp_client_module, "streamable_http_client") # test-quality-ok: exercises MCPClient wiring; the walk itself is covered sessionless in test_tools.py + @patch.object(mcp_client_module, "ClientSession") # test-quality-ok: exercises MCPClient wiring; the walk itself is covered sessionless in test_tools.py + async def test_list_tools_swallows_mid_walk_error_without_raise_on_error( + self, + mock_session_class, + mock_transport, + ): + """Test a mid-walk failure returns [] when raise_on_error is False.""" + mock_transport_ctx = AsyncMock() + mock_transport.return_value = mock_transport_ctx + mock_transport_instance = MagicMock() + mock_transport_ctx.__aenter__ = AsyncMock(return_value=mock_transport_instance) + + mock_session_ctx = AsyncMock() + mock_session_class.return_value = mock_session_ctx + mock_session_instance = AsyncMock() + mock_session_ctx.__aenter__ = AsyncMock(return_value=mock_session_instance) + + mock_session_instance.list_tools.side_effect = [ + ListToolsResult( + tools=[MCPTool(name="tool_0", description="Tool 0", inputSchema={})], + nextCursor="page-2", + ), + RuntimeError("transient upstream failure"), + ] + + client = MCPClient("http://example.com") + result = await client.list_tools() + + assert result == [] + assert mock_session_instance.list_tools.call_count == 2 + @pytest.mark.asyncio @patch.object(mcp_client_module, "streamable_http_client") @patch.object(mcp_client_module, "ClientSession") diff --git a/tests/pass_through_unit_tests/test_websearch_interception_e2e.py b/tests/pass_through_unit_tests/test_websearch_interception_e2e.py index 091ea106b91..fd95b7fa8f2 100644 --- a/tests/pass_through_unit_tests/test_websearch_interception_e2e.py +++ b/tests/pass_through_unit_tests/test_websearch_interception_e2e.py @@ -937,6 +937,14 @@ async def test_pre_request_hook_modifies_request_body(): print("✅ WebSearchInterceptionLogger initialized") + mock_router = MagicMock() + mock_router.search_tools = [ + { + "search_tool_name": "test-search-tool", + "litellm_params": {"search_provider": "tavily"}, + } + ] + # Track what actually gets sent to the API captured_request = {} @@ -987,6 +995,9 @@ async def test_pre_request_hook_modifies_request_body(): with patch( "litellm.llms.anthropic.experimental_pass_through.messages.handler.anthropic_messages_handler", side_effect=mock_anthropic_messages_handler, + ), patch( # test-quality-ok: the hook imports this process-global router at call time; no injection seam exists to register search_tools + "litellm.proxy.proxy_server.llm_router", + mock_router, ): print( diff --git a/tests/proxy_migration_tests/test_image_bedrock_realtime_extra.py b/tests/proxy_migration_tests/test_image_bedrock_realtime_extra.py new file mode 100644 index 00000000000..ed21734c5fc --- /dev/null +++ b/tests/proxy_migration_tests/test_image_bedrock_realtime_extra.py @@ -0,0 +1,58 @@ +"""Image-level check that the built proxy image can import the Bedrock realtime SDK. + +Bedrock Nova Sonic (`/v1/realtime`) imports `aws_sdk_bedrock_runtime` lazily on the +first session, so an image whose `uv sync` stages skip the `bedrock-realtime` extra +boots, passes health checks, and then fails every Nova Sonic session with +"Missing aws_sdk_bedrock_runtime". Importing inside the built image is what catches +that class of regression (missing extra, lockfile drift, a stage that syncs a +different set of extras), which a static Dockerfile check cannot. + +Gated on LITELLM_IMAGE like the other image checks in this directory; exercised +where an image has been built (the image-scan workflow). Requires a working docker CLI. +""" + +import os +import shutil +import subprocess +from typing import Final + +import pytest + +IMAGE: Final = os.getenv("LITELLM_IMAGE") +NON_ROOT_UID: Final = "12345:0" +IMPORT_PROBE: Final = "import aws_sdk_bedrock_runtime, smithy_aws_core; print('bedrock-realtime ok')" + +pytestmark = [ + pytest.mark.skipif(IMAGE is None, reason="requires a built image (set LITELLM_IMAGE)"), + pytest.mark.skipif(shutil.which("docker") is None, reason="requires the docker CLI"), +] + + +def test_image_imports_bedrock_realtime_sdk(): + assert IMAGE is not None + + probe: Final = subprocess.run( + [ + "docker", + "run", + "--rm", + "--network", + "none", + "--user", + NON_ROOT_UID, + "--entrypoint", + "python", + IMAGE, + "-c", + IMPORT_PROBE, + ], + capture_output=True, + text=True, + check=False, + ) + + assert probe.returncode == 0 and "bedrock-realtime ok" in probe.stdout, ( + f"{IMAGE} cannot import aws_sdk_bedrock_runtime as uid {NON_ROOT_UID}, so Bedrock Nova Sonic " + "/v1/realtime sessions fail with 'Missing aws_sdk_bedrock_runtime'. Is `--extra bedrock-realtime` " + f"passed to every `uv sync` in its Dockerfile?\nstdout:\n{probe.stdout}\nstderr:\n{probe.stderr}" + ) diff --git a/tests/proxy_unit_tests/test_check_batch_cost.py b/tests/proxy_unit_tests/test_check_batch_cost.py index b4fe9347581..ff5e8f89d64 100644 --- a/tests/proxy_unit_tests/test_check_batch_cost.py +++ b/tests/proxy_unit_tests/test_check_batch_cost.py @@ -9,16 +9,40 @@ ARN unified_object_id) batches with no managed unified id. import asyncio import json from contextlib import contextmanager +from typing import TYPE_CHECKING from unittest.mock import AsyncMock, MagicMock, patch import pytest from fastapi import HTTPException +if TYPE_CHECKING: + from litellm.batches.batch_utils import BatchCostUsageResult + _IS_B64 = "litellm.proxy.openai_files_endpoints.common_utils._is_base64_encoded_unified_file_id" _CLAIM_UNIFIED_BATCH_ID = "dW5pZmllZF9iYXRjaF9pZA==" _CLAIM_OUTPUT_FILE_ID = "file-output-123" +def _batch_cost_result( + cost: float, + usage: dict, + models: list[str], + successful_requests: int = 1, + failed_requests: int = 0, +) -> "BatchCostUsageResult": + """Build the BatchCostUsageResult calculate_batch_cost_and_usage now returns, + for mocking it in tests that only care about cost/usage/models.""" + from litellm.batches.batch_utils import BatchCostUsageResult + + return BatchCostUsageResult( + cost=cost, + usage=usage, + models=models, + successful_requests=successful_requests, + failed_requests=failed_requests, + ) + + def _unmanaged_vertex_file_object( input_file_id="gs://bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash/abc.jsonl", status="validating", @@ -327,7 +351,7 @@ class TestCheckBatchCost: patch( "litellm.batches.batch_utils.calculate_batch_cost_and_usage", new_callable=AsyncMock, - return_value=( + return_value=_batch_cost_result( 0.01, {"prompt_tokens": 10, "completion_tokens": 5}, ["gpt-4"], @@ -432,7 +456,7 @@ class TestCheckBatchCost: patch( "litellm.batches.batch_utils.calculate_batch_cost_and_usage", new_callable=AsyncMock, - return_value=(0.01, {"prompt_tokens": 10, "completion_tokens": 5}, ["claude-haiku-4-5"]), + return_value=_batch_cost_result(0.01, {"prompt_tokens": 10, "completion_tokens": 5}, ["claude-haiku-4-5"]), ), patch( "litellm.litellm_core_utils.get_llm_provider_logic.get_llm_provider", @@ -535,7 +559,9 @@ class TestCheckBatchCost: patch( "litellm.batches.batch_utils.calculate_batch_cost_and_usage", new_callable=AsyncMock, - return_value=(0.0052, {"prompt_tokens": 1400, "completion_tokens": 600}, ["claude-haiku-4-5"]), + return_value=_batch_cost_result( + 0.0052, {"prompt_tokens": 1400, "completion_tokens": 600}, ["claude-haiku-4-5"] + ), ) as mock_calculate, patch( "litellm.litellm_core_utils.get_llm_provider_logic.get_llm_provider", @@ -634,7 +660,7 @@ class TestCheckBatchCost: patch( "litellm.batches.batch_utils.calculate_batch_cost_and_usage", new_callable=AsyncMock, - return_value=( + return_value=_batch_cost_result( 0.01, {"prompt_tokens": 10, "completion_tokens": 5}, ["gpt-4"], @@ -764,7 +790,7 @@ class TestCheckBatchCost: patch( "litellm.batches.batch_utils.calculate_batch_cost_and_usage", new_callable=AsyncMock, - return_value=( + return_value=_batch_cost_result( 0.01, {"prompt_tokens": 10, "completion_tokens": 5}, ["gpt-4"], @@ -1312,7 +1338,7 @@ class TestCheckBatchCost: patch( "litellm.batches.batch_utils.calculate_batch_cost_and_usage", new_callable=AsyncMock, - return_value=( + return_value=_batch_cost_result( 0.01, {"prompt_tokens": 10, "completion_tokens": 5}, ["gpt-4"], @@ -1347,6 +1373,114 @@ class TestCheckBatchCost: update_data["status"] == terminal_status ), f"billed {terminal_status} batch must keep its real terminal status in the DB" + @pytest.mark.asyncio + async def test_error_file_failures_add_to_failed_request_count( + self, check_batch_cost_instance, mock_prisma_client, mock_llm_router + ): + """OpenAI-shaped providers report per-request failures only in a separate + error file. The poller prices from the output file, so without also counting + the error file's lines, batch_failed_requests on the spend log undercounts: + regression test for the poller path merging error-file failures. + """ + import base64 + from unittest.mock import patch + + import httpx + import respx + + from litellm.litellm_core_utils.litellm_logging import Logging + + mock_prisma_client.db.litellm_managedobjecttable.update_many = AsyncMock(return_value=1) + mock_prisma_client.db.litellm_managedobjecttable.update = AsyncMock() + mock_prisma_client.db.litellm_usertable.find_unique = AsyncMock(return_value=None) + + mock_job = MagicMock() + mock_job.id = "job-error-file-1" + mock_job.unified_object_id = base64.urlsafe_b64encode( + b"litellm_proxy;model_id:model-123;llm_batch_id:batch-456" + ).decode() + mock_job.created_by = "user-1" + mock_prisma_client.db.litellm_managedobjecttable.find_many = AsyncMock(return_value=[mock_job]) + + mock_response = MagicMock() + mock_response.status = "completed" + mock_response.output_file_id = "file-output-123" + mock_response.error_file_id = "file-error-456" + mock_response.model_dump_json.return_value = '{"id":"batch-1","status":"completed"}' + mock_llm_router.aretrieve_batch = AsyncMock(return_value=mock_response) + mock_llm_router.get_deployment_credentials_with_provider = MagicMock(return_value={"api_key": "sk-test"}) + + mock_deployment = MagicMock() + mock_deployment.litellm_params.custom_llm_provider = "openai" + mock_deployment.litellm_params.model = "gpt-4" + mock_deployment.model_info.model_dump.return_value = {} + mock_llm_router.get_deployment = MagicMock(return_value=mock_deployment) + + succeeded_line = json.dumps( + { + "custom_id": "req-1", + "response": { + "status_code": 200, + "body": { + "id": "chatcmpl-1", + "object": "chat.completion", + "model": "gpt-4", + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": "hi"}, + "finish_reason": "stop", + } + ], + "usage": { + "prompt_tokens": 10, + "completion_tokens": 5, + "total_tokens": 15, + }, + }, + }, + "error": None, + } + ) + rejected_line = json.dumps( + { + "custom_id": "req-2", + "response": { + "status_code": 400, + "body": {"error": {"message": "bad request"}}, + }, + "error": None, + } + ) + error_file_lines = "\n".join( + json.dumps({"custom_id": custom_id, "error": {"message": "rejected"}}) for custom_id in ("req-3", "req-4") + ) + + with ( + respx.mock(assert_all_called=True) as provider, + patch.object( # test-quality-ok: the poller builds Logging inline, the only seam to its handler kwargs + Logging, "async_success_handler", new_callable=AsyncMock + ) as success_handler, + ): + provider.get("https://api.openai.com/v1/files/file-output-123/content").mock( + return_value=httpx.Response(200, content=f"{succeeded_line}\n{rejected_line}\n".encode()) + ) + provider.get("https://api.openai.com/v1/files/file-error-456/content").mock( + return_value=httpx.Response(200, content=f"{error_file_lines}\n\n".encode()) + ) + await check_batch_cost_instance.check_batch_cost() + + spend_log_calls = [call.kwargs for call in success_handler.await_args_list if "batch_cost" in call.kwargs] + assert len(spend_log_calls) == 1 + handler_kwargs = spend_log_calls[0] + assert handler_kwargs["batch_successful_requests"] == 1 + assert handler_kwargs["batch_failed_requests"] == 3, ( + "2 error-file lines must add to the output file's 1 rejected request" + ) + assert handler_kwargs["batch_models"] == ["gpt-4"] + assert handler_kwargs["batch_usage"].total_tokens == 15 + assert handler_kwargs["batch_cost"] > 0 + @pytest.mark.asyncio async def test_terminal_batch_with_missing_output_file_is_retired_unbilled( self, check_batch_cost_instance, mock_prisma_client, mock_llm_router @@ -1518,7 +1652,7 @@ class TestCheckBatchCost: patch( "litellm.batches.batch_utils.calculate_batch_cost_and_usage", new_callable=AsyncMock, - return_value=( + return_value=_batch_cost_result( 0.01, {"prompt_tokens": 10, "completion_tokens": 5}, ["gpt-4"], @@ -1776,7 +1910,7 @@ class TestUnmanagedVertexRouting: patch( "litellm.batches.batch_utils.calculate_batch_cost_and_usage", new_callable=AsyncMock, - return_value=( + return_value=_batch_cost_result( 0.01, {"prompt_tokens": 10, "completion_tokens": 5}, ["gemini-2.5-flash"], @@ -2006,7 +2140,7 @@ class TestUnmanagedBedrockRouting: patch( "litellm.batches.batch_utils.calculate_batch_cost_and_usage", new_callable=AsyncMock, - return_value=( + return_value=_batch_cost_result( 0.02, {"prompt_tokens": 10, "completion_tokens": 5}, ["claude-sonnet-4"], @@ -2198,7 +2332,7 @@ class TestManagedOutputFileIdEncodesPublicModelGroup: patch( "litellm.batches.batch_utils.calculate_batch_cost_and_usage", new_callable=AsyncMock, - return_value=(0.01, {"prompt_tokens": 10}, ["gpt-5.5"]), + return_value=_batch_cost_result(0.01, {"prompt_tokens": 10}, ["gpt-5.5"]), ), patch("litellm.litellm_core_utils.litellm_logging.Logging") as logging_cls, ): @@ -2826,7 +2960,7 @@ class TestMultiPodBatchCostClaim: patch( "litellm.batches.batch_utils.calculate_batch_cost_and_usage", new_callable=AsyncMock, - return_value=(0.01, {"prompt_tokens": 10, "completion_tokens": 5}, ["gpt-4"]), + return_value=_batch_cost_result(0.01, {"prompt_tokens": 10, "completion_tokens": 5}, ["gpt-4"]), ), patch( "litellm.litellm_core_utils.get_llm_provider_logic.get_llm_provider", diff --git a/tests/proxy_unit_tests/test_proxy_utils.py b/tests/proxy_unit_tests/test_proxy_utils.py index 3bde72ccd49..35de9961054 100644 --- a/tests/proxy_unit_tests/test_proxy_utils.py +++ b/tests/proxy_unit_tests/test_proxy_utils.py @@ -1317,6 +1317,62 @@ def test_proxy_config_state_post_init_callback_call(monkeypatch): assert config["litellm_settings"]["default_team_settings"][0]["team_id"] == "test" +@pytest.mark.asyncio +async def test_default_team_settings_newrelic_resolves_traces_and_metrics(): + """Static `default_team_settings` is the config-file twin of POST /team/callback. + + A team pinned to New Relic through `default_team_settings` must reach the + same two loggers the dynamic path does: the per-team metrics logger (cost + and usage) and the trace logger (LLM/agent spans). This proves the static + path resolves both, not just one, so the config-file customer gets the + same per-team routing as the API customer. + """ + from litellm.litellm_core_utils.litellm_logging import Logging + from litellm.proxy.litellm_pre_call_utils import LiteLLMProxyRequestSetup + from litellm.proxy.proxy_server import ProxyConfig + + pc = ProxyConfig() + pc.config = { + "litellm_settings": { + "default_team_settings": [ + { + "team_id": "team-a", + "success_callback": ["newrelic"], + "newrelic_api_key": "team-a-ingest-key", + "newrelic_region": "eu", + } + ] + } + } + + callback_metadata = LiteLLMProxyRequestSetup.add_team_based_callbacks_from_config( + team_id="team-a", + proxy_config=pc, + ) + + assert callback_metadata is not None + assert callback_metadata.success_callback == ["newrelic"] + assert callback_metadata.callback_vars == { + "newrelic_api_key": "team-a-ingest-key", + "newrelic_region": "eu", + } + + logging_obj = Logging( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "hi"}], + stream=False, + call_type="completion", + start_time=None, + litellm_call_id="static-nr-1", + function_id="static-nr-1", + ) + logging_obj._trusted_callback_vars = tuple(callback_metadata.callback_vars.items()) + + resolved = logging_obj._resolve_dynamic_callback_string("newrelic") + resolved_names = {type(logger).__name__ for logger in resolved} + assert resolved_names == {"NewRelicMetricsLogger", "NewRelicLogger"} + + def test_proxy_config_state_get_config_state_error(): """ Ensures that get_config_state does not raise an error when the config is not a valid dictionary diff --git a/tests/router_unit_tests/test_router_anthropic_messages_fallback.py b/tests/router_unit_tests/test_router_anthropic_messages_fallback.py new file mode 100644 index 00000000000..0c4d1dfc21e --- /dev/null +++ b/tests/router_unit_tests/test_router_anthropic_messages_fallback.py @@ -0,0 +1,402 @@ +""" +Unit tests for safeguard-refusal fallback on the /v1/messages router surface. + +An Anthropic safeguard refusal is an HTTP 200 whose body carries +stop_reason "refusal" plus a stop_details object; the router converts it +into a ContentPolicyViolationError so the content-policy fallback chain +runs, but only when a matching fallback is configured. A plain refusal +without stop_details, or any refusal with nothing configured, must reach +the client byte-identical. + +The upstream is faked at the HTTP boundary by intercepting the third-party +transport (httpx.AsyncClient.send), so requests run litellm's real +transformation, allowlist, and streaming pipeline end to end. +""" + +import json +from typing import Any, AsyncIterator +from unittest.mock import patch + +import httpx +import pytest + +from litellm import Router +from litellm.router_utils.fallback_event_handlers import ( + PRE_ROUTING_SELECTED_MODEL_KEY, + record_pre_routing_selection, +) + +REFUSAL_RESPONSE: dict[str, Any] = { + "id": "msg_refusal", + "type": "message", + "role": "assistant", + "model": "claude-fable-5", + "content": [], + "stop_reason": "refusal", + "stop_sequence": None, + "stop_details": {"category": "cyber", "explanation": "flagged"}, + "usage": {"input_tokens": 25, "output_tokens": 1}, +} + +PLAIN_REFUSAL_RESPONSE: dict[str, Any] = {k: v for k, v in REFUSAL_RESPONSE.items() if k != "stop_details"} + +OK_RESPONSE: dict[str, Any] = { + "id": "msg_ok", + "type": "message", + "role": "assistant", + "model": "claude-opus-5", + "content": [{"type": "text", "text": "hello"}], + "stop_reason": "end_turn", + "stop_sequence": None, + "usage": {"input_tokens": 25, "output_tokens": 2}, +} + + +def _sse(event: str, data: dict[str, Any]) -> bytes: + return f"event: {event}\ndata: {json.dumps(data)}\n\n".encode() + + +REFUSAL_STREAM_FRAMES: tuple[bytes, ...] = ( + _sse("message_start", {"type": "message_start", "message": {**REFUSAL_RESPONSE, "stop_reason": None}}), + _sse( + "message_delta", + { + "type": "message_delta", + "delta": {"stop_reason": "refusal", "stop_details": {"category": "cyber"}}, + "usage": {"output_tokens": 1}, + }, + ), + _sse("message_stop", {"type": "message_stop"}), +) + +OK_STREAM_FRAMES: tuple[bytes, ...] = ( + _sse("message_start", {"type": "message_start", "message": {**OK_RESPONSE, "stop_reason": None}}), + _sse( + "content_block_delta", + {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "hello"}}, + ), + _sse("message_stop", {"type": "message_stop"}), +) + + +def _split_frames_mid_data_line(frames: tuple[bytes, ...]) -> tuple[bytes, ...]: + """Split each frame's data line in half, modeling a transport chunk boundary.""" + return tuple(part for frame in frames for part in (frame[: len(frame) // 2], frame[len(frame) // 2 :])) + + +class _FrameStream(httpx.AsyncByteStream): + def __init__(self, frames: tuple[bytes, ...]) -> None: + self._frames = frames + + async def __aiter__(self) -> AsyncIterator[bytes]: + for frame in self._frames: + yield frame + + async def aclose(self) -> None: + return None + + +class FakeAnthropicUpstream: + """Intercepts the third-party transport (httpx.AsyncClient.send): refuses on fable + models, answers on others. The router deliberately does not forward caller-injected + clients, so the transport is the seam that exercises the real litellm pipeline.""" + + def __init__( + self, + refusal_body: dict[str, Any] = REFUSAL_RESPONSE, + refusal_frames: tuple[bytes, ...] = REFUSAL_STREAM_FRAMES, + ) -> None: + self.refusal_body = refusal_body + self.refusal_frames = refusal_frames + self.calls: list[str] = [] + self.bodies: list[dict[str, Any]] = [] + + async def send(self, request: httpx.Request, **kwargs: Any) -> httpx.Response: + body = json.loads(request.content or b"{}") + model = body.get("model", "") + self.calls.append(model) + self.bodies.append(body) + refuses = "fable" in model + if body.get("stream"): + frames = self.refusal_frames if refuses else OK_STREAM_FRAMES + return httpx.Response( + 200, + stream=_FrameStream(frames), + headers={"content-type": "text/event-stream"}, + request=request, + ) + return httpx.Response(200, json=self.refusal_body if refuses else OK_RESPONSE, request=request) + + def install(self): + async def _send(_client: httpx.AsyncClient, request: httpx.Request, **kwargs: Any) -> httpx.Response: + return await self.send(request, **kwargs) + + return patch("httpx.AsyncClient.send", new=_send) + + +FABLE_TIER = { + "model_name": "fable-tier", + "litellm_params": {"model": "anthropic/claude-fable-5", "api_key": "sk-test"}, +} +OPUS_TARGET = { + "model_name": "opus-target", + "litellm_params": {"model": "anthropic/claude-opus-5", "api_key": "sk-test"}, +} + + +def _router(content_policy_fallbacks: list | None) -> Router: + return Router(model_list=[FABLE_TIER, OPUS_TARGET], content_policy_fallbacks=content_policy_fallbacks) + + +async def _collect(stream: AsyncIterator[bytes]) -> bytes: + return b"".join([chunk async for chunk in stream]) + + +@pytest.mark.asyncio +async def test_non_streaming_refusal_with_fallback_row_returns_fallback_response(): + fake = FakeAnthropicUpstream() + router = _router(content_policy_fallbacks=[{"fable-tier": ["opus-target"]}]) + + with fake.install(): + response = await router.aanthropic_messages( + model="fable-tier", max_tokens=16, messages=[{"role": "user", "content": "hi"}] + ) + + assert response["stop_reason"] == "end_turn" + assert response["id"] == "msg_ok" + assert len(fake.calls) == 2 + assert "claude-opus-5" in fake.calls[1] + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "content_policy_fallbacks, upstream_body", + [ + (None, REFUSAL_RESPONSE), + ([{"unrelated-group": ["opus-target"]}], REFUSAL_RESPONSE), + ([{"fable-tier": ["opus-target"]}], PLAIN_REFUSAL_RESPONSE), + ], + ids=["nothing-configured", "row-for-other-group", "refusal-without-stop-details"], +) +async def test_non_streaming_refusal_passes_through_untouched(content_policy_fallbacks, upstream_body): + fake = FakeAnthropicUpstream(refusal_body=upstream_body) + router = _router(content_policy_fallbacks=content_policy_fallbacks) + + with fake.install(): + response = await router.aanthropic_messages( + model="fable-tier", max_tokens=16, messages=[{"role": "user", "content": "hi"}] + ) + + assert response["stop_reason"] == "refusal" + assert response.get("stop_details") == upstream_body.get("stop_details") + assert len(fake.calls) == 1 + + +@pytest.mark.asyncio +async def test_streaming_refusal_with_fallback_row_streams_fallback_frames(): + fake = FakeAnthropicUpstream() + router = _router(content_policy_fallbacks=[{"fable-tier": ["opus-target"]}]) + + with fake.install(): + stream = await router.aanthropic_messages( + model="fable-tier", max_tokens=16, stream=True, messages=[{"role": "user", "content": "hi"}] + ) + body = await _collect(stream) + + assert b'"refusal"' not in body + assert b"text_delta" in body + assert len(fake.calls) == 2 + + +@pytest.mark.asyncio +async def test_streaming_refusal_split_across_chunks_still_falls_back(): + fake = FakeAnthropicUpstream(refusal_frames=_split_frames_mid_data_line(REFUSAL_STREAM_FRAMES)) + router = _router(content_policy_fallbacks=[{"fable-tier": ["opus-target"]}]) + + with fake.install(): + stream = await router.aanthropic_messages( + model="fable-tier", max_tokens=16, stream=True, messages=[{"role": "user", "content": "hi"}] + ) + body = await _collect(stream) + + assert b'"refusal"' not in body + assert b"text_delta" in body + assert len(fake.calls) == 2 + + +@pytest.mark.asyncio +async def test_streaming_refusal_without_fallback_row_passes_frames_through(): + fake = FakeAnthropicUpstream() + router = _router(content_policy_fallbacks=None) + + with fake.install(): + stream = await router.aanthropic_messages( + model="fable-tier", max_tokens=16, stream=True, messages=[{"role": "user", "content": "hi"}] + ) + body = await _collect(stream) + + assert b'"stop_reason": "refusal"' in body + assert b"stop_details" in body + assert len(fake.calls) == 1 + + +@pytest.mark.asyncio +async def test_streaming_refusal_on_routed_tier_matches_tier_keyed_row_without_inbound_metadata(): + """The pre-routing hook's tier stamp must reach the mid-stream fallback lookup even when the + request carries no metadata bucket at all (the snapshot is taken before the request runs).""" + fake = FakeAnthropicUpstream() + smart_router = { + "model_name": "smart-router", + "litellm_params": { + "model": "auto_router/complexity_router", + "complexity_router_config": { + "tiers": {"SIMPLE": "fable-tier", "MEDIUM": "fable-tier", "COMPLEX": "fable-tier"} + }, + "complexity_router_default_model": "fable-tier", + }, + "model_info": {"id": "router-1", "db_model": True}, + } + router = Router( + model_list=[FABLE_TIER, OPUS_TARGET, smart_router], + content_policy_fallbacks=[{"fable-tier": ["opus-target"]}], + ignore_invalid_deployments=True, + ) + + with fake.install(): + stream = await router.aanthropic_messages( + model="smart-router", max_tokens=16, stream=True, messages=[{"role": "user", "content": "hi"}] + ) + body = await _collect(stream) + + assert b'"refusal"' not in body + assert b"text_delta" in body + assert len(fake.calls) == 2 + + +@pytest.mark.asyncio +async def test_caller_forged_tier_stamp_cannot_pick_the_streaming_fallback_chain(): + fake = FakeAnthropicUpstream() + router = _router(content_policy_fallbacks=[{"forged-tier": ["opus-target"]}]) + + with fake.install(): + stream = await router.aanthropic_messages( + model="fable-tier", + max_tokens=16, + stream=True, + messages=[{"role": "user", "content": "hi"}], + litellm_metadata={PRE_ROUTING_SELECTED_MODEL_KEY: "forged-tier"}, + ) + body = await _collect(stream) + + assert b'"stop_reason": "refusal"' in body + assert len(fake.calls) == 1 + + +@pytest.mark.asyncio +async def test_tier_stamp_never_reaches_provider_bound_metadata(): + """On /v1/messages the top-level metadata dict is Anthropic's own request field, so the + routed-tier stamp must never appear in any upstream body even when the client sends one.""" + fake = FakeAnthropicUpstream() + smart_router = { + "model_name": "smart-router", + "litellm_params": { + "model": "auto_router/complexity_router", + "complexity_router_config": { + "tiers": {"SIMPLE": "fable-tier", "MEDIUM": "fable-tier", "COMPLEX": "fable-tier"} + }, + "complexity_router_default_model": "fable-tier", + }, + "model_info": {"id": "router-1", "db_model": True}, + } + router = Router( + model_list=[FABLE_TIER, OPUS_TARGET, smart_router], + content_policy_fallbacks=[{"fable-tier": ["opus-target"]}], + ignore_invalid_deployments=True, + ) + + with fake.install(): + response = await router.aanthropic_messages( + model="smart-router", + max_tokens=16, + messages=[{"role": "user", "content": "hi"}], + metadata={"user_id": "u1"}, + ) + + assert response["stop_reason"] == "end_turn" + assert len(fake.bodies) == 2 + for body in fake.bodies: + assert body.get("metadata") == {"user_id": "u1"} + + +def test_record_pre_routing_selection_writes_only_the_internal_bucket(): + """The Anthropic request's own metadata field must never carry the tier stamp.""" + kwargs = {"metadata": {"user_id": "u1"}, "litellm_metadata": {}} + + record_pre_routing_selection(kwargs, "tier-x") + + assert kwargs["litellm_metadata"] == {PRE_ROUTING_SELECTED_MODEL_KEY: "tier-x"} + assert kwargs["metadata"] == {"user_id": "u1"} + + +def test_refusal_gate_keys_on_pre_routing_tier_stamp(): + router = _router(content_policy_fallbacks=[{"tier-group": ["opus-target"]}]) + + def anthropic_messages(**kwargs: Any) -> None: + return None + + refusal_kwargs = {"litellm_metadata": {PRE_ROUTING_SELECTED_MODEL_KEY: "tier-group"}} + assert ( + router._should_raise_anthropic_refusal_error( + model="router-group", + original_generic_function=anthropic_messages, + response=dict(REFUSAL_RESPONSE), + kwargs=refusal_kwargs, + ) + is True + ) + assert ( + router._should_raise_anthropic_refusal_error( + model="router-group", + original_generic_function=anthropic_messages, + response=dict(REFUSAL_RESPONSE), + kwargs={}, + ) + is False + ) + + +def test_has_content_policy_fallback_default_fallbacks_arm(): + router = Router(model_list=[OPUS_TARGET], fallbacks=[{"*": ["opus-target"]}]) + + assert router._has_content_policy_fallback("any-group", {}) is True + assert router._has_content_policy_fallback("any-group", {"content_policy_fallbacks": [{"other": ["x"]}]}) is False + + +def test_get_fallback_model_group_for_lookup_groups_orders_tier_before_requested(): + router = _router(content_policy_fallbacks=None) + fallbacks = [{"tier1": ["backup-a"]}, {"smart-router": ["backup-b"]}] + + assert router._get_fallback_model_group_for_lookup_groups( + fallbacks=fallbacks, lookup_groups=("tier1", "smart-router") + ) == ["backup-a"] + assert router._get_fallback_model_group_for_lookup_groups( + fallbacks=fallbacks, lookup_groups=("tier9", "smart-router") + ) == ["backup-b"] + assert router._get_fallback_model_group_for_lookup_groups(fallbacks=fallbacks, lookup_groups=()) is None + + +def test_refusal_gate_ignores_other_generic_call_types(): + router = _router(content_policy_fallbacks=[{"fable-tier": ["opus-target"]}]) + + def aresponses(**kwargs: Any) -> None: + return None + + assert ( + router._should_raise_anthropic_refusal_error( + model="fable-tier", + original_generic_function=aresponses, + response=dict(REFUSAL_RESPONSE), + kwargs={}, + ) + is False + ) diff --git a/tests/router_unit_tests/test_router_helper_utils.py b/tests/router_unit_tests/test_router_helper_utils.py index dcd2e9edf7b..7dbac243d55 100644 --- a/tests/router_unit_tests/test_router_helper_utils.py +++ b/tests/router_unit_tests/test_router_helper_utils.py @@ -2294,6 +2294,7 @@ def search_tools(): "search_provider": "perplexity", "api_key": "test-api-key", "api_base": "https://api.perplexity.ai", + "mode": "turbo", }, }, { @@ -2302,6 +2303,7 @@ def search_tools(): "search_provider": "perplexity", "api_key": "test-api-key-2", "api_base": "https://api.perplexity.ai", + "mode": "turbo", }, }, ] @@ -2393,6 +2395,7 @@ async def test_asearch_with_fallbacks_helper(search_tools): assert "search_provider" in kwargs assert kwargs["search_provider"] == "perplexity" assert "api_key" in kwargs + assert kwargs["mode"] == "turbo" assert kwargs["query"] == "helper test query" return mock_response diff --git a/tests/test_litellm/batches/test_batch_utils.py b/tests/test_litellm/batches/test_batch_utils.py index 41b4bb8cf76..c86c7c4df03 100644 --- a/tests/test_litellm/batches/test_batch_utils.py +++ b/tests/test_litellm/batches/test_batch_utils.py @@ -211,10 +211,10 @@ def test_estimate_tokens_never_zero_for_short_rows(): def test_output_models_uses_model_name_override(monkeypatch): monkeypatch.setattr(litellm, "completion_cost", lambda **kw: 0.0) - _, _, models = bu._aggregate_batch_cost_usage_models( + result = bu._aggregate_batch_cost_usage_models( entries=[_success_row(model="ignored")], custom_llm_provider="openai", model_name="forced-model" ) - assert models == ["forced-model"] + assert result.models == ["forced-model"] def test_output_models_collects_from_successful_only(monkeypatch): @@ -224,15 +224,15 @@ def test_output_models_collects_from_successful_only(monkeypatch): _failed_row(model="should-be-skipped"), _success_row(model="claude-3"), ] - _, _, models = bu._aggregate_batch_cost_usage_models(entries=rows, custom_llm_provider="openai") - assert models == ["gpt-4o", "claude-3"] + result = bu._aggregate_batch_cost_usage_models(entries=rows, custom_llm_provider="openai") + assert result.models == ["gpt-4o", "claude-3"] def test_output_models_skips_successful_without_model(monkeypatch): monkeypatch.setattr(litellm, "completion_cost", lambda **kw: 0.0) rows = [{"response": {"status_code": 200, "body": {}}}] - _, _, models = bu._aggregate_batch_cost_usage_models(entries=rows, custom_llm_provider="openai") - assert models == [] + result = bu._aggregate_batch_cost_usage_models(entries=rows, custom_llm_provider="openai") + assert result.models == [] # =========================================================================== # @@ -399,8 +399,8 @@ def test_total_usage_sums_successful_only(monkeypatch): _failed_row(), # excluded _success_row(usage=_usage(20, 10)), # 30 ] - _, usage, _ = bu._aggregate_batch_cost_usage_models(entries=rows, custom_llm_provider="openai") - assert (usage.prompt_tokens, usage.completion_tokens, usage.total_tokens) == ( + result = bu._aggregate_batch_cost_usage_models(entries=rows, custom_llm_provider="openai") + assert (result.usage.prompt_tokens, result.usage.completion_tokens, result.usage.total_tokens) == ( 30, 15, 45, @@ -418,7 +418,7 @@ def test_total_usage_and_cost_normalize_mixed_responses_and_chat(): ) chat_row = _success_row(usage=_usage(10, 5)) - cost, usage, _ = bu._aggregate_batch_cost_usage_models( + result = bu._aggregate_batch_cost_usage_models( entries=[responses_row, chat_row], custom_llm_provider="openai", model_info={ @@ -427,22 +427,79 @@ def test_total_usage_and_cost_normalize_mixed_responses_and_chat(): }, ) - assert usage.prompt_tokens == 30 - assert usage.completion_tokens == 12 - assert usage.total_tokens == 42 - assert usage.cache_read_input_tokens == 3 - assert cost == pytest.approx((30 * 0.00125) + (12 * 0.005)) + assert result.usage.prompt_tokens == 30 + assert result.usage.completion_tokens == 12 + assert result.usage.total_tokens == 42 + assert result.usage.cache_read_input_tokens == 3 + assert result.cost == pytest.approx((30 * 0.00125) + (12 * 0.005)) def test_total_usage_empty_is_zero(): - cost, usage, models = bu._aggregate_batch_cost_usage_models(entries=[], custom_llm_provider="openai") - assert cost == 0.0 - assert models == [] - assert (usage.prompt_tokens, usage.completion_tokens, usage.total_tokens) == ( + result = bu._aggregate_batch_cost_usage_models(entries=[], custom_llm_provider="openai") + assert result.cost == 0.0 + assert result.models == [] + assert (result.usage.prompt_tokens, result.usage.completion_tokens, result.usage.total_tokens) == ( 0, 0, 0, ) + assert result.successful_requests == 0 + assert result.failed_requests == 0 + + +def test_total_usage_includes_reasoning_tokens(monkeypatch): + monkeypatch.setattr(litellm, "completion_cost", lambda **kw: 0.0) + rows = [ + _success_row( + usage={ + "prompt_tokens": 10, + "completion_tokens": 50, + "total_tokens": 60, + "completion_tokens_details": {"reasoning_tokens": 30}, + } + ), + _success_row( + usage={ + "prompt_tokens": 5, + "completion_tokens": 20, + "total_tokens": 25, + "completion_tokens_details": {"reasoning_tokens": 8}, + } + ), + _failed_row(), # excluded, must not contribute reasoning tokens either + ] + result = bu._aggregate_batch_cost_usage_models(entries=rows, custom_llm_provider="openai") + assert result.usage.completion_tokens_details is not None + assert result.usage.completion_tokens_details.reasoning_tokens == 38 + + +def test_aggregate_counts_successful_and_failed_requests(monkeypatch): + monkeypatch.setattr(litellm, "completion_cost", lambda **kw: 0.0) + rows = [ + _success_row(usage=_usage(10, 5)), + _failed_row(), + _success_row(usage=_usage(20, 10)), + _failed_row(), + _failed_row(), + ] + result = bu._aggregate_batch_cost_usage_models(entries=rows, custom_llm_provider="openai") + assert result.successful_requests == 2 + assert result.failed_requests == 3 + assert result.successful_requests + result.failed_requests == len(rows) + + +def test_aggregate_returns_batch_cost_usage_result_dataclass(monkeypatch): + monkeypatch.setattr(litellm, "completion_cost", lambda **kw: 1.0) + result = bu._aggregate_batch_cost_usage_models( + entries=[_success_row(usage=_usage(10, 5))], custom_llm_provider="openai" + ) + assert isinstance(result, bu.BatchCostUsageResult) + assert (result.cost, result.models, result.successful_requests, result.failed_requests) == ( + 1.0, + ["gpt-4o"], + 1, + 0, + ) # =========================================================================== # @@ -465,15 +522,22 @@ def test_cost_from_content_completion_cost_path(monkeypatch): _success_row(usage=_usage(20, 10)), ] - total, _, _ = bu._aggregate_batch_cost_usage_models(entries=rows, custom_llm_provider="openai") + result = bu._aggregate_batch_cost_usage_models(entries=rows, custom_llm_provider="openai") - assert total == 1.0 # 2 successful * 0.5 + assert result.cost == 1.0 # 2 successful * 0.5 assert len(calls) == 2 # failed row not costed + assert result.successful_requests == 2 + assert result.failed_requests == 1 def test_empty_body_line_does_not_zero_whole_batch(): """A status-200 row with an empty body makes litellm.completion_cost raise; - that line must be skipped instead of zeroing the whole batch.""" + that line must be skipped from pricing instead of zeroing the whole batch. + + The provider still reported it as a success, so it stays in + successful_requests and out of failed_requests - otherwise the counts stop + reconciling with the provider's own request_counts over a litellm-side + pricing gap the customer never caused.""" rows = [ _success_row(usage=_usage(10, 5)), { @@ -483,11 +547,12 @@ def test_empty_body_line_does_not_zero_whole_batch(): _success_row(usage=_usage(20, 10)), ] - cost, usage, models = bu._aggregate_batch_cost_usage_models(entries=rows, custom_llm_provider="openai") + result = bu._aggregate_batch_cost_usage_models(entries=rows, custom_llm_provider="openai") - assert cost > 0.0 - assert (usage.prompt_tokens, usage.completion_tokens, usage.total_tokens) == (30, 15, 45) - assert models == ["gpt-4o", "gpt-4o"] + assert result.cost > 0.0 + assert (result.usage.prompt_tokens, result.usage.completion_tokens, result.usage.total_tokens) == (30, 15, 45) + assert result.models == ["gpt-4o", "gpt-4o"] + assert (result.successful_requests, result.failed_requests) == (3, 0) def test_cost_from_content_model_info_path(monkeypatch): @@ -500,13 +565,13 @@ def test_cost_from_content_model_info_path(monkeypatch): _success_row(usage=_usage(20, 10)), ] - total, _, _ = bu._aggregate_batch_cost_usage_models( + result = bu._aggregate_batch_cost_usage_models( entries=rows, custom_llm_provider="openai", model_info={"input_cost_per_token": 0.0}, # type: ignore[arg-type] # truthy -> model_info path ) - assert total == pytest.approx(0.6) # 2 * (0.1 + 0.2) + assert result.cost == pytest.approx(0.6) # 2 * (0.1 + 0.2) def test_aggregate_consumes_entries_in_a_single_pass(monkeypatch): @@ -516,11 +581,13 @@ def test_aggregate_consumes_entries_in_a_single_pass(monkeypatch): monkeypatch.setattr(litellm, "completion_cost", lambda **kw: 0.5) one_shot = (row for row in [_success_row(usage=_usage(10, 5)), _failed_row(), _success_row(usage=_usage(20, 10))]) - cost, usage, models = bu._aggregate_batch_cost_usage_models(entries=one_shot, custom_llm_provider="openai") + result = bu._aggregate_batch_cost_usage_models(entries=one_shot, custom_llm_provider="openai") - assert cost == 1.0 - assert (usage.prompt_tokens, usage.completion_tokens, usage.total_tokens) == (30, 15, 45) - assert models == ["gpt-4o", "gpt-4o"] + assert result.cost == 1.0 + assert (result.usage.prompt_tokens, result.usage.completion_tokens, result.usage.total_tokens) == (30, 15, 45) + assert result.models == ["gpt-4o", "gpt-4o"] + assert result.successful_requests == 2 + assert result.failed_requests == 1 # =========================================================================== # @@ -534,7 +601,13 @@ async def test_calculate_vertex_disable_transform_path(monkeypatch): monkeypatch.setattr( bu, "calculate_vertex_ai_batch_cost_and_usage", - lambda content, model: (9.9, Usage(prompt_tokens=1, completion_tokens=2, total_tokens=3)), + lambda content, model: bu.BatchCostUsageResult( + cost=9.9, + usage=Usage(prompt_tokens=1, completion_tokens=2, total_tokens=3), + models=["gemini-2.0-flash-001"], + successful_requests=1, + failed_requests=0, + ), ) # generic path must NOT be taken monkeypatch.setattr( @@ -543,12 +616,12 @@ async def test_calculate_vertex_disable_transform_path(monkeypatch): lambda **kw: pytest.fail("generic path should not run"), ) - cost, usage, models = await bu.calculate_batch_cost_and_usage( + result = await bu.calculate_batch_cost_and_usage( file_content_dictionary=[], custom_llm_provider="vertex_ai", model_name="gemini-2.0-flash-001" ) - assert cost == 9.9 - assert usage.total_tokens == 3 - assert models == ["gemini-2.0-flash-001"] + assert result.cost == 9.9 + assert result.usage.total_tokens == 3 + assert result.models == ["gemini-2.0-flash-001"] @pytest.mark.asyncio @@ -562,12 +635,12 @@ async def test_calculate_vertex_disable_transform_needs_model_name(monkeypatch): lambda content, model: pytest.fail("raw vertex path should not run"), ) - cost, usage, models = await bu.calculate_batch_cost_and_usage( + result = await bu.calculate_batch_cost_and_usage( file_content_dictionary=[], custom_llm_provider="vertex_ai" ) - assert cost == 0.0 - assert usage.total_tokens == 0 - assert models == [] + assert result.cost == 0.0 + assert result.usage.total_tokens == 0 + assert result.models == [] # =========================================================================== # @@ -600,14 +673,16 @@ def test_vertex_cost_and_usage_aggregation(monkeypatch): }, ] - cost, usage = bu.calculate_vertex_ai_batch_cost_and_usage(responses, "gemini-x") + result = bu.calculate_vertex_ai_batch_cost_and_usage(responses, "gemini-x") - assert cost == pytest.approx(0.6) # 2 * (0.1 + 0.2) - assert (usage.prompt_tokens, usage.completion_tokens, usage.total_tokens) == ( + assert result.cost == pytest.approx(0.6) # 2 * (0.1 + 0.2) + assert (result.usage.prompt_tokens, result.usage.completion_tokens, result.usage.total_tokens) == ( 30, 15, 45, ) + assert result.successful_requests == 2 + assert result.failed_requests == 0 def test_vertex_cost_skips_none_response_body(monkeypatch): @@ -627,10 +702,12 @@ def test_vertex_cost_skips_none_response_body(monkeypatch): }, ] - cost, usage = bu.calculate_vertex_ai_batch_cost_and_usage(responses, "gemini-x") + result = bu.calculate_vertex_ai_batch_cost_and_usage(responses, "gemini-x") - assert cost == pytest.approx(1.0) # only one line costed - assert usage.total_tokens == 10 + assert result.cost == pytest.approx(1.0) # only one line costed + assert result.usage.total_tokens == 10 + assert result.successful_requests == 1 + assert result.failed_requests == 1 def test_vertex_usage_total_token_fallback(monkeypatch): @@ -640,8 +717,8 @@ def test_vertex_usage_total_token_fallback(monkeypatch): monkeypatch.setattr(cc, "batch_cost_calculator", lambda **kw: (0.0, 0.0)) responses = [{"response": {"usageMetadata": {"promptTokenCount": 8, "candidatesTokenCount": 4}}}] - _, usage = bu.calculate_vertex_ai_batch_cost_and_usage(responses, "gemini-x") - assert usage.total_tokens == 12 + result = bu.calculate_vertex_ai_batch_cost_and_usage(responses, "gemini-x") + assert result.usage.total_tokens == 12 def test_vertex_cost_error_in_line_is_swallowed(monkeypatch): @@ -664,9 +741,9 @@ def test_vertex_cost_error_in_line_is_swallowed(monkeypatch): } ] - cost, usage = bu.calculate_vertex_ai_batch_cost_and_usage(responses, "gemini-x") - assert cost == 0.0 - assert usage.total_tokens == 10 + result = bu.calculate_vertex_ai_batch_cost_and_usage(responses, "gemini-x") + assert result.cost == 0.0 + assert result.usage.total_tokens == 10 # =========================================================================== # @@ -679,13 +756,11 @@ async def test_calculate_batch_cost_and_usage_orchestration(monkeypatch): rows = [_success_row(model="gpt-4o", usage=_usage(10, 5))] monkeypatch.setattr(litellm, "completion_cost", lambda **kw: 2.5) - cost, usage, models = await bu.calculate_batch_cost_and_usage( - file_content_dictionary=rows, custom_llm_provider="openai" - ) + result = await bu.calculate_batch_cost_and_usage(file_content_dictionary=rows, custom_llm_provider="openai") - assert cost == 2.5 - assert (usage.prompt_tokens, usage.completion_tokens, usage.total_tokens) == (10, 5, 15) - assert models == ["gpt-4o"] + assert result.cost == 2.5 + assert (result.usage.prompt_tokens, result.usage.completion_tokens, result.usage.total_tokens) == (10, 5, 15) + assert result.models == ["gpt-4o"] # =========================================================================== # @@ -940,7 +1015,7 @@ async def test_handle_completed_vertex_batch_computes_cost_usage_and_models(monk monkeypatch.setattr(files_main, "afile_content", fake_afile_content) - cost, usage, models = await bu._handle_completed_batch( + result = await bu._handle_completed_batch( _batch("gs://litellm-bucket/output/predictions.jsonl"), custom_llm_provider="vertex_ai", litellm_params={"vertex_project": "proj-1", "vertex_location": "us-central1"}, @@ -952,10 +1027,12 @@ async def test_handle_completed_vertex_batch_computes_cost_usage_and_models(monk assert batch_input < pricing["input_cost_per_token"] assert batch_output < pricing["output_cost_per_token"] - assert cost > 0 - assert cost == pytest.approx(30 * batch_input + 15 * batch_output) - assert (usage.prompt_tokens, usage.completion_tokens, usage.total_tokens) == (30, 15, 45) - assert models == ["gemini-3.6-flash", "gemini-3.6-flash"] + assert result.cost > 0 + assert result.cost == pytest.approx(30 * batch_input + 15 * batch_output) + assert (result.usage.prompt_tokens, result.usage.completion_tokens, result.usage.total_tokens) == (30, 15, 45) + assert result.models == ["gemini-3.6-flash", "gemini-3.6-flash"] + assert result.successful_requests == 2 + assert result.failed_requests == 0 @pytest.mark.asyncio @@ -1033,11 +1110,121 @@ async def test_handle_completed_batch_orchestration(monkeypatch): monkeypatch.setattr(bu, "_fetch_batch_output_file_content", fake_fetch) monkeypatch.setattr(litellm, "completion_cost", lambda **kw: 3.3) - cost, usage, models = await bu._handle_completed_batch(_batch("of"), custom_llm_provider="openai") + result = await bu._handle_completed_batch(_batch("of"), custom_llm_provider="openai") - assert cost == 3.3 - assert (usage.prompt_tokens, usage.completion_tokens, usage.total_tokens) == (10, 5, 15) - assert models == ["gpt-4o"] + assert result.cost == 3.3 + assert (result.usage.prompt_tokens, result.usage.completion_tokens, result.usage.total_tokens) == (10, 5, 15) + assert result.models == ["gpt-4o"] + + +@pytest.mark.asyncio +async def test_handle_completed_batch_counts_error_file_failures(monkeypatch): + """Regression test: OpenAI writes per-request failures (e.g. a rejected param) + to a separate error_file_id, never into the output file - so failed_requests + must include them or it silently undercounts real batch failures.""" + from litellm.types.llms.openai import Batch + + rows = [_success_row(model="gpt-5-mini", usage=_usage(24, 107))] + error_rows = [ + { + "id": "batch_req_err1", + "custom_id": "req-2-bad", + "response": {"status_code": 400, "body": {"error": {"message": "Invalid 'temperature'"}}}, + "error": None, + } + ] + + async def fake_fetch(batch, custom_llm_provider, litellm_params=None): + return _vertex_jsonl(rows) + + async def fake_afile_content(**kw): + return type("R", (), {"content": _vertex_jsonl(error_rows)})() + + import litellm.files.main as files_main + + monkeypatch.setattr(bu, "_fetch_batch_output_file_content", fake_fetch) + monkeypatch.setattr(files_main, "afile_content", fake_afile_content) + monkeypatch.setattr(litellm, "completion_cost", lambda **kw: 0.0) + + batch = Batch( + id="b", + completion_window="24h", + created_at=1, + endpoint="/v1/chat/completions", + input_file_id="f", + object="batch", + status="completed", + output_file_id="of", + error_file_id="ef", + ) + + result = await bu._handle_completed_batch(batch, custom_llm_provider="openai") + + assert result.successful_requests == 1 + assert result.failed_requests == 1 + + +@pytest.mark.asyncio +async def test_handle_completed_batch_decodes_model_encoded_error_file_id(monkeypatch): + """A model-encoded error file id must be decoded to the raw provider id before + the fetch, exactly like the output file id. Sending the encoded id straight to + the provider 404s, and the swallowed fetch failure silently reports 0 failures.""" + import base64 + + from litellm.types.llms.openai import Batch + + provider_error_file_id = "file-real-error-id" + encoded_error_file_id = "file-" + base64.urlsafe_b64encode( + f"litellm:{provider_error_file_id};model,model-abc".encode() + ).decode().rstrip("=") + + requested_file_ids = [] + + async def fake_fetch(batch, custom_llm_provider, litellm_params=None): + return _vertex_jsonl([_success_row(model="gpt-4o", usage=_usage(10, 5))]) + + async def fake_afile_content(**kw): + requested_file_ids.append(kw["file_id"]) + return type("R", (), {"content": _vertex_jsonl([{"custom_id": "bad-1"}])})() + + import litellm.files.main as files_main + + monkeypatch.setattr(bu, "_fetch_batch_output_file_content", fake_fetch) + monkeypatch.setattr(files_main, "afile_content", fake_afile_content) + monkeypatch.setattr(litellm, "completion_cost", lambda **kw: 0.0) + + batch = Batch( + id="b", + completion_window="24h", + created_at=1, + endpoint="/v1/chat/completions", + input_file_id="f", + object="batch", + status="completed", + output_file_id="of", + error_file_id=encoded_error_file_id, + ) + + result = await bu._handle_completed_batch(batch, custom_llm_provider="openai") + + assert requested_file_ids == [provider_error_file_id] + assert result.failed_requests == 1 + + +@pytest.mark.asyncio +async def test_handle_completed_batch_no_error_file_id_reports_zero_error_failures(monkeypatch): + rows = [_success_row(model="gpt-4o", usage=_usage(10, 5))] + + async def fake_fetch(batch, custom_llm_provider, litellm_params=None): + return _vertex_jsonl(rows) + + monkeypatch.setattr(bu, "_fetch_batch_output_file_content", fake_fetch) + monkeypatch.setattr(litellm, "completion_cost", lambda **kw: 0.0) + + result = await bu._handle_completed_batch(_batch("of"), custom_llm_provider="openai") + + assert result.successful_requests == 1 + assert result.failed_requests == 0 @pytest.mark.asyncio @@ -1054,11 +1241,13 @@ async def test_handle_completed_batch_no_output_file_is_zero(monkeypatch): monkeypatch.setattr(bu, "_fetch_batch_output_file_content", _must_not_fetch) - cost, usage, models = await bu._handle_completed_batch(_batch(None), custom_llm_provider="openai") + result = await bu._handle_completed_batch(_batch(None), custom_llm_provider="openai") - assert cost == 0.0 - assert (usage.prompt_tokens, usage.completion_tokens, usage.total_tokens) == (0, 0, 0) - assert models == [] + assert result.cost == 0.0 + assert (result.usage.prompt_tokens, result.usage.completion_tokens, result.usage.total_tokens) == (0, 0, 0) + assert result.models == [] + assert result.successful_requests == 0 + assert result.failed_requests == 0 @pytest.mark.asyncio @@ -1075,19 +1264,25 @@ async def test_handle_completed_batch_vertex_disable_transform_path(monkeypatch) def fake_vertex_calc(content, model): seen["content"] = content seen["model"] = model - return 7.7, Usage(prompt_tokens=1, completion_tokens=2, total_tokens=3) + return bu.BatchCostUsageResult( + cost=7.7, + usage=Usage(prompt_tokens=1, completion_tokens=2, total_tokens=3), + models=["gemini-x"], + successful_requests=1, + failed_requests=0, + ) monkeypatch.setattr(bu, "calculate_vertex_ai_batch_cost_and_usage", fake_vertex_calc) - cost, usage, models = await bu._handle_completed_batch( + result = await bu._handle_completed_batch( _batch("gs://litellm-bucket/output/predictions.jsonl"), custom_llm_provider="vertex_ai", model_name="gemini-x", ) - assert cost == 7.7 - assert usage.total_tokens == 3 - assert models == ["gemini-x"] + assert result.cost == 7.7 + assert result.usage.total_tokens == 3 + assert result.models == ["gemini-x"] assert seen["content"] == raw_rows assert seen["model"] == "gemini-x" @@ -1189,14 +1384,14 @@ def test_bedrock_cost_uses_deployment_model_name(): "recordId": "1", "modelOutput": {"model": "claude-sonnet-4-6", "usage": {"input_tokens": 13, "output_tokens": 5}}, } - cost, _, models = bu._aggregate_batch_cost_usage_models( + result = bu._aggregate_batch_cost_usage_models( entries=[row], custom_llm_provider="bedrock", model_name="us.anthropic.claude-sonnet-4-6", model_info={}, ) - assert cost > 0 - assert models == ["us.anthropic.claude-sonnet-4-6"] + assert result.cost > 0 + assert result.models == ["us.anthropic.claude-sonnet-4-6"] def test_anthropic_total_usage_sums_succeeded_only(monkeypatch): @@ -1208,8 +1403,10 @@ def test_anthropic_total_usage_sums_succeeded_only(monkeypatch): _anthropic_errored_row(), _anthropic_succeeded_row(usage=_anthropic_usage(20, 10, cache_read=100)), ] - _, usage, _ = bu._aggregate_batch_cost_usage_models(entries=rows, custom_llm_provider="anthropic") - assert (usage.prompt_tokens, usage.completion_tokens, usage.total_tokens) == (130, 15, 145) + result = bu._aggregate_batch_cost_usage_models(entries=rows, custom_llm_provider="anthropic") + assert (result.usage.prompt_tokens, result.usage.completion_tokens, result.usage.total_tokens) == (130, 15, 145) + assert result.successful_requests == 2 + assert result.failed_requests == 1 def test_anthropic_total_usage_aggregates_cache_token_details(monkeypatch): @@ -1221,11 +1418,11 @@ def test_anthropic_total_usage_aggregates_cache_token_details(monkeypatch): _anthropic_errored_row(), _anthropic_succeeded_row(usage=_anthropic_usage(50, 20, cache_creation=300, cache_read=700)), ] - _, usage, _ = bu._aggregate_batch_cost_usage_models(entries=rows, custom_llm_provider="anthropic") - assert usage.prompt_tokens_details.cached_tokens == 8700 - assert usage.prompt_tokens_details.cache_creation_tokens == 2300 - assert usage.cache_read_input_tokens == 8700 - assert usage.cache_creation_input_tokens == 2300 + result = bu._aggregate_batch_cost_usage_models(entries=rows, custom_llm_provider="anthropic") + assert result.usage.prompt_tokens_details.cached_tokens == 8700 + assert result.usage.prompt_tokens_details.cache_creation_tokens == 2300 + assert result.usage.cache_read_input_tokens == 8700 + assert result.usage.cache_creation_input_tokens == 2300 def test_total_usage_without_cache_tokens_has_no_prompt_details(monkeypatch): @@ -1236,9 +1433,9 @@ def test_total_usage_without_cache_tokens_has_no_prompt_details(monkeypatch): "response": {"status_code": 200, "body": {"model": "gpt-5.2", "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15}}}, } ] - _, usage, _ = bu._aggregate_batch_cost_usage_models(entries=rows, custom_llm_provider="openai") - assert (usage.prompt_tokens, usage.completion_tokens, usage.total_tokens) == (10, 5, 15) - assert usage.prompt_tokens_details is None + result = bu._aggregate_batch_cost_usage_models(entries=rows, custom_llm_provider="openai") + assert (result.usage.prompt_tokens, result.usage.completion_tokens, result.usage.total_tokens) == (10, 5, 15) + assert result.usage.prompt_tokens_details is None def test_anthropic_cost_applies_batch_discount_and_cache_pricing(): @@ -1249,14 +1446,14 @@ def test_anthropic_cost_applies_batch_discount_and_cache_pricing(): _anthropic_errored_row(), ] - total, _, _ = bu._aggregate_batch_cost_usage_models( + result = bu._aggregate_batch_cost_usage_models( entries=rows, custom_llm_provider="anthropic", model_info=_ANTHROPIC_MODEL_INFO, # type: ignore[arg-type] ) expected_half_price = (1000 * 3e-6 + 8000 * 3e-7 + 2000 * 3.75e-6 + 200 * 15e-6) / 2 - assert total == pytest.approx(expected_half_price) + assert result.cost == pytest.approx(expected_half_price) def test_anthropic_cost_without_model_info_uses_batch_cost_calculator(monkeypatch): @@ -1275,11 +1472,9 @@ def test_anthropic_cost_without_model_info_uses_batch_cost_calculator(monkeypatc lambda **kw: pytest.fail("anthropic rows must not go through completion_cost"), ) - total, _, _ = bu._aggregate_batch_cost_usage_models( - entries=[_anthropic_succeeded_row()], custom_llm_provider="anthropic" - ) + result = bu._aggregate_batch_cost_usage_models(entries=[_anthropic_succeeded_row()], custom_llm_provider="anthropic") - assert total == pytest.approx(0.3) + assert result.cost == pytest.approx(0.3) assert seen[0]["model"] == "claude-sonnet-4-5-20250929" assert seen[0]["custom_llm_provider"] == "anthropic" assert seen[0]["usage"].prompt_tokens == 10 @@ -1293,8 +1488,8 @@ def test_anthropic_batch_models_collected_from_succeeded_rows(monkeypatch): _anthropic_succeeded_row(model="claude-sonnet-4-5-20250929"), _anthropic_errored_row(), ] - _, _, models = bu._aggregate_batch_cost_usage_models(entries=rows, custom_llm_provider="anthropic") - assert models == ["claude-sonnet-4-5-20250929"] + result = bu._aggregate_batch_cost_usage_models(entries=rows, custom_llm_provider="anthropic") + assert result.models == ["claude-sonnet-4-5-20250929"] @pytest.mark.asyncio @@ -1304,16 +1499,16 @@ async def test_calculate_batch_cost_and_usage_anthropic_end_to_end(): _anthropic_errored_row(), ] - cost, usage, models = await bu.calculate_batch_cost_and_usage( + result = await bu.calculate_batch_cost_and_usage( file_content_dictionary=rows, custom_llm_provider="anthropic", model_name="claude-sonnet-4-5", model_info=_ANTHROPIC_MODEL_INFO, # type: ignore[arg-type] ) - assert cost == pytest.approx(1000 * 3e-6 / 2 + 8000 * 3e-7 / 2 + 2000 * 3.75e-6 / 2 + 200 * 15e-6 / 2) - assert (usage.prompt_tokens, usage.completion_tokens, usage.total_tokens) == (11000, 200, 11200) - assert models == ["claude-sonnet-4-5"] + assert result.cost == pytest.approx(1000 * 3e-6 / 2 + 8000 * 3e-7 / 2 + 2000 * 3.75e-6 / 2 + 200 * 15e-6 / 2) + assert (result.usage.prompt_tokens, result.usage.completion_tokens, result.usage.total_tokens) == (11000, 200, 11200) + assert result.models == ["claude-sonnet-4-5"] def test_extract_credentials_forwards_the_trusted_model_credential_snapshot(): @@ -1421,24 +1616,24 @@ async def test_handle_completed_bedrock_batch_prices_from_deployment_model(monke monkeypatch.setattr(bu, "_fetch_batch_output_file_content", fake_fetch) - cost, usage, _ = await bu._handle_completed_batch( + result = await bu._handle_completed_batch( _batch("of"), custom_llm_provider="bedrock", model_name="bedrock/global.anthropic.claude-sonnet-4-6", ) - assert (usage.prompt_tokens, usage.completion_tokens, usage.total_tokens) == (1800, 1000, 2800) + assert (result.usage.prompt_tokens, result.usage.completion_tokens, result.usage.total_tokens) == (1800, 1000, 2800) # 3e-06 / 1.5e-05 on-demand, halved for batch. - assert cost == pytest.approx(1800 * 3e-06 / 2 + 1000 * 1.5e-05 / 2) + assert result.cost == pytest.approx(1800 * 3e-06 / 2 + 1000 * 1.5e-05 / 2) # The response model alone cannot price a bedrock batch: this is the $0 bug. - zero_cost, zero_usage, _ = await bu._handle_completed_batch( + zero_result = await bu._handle_completed_batch( _batch("of"), custom_llm_provider="bedrock", model_name=None, ) - assert zero_cost == 0.0 - assert zero_usage.total_tokens == 2800 + assert zero_result.cost == 0.0 + assert zero_result.usage.total_tokens == 2800 @pytest.mark.asyncio @@ -1451,7 +1646,7 @@ async def test_handle_completed_batch_honors_deployment_pricing(monkeypatch) -> monkeypatch.setattr(bu, "_fetch_batch_output_file_content", fake_fetch) - free_cost, _, _ = await bu._handle_completed_batch( + free_result = await bu._handle_completed_batch( _batch("of"), custom_llm_provider="vertex_ai", model_name="vertex_ai/gemini-2.5-flash", @@ -1462,15 +1657,15 @@ async def test_handle_completed_batch_honors_deployment_pricing(monkeypatch) -> "output_cost_per_token_batches": 0.0, }, ) - assert free_cost == 0.0 + assert free_result.cost == 0.0 - billed_cost, _, _ = await bu._handle_completed_batch( + billed_result = await bu._handle_completed_batch( _batch("of"), custom_llm_provider="vertex_ai", model_name="vertex_ai/gemini-2.5-flash", model_info=None, ) - assert billed_cost > 0.0 + assert billed_result.cost > 0.0 # =========================================================================== # diff --git a/tests/test_litellm/batches/test_responses_batch_cost.py b/tests/test_litellm/batches/test_responses_batch_cost.py index 7ce026bd103..b634f5f73db 100644 --- a/tests/test_litellm/batches/test_responses_batch_cost.py +++ b/tests/test_litellm/batches/test_responses_batch_cost.py @@ -71,24 +71,24 @@ async def test_responses_batch_reconciles_to_real_tokens_and_spend(local_model_c input_tokens = 33 output_tokens = 57 - cost, usage, models = await bu.calculate_batch_cost_and_usage( + result = await bu.calculate_batch_cost_and_usage( file_content_dictionary=[_responses_line(input_tokens, output_tokens)], custom_llm_provider="openai", model_name=MODEL, model_info=model_info, ) - assert (usage.prompt_tokens, usage.completion_tokens, usage.total_tokens) == ( + assert (result.usage.prompt_tokens, result.usage.completion_tokens, result.usage.total_tokens) == ( input_tokens, output_tokens, input_tokens + output_tokens, ) - assert models == [MODEL] - assert cost == pytest.approx( + assert result.models == [MODEL] + assert result.cost == pytest.approx( input_tokens * model_info["input_cost_per_token_batches"] + output_tokens * model_info["output_cost_per_token_batches"] ) - assert cost > 0.0 + assert result.cost > 0.0 async def test_mixed_shape_batch_output_sums_across_both_line_shapes(local_model_cost_map): @@ -96,15 +96,15 @@ async def test_mixed_shape_batch_output_sums_across_both_line_shapes(local_model batch's declared endpoint rather than each line's shape would miss this.""" model_info = litellm.get_model_info(model=MODEL, custom_llm_provider="openai") - cost, usage, _ = await bu.calculate_batch_cost_and_usage( + result = await bu.calculate_batch_cost_and_usage( file_content_dictionary=[_responses_line(100, 50), _chat_line(33, 57)], custom_llm_provider="openai", model_name=MODEL, model_info=model_info, ) - assert (usage.prompt_tokens, usage.completion_tokens, usage.total_tokens) == (133, 107, 240) - assert cost == pytest.approx( + assert (result.usage.prompt_tokens, result.usage.completion_tokens, result.usage.total_tokens) == (133, 107, 240) + assert result.cost == pytest.approx( 133 * model_info["input_cost_per_token_batches"] + 107 * model_info["output_cost_per_token_batches"] ) diff --git a/tests/test_litellm/caching/test_redis_connection_pool.py b/tests/test_litellm/caching/test_redis_connection_pool.py index c824d3e7a0e..54dbe5361d7 100644 --- a/tests/test_litellm/caching/test_redis_connection_pool.py +++ b/tests/test_litellm/caching/test_redis_connection_pool.py @@ -1,15 +1,14 @@ -""" -Regression tests for Redis connection pool leak fixes (RC1-RC5). - -Tests are pure unit tests — no Redis server required. -""" - from unittest.mock import AsyncMock, MagicMock, patch import pytest -import redis.asyncio as async_redis -from litellm._redis import get_redis_async_client, get_redis_connection_pool +from litellm._redis import ( + _coerce_redis_kwargs_types, + _get_redis_client_logic, + _get_redis_env_kwarg_mapping, + get_redis_async_client, + get_redis_connection_pool, +) def test_url_config_uses_passed_pool(): @@ -60,16 +59,14 @@ def test_max_connections_url_config_string_value(monkeypatch): assert pool.max_connections == 25 -def test_max_connections_url_config_invalid_value(): - """Invalid max_connections should be silently ignored, falling back - to the pool default (50 for BlockingConnectionPool).""" - with patch("litellm._redis._get_redis_client_logic") as mock_logic: - mock_logic.return_value = { - "url": "redis://localhost:6379/0", - "max_connections": "not_a_number", - } +def test_max_connections_url_config_invalid_value(monkeypatch): + """Invalid max_connections from an env var should be silently dropped, + falling back to the pool default (50 for BlockingConnectionPool).""" + monkeypatch.setenv("REDIS_URL", "redis://localhost:6379/0") + monkeypatch.delenv("REDIS_HOST", raising=False) + monkeypatch.setenv("REDIS_MAX_CONNECTIONS", "not_a_number") - pool = get_redis_connection_pool() + pool = get_redis_connection_pool() # BlockingConnectionPool default is 50 assert pool.max_connections == 50 @@ -128,3 +125,173 @@ async def test_disconnect_idempotent(): await cache.disconnect() await cache.disconnect() # should not raise + + +def test_coerce_redis_kwargs_types_int(): + """String values for int-typed Redis params are coerced to int.""" + result = _coerce_redis_kwargs_types({"health_check_interval": "30", "port": "6380", "db": "1"}) + assert result["health_check_interval"] == 30 + assert isinstance(result["health_check_interval"], int) + assert result["port"] == 6380 + assert result["db"] == 1 + + +def test_coerce_redis_kwargs_types_bool(): + """String values for bool-typed Redis params are coerced to bool.""" + result = _coerce_redis_kwargs_types({"ssl": "true", "decode_responses": "false"}) + assert result["ssl"] is True + assert result["decode_responses"] is False + + +def test_coerce_redis_kwargs_types_none_default_numeric(): + """String values for known None-default numeric params are coerced.""" + result = _coerce_redis_kwargs_types({"max_connections": "20", "socket_timeout": "5.5"}) + assert result["max_connections"] == 20 + assert isinstance(result["max_connections"], int) + assert result["socket_timeout"] == 5.5 + assert isinstance(result["socket_timeout"], float) + + +def _redis_signature_pre_8x( + socket_timeout=None, + socket_connect_timeout=None, + max_connections=None, + health_check_interval=0, +): + """Stand-in for the redis-py <= 7.x Redis signature, where the timeout defaults are None.""" + + +def _redis_signature_8x( + socket_timeout=5, + socket_connect_timeout=5, + max_connections=None, + health_check_interval=0, +): + """Stand-in for the redis-py 8.x Redis signature, where the timeout defaults became int 5.""" + + +@pytest.mark.parametrize( + "client", + [_redis_signature_pre_8x, _redis_signature_8x], + ids=["redis-py<=7.x", "redis-py-8.x"], +) +def test_coerce_fractional_socket_timeout_survives_signature_default_change(client): + """redis-py 8.x changed socket_timeout's default from None to int 5. Deriving the + target type from the signature default made int("5.5") raise, so the key was dropped + and REDIS_SOCKET_TIMEOUT=5.5 silently disappeared on 8.x.""" + result = _coerce_redis_kwargs_types( + {"socket_timeout": "5.5", "socket_connect_timeout": "2.5", "max_connections": "20"}, + client=client, + ) + + assert result["socket_timeout"] == pytest.approx(5.5) + assert isinstance(result["socket_timeout"], float) + assert result["socket_connect_timeout"] == pytest.approx(2.5) + assert isinstance(result["socket_connect_timeout"], float) + assert result["max_connections"] == 20 + assert isinstance(result["max_connections"], int) + + +def test_coerce_invalid_socket_timeout_is_still_dropped(): + """Garbage must not survive the explicit-type path; Redis falls back to its own default.""" + result = _coerce_redis_kwargs_types({"socket_timeout": "not_a_number"}, client=_redis_signature_8x) + + assert "socket_timeout" not in result + + +def test_coerce_redis_kwargs_types_invalid_drops_key(): + """A string that cannot be coerced to the expected numeric type is dropped.""" + result = _coerce_redis_kwargs_types({"health_check_interval": "not_a_number"}) + assert "health_check_interval" not in result + + +def test_coerce_redis_kwargs_types_non_string_unchanged(): + """Non-string values pass through without modification.""" + result = _coerce_redis_kwargs_types({"health_check_interval": 30, "ssl": True}) + assert result["health_check_interval"] == 30 + assert result["ssl"] is True + + +def test_health_check_interval_from_env_is_int(monkeypatch): + monkeypatch.setenv("REDIS_HOST", "localhost") + monkeypatch.setenv("REDIS_HEALTH_CHECK_INTERVAL", "30") + + pool = get_redis_connection_pool() + + assert pool is not None + interval = pool.connection_kwargs.get("health_check_interval") + assert interval == 30 + assert isinstance(interval, int), f"Expected int, got {type(interval)}: {interval!r}" + + +def _signature_without_defaults(testkey): + """Stand-in for a client whose parameter declares no default at all.""" + + +def _signature_with_float_default(myparam=1.0): + """Stand-in for a client whose parameter declares a float default.""" + + +def test_coerce_redis_kwargs_types_empty_default_param_unchanged(): + """String params whose signature entry has no default (inspect.Parameter.empty) are left as-is.""" + result = _coerce_redis_kwargs_types({"testkey": "some_value"}, client=_signature_without_defaults) + + assert result["testkey"] == "some_value" + assert isinstance(result["testkey"], str) + + +def test_coerce_redis_kwargs_types_float_valid(): + """String values for params whose signature default is a float are coerced to float.""" + result = _coerce_redis_kwargs_types({"myparam": "3.14"}, client=_signature_with_float_default) + + assert result["myparam"] == pytest.approx(3.14) + assert isinstance(result["myparam"], float) + + +def test_coerce_redis_kwargs_types_float_invalid_drops_key(): + """An unconvertible string for a float-default param is dropped from the result.""" + result = _coerce_redis_kwargs_types({"myparam": "not_a_float"}, client=_signature_with_float_default) + + assert "myparam" not in result + + +@pytest.mark.parametrize( + ("raw", "expected"), + [("false", False), ("true", True), ("0", False), ("1", True)], +) +def test_coerce_socket_keepalive_string(raw, expected): + """socket_keepalive's signature default is None, so it needs an explicit bool + coercion: a leftover "false" string is truthy and enables keepalive.""" + result = _coerce_redis_kwargs_types({"socket_keepalive": raw}) + + assert result["socket_keepalive"] is expected + + +def test_get_redis_client_logic_coerces_cluster_only_kwargs(monkeypatch): + """Cluster-only kwargs (absent from redis.Redis's signature) must still be + coerced when routing to a cluster, or Helm-stringified values reach + RedisCluster as strings.""" + for envvar in (*_get_redis_env_kwarg_mapping(), "REDIS_CLUSTER_NODES", "REDIS_SENTINEL_NODES"): + monkeypatch.delenv(envvar, raising=False) + + result = _get_redis_client_logic( + startup_nodes='[{"host": "localhost", "port": 7000}]', + cluster_error_retry_attempts="5", + require_full_coverage="false", + health_check_interval="30", + ) + + assert result["cluster_error_retry_attempts"] == 5 + assert isinstance(result["cluster_error_retry_attempts"], int) + assert result["require_full_coverage"] is False + assert result["health_check_interval"] == 30 + assert isinstance(result["health_check_interval"], int) + + +def test_get_redis_client_logic_raises_without_host_or_url(monkeypatch): + """_get_redis_client_logic raises ValueError when neither host nor url is provided.""" + for envvar in (*_get_redis_env_kwarg_mapping(), "REDIS_CLUSTER_NODES", "REDIS_SENTINEL_NODES"): + monkeypatch.delenv(envvar, raising=False) + + with pytest.raises(ValueError, match="Either 'host' or 'url' must be specified for redis"): + _get_redis_client_logic() diff --git a/tests/test_litellm/endpoints/__init__.py b/tests/test_litellm/endpoints/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/test_litellm/endpoints/speech/__init__.py b/tests/test_litellm/endpoints/speech/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/test_litellm/endpoints/speech/speech_to_completion_bridge/__init__.py b/tests/test_litellm/endpoints/speech/speech_to_completion_bridge/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/test_litellm/endpoints/speech/speech_to_completion_bridge/test_transformation.py b/tests/test_litellm/endpoints/speech/speech_to_completion_bridge/test_transformation.py new file mode 100644 index 00000000000..953f028af3c --- /dev/null +++ b/tests/test_litellm/endpoints/speech/speech_to_completion_bridge/test_transformation.py @@ -0,0 +1,117 @@ +import base64 +from typing import Final +from unittest.mock import MagicMock + +import pytest + +import litellm +from litellm.constants import OPENAI_CHAT_COMPLETION_PARAMS +from litellm.endpoints.speech.speech_to_completion_bridge.transformation import ( + SpeechToCompletionBridgeTransformationHandler, +) +from litellm.types.utils import ChatCompletionAudioResponse, Choices, Message, ModelResponse + +GEMINI_TTS_MODEL: Final = "gemini-3.1-flash-tts-preview" +PCM_BYTES: Final = b"\x01\x02\x03\x04" * 6 + + +def _model_response(model: str, pcm: bytes) -> ModelResponse: + audio: Final = ChatCompletionAudioResponse( + data=base64.b64encode(pcm).decode(), expires_at=0, transcript="hello" + ) + return ModelResponse(model=model, choices=[Choices(message=Message(content=None, audio=audio))]) + + +def _bridge_request(response_format: str | None) -> dict: + optional_params: Final = ( + {"temperature": 0.4} if response_format is None else {"temperature": 0.4, "response_format": response_format} + ) + return SpeechToCompletionBridgeTransformationHandler().transform_request( + model=GEMINI_TTS_MODEL, + input="Hello from LiteLLM", + voice="Kore", + optional_params=optional_params, + litellm_params={}, + headers={}, + litellm_logging_obj=MagicMock(), + custom_llm_provider="gemini", + ) + + +@pytest.mark.parametrize("response_format", ["wav", "pcm", None]) +def test_gemini_tts_request_keeps_speech_response_format_out_of_chat_params(response_format: str | None) -> None: + request: Final = _bridge_request(response_format) + + assert "response_format" not in request + assert request["audio"] == {"voice": "Kore", "format": "pcm16"} + assert request["temperature"] == 0.4 + assert request["modalities"] == ["audio"] + + gemini_params: Final = litellm.get_optional_params( + model=GEMINI_TTS_MODEL, + custom_llm_provider="gemini", + **{param: value for param, value in request.items() if param in OPENAI_CHAT_COMPLETION_PARAMS}, + ) + assert gemini_params["speechConfig"] == {"voiceConfig": {"prebuiltVoiceConfig": {"voiceName": "Kore"}}} + assert "responseMimeType" not in gemini_params + + +def test_non_gemini_request_forwards_speech_response_format_as_audio_format() -> None: + request: Final = SpeechToCompletionBridgeTransformationHandler().transform_request( + model="gpt-4o-audio-preview", + input="Hello from LiteLLM", + voice="alloy", + optional_params={"response_format": "wav"}, + litellm_params={}, + headers={}, + litellm_logging_obj=MagicMock(), + custom_llm_provider="openai", + ) + + assert "response_format" not in request + assert request["audio"] == {"voice": "alloy", "format": "wav"} + + +@pytest.mark.parametrize("response_format", ["mp3", "flac", "opus", "aac"]) +def test_gemini_tts_request_rejects_formats_gemini_cannot_produce(response_format: str) -> None: + with pytest.raises(litellm.BadRequestError) as excinfo: + _bridge_request(response_format) + + assert excinfo.value.status_code == 400 + assert response_format in str(excinfo.value) + assert "pcm" in str(excinfo.value) + assert "wav" in str(excinfo.value) + + +def test_gemini_tts_pcm_response_returns_raw_pcm_bytes() -> None: + response: Final = SpeechToCompletionBridgeTransformationHandler().transform_response( + model_response=_model_response(GEMINI_TTS_MODEL, PCM_BYTES), + response_format="pcm", + ) + + assert response.response.content == PCM_BYTES + assert response.response.headers["content-type"] == "audio/pcm" + + +@pytest.mark.parametrize("response_format", ["wav", None]) +def test_gemini_tts_wav_and_default_responses_wrap_pcm_in_wav(response_format: str | None) -> None: + response: Final = SpeechToCompletionBridgeTransformationHandler().transform_response( + model_response=_model_response(GEMINI_TTS_MODEL, PCM_BYTES), + response_format=response_format, + ) + + body: Final = response.response.content + assert body[:4] == b"RIFF" + assert body[8:12] == b"WAVE" + assert body[44:] == PCM_BYTES + assert response.response.headers["content-type"] == "audio/wav" + + +def test_non_gemini_response_keeps_original_bytes_and_mpeg_content_type() -> None: + response: Final = SpeechToCompletionBridgeTransformationHandler().transform_response( + model_response=_model_response("gpt-4o-audio-preview", PCM_BYTES), + response_format="mp3", + ) + + assert response.response.content == PCM_BYTES + assert response.response.headers["content-type"] == "audio/mpeg" diff --git a/tests/test_litellm/enterprise/proxy/test_managed_files_access_check.py b/tests/test_litellm/enterprise/proxy/test_managed_files_access_check.py index c75c8099ea1..ad46798b788 100644 --- a/tests/test_litellm/enterprise/proxy/test_managed_files_access_check.py +++ b/tests/test_litellm/enterprise/proxy/test_managed_files_access_check.py @@ -10,11 +10,14 @@ with deployment credentials, bypassing the managed files access-control hooks. import base64 import pytest +from types import SimpleNamespace from unittest.mock import AsyncMock, MagicMock, patch from fastapi import HTTPException -from litellm.proxy._types import UserAPIKeyAuth +from litellm.caching.dual_cache import DualCache +from litellm.proxy._types import CallTypes, UserAPIKeyAuth +from litellm.types.utils import LiteLLMBatch def _make_user_api_key_dict(user_id: str) -> UserAPIKeyAuth: @@ -161,6 +164,108 @@ async def test_service_account_blocked_from_other_team_file(): assert exc_info.value.status_code == 403 +# --- Keyless key must not be locked out of the batch it created --- + + +def _make_unified_batch_id() -> str: + raw = "litellm_proxy;model_id:my-model-id;llm_batch_id:batch_raw_123" + return base64.urlsafe_b64encode(raw.encode()).decode().rstrip("=") + + +def _make_managed_files_instance_with_object_store(): + """Managed-files hook backed by an in-memory stand-in for the managed + object table, so create and retrieve exercise the same stored row.""" + from litellm_enterprise.proxy.hooks.managed_files import ( + _PROXY_LiteLLMManagedFiles, + ) + + store = {} + + async def upsert(where, data): + store[where["unified_object_id"]] = SimpleNamespace(**data["create"]) + + async def find_first(where): + return store.get(where["unified_object_id"]) + + mock_prisma = MagicMock() + mock_prisma.db.litellm_managedobjecttable.upsert = AsyncMock(side_effect=upsert) + mock_prisma.db.litellm_managedobjecttable.find_first = AsyncMock( + side_effect=find_first + ) + + return ( + _PROXY_LiteLLMManagedFiles( + internal_usage_cache=DualCache(), + prisma_client=mock_prisma, + ), + store, + ) + + +async def _store_batch(managed_files, unified_batch_id: str, creator: UserAPIKeyAuth): + await managed_files.store_unified_object_id( + unified_object_id=unified_batch_id, + file_object=LiteLLMBatch( + id="batch_raw_123", + completion_window="24h", + created_at=0, + endpoint="/v1/chat/completions", + input_file_id="file-1", + object="batch", + status="validating", + ), + litellm_parent_otel_span=None, + model_object_id="batch_raw_123", + file_purpose="batch", + user_api_key_dict=creator, + ) + + +@pytest.mark.asyncio +async def test_keyless_key_can_retrieve_the_batch_it_created(): + """Regression: a key with no user_id and no team_id (what `/key/generate` + by a proxy admin and service-account keys produce) stamped + `created_by=None` and was then denied its own managed batch with + "User None does not have access".""" + unified_batch_id = _make_unified_batch_id() + managed_files, store = _make_managed_files_instance_with_object_store() + keyless = UserAPIKeyAuth(api_key="sk-keyless", parent_otel_span=None) + + await _store_batch(managed_files, unified_batch_id, keyless) + assert store[unified_batch_id].created_by == f"key:{keyless.token}" + + data = {"batch_id": unified_batch_id} + await managed_files.async_pre_call_hook( + user_api_key_dict=keyless, + cache=DualCache(), + data=data, + call_type=CallTypes.aretrieve_batch.value, + ) + assert data["batch_id"] == "batch_raw_123" + assert data["model"] == "my-model-id" + + +@pytest.mark.asyncio +async def test_other_keyless_key_still_denied_the_batch(): + unified_batch_id = _make_unified_batch_id() + managed_files, _ = _make_managed_files_instance_with_object_store() + + await _store_batch( + managed_files, + unified_batch_id, + UserAPIKeyAuth(api_key="sk-creator", parent_otel_span=None), + ) + + with pytest.raises(HTTPException) as exc_info: + await managed_files.async_pre_call_hook( + user_api_key_dict=UserAPIKeyAuth(api_key="sk-other", parent_otel_span=None), + cache=DualCache(), + data={"batch_id": unified_batch_id}, + call_type=CallTypes.aretrieve_batch.value, + ) + assert exc_info.value.status_code == 403 + + # --- Option C fix test: check_batch_cost bypasses managed files hook --- diff --git a/tests/test_litellm/enterprise/proxy/test_managed_files_hook.py b/tests/test_litellm/enterprise/proxy/test_managed_files_hook.py index eddfc4fbd34..f3ad8a8592e 100644 --- a/tests/test_litellm/enterprise/proxy/test_managed_files_hook.py +++ b/tests/test_litellm/enterprise/proxy/test_managed_files_hook.py @@ -527,13 +527,33 @@ async def test_afile_list_orders_newest_first_and_breaks_ties_on_the_cursor_colu @pytest.mark.asyncio -async def test_afile_list_denies_a_caller_without_a_user_or_team(): +async def test_afile_list_scopes_a_keyless_key_to_its_own_hashed_token(): + caller = UserAPIKeyAuth(api_key="sk-test", parent_otel_span=None) + managed_files, table = _make_managed_files_over_rows( + [ + _make_managed_file_row("unified-mine", created_by=f"key:{caller.token}"), + _make_managed_file_row("unified-theirs", created_by="other-user"), + ] + ) + + response = await managed_files.afile_list( + purpose=None, + litellm_parent_otel_span=None, + user_api_key_dict=caller, + ) + + assert [file.id for file in response.data] == ["unified-mine"] + assert table.find_many_calls[0]["where"] == {"created_by": f"key:{caller.token}"} + + +@pytest.mark.asyncio +async def test_afile_list_denies_a_caller_with_no_identity_at_all(): managed_files, table = _make_managed_files_over_rows([_make_managed_file_row("unified-mine")]) response = await managed_files.afile_list( purpose=None, litellm_parent_otel_span=None, - user_api_key_dict=UserAPIKeyAuth(api_key="sk-test", parent_otel_span=None), + user_api_key_dict=UserAPIKeyAuth(parent_otel_span=None), ) assert response.data == [] diff --git a/tests/test_litellm/experimental_mcp_client/test_tools.py b/tests/test_litellm/experimental_mcp_client/test_tools.py index 89f67452f29..6645b06664d 100644 --- a/tests/test_litellm/experimental_mcp_client/test_tools.py +++ b/tests/test_litellm/experimental_mcp_client/test_tools.py @@ -8,11 +8,13 @@ from mcp.types import ( CallToolRequestParams, CallToolResult, ListToolsResult, + PaginatedRequestParams, TextContent, ) from mcp.types import Tool as MCPTool from litellm.experimental_mcp_client.tools import ( + list_tools_with_pagination, transform_mcp_tool_to_anthropic_tool, _get_function_arguments, _normalize_mcp_input_schema, @@ -106,6 +108,134 @@ async def test_load_mcp_tools_openai_format(mock_session, mock_list_tools_result mock_session.list_tools.assert_called_once() +@pytest.mark.asyncio() +async def test_load_mcp_tools_follows_pagination(mock_session): + mock_session.list_tools.side_effect = [ + ListToolsResult( + tools=[ + MCPTool(name="tool_a", description="a", inputSchema={}), + MCPTool(name="tool_b", description="b", inputSchema={}), + ], + nextCursor="page-2", + ), + ListToolsResult(tools=[MCPTool(name="tool_c", description="c", inputSchema={})]), + ] + result = await load_mcp_tools(mock_session, format="mcp") + assert [tool.name for tool in result] == ["tool_a", "tool_b", "tool_c"] + assert mock_session.list_tools.call_count == 2 + second_call_params = mock_session.list_tools.call_args_list[1].kwargs["params"] + assert isinstance(second_call_params, PaginatedRequestParams) + assert second_call_params.cursor == "page-2" + + +@pytest.mark.asyncio() +async def test_pagination_walk_stops_at_page_cap(mock_session, monkeypatch): + monkeypatch.setattr("litellm.experimental_mcp_client.tools.MCP_TOOL_LISTING_MAX_PAGES", 2) + mock_session.list_tools.side_effect = [ + ListToolsResult( + tools=[MCPTool(name="tool_0", description="0", inputSchema={})], + nextCursor="page-2", + ), + ListToolsResult( + tools=[MCPTool(name="tool_1", description="1", inputSchema={})], + nextCursor="page-3", + ), + ListToolsResult(tools=[MCPTool(name="tool_2", description="2", inputSchema={})]), + ] + result = await list_tools_with_pagination(mock_session) + assert [tool.name for tool in result] == ["tool_0", "tool_1"] + assert mock_session.list_tools.call_count == 2 + + +@pytest.mark.asyncio() +async def test_pagination_walk_stops_on_repeated_cursor(mock_session): + mock_session.list_tools.side_effect = [ + ListToolsResult( + tools=[MCPTool(name="tool_0", description="0", inputSchema={})], + nextCursor="same-cursor", + ), + ListToolsResult( + tools=[MCPTool(name="tool_1", description="1", inputSchema={})], + nextCursor="same-cursor", + ), + ] + result = await list_tools_with_pagination(mock_session) + assert [tool.name for tool in result] == ["tool_0", "tool_1"] + assert mock_session.list_tools.call_count == 2 + + +@pytest.mark.asyncio() +async def test_pagination_walk_treats_empty_cursor_as_terminal(mock_session): + mock_session.list_tools.side_effect = [ + ListToolsResult( + tools=[MCPTool(name="tool_0", description="0", inputSchema={})], + nextCursor="", + ), + ] + result = await list_tools_with_pagination(mock_session) + assert [tool.name for tool in result] == ["tool_0"] + mock_session.list_tools.assert_called_once() + + +@pytest.mark.asyncio() +async def test_pagination_walk_stops_at_whole_walk_deadline(mock_session, monkeypatch): + import anyio + + from litellm.experimental_mcp_client.tools import list_tools_with_pagination + + monkeypatch.setattr("litellm.experimental_mcp_client.tools.MCP_CLIENT_TIMEOUT", 0.2) + monkeypatch.setattr("litellm.experimental_mcp_client.tools.MCP_TOOL_LISTING_TIMEOUT", 0.2) + + async def slow_page(params=None): + await anyio.sleep(0.15) + idx = int(params.cursor) if params is not None else 0 + return ListToolsResult( + tools=[MCPTool(name=f"tool_{idx}", description=str(idx), inputSchema={})], + nextCursor=str(idx + 1), + ) + + mock_session.list_tools = slow_page + result = await list_tools_with_pagination(mock_session) + + assert [tool.name for tool in result] == ["tool_0"] + + +@pytest.mark.asyncio() +async def test_pagination_walk_honors_explicit_deadline_over_globals(mock_session, monkeypatch): + import anyio + + from litellm.experimental_mcp_client.tools import list_tools_with_pagination + + monkeypatch.setattr("litellm.experimental_mcp_client.tools.MCP_CLIENT_TIMEOUT", 0.1) + monkeypatch.setattr("litellm.experimental_mcp_client.tools.MCP_TOOL_LISTING_TIMEOUT", 0.1) + + async def slow_page(params=None): + await anyio.sleep(0.15) + idx = int(params.cursor) if params is not None else 0 + tools = [MCPTool(name=f"tool_{idx}", description=str(idx), inputSchema={})] + if idx == 0: + return ListToolsResult(tools=tools, nextCursor="1") + return ListToolsResult(tools=tools) + + mock_session.list_tools = slow_page + result = await list_tools_with_pagination(mock_session, listing_deadline=2.0) + + assert [tool.name for tool in result] == ["tool_0", "tool_1"] + + +@pytest.mark.asyncio() +async def test_load_mcp_tools_openai_format_spans_pages(mock_session): + mock_session.list_tools.side_effect = [ + ListToolsResult( + tools=[MCPTool(name="tool_a", description="a", inputSchema={})], + nextCursor="page-2", + ), + ListToolsResult(tools=[MCPTool(name="tool_b", description="b", inputSchema={})]), + ] + result = await load_mcp_tools(mock_session, format="openai") + assert [t["function"]["name"] for t in result] == ["tool_a", "tool_b"] + + def test_get_function_arguments(): # Test with string arguments function = {"arguments": '{"test": "value"}'} diff --git a/tests/test_litellm/integrations/SlackAlerting/test_slack_alerting.py b/tests/test_litellm/integrations/SlackAlerting/test_slack_alerting.py index cfbd3e76a88..55e2dcdc270 100644 --- a/tests/test_litellm/integrations/SlackAlerting/test_slack_alerting.py +++ b/tests/test_litellm/integrations/SlackAlerting/test_slack_alerting.py @@ -12,7 +12,7 @@ import litellm from litellm.caching.caching import DualCache from litellm.integrations.SlackAlerting.slack_alerting import SlackAlerting from litellm.proxy._types import CallInfo, Litellm_EntityType -from litellm.types.integrations.slack_alerting import SlackAlertingCacheKeys +from litellm.types.integrations.slack_alerting import AlertType, SlackAlertingCacheKeys class TestSlackAlerting(unittest.TestCase): @@ -366,3 +366,56 @@ async def test_scheduled_daily_report_threads_the_pod_lock_manager_through(): _, kwargs = slack_alerting._run_scheduler_helper.await_args assert kwargs["pod_lock_manager"] is pod_lock_manager + + +def _slack_alerting_with_env_resolution() -> SlackAlerting: + slack_alerting: Final = SlackAlerting(alerting=["slack"], internal_usage_cache=DualCache()) + slack_alerting.periodic_started = True + return slack_alerting + + +@pytest.mark.asyncio +async def test_send_alert_falls_back_to_alerting_webhook_url_env(monkeypatch): + monkeypatch.delenv("SLACK_WEBHOOK_URL", raising=False) + monkeypatch.setenv("ALERTING_WEBHOOK_URL", "https://chat.example.com/hooks/abc") + slack_alerting: Final = _slack_alerting_with_env_resolution() + + await slack_alerting.send_alert( + message="budget crossed", + level="High", + alert_type=AlertType.budget_alerts, + alerting_metadata={}, + ) + + assert slack_alerting.log_queue[0]["url"] == "https://chat.example.com/hooks/abc" + + +@pytest.mark.asyncio +async def test_send_alert_prefers_slack_webhook_url_over_fallback(monkeypatch): + monkeypatch.setenv("SLACK_WEBHOOK_URL", "https://hooks.slack.com/services/T0/B0/X0") + monkeypatch.setenv("ALERTING_WEBHOOK_URL", "https://chat.example.com/hooks/abc") + slack_alerting: Final = _slack_alerting_with_env_resolution() + + await slack_alerting.send_alert( + message="budget crossed", + level="High", + alert_type=AlertType.budget_alerts, + alerting_metadata={}, + ) + + assert slack_alerting.log_queue[0]["url"] == "https://hooks.slack.com/services/T0/B0/X0" + + +@pytest.mark.asyncio +async def test_send_alert_raises_when_no_webhook_url_configured(monkeypatch): + monkeypatch.delenv("SLACK_WEBHOOK_URL", raising=False) + monkeypatch.delenv("ALERTING_WEBHOOK_URL", raising=False) + slack_alerting: Final = _slack_alerting_with_env_resolution() + + with pytest.raises(ValueError, match="SLACK_WEBHOOK_URL / ALERTING_WEBHOOK_URL"): + await slack_alerting.send_alert( + message="budget crossed", + level="High", + alert_type=AlertType.budget_alerts, + alerting_metadata={}, + ) diff --git a/tests/test_litellm/integrations/SlackAlerting/test_slack_alerting_digest.py b/tests/test_litellm/integrations/SlackAlerting/test_slack_alerting_digest.py index edce5c5f3a2..d614823c0ef 100644 --- a/tests/test_litellm/integrations/SlackAlerting/test_slack_alerting_digest.py +++ b/tests/test_litellm/integrations/SlackAlerting/test_slack_alerting_digest.py @@ -79,6 +79,23 @@ class TestDigestMode(unittest.IsolatedAsyncioTestCase): self.assertEqual(len(self.slack_alerting.digest_buckets), 2) + async def test_digest_falls_back_to_alerting_webhook_url_env(self): + """With SLACK_WEBHOOK_URL unset, the digest entry resolves ALERTING_WEBHOOK_URL instead.""" + env = {k: v for k, v in os.environ.items() if k != "SLACK_WEBHOOK_URL"} + env["ALERTING_WEBHOOK_URL"] = "https://chat.example.com/hooks/abc" + with unittest.mock.patch.dict(os.environ, env, clear=True): + await self.slack_alerting.send_alert( + message="`Requests are hanging`", + level="Medium", + alert_type=AlertType.llm_requests_hanging, + alerting_metadata={}, + request_model="gemini-2.5-flash", + api_base="None", + ) + + bucket = list(self.slack_alerting.digest_buckets.values())[0] + self.assertEqual(bucket["webhook_url"], "https://chat.example.com/hooks/abc") + async def test_non_digest_alert_goes_to_queue(self): """Alert types without digest enabled should go straight to the log queue.""" message = "Budget exceeded" diff --git a/tests/test_litellm/integrations/SlackAlerting/test_user_spend_alerts.py b/tests/test_litellm/integrations/SlackAlerting/test_user_spend_alerts.py new file mode 100644 index 00000000000..45e1acecec8 --- /dev/null +++ b/tests/test_litellm/integrations/SlackAlerting/test_user_spend_alerts.py @@ -0,0 +1,193 @@ +import datetime +from typing import Final +from unittest.mock import AsyncMock, patch + +import pytest +from pydantic import ValidationError + +from litellm.integrations.SlackAlerting.slack_alerting import SlackAlerting +from litellm.integrations.SlackAlerting.user_spend_alerts import ( + UserSpendRow, + evaluate_user_spend, +) +from litellm.types.integrations.slack_alerting import ( + DEFAULT_ALERT_TYPES, + AlertType, + SlackAlertingArgs, +) + +TODAY: Final = datetime.date(2026, 8, 15) + + +def _row( + daily_spend: float = 0.0, + monthly_spend: float = 0.0, + baseline_spend: float = 0.0, +) -> UserSpendRow: + return UserSpendRow( + user_id="user-1", + daily_spend=daily_spend, + monthly_spend=monthly_spend, + baseline_spend=baseline_spend, + ) + + +def _evaluate(row: UserSpendRow, args: SlackAlertingArgs, thresholds: bool = True, anomalies: bool = True): + return evaluate_user_spend( + row=row, + args=args, + today=TODAY, + thresholds_enabled=thresholds, + anomalies_enabled=anomalies, + ) + + +def test_daily_threshold_crossed(): + args: Final = SlackAlertingArgs(daily_spend_per_user_threshold=50.0, spend_anomaly_min_spend=1000.0) + events: Final = _evaluate(_row(daily_spend=75.0, monthly_spend=75.0), args) + assert [e.kind for e in events] == ["daily_threshold"] + assert "`$75.00`" in events[0].message + assert "`$50.00`" in events[0].message + assert events[0].alert_type == AlertType.user_spend_thresholds + assert events[0].cache_key == "user_spend_alert_daily_user-1_2026-08-15" + + +def test_daily_threshold_not_crossed(): + args: Final = SlackAlertingArgs(daily_spend_per_user_threshold=50.0, spend_anomaly_min_spend=1000.0) + assert _evaluate(_row(daily_spend=49.99, monthly_spend=49.99), args) == () + + +def test_thresholds_unset_by_default(): + args: Final = SlackAlertingArgs(spend_anomaly_min_spend=1000.0) + assert _evaluate(_row(daily_spend=999.0, monthly_spend=999.0), args) == () + + +def test_monthly_threshold_crossed(): + args: Final = SlackAlertingArgs(monthly_spend_per_user_threshold=200.0, spend_anomaly_min_spend=1000.0) + events: Final = _evaluate(_row(daily_spend=5.0, monthly_spend=250.0), args) + assert [e.kind for e in events] == ["monthly_threshold"] + assert events[0].cache_key == "user_spend_alert_monthly_user-1_2026-08" + + +def test_thresholds_disabled_suppresses_threshold_events(): + args: Final = SlackAlertingArgs( + daily_spend_per_user_threshold=50.0, + monthly_spend_per_user_threshold=200.0, + spend_anomaly_min_spend=1000.0, + ) + assert _evaluate(_row(daily_spend=75.0, monthly_spend=250.0), args, thresholds=False) == () + + +def test_anomaly_detected_above_multiple_of_baseline(): + args: Final = SlackAlertingArgs(spend_anomaly_multiplier=3.0, spend_anomaly_min_spend=10.0) + events: Final = _evaluate( + _row(daily_spend=70.0, monthly_spend=100.0, baseline_spend=70.0), args + ) + assert [e.kind for e in events] == ["anomaly"] + assert events[0].alert_type == AlertType.user_spend_anomalies + assert "`$10.00`" in events[0].message + assert events[0].cache_key == "user_spend_alert_anomaly_user-1_2026-08-15" + + +def test_no_anomaly_within_baseline_multiple(): + args: Final = SlackAlertingArgs(spend_anomaly_multiplier=3.0, spend_anomaly_min_spend=10.0) + assert ( + _evaluate(_row(daily_spend=25.0, monthly_spend=100.0, baseline_spend=70.0), args) == () + ) + + +def test_no_anomaly_below_min_spend_floor(): + args: Final = SlackAlertingArgs(spend_anomaly_multiplier=3.0, spend_anomaly_min_spend=10.0) + assert _evaluate(_row(daily_spend=9.0, monthly_spend=9.0, baseline_spend=0.1), args) == () + + +def test_anomaly_for_new_user_without_baseline(): + args: Final = SlackAlertingArgs(spend_anomaly_multiplier=3.0, spend_anomaly_min_spend=10.0) + events: Final = _evaluate(_row(daily_spend=15.0, monthly_spend=15.0), args) + assert [e.kind for e in events] == ["anomaly"] + + +def test_sparse_baseline_averages_over_full_window(): + args: Final = SlackAlertingArgs( + spend_anomaly_multiplier=3.0, spend_anomaly_min_spend=10.0, spend_anomaly_baseline_days=7 + ) + events: Final = _evaluate(_row(daily_spend=13.0, monthly_spend=20.0, baseline_spend=7.0), args) + assert [e.kind for e in events] == ["anomaly"] + + +def test_anomalies_not_in_default_alert_types(): + assert AlertType.user_spend_anomalies not in DEFAULT_ALERT_TYPES + assert AlertType.user_spend_thresholds in DEFAULT_ALERT_TYPES + + +def test_invalid_config_rejected(): + with pytest.raises(ValidationError, match="daily_spend_per_user_threshold"): + SlackAlertingArgs(daily_spend_per_user_threshold=0) + with pytest.raises(ValidationError, match="spend_anomaly_baseline_days"): + SlackAlertingArgs(spend_anomaly_baseline_days=0) + with pytest.raises(ValidationError, match="user_spend_check_interval"): + SlackAlertingArgs(user_spend_check_interval=10) + + +def test_non_finite_config_rejected(): + with pytest.raises(ValidationError, match="daily_spend_per_user_threshold"): + SlackAlertingArgs(daily_spend_per_user_threshold=float("inf")) + with pytest.raises(ValidationError, match="spend_anomaly_multiplier"): + SlackAlertingArgs(spend_anomaly_multiplier=float("nan")) + with pytest.raises(ValidationError, match="spend_anomaly_min_spend"): + SlackAlertingArgs(spend_anomaly_min_spend=float("inf")) + with pytest.raises(ValidationError, match="user_spend_check_interval"): + SlackAlertingArgs(user_spend_check_interval=float("inf")) + + +def test_anomalies_disabled_suppresses_anomaly_events(): + args: Final = SlackAlertingArgs(spend_anomaly_multiplier=3.0, spend_anomaly_min_spend=10.0) + assert _evaluate(_row(daily_spend=500.0, monthly_spend=500.0), args, anomalies=False) == () + + +@pytest.mark.asyncio +async def test_send_user_spend_alerts_sends_and_dedupes(): + slack_alerting: Final = SlackAlerting( + alerting=["slack"], + alerting_args={"daily_spend_per_user_threshold": 50.0, "spend_anomaly_min_spend": 1000.0}, + ) + mock_prisma: Final = AsyncMock() + mock_prisma.db.query_raw = AsyncMock( + return_value=[ + { + "user_id": "user-1", + "daily_spend": 75.0, + "monthly_spend": 75.0, + "baseline_spend": 0.0, + }, + { + "user_id": "user-2", + "daily_spend": 60.0, + "monthly_spend": 60.0, + "baseline_spend": 0.0, + }, + ] + ) + with patch.object(slack_alerting, "send_alert", new_callable=AsyncMock) as mock_send_alert: + await slack_alerting.send_user_spend_alerts(prisma_client=mock_prisma) + assert mock_send_alert.call_count == 1 + sent_kwargs: Final = mock_send_alert.call_args.kwargs + assert sent_kwargs["alert_type"] == AlertType.user_spend_thresholds + assert "User Daily Spend Threshold Crossed" in sent_kwargs["message"] + assert "`user-1`" in sent_kwargs["message"] + assert "`user-2`" in sent_kwargs["message"] + + await slack_alerting.send_user_spend_alerts(prisma_client=mock_prisma) + assert mock_send_alert.call_count == 1 + + +@pytest.mark.asyncio +async def test_send_user_spend_alerts_noop_when_alert_types_disabled(): + slack_alerting: Final = SlackAlerting( + alerting=["slack"], + alert_types=[AlertType.budget_alerts], + alerting_args={"daily_spend_per_user_threshold": 50.0}, + ) + mock_prisma: Final = AsyncMock() + await slack_alerting.send_user_spend_alerts(prisma_client=mock_prisma) + mock_prisma.db.query_raw.assert_not_called() diff --git a/tests/test_litellm/integrations/datadog/test_datadog_llm_obs.py b/tests/test_litellm/integrations/datadog/test_datadog_llm_obs.py new file mode 100644 index 00000000000..2d0605e3b7f --- /dev/null +++ b/tests/test_litellm/integrations/datadog/test_datadog_llm_obs.py @@ -0,0 +1,469 @@ +""" +Regression tests for the Datadog LLM Observability payload schema (issue #35786). + +Datadog renders tool calls, tool results and prompt-cache savings only from the fields its +own schema names. These assert on the payload `create_llm_obs_payload` actually hands the +intake, so a regression that moves data back into `meta.metadata` fails here. + +Fixtures mirror what a live proxy run recorded on the callback, including the provider +spelling of prompt-cache counts (`prompt_tokens_details.cached_tokens`). +""" + +import json +import os +from datetime import datetime, timedelta +from typing import Any +from unittest.mock import patch + +import pytest + +from litellm.integrations.datadog.datadog_llm_obs import DataDogLLMObsLogger +from litellm.litellm_core_utils.safe_json_dumps import safe_dumps + +TOOL_DEFINITION: dict[str, Any] = { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get current weather for a city", + "parameters": { + "type": "object", + "properties": {"city": {"type": "string"}}, + "required": ["city"], + }, + }, +} + +ASSISTANT_TOOL_CALL: dict[str, Any] = { + "id": "call_abc123", + "type": "function", + "function": {"name": "get_weather", "arguments": '{"city":"Paris","unit":"c"}'}, +} + + +@pytest.fixture +def logger() -> DataDogLLMObsLogger: + with patch.dict(os.environ, {"DD_API_KEY": "k", "DD_SITE": "us5.datadoghq.com"}, clear=True): + with patch("asyncio.create_task"): + return DataDogLLMObsLogger() + + +NOT_GIVEN: Any = object() + + +def build_payload( + messages: Any = NOT_GIVEN, + response_message: dict[str, Any] | None = None, + usage_object: dict[str, Any] | None = None, + model_parameters: dict[str, Any] | None = None, + prompt_tokens: int = 4447, +) -> dict[str, Any]: + return { + "standard_logging_object": { + "call_type": "acompletion", + "messages": [{"role": "user", "content": "hi"}] if messages is NOT_GIVEN else messages, + "response": {"choices": [{"message": response_message or {"role": "assistant", "content": "hello"}}]}, + "model_parameters": model_parameters or {}, + "metadata": {"usage_object": usage_object} if usage_object is not None else {}, + "prompt_tokens": prompt_tokens, + "completion_tokens": 507, + "total_tokens": prompt_tokens + 507, + "response_cost": 0.02, + "status": "success", + }, + "litellm_params": {"metadata": {}}, + } + + +def build(logger: DataDogLLMObsLogger, **kwargs: Any) -> dict[str, Any]: + """Build a span and read it back as the JSON the intake receives, not as Python objects.""" + start = datetime(2026, 9, 1, 12, 0, 0) + payload = logger.create_llm_obs_payload(build_payload(**kwargs), start, start + timedelta(seconds=2)) + return json.loads(safe_dumps(payload)) + + +def test_output_tool_calls_use_the_datadog_tool_call_schema(logger: DataDogLLMObsLogger) -> None: + """Datadog reads name/arguments/tool_id off the tool call; OpenAI nests them under `function`.""" + payload = build( + logger, + response_message={"role": "assistant", "content": None, "tool_calls": [ASSISTANT_TOOL_CALL]}, + ) + + message = payload["meta"]["output"]["messages"][0] + assert message["tool_calls"] == [ + { + "name": "get_weather", + "arguments": {"city": "Paris", "unit": "c"}, + "tool_id": "call_abc123", + "type": "function", + } + ] + assert "function" not in message["tool_calls"][0] + + +def test_tool_calls_are_not_duplicated_into_metadata(logger: DataDogLLMObsLogger) -> None: + """The flat `output_tool_calls.*` keys were a second copy of a fact that now has its own field.""" + payload = build( + logger, + response_message={"role": "assistant", "content": None, "tool_calls": [ASSISTANT_TOOL_CALL]}, + ) + + assert [key for key in payload["meta"]["metadata"] if "tool_calls." in key] == [] + + +def test_tool_result_message_links_back_to_its_tool_call(logger: DataDogLLMObsLogger) -> None: + """Datadog pairs a result with its call through tool_id, and names the tool from the call.""" + payload = build( + logger, + messages=[ + {"role": "user", "content": "Weather in Paris?"}, + {"role": "assistant", "content": None, "tool_calls": [ASSISTANT_TOOL_CALL]}, + {"role": "tool", "tool_call_id": "call_abc123", "content": '{"temp_c": 18}'}, + ], + ) + + tool_message = payload["meta"]["input"]["messages"][2] + assert tool_message["tool_results"] == [ + {"name": "get_weather", "result": '{"temp_c": 18}', "tool_id": "call_abc123", "type": "function"} + ] + + +def test_tool_result_without_a_matching_call_still_reports_its_id(logger: DataDogLLMObsLogger) -> None: + """A truncated conversation loses the call, so the name is unknown but the link must survive.""" + payload = build( + logger, + messages=[{"role": "tool", "tool_call_id": "call_orphan", "content": "42"}], + ) + + assert payload["meta"]["input"]["messages"][0]["tool_results"] == [ + {"name": "", "result": "42", "tool_id": "call_orphan", "type": "function"} + ] + + +def test_cache_tokens_are_reported_as_span_metrics(logger: DataDogLLMObsLogger) -> None: + """ + Datadog charts cache savings from span metrics; nested usage_object is not read for it. + + litellm's normalized prompt count includes both cache categories, so the three cache + metrics must partition input_tokens: read + write + non_cached == input. + """ + payload = build( + logger, + usage_object={"prompt_tokens_details": {"cached_tokens": 4300, "cache_write_tokens": 95}}, + ) + + metrics = payload["metrics"] + assert metrics["cache_read_input_tokens"] == 4300.0 + assert metrics["cache_write_input_tokens"] == 95.0 + assert metrics["non_cached_input_tokens"] == 4447.0 - 4300.0 - 95.0 + assert ( + metrics["cache_read_input_tokens"] + metrics["cache_write_input_tokens"] + metrics["non_cached_input_tokens"] + == metrics["input_tokens"] + ) + + +def test_cache_write_tokens_are_not_counted_as_non_cached(logger: DataDogLLMObsLogger) -> None: + """A cache-priming request must not report its primed prefix as full-price uncached input.""" + payload = build(logger, usage_object={"prompt_tokens_details": {"cache_write_tokens": 4000}}) + + assert payload["metrics"]["cache_write_input_tokens"] == 4000.0 + assert payload["metrics"]["non_cached_input_tokens"] == 4447.0 - 4000.0 + assert "cache_read_input_tokens" not in payload["metrics"] + + +def test_a_fully_cached_request_reports_a_zero_non_cached_count(logger: DataDogLLMObsLogger) -> None: + """Zero residual is real data: everything was served from cache. Inconsistent counts clamp to it.""" + payload = build( + logger, + usage_object={"prompt_tokens_details": {"cached_tokens": 4352, "cache_write_tokens": 95}}, + ) + + assert payload["metrics"]["non_cached_input_tokens"] == 0.0 + + +def test_anthropic_top_level_cache_keys_are_read(logger: DataDogLLMObsLogger) -> None: + """A raw Anthropic usage dict records the counts top level, not under prompt_tokens_details.""" + payload = build( + logger, + usage_object={"cache_read_input_tokens": 4300, "cache_creation_input_tokens": 95}, + ) + + metrics = payload["metrics"] + assert metrics["cache_read_input_tokens"] == 4300.0 + assert metrics["cache_write_input_tokens"] == 95.0 + assert metrics["non_cached_input_tokens"] == 4447.0 - 4300.0 - 95.0 + + +def test_cache_metrics_come_from_the_normalized_field_not_the_anthropic_one(logger: DataDogLLMObsLogger) -> None: + """ + litellm normalizes every provider's cache counters into prompt_tokens_details. + + A real cached request from a non-Anthropic provider carries only `cached_tokens`, so + reading the Anthropic-specific `cache_read_input_tokens` key reports nothing for it. + """ + payload = build( + logger, + usage_object={"prompt_tokens_details": {"audio_tokens": None, "cached_tokens": 4096}}, + prompt_tokens=4335, + ) + + assert payload["metrics"]["cache_read_input_tokens"] == 4096.0 + assert payload["metrics"]["non_cached_input_tokens"] == 4335.0 - 4096.0 + + +@pytest.mark.parametrize( + "usage_object", + [ + {"prompt_tokens_details": {"cache_write_tokens": 95}}, + {"prompt_tokens_details": {"cache_creation_tokens": 95}}, + {"cache_creation_input_tokens": 95}, + ], +) +def test_every_spelling_of_cache_write_tokens_is_read( + logger: DataDogLLMObsLogger, usage_object: dict[str, Any] +) -> None: + """A raw usage dict that bypassed litellm's normalizer can carry any provider's spelling.""" + payload = build(logger, usage_object=usage_object) + + assert payload["metrics"]["cache_write_input_tokens"] == 95.0 + + +def test_a_cache_read_does_not_emit_a_zero_cache_write(logger: DataDogLLMObsLogger) -> None: + """A zero write on every cache-read span would drag Datadog's cache-write average to nothing.""" + payload = build(logger, usage_object={"prompt_tokens_details": {"cached_tokens": 4096}}) + + assert payload["metrics"]["cache_read_input_tokens"] == 4096.0 + assert "cache_write_input_tokens" not in payload["metrics"] + + +def test_no_cache_keys_when_the_provider_reports_no_caching(logger: DataDogLLMObsLogger) -> None: + """An uncached request must not gain zero-valued cache metrics that dilute cache dashboards.""" + payload = build(logger, usage_object={"prompt_tokens_details": None}) + + assert "cache_read_input_tokens" not in payload["metrics"] + assert "cache_write_input_tokens" not in payload["metrics"] + assert "non_cached_input_tokens" not in payload["metrics"] + + +def test_tool_definitions_are_sent_on_meta(logger: DataDogLLMObsLogger) -> None: + payload = build(logger, model_parameters={"tools": [TOOL_DEFINITION]}) + + assert payload["meta"]["tool_definitions"] == [ + { + "name": "get_weather", + "description": "Get current weather for a city", + "schema": {"type": "object", "properties": {"city": {"type": "string"}}, "required": ["city"]}, + } + ] + + +def test_tool_definitions_accept_the_bare_anthropic_shape(logger: DataDogLLMObsLogger) -> None: + """The Anthropic surface declares tools unwrapped, with input_schema instead of parameters.""" + payload = build( + logger, + model_parameters={"tools": [{"name": "get_weather", "description": "d", "input_schema": {"type": "object"}}]}, + ) + + assert payload["meta"]["tool_definitions"] == [ + {"name": "get_weather", "description": "d", "schema": {"type": "object"}} + ] + + +def test_meta_omits_tool_definitions_when_no_tools_were_offered(logger: DataDogLLMObsLogger) -> None: + assert "tool_definitions" not in build(logger)["meta"] + + +def test_unparseable_tool_arguments_are_preserved_rather_than_dropped(logger: DataDogLLMObsLogger) -> None: + """A truncated argument string is still the only record of what the model tried to call.""" + payload = build( + logger, + response_message={ + "role": "assistant", + "content": None, + "tool_calls": [{"id": "c1", "type": "function", "function": {"name": "f", "arguments": '{"city":'}}], + }, + ) + + assert payload["meta"]["output"]["messages"][0]["tool_calls"][0]["arguments"] == '{"city":' + + +def test_oversized_tool_arguments_ship_unparsed(logger: DataDogLLMObsLogger) -> None: + """ + Decoding attacker-sized compact JSON multiplies memory for a span that is only logging. + + This payload is perfectly valid JSON, so the only reason it arrives as a string is the + size bound; a smaller copy of the same shape comes back as an object below. + """ + oversized = '{"a":"' + "x" * 300_000 + '"}' + + payload = build( + logger, + response_message={ + "role": "assistant", + "content": None, + "tool_calls": [{"id": "c1", "type": "function", "function": {"name": "f", "arguments": oversized}}], + }, + ) + + assert payload["meta"]["output"]["messages"][0]["tool_calls"][0]["arguments"] == oversized + + +def test_valid_arguments_below_the_bound_still_parse(logger: DataDogLLMObsLogger) -> None: + """The size bound must not swallow ordinary arguments; this is the oversized test's control.""" + payload = build( + logger, + response_message={ + "role": "assistant", + "content": None, + "tool_calls": [ + {"id": "c1", "type": "function", "function": {"name": "f", "arguments": '{"a":"' + "x" * 64 + '"}'}} + ], + }, + ) + + assert payload["meta"]["output"]["messages"][0]["tool_calls"][0]["arguments"] == {"a": "x" * 64} + + +def test_a_result_is_named_even_when_its_call_had_unparseable_arguments(logger: DataDogLLMObsLogger) -> None: + """Correlating a result to its call reads ids and names, so bad arguments cannot break linking.""" + payload = build( + logger, + messages=[ + { + "role": "assistant", + "content": None, + "tool_calls": [ + {"id": "call_abc123", "type": "function", "function": {"name": "get_weather", "arguments": "{"}} + ], + }, + {"role": "tool", "tool_call_id": "call_abc123", "content": "18C"}, + ], + ) + + assert payload["meta"]["input"]["messages"][1]["tool_results"] == [ + {"name": "get_weather", "result": "18C", "tool_id": "call_abc123", "type": "function"} + ] + + +def test_deeply_nested_tool_arguments_do_not_drop_the_span(logger: DataDogLLMObsLogger) -> None: + """json.loads raises RecursionError, not JSONDecodeError, on hostile nesting.""" + payload = build( + logger, + response_message={ + "role": "assistant", + "content": None, + "tool_calls": [{"id": "c1", "type": "function", "function": {"name": "f", "arguments": "[" * 50_000}}], + }, + ) + + assert payload["meta"]["output"]["messages"][0]["tool_calls"][0]["arguments"] == "[" * 50_000 + + +def test_tool_arguments_that_parse_to_a_non_object_stay_a_string(logger: DataDogLLMObsLogger) -> None: + """Datadog types arguments as an object, so a bare JSON scalar must not land there as one.""" + payload = build( + logger, + response_message={ + "role": "assistant", + "content": None, + "tool_calls": [{"id": "c1", "type": "function", "function": {"name": "f", "arguments": "42"}}], + }, + ) + + assert payload["meta"]["output"]["messages"][0]["tool_calls"][0]["arguments"] == "42" + + +def test_a_tool_without_a_name_is_not_offered_as_a_definition(logger: DataDogLLMObsLogger) -> None: + """A nameless tool cannot be matched to a call, so it is dropped rather than sent blank.""" + payload = build(logger, model_parameters={"tools": [{"function": {"description": "no name"}}, TOOL_DEFINITION]}) + + assert [tool["name"] for tool in payload["meta"]["tool_definitions"]] == ["get_weather"] + + +def test_a_tool_definition_without_a_schema_omits_the_field(logger: DataDogLLMObsLogger) -> None: + """An empty schema object would read as a tool that takes no arguments, which is a different claim.""" + payload = build(logger, model_parameters={"tools": [{"name": "ping", "description": "d"}]}) + + assert payload["meta"]["tool_definitions"] == [{"name": "ping", "description": "d"}] + + +def test_a_non_dict_message_still_reaches_datadog(logger: DataDogLLMObsLogger) -> None: + """Callers can log arbitrary message payloads, and dropping the span over one loses the request.""" + payload = build(logger, messages=["just a bare string"]) + + assert payload["meta"]["input"]["messages"] == [{"input": "just a bare string"}] + + +def test_messages_logged_as_a_bare_string_still_reach_datadog(logger: DataDogLLMObsLogger) -> None: + payload = build(logger, messages="the whole prompt as one string") + + assert payload["meta"]["input"]["messages"] == [{"input": "the whole prompt as one string"}] + + +def test_non_chat_call_types_log_an_empty_input(logger: DataDogLLMObsLogger) -> None: + """Embedding and image calls carry no messages; fabricating an "None" turn misreads in Datadog.""" + payload = build(logger, messages=None) + + assert payload["meta"]["input"]["messages"] == [] + + +def test_anthropic_tool_blocks_map_to_tool_calls_and_results(logger: DataDogLLMObsLogger) -> None: + """/v1/messages carries tool traffic as content blocks, not OpenAI fields.""" + payload = build( + logger, + messages=[ + {"role": "user", "content": [{"type": "text", "text": "Weather in Tokyo?"}]}, + { + "role": "assistant", + "content": [{"type": "tool_use", "id": "toolu_1", "name": "get_weather", "input": {"city": "Tokyo"}}], + }, + {"role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_1", "content": "18C"}]}, + ], + ) + + assistant, result_turn = payload["meta"]["input"]["messages"][1:3] + assert assistant["tool_calls"] == [ + {"name": "get_weather", "arguments": {"city": "Tokyo"}, "tool_id": "toolu_1", "type": "tool_use"} + ] + assert result_turn["tool_results"] == [ + {"name": "get_weather", "result": "18C", "tool_id": "toolu_1", "type": "function"} + ] + + +def test_content_with_no_text_parts_is_preserved_not_blanked(logger: DataDogLLMObsLogger) -> None: + """A content list the mapper does not understand must ride along, not be erased.""" + blocks = [{"type": "image_url", "image_url": {"url": "https://example.com/x.png"}}] + payload = build(logger, messages=[{"role": "user", "content": blocks}]) + + assert payload["meta"]["input"]["messages"][0]["content"] == blocks + + +def test_multimodal_content_parts_are_flattened_to_text(logger: DataDogLLMObsLogger) -> None: + """Datadog types Message.content as a string, so content lists collapse to their text.""" + payload = build( + logger, + messages=[ + {"role": "user", "content": [{"type": "text", "text": "describe "}, {"type": "text", "text": "this"}]} + ], + ) + + assert payload["meta"]["input"]["messages"][0]["content"] == "describe this" + + +def test_mapping_input_messages_does_not_mutate_the_shared_payload(logger: DataDogLLMObsLogger) -> None: + """Sibling callbacks read the same messages list, so flattening must not write through it.""" + messages: list[dict[str, Any]] = [{"role": "user", "content": [{"type": "text", "text": "hi"}]}] + kwargs = build_payload(messages=messages) + start = datetime(2026, 9, 1, 12, 0, 0) + + logger.create_llm_obs_payload(kwargs, start, start + timedelta(seconds=1)) + + assert messages[0]["content"] == [{"type": "text", "text": "hi"}] + + +def test_reasoning_content_survives_the_mapping(logger: DataDogLLMObsLogger) -> None: + payload = build( + logger, + response_message={"role": "assistant", "content": "answer", "reasoning_content": "thinking"}, + ) + + assert payload["meta"]["output"]["messages"][0]["reasoning_content"] == "thinking" diff --git a/tests/test_litellm/integrations/otel/test_otel_v2_components.py b/tests/test_litellm/integrations/otel/test_otel_v2_components.py index 115e385eda4..4aa28b5abfd 100644 --- a/tests/test_litellm/integrations/otel/test_otel_v2_components.py +++ b/tests/test_litellm/integrations/otel/test_otel_v2_components.py @@ -3,6 +3,7 @@ baggage helpers, metrics, the typed coercion helpers, mapper branches, span-name builders, and the registry validator's failure paths. Needs the OTel SDK.""" import json +from dataclasses import replace import pytest @@ -215,6 +216,27 @@ def test_genai_mapper_all_request_params(): assert attrs["server.port"] == 443 +def test_genai_mapper_cache_token_attrs(): + cached = replace( + _full_llm_call(), + usage=LLMUsage( + input_tokens=10, + output_tokens=5, + total_tokens=15, + cache_creation_input_tokens=7, + cache_read_input_tokens=3, + ), + ) + attrs = GenAIMapper().map(cached) + assert attrs[GenAI.USAGE_CACHE_CREATION_INPUT_TOKENS] == 7 + assert attrs[GenAI.USAGE_CACHE_READ_INPUT_TOKENS] == 3 + + # No cache usage keeps the span sparse: neither key present. + uncached = GenAIMapper().map(_full_llm_call()) + assert GenAI.USAGE_CACHE_CREATION_INPUT_TOKENS not in uncached + assert GenAI.USAGE_CACHE_READ_INPUT_TOKENS not in uncached + + def test_genai_mapper_stamps_input_output_messages(): data = LLMCallSpanData( operation=GenAIOperation.CHAT, diff --git a/tests/test_litellm/integrations/otel/test_otel_v2_dynamic.py b/tests/test_litellm/integrations/otel/test_otel_v2_dynamic.py index 1da8720d1aa..29772eb92c7 100644 --- a/tests/test_litellm/integrations/otel/test_otel_v2_dynamic.py +++ b/tests/test_litellm/integrations/otel/test_otel_v2_dynamic.py @@ -2,21 +2,33 @@ import base64 - +import pytest from opentelemetry.trace import NoOpTracer from litellm.integrations.otel.model.config import ExporterSpec, OpenTelemetryV2Config +from litellm.integrations.otel.plumbing.providers import parse_headers +from litellm.integrations.otel.plumbing.routing import TenantTracerCache from litellm.integrations.otel.presets import ( + DYNAMIC_HEADERS_BY_CALLBACK, dynamic_otlp_endpoint, dynamic_otlp_headers, project_routing_headers, ) -from litellm.integrations.otel.plumbing.providers import parse_headers -from litellm.integrations.otel.plumbing.routing import TenantTracerCache def _cache(callback_name, exporters=None): - cfg = OpenTelemetryV2Config(exporters=exporters or [ExporterSpec(kind="in_memory")]) + # A credential-routing callback always contributes an owned OTLP exporter + # from its preset, so default the fixture to one (a simple processor, no + # background flush thread); otherwise its dynamic credentials have nowhere + # to stamp and the route stays on the default tracer. + if exporters is None: + owned = ( + [ExporterSpec(kind="otlp_http", owner=callback_name, use_simple_processor=True)] + if callback_name in DYNAMIC_HEADERS_BY_CALLBACK + else [] + ) + exporters = [ExporterSpec(kind="in_memory"), *owned] + cfg = OpenTelemetryV2Config(exporters=exporters) return TenantTracerCache(cfg, callback_name, "litellm") @@ -507,6 +519,127 @@ def test_newrelic_provider_cached_per_key_and_region(): assert len(cache._providers) == 3 +# --- credential routes must detach: their tenant backend never receives the --- # +# --- operator-side request-root span, so a parented LLM span is orphaned. --- # + + +@pytest.mark.parametrize( + "callback, dynamic_params", + [ + ("newrelic", {"newrelic_api_key": "NRAL-KEY"}), + ("arize", {"arize_space_id": "S", "arize_api_key": "K"}), + ("langfuse_otel", {"langfuse_public_key": "pk", "langfuse_secret_key": "sk"}), + ("weave_otel", {"wandb_api_key": "w", "weave_project_id": "p"}), + ], +) +def test_credential_route_detaches_from_request_trace(callback, dynamic_params): + # The request root, auth, guardrail and db spans stay on the operator's + # default backend; a credential-routed LLM span exports to the tenant's own + # account, which never sees that root. Parenting it there leaves it + # orphaned ("Missing parent"/fragmented), so a credential route must root + # its own trace and link back, exactly as a Phoenix project route does. + cache = _cache( + callback, + exporters=[ + ExporterSpec(kind="in_memory"), + ExporterSpec(kind="otlp_http", owner=callback, use_simple_processor=True), + ], + ) + default = NoOpTracer() + routed = cache.route_for(default, dynamic_params) + assert routed.tracer is not default + assert routed.detached is True # own trace + link back, never parented cross-account + cache.release(routed.provider) + + +def test_credential_route_without_owned_otlp_exporter_stays_parented(): + # A callback owning only a console/in_memory exporter has nowhere to stamp + # the dynamic credentials, so the span exports to the operator's default + # backend unchanged. Detaching there would orphan it on the very backend + # that holds its parent, so it must stay parented (mirrors the project guard). + cache = _cache("newrelic", exporters=[ExporterSpec(kind="in_memory")]) + default = NoOpTracer() + routed = cache.route_for(default, {"newrelic_api_key": "NRAL-KEY"}) + assert routed.tracer is default # no scoped provider built + assert routed.detached is False + assert cache._providers == {} + + +@pytest.mark.parametrize("typo_kind", ["otlp", "grcp", "htttp", "otlphttp"]) +def test_credential_route_with_unresolvable_exporter_kind_stays_parented(typo_kind): + # An owned exporter whose kind does not resolve to a real OTLP exporter + # (a typo or an unavailable protocol) falls back to a header-ignoring + # console exporter, so the dynamic credentials never reach a tenant backend. + # A denylist would wrongly treat it as routable and detach the span onto the + # operator's console, orphaning it; routability must instead follow the same + # kind resolution the exporter build uses. + cache = _cache( + "newrelic", + exporters=[ExporterSpec(kind="in_memory"), ExporterSpec(kind=typo_kind, owner="newrelic")], + ) + default = NoOpTracer() + routed = cache.route_for(default, {"newrelic_api_key": "NRAL-KEY"}) + assert routed.tracer is default # no scoped provider built + assert routed.detached is False + assert cache._providers == {} + + +def test_credential_routed_span_roots_new_trace_and_links_back(): + # Beyond the detached flag: an emitted credential-routed span must actually + # root its own trace (a fresh trace id, no parent) and carry a link back to + # the request trace, so the tenant account can correlate it without holding + # the operator-side root it never received. + from opentelemetry import trace + from opentelemetry.sdk.trace import TracerProvider + from opentelemetry.sdk.trace.export import SimpleSpanProcessor + from opentelemetry.sdk.trace.export.in_memory_span_exporter import ( + InMemorySpanExporter, + ) + + from litellm.integrations.otel.logger import _request_trace_links + + default_exporter = InMemorySpanExporter() + default_provider = TracerProvider() + default_provider.add_span_processor(SimpleSpanProcessor(default_exporter)) + default = default_provider.get_tracer("litellm") + + cache = _cache( + "newrelic", + exporters=[ExporterSpec(kind="otlp_http", owner="newrelic", use_simple_processor=True)], + ) + with default.start_as_current_span("chat gemini-flash") as request_root: + request_ctx = trace.set_span_in_context(request_root) + route = cache.route_for(default, {"newrelic_api_key": "NRAL-KEY"}) + assert route.detached is True + from opentelemetry.trace import INVALID_SPAN, set_span_in_context + + with route.tracer.start_as_current_span( + "chat gemini-flash", + context=set_span_in_context(INVALID_SPAN, request_ctx), + links=_request_trace_links(request_ctx), + ) as tenant_span: + tenant_ctx = tenant_span.get_span_context() + cache.release(route.provider) + + root_ctx = request_root.get_span_context() + assert tenant_ctx.trace_id != root_ctx.trace_id # fresh trace, not parented + (link,) = tenant_span.links + assert link.context.trace_id == root_ctx.trace_id # linked back to the request trace + + +def test_service_name_route_stays_parented_unlike_credential_route(): + # Guard the boundary the fix must NOT cross: service.name routing relabels + # the span on the SAME operator backend, where the request root is present, + # so it stays parented. Only credential/project routes (different backend) + # detach. + cache = _cache("otel") + default = NoOpTracer() + routed = cache.route_for(default, None, {"otel_service_name": "payments-gateway"}) + assert routed.tracer is not default + assert routed.detached is False + cache.release(routed.provider) + + def test_requires_headers_spec_skipped_without_headers(): from litellm.integrations.otel.plumbing.providers import build_tracer_provider diff --git a/tests/test_litellm/integrations/otel/test_otel_v2_sources_of_truth.py b/tests/test_litellm/integrations/otel/test_otel_v2_sources_of_truth.py index baa72b5a7fe..99d706a9c44 100644 --- a/tests/test_litellm/integrations/otel/test_otel_v2_sources_of_truth.py +++ b/tests/test_litellm/integrations/otel/test_otel_v2_sources_of_truth.py @@ -525,6 +525,96 @@ def test_llm_call_adapter_extracts_all_fields(): assert data.identity.key_hash == "hsh" +def test_llm_call_adapter_extracts_cache_tokens_from_usage_object(): + payload = _sample_payload() + payload["metadata"] = { + **payload["metadata"], + "usage_object": { + "prompt_tokens": 10, + "completion_tokens": 5, + "cache_creation_input_tokens": 7, + "cache_read_input_tokens": 3, + }, + } + data = LLMCallSpanData.from_standard_logging_payload(payload) + assert data.usage.cache_creation_input_tokens == 7 + assert data.usage.cache_read_input_tokens == 3 + + +def test_llm_call_adapter_normalizes_nested_cache_tokens(): + cases: Final = ( + ({"prompt_tokens_details": {"cached_tokens": 3}}, 3, None), + ({"prompt_cache_hit_tokens": 11}, 11, None), + ({"prompt_tokens_details": {"cache_write_tokens": 7}}, None, 7), + ({"prompt_tokens_details": {"cache_creation_tokens": 13}}, None, 13), + ({"prompt_tokens_details": {"cache_creation_input_tokens": 17}}, None, 17), + ) + for usage_object, expected_read, expected_creation in cases: + case_payload = _sample_payload(metadata={"usage_object": usage_object}) + data = LLMCallSpanData.from_standard_logging_payload(case_payload) + assert data.usage.cache_read_input_tokens == expected_read + assert data.usage.cache_creation_input_tokens == expected_creation + + +def test_llm_call_adapter_prefers_nested_count_over_zero_top_level(): + payload = _sample_payload( + metadata={ + "usage_object": { + "cache_read_input_tokens": 0, + "cache_creation_input_tokens": 0, + "prompt_tokens_details": {"cached_tokens": 5, "cache_write_tokens": 7}, + } + } + ) + data = LLMCallSpanData.from_standard_logging_payload(payload) + assert data.usage.cache_read_input_tokens == 5 + assert data.usage.cache_creation_input_tokens == 7 + + +def test_llm_call_adapter_ignores_invalid_cache_values_before_valid_fallbacks(): + payload = _sample_payload( + metadata={ + "usage_object": { + "cache_read_input_tokens": -1, + "cache_creation_input_tokens": "5.0", + "prompt_tokens_details": {"cached_tokens": 5, "cache_write_tokens": 7}, + } + } + ) + data = LLMCallSpanData.from_standard_logging_payload(payload) + assert data.usage.cache_read_input_tokens == 5 + assert data.usage.cache_creation_input_tokens == 7 + + +def test_llm_call_adapter_ignores_non_finite_cache_values(): + payload = _sample_payload( + metadata={ + "usage_object": { + "prompt_tokens_details": {"cached_tokens": float("nan")}, + } + } + ) + data = LLMCallSpanData.from_standard_logging_payload(payload) + assert data.usage.cache_read_input_tokens is None + + +def test_llm_call_adapter_preserves_explicit_zero_and_omits_missing_cache_tokens(): + for usage_object, expected_read, expected_creation in ( + ({"prompt_tokens_details": {"cached_tokens": 0}}, 0, None), + ({}, None, None), + ): + case_payload = _sample_payload(metadata={"usage_object": usage_object}) + data = LLMCallSpanData.from_standard_logging_payload(case_payload) + assert data.usage.cache_read_input_tokens == expected_read + assert data.usage.cache_creation_input_tokens == expected_creation + + +def test_llm_call_adapter_cache_tokens_none_without_usage_object(): + data = LLMCallSpanData.from_standard_logging_payload(_sample_payload()) + assert data.usage.cache_creation_input_tokens is None + assert data.usage.cache_read_input_tokens is None + + def test_llm_call_adapter_failure_path(): payload = _sample_payload( status="failure", diff --git a/tests/test_litellm/integrations/test_anthropic_cache_control_hook.py b/tests/test_litellm/integrations/test_anthropic_cache_control_hook.py index 3a736e2a889..e995cbae782 100644 --- a/tests/test_litellm/integrations/test_anthropic_cache_control_hook.py +++ b/tests/test_litellm/integrations/test_anthropic_cache_control_hook.py @@ -1611,6 +1611,20 @@ class TestEnableAnthropicPromptCaching: monkeypatch.setattr(litellm, "enable_anthropic_prompt_caching", True) assert self._points(model="anthropic.claude-3-5-sonnet-20240620-v1:0", provider="bedrock") == [] + @pytest.mark.parametrize("model", ["us.xai.grok-4.6", "global.xai.grok-4.6"]) + def test_bedrock_grok_not_injected(self, monkeypatch, local_model_cost_map, model): + """Bedrock supports only implicit prompt caching for Grok: explicit cachePoint + breakpoints make it reject the whole request ("You invoked an unsupported model + or your request did not allow prompt caching"), so supports_prompt_caching stays + false, while implicit cache hits still bill at the cache-read rate.""" + from litellm.utils import supports_prompt_caching + + monkeypatch.setattr(litellm, "enable_anthropic_prompt_caching", True) + assert supports_prompt_caching(model=model, custom_llm_provider="bedrock") is False + assert self._points(model=model, provider="bedrock") == [] + entry = litellm.model_cost[model] + assert 0 < entry["cache_read_input_token_cost"] < entry["input_cost_per_token"] + def test_stands_down_when_client_sent_cache_control(self, monkeypatch): monkeypatch.setattr(litellm, "enable_anthropic_prompt_caching", True) messages = [ diff --git a/tests/test_litellm/integrations/test_custom_guardrail.py b/tests/test_litellm/integrations/test_custom_guardrail.py index d978eb48c12..7d70b9a8862 100644 --- a/tests/test_litellm/integrations/test_custom_guardrail.py +++ b/tests/test_litellm/integrations/test_custom_guardrail.py @@ -2237,3 +2237,202 @@ class TestRecordsOwnGuardrailInformation: ) assert _guardrail_entries(request_data) == [] + + +class _ApplyOnlyObserver(CustomGuardrail): + """Overrides only apply_guardrail, like panw_prisma_airs; inherits async_logging_hook.""" + + def __init__(self, block: bool = False): + from litellm.types.guardrails import GuardrailEventHooks + + super().__init__(guardrail_name="apply-only-observer", event_hook=GuardrailEventHooks.logging_only) + self.block = block + self.calls: list = [] + + @log_guardrail_information + async def apply_guardrail(self, inputs, request_data, input_type, logging_obj=None): + from fastapi import HTTPException + + self.calls.append((input_type, list(inputs.get("texts") or []))) + if self.block: + raise HTTPException(status_code=400, detail={"error": "flagged"}) + return GenericGuardrailAPIInputs(texts=["[MASKED]" for _ in inputs.get("texts") or []]) + + +def _logged_call(messages: list | str) -> tuple[dict, object]: + from litellm.types.utils import Choices, Message, ModelResponse + + response = ModelResponse(choices=[Choices(message=Message(role="assistant", content="general kenobi"))]) + kwargs = { + "model": "gpt-5.4-mini", + "messages": messages, + "litellm_call_id": "call-1", + "litellm_params": {"metadata": {"user_api_key_user_id": "u1"}}, + "optional_params": {}, + "standard_logging_object": {"guardrail_information": None}, + } + return kwargs, response + + +class TestLoggingOnlyApplyGuardrail: + """LIT-4876 regression: a guardrail in mode logging_only that implements only + apply_guardrail must still run against the logged request and response and + record guardrail_information, instead of inheriting the CustomLogger no-op.""" + + @pytest.mark.asyncio + async def test_runs_apply_guardrail_observe_only_and_records_verdict(self): + guardrail = _ApplyOnlyObserver() + messages = [{"role": "user", "content": "hello there"}] + kwargs, response = _logged_call(messages) + + out_kwargs, out_response = await guardrail.async_logging_hook(kwargs, response, CallTypes.acompletion.value) + + assert guardrail.calls == [("request", ["hello there"]), ("response", ["general kenobi"])] + assert out_kwargs["messages"] == [{"role": "user", "content": "hello there"}] + assert out_response.choices[0].message.content == "general kenobi" + entries = out_kwargs["standard_logging_object"]["guardrail_information"] + assert [e["guardrail_name"] for e in entries] == ["apply-only-observer", "apply-only-observer"] + assert {e["guardrail_mode"] for e in entries} == {"logging_only"} + assert {e["guardrail_status"] for e in entries} == {"success"} + assert "standard_logging_guardrail_information" not in kwargs["litellm_params"]["metadata"] + assert kwargs["standard_logging_object"] == {"guardrail_information": None} + + @pytest.mark.asyncio + async def test_appends_to_pre_call_verdicts_without_duplicating_them(self): + guardrail = _ApplyOnlyObserver() + kwargs, response = _logged_call([{"role": "user", "content": "hello there"}]) + pre_call_entry = {"guardrail_name": "pii-blocker", "guardrail_mode": "pre_call", "guardrail_status": "success"} + kwargs["litellm_params"]["metadata"]["standard_logging_guardrail_information"] = [pre_call_entry] + kwargs["standard_logging_object"]["guardrail_information"] = [pre_call_entry] + + out_kwargs, _ = await guardrail.async_logging_hook(kwargs, response, CallTypes.acompletion.value) + + entries = out_kwargs["standard_logging_object"]["guardrail_information"] + assert [e["guardrail_name"] for e in entries] == ["pii-blocker", "apply-only-observer", "apply-only-observer"] + assert kwargs["litellm_params"]["metadata"]["standard_logging_guardrail_information"] == [pre_call_entry] + + @pytest.mark.asyncio + async def test_request_copy_failure_is_swallowed(self): + import threading + + guardrail = _ApplyOnlyObserver() + kwargs, response = _logged_call([{"role": "user", "content": "hello there", "lock": threading.Lock()}]) + + out_kwargs, out_response = await guardrail.async_logging_hook(kwargs, response, CallTypes.acompletion.value) + + assert guardrail.calls == [] + assert out_kwargs is kwargs + assert out_response is response + + @pytest.mark.asyncio + async def test_block_verdict_is_recorded_without_raising(self): + guardrail = _ApplyOnlyObserver(block=True) + kwargs, response = _logged_call([{"role": "user", "content": "flagged content"}]) + + out_kwargs, _ = await guardrail.async_logging_hook(kwargs, response, CallTypes.acompletion.value) + + assert guardrail.calls == [("request", ["flagged content"])] + entries = out_kwargs["standard_logging_object"]["guardrail_information"] + assert [e["guardrail_status"] for e in entries] == ["guardrail_intervened"] + + @pytest.mark.asyncio + async def test_call_type_without_translation_is_skipped(self): + guardrail = _ApplyOnlyObserver() + kwargs, response = _logged_call([{"role": "user", "content": "hello there"}]) + + out_kwargs, _ = await guardrail.async_logging_hook(kwargs, response, CallTypes.amoderation.value) + + assert guardrail.calls == [] + assert out_kwargs["standard_logging_object"]["guardrail_information"] is None + + @pytest.mark.asyncio + async def test_aembedding_scans_logged_input(self): + from litellm.types.utils import EmbeddingResponse + + guardrail = _ApplyOnlyObserver() + kwargs, _ = _logged_call("hello there") + response = EmbeddingResponse(data=[{"embedding": [0.1], "index": 0, "object": "embedding"}]) + + out_kwargs, out_response = await guardrail.async_logging_hook(kwargs, response, CallTypes.aembedding.value) + + assert guardrail.calls == [("request", ["hello there"])] + assert out_kwargs["messages"] == "hello there" + assert out_response is response + entries = out_kwargs["standard_logging_object"]["guardrail_information"] + assert [e["guardrail_status"] for e in entries] == ["success"] + + @pytest.mark.asyncio + async def test_native_lifecycle_hook_guardrail_is_left_alone(self): + class _NativeHooks(_ApplyOnlyObserver): + use_native_lifecycle_hooks = True + + guardrail = _NativeHooks() + kwargs, response = _logged_call([{"role": "user", "content": "hello there"}]) + + out_kwargs, out_response = await guardrail.async_logging_hook(kwargs, response, CallTypes.acompletion.value) + + assert guardrail.calls == [] + assert out_kwargs is kwargs + assert out_response is response + + @pytest.mark.asyncio + async def test_aresponses_scans_logged_messages_when_input_is_cleared(self): + from litellm.types.llms.openai import ResponsesAPIResponse + + guardrail = _ApplyOnlyObserver() + kwargs, _ = _logged_call([{"role": "user", "content": "hello there"}]) + kwargs["input"] = None + response = ResponsesAPIResponse( + id="resp_1", + created_at=1, + model="gpt-5.4-mini", + object="response", + status="completed", + output=[ + { + "type": "message", + "id": "msg_1", + "status": "completed", + "role": "assistant", + "content": [{"type": "output_text", "text": "general kenobi"}], + } + ], + ) + + out_kwargs, _ = await guardrail.async_logging_hook(kwargs, response, CallTypes.aresponses.value) + + assert guardrail.calls == [("request", ["hello there"]), ("response", ["general kenobi"])] + entries = out_kwargs["standard_logging_object"]["guardrail_information"] + assert [e["guardrail_status"] for e in entries] == ["success", "success"] + + @pytest.mark.asyncio + async def test_async_success_handler_records_verdict_in_standard_logging_object(self): + import datetime as dt + + from litellm.litellm_core_utils.litellm_logging import Logging + + guardrail = _ApplyOnlyObserver() + guardrail.default_on = True + messages = [{"role": "user", "content": "hello there"}] + _, response = _logged_call(messages) + logging_obj = Logging( + model="gpt-5.4-mini", + messages=messages, + stream=False, + call_type=CallTypes.acompletion.value, + start_time=dt.datetime.now(), + litellm_call_id="call-1", + function_id="fn-1", + dynamic_async_success_callbacks=[guardrail], + ) + logging_obj.update_environment_variables( + litellm_params={"metadata": {}}, optional_params={}, model="gpt-5.4-mini", custom_llm_provider="openai" + ) + + await logging_obj.async_success_handler( + result=response, start_time=dt.datetime.now(), end_time=dt.datetime.now() + ) + + assert guardrail.calls == [("request", ["hello there"]), ("response", ["general kenobi"])] + entries = logging_obj.model_call_details["standard_logging_object"]["guardrail_information"] + assert [e["guardrail_status"] for e in entries] == ["success", "success"] diff --git a/tests/test_litellm/integrations/test_prometheus_client_ip_user_agent.py b/tests/test_litellm/integrations/test_prometheus_client_ip_user_agent.py index 029b097cb75..ea661d2ea78 100644 --- a/tests/test_litellm/integrations/test_prometheus_client_ip_user_agent.py +++ b/tests/test_litellm/integrations/test_prometheus_client_ip_user_agent.py @@ -93,6 +93,7 @@ async def test_async_post_call_success_hook_includes_client_ip_user_agent(): logger._increment_token_metrics = MagicMock() logger._increment_remaining_budget_metrics = AsyncMock() logger._set_virtual_key_rate_limit_metrics = MagicMock() + logger._set_key_and_team_rate_limit_metrics = MagicMock() logger._set_latency_metrics = MagicMock() logger.set_llm_deployment_success_metrics = MagicMock() logger._increment_cache_metrics = MagicMock() diff --git a/tests/test_litellm/integrations/test_prometheus_rate_limit_labels.py b/tests/test_litellm/integrations/test_prometheus_rate_limit_labels.py index 9c6d2e018ff..bf1d68c7714 100644 --- a/tests/test_litellm/integrations/test_prometheus_rate_limit_labels.py +++ b/tests/test_litellm/integrations/test_prometheus_rate_limit_labels.py @@ -13,6 +13,7 @@ Covers two follow-up gaps to the unified rate-limit error work: 429s don't silently break when the new class lands. """ +from collections.abc import Mapping from unittest.mock import MagicMock, patch import pytest @@ -471,3 +472,254 @@ def test_should_ignore_non_int_v3_header_values(bad_value): logger.litellm_remaining_api_key_tokens_for_model.labels.return_value.set.assert_called_once_with( sys.maxsize ) + + +KEY_AND_TEAM_RATE_LIMIT_METRICS = ( + "litellm_api_key_rate_limit_allowed_metric", + "litellm_api_key_rate_limit_used_metric", + "litellm_team_rate_limit_allowed_metric", + "litellm_team_rate_limit_used_metric", +) + + +def _clear_prometheus_registry() -> None: + from prometheus_client import REGISTRY + + for collector in list(REGISTRY._collector_to_names.keys()): + try: + REGISTRY.unregister(collector) + except Exception: + pass + + +def _collected_samples(metric_name: str) -> dict[tuple[tuple[str, str], ...], float]: + from prometheus_client import REGISTRY + + return { + tuple(sorted(sample.labels.items())): sample.value + for metric in REGISTRY.collect() + for sample in metric.samples + if sample.name == metric_name + } + + +def _success_kwargs_with_rate_limit_headers(additional_headers: Mapping[str, object] | None) -> dict[str, object]: + return { + "model": "claude-haiku-4-5", + "litellm_params": {"metadata": {}}, + "standard_logging_object": { + "id": "t", + "call_type": "completion", + "response_cost": 0.001, + "status": "success", + "total_tokens": 20, + "prompt_tokens": 15, + "completion_tokens": 5, + "startTime": 1.0, + "endTime": 2.0, + "completionStartTime": 1.5, + "model": "claude-haiku-4-5", + "model_id": "model-123", + "model_group": "anthropic-haiku-4-5", + "api_base": "https://api.anthropic.com", + "custom_llm_provider": "anthropic", + "request_tags": [], + "end_user": None, + "cache_hit": False, + "stream": False, + "response": None, + "model_parameters": None, + "metadata": { + "user_api_key_hash": "key-hash", + "user_api_key_alias": "key-alias", + "user_api_key_team_id": "team-id", + "user_api_key_team_alias": "team-alias", + "user_api_key_user_id": "u", + "user_api_key_user_email": "e@x.com", + "user_api_key_org_id": None, + "user_api_key_org_alias": None, + "requester_metadata": None, + "user_api_key_end_user_id": None, + "usage_object": None, + }, + "hidden_params": { + "litellm_overhead_time_ms": None, + "additional_headers": additional_headers, + }, + }, + } + + +async def _run_success_event( + additional_headers: Mapping[str, object] | None, logger: PrometheusLogger | None = None +) -> None: + import datetime + + now = datetime.datetime.now() + await (logger or PrometheusLogger()).async_log_success_event( + _success_kwargs_with_rate_limit_headers(additional_headers), None, now, now + ) + + +@pytest.mark.asyncio +async def test_should_emit_key_and_team_rate_limit_allowed_and_used_from_v3_headers(): + """ + LIT-1672: the v3 limiter mirrors ``x-ratelimit-{api_key,team}-{limit,remaining}-*`` + into the logging payload. The gauges must expose the configured limit as-is + and the window consumption as ``limit - remaining`` for each key / team + dimension, split by ``rate_limit_type``. + """ + _clear_prometheus_registry() + try: + await _run_success_event( + { + "x-ratelimit-api_key-limit-requests": 10, + "x-ratelimit-api_key-remaining-requests": 7, + "x-ratelimit-api_key-limit-tokens": 20000, + "x-ratelimit-api_key-remaining-tokens": 19947, + "x-ratelimit-team-limit-requests": 50, + "x-ratelimit-team-remaining-requests": 47, + "x-ratelimit-team-limit-tokens": 40000, + "x-ratelimit-team-remaining-tokens": 39960, + "x-ratelimit-model_per_key-limit-requests": 5, + "x-ratelimit-model_per_key-remaining-requests": 1, + } + ) + + key_requests = ( + ("api_key_alias", "key-alias"), + ("hashed_api_key", "key-hash"), + ("rate_limit_type", "requests"), + ) + key_tokens = ( + ("api_key_alias", "key-alias"), + ("hashed_api_key", "key-hash"), + ("rate_limit_type", "tokens"), + ) + team_requests = ( + ("rate_limit_type", "requests"), + ("team", "team-id"), + ("team_alias", "team-alias"), + ) + team_tokens = ( + ("rate_limit_type", "tokens"), + ("team", "team-id"), + ("team_alias", "team-alias"), + ) + + assert _collected_samples("litellm_api_key_rate_limit_allowed_metric") == { + key_requests: 10, + key_tokens: 20000, + } + assert _collected_samples("litellm_api_key_rate_limit_used_metric") == { + key_requests: 3, + key_tokens: 53, + } + assert _collected_samples("litellm_team_rate_limit_allowed_metric") == { + team_requests: 50, + team_tokens: 40000, + } + assert _collected_samples("litellm_team_rate_limit_used_metric") == { + team_requests: 3, + team_tokens: 40, + } + finally: + _clear_prometheus_registry() + + +@pytest.mark.asyncio +async def test_should_emit_only_the_dimensions_the_limiter_enforced(): + """ + A key with only ``rpm_limit`` set and no team limits produces only the + key/requests headers, so no tokens series and no team series may appear + (a phantom 0 or sys.maxsize series would misreport an unlimited dimension). + """ + _clear_prometheus_registry() + try: + await _run_success_event( + { + "x-ratelimit-api_key-limit-requests": 10, + "x-ratelimit-api_key-remaining-requests": 10, + } + ) + + key_requests = ( + ("api_key_alias", "key-alias"), + ("hashed_api_key", "key-hash"), + ("rate_limit_type", "requests"), + ) + assert _collected_samples("litellm_api_key_rate_limit_allowed_metric") == {key_requests: 10} + assert _collected_samples("litellm_api_key_rate_limit_used_metric") == {key_requests: 0} + assert _collected_samples("litellm_team_rate_limit_allowed_metric") == {} + assert _collected_samples("litellm_team_rate_limit_used_metric") == {} + finally: + _clear_prometheus_registry() + + +@pytest.mark.asyncio +async def test_should_drop_key_and_team_series_once_the_limiter_stops_reporting_a_limit(): + """ + Removing a key's ``rpm_limit`` / ``tpm_limit`` (or a team's ``tpm_limit``) + makes the v3 limiter stop emitting that descriptor's headers on later + requests. The old allowed/used samples must disappear instead of keeping + a limit that no longer exists on the scrape. + """ + _clear_prometheus_registry() + try: + logger = PrometheusLogger() + await _run_success_event( + { + "x-ratelimit-api_key-limit-requests": 10, + "x-ratelimit-api_key-remaining-requests": 7, + "x-ratelimit-api_key-limit-tokens": 20000, + "x-ratelimit-api_key-remaining-tokens": 19947, + "x-ratelimit-team-limit-requests": 50, + "x-ratelimit-team-remaining-requests": 47, + "x-ratelimit-team-limit-tokens": 40000, + "x-ratelimit-team-remaining-tokens": 39960, + }, + logger=logger, + ) + await _run_success_event( + { + "x-ratelimit-team-limit-requests": 50, + "x-ratelimit-team-remaining-requests": 46, + }, + logger=logger, + ) + + team_requests = ( + ("rate_limit_type", "requests"), + ("team", "team-id"), + ("team_alias", "team-alias"), + ) + assert _collected_samples("litellm_api_key_rate_limit_allowed_metric") == {} + assert _collected_samples("litellm_api_key_rate_limit_used_metric") == {} + assert _collected_samples("litellm_team_rate_limit_allowed_metric") == {team_requests: 50} + assert _collected_samples("litellm_team_rate_limit_used_metric") == {team_requests: 4} + finally: + _clear_prometheus_registry() + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "additional_headers", + [ + None, + {"x-ratelimit-model_per_key-remaining-requests": 42}, + {"x-ratelimit-api_key-limit-requests": 10}, + {"x-ratelimit-api_key-limit-requests": "10", "x-ratelimit-api_key-remaining-requests": "7"}, + {"x-ratelimit-team-limit-tokens": True, "x-ratelimit-team-remaining-tokens": 5}, + ], +) +async def test_should_emit_no_key_or_team_rate_limit_series_without_a_complete_int_pair( + additional_headers, +): + _clear_prometheus_registry() + try: + await _run_success_event(additional_headers) + + for metric_name in KEY_AND_TEAM_RATE_LIMIT_METRICS: + assert _collected_samples(metric_name) == {}, metric_name + finally: + _clear_prometheus_registry() diff --git a/tests/test_litellm/integrations/test_prometheus_requested_model_cardinality.py b/tests/test_litellm/integrations/test_prometheus_requested_model_cardinality.py new file mode 100644 index 00000000000..519a13751f1 --- /dev/null +++ b/tests/test_litellm/integrations/test_prometheus_requested_model_cardinality.py @@ -0,0 +1,279 @@ +""" +LIT-6611: every unique client-supplied model name that fails routing used to +mint permanent Prometheus series carrying ``requested_model=""`` on the +proxy request metrics and the deployment metrics, with no eviction. The fix +collapses any requested model the router does not recognize (and no wildcard +pattern matches) into the single ``other`` label bucket, while recognized +names, aliases, and wildcard-matched names keep their own label values. +""" + +import sys +import types +from unittest.mock import patch + +import pytest +from prometheus_client import REGISTRY + +import litellm +from litellm.integrations.prometheus import ( + UNRECOGNIZED_REQUESTED_MODEL_LABEL, + PrometheusLogger, +) +from litellm.proxy._types import UserAPIKeyAuth + + +class _ClientSideError(Exception): + status_code = 400 + + +@pytest.fixture(autouse=True) +def cleanup_prometheus_registry(): + for collector in list(REGISTRY._collector_to_names.keys()): + try: + REGISTRY.unregister(collector) + except Exception: + pass + + yield + + for collector in list(REGISTRY._collector_to_names.keys()): + try: + REGISTRY.unregister(collector) + except Exception: + pass + + +@pytest.fixture +def router(): + return litellm.Router( + model_list=[ + { + "model_name": "gpt-4o-mini", + "litellm_params": {"model": "openai/gpt-4o-mini", "api_key": "fake-key"}, + }, + { + "model_name": "openai/*", + "litellm_params": {"model": "openai/*", "api_key": "fake-key"}, + }, + ], + model_group_alias={"gpt4o-alias": "gpt-4o-mini"}, + ) + + +@pytest.fixture +def team_router(): + return litellm.Router( + model_list=[ + { + "model_name": "team-internal-gpt", + "litellm_params": {"model": "openai/gpt-4o-mini", "api_key": "fake-key"}, + "model_info": {"team_id": "team-1", "team_public_model_name": "team-alias-gpt"}, + }, + { + "model_name": "team-internal-bedrock", + "litellm_params": {"model": "openai/*", "api_key": "fake-key"}, + "model_info": {"team_id": "team-1", "team_public_model_name": "team-models/*"}, + }, + ] + ) + + +def _requested_model_values(metric) -> set[str]: + index = metric._labelnames.index("requested_model") + return {sample_key[index] for sample_key in metric._metrics} + + +def _series_count(metric) -> int: + return len(metric._metrics) + + +def _total_value(metric) -> float: + return sum(child._value.get() for child in metric._metrics.values()) + + +async def _fire_proxy_failure(logger: PrometheusLogger, model: str) -> None: + await logger.async_post_call_failure_hook( + request_data={"model": model, "metadata": {}, "proxy_server_request": {}}, + original_exception=_ClientSideError(f"model {model} does not exist"), + user_api_key_dict=UserAPIKeyAuth(api_key="hashed-key-1"), + ) + + +@pytest.mark.asyncio +async def test_unknown_models_collapse_to_one_series_on_proxy_request_metrics(router): + logger = PrometheusLogger() + + with patch("litellm.proxy.proxy_server.llm_router", router, create=True): # test-quality-ok: production reads proxy_server.llm_router lazily, no injection seam + for index in range(25): + await _fire_proxy_failure(logger, f"agent-typo-{index}") + + for metric in ( + logger.litellm_proxy_failed_requests_metric, + logger.litellm_proxy_total_requests_metric, + ): + assert _requested_model_values(metric) == {UNRECOGNIZED_REQUESTED_MODEL_LABEL} + assert _series_count(metric) == 1 + assert _total_value(metric) == 25 + + +@pytest.mark.asyncio +async def test_known_alias_and_wildcard_models_keep_their_own_labels(router): + logger = PrometheusLogger() + + with patch("litellm.proxy.proxy_server.llm_router", router, create=True): # test-quality-ok: production reads proxy_server.llm_router lazily, no injection seam + await _fire_proxy_failure(logger, "gpt-4o-mini") + await _fire_proxy_failure(logger, "gpt4o-alias") + await _fire_proxy_failure(logger, "openai/gpt-4o-audio-preview") + await _fire_proxy_failure(logger, "agent-typo-hallucinated") + + for metric in ( + logger.litellm_proxy_failed_requests_metric, + logger.litellm_proxy_total_requests_metric, + ): + assert _requested_model_values(metric) == { + "gpt-4o-mini", + "gpt4o-alias", + "openai/gpt-4o-audio-preview", + UNRECOGNIZED_REQUESTED_MODEL_LABEL, + } + + +@pytest.mark.asyncio +async def test_team_alias_and_team_wildcard_models_keep_their_own_labels(team_router): + logger = PrometheusLogger() + + with patch("litellm.proxy.proxy_server.llm_router", team_router, create=True): # test-quality-ok: production reads proxy_server.llm_router lazily, no injection seam + await _fire_proxy_failure(logger, "team-alias-gpt") + await _fire_proxy_failure(logger, "team-models/gpt-4o-audio-preview") + await _fire_proxy_failure(logger, "agent-typo-hallucinated") + + for metric in ( + logger.litellm_proxy_failed_requests_metric, + logger.litellm_proxy_total_requests_metric, + ): + assert _requested_model_values(metric) == { + "team-alias-gpt", + "team-models/gpt-4o-audio-preview", + UNRECOGNIZED_REQUESTED_MODEL_LABEL, + } + + +@pytest.mark.asyncio +async def test_unknown_models_collapse_to_other_when_router_is_unavailable(): + logger = PrometheusLogger() + + with patch("litellm.proxy.proxy_server.llm_router", None, create=True): # test-quality-ok: production reads proxy_server.llm_router lazily, no injection seam + await _fire_proxy_failure(logger, "agent-typo-no-router") + await _fire_proxy_failure(logger, "gpt-4o-mini") + + assert _requested_model_values(logger.litellm_proxy_failed_requests_metric) == { + UNRECOGNIZED_REQUESTED_MODEL_LABEL + } + + +@pytest.mark.asyncio +async def test_sdk_router_originated_metrics_keep_labels_without_proxy_router(): + logger = PrometheusLogger() + + with patch("litellm.proxy.proxy_server.llm_router", None, create=True): # test-quality-ok: production reads proxy_server.llm_router lazily, no injection seam + logger.set_llm_deployment_failure_metrics( + request_kwargs={ + "model": "sdk-deployment-group", + "litellm_params": {"metadata": {}}, + "standard_logging_object": {}, + "exception": _ClientSideError("model does not exist"), + } + ) + await logger.log_failure_fallback_event( + original_model_group="sdk-fallback-group", + kwargs={"model": "sdk-fallback-group", "metadata": {}}, + original_exception=_ClientSideError("upstream unavailable"), + ) + + assert _requested_model_values(logger.litellm_deployment_failure_responses) == {"sdk-deployment-group"} + assert _requested_model_values(logger.litellm_deployment_failed_fallbacks) == {"sdk-fallback-group"} + + +@pytest.mark.asyncio +async def test_sdk_fallback_labels_survive_non_import_errors_from_proxy_module(monkeypatch): + logger = PrometheusLogger() + broken_proxy_module = types.ModuleType("litellm.proxy.proxy_server") + + def _raise_value_error(_name: str): + raise ValueError("bad proxy env var") + + broken_proxy_module.__getattr__ = _raise_value_error # test-quality-ok: reproduces a proxy_server import raising non-ImportError, no injection seam + monkeypatch.setitem(sys.modules, "litellm.proxy.proxy_server", broken_proxy_module) # test-quality-ok: reproduces a proxy_server import raising non-ImportError, no injection seam + + await logger.log_failure_fallback_event( + original_model_group="sdk-fallback-group", + kwargs={"model": "sdk-fallback-group", "metadata": {}}, + original_exception=_ClientSideError("upstream unavailable"), + ) + + assert _requested_model_values(logger.litellm_deployment_failed_fallbacks) == {"sdk-fallback-group"} + + +def test_unknown_models_collapse_to_one_series_on_deployment_metrics(router): + logger = PrometheusLogger() + + with patch("litellm.proxy.proxy_server.llm_router", router, create=True): # test-quality-ok: production reads proxy_server.llm_router lazily, no injection seam + for index in range(25): + logger.set_llm_deployment_failure_metrics( + request_kwargs={ + "model": f"agent-typo-{index}", + "litellm_params": {"metadata": {}}, + "standard_logging_object": {}, + "exception": _ClientSideError("model does not exist"), + } + ) + logger.set_llm_deployment_failure_metrics( + request_kwargs={ + "model": "gpt-4o-mini", + "litellm_params": {"metadata": {}}, + "standard_logging_object": {}, + "exception": _ClientSideError("all deployments cooling down"), + } + ) + + for metric in ( + logger.litellm_deployment_failure_responses, + logger.litellm_deployment_total_requests, + ): + assert _requested_model_values(metric) == { + UNRECOGNIZED_REQUESTED_MODEL_LABEL, + "gpt-4o-mini", + } + assert _series_count(metric) == 2 + assert _total_value(metric) == 26 + + +@pytest.mark.asyncio +async def test_fallback_event_requested_model_is_bounded(router): + logger = PrometheusLogger() + kwargs = {"model": "gpt-4o-mini", "metadata": {}} + + with patch("litellm.proxy.proxy_server.llm_router", router, create=True): # test-quality-ok: production reads proxy_server.llm_router lazily, no injection seam + await logger.log_failure_fallback_event( + original_model_group="agent-typo-hallucinated", + kwargs=kwargs, + original_exception=_ClientSideError("model does not exist"), + ) + await logger.log_success_fallback_event( + original_model_group="agent-typo-hallucinated", + kwargs=kwargs, + original_exception=_ClientSideError("model does not exist"), + ) + await logger.log_failure_fallback_event( + original_model_group="gpt-4o-mini", + kwargs=kwargs, + original_exception=_ClientSideError("upstream unavailable"), + ) + + assert _requested_model_values(logger.litellm_deployment_failed_fallbacks) == { + UNRECOGNIZED_REQUESTED_MODEL_LABEL, + "gpt-4o-mini", + } + assert _requested_model_values(logger.litellm_deployment_successful_fallbacks) == { + UNRECOGNIZED_REQUESTED_MODEL_LABEL + } diff --git a/tests/test_litellm/integrations/test_s3.py b/tests/test_litellm/integrations/test_s3.py index 7e997870852..58b15b79e76 100644 --- a/tests/test_litellm/integrations/test_s3.py +++ b/tests/test_litellm/integrations/test_s3.py @@ -2,26 +2,27 @@ from datetime import datetime from unittest.mock import MagicMock, patch import litellm +from litellm.constants import MAX_S3_OBJECT_DOWNLOAD_FILENAME_BYTES, MAX_S3_OBJECT_KEY_BYTES from litellm.integrations.s3 import S3Logger TEST_KMS_KEY_ARN = "arn:aws:kms:us-east-1:111122223333:key/test-key-id" -def _standard_logging_payload() -> dict: +def _standard_logging_payload(response_id: str = "chatcmpl-test-id") -> dict: return { - "id": "chatcmpl-test-id", + "id": response_id, "metadata": {"user_api_key_team_alias": None}, } -def _log_event_kwargs() -> dict: +def _log_event_kwargs(response_id: str = "chatcmpl-test-id") -> dict: return { "litellm_params": {"metadata": {}}, - "standard_logging_object": _standard_logging_payload(), + "standard_logging_object": _standard_logging_payload(response_id), } -def _run_log_event(callback_params: dict) -> MagicMock: +def _run_log_event(callback_params: dict, response_id: str = "chatcmpl-test-id") -> MagicMock: original = litellm.s3_callback_params litellm.s3_callback_params = callback_params try: @@ -30,8 +31,8 @@ def _run_log_event(callback_params: dict) -> MagicMock: mock_boto3_client.return_value = mock_s3_client logger = S3Logger() logger.log_event( - kwargs=_log_event_kwargs(), - response_obj={}, + kwargs=_log_event_kwargs(response_id), + response_obj={"id": response_id}, start_time=datetime(2026, 7, 30, 12, 0, 0), end_time=datetime(2026, 7, 30, 12, 0, 1), print_verbose=lambda *args, **kwargs: None, @@ -154,3 +155,30 @@ def test_non_string_key_id_is_dropped_and_valid_algorithm_is_kept(): put_object_kwargs = mock_s3_client.put_object.call_args.kwargs assert put_object_kwargs["ServerSideEncryption"] == "aws:kms" assert "SSEKMSKeyId" not in put_object_kwargs + + +def test_put_object_key_and_filename_are_bounded_for_an_oversized_response_id(): + """The sync logger bounds both the key and the Content-Disposition filename.""" + mock_s3_client = _run_log_event( + {"s3_bucket_name": "test-bucket", "s3_region_name": "us-west-2", "s3_path": "logs"}, + response_id="resp_" + "A" * 1100, + ) + + put_object_kwargs = mock_s3_client.put_object.call_args.kwargs + assert len(put_object_kwargs["Key"].encode("utf-8")) <= MAX_S3_OBJECT_KEY_BYTES + assert put_object_kwargs["Key"].startswith("logs/2026-07-30/time-12-00-00-000000_resp_") + filename = put_object_kwargs["ContentDisposition"].removeprefix('inline; filename="').removesuffix('"') + assert len(filename.encode("utf-8")) <= MAX_S3_OBJECT_DOWNLOAD_FILENAME_BYTES + + +def test_put_object_keeps_the_configured_path_intact_when_only_the_id_has_to_shrink(): + """A long configured s3_path survives whole when the id can be shortened instead.""" + long_path = "litellm-prod-logs/" + "t" * 921 + mock_s3_client = _run_log_event( + {"s3_bucket_name": "test-bucket", "s3_region_name": "us-west-2", "s3_path": long_path}, + response_id="resp_" + "B" * 100, + ) + + key = mock_s3_client.put_object.call_args.kwargs["Key"] + assert key.startswith(long_path + "/2026-07-30/") + assert len(key.encode("utf-8")) == MAX_S3_OBJECT_KEY_BYTES diff --git a/tests/test_litellm/integrations/test_s3_v2.py b/tests/test_litellm/integrations/test_s3_v2.py index 51671d5101e..a037284d7c1 100644 --- a/tests/test_litellm/integrations/test_s3_v2.py +++ b/tests/test_litellm/integrations/test_s3_v2.py @@ -1170,6 +1170,294 @@ def test_create_s3_batch_logging_element_flat_key_for_arn_response_id(): assert file_segment.endswith("model-invocation-job_gl18r6skk9yy.json") +# -------------------------------------------------------------- +# object keys bounded to S3's 1024 UTF-8 byte limit +# -------------------------------------------------------------- +def _oversized_response_id() -> str: + return "resp_" + "A" * 1100 + + +def test_s3_object_key_at_the_byte_limit_is_left_alone(): + """A key that still fits is left byte-identical.""" + from litellm.constants import MAX_S3_OBJECT_KEY_BYTES + from litellm.integrations.s3 import get_s3_object_key + + start_time = datetime(2026, 8, 24, 6, 18, 41, 948021) + fixed_len = len("input/2026-08-24/.json") + file_name = "x" * (MAX_S3_OBJECT_KEY_BYTES - fixed_len) + + key = get_s3_object_key(s3_path="input", prefix="", start_time=start_time, s3_file_name=file_name) + + assert key == f"input/2026-08-24/{file_name}.json" + assert len(key.encode("utf-8")) == MAX_S3_OBJECT_KEY_BYTES + + +def test_s3_object_key_is_bounded_for_oversized_response_id(): + """An oversized Responses API id is shortened to a readable head plus a digest.""" + import hashlib + + from litellm.constants import MAX_S3_OBJECT_KEY_BYTES + from litellm.integrations.s3 import get_s3_object_key + + start_time = datetime(2026, 8, 24, 6, 18, 41, 948021) + file_name = f"time-06-18-41-948021_{_oversized_response_id()}" + + key = get_s3_object_key(s3_path="input", prefix="DefaultTeamProd/", start_time=start_time, s3_file_name=file_name) + + assert len(key.encode("utf-8")) <= MAX_S3_OBJECT_KEY_BYTES + assert key.startswith("input/DefaultTeamProd/2026-08-24/time-06-18-41-948021_resp_") + assert key.endswith(f"_{hashlib.sha256(file_name.encode('utf-8')).hexdigest()}.json") + + +@pytest.mark.parametrize( + "s3_path,prefix", + [ + ("input", ""), + ("a" * 900, ""), + ("input", "team-" + "b" * 900 + "/"), + ("c" * 600, "team-" + "d" * 600 + "/key-" + "e" * 600 + "/"), + # many short segments, so the trim lands exactly on the budget edge + ("", "ssss/" * 200), + ], +) +def test_s3_object_key_is_bounded_for_long_paths_and_aliases(s3_path: str, prefix: str): + """Long paths, team aliases and key aliases stay within the cap.""" + from litellm.constants import MAX_S3_OBJECT_KEY_BYTES + from litellm.integrations.s3 import get_s3_object_key + + key = get_s3_object_key( + s3_path=s3_path, + prefix=prefix, + start_time=datetime(2026, 8, 24, 6, 18, 41, 948021), + s3_file_name=f"time-06-18-41-948021_{_oversized_response_id()}", + ) + + assert len(key.encode("utf-8")) <= MAX_S3_OBJECT_KEY_BYTES + assert key.endswith(".json") + assert "/2026-08-24/" in key or key.startswith("2026-08-24/") + assert "/" not in key.rsplit("2026-08-24/", 1)[1] + + +def test_s3_object_key_trimmed_prefixes_stay_distinct_per_operator(): + """Prefixes that differ only past the trim point keep separate folders.""" + from litellm.constants import MAX_S3_OBJECT_KEY_BYTES + from litellm.integrations.s3 import get_s3_object_key + + start_time = datetime(2026, 8, 24, 6, 18, 41, 948021) + keys = [ + get_s3_object_key( + s3_path="input", + prefix="team-" + "b" * 1000 + suffix + "/", + start_time=start_time, + s3_file_name=f"time-06-18-41-948021_{_oversized_response_id()}", + ) + for suffix in ("-one", "-two") + ] + + assert keys[0] != keys[1] + assert all(key.startswith("input/team-" + "b" * 900) for key in keys) + assert all(len(key.encode("utf-8")) == MAX_S3_OBJECT_KEY_BYTES for key in keys) + + +def test_s3_object_key_bounded_prefix_never_splits_a_multibyte_character(): + """A multibyte prefix is trimmed on a character boundary.""" + from litellm.constants import MAX_S3_OBJECT_KEY_BYTES + from litellm.integrations.s3 import get_s3_object_key + + s3_path = "\u65e5\u672c\u8a9e" * 200 + + key = get_s3_object_key( + s3_path=s3_path, + prefix="\u30c1\u30fc\u30e0" * 200 + "/", + start_time=datetime(2026, 8, 24, 6, 18, 41, 948021), + s3_file_name=f"time-06-18-41-948021_{_oversized_response_id()}", + ) + + assert len(key.encode("utf-8")) <= MAX_S3_OBJECT_KEY_BYTES + assert key.startswith(s3_path[:100]) + assert "\ufffd" not in key + + +def test_s3_object_key_stays_unique_for_ids_sharing_a_head(): + """Ids sharing a visible head still get distinct keys.""" + from litellm.integrations.s3 import get_s3_object_key + + start_time = datetime(2026, 8, 24, 6, 18, 41, 948021) + keys = { + get_s3_object_key( + s3_path="input", + prefix="", + start_time=start_time, + s3_file_name=f"time-06-18-41-948021_{_oversized_response_id()}{suffix}", + ) + for suffix in ("first", "second", "third") + } + + assert len(keys) == 3 + + +def test_s3_object_key_bounding_matches_the_documented_layout(): + """The bounded key is `//_.json`.""" + import hashlib + + from litellm.integrations.s3 import get_s3_object_key + + file_name = f"time-06-18-41-948021_{_oversized_response_id()}" + + key = get_s3_object_key( + s3_path="input", + prefix="team/", + start_time=datetime(2026, 8, 24, 6, 18, 41, 948021), + s3_file_name=file_name, + ) + + digest = hashlib.sha256(file_name.encode("utf-8")).hexdigest() + assert key == f"input/team/2026-08-24/{file_name[:64]}_{digest}.json" + + +def test_s3_object_key_keeps_the_configured_prefix_when_only_the_id_overflows(): + """A 940 byte configured prefix survives whole when only the id overflows.""" + from litellm.constants import MAX_S3_OBJECT_KEY_BYTES + from litellm.integrations.s3 import get_s3_object_key + + prefix = "team-" + "b" * 934 + "/" + + key = get_s3_object_key( + s3_path="", + prefix=prefix, + start_time=datetime(2026, 8, 24, 6, 18, 41, 948021), + s3_file_name=f"time-06-18-41-948021_{_oversized_response_id()}", + ) + + assert key.startswith(prefix + "2026-08-24/") + assert len(key.encode("utf-8")) == MAX_S3_OBJECT_KEY_BYTES + + +def test_s3_object_key_spends_the_whole_budget_when_the_prefix_must_be_trimmed(): + """A trimmed prefix keeps every byte the budget allows, not whole segments.""" + from litellm.constants import MAX_S3_OBJECT_KEY_BYTES + from litellm.integrations.s3 import get_s3_object_key + + s3_path = "p" * 400 + "/" + "q" * 600 + + key = get_s3_object_key( + s3_path=s3_path, + prefix="", + start_time=datetime(2026, 8, 24, 6, 18, 41, 948021), + s3_file_name="time-06-18-41-948021_abc", + ) + + assert len(key.encode("utf-8")) == MAX_S3_OBJECT_KEY_BYTES + assert key.startswith("p" * 400 + "/" + "q" * 500) + + +def test_s3_object_key_keeps_a_single_segment_path_as_far_as_it_fits(): + """A path with no separator is kept as far as it fits, never dropped to the bucket root.""" + from litellm.constants import MAX_S3_OBJECT_KEY_BYTES + from litellm.integrations.s3 import get_s3_object_key + + key = get_s3_object_key( + s3_path="a" * 1050, + prefix="", + start_time=datetime(2026, 8, 24, 6, 18, 41, 948021), + s3_file_name="time-06-18-41-948021_chatcmpl-xyz", + ) + + assert len(key.encode("utf-8")) == MAX_S3_OBJECT_KEY_BYTES + assert key.startswith("a" * 900) + + +def test_create_s3_batch_logging_element_bounds_key_and_keeps_full_response_id(): + """The batch element bounds the key and keeps the full response id in the payload.""" + from litellm.constants import MAX_S3_OBJECT_KEY_BYTES + + logger = S3Logger(s3_use_team_prefix=True, s3_use_key_prefix=True) + response_id = _oversized_response_id() + payload = StandardLoggingPayload( + id=response_id, + metadata={"user_api_key_team_alias": "DefaultTeamProd", "user_api_key_alias": "prod-key"}, + messages=[], + ) + + result = logger.create_s3_batch_logging_element(datetime(2026, 8, 24, 6, 18, 41, 948021), payload) + + assert result is not None + assert len(result.s3_object_key.encode("utf-8")) <= MAX_S3_OBJECT_KEY_BYTES + assert result.s3_object_key.startswith("DefaultTeamProd/prod-key/2026-08-24/") + assert result.payload["id"] == response_id + + +def test_s3_object_download_filename_is_bounded_for_oversized_response_id(): + """The Content-Disposition filename is bounded too, or the PUT fails with MetadataTooLarge.""" + from litellm.constants import MAX_S3_OBJECT_DOWNLOAD_FILENAME_BYTES + from litellm.integrations.s3 import get_s3_object_download_filename + + file_name = get_s3_object_download_filename(datetime(2026, 8, 24, 6, 18, 41, 948021), _oversized_response_id()) + + assert len(file_name.encode("utf-8")) <= MAX_S3_OBJECT_DOWNLOAD_FILENAME_BYTES + assert file_name.startswith("time-2026-08-24T06-18-41-948021_resp_") + assert file_name.endswith(".json") + + +def test_s3_object_download_filenames_stay_distinct_when_shortened(): + """Shortened filenames stay distinct.""" + from litellm.integrations.s3 import get_s3_object_download_filename + + start_time = datetime(2026, 8, 24, 6, 18, 41, 948021) + file_names = { + get_s3_object_download_filename(start_time, _oversized_response_id() + suffix) + for suffix in ("first", "second", "third") + } + + assert len(file_names) == 3 + + +def test_s3_object_download_filename_short_id_is_unchanged(): + """An ordinary response id keeps the filename it had before.""" + from litellm.integrations.s3 import get_s3_object_download_filename + + file_name = get_s3_object_download_filename(datetime(2026, 8, 24, 6, 18, 41, 948021), "resp_abc123") + + assert file_name == "time-2026-08-24T06-18-41-948021_resp_abc123.json" + + +def test_create_s3_batch_logging_element_bounds_the_download_filename(): + """The batch element carries a bounded Content-Disposition filename.""" + from litellm.constants import MAX_S3_OBJECT_DOWNLOAD_FILENAME_BYTES + + logger = S3Logger() + payload = StandardLoggingPayload(id=_oversized_response_id(), metadata={}, messages=[]) + + result = logger.create_s3_batch_logging_element(datetime(2026, 8, 24, 6, 18, 41, 948021), payload) + + assert result is not None + assert len(result.s3_object_download_filename.encode("utf-8")) <= MAX_S3_OBJECT_DOWNLOAD_FILENAME_BYTES + + +@pytest.mark.asyncio +async def test_audit_log_object_key_is_bounded_for_a_long_configured_path(): + """Audit log keys are bounded by the same builder.""" + from litellm.constants import MAX_S3_OBJECT_KEY_BYTES + + logger = S3Logger() + logger.s3_path = "audit-archive/" + "z" * 1100 + + await logger.async_log_audit_log_event({"id": "1a4f7bd0-6f1e-4d0a-9b3c-9f2e1d5a7c88"}) + + assert len(logger.log_queue) == 1 + assert len(logger.log_queue[0].s3_object_key.encode("utf-8")) <= MAX_S3_OBJECT_KEY_BYTES + assert logger.log_queue[0].s3_object_key.startswith("audit-archive/" + "z" * 900) + + +def test_s3_object_download_filename_drops_characters_that_break_the_header(): + """A quote or separator in the response id cannot escape the quoted header value.""" + from litellm.integrations.s3 import get_s3_object_download_filename + + file_name = get_s3_object_download_filename(datetime(2026, 8, 24, 6, 18, 41, 948021), 'resp_a"b/c') + + assert file_name == "time-2026-08-24T06-18-41-948021_resp_a_b_c.json" + + # -------------------------------------------------------------- # params_source / s3_callback_params_override (audit-log decoupling) # -------------------------------------------------------------- diff --git a/tests/test_litellm/integrations/test_shadow_eval_logger.py b/tests/test_litellm/integrations/test_shadow_eval_logger.py index f9c287fc7b7..5628d69de26 100644 --- a/tests/test_litellm/integrations/test_shadow_eval_logger.py +++ b/tests/test_litellm/integrations/test_shadow_eval_logger.py @@ -58,12 +58,14 @@ def _prisma(jobs=(), attempt_counts=(), attempt_costs=()) -> MagicMock: return prisma -def _job_record(job: ActiveShadowEvalJob, api_key_id="key-hash") -> MagicMock: +def _job_record(job: ActiveShadowEvalJob, target_type="key", target_id="key-hash") -> MagicMock: record = MagicMock() for field, value in dict( id=job.id, - api_key_id=api_key_id, + target_type=target_type, + target_id=target_id, router_name=job.router_name, + router_names=job.router_names, direction=job.direction, baseline_model=job.baseline_model, shadow_percentage=job.shadow_percentage, @@ -80,6 +82,7 @@ def _router( shadow_text="shadow answer", judge_json='{"preference": "A", "confidence": 0.9, "reasoning": "x"}', classifier_cost=None, + sibling_router_texts=None, ): """One mock router serving the shadow call first, the judge call second, told apart by the internal-origin stamp rather than the model, since a reverse job's shadow arm names @@ -99,6 +102,15 @@ def _router( decision["classifier_cost"] = classifier_cost kwargs["metadata"]["routing_decision"] = decision return {"choices": [{"message": {"content": shadow_text}}], "usage": {"completion_tokens": 5}} + if sibling_router_texts and kwargs["model"] in sibling_router_texts: + kwargs["metadata"]["routing_decision"] = { + "tier_label": "MEDIUM", + "routed_model": f"{kwargs['model']}-pick", + } + return { + "choices": [{"message": {"content": sibling_router_texts[kwargs["model"]]}}], + "usage": {"completion_tokens": 5}, + } return ModelResponse( model=kwargs["model"], choices=[{"index": 0, "finish_reason": "stop", "message": {"role": "assistant", "content": shadow_text}}], @@ -123,7 +135,7 @@ def _spend_counter(store=None): return counter, read, write -def _logger(router=None, prisma=None, jobs=(), counter_store=None) -> ShadowEvalLogger: +def _logger(router=None, prisma=None, jobs=(), counter_store=None, jobs_by_target=None) -> ShadowEvalLogger: cache = InMemoryCache(max_size_in_memory=4, default_ttl=60) counter, read, write = _spend_counter(counter_store) funnel_events = [] @@ -137,8 +149,9 @@ def _logger(router=None, prisma=None, jobs=(), counter_store=None) -> ShadowEval ) logger._test_counter = counter logger._test_funnel = funnel_events - if jobs: - cache.set_cache("shadow_eval:active_jobs", {"key-hash": tuple(jobs)}) + seeded = jobs_by_target if jobs_by_target is not None else ({("key", "key-hash"): tuple(jobs)} if jobs else None) + if seeded is not None: + cache.set_cache("shadow_eval:active_jobs", seeded) return logger @@ -837,6 +850,86 @@ class TestSuccessHookSkipChain: prisma.db.litellm_shadowevalattempt.create.assert_not_called() +JWT_IDENTITY = {"user_api_key_hash": None, "user_api_key_team_id": "team-eng", "user_api_key_user_id": "dev-alice"} + + +@pytest.mark.asyncio +class TestTargetMatching: + """A request qualifies for a job through ANY of its resolved identities: key hash, + team id, or user id. Team and user jobs must therefore sample JWT-authenticated + traffic, which carries no key hash at all.""" + + @pytest.mark.parametrize( + "target,sampled", + [ + (("team", "team-eng"), True), + (("user", "dev-alice"), True), + (("key", "some-key"), False), + ], + ids=["team-job-samples-jwt-traffic", "user-job-samples-jwt-traffic", "key-jobs-never-match-keyless-traffic"], + ) + async def test_jwt_shaped_traffic_matches_team_and_user_jobs_but_no_key_job(self, target, sampled): + prisma = _prisma() + router = _router() + logger = _logger(router=router, prisma=prisma, jobs_by_target={target: (_job(),)}) + hook_kwargs = _success_kwargs() + hook_kwargs["standard_logging_object"]["metadata"] = dict(JWT_IDENTITY) + + await logger.async_log_success_event(hook_kwargs, RESPONSE, None, None) + await _drain(logger) + + if sampled: + prisma.db.litellm_shadowevalattempt.create.assert_awaited_once() + assert prisma.db.litellm_shadowevalattempt.create.call_args.kwargs["data"]["job_id"] == "job-1" + else: + router.acompletion.assert_not_called() + prisma.db.litellm_shadowevalattempt.create.assert_not_called() + + async def test_an_event_with_no_identity_early_returns_without_a_cache_read(self): + prisma = _prisma() + router = _router() + cache = MagicMock(spec=InMemoryCache) + cache.async_get_cache = AsyncMock() + logger = ShadowEvalLogger( + router_provider=lambda: router, + prisma_provider=lambda: prisma, + jobs_cache=cache, + ) + hook_kwargs = _success_kwargs() + hook_kwargs["standard_logging_object"]["metadata"] = {} + + await logger.async_log_success_event(hook_kwargs, RESPONSE, None, None) + + cache.async_get_cache.assert_not_awaited() + router.acompletion.assert_not_called() + prisma.db.litellm_shadowevalattempt.create.assert_not_called() + + async def test_an_event_matching_a_key_job_and_a_team_job_fires_both(self): + """A request's key and its team can each hold a job; the two are separately + budgeted experiments, so both fire and each counts its own start.""" + prisma = _prisma() + logger = _logger( + router=_router(), + prisma=prisma, + jobs_by_target={ + ("key", "key-hash"): (_job(id="key-job"),), + ("team", "team-eng"): (_job(id="team-job"),), + }, + ) + hook_kwargs = _success_kwargs() + hook_kwargs["standard_logging_object"]["metadata"] = { + "user_api_key_hash": "key-hash", + "user_api_key_team_id": "team-eng", + } + + await logger.async_log_success_event(hook_kwargs, RESPONSE, None, None) + await _drain(logger) + + rows = [call.kwargs["data"] for call in prisma.db.litellm_shadowevalattempt.create.call_args_list] + assert sorted(row["job_id"] for row in rows) == ["key-job", "team-job"] + assert logger._job_starts == {"key-job": 1, "team-job": 1} + + @pytest.mark.asyncio class TestActiveJobsCache: async def test_cache_miss_reads_db_once_then_serves_from_cache(self): @@ -851,8 +944,8 @@ class TestActiveJobsCache: first = await logger._active_jobs() second = await logger._active_jobs() - assert [job.id for job in first["key-hash"]] == ["job-1"] - assert second["key-hash"][0].attempts == 7 + assert [job.id for job in first[("key", "key-hash")]] == ["job-1"] + assert second[("key", "key-hash")][0].attempts == 7 assert prisma.db.litellm_shadowevaljob.find_many.await_count == 1 where = prisma.db.litellm_shadowevaljob.find_many.call_args.kwargs["where"] assert where["stopped_at"] is None @@ -899,8 +992,8 @@ class TestActiveJobsCache: jobs = await logger._active_jobs() assert logger._job_starts == {} - assert jobs["key-hash"][0].attempts == 7 - assert jobs["key-hash"][0].spend == 0.05 + assert jobs[("key", "key-hash")][0].attempts == 7 + assert jobs[("key", "key-hash")][0].spend == 0.05 @pytest.mark.asyncio @@ -1128,29 +1221,36 @@ class TestJobValidation: {"direction": "reverse"}, {"baseline_model": "baseline-model"}, {"direction": "sideways", "baseline_model": "baseline-model"}, + {"direction": "reverse", "baseline_model": "baseline-model", "router_names": ("a", "b")}, ], - ids=["reverse-without-baseline", "forward-with-baseline", "unknown-direction"], + ids=["reverse-without-baseline", "forward-with-baseline", "unknown-direction", "reverse-with-router-set"], ) def test_unsamplable_shapes_are_rejected(self, overrides): with pytest.raises(ValidationError): _job(**overrides) - def test_shadow_target_follows_direction(self): - assert _job().shadow_target == "my-router" - assert _reverse_job().shadow_target == "baseline-model" + def test_arm_target_follows_direction(self): + assert _job().arm_target("my-router") == "my-router" + assert _reverse_job().arm_target("my-router") == "baseline-model" + + def test_rows_from_before_router_names_carry_their_set_in_router_name(self): + assert _job().arm_router_names == ("my-router",) + assert _job(router_names=("my-router", "alt-router")).arm_router_names == ("my-router", "alt-router") @pytest.mark.asyncio class TestDirection: @pytest.mark.parametrize( - "job,routed_by,sampled", + "job,routed_by,attempt_rows", [ - (_job(), None, True), - (_job(), "my-router", False), - (_job(), "other-router", True), - (_reverse_job(), "my-router", True), - (_reverse_job(), None, False), - (_reverse_job(), "other-router", False), + (_job(), None, 1), + (_job(), "my-router", 0), + (_job(), "other-router", 1), + (_reverse_job(), "my-router", 1), + (_reverse_job(), None, 0), + (_reverse_job(), "other-router", 0), + (_job(router_names=("my-router", "alt-router")), "alt-router", 0), + (_job(router_names=("my-router", "alt-router")), "other-router", 2), ], ids=[ "forward-samples-unrouted", @@ -1159,20 +1259,24 @@ class TestDirection: "reverse-samples-its-own-router", "reverse-skips-unrouted", "reverse-skips-another-router", + "forward-skips-any-candidates-own-traffic", + "forward-multi-samples-once-per-arm", ], ) - async def test_direction_decides_which_traffic_is_sampled(self, job, routed_by, sampled): + async def test_direction_decides_which_traffic_is_sampled(self, job, routed_by, attempt_rows): """The two directions partition the key's traffic: whatever one samples, the other - skips, so a key running both never judges the same turn twice for the same reason.""" + skips, so a key running both never judges the same turn twice for the same reason. + A multi-router job extends the forward skip to every candidate: a request one + candidate served must not be judged as the incumbent against another candidate.""" prisma = _prisma() - logger = _logger(router=_router(), prisma=prisma, jobs=(job,)) + logger = _logger(router=_router(sibling_router_texts={"alt-router": "alt answer"}), prisma=prisma, jobs=(job,)) await logger.async_log_success_event( _success_kwargs(request_metadata=_routed_by(routed_by) if routed_by else {}), RESPONSE, None, None ) await _drain(logger) - assert prisma.db.litellm_shadowevalattempt.create.await_count == int(sampled) + assert prisma.db.litellm_shadowevalattempt.create.await_count == attempt_rows async def test_reverse_duplicates_against_the_baseline_model(self): prisma = _prisma() @@ -1234,6 +1338,134 @@ class TestDirection: assert logger._job_starts == {"forward-job": 1, "reverse-job": 1} +@pytest.mark.asyncio +class TestMultiRouterArms: + async def test_every_arm_judges_the_same_request_and_stamps_its_own_row(self): + """One sampled request, one row per candidate router, both judged against the same + real response: the paired comparison that makes multi-router win rates comparable.""" + prisma = _prisma() + router = _router(sibling_router_texts={"alt-router": "alt answer"}) + logger = _logger(router=router, prisma=prisma) + + await logger._run_shadow_eval( + job=_job(router_names=("my-router", "alt-router")), + request_id="req-1", + messages=({"role": "user", "content": "hi"},), + real_text="real answer", + real_model="claude-opus", + real_cost=0.001, + real_classifier_cost=0.0, + real_cache_hit=False, + control_tier=None, + shadow_params={}, + parent_metadata={}, + ) + + rows = [call.kwargs["data"] for call in prisma.db.litellm_shadowevalattempt.create.await_args_list] + assert [row["router_name"] for row in rows] == ["my-router", "alt-router"] + assert {row["request_id"] for row in rows} == {"req-1"} + assert [row["shadow_model"] for row in rows] == ["cheap-model", "alt-router-pick"] + assert all(row["outcome"] in ("real", "shadow", "tie") for row in rows) + assert all(row["real_cost"] == 0.001 for row in rows) + + async def test_a_single_router_job_stamps_its_router_on_the_row(self): + prisma = _prisma() + logger = _logger(router=_router(), prisma=prisma) + + await logger._run_shadow_eval( + job=_job(), + request_id="req-1", + messages=({"role": "user", "content": "hi"},), + real_text="real answer", + real_model="claude-opus", + real_cost=0.0, + real_classifier_cost=0.0, + real_cache_hit=False, + control_tier=None, + shadow_params={}, + parent_metadata={}, + ) + + row = prisma.db.litellm_shadowevalattempt.create.call_args.kwargs["data"] + assert row["router_name"] == "my-router" + + async def test_one_arms_failure_never_silences_the_sibling(self): + prisma = _prisma() + router = _router(sibling_router_texts={"alt-router": "alt answer"}) + healthy = router.acompletion.side_effect + + async def first_arm_explodes(**kwargs): + if kwargs["model"] == "my-router": + raise RuntimeError("provider exploded") + return await healthy(**kwargs) + + router.acompletion.side_effect = first_arm_explodes + logger = _logger(router=router, prisma=prisma) + + await logger._run_shadow_eval( + job=_job(router_names=("my-router", "alt-router")), + request_id="req-1", + messages=({"role": "user", "content": "hi"},), + real_text="real answer", + real_model="claude-opus", + real_cost=0.0, + real_classifier_cost=0.0, + real_cache_hit=False, + control_tier=None, + shadow_params={}, + parent_metadata={}, + ) + + rows = [call.kwargs["data"] for call in prisma.db.litellm_shadowevalattempt.create.await_args_list] + assert [row["router_name"] for row in rows] == ["my-router", "alt-router"] + assert rows[0]["outcome"] == "error" + assert "provider exploded" in rows[0]["error"] + assert rows[1]["outcome"] in ("real", "shadow", "tie") + + async def test_the_turn_valve_counts_every_arm_a_start_will_write(self): + """max_turns is a row ceiling and one sampled request writes one row per arm, so + admission pre-counts the arms: a two-arm job with two turns of budget admits one + request, not two.""" + prisma = _prisma() + router = _router(sibling_router_texts={"alt-router": "alt answer"}) + logger = _logger( + router=router, prisma=prisma, jobs=(_job(router_names=("my-router", "alt-router"), max_turns=2),) + ) + + await logger.async_log_success_event(_success_kwargs(request_id="req-1"), RESPONSE, None, None) + await logger.async_log_success_event(_success_kwargs(request_id="req-2"), RESPONSE, None, None) + await _drain(logger) + + rows = [call.kwargs["data"] for call in prisma.db.litellm_shadowevalattempt.create.await_args_list] + assert {row["request_id"] for row in rows} == {"req-1"} + assert len(rows) == 2 + + async def test_a_withheld_request_runs_no_arm_and_counts_once(self): + """The budget gates run once per sampled request, before any arm: funnel counters + stay per-request, so coverage math is arm-count independent.""" + prisma = _prisma() + router = _router(sibling_router_texts={"alt-router": "alt answer"}) + logger = _logger(router=router, prisma=prisma) + + await logger._run_shadow_eval( + job=_job(router_names=("my-router", "alt-router"), max_budget=1.0, spend=2.0), + request_id="req-1", + messages=({"role": "user", "content": "hi"},), + real_text="real answer", + real_model="claude-opus", + real_cost=0.0, + real_classifier_cost=0.0, + real_cache_hit=False, + control_tier=None, + shadow_params={}, + parent_metadata={}, + ) + + router.acompletion.assert_not_called() + prisma.db.litellm_shadowevalattempt.create.assert_not_called() + assert logger._test_funnel == [("job-1", "withheld")] + + @pytest.mark.asyncio class TestActiveJobsFailClosed: async def test_a_row_the_sampler_cannot_read_is_dropped_not_guessed(self): @@ -1249,13 +1481,14 @@ class TestActiveJobsFailClosed: jobs_cache=InMemoryCache(max_size_in_memory=4, default_ttl=60), ) - assert [job.id for job in (await logger._active_jobs())["key-hash"]] == ["job-ok"] + assert [job.id for job in (await logger._active_jobs())[("key", "key-hash")]] == ["job-ok"] - async def test_both_of_a_key_s_jobs_survive_the_lookup(self): + async def test_every_targets_jobs_survive_the_lookup_keyed_by_type_and_id(self): records = [ _job_record(_job(id="job-forward")), _job_record(_reverse_job(id="job-reverse")), - _job_record(_job(id="job-other"), api_key_id="other-key"), + _job_record(_job(id="job-other"), target_id="other-key"), + _job_record(_job(id="job-team"), target_type="team", target_id="team-eng"), ] prisma = _prisma(jobs=records, attempt_counts=[("job-reverse", 3)]) logger = ShadowEvalLogger( @@ -1266,9 +1499,11 @@ class TestActiveJobsFailClosed: jobs = await logger._active_jobs() - assert sorted(job.id for job in jobs["key-hash"]) == ["job-forward", "job-reverse"] - assert [job.id for job in jobs["other-key"]] == ["job-other"] - assert {job.id: job.attempts for job in jobs["key-hash"]}["job-reverse"] == 3 + assert sorted(job.id for job in jobs[("key", "key-hash")]) == ["job-forward", "job-reverse"] + assert [job.id for job in jobs[("key", "other-key")]] == ["job-other"] + assert [job.id for job in jobs[("team", "team-eng")]] == ["job-team"] + assert ("team-eng",) not in jobs and "team-eng" not in jobs + assert {job.id: job.attempts for job in jobs[("key", "key-hash")]}["job-reverse"] == 3 def _failing_router(): diff --git a/tests/test_litellm/litellm_core_utils/audio_utils/test_subtitle_utils.py b/tests/test_litellm/litellm_core_utils/audio_utils/test_subtitle_utils.py index dcc4163ff10..210513f4967 100644 --- a/tests/test_litellm/litellm_core_utils/audio_utils/test_subtitle_utils.py +++ b/tests/test_litellm/litellm_core_utils/audio_utils/test_subtitle_utils.py @@ -1,5 +1,6 @@ from litellm.litellm_core_utils.audio_utils.subtitle_utils import ( SubtitleToken, + _merge_tokens_into_words, render_subtitle_tokens_as_srt, render_subtitle_tokens_as_vtt, synthesize_subtitle_document, @@ -23,25 +24,59 @@ class TestRenderSubtitleTokensAsSrt: "1\n00:00:00,000 --> 00:00:01,000\nHi.\n\n2\n00:00:01,500 --> 00:00:02,500\nHey.\n" ) - def test_token_cap_starts_a_new_cue_after_15_tokens(self): - tokens = tuple( - SubtitleToken(text=f"{index} ", start_ms=index * 100, end_ms=index * 100 + 100) for index in range(16) + def test_width_budget_starts_a_new_cue_at_word_boundaries(self): + tokens = tuple(SubtitleToken(text="abcdefghi ", start_ms=i * 100, end_ms=i * 100 + 90) for i in range(20)) + result = render_subtitle_tokens_as_srt(tokens) + texts = [cue.split("\n", 2)[2] for cue in result.strip().split("\n\n")] + assert len(texts) == 3 + assert all(len(text) <= 84 for text in texts) + assert all(set(text.split()) == {"abcdefghi"} for text in texts) + + def test_duration_cap_starts_a_new_cue_before_word_crossing_7000ms(self): + tokens = ( + SubtitleToken(text="Alpha ", start_ms=0, end_ms=3400), + SubtitleToken(text="beta ", start_ms=3400, end_ms=6800), + SubtitleToken(text="gamma", start_ms=6800, end_ms=7400), ) assert render_subtitle_tokens_as_srt(tokens) == ( - "1\n00:00:00,000 --> 00:00:01,500\n0 1 2 3 4 5 6 7 8 9 10 11 12 13 14\n" - "\n2\n00:00:01,500 --> 00:00:01,600\n15\n" + "1\n00:00:00,000 --> 00:00:06,800\nAlpha beta\n\n2\n00:00:06,800 --> 00:00:07,400\ngamma\n" ) - def test_duration_cap_starts_a_new_cue_at_5000ms(self): + def test_silence_gap_starts_a_new_cue(self): tokens = ( SubtitleToken(text="Alpha ", start_ms=0, end_ms=400), - SubtitleToken(text="beta ", start_ms=2000, end_ms=2400), - SubtitleToken(text="gamma.", start_ms=5000, end_ms=5400), + SubtitleToken(text="beta", start_ms=2000, end_ms=2400), ) assert render_subtitle_tokens_as_srt(tokens) == ( - "1\n00:00:00,000 --> 00:00:02,400\nAlpha beta\n\n2\n00:00:05,000 --> 00:00:05,400\ngamma.\n" + "1\n00:00:00,000 --> 00:00:00,400\nAlpha\n\n2\n00:00:02,000 --> 00:00:02,400\nbeta\n" ) + def test_sentence_final_punctuation_starts_a_new_cue(self): + tokens = ( + SubtitleToken(text="Done. ", start_ms=0, end_ms=400), + SubtitleToken(text="Next", start_ms=500, end_ms=800), + ) + assert render_subtitle_tokens_as_srt(tokens) == ( + "1\n00:00:00,000 --> 00:00:00,400\nDone.\n\n2\n00:00:00,500 --> 00:00:00,800\nNext\n" + ) + + def test_subword_tokens_merge_into_words_before_grouping(self): + tokens = ( + SubtitleToken(text=" hel", start_ms=0, end_ms=150), + SubtitleToken(text="lo", start_ms=150, end_ms=300), + SubtitleToken(text=" world.", start_ms=350, end_ms=600), + ) + assert render_subtitle_tokens_as_srt(tokens) == "1\n00:00:00,000 --> 00:00:00,600\nhello world.\n" + + def test_cjk_tokens_merge_and_keep_punctuation_attached(self): + tokens = ( + SubtitleToken(text="編", start_ms=0, end_ms=100), + SubtitleToken(text="集", start_ms=100, end_ms=200), + SubtitleToken(text="、", start_ms=200, end_ms=250), + SubtitleToken(text="保存", start_ms=250, end_ms=400), + ) + assert [word.text for word in _merge_tokens_into_words(tokens)] == ["編", "集、", "保存"] + def test_timestampless_token_joins_the_current_cue(self): tokens = ( SubtitleToken(text="Hello ", start_ms=0, end_ms=500), diff --git a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py index 3c4121977de..0e1c832ebf5 100644 --- a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py +++ b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py @@ -32,6 +32,8 @@ from litellm.litellm_core_utils.llm_cost_calc.utils import ( TokenTypeCostBreakdown, _calculate_input_cost, _get_token_base_cost, + _is_off_peak, + _is_within_off_peak_window, calculate_cache_writing_cost, generic_cost_per_token, get_token_type_cost_breakdown, @@ -409,6 +411,377 @@ def test_get_token_base_cost_picks_highest_crossed_tier(): assert prompt_base_cost == 9e-6 +def test_is_within_off_peak_window_same_day(): + from datetime import datetime, timezone + + window = "09:00-17:00" + assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 12, 0, tzinfo=timezone.utc)) is True + assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 8, 59, tzinfo=timezone.utc)) is False + assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 9, 0, tzinfo=timezone.utc)) is True + assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 17, 0, tzinfo=timezone.utc)) is False + + +def test_is_within_off_peak_window_wraps_midnight(): + from datetime import datetime, timezone + + window = "16:30-00:30" + assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc)) is True + assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 0, 15, tzinfo=timezone.utc)) is True + assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 16, 30, tzinfo=timezone.utc)) is True + assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 0, 30, tzinfo=timezone.utc)) is False + assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 12, 0, tzinfo=timezone.utc)) is False + + +def test_is_within_off_peak_window_equal_start_and_end_covers_whole_day(): + """An equal start and end is the natural way to spell off-peak all day. It used to take the + non-wrap branch, where start <= now < end can never hold, so it matched nothing and billed at + standard rates around the clock without raising or logging anything.""" + from datetime import datetime, timezone + + for window in ("00:00-00:00", "10:00-10:00"): + for hour in range(24): + assert ( + _is_within_off_peak_window(window, datetime(2026, 1, 1, hour, 0, tzinfo=timezone.utc)) is True + ), f"{window} should cover {hour:02d}:00" + + +def test_is_within_off_peak_window_multiple_windows(): + from datetime import datetime, timezone + + # Providers like DeepSeek V4 have more than one daily peak/off-peak window. + windows = ["01:00-05:00", "13:00-16:00"] + assert _is_within_off_peak_window(windows, datetime(2026, 1, 1, 3, 0, tzinfo=timezone.utc)) is True + assert _is_within_off_peak_window(windows, datetime(2026, 1, 1, 14, 30, tzinfo=timezone.utc)) is True + assert _is_within_off_peak_window(windows, datetime(2026, 1, 1, 9, 0, tzinfo=timezone.utc)) is False + # a malformed entry in the list is ignored, valid entries still match + assert _is_within_off_peak_window(["bad", "13:00-16:00"], datetime(2026, 1, 1, 14, 0, tzinfo=timezone.utc)) is True + assert _is_within_off_peak_window([], datetime(2026, 1, 1, 14, 0, tzinfo=timezone.utc)) is False + + +def test_is_within_off_peak_window_normalizes_timezone_aware_input(): + from datetime import datetime, timedelta, timezone + + # A caller may pass a non-UTC aware datetime; the window is UTC and must be + # evaluated in UTC, not against the caller's wall-clock. 09:00 at UTC+8 is + # 01:00 UTC, inside the 01:00-05:00 window. + tz_plus_8 = timezone(timedelta(hours=8)) + assert _is_within_off_peak_window("01:00-05:00", datetime(2026, 1, 1, 9, 0, tzinfo=tz_plus_8)) is True + assert _is_within_off_peak_window("01:00-05:00", datetime(2026, 1, 1, 12, 0, tzinfo=tz_plus_8)) is True + # 06:00 at UTC+8 is 22:00 UTC the previous day, outside the window + assert _is_within_off_peak_window("01:00-05:00", datetime(2026, 1, 1, 6, 0, tzinfo=tz_plus_8)) is False + + +def test_is_within_off_peak_window_malformed_returns_false(): + from datetime import datetime, timezone + + now = datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc) + assert _is_within_off_peak_window("not-a-window", now) is False + assert _is_within_off_peak_window("16:30", now) is False + assert _is_within_off_peak_window("25:00-26:00", now) is False + + +def test_is_off_peak_weekday_qualified_windows_deepseek_schedule(): + """DeepSeek since 2026-08-23: peak is 01:00-04:00 and 06:00-10:00 UTC on weekdays only, with + weekends off-peak around the clock. The weekday axis is not a filter on one window set; on + two days of seven the off-peak window becomes the whole day, so the schedule needs two + day-qualified rules. The weekend instants inside would-be peak hours are the ones a + time-only implementation bills wrong.""" + from datetime import datetime, timezone + + deepseek = { + "windows": [ + {"hours_utc": ["00:00-01:00", "04:00-06:00", "10:00-00:00"], "weekdays": [1, 2, 3, 4, 5]}, + {"hours_utc": "00:00-00:00", "weekdays": [6, 7]}, + ], + } + peak_instants = [ + datetime(2026, 8, 24, 1, 30, tzinfo=timezone.utc), + datetime(2026, 8, 26, 7, 0, tzinfo=timezone.utc), + datetime(2026, 8, 28, 9, 59, tzinfo=timezone.utc), + ] + off_peak_instants = [ + datetime(2026, 8, 23, 1, 30, tzinfo=timezone.utc), + datetime(2026, 8, 29, 2, 0, tzinfo=timezone.utc), + datetime(2026, 8, 30, 8, 0, tzinfo=timezone.utc), + datetime(2026, 8, 26, 5, 0, tzinfo=timezone.utc), + datetime(2026, 8, 28, 16, 30, tzinfo=timezone.utc), + datetime(2026, 8, 24, 0, 30, tzinfo=timezone.utc), + ] + for when in peak_instants: + assert _is_off_peak(deepseek, when) is False, f"{when.isoformat()} should bill peak" + for when in off_peak_instants: + assert _is_off_peak(deepseek, when) is True, f"{when.isoformat()} should bill off-peak" + + +def test_is_off_peak_weekday_timezone_reads_vendor_calendar(): + """The UTC and Asia/Shanghai calendars only disagree about the date over 16:00-24:00 UTC, so + a window in that stretch is the one place a vendor-local weekday differs from a UTC one: + 2026-08-28T16:30Z is Friday in UTC but already Saturday in Beijing.""" + from datetime import datetime, timezone + + shanghai_saturday = { + "weekday_timezone": "Asia/Shanghai", + "windows": [{"hours_utc": "16:00-17:00", "weekdays": [6]}], + } + assert _is_off_peak(shanghai_saturday, datetime(2026, 8, 28, 16, 30, tzinfo=timezone.utc)) is True + assert _is_off_peak(shanghai_saturday, datetime(2026, 8, 29, 16, 30, tzinfo=timezone.utc)) is False + + +def test_is_off_peak_weekdays_default_utc_calendar_and_accept_names(): + from datetime import datetime, timezone + + named_weekend = {"windows": [{"hours_utc": "00:00-00:00", "weekdays": ["Sat", "sunday"]}]} + assert _is_off_peak(named_weekend, datetime(2026, 8, 29, 12, 0, tzinfo=timezone.utc)) is True + assert _is_off_peak(named_weekend, datetime(2026, 8, 28, 12, 0, tzinfo=timezone.utc)) is False + + utc_friday = {"windows": [{"hours_utc": "16:00-17:00", "weekdays": [5]}]} + assert _is_off_peak(utc_friday, datetime(2026, 8, 28, 16, 30, tzinfo=timezone.utc)) is True + assert _is_off_peak(utc_friday, datetime(2026, 8, 29, 16, 30, tzinfo=timezone.utc)) is False + + +def test_is_off_peak_naive_current_time_read_as_utc(): + from datetime import datetime + + block = {"windows": [{"hours_utc": "16:00-17:00", "weekdays": [5]}]} + assert _is_off_peak(block, datetime(2026, 8, 28, 16, 30)) is True + assert _is_off_peak(block, datetime(2026, 8, 29, 16, 30)) is False + + +def test_is_off_peak_invalid_weekday_timezone_falls_back_to_utc(): + from datetime import datetime, timezone + + block = {"weekday_timezone": "Not/AZone", "windows": [{"hours_utc": "16:00-17:00", "weekdays": [5]}]} + assert _is_off_peak(block, datetime(2026, 8, 28, 16, 30, tzinfo=timezone.utc)) is True + assert _is_off_peak(block, datetime(2026, 8, 29, 16, 30, tzinfo=timezone.utc)) is False + + +def test_is_off_peak_ignores_malformed_weekday_rules(): + from datetime import datetime, timezone + + when = datetime(2026, 8, 29, 12, 0, tzinfo=timezone.utc) + assert _is_off_peak({"windows": [{"hours_utc": "00:00-00:00", "weekdays": []}]}, when) is False + assert _is_off_peak({"windows": [{"hours_utc": "00:00-00:00", "weekdays": [0, 8, "noday", True]}]}, when) is False + assert _is_off_peak({"windows": [{"weekdays": [6]}]}, when) is False + assert _is_off_peak({"windows": [{"hours_utc": 1630}]}, when) is False + assert _is_off_peak({"windows": ["00:00-00:00"]}, when) is False + assert _is_off_peak({"windows": "00:00-00:00"}, when) is False + assert _is_off_peak({"hours_utc": 1630}, when) is False + assert _is_off_peak({}, when) is False + + +def test_is_off_peak_flat_hours_and_windows_are_a_union(): + from datetime import datetime, timezone + + block = { + "hours_utc": "04:00-06:00", + "windows": [{"hours_utc": "00:00-00:00", "weekdays": [7]}], + } + assert _is_off_peak(block, datetime(2026, 8, 28, 5, 0, tzinfo=timezone.utc)) is True + assert _is_off_peak(block, datetime(2026, 8, 30, 20, 0, tzinfo=timezone.utc)) is True + assert _is_off_peak(block, datetime(2026, 8, 28, 20, 0, tzinfo=timezone.utc)) is False + + +def test_get_token_base_cost_weekend_only_off_peak_rate(): + from datetime import datetime, timezone + from typing import cast + + from litellm.types.utils import ModelInfo + + model_info = cast( + ModelInfo, + { + "input_cost_per_token": 1e-6, + "output_cost_per_token": 2e-6, + "off_peak_pricing": { + "windows": [ + {"hours_utc": ["00:00-01:00", "04:00-06:00", "10:00-00:00"], "weekdays": [1, 2, 3, 4, 5]}, + {"hours_utc": "00:00-00:00", "weekdays": [6, 7]}, + ], + "input_cost_per_token": 5e-7, + "output_cost_per_token": 1e-6, + }, + }, + ) + usage = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150) + + saturday_peak_hours = _get_token_base_cost( + model_info, usage, current_time=datetime(2026, 8, 29, 2, 0, tzinfo=timezone.utc) + ) + assert saturday_peak_hours[:2] == (5e-7, 1e-6) + + monday_same_hours = _get_token_base_cost( + model_info, usage, current_time=datetime(2026, 8, 24, 2, 0, tzinfo=timezone.utc) + ) + assert monday_same_hours[:2] == (1e-6, 2e-6) + + +def test_get_token_base_cost_applies_off_peak_pricing(): + from datetime import datetime, timezone + from typing import cast + + from litellm.types.utils import ModelInfo + + model_info = cast( + ModelInfo, + { + "input_cost_per_token": 1e-6, + "output_cost_per_token": 2e-6, + "cache_read_input_token_cost": 1e-7, + "off_peak_pricing": { + "hours_utc": "16:30-00:30", + "input_cost_per_token": 5e-7, + "output_cost_per_token": 1e-6, + "cache_read_input_token_cost": 5e-8, + }, + }, + ) + usage = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150) + + off_peak = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc)) + assert off_peak[0] == 5e-7 + assert off_peak[1] == 1e-6 + assert off_peak[4] == 5e-8 + + peak = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 12, 0, tzinfo=timezone.utc)) + assert peak[0] == 1e-6 + assert peak[1] == 2e-6 + assert peak[4] == 1e-7 + + +def test_get_token_base_cost_non_mapping_off_peak_block_bills_standard_rates(): + """A truthy non-mapping off_peak_pricing value (a bare string or a list in + YAML) must bill standard rates rather than raising, matching how every + other malformed piece of the block behaves. + """ + from datetime import datetime, timezone + from typing import cast + + from litellm.types.utils import ModelInfo + + usage = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150) + when = datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc) + + for malformed_block in ("16:00-19:00", ["16:00-19:00"], 5e-7, True): + model_info = cast( + ModelInfo, + { + "input_cost_per_token": 1e-6, + "output_cost_per_token": 2e-6, + "off_peak_pricing": malformed_block, + }, + ) + result = _get_token_base_cost(model_info, usage, current_time=when) + assert result[0] == 1e-6 + assert result[1] == 2e-6 + + +def test_get_token_base_cost_off_peak_falls_back_to_standard_when_unset(): + from datetime import datetime, timezone + from typing import cast + + from litellm.types.utils import ModelInfo + + model_info = cast( + ModelInfo, + { + "input_cost_per_token": 1e-6, + "output_cost_per_token": 2e-6, + "off_peak_pricing": {"hours_utc": "16:30-00:30", "input_cost_per_token": 5e-7}, + }, + ) + usage = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150) + + result = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc)) + assert result[0] == 5e-7 + assert result[1] == 2e-6 + + +def test_get_token_base_cost_off_peak_wins_over_threshold(): + from datetime import datetime, timezone + from typing import cast + + from litellm.types.utils import ModelInfo + + model_info = cast( + ModelInfo, + { + "input_cost_per_token": 1e-6, + "output_cost_per_token": 2e-6, + "input_cost_per_token_above_200k_tokens": 3e-6, + "output_cost_per_token_above_200k_tokens": 4e-6, + "off_peak_pricing": { + "hours_utc": "16:30-00:30", + "input_cost_per_token": 5e-7, + "output_cost_per_token": 1e-6, + }, + }, + ) + usage = Usage(prompt_tokens=250000, completion_tokens=250000, total_tokens=500000) + + off_peak = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc)) + assert off_peak[0] == 5e-7 + assert off_peak[1] == 1e-6 + + peak = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 12, 0, tzinfo=timezone.utc)) + assert peak[0] == 3e-6 + assert peak[1] == 4e-6 + + +def test_get_model_info_propagates_off_peak_fields(): + model_name = "test-off-peak-model" + off_peak_pricing = { + "hours_utc": "16:30-00:30", + "input_cost_per_token": 5e-7, + "output_cost_per_token": 1e-6, + "cache_read_input_token_cost": 5e-8, + } + litellm.register_model( + { + model_name: { + "litellm_provider": "openai", + "mode": "chat", + "input_cost_per_token": 1e-6, + "output_cost_per_token": 2e-6, + "off_peak_pricing": off_peak_pricing, + } + } + ) + info = litellm.get_model_info(model=model_name) + assert info["off_peak_pricing"] == off_peak_pricing + + +def test_get_token_base_cost_off_peak_wins_over_tiered_pricing(): + """Tiered pricing resolves base rates on its own path and returns early, so off-peak has to + be applied there too or a model carrying both would silently bill the tier rate all day.""" + from datetime import datetime, timezone + + model_name = "litellm-test-off-peak-tiered" + litellm.register_model( + { + model_name: { + "litellm_provider": "openai", + "mode": "chat", + "tiered_pricing": [ + {"range": [0, 128000], "input_cost_per_token": 3e-6, "output_cost_per_token": 6e-6}, + ], + "off_peak_pricing": { + "hours_utc": "16:30-00:30", + "input_cost_per_token": 5e-7, + "output_cost_per_token": 1e-6, + }, + } + } + ) + info = litellm.get_model_info(model=model_name) + usage = Usage(prompt_tokens=1_000, completion_tokens=100, total_tokens=1_100) + + inside = _get_token_base_cost(info, usage, current_time=datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc)) + assert inside[:2] == (5e-7, 1e-6) + + outside = _get_token_base_cost(info, usage, current_time=datetime(2026, 1, 1, 12, 0, tzinfo=timezone.utc)) + assert outside[:2] == (3e-6, 6e-6) + + def test_generic_cost_per_token_gpt54_above_272k_tokens(_local_model_cost_map): """GPT-5.4/5.4-pro: prompts >272K input tokens priced at 2x input, 1.5x output.""" model = "gpt-5.4" diff --git a/tests/test_litellm/litellm_core_utils/llm_response_utils/test_response_metadata.py b/tests/test_litellm/litellm_core_utils/llm_response_utils/test_response_metadata.py index 996530daa2e..a06b6bbf3cc 100644 --- a/tests/test_litellm/litellm_core_utils/llm_response_utils/test_response_metadata.py +++ b/tests/test_litellm/litellm_core_utils/llm_response_utils/test_response_metadata.py @@ -5,6 +5,7 @@ Covers the callback_duration_ms timing metric that flows from the Logging object through _hidden_params to the x-litellm-callback-duration-ms response header. """ +import asyncio import datetime from unittest.mock import MagicMock @@ -13,6 +14,7 @@ import litellm.proxy.common_request_processing as common_request_processing_mod from litellm.litellm_core_utils.litellm_logging import Logging from litellm.litellm_core_utils.llm_response_utils.response_metadata import ( ResponseMetadata, + response_timing_metrics, update_response_metadata, ) from litellm.proxy._types import UserAPIKeyAuth @@ -124,6 +126,158 @@ class TestDictResultsSkipMetadataUpdate: logging_obj._response_cost_calculator.assert_not_called() assert "_hidden_params" not in anthropic_response + def test_update_response_metadata_keeps_timing_on_logging_obj_for_dict_results(self): + """LIT-5466: the /v1/messages dict cannot carry _hidden_params, so its timing + (the input to x-litellm-overhead-duration-ms and the SLP litellm_overhead_time_ms) + lands on the logging object instead - still without recomputing cost.""" + anthropic_response = {"id": "msg_123", "type": "message", "role": "assistant", "content": []} + logging_obj = MagicMock() + logging_obj.model_call_details = {"llm_api_duration_ms": 900.0} + logging_obj.caching_details = None + + update_response_metadata( + result=anthropic_response, + logging_obj=logging_obj, + model="openai/gpt-4o-mini", + kwargs={}, + start_time=datetime.datetime(2025, 1, 1, 0, 0, 0), + end_time=datetime.datetime(2025, 1, 1, 0, 0, 1), + ) + + logging_obj.set_response_timing_metrics.assert_called_once_with( + {"_response_ms": 1000.0, "litellm_overhead_time_ms": 100.0} + ) + logging_obj._response_cost_calculator.assert_not_called() + assert "_hidden_params" not in anthropic_response + + def test_update_response_metadata_keeps_timing_for_stream_wrapper_without_hidden_params(self): + """The /v1/messages bridge streams a bare async generator, which cannot hold + _hidden_params either; when the provider duration is unknown only the total is kept.""" + + async def sse_stream(): + yield b"event: message_start\n\n" + + logging_obj = MagicMock() + logging_obj.model_call_details = {} + logging_obj.caching_details = None + + async def drive(): + stream = sse_stream() + try: + update_response_metadata( + result=stream, + logging_obj=logging_obj, + model="openai/gpt-4o-mini", + kwargs={}, + start_time=datetime.datetime(2025, 1, 1, 0, 0, 0), + end_time=datetime.datetime(2025, 1, 1, 0, 0, 0, 250000), + ) + finally: + await stream.aclose() + + asyncio.run(drive()) + + logging_obj.set_response_timing_metrics.assert_called_once_with({"_response_ms": 250.0}) + logging_obj._response_cost_calculator.assert_not_called() + + def test_update_response_metadata_leaves_logging_obj_alone_for_objects_with_hidden_params(self): + """ModelResponse keeps carrying its own timing; the logging-object carrier is not written.""" + result = ModelResponse() + logging_obj = MagicMock() + logging_obj.model_call_details = {"llm_api_duration_ms": 900.0} + logging_obj.caching_details = None + logging_obj._response_cost_calculator = MagicMock(return_value=0.001) + logging_obj.litellm_call_id = "test-call-id" + + update_response_metadata( + result=result, + logging_obj=logging_obj, + model="gpt-4", + kwargs={}, + start_time=datetime.datetime(2025, 1, 1, 0, 0, 0), + end_time=datetime.datetime(2025, 1, 1, 0, 0, 1), + ) + + logging_obj.set_response_timing_metrics.assert_not_called() + assert result._hidden_params["litellm_overhead_time_ms"] == 100.0 + + def test_update_response_metadata_omits_overhead_for_completed_stream(self): + """LIT-5466: the Responses streaming iterator finishes the whole stream before updating + metadata, and the provider call it recorded stopped at the first byte.""" + result = ModelResponse() + logging_obj = MagicMock() + logging_obj.model_call_details = {"llm_api_duration_ms": 200.0} + logging_obj.caching_details = None + logging_obj._response_cost_calculator = MagicMock(return_value=0.001) + logging_obj.litellm_call_id = "test-call-id" + + update_response_metadata( + result=result, + logging_obj=logging_obj, + model="gpt-4", + kwargs={}, + start_time=datetime.datetime(2025, 1, 1, 0, 0, 0), + end_time=datetime.datetime(2025, 1, 1, 0, 0, 1), + include_overhead=False, + ) + + assert result._hidden_params["_response_ms"] == 1000.0 + assert "litellm_overhead_time_ms" not in result._hidden_params + + +class TestResponseTimingMetrics: + """response_timing_metrics() is the single source of _response_ms / litellm_overhead_time_ms.""" + + START = datetime.datetime(2025, 1, 1, 0, 0, 0) + END = datetime.datetime(2025, 1, 1, 0, 0, 1) + + def _make_logging_obj(self, llm_api_duration_ms=None, caching_details=None): + logging_obj = MagicMock() + logging_obj.model_call_details = {} + if llm_api_duration_ms is not None: + logging_obj.model_call_details["llm_api_duration_ms"] = llm_api_duration_ms + logging_obj.caching_details = caching_details + return logging_obj + + def test_overhead_is_total_minus_provider_call(self): + logging_obj = self._make_logging_obj(llm_api_duration_ms=900.0) + assert response_timing_metrics(self.START, self.END, logging_obj) == { + "_response_ms": 1000.0, + "litellm_overhead_time_ms": 100.0, + } + + def test_overhead_omitted_when_no_provider_or_cache_duration_recorded(self): + logging_obj = self._make_logging_obj() + assert response_timing_metrics(self.START, self.END, logging_obj) == {"_response_ms": 1000.0} + + def test_cache_hit_overhead_is_total_minus_cache_read(self): + logging_obj = self._make_logging_obj( + llm_api_duration_ms=900.0, caching_details={"cache_hit": True, "cache_duration_ms": 250.0} + ) + assert response_timing_metrics(self.START, self.END, logging_obj) == { + "_response_ms": 1000.0, + "litellm_overhead_time_ms": 750.0, + } + + def test_cache_miss_ignores_cache_duration(self): + logging_obj = self._make_logging_obj(caching_details={"cache_hit": False, "cache_duration_ms": 250.0}) + assert response_timing_metrics(self.START, self.END, logging_obj) == {"_response_ms": 1000.0} + + def test_cache_hit_without_recorded_cache_duration_falls_back_to_provider_call(self): + logging_obj = self._make_logging_obj(llm_api_duration_ms=900.0, caching_details={"cache_hit": True}) + assert response_timing_metrics(self.START, self.END, logging_obj) == { + "_response_ms": 1000.0, + "litellm_overhead_time_ms": 100.0, + } + + def test_overhead_omitted_when_caller_measured_a_wider_window(self): + """A stream read to completion times the provider call to first byte, so the rest of the + stream is token generation, not LiteLLM overhead.""" + logging_obj = self._make_logging_obj(llm_api_duration_ms=200.0) + assert response_timing_metrics(self.START, self.END, logging_obj, include_overhead=False) == { + "_response_ms": 1000.0 + } + class TestCallbackDurationInCustomHeaders: """Test that callback_duration_ms flows into get_custom_headers.""" diff --git a/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_common_utils.py b/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_common_utils.py index aec6d12069f..c037f928593 100644 --- a/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_common_utils.py +++ b/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_common_utils.py @@ -1,3 +1,4 @@ +import functools import json import os from unittest.mock import MagicMock, patch @@ -1094,3 +1095,462 @@ def test_drop_tool_reference_parts_leaves_non_tool_messages_alone(): assert result[0] == user_message assert result[2]["content"] == "" + + +class TestFlattenTopLevelSchemaCombinators: + def _customer_anyof_schema(self): + return { + "type": "object", + "anyOf": [ + { + "properties": {"id": {"type": "string"}, "enabled": {"type": "boolean"}}, + "required": ["id", "enabled"], + }, + { + "properties": {"id": {"type": "string"}, "schedule": {"type": "string"}}, + "required": ["id", "schedule"], + }, + ], + "properties": {"id": {"type": "string"}}, + "required": ["id"], + } + + def test_merges_anyof_branches_into_object_schema(self): + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + flatten_top_level_schema_combinators, + ) + + result = flatten_top_level_schema_combinators(self._customer_anyof_schema()) + + assert "anyOf" not in result + assert result["type"] == "object" + assert set(result["properties"]) == {"id", "enabled", "schedule"} + assert result["properties"]["enabled"] == {"type": "boolean"} + assert result["required"] == ["id"] + + def test_typeless_anyof_of_object_branches_gets_intersected_required(self): + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + flatten_top_level_schema_combinators, + ) + + schema = { + "anyOf": [ + { + "properties": {"id": {"type": "string"}, "enabled": {"type": "boolean"}}, + "required": ["id", "enabled"], + }, + { + "properties": {"id": {"type": "string"}, "schedule": {"type": "string"}}, + "required": ["id", "schedule"], + }, + ] + } + + result = flatten_top_level_schema_combinators(schema) + + assert result["type"] == "object" + assert "anyOf" not in result + assert result["required"] == ["id"] + + def test_allof_required_is_the_union_of_branches(self): + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + flatten_top_level_schema_combinators, + ) + + schema = { + "type": "object", + "allOf": [ + {"properties": {"id": {"type": "string"}}, "required": ["id"]}, + {"properties": {"enabled": {"type": "boolean"}}, "required": ["enabled"]}, + ], + } + + result = flatten_top_level_schema_combinators(schema) + + assert "allOf" not in result + assert result["required"] == ["enabled", "id"] + assert set(result["properties"]) == {"id", "enabled"} + + def test_top_level_schema_wins_property_collisions(self): + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + flatten_top_level_schema_combinators, + ) + + schema = { + "type": "object", + "anyOf": [ + {"properties": {"id": {"type": "integer"}}}, + {"properties": {"id": {"type": "number"}}}, + ], + "properties": {"id": {"type": "string"}}, + } + + result = flatten_top_level_schema_combinators(schema) + + assert result["properties"]["id"] == {"type": "string"} + + def test_drops_openai_rejected_scalar_keys_on_object_schema(self): + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + flatten_top_level_schema_combinators, + ) + + schema = { + "type": "object", + "properties": {"id": {"type": "string"}}, + "enum": [{"id": "a"}], + "const": {"id": "a"}, + "not": {"required": ["other"]}, + } + + result = flatten_top_level_schema_combinators(schema) + + assert "enum" not in result + assert "const" not in result + assert "not" not in result + assert result["properties"] == {"id": {"type": "string"}} + + def test_resolves_local_ref_branches_from_defs(self): + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + flatten_top_level_schema_combinators, + ) + + schema = { + "anyOf": [{"$ref": "#/$defs/Enable"}, {"$ref": "#/$defs/Schedule"}], + "$defs": { + "Enable": { + "properties": {"id": {"type": "string"}, "enabled": {"type": "boolean"}}, + "required": ["id", "enabled"], + }, + "Schedule": { + "properties": {"id": {"type": "string"}, "schedule": {"type": "string"}}, + "required": ["id", "schedule"], + }, + }, + } + + result = flatten_top_level_schema_combinators(schema) + + assert "anyOf" not in result + assert result["type"] == "object" + assert set(result["properties"]) == {"id", "enabled", "schedule"} + assert result["required"] == ["id"] + assert "$defs" in result + + def test_flattens_nested_combinator_branch_from_definitions(self): + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + flatten_top_level_schema_combinators, + ) + + schema = { + "type": "object", + "oneOf": [ + {"$ref": "#/definitions/Toggle"}, + {"allOf": [{"properties": {"schedule": {"type": "string"}}, "required": ["schedule"]}]}, + ], + "definitions": {"Toggle": {"properties": {"enabled": {"type": "boolean"}}, "required": ["enabled"]}}, + } + + result = flatten_top_level_schema_combinators(schema) + + assert "oneOf" not in result + assert set(result["properties"]) == {"enabled", "schedule"} + assert "required" not in result + + def test_unresolvable_ref_branch_leaves_schema_untouched(self): + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + flatten_top_level_schema_combinators, + ) + + schema = { + "type": "object", + "anyOf": [{"$ref": "https://example.com/schemas/automation.json"}], + "properties": {"id": {"type": "string"}}, + } + + assert flatten_top_level_schema_combinators(schema) is schema + + def test_self_referencing_ref_branch_leaves_schema_untouched(self): + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + flatten_top_level_schema_combinators, + ) + + schema = { + "type": "object", + "anyOf": [{"$ref": "#/$defs/Node"}], + "$defs": {"Node": {"type": "object", "anyOf": [{"$ref": "#/$defs/Node"}]}}, + } + + assert flatten_top_level_schema_combinators(schema) is schema + + @pytest.mark.parametrize("boolean_branch", [True, False]) + def test_boolean_branch_leaves_schema_untouched(self, boolean_branch): + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + flatten_top_level_schema_combinators, + ) + + schema = { + "type": "object", + "anyOf": [boolean_branch, {"properties": {"id": {"type": "string"}}, "required": ["id"]}], + } + + assert flatten_top_level_schema_combinators(schema) is schema + + def test_root_required_is_combined_with_branch_required(self): + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + flatten_top_level_schema_combinators, + ) + + allof_schema = { + "type": "object", + "required": ["id"], + "properties": {"id": {"type": "string"}}, + "allOf": [{"properties": {"enabled": {"type": "boolean"}}, "required": ["enabled"]}], + } + anyof_schema = { + "type": "object", + "required": ["id"], + "properties": {"id": {"type": "string"}}, + "anyOf": [ + {"properties": {"name": {"type": "string"}, "a": {"type": "string"}}, "required": ["name", "a"]}, + {"properties": {"name": {"type": "string"}, "b": {"type": "string"}}, "required": ["name", "b"]}, + ], + } + + assert flatten_top_level_schema_combinators(allof_schema)["required"] == ["enabled", "id"] + assert flatten_top_level_schema_combinators(anyof_schema)["required"] == ["id", "name"] + + def test_repeated_refs_are_expanded_once(self): + import time + + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + flatten_top_level_schema_combinators, + ) + + fan_out, chain_length = 8, 8 + schema = { + "type": "object", + "anyOf": [{"$ref": "#/$defs/Level0"}], + "$defs": { + **{ + f"Level{level}": {"anyOf": [{"$ref": f"#/$defs/Level{level + 1}"}] * fan_out} + for level in range(chain_length) + }, + f"Level{chain_length}": {"type": "object", "properties": {"id": {"type": "string"}}}, + }, + } + + started = time.perf_counter() + result = flatten_top_level_schema_combinators(schema) + + assert time.perf_counter() - started < 5 + assert "anyOf" not in result + assert result["properties"] == {"id": {"type": "string"}} + + def test_nesting_past_the_depth_cap_leaves_schema_untouched(self): + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + flatten_top_level_schema_combinators, + ) + + def nested(levels): + leaf = {"type": "object", "properties": {"id": {"type": "string"}}} + return functools.reduce(lambda inner, _: {"type": "object", "anyOf": [inner]}, range(levels), leaf) + + shallow, deep = nested(20), nested(40) + + assert "anyOf" not in flatten_top_level_schema_combinators(shallow) + assert flatten_top_level_schema_combinators(deep) is deep + + @pytest.mark.parametrize( + "branches", + [ + [{"required": ["enabled"]}, {"required": ["schedule"]}], + [{"type": "object", "required": ["enabled"]}, {"type": "object", "required": ["schedule"]}], + ], + ) + def test_typeless_root_with_properties_flattens_branches_without_properties(self, branches): + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + flatten_top_level_schema_combinators, + ) + + schema = { + "properties": {"id": {"type": "string"}, "enabled": {"type": "boolean"}, "schedule": {"type": "string"}}, + "required": ["id"], + "anyOf": branches, + } + + result = flatten_top_level_schema_combinators(schema) + + assert "anyOf" not in result + assert result["type"] == "object" + assert set(result["properties"]) == {"id", "enabled", "schedule"} + assert result["required"] == ["id"] + + def test_typeless_root_flattens_typed_object_branches_without_properties(self): + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + flatten_top_level_schema_combinators, + ) + + schema = { + "anyOf": [ + {"type": "object", "properties": {"id": {"type": "string"}}}, + {"type": "object", "required": ["id"]}, + ] + } + + result = flatten_top_level_schema_combinators(schema) + + assert "anyOf" not in result + assert result["type"] == "object" + assert result["properties"] == {"id": {"type": "string"}} + assert "required" not in result + + def test_non_object_union_passes_through_unchanged(self): + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + flatten_top_level_schema_combinators, + ) + + schema = {"anyOf": [{"type": "string"}, {"type": "number"}]} + + assert flatten_top_level_schema_combinators(schema) is schema + + def test_schema_without_rejected_keys_is_returned_as_is(self): + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + flatten_top_level_schema_combinators, + ) + + schema = {"type": "object", "properties": {"nested": {"anyOf": [{"type": "string"}, {"type": "null"}]}}} + + assert flatten_top_level_schema_combinators(schema) is schema + + def test_input_schema_is_never_mutated(self): + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + flatten_top_level_schema_combinators, + ) + + schema = self._customer_anyof_schema() + snapshot = json.loads(json.dumps(schema)) + + flatten_top_level_schema_combinators(schema) + + assert schema == snapshot + + +class TestToolWithFlattenedParameters: + def _anyof_tool(self): + return { + "type": "function", + "function": { + "name": "automation_update", + "description": "Update an automation", + "parameters": { + "type": "object", + "anyOf": [ + { + "properties": {"id": {"type": "string"}, "enabled": {"type": "boolean"}}, + "required": ["id", "enabled"], + }, + { + "properties": {"id": {"type": "string"}, "schedule": {"type": "string"}}, + "required": ["id", "schedule"], + }, + ], + "properties": {"id": {"type": "string"}}, + "required": ["id"], + }, + }, + } + + def test_flattens_anyof_parameters_into_new_tool(self): + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + tool_with_flattened_parameters, + ) + + tool = self._anyof_tool() + result = tool_with_flattened_parameters(tool) + + assert result is not tool + parameters = result["function"]["parameters"] + assert "anyOf" not in parameters + assert parameters["type"] == "object" + assert set(parameters["properties"]) == {"id", "enabled", "schedule"} + assert parameters["required"] == ["id"] + assert result["function"]["name"] == "automation_update" + assert tool == self._anyof_tool() + + def test_clean_parameters_return_the_same_tool_object(self): + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + tool_with_flattened_parameters, + ) + + tool = { + "type": "function", + "function": { + "name": "lookup", + "parameters": {"type": "object", "properties": {"id": {"type": "string"}}, "required": ["id"]}, + }, + } + + assert tool_with_flattened_parameters(tool) is tool + + @pytest.mark.parametrize( + "tool", + [ + {"type": "function"}, + {"type": "function", "function": "not-a-dict"}, + {"type": "function", "function": {"name": "no_params"}}, + {"type": "function", "function": {"name": "bad_params", "parameters": "not-a-dict"}}, + ], + ) + def test_non_dict_function_or_parameters_return_the_same_tool_object(self, tool): + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + tool_with_flattened_parameters, + ) + + assert tool_with_flattened_parameters(tool) is tool + + +class TestRequestContainsImageContent: + """One detector for every dialect that reaches pre-routing hooks untranslated.""" + + @pytest.mark.parametrize( + "part", + [ + {"type": "image_url", "image_url": {"url": "data:image/png;base64,aGk="}}, + {"type": "input_image", "image_url": "data:image/png;base64,aGk="}, + {"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": "aGk="}}, + { + "type": "tool_result", + "tool_use_id": "tu_1", + "content": [{"type": "image", "source": {"type": "base64", "data": "aGk="}}], + }, + ], + ) + def test_detects_every_image_dialect_including_tool_results(self, part): + from litellm.litellm_core_utils.prompt_templates.common_utils import request_contains_image_content + + messages = [{"role": "user", "content": [{"type": "text", "text": "hi"}, part]}] + assert request_contains_image_content(messages) is True + + @pytest.mark.parametrize( + "messages", + [ + [{"role": "user", "content": "plain string"}], + [{"role": "user", "content": [{"type": "text", "text": "hi"}]}], + [{"role": "user", "content": [{"type": "input_audio", "input_audio": {"data": "x"}}]}], + [{"role": "user", "content": [{"type": "tool_result", "content": [{"type": "text", "text": "ok"}]}]}], + [{"role": "user", "content": None}], + [], + ], + ) + def test_ignores_text_audio_and_degenerate_shapes(self, messages): + from litellm.litellm_core_utils.prompt_templates.common_utils import request_contains_image_content + + assert request_contains_image_content(messages) is False + + def test_hostile_nesting_is_depth_bounded(self): + from litellm.litellm_core_utils.prompt_templates.common_utils import request_contains_image_content + + nested: dict = {"type": "image", "source": {"type": "base64", "data": "aGk="}} + for _ in range(50): + nested = {"type": "tool_result", "content": [nested]} + assert request_contains_image_content([{"role": "user", "content": [nested]}]) is False diff --git a/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py b/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py index 72d26f31c60..dd2d45f00c6 100644 --- a/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py +++ b/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py @@ -2932,6 +2932,28 @@ def test_add_cache_point_tool_block_passes_ttl_for_claude_4_5(monkeypatch): monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", old_env) +def test_add_cache_point_tool_block_stands_down_for_model_without_prompt_caching(monkeypatch): + """A tool carrying cache_control must not become a cachePoint for a Bedrock model + whose cost-map entry lacks prompt caching support, since Bedrock rejects the whole + request. An unmapped id keeps emitting so ARN deployments do not lose caching.""" + from litellm.litellm_core_utils.prompt_templates.factory import ( + add_cache_point_tool_block, + ) + + monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") + monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url="")) + tool = {"cache_control": {"type": "ephemeral"}} + + assert add_cache_point_tool_block(tool, model="nvidia.nemotron-super-3-120b") is None + assert add_cache_point_tool_block(tool, model="us.nvidia.nemotron-super-3-120b") is None + assert add_cache_point_tool_block( + tool, model="arn:aws:bedrock:us-east-1:123456789012:application-inference-profile/abc123" + ) == {"cachePoint": {"type": "default"}} + assert add_cache_point_tool_block(tool, model="us.anthropic.claude-sonnet-4-5-20250929-v1:0") == { + "cachePoint": {"type": "default"} + } + + def test_bedrock_tools_pt_passes_ttl_for_claude_4_5(monkeypatch): """ End-to-end: _bedrock_tools_pt should produce cachePoint blocks with ttl @@ -3627,3 +3649,67 @@ def test_convert_gemini_tool_call_result_answers_tool_reference_only_result(): ) assert result == {"function_response": {"name": "ToolSearch", "response": {"content": ""}}} + + +def test_convert_to_anthropic_tool_invoke_degrades_unpaired_server_tool_use(): + """A replayed srvtoolu_ call whose server tool result is not available + (e.g. the Responses bridge replays items without provider_specific_fields) + must become a plain client tool_use so the client's tool_result can pair + with it. A dangling server_tool_use makes Anthropic 400 the request with + "unexpected `tool_use_id` found in `tool_result` blocks".""" + from litellm.litellm_core_utils.prompt_templates.factory import convert_to_anthropic_tool_invoke + + result = convert_to_anthropic_tool_invoke( + tool_calls=[ + { + "id": "srvtoolu_01Unpaired", + "type": "function", + "function": {"name": "web_search", "arguments": '{"query": "zig version"}'}, + } + ], + web_search_results=None, + tool_results=None, + ) + + assert result == [ + { + "type": "tool_use", + "id": "srvtoolu_01Unpaired", + "name": "web_search", + "input": {"query": "zig version"}, + } + ] + + +def test_convert_to_anthropic_tool_invoke_keeps_paired_server_tool_use(): + """When the paired server tool result is available, the srvtoolu_ call is + still reconstructed as server_tool_use followed by its result block.""" + from litellm.litellm_core_utils.prompt_templates.factory import convert_to_anthropic_tool_invoke + + server_result = { + "type": "web_search_tool_result", + "tool_use_id": "srvtoolu_01Paired", + "content": [{"type": "web_search_result", "url": "https://ziglang.org", "title": "Zig"}], + } + + result = convert_to_anthropic_tool_invoke( + tool_calls=[ + { + "id": "srvtoolu_01Paired", + "type": "function", + "function": {"name": "web_search", "arguments": '{"query": "zig version"}'}, + } + ], + web_search_results=[server_result], + tool_results=None, + ) + + assert result == [ + { + "type": "server_tool_use", + "id": "srvtoolu_01Paired", + "name": "web_search", + "input": {"query": "zig version"}, + }, + server_result, + ] diff --git a/tests/test_litellm/litellm_core_utils/test_audio_utils.py b/tests/test_litellm/litellm_core_utils/test_audio_utils.py index 0e8176fffce..155f6680416 100644 --- a/tests/test_litellm/litellm_core_utils/test_audio_utils.py +++ b/tests/test_litellm/litellm_core_utils/test_audio_utils.py @@ -347,3 +347,65 @@ class TestNormalizeTranscriptionLanguageToBcp47: ) assert normalize_transcription_language_to_bcp47(language) == expected + + +class TestResolveSpeechMediaType: + @pytest.mark.parametrize( + ("upstream_content_type", "response_format", "expected"), + [ + ("audio/wav", None, "audio/wav"), + ("AUDIO/WAV", None, "audio/wav"), + ("audio/flac; charset=binary", "mp3", "audio/flac"), + ("application/json", "flac", "audio/flac"), + ("application/octet-stream", "pcm", "audio/pcm"), + (None, "wav", "audio/wav"), + (None, "WAV", "audio/wav"), + (None, "opus", "audio/opus"), + (None, "aac", "audio/aac"), + (None, "mp3", "audio/mpeg"), + (None, "mp4", "audio/mpeg"), + (None, "bogus", "audio/mpeg"), + (None, None, "audio/mpeg"), + ("", None, "audio/mpeg"), + ], + ) + def test_resolution(self, upstream_content_type, response_format, expected): + from litellm.litellm_core_utils.audio_utils.utils import resolve_speech_media_type + + resolved = resolve_speech_media_type( + upstream_content_type=upstream_content_type, + response_format=response_format, + ) + assert resolved == expected + + +class TestSpeechMediaTypeFromAudioBytes: + @pytest.mark.parametrize( + ("audio", "expected"), + [ + (b"RIFF\x24\x00\x00\x00WAVEfmt ", "audio/wav"), + (b"fLaC\x00\x00\x00\x22", "audio/flac"), + (b"OggS" + b"\x00" * 24 + b"OpusHead", "audio/opus"), + (b"OggS" + b"\x00" * 24 + b"\x01vorbis", "audio/ogg"), + (b"ID3\x04\x00\x00\x00\x00\x00\x00", "audio/mpeg"), + (b"\xff\xfb\x90\x64", "audio/mpeg"), + (b"\xff\xf3\x80\x00", "audio/mpeg"), + (b"\xff\xf1\x50\x80", "audio/aac"), + (b"\xff\xf9\x50\x80", "audio/aac"), + (b"RIFF\x24\x00\x00\x00AVI LIST", None), + (b"\xff\xff\xff\xff\xff\xff", None), + (b"\xff\xfb\xf0\x00", None), + (b"\xff\xfb\x9c\x00", None), + (b"\xff\xeb\x90\x00", None), + (b"\xff\xf1\xf4\x80", None), + (b"\xff\x00\x00\x00", None), + (b"\x00\x01\x02\x03\x04\x05", None), + (b"\xff\xfb", None), + (b"\xff", None), + (b"", None), + ], + ) + def test_sniffing(self, audio, expected): + from litellm.litellm_core_utils.audio_utils.utils import speech_media_type_from_audio_bytes + + assert speech_media_type_from_audio_bytes(audio) == expected diff --git a/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py b/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py index 0587628e2fe..b1e8163b91d 100644 --- a/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py +++ b/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py @@ -366,7 +366,7 @@ def test_shipped_rules_stack_adaptive_and_mid_conversation_flags(shipped_cost_ma def test_shipped_rules_flag_unmapped_fable_as_always_on_thinking(shipped_cost_map): """An unmapped Fable/Mythos id picks up ``thinking_always_on`` from the claude-always-on-thinking rule, while other unmapped Claudes stay unflagged.""" - model = "claude-fable-5-1" + model = "claude-fable-6-1" assert model not in litellm.model_cost info = litellm.get_model_info(model, custom_llm_provider="anthropic") assert info["thinking_always_on"] is True @@ -419,7 +419,7 @@ def test_shipped_rules_cover_new_families_like_fable_at_5_plus(shipped_cost_map) """Both version gates accept any claude-- id at major 5 or higher, bare major or major-minor, so a new family shaped like claude-fable-5 gets adaptive thinking and mid-conversation system support without a cost-map entry.""" - model = "claude-fable-5-1" + model = "claude-fable-6-1" assert model not in litellm.model_cost info = litellm.get_model_info(model, custom_llm_provider="anthropic") assert info["supports_mid_conversation_system"] is True diff --git a/tests/test_litellm/litellm_core_utils/test_get_llm_provider_endpoint_match.py b/tests/test_litellm/litellm_core_utils/test_get_llm_provider_endpoint_match.py index 6cacd119030..419ca104bb1 100644 --- a/tests/test_litellm/litellm_core_utils/test_get_llm_provider_endpoint_match.py +++ b/tests/test_litellm/litellm_core_utils/test_get_llm_provider_endpoint_match.py @@ -184,3 +184,26 @@ class TestTogetherApiBaseResolvesProvider: assert provider == "together_ai" assert api_base == "https://api.together.ai/v1" + + +class TestGigachatApiBaseResolvesProvider: + """ + Regression for the GigaChat api_base branch: the provider-mapping chain + carried an ``endpoint == "https://gigachat.devices.sberbank.ru/api/v1"`` + elif, but the URL was never added to ``openai_compatible_endpoints``, so + the endpoint loop never fired the branch and a caller-supplied GigaChat + api_base raised BadRequestError instead of resolving to ``gigachat``. + """ + + def test_gigachat_api_base_resolves_to_gigachat(self, monkeypatch): + monkeypatch.setenv("GIGACHAT_API_KEY", "gigachat-key-from-env") + + model, provider, dynamic_api_key, returned_api_base = get_llm_provider( + model="GigaChat-2", + api_base="https://gigachat.devices.sberbank.ru/api/v1", + ) + + assert provider == "gigachat" + assert dynamic_api_key == "gigachat-key-from-env" + assert returned_api_base == "https://gigachat.devices.sberbank.ru/api/v1" + assert model == "GigaChat-2" diff --git a/tests/test_litellm/litellm_core_utils/test_get_model_cost_map.py b/tests/test_litellm/litellm_core_utils/test_get_model_cost_map.py index 8c0e8ee5d02..a374e03d1c7 100644 --- a/tests/test_litellm/litellm_core_utils/test_get_model_cost_map.py +++ b/tests/test_litellm/litellm_core_utils/test_get_model_cost_map.py @@ -256,14 +256,12 @@ def test_get_model_cost_map_stamps_loaded_at(monkeypatch): from litellm.litellm_core_utils import get_model_cost_map as module monkeypatch.setattr(module._cost_map_source_info, "loaded_at", None) - monkeypatch.setattr( - module.GetModelCostMap, - "fetch_remote_model_cost_map", - staticmethod(lambda url, timeout=5: _load_root_cost_map()), + client, _calls = _mock_client( + [httpx.Response(200, content=_real_map_bytes())], client_cls=httpx.Client ) before = datetime.now(timezone.utc) - module.get_model_cost_map(url="https://example.invalid/cost_map.json") + module.get_model_cost_map(url="https://example.invalid/cost_map.json", client=client) loaded_at = module.get_model_cost_map_loaded_at() assert loaded_at is not None @@ -308,7 +306,7 @@ def _unset_local_cost_map_env(monkeypatch): monkeypatch.delenv("LITELLM_LOCAL_MODEL_COST_MAP", raising=False) -def _mock_client(outcomes): +def _mock_client(outcomes, client_cls=httpx.AsyncClient): """httpx client over a MockTransport serving one outcome per request; an exception instance is raised.""" calls = {"count": 0} @@ -320,7 +318,7 @@ def _mock_client(outcomes): raise outcome return outcome - return httpx.AsyncClient(transport=httpx.MockTransport(handler)), calls + return client_cls(transport=httpx.MockTransport(handler)), calls @pytest.mark.asyncio @@ -450,3 +448,97 @@ async def test_refetch_respects_local_env_override(monkeypatch): ) assert isinstance(result, ModelCostMapReloaded) assert len(result.model_cost_map) > 100 + + +# --------------------------------------------------------------------------- +# get_model_cost_map: the boot-time load retries transient failures like a reload does +# --------------------------------------------------------------------------- + +from litellm.litellm_core_utils.get_model_cost_map import ( + get_model_cost_map, + get_model_cost_map_source_info, +) + + +class _SyncSleepRecorder: + """Injected in place of time.sleep so the boot path's waits are asserted without delay.""" + + def __init__(self): + self.waits = [] + + def __call__(self, seconds: float) -> None: + self.waits.append(seconds) + + +def test_boot_load_retries_transient_failures_instead_of_falling_back(): + """A refused connection then a 503 at pod boot used to pin the process to the bundled + backup for its lifetime; both are transient and must be retried before giving up.""" + client, calls = _mock_client( + [ + httpx.ConnectError("connection refused"), + httpx.Response(503), + httpx.Response(200, content=_real_map_bytes()), + ], + client_cls=httpx.Client, + ) + sleeper = _SyncSleepRecorder() + + cost_map = get_model_cost_map(url=_URL, sleep=sleeper, rng=random.Random(0), client=client) + + assert calls["count"] == 3 + assert len(sleeper.waits) == 2 + assert 2.0 <= sleeper.waits[0] < 3.0 + assert 4.0 <= sleeper.waits[1] < 5.0 + source = get_model_cost_map_source_info() + assert source["source"] == "remote" + assert source["fallback_reason"] is None + assert cost_map.keys() >= _load_root_cost_map().keys() - {"sample_spec", FALLBACK_GENERALIZATIONS_KEY} + + +def test_boot_load_honors_retry_after_then_falls_back_after_max_attempts(): + """An outage longer than the retry budget still ends on the bundled backup, and the + recorded fallback reason says how many attempts were spent so operators can tell.""" + client, calls = _mock_client( + [httpx.Response(429, headers={"Retry-After": "7"})], client_cls=httpx.Client + ) + sleeper = _SyncSleepRecorder() + + cost_map = get_model_cost_map(url=_URL, sleep=sleeper, rng=random.Random(0), client=client) + + assert calls["count"] == 3 + assert sleeper.waits == [7.0, 7.0] + source = get_model_cost_map_source_info() + assert source["source"] == "local" + assert "after 3 attempts" in source["fallback_reason"] + assert len(cost_map) > 100 + + +def test_boot_load_does_not_retry_permanent_failures(): + """A 404 or a malformed URL cannot heal by waiting: one attempt, no sleeps, backup.""" + client, calls = _mock_client([httpx.Response(404)], client_cls=httpx.Client) + sleeper = _SyncSleepRecorder() + + get_model_cost_map(url=_URL, sleep=sleeper, rng=random.Random(0), client=client) + assert calls["count"] == 1 + assert sleeper.waits == [] + assert get_model_cost_map_source_info()["source"] == "local" + + get_model_cost_map(url="not a url", sleep=sleeper, rng=random.Random(0)) + assert sleeper.waits == [] + assert get_model_cost_map_source_info()["source"] == "local" + + +def test_boot_load_respects_local_env_override(monkeypatch): + """LITELLM_LOCAL_MODEL_COST_MAP=True still short-circuits to the backup with zero HTTP.""" + monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") + + def _fail(request): + raise AssertionError("no HTTP request should be made when local map is forced") + + cost_map = get_model_cost_map( + url=_URL, + sleep=_SyncSleepRecorder(), + client=httpx.Client(transport=httpx.MockTransport(_fail)), + ) + assert len(cost_map) > 100 + assert get_model_cost_map_source_info()["is_env_forced"] is True diff --git a/tests/test_litellm/litellm_core_utils/test_get_supported_openai_params.py b/tests/test_litellm/litellm_core_utils/test_get_supported_openai_params.py index cb4e72ab3ad..722818598af 100644 --- a/tests/test_litellm/litellm_core_utils/test_get_supported_openai_params.py +++ b/tests/test_litellm/litellm_core_utils/test_get_supported_openai_params.py @@ -188,6 +188,8 @@ class TestDeclaredAuthenticatingProvider: ("gpt-4o", "github_copilot", "github_copilot"), ("openai/gpt-4o", None, None), ("gpt-4o", "openai", None), + ("github_copilot", None, None), + ("chatgpt", None, None), ], ) def test_names_only_the_providers_whose_resolution_authenticates(self, model, provider, expected): diff --git a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py index 1193160c831..366f61ded49 100644 --- a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py +++ b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py @@ -580,9 +580,17 @@ class TestRetrieveBatchCostPassesModelIdentity: captured: dict[str, object] = {} - async def fake_handle_completed_batch(**kwargs: object) -> tuple[float, Usage, list[str]]: + from litellm.batches.batch_utils import BatchCostUsageResult + + async def fake_handle_completed_batch(**kwargs: object) -> BatchCostUsageResult: captured.update(kwargs) - return 1.25, Usage(prompt_tokens=1800, completion_tokens=1000, total_tokens=2800), ["m"] + return BatchCostUsageResult( + cost=1.25, + usage=Usage(prompt_tokens=1800, completion_tokens=1000, total_tokens=2800), + models=["m"], + successful_requests=1, + failed_requests=0, + ) monkeypatch.setattr(logging_module, "_handle_completed_batch", fake_handle_completed_batch) @@ -3727,6 +3735,60 @@ def test_get_standard_logging_object_payload_includes_litellm_call_id(logging_ob assert payload["litellm_call_id"] == call_id +def test_get_standard_logging_object_payload_carries_matched_access_groups(logging_obj): + """Access groups stamped at auth time reach the logging payload, so integrations see what a request billed.""" + from datetime import datetime + + from litellm.litellm_core_utils.litellm_logging import ( + get_standard_logging_object_payload, + ) + + now = datetime.now() + payload = get_standard_logging_object_payload( + kwargs={ + "model": "gpt-4o", + "messages": [], + "litellm_params": { + "metadata": { + "user_api_key_matched_model_access_groups": ["premium-pool", "shared-pool"] + }, + "proxy_server_request": {"body": {}}, + }, + }, + init_response_obj={}, + start_time=now, + end_time=now, + logging_obj=logging_obj, + status="success", + ) + + assert payload is not None + assert payload["request_model_access_groups"] == ("premium-pool", "shared-pool") + + +def test_get_standard_logging_object_payload_has_no_access_groups_when_unstamped( + logging_obj, +): + from datetime import datetime + + from litellm.litellm_core_utils.litellm_logging import ( + get_standard_logging_object_payload, + ) + + now = datetime.now() + payload = get_standard_logging_object_payload( + kwargs={"model": "gpt-4o", "messages": []}, + init_response_obj={}, + start_time=now, + end_time=now, + logging_obj=logging_obj, + status="success", + ) + + assert payload is not None + assert payload["request_model_access_groups"] == () + + def test_get_standard_logging_object_payload_preserves_absent_end_user_as_none(logging_obj): from datetime import datetime from typing import Final @@ -5417,6 +5479,97 @@ def test_pre_call_redacts_and_masks_raw_request(logging_obj): assert "key=*****" in raw_api_base +def _streaming_logging_obj_with_callbacks(callbacks: list[CustomLogger]): + import datetime + + obj = LitellmLogging( + model="anthropic/claude-opus-5", + messages=[{"role": "user", "content": "hi"}], + stream=True, + call_type="completion", + start_time=datetime.datetime.now(), + litellm_call_id="slot-leak-test", + function_id="slot-leak-test", + ) + obj.model_call_details["litellm_params"] = {"metadata": {}} + return patch.object(obj, "get_combined_callback_list", return_value=callbacks), obj + + +def _assembled_stream_result(): + response = ModelResponse() + response.choices[0].message.content = "hello" + return response + + +@pytest.mark.asyncio +async def test_streaming_success_callbacks_survive_logging_hook_failure(): + """Regression for leaked max_parallel_requests slots: a raising + async_logging_hook must not abort the success-callback loop that + releases the rate-limiter slot.""" + broken = CustomLogger() + broken.async_logging_hook = AsyncMock(side_effect=RuntimeError("broken stream payload")) + releasing = CustomLogger() + releasing.async_log_success_event = AsyncMock() + + patcher, logging_obj = _streaming_logging_obj_with_callbacks([broken, releasing]) + with patcher: + await logging_obj.async_success_handler(result=_assembled_stream_result()) + + releasing.async_log_success_event.assert_awaited_once() + + +@pytest.mark.asyncio +async def test_streaming_success_callbacks_survive_cost_calculation_failure(): + releasing = CustomLogger() + releasing.async_log_success_event = AsyncMock() + + patcher, logging_obj = _streaming_logging_obj_with_callbacks([releasing]) + with patcher, patch.object( + logging_obj, "_response_cost_calculator", side_effect=ValueError("bad usage block") + ): + await logging_obj.async_success_handler(result=_assembled_stream_result()) + + assert logging_obj.model_call_details["response_cost"] is None + releasing.async_log_success_event.assert_awaited_once() + + +@pytest.mark.asyncio +async def test_streaming_success_callbacks_survive_standard_logging_payload_failure(): + releasing = CustomLogger() + releasing.async_log_success_event = AsyncMock() + + patcher, logging_obj = _streaming_logging_obj_with_callbacks([releasing]) + with patcher, patch.object( + logging_obj, "_build_standard_logging_payload", side_effect=ValueError("incomplete stream") + ): + await logging_obj.async_success_handler(result=_assembled_stream_result()) + + assert logging_obj.model_call_details.get("standard_logging_object") is None + releasing.async_log_success_event.assert_awaited_once() + + +@pytest.mark.asyncio +async def test_streaming_success_callbacks_survive_guardrail_logging_hook_failure(): + from litellm.integrations.custom_guardrail import CustomGuardrail + + skipping = CustomGuardrail(guardrail_name="skipping-guardrail") + skipping.should_run_guardrail = MagicMock(return_value=False) + skipping.async_logging_hook = AsyncMock() + raising = CustomGuardrail(guardrail_name="raising-guardrail") + raising.should_run_guardrail = MagicMock(return_value=True) + raising.async_logging_hook = AsyncMock(side_effect=RuntimeError("guardrail hook failed")) + releasing = CustomLogger() + releasing.async_log_success_event = AsyncMock() + + patcher, logging_obj = _streaming_logging_obj_with_callbacks([skipping, raising, releasing]) + with patcher: + await logging_obj.async_success_handler(result=_assembled_stream_result()) + + skipping.async_logging_hook.assert_not_awaited() + raising.async_logging_hook.assert_awaited_once() + releasing.async_log_success_event.assert_awaited_once() + + def _resolve(custom_llm_provider, litellm_params, optional_params, model): from litellm.litellm_core_utils.litellm_logging import ( _resolve_vertex_location_for_cost, @@ -5932,3 +6085,171 @@ async def test_prompt_hook_injection_marker_recorded_for_every_surface(logging_o request_kwargs=untouched, ) assert "litellm_gateway_injected_cache" not in untouched["metadata"] + + +def test_get_standard_logging_object_payload_reads_overhead_from_logging_obj_for_dict_results(logging_obj): + """LIT-5466: /v1/messages returns a plain dict with no _hidden_params, so the overhead + recorded on the logging object must reach hidden_params.litellm_overhead_time_ms (SpendLogs).""" + import datetime + + from litellm.litellm_core_utils.litellm_logging import ( + get_standard_logging_object_payload, + ) + + logging_obj.set_response_timing_metrics({"_response_ms": 1000.0, "litellm_overhead_time_ms": 100.0}) + now = datetime.datetime.now() + payload = get_standard_logging_object_payload( + kwargs={"litellm_call_id": "call-1", "model": "gpt-4o", "messages": []}, + init_response_obj={"id": "msg_1", "type": "message", "role": "assistant", "content": []}, + start_time=now, + end_time=now, + logging_obj=logging_obj, + status="success", + ) + + assert payload is not None + assert payload["hidden_params"]["litellm_overhead_time_ms"] == 100.0 + + +def test_get_standard_logging_object_payload_survives_logging_obj_without_timing_metrics(logging_obj): + """The payload is built inside a blanket except that returns None, so a logging object without + the timing carrier (custom subclasses, older pickles) must not silently drop every spend log.""" + import datetime + + from litellm.litellm_core_utils.litellm_logging import ( + get_standard_logging_object_payload, + ) + + del logging_obj.response_timing_metrics + now = datetime.datetime.now() + payload = get_standard_logging_object_payload( + kwargs={"litellm_call_id": "call-1", "model": "gpt-4o", "messages": []}, + init_response_obj={"id": "msg_1", "type": "message", "role": "assistant", "content": []}, + start_time=now, + end_time=now, + logging_obj=logging_obj, + status="success", + ) + + assert payload is not None + assert payload["hidden_params"]["litellm_overhead_time_ms"] is None + + +def test_get_standard_logging_object_payload_failure_status_keeps_overhead_none(logging_obj): + """A post_call guardrail can fail the request after the upstream call succeeded; the failure + payload keeps litellm_overhead_time_ms None, matching responses that carry their own _hidden_params.""" + import datetime + + from litellm.litellm_core_utils.litellm_logging import ( + get_standard_logging_object_payload, + ) + + logging_obj.set_response_timing_metrics({"_response_ms": 1000.0, "litellm_overhead_time_ms": 100.0}) + now = datetime.datetime.now() + payload = get_standard_logging_object_payload( + kwargs={"litellm_call_id": "call-1", "model": "gpt-4o", "messages": []}, + init_response_obj={}, + start_time=now, + end_time=now, + logging_obj=logging_obj, + status="failure", + ) + + assert payload is not None + assert payload["hidden_params"]["litellm_overhead_time_ms"] is None + + +def test_get_standard_logging_object_payload_prefers_response_hidden_params_overhead(logging_obj): + """A response that carries its own litellm_overhead_time_ms (chat completions) wins over the logging object.""" + import datetime + + from litellm.litellm_core_utils.litellm_logging import ( + get_standard_logging_object_payload, + ) + + logging_obj.set_response_timing_metrics({"_response_ms": 1000.0, "litellm_overhead_time_ms": 100.0}) + response = ModelResponse() + response._hidden_params = {"litellm_overhead_time_ms": 5.0} + now = datetime.datetime.now() + payload = get_standard_logging_object_payload( + kwargs={"litellm_call_id": "call-1", "model": "gpt-4o", "messages": []}, + init_response_obj=response, + start_time=now, + end_time=now, + logging_obj=logging_obj, + status="success", + ) + + assert payload is not None + assert payload["hidden_params"]["litellm_overhead_time_ms"] == 5.0 + + +def test_response_timing_metrics_survive_deepcopy(logging_obj): + """Proxy pre-call hooks deep-copy the logging object; the timing carrier must stay copyable.""" + import copy + + assert logging_obj.response_timing_metrics == {} + logging_obj.set_response_timing_metrics({"_response_ms": 12.5}) + + assert copy.deepcopy(logging_obj).response_timing_metrics == {"_response_ms": 12.5} + + +def test_passthrough_embeddings_result_swapped_for_callbacks(): + """ + Regression: for gigachat passthrough /embeddings, normalize_logging_result + produces an EmbeddingResponse, but the result swap only accepted + ModelResponse, so callbacks kept receiving the raw httpx.Response (which + crashes attribute readers like OTEL). The swap must cover + EmbeddingResponse too. + """ + import datetime as dt + + from litellm.types.utils import EmbeddingResponse + + logging_obj = LitellmLogging( + model="EmbeddingsGigaR", + messages=[], + stream=False, + call_type="allm_passthrough_route", + start_time=time.time(), + litellm_call_id="passthrough-embed-call-id", + function_id="passthrough-embed-fn-id", + ) + logging_obj.update_environment_variables( + litellm_params={}, + optional_params={}, + model="EmbeddingsGigaR", + custom_llm_provider="gigachat", + endpoint="/embeddings", + request_data={"model": "EmbeddingsGigaR", "input": ["hello"]}, + input=["hello"], + ) + + httpx_response = httpx.Response( + 200, + json={ + "object": "list", + "data": [ + { + "object": "embedding", + "embedding": [0.1, 0.2, 0.3], + "index": 0, + "usage": {"prompt_tokens": 5}, + } + ], + "model": "EmbeddingsGigaR", + }, + request=httpx.Request( + "POST", "https://gigachat.devices.sberbank.ru/api/v1/embeddings" + ), + ) + + _, _, swapped_result = logging_obj._success_handler_helper_fn( + result=httpx_response, + start_time=dt.datetime.now(), + end_time=dt.datetime.now(), + cache_hit=False, + ) + + assert isinstance(swapped_result, EmbeddingResponse) + assert swapped_result.data[0]["embedding"] == [0.1, 0.2, 0.3] diff --git a/tests/test_litellm/litellm_core_utils/test_streaming_handler.py b/tests/test_litellm/litellm_core_utils/test_streaming_handler.py index 7f54fbfb4c2..4c99bce2b0f 100644 --- a/tests/test_litellm/litellm_core_utils/test_streaming_handler.py +++ b/tests/test_litellm/litellm_core_utils/test_streaming_handler.py @@ -4692,3 +4692,82 @@ async def test_async_stream_assembled_response_keeps_vertex_traffic_type(logging assembled = litellm.stream_chunk_builder(chunks=received, messages=[{"role": "user", "content": "hi"}]) assert assembled is not None assert assembled._hidden_params["provider_specific_fields"]["traffic_type"] == "ON_DEMAND_FLEX" + + +class TestStableStreamingResponseId: + """ + All chunks of one streamed response must share the same top-level id + (OpenAI streaming contract). Providers streaming via GenericStreamingChunk + (e.g. GigaChat) do not propagate an upstream response id, so + CustomStreamWrapper must pin the id from the first chunk it creates, + mirroring the existing `created` pinning (issue #11437). + + Clients such as goose merge streamed deltas into one assistant message by + chunk id; per-chunk ids split a single reply into many messages. + """ + + def test_generic_chunks_share_one_id(self): + def _generic_chunks(): + return iter( + [ + { + "text": "Hello", + "tool_use": None, + "is_finished": False, + "finish_reason": "", + "usage": None, + "index": 0, + }, + { + "text": " world", + "tool_use": None, + "is_finished": False, + "finish_reason": "", + "usage": None, + "index": 0, + }, + { + "text": "", + "tool_use": None, + "is_finished": True, + "finish_reason": "stop", + "usage": { + "prompt_tokens": 1, + "completion_tokens": 2, + "total_tokens": 3, + }, + "index": 0, + }, + ] + ) + + wrapper = CustomStreamWrapper( + completion_stream=_generic_chunks(), + model="gigachat/GigaChat-2-Max", + logging_obj=MagicMock(), + custom_llm_provider="gigachat", + ) + ids = [chunk.id for chunk in wrapper if chunk.id] + assert ids, "no chunks emitted" + assert len(set(ids)) == 1, f"chunk ids differ across one stream: {ids}" + + def test_creator_pins_id_from_first_chunk(self): + wrapper = CustomStreamWrapper( + completion_stream=iter([]), + model="gigachat/GigaChat-2-Max", + logging_obj=MagicMock(), + custom_llm_provider="gigachat", + ) + first = wrapper.model_response_creator() + assert wrapper.response_id == first.id + assert wrapper.model_response_creator().id == first.id + + def test_provider_supplied_id_still_wins(self): + wrapper = CustomStreamWrapper( + completion_stream=iter([]), + model="gigachat/GigaChat-2-Max", + logging_obj=MagicMock(), + custom_llm_provider="gigachat", + ) + wrapper.response_id = "chatcmpl-from-provider" + assert wrapper.model_response_creator().id == "chatcmpl-from-provider" diff --git a/tests/test_litellm/litellm_core_utils/test_token_counter.py b/tests/test_litellm/litellm_core_utils/test_token_counter.py index 572b505e94c..4694fa8fbed 100644 --- a/tests/test_litellm/litellm_core_utils/test_token_counter.py +++ b/tests/test_litellm/litellm_core_utils/test_token_counter.py @@ -1377,3 +1377,38 @@ def test_anthropic_document_title_and_context_add_their_tokens(): {"type": "document", "source": source}, ] ) + + +def test_openai_file_block_prices_like_the_equivalent_anthropic_document(): + """An inline `file` is a `document` in the chat-completions dialect, so it must price identically, not raise. + + Before the fix `file` was missing from the content-block match even though `ChatCompletionFileObject` + is in the union this counter accepts, so every local count of a Responses `input_file` raised + `Invalid content item type: file` and surfaced as a 500 on /v1/responses/input_tokens. + """ + prompt = {"type": "text", "text": "Summarize this file."} + inline_file = { + "type": "file", + "file": {"filename": "report.pdf", "file_data": "data:application/pdf;base64,JVBERi0xLjQK"}, + } + document = { + "type": "document", + "title": "report.pdf", + "source": {"type": "base64", "media_type": "application/pdf", "data": "JVBERi0xLjQK"}, + } + + assert _count_user_content([prompt, inline_file]) == _count_user_content([prompt, document]) + assert _count_user_content([prompt, inline_file]) > _count_user_content([prompt]) + + +def test_openai_file_block_without_inline_bytes_counts_what_it_carries(): + """A `file` block naming an uploaded file has no bytes to price, so it adds only the filename's tokens.""" + prompt = {"type": "text", "text": "Summarize this file."} + + by_id = {"type": "file", "file": {"file_id": "file-abc123"}} + assert _count_user_content([prompt, by_id]) == _count_user_content([prompt]) + + named = {"type": "file", "file": {"file_id": "file-abc123", "filename": "report.pdf"}} + assert _count_user_content([prompt, named]) == _count_user_content( + [prompt, {"type": "text", "text": "report.pdf"}] + ) diff --git a/tests/test_litellm/llms/anthropic/chat/guardrail_translation/test_anthropic_guardrail_handler.py b/tests/test_litellm/llms/anthropic/chat/guardrail_translation/test_anthropic_guardrail_handler.py index af3ccd65b11..0fe7730e91e 100644 --- a/tests/test_litellm/llms/anthropic/chat/guardrail_translation/test_anthropic_guardrail_handler.py +++ b/tests/test_litellm/llms/anthropic/chat/guardrail_translation/test_anthropic_guardrail_handler.py @@ -1490,6 +1490,92 @@ class MockCanaryMaskingGuardrail(CustomGuardrail): return inputs +class TestAnthropicMessagesImageSources: + """An Anthropic image block has three source shapes (`AnthropicMessagesImageParam.source`). + + Only the base64 one carries "data", so reading that key alone drops url images + entirely -- for every guardrail consuming GenericGuardrailAPIInputs["images"], + not just Bedrock. + """ + + def _data(self, messages): + return {"model": "claude-sonnet-4-5", "messages": messages} + + async def _images_seen(self, content) -> list[str]: + handler = AnthropicMessagesHandler() + + class ImageRecordingGuardrail(MockCanaryMaskingGuardrail): + def __init__(self): + super().__init__() + self.seen_images: list[str] = [] # mutable-ok: accumulator for the assertion + + async def apply_guardrail(self, inputs, request_data, input_type, logging_obj=None): + self.seen_images.extend(inputs.get("images") or []) + return await super().apply_guardrail(inputs, request_data, input_type, logging_obj) + + guardrail = ImageRecordingGuardrail() + # The text block is what gets the guardrail invoked at all: a message with + # no text gives the handler nothing to scan, so it never reaches the + # guardrail and every source shape would look equally "dropped". + await handler.process_input_messages( + data=self._data([{"role": "user", "content": [{"type": "text", "text": "describe it"}, *content]}]), + guardrail_to_apply=guardrail, + ) + return guardrail.seen_images + + @pytest.mark.asyncio + async def test_url_source_reaches_the_guardrail(self): + """A url source has no "data" key, so it used to yield nothing at all.""" + seen = await self._images_seen( + [{"type": "image", "source": {"type": "url", "url": "https://example.com/a.png"}}] + ) + + assert seen == ["https://example.com/a.png"] + + @pytest.mark.asyncio + async def test_base64_source_carries_its_media_type(self): + """Bare base64 leaves the consumer no way to recover the format. + + An API like Bedrock's ApplyGuardrail needs it to build the request, so the + media_type travels with the payload as a data URI. + """ + seen = await self._images_seen( + [{"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": "AAAA"}}] + ) + + assert seen == ["data:image/png;base64,AAAA"] + + @pytest.mark.asyncio + async def test_base64_source_without_a_media_type_is_passed_through(self): + """There is no format to attach, so the payload goes through unchanged.""" + seen = await self._images_seen([{"type": "image", "source": {"type": "base64", "data": "AAAA"}}]) + + assert seen == ["AAAA"] + + @pytest.mark.asyncio + async def test_file_source_yields_nothing(self): + """The bytes live behind the Files API and this extractor has no client. + + Documented as a known gap rather than silently handed on as a file_id string, + which a consumer would try to decode as an image. + """ + seen = await self._images_seen([{"type": "image", "source": {"type": "file", "file_id": "file_abc"}}]) + + assert seen == [] + + @pytest.mark.asyncio + async def test_a_malformed_source_is_dropped_rather_than_passed_on(self): + seen = await self._images_seen( + [ + {"type": "image", "source": {"type": "base64"}}, + {"type": "image", "source": {"type": "url"}}, + {"type": "image", "source": {"type": "base64", "data": ""}}, + ] + ) + + assert seen == [] + + class TestAnthropicMessagesToolResultScanning: """LIT-5251: tool_result blocks carry whatever a client's local tool fetched, so they are the request-path payload an indirect prompt injection actually arrives in. diff --git a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py index bd750a47f63..043537f8c1f 100644 --- a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py +++ b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py @@ -3,11 +3,13 @@ import threading from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer from unittest.mock import AsyncMock, MagicMock, patch +import httpx import pytest import litellm from litellm.constants import RESPONSE_FORMAT_TOOL_NAME from litellm.llms.anthropic.chat.handler import ModelResponseIterator, make_call +from litellm.llms.custom_httpx.http_handler import HTTPHandler from litellm.types.llms.openai import ( ChatCompletionToolCallChunk, ChatCompletionToolCallFunctionChunk, @@ -46,6 +48,43 @@ async def test_make_call_passes_logging_obj_to_client_post(): assert call_kwargs.get("logging_obj") is logging_obj +def test_anthropic_completion_does_not_send_deployment_default_limits(): + captured_requests: list[httpx.Request] = [] + + def respond(request: httpx.Request) -> httpx.Response: + captured_requests.append(request) + return httpx.Response( + 200, + json={ + "id": "msg_default_limits", + "type": "message", + "role": "assistant", + "model": "claude-3-5-haiku-20241022", + "content": [{"type": "text", "text": "Hello"}], + "stop_reason": "end_turn", + "stop_sequence": None, + "usage": {"input_tokens": 1, "output_tokens": 1}, + }, + ) + + client = HTTPHandler(client=httpx.Client(transport=httpx.MockTransport(respond))) + try: + litellm.completion( + model="anthropic/claude-3-5-haiku-20241022", + messages=[{"role": "user", "content": "Hello"}], + api_key="test-key", + client=client, + default_api_key_rpm_limit=60, + default_api_key_tpm_limit=5000000, + ) + finally: + client.close() + + request_body = json.loads(captured_requests[0].content) + assert "default_api_key_rpm_limit" not in request_body + assert "default_api_key_tpm_limit" not in request_body + + def test_redacted_thinking_content_block_delta(): chunk = { "type": "content_block_start", diff --git a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py index 25e2c3cda80..0f9f8259bef 100644 --- a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py +++ b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py @@ -6176,9 +6176,10 @@ def test_is_anthropic_usage_object_rejects_responses_api_usage(): [ # always-on-thinking models reject thinking.type=disabled with a 400 ("claude-fable-5", True), + ("claude-fable-5-1", True), ("claude-mythos-5", True), # unmapped future family member -> claude-always-on-thinking fallback rule - ("claude-fable-5-1", True), + ("claude-fable-6-1", True), # adaptive-capable models that ACCEPT disabled must keep it verbatim ("claude-opus-5", False), ("claude-sonnet-5", False), @@ -6207,3 +6208,179 @@ def test_disabled_thinking_omitted_only_for_always_on_models( assert "thinking" not in request else: assert request["thinking"] == {"type": "disabled"} + + +@pytest.mark.parametrize( + "tool_choice", + ["required", {"type": "required"}, {"type": "function", "function": {"name": "get_weather"}}], +) +def test_forced_tool_choice_raises_clean_error_on_fable_5_1_without_drop_params( + local_model_cost_map, tool_choice, monkeypatch +): + """Fable 5.1 400s on tool_choice type any/tool (thinking is always on and a + forced call would skip it); without drop_params the caller gets a clean + client-side 400 that explains the workaround, not a provider error.""" + monkeypatch.setattr(litellm, "drop_params", False) + config = AnthropicConfig() + + with pytest.raises(litellm.utils.UnsupportedParamsError, match="forced tool use"): + config.map_openai_params( + non_default_params={"tool_choice": tool_choice}, + optional_params={}, + model="claude-fable-5-1", + drop_params=False, + ) + + +@pytest.mark.parametrize( + "tool_choice", + ["required", {"type": "required"}, {"type": "function", "function": {"name": "get_weather"}}], +) +def test_forced_tool_choice_downgraded_to_auto_on_fable_5_1_with_drop_params( + local_model_cost_map, tool_choice +): + config = AnthropicConfig() + + result = config.map_openai_params( + non_default_params={"tool_choice": tool_choice}, + optional_params={}, + model="claude-fable-5-1", + drop_params=True, + ) + + assert result["tool_choice"] == {"type": "auto"} + + +def test_forced_tool_choice_downgrade_keeps_parallel_tool_calls_flag(local_model_cost_map): + config = AnthropicConfig() + + result = config.map_openai_params( + non_default_params={"tool_choice": "required", "parallel_tool_calls": False}, + optional_params={}, + model="claude-fable-5-1", + drop_params=True, + ) + + assert result["tool_choice"] == {"type": "auto", "disable_parallel_tool_use": True} + + +@pytest.mark.parametrize("tool_choice, expected_type", [("auto", "auto"), ("none", "none")]) +def test_unforced_tool_choice_forwarded_on_fable_5_1( + local_model_cost_map, tool_choice, expected_type, monkeypatch +): + monkeypatch.setattr(litellm, "drop_params", False) + config = AnthropicConfig() + + result = config.map_openai_params( + non_default_params={"tool_choice": tool_choice}, + optional_params={}, + model="claude-fable-5-1", + drop_params=False, + ) + + assert result["tool_choice"]["type"] == expected_type + + +@pytest.mark.parametrize("model", ["claude-fable-5", "claude-opus-5", "claude-sonnet-5"]) +def test_forced_tool_choice_forwarded_on_models_that_support_it( + local_model_cost_map, model, monkeypatch +): + monkeypatch.setattr(litellm, "drop_params", False) + config = AnthropicConfig() + + result = config.map_openai_params( + non_default_params={"tool_choice": "required"}, + optional_params={}, + model=model, + drop_params=True, + ) + + assert result["tool_choice"] == {"type": "any"} + + +def test_forced_tool_choice_gating_driven_by_model_map_flag(local_model_cost_map, monkeypatch): + """The gate must read ``supports_forced_tool_use`` from the model map, not + the model name: a flagged entry gates a model whose name says nothing.""" + monkeypatch.setitem(litellm.model_cost, "claude-zeta-9", {"supports_forced_tool_use": False}) + config = AnthropicConfig() + + result = config.map_openai_params( + non_default_params={"tool_choice": "required"}, + optional_params={}, + model="claude-zeta-9", + drop_params=True, + ) + + assert result["tool_choice"] == {"type": "auto"} + + +def test_anthropic_drop_params_keeps_format_only_output_config(monkeypatch): + """``drop_params=True`` must not consume ``output_config.format``: the drop + gate is an effort gate and ``format`` is a structured-output field.""" + monkeypatch.setattr(litellm, "drop_params", True) + config = AnthropicConfig() + schema_format = { + "type": "json_schema", + "schema": {"type": "object", "properties": {"z": {"type": "integer"}}}, + } + + result = config.transform_request( + model="claude-3-haiku-20240307", + messages=[{"role": "user", "content": "Hello"}], + optional_params={"output_config": {"format": schema_format}}, + litellm_params={}, + headers={}, + ) + + assert result.get("output_config") == {"format": schema_format} + + +def test_anthropic_drop_params_reduces_mixed_output_config_to_format(monkeypatch): + """``drop_params=True`` drops the effort key on unsupported models but keeps + ``format`` so structured outputs still reach the provider.""" + monkeypatch.setattr(litellm, "drop_params", True) + config = AnthropicConfig() + schema_format = { + "type": "json_schema", + "schema": {"type": "object", "properties": {"z": {"type": "integer"}}}, + } + + result = config.transform_request( + model="claude-3-haiku-20240307", + messages=[{"role": "user", "content": "Hello"}], + optional_params={"output_config": {"effort": "low", "format": schema_format}}, + litellm_params={}, + headers={}, + ) + + assert result.get("output_config") == {"format": schema_format} + + +def test_response_format_tool_path_skips_forced_tool_choice_when_unsupported(local_model_cost_map, monkeypatch): + """Backstop: on the tool-based structured-output path, a model flagged + ``supports_forced_tool_use: false`` must not get the forced response-format + tool_choice the provider would 400 on.""" + monkeypatch.setitem( + litellm.model_cost, + "claude-test-no-forced-tools", + {"litellm_provider": "anthropic", "mode": "chat", "supports_forced_tool_use": False}, + ) + config = AnthropicConfig() + + result = config.map_openai_params( + non_default_params={ + "response_format": { + "type": "json_schema", + "json_schema": { + "name": "test_schema", + "schema": {"type": "object", "properties": {"result": {"type": "string"}}}, + }, + } + }, + optional_params={}, + model="claude-test-no-forced-tools", + drop_params=False, + ) + + assert "tools" in result + assert "tool_choice" not in result diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py index ea1813acb82..2d74c00071b 100644 --- a/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py +++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py @@ -1013,12 +1013,15 @@ def test_translate_openai_content_to_anthropic_thinking_and_redacted_thinking(): assert result[1]["data"] == "REDACTED" -def test_translate_openai_content_to_anthropic_drops_empty_thinking_blocks(): - """LIT-6357 non-streaming producer half: a bridged reasoning model whose - thinking_blocks entry has empty or whitespace-only text (signed or not) - must not surface as {"type": "thinking", "thinking": ""} — clients replay - it as history and Anthropic 400s with "each thinking block must contain - thinking". Non-empty thinking and redacted_thinking pass through.""" +def test_translate_openai_content_to_anthropic_drops_empty_unsigned_thinking_blocks(): + """LIT-6357 non-streaming producer half, narrowed to unsigned blocks: a + bridged reasoning model whose thinking_blocks entry has empty or + whitespace-only text and no signature must not surface as + {"type": "thinking", "thinking": ""}. A signature-only block (Bedrock + Converse adaptive thinking) must be emitted so the client keeps the + signature for tool-use replay; the inbound strip self-heals it if the + client loops it back. Non-empty thinking and redacted_thinking pass + through.""" openai_choices = [ Choices( message=Message( @@ -1037,9 +1040,11 @@ def test_translate_openai_content_to_anthropic_drops_empty_thinking_blocks(): adapter = LiteLLMAnthropicMessagesAdapter() result = adapter._translate_openai_content_to_anthropic(choices=openai_choices) - assert [b["type"] for b in result] == ["thinking", "redacted_thinking", "text"] - assert result[0]["thinking"] == "real plan" - assert result[1]["data"] == "REDACTED" + assert [b["type"] for b in result] == ["thinking", "thinking", "redacted_thinking", "text"] + assert result[0]["thinking"] == "" + assert result[0]["signature"] == "sig_abc" + assert result[1]["thinking"] == "real plan" + assert result[2]["data"] == "REDACTED" def test_translate_streaming_openai_chunk_to_anthropic_thinking_delta(): diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_streaming_iterator_first_delta.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_streaming_iterator_first_delta.py index 6268cd01efe..17d42f55ae0 100644 --- a/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_streaming_iterator_first_delta.py +++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_streaming_iterator_first_delta.py @@ -1048,19 +1048,20 @@ def _empty_thinking_then_tool_chunks(thinking: str = "", signature: str = "") -> @pytest.mark.parametrize("is_async", [False, True]) @pytest.mark.parametrize( "thinking,signature", - [("", ""), (" \n\t ", ""), ("", "sig_abc")], - ids=["empty", "whitespace-only", "empty-but-signed"], + [("", ""), (" \n\t ", "")], + ids=["empty", "whitespace-only"], ) @pytest.mark.asyncio async def test_contentless_thinking_chunk_opens_no_thinking_block(is_async: bool, thinking: str, signature: str): """LIT-6357 producer half: a reasoning model that goes straight to tool - calls streams a ``thinking_blocks`` entry with no real thinking text; the - wrapper used to open ``{"type": "thinking", "thinking": ""}`` for it and - close the block with no delta. Clients (Claude Code) replay that block as - history and Anthropic rejects the next tool-loop request with - "each thinking block must contain thinking" — empty-but-signed included. - The contentless chunk must open nothing; the tool_use block must be - unaffected.""" + calls streams a ``thinking_blocks`` entry with no real thinking text and + no signature; the wrapper used to open ``{"type": "thinking", + "thinking": ""}`` for it and close the block with no delta. Clients + (Claude Code) replay that block as history and Anthropic rejects the next + tool-loop request with "each thinking block must contain thinking". + The contentless unsigned chunk must open nothing; the tool_use block must + be unaffected. A SIGNED contentless chunk is different: see + test_signature_only_thinking_chunk_opens_signed_block.""" chunks = _empty_thinking_then_tool_chunks(thinking, signature) if is_async: wrapper = AnthropicStreamWrapper(completion_stream=_AsyncStream(chunks), model="claude-x") @@ -1138,11 +1139,38 @@ async def test_early_signature_on_blank_thinking_chunk_is_carried_to_the_opened_ @pytest.mark.parametrize("is_async", [False, True]) @pytest.mark.asyncio -async def test_early_signature_discarded_when_first_block_is_not_thinking(is_async: bool): - """An early signature from a skipped blank thinking chunk must not leak - into a text or tool_use first block, and must not resurrect an empty - thinking block on its own (an empty-but-signed block is exactly what - Anthropic rejects).""" +async def test_signature_only_thinking_chunk_opens_signed_block(is_async: bool): + """Bedrock Converse under adaptive thinking emits a reasoning delta with + empty text and only a signature. The signed chunk must open a thinking + block that carries the signature to the client (needed to replay reasoning + across tool-use turns); the tool_use block must be unaffected. Dropping it + like the unsigned case regressed the claude_code thinking e2e cells.""" + chunks = _empty_thinking_then_tool_chunks("", "sig_bedrock") + if is_async: + wrapper = AnthropicStreamWrapper(completion_stream=_AsyncStream(chunks), model="claude-x") + events = await _drain_async(wrapper) + else: + wrapper = AnthropicStreamWrapper(completion_stream=iter(chunks), model="claude-x") + events = _drain_sync(wrapper) + + starts = _thinking_block_starts(events) + assert len(starts) == 1 + assert starts[0].get("signature") == "sig_bedrock" or _signature_deltas(events) == ["sig_bedrock"] + assert _thinking_deltas(events) == [] + tool_starts = [ + e["content_block"] + for e in events + if e.get("type") == "content_block_start" and e["content_block"].get("type") == "tool_use" + ] + assert [b["name"] for b in tool_starts] == ["get_weather"] + _assert_deltas_match_their_block_type(events) + + +@pytest.mark.parametrize("is_async", [False, True]) +@pytest.mark.asyncio +async def test_signature_only_thinking_chunk_before_text_leaks_no_signature(is_async: bool): + """The signed thinking block a signature-only chunk opens must stay its + own block: the text block that follows carries no signature.""" chunks = [ _thinking_chunk("", signature="sig_early"), _make_chunk(Delta(content="Hello")), @@ -1155,7 +1183,9 @@ async def test_early_signature_discarded_when_first_block_is_not_thinking(is_asy wrapper = AnthropicStreamWrapper(completion_stream=iter(chunks), model="claude-x") events = _drain_sync(wrapper) - assert _thinking_block_starts(events) == [] + starts = _thinking_block_starts(events) + assert len(starts) == 1 + assert starts[0].get("signature") == "sig_early" or _signature_deltas(events) == ["sig_early"] text_starts = [ e["content_block"] for e in events diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_messages_effort.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_messages_effort.py index e9d4d625421..daaa110e7b9 100644 --- a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_messages_effort.py +++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_messages_effort.py @@ -10,6 +10,10 @@ from litellm.llms.anthropic.common_utils import AnthropicError from litellm.llms.anthropic.experimental_pass_through.messages.transformation import ( AnthropicMessagesConfig, ) +from litellm.llms.openai_like.json_loader import SimpleProviderConfig +from litellm.llms.openai_like.messages.transformation import ( + JSONProviderAnthropicMessagesConfig, +) def _claude_code_payload(effort="medium", max_tokens=8192, **output_config_extra): @@ -294,3 +298,39 @@ def test_non_adaptive_request_without_effort_is_untouched(): assert "thinking" not in result assert "output_config" not in result + + +def test_reasoning_effort_budget_capped_below_max_tokens(): + result = _transform("claude-haiku-4-5", {"max_tokens": 4000, "reasoning_effort": "xhigh"}) + + assert result["thinking"] == {"type": "enabled", "budget_tokens": 3999} + assert result["max_tokens"] == 4000 + + +def test_reasoning_effort_thinking_dropped_when_min_budget_cannot_fit(): + result = _transform("claude-haiku-4-5", {"max_tokens": 1024, "reasoning_effort": "xhigh"}) + + assert "thinking" not in result + assert result["max_tokens"] == 1024 + + +def test_reasoning_effort_budget_capped_for_openai_like_messages_upstream(): + provider = SimpleProviderConfig( + "meta", + { + "base_url": "https://api.meta.ai/v1", + "api_key_env": "META_API_KEY", + "supported_endpoints": ["/v1/messages"], + }, + ) + + result = JSONProviderAnthropicMessagesConfig(provider).transform_anthropic_messages_request( + model="muse-spark-1.2", + messages=[{"role": "user", "content": "Hello"}], + anthropic_messages_optional_request_params={"max_tokens": 4000, "reasoning_effort": "xhigh"}, + litellm_params={}, + headers={}, + ) + + assert result["thinking"] == {"type": "enabled", "budget_tokens": 3999} + assert result["max_tokens"] == 4000 diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_reasoning_effort_translation.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_reasoning_effort_translation.py index c9170efd18a..7e2fa356685 100644 --- a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_reasoning_effort_translation.py +++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_reasoning_effort_translation.py @@ -70,7 +70,7 @@ def test_reasoning_effort_none_clears_thinking_and_output_config(): def test_reasoning_effort_on_non_adaptive_model_uses_thinking_budget(): config = AnthropicMessagesConfig() - optional_params = {"max_tokens": 1024, "reasoning_effort": "high"} + optional_params = {"max_tokens": 8192, "reasoning_effort": "high"} result = config.transform_anthropic_messages_request( model="claude-opus-4-5", @@ -86,7 +86,7 @@ def test_reasoning_effort_on_non_adaptive_model_uses_thinking_budget(): assert isinstance(thinking, dict) assert thinking.get("type") == "enabled" assert isinstance(thinking.get("budget_tokens"), int) - assert thinking["budget_tokens"] >= 1024 + assert 1024 <= thinking["budget_tokens"] < result["max_tokens"] @pytest.mark.parametrize("bad_effort", ["invalid", "disabled", ""]) diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_streaming_iterator.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_streaming_iterator.py index b8ce11db8d1..11a048edc1f 100644 --- a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_streaming_iterator.py +++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_streaming_iterator.py @@ -6,9 +6,7 @@ import pytest from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj -from litellm.llms.anthropic.experimental_pass_through.messages import ( - streaming_iterator as streaming_iterator_module, -) +from litellm.llms.anthropic.experimental_pass_through.messages import streaming_iterator as streaming_iterator_module from litellm.llms.anthropic.experimental_pass_through.messages.streaming_iterator import ( INCOMPLETE_STREAM_ERROR_MESSAGE, AnthropicMessagesStreamHiddenParams, @@ -338,47 +336,6 @@ async def _events_then_hang(events): await asyncio.Event().wait() -@pytest.mark.asyncio -async def test_async_sse_wrapper_logs_partial_chunks_on_client_disconnect(): - """ - Regression test for LIT-5839: a client disconnect tears the generator - down with GeneratorExit at the yield, which used to skip the post-loop - logging dispatch entirely, so the partial output tokens the provider - already generated (and billed) never reached spend tracking. - """ - iterator = _RecordingLoggingIterator( - litellm_logging_obj=_make_logging_obj("test_disconnect_logs_partial_chunks"), - request_body={}, - ) - wrapped = iterator.async_sse_wrapper(_events_then_hang(TRUNCATED_TOOL_USE_EVENTS)) - streamed = [await wrapped.__anext__() for _ in range(len(TRUNCATED_TOOL_USE_EVENTS))] - assert iterator.logging_call_count == 0 - - await wrapped.aclose() - - assert iterator.logging_call_count == 1 - assert iterator.logged_chunks == streamed - - -@pytest.mark.asyncio -async def test_async_sse_wrapper_logs_partial_chunks_on_cancellation(): - iterator = _RecordingLoggingIterator( - litellm_logging_obj=_make_logging_obj("test_cancellation_logs_partial_chunks"), - request_body={}, - ) - wrapped = iterator.async_sse_wrapper(_events_then_hang(TRUNCATED_TOOL_USE_EVENTS)) - streamed = [await wrapped.__anext__() for _ in range(len(TRUNCATED_TOOL_USE_EVENTS))] - - consume_task = asyncio.ensure_future(wrapped.__anext__()) - await asyncio.sleep(0.01) - consume_task.cancel() - with pytest.raises(asyncio.CancelledError): - await consume_task - - assert iterator.logging_call_count == 1 - assert iterator.logged_chunks == streamed - - @pytest.mark.asyncio async def test_async_sse_wrapper_skips_logging_on_disconnect_before_first_chunk(): iterator = _RecordingLoggingIterator( @@ -408,6 +365,561 @@ def test_incomplete_stream_error_sse_event_is_valid_anthropic_error(): assert event.endswith("\n\n") +_STREAM_PREFIX = ( + {"type": "message_start", "message": {"id": "msg_1", "usage": {"input_tokens": 52, "output_tokens": 1}}}, + {"type": "content_block_start", "index": 0, "content_block": {"type": "text", "text": ""}}, + {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "The Roman"}}, +) +_STREAM_TAIL = ( + {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": " Empire ..."}}, + {"type": "content_block_stop", "index": 0}, + {"type": "message_delta", "delta": {"stop_reason": "end_turn"}, "usage": {"output_tokens": 64}}, + {"type": "message_stop"}, +) + + +def _output_tokens_from_logged_chunks(chunks: list[bytes]) -> int | None: + """Read the last output_tokens the billing path would see from the SSE bytes.""" + latest: int | None = None + for raw in chunks: + for line in raw.decode().splitlines(): + if not line.startswith("data:"): + continue + data = json.loads(line[len("data:"):].strip()) + usage = data.get("usage") if isinstance(data, dict) else None + if isinstance(usage, dict) and usage.get("output_tokens") is not None: + latest = usage["output_tokens"] + return latest + + +@pytest.mark.asyncio +async def test_async_sse_wrapper_bills_full_stream_after_client_disconnect(): + """ + Regression: on a client disconnect mid-stream the upstream provider keeps + generating (and billing) the full response. The wrapper must keep draining + that upstream to its terminal ``message_delta`` and bill the real + output_tokens (64), not the partial count the client drained before leaving + (the message_start placeholder, 1). + + A ``tail_gated`` event holds back the stream tail until the client has + disconnected, so the tail can only be captured by a drain that survives the + client teardown - exactly the path the previous implementation dropped. + """ + tail_gated = asyncio.Event() + upstream_fully_drained = asyncio.Event() + + async def _gated_stream(): + for event in _STREAM_PREFIX: + yield event + await tail_gated.wait() + for event in _STREAM_TAIL: + yield event + upstream_fully_drained.set() + + iterator = _RecordingLoggingIterator( + litellm_logging_obj=_make_logging_obj("test_bills_full_stream_after_disconnect"), + request_body={}, + ) + + gen = iterator.async_sse_wrapper(_gated_stream()) + + client_chunks = [] + async for chunk in gen: + client_chunks.append(chunk) + if len(client_chunks) == len(_STREAM_PREFIX): + break + await gen.aclose() + + tail_gated.set() + await asyncio.wait_for(upstream_fully_drained.wait(), timeout=5) + for _ in range(100): + if iterator.logged_chunks: + break + await asyncio.sleep(0.01) + + assert len(client_chunks) == len(_STREAM_PREFIX) + + assert iterator.logged_chunks, "pump never billed after client disconnect" + assert _output_tokens_from_logged_chunks(iterator.logged_chunks) == 64 + assert any(c.startswith(b"event: message_stop\n") for c in iterator.logged_chunks) + assert not any(c.startswith(b"event: error\n") for c in iterator.logged_chunks) + + +@pytest.mark.asyncio +async def test_async_sse_wrapper_bills_full_stream_when_client_reads_all(): + """Happy path: when the client drains the whole stream, billing still sees + the terminal output_tokens (64) and the client gets every chunk.""" + tail_gated = asyncio.Event() + tail_gated.set() # no gating; full stream flows immediately + + async def _full_stream(): + for event in (*_STREAM_PREFIX, *_STREAM_TAIL): + yield event + + iterator = _RecordingLoggingIterator( + litellm_logging_obj=_make_logging_obj("test_bills_full_stream_happy_path"), + request_body={}, + ) + client_chunks = [chunk async for chunk in iterator.async_sse_wrapper(_full_stream())] + + for _ in range(100): + if iterator.logged_chunks: + break + await asyncio.sleep(0.01) + + assert len(client_chunks) == len(_STREAM_PREFIX) + len(_STREAM_TAIL) + assert _output_tokens_from_logged_chunks(iterator.logged_chunks) == 64 + assert not any(c.startswith(b"event: error\n") for c in iterator.logged_chunks) + + +@pytest.mark.asyncio +async def test_async_sse_wrapper_dispatches_deferred_logging_when_client_disconnects_mid_tail(): + """ + Regression: when the pump finishes draining while the client is still + connected, ``_handle_streaming_logging`` defers billing for the proxy's + post-response hook (``ProxyLogging._fire_deferred_stream_logging``), which + only fires on a normally completed response. If the client then disconnects + before consuming the queued tail, the response generator tears down via + GeneratorExit and that hook never runs. The relay teardown must dispatch + the stored deferred billing itself, or the request logs no spend at all. + """ + dispatched = [] + deferred_fired = asyncio.Event() + + def _deferred_stream_complete(logging_coroutine): + dispatched.append(logging_coroutine) + + async def _consume(): + logging_coroutine.close() + deferred_fired.set() + + return _consume() + + logging_obj = _make_logging_obj("test_deferred_dispatch_on_disconnect_mid_tail") + logging_obj._on_deferred_stream_complete = _deferred_stream_complete + iterator = BaseAnthropicMessagesStreamingIterator(litellm_logging_obj=logging_obj, request_body={}) + + async def _full_stream(): + for event in (*_STREAM_PREFIX, *_STREAM_TAIL): + yield event + + gen = iterator.async_sse_wrapper(_full_stream()) + client_chunks = [] + async for chunk in gen: + client_chunks.append(chunk) + if len(client_chunks) == len(_STREAM_PREFIX): + break + + for _ in range(100): + if getattr(logging_obj, "_deferred_stream_complete_args", None) is not None: + break + await asyncio.sleep(0.01) + assert getattr(logging_obj, "_deferred_stream_complete_args", None) is not None, "pump never deferred billing" + + await gen.aclose() + + assert len(dispatched) == 1, "relay teardown did not dispatch the deferred billing" + assert logging_obj._on_deferred_stream_complete is None + assert logging_obj._deferred_stream_complete_args is None + await asyncio.wait_for(deferred_fired.wait(), timeout=5) + + +class _ProviderStreamError(Exception): + """Stand-in for a provider-specific streaming failure carrying a status code.""" + + def __init__(self, message: str, status_code: int): + super().__init__(message) + self.status_code = status_code + + +@pytest.mark.asyncio +async def test_async_sse_wrapper_reraises_upstream_error_to_connected_client(): + """ + Regression: an upstream failure (Bedrock read / decode / chunk-conversion) + before message_stop must propagate the ORIGINAL provider exception to a + still-connected client, so the proxy's failure handling keeps the + provider-specific status. The pump must not swallow it into a generic + api_error event + normal termination. + """ + + async def _failing_stream(): + yield {"type": "message_start", "message": {"id": "msg_1", "usage": {"input_tokens": 52, "output_tokens": 1}}} + yield {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "partial"}} + raise _ProviderStreamError("bedrock stream blew up", status_code=529) + + iterator = _RecordingLoggingIterator( + litellm_logging_obj=_make_logging_obj("test_reraises_upstream_error"), + request_body={}, + ) + + received = [] + + async def _drain(): + async for chunk in iterator.async_sse_wrapper(_failing_stream()): + received.append(chunk) + + with pytest.raises(_ProviderStreamError) as excinfo: + await _drain() + + assert excinfo.value.status_code == 529 + assert received + assert not any(c.startswith(b"event: error\n") for c in received) + assert iterator.logged_chunks == [] + + +@pytest.mark.asyncio +async def test_async_sse_wrapper_salvages_partial_spend_on_upstream_error_after_disconnect(): + """ + When the upstream errors AFTER the client has already disconnected there is + no live client to re-raise to and no failure hook will run, so the pump + salvages partial spend from what it collected instead of dropping the row. + """ + tail_gated = asyncio.Event() + + async def _gated_failing_stream(): + yield {"type": "message_start", "message": {"id": "msg_1", "usage": {"input_tokens": 52, "output_tokens": 1}}} + yield {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "partial"}} + await tail_gated.wait() + raise _ProviderStreamError("late failure", status_code=500) + + iterator = _RecordingLoggingIterator( + litellm_logging_obj=_make_logging_obj("test_salvage_partial_on_late_error"), + request_body={}, + ) + + gen = iterator.async_sse_wrapper(_gated_failing_stream()) + received = [await gen.__anext__(), await gen.__anext__()] + await gen.aclose() # client disconnects before the upstream error + + tail_gated.set() # let the upstream raise now, after disconnect + for _ in range(100): + if iterator.logged_chunks: + break + await asyncio.sleep(0.01) + + assert len(received) == 2 + assert iterator.logged_chunks == received + + +@pytest.mark.asyncio +async def test_async_sse_wrapper_salvages_spend_when_queued_error_is_never_consumed(): + """ + When the upstream errors while the client is still connected, the pump + forwards the exception through the queue expecting the relay to re-raise it + into the proxy's failure handling. If the client disconnects before + consuming that queued exception, the handoff never happens and no failure + hook runs, so the pump must notice the unconsumed exception at teardown and + salvage partial spend instead of dropping the row entirely. + """ + upstream_errored = asyncio.Event() + + async def _failing_stream(): + yield {"type": "message_start", "message": {"id": "msg_1", "usage": {"input_tokens": 52, "output_tokens": 1}}} + yield {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "partial"}} + upstream_errored.set() + raise _ProviderStreamError("mid-stream failure", status_code=500) + + iterator = _RecordingLoggingIterator( + litellm_logging_obj=_make_logging_obj("test_salvage_on_unconsumed_queued_error"), + request_body={}, + ) + + gen = iterator.async_sse_wrapper(_failing_stream()) + received = [await gen.__anext__(), await gen.__anext__()] + await upstream_errored.wait() # exception is now queued behind the consumed chunks + await gen.aclose() # client disconnects without ever consuming the queued exception + + for _ in range(100): + if iterator.logged_chunks: + break + await asyncio.sleep(0.01) + + assert iterator.logging_call_count == 1 + assert iterator.logged_chunks == received + + +@pytest.mark.asyncio +async def test_async_sse_wrapper_applies_backpressure_to_slow_client(monkeypatch): + """ + Regression: the relay queue is bounded, so a slow client throttles the + upstream read instead of letting the pump buffer the whole response in + memory. With a tiny queue and a client that reads a single chunk, the pump + must stall after producing only a queue's worth of chunks ahead, not race + to the end of a large stream. + """ + monkeypatch.setattr(streaming_iterator_module, "ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE", 2) + + total = 200 + produced = 0 + + async def _fast_stream(): + nonlocal produced + for i in range(total): + produced += 1 + yield {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": f"t{i}"}} + + iterator = _make_iterator("test_backpressure_slow_client") + gen = iterator.async_sse_wrapper(_fast_stream()) + try: + await gen.__anext__() + for _ in range(500): + await asyncio.sleep(0) + assert produced <= 2 + 3, f"pump ran ahead unthrottled: produced {produced} of {total}" + assert produced < total + finally: + await gen.aclose() + + +@pytest.mark.asyncio +async def test_async_sse_wrapper_bills_partial_when_detached_drain_cap_reached(monkeypatch): + """ + Regression: when the concurrent detached-drain cap is already reached, a + pump whose client has disconnected must bill what it collected instead of + continuing to drain (and accumulating) the rest of a large upstream stream, + so slow/abandoned clients can't pin unbounded worker state. + + The cap slot set is pre-occupied so the single slot is unavailable when this + pump reaches its first post-disconnect chunk; that isolates the cap decision + from multi-pump scheduling races. + """ + monkeypatch.setattr(streaming_iterator_module, "ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS", 1) + monkeypatch.setattr(streaming_iterator_module, "ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE", 4) + + async def _hold_slot(): + await asyncio.sleep(3600) + + holder = asyncio.ensure_future(_hold_slot()) + streaming_iterator_module._DETACHED_STREAM_DRAINS.add(holder) + tail_reached = False + + async def _long_stream(): + nonlocal tail_reached + yield {"type": "message_start", "message": {"id": "m", "usage": {"input_tokens": 5, "output_tokens": 1}}} + yield {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "x"}} + for i in range(100): + yield {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": f"more{i}"}} + tail_reached = True + yield {"type": "message_delta", "delta": {"stop_reason": "end_turn"}, "usage": {"output_tokens": 42}} + yield {"type": "message_stop"} + + iterator = _RecordingLoggingIterator(litellm_logging_obj=_make_logging_obj("drain_cap_full"), request_body={}) + try: + gen = iterator.async_sse_wrapper(_long_stream()) + await gen.__anext__() # message_start + await gen.__anext__() # first delta + await gen.aclose() # client disconnects; 100+ chunks remain upstream + + for _ in range(200): + if iterator.logged_chunks: + break + await asyncio.sleep(0) + + assert iterator.logged_chunks, "capped pump never billed" + assert len(iterator.logged_chunks) <= 2 + streaming_iterator_module.ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE + assert len(iterator.logged_chunks) < 100 + assert not any(c.startswith(b"event: message_stop\n") for c in iterator.logged_chunks) + assert tail_reached is False, "pump kept draining past the cap instead of stopping" + finally: + holder.cancel() + streaming_iterator_module._DETACHED_STREAM_DRAINS.discard(holder) + + +@pytest.mark.asyncio +async def test_async_sse_wrapper_bills_partial_when_detached_drains_disabled(monkeypatch): + """ + Regression: ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS=0 must disable + detached draining entirely, not just shrink the cap. With no slots ever + available, the very first post-disconnect chunk must fall back to partial + spend logging instead of hanging on a cap that's unreachable. + """ + monkeypatch.setattr(streaming_iterator_module, "ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS", 0) + monkeypatch.setattr(streaming_iterator_module, "ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE", 4) + + tail_reached = False + + async def _long_stream(): + nonlocal tail_reached + yield {"type": "message_start", "message": {"id": "m", "usage": {"input_tokens": 5, "output_tokens": 1}}} + yield {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "x"}} + for i in range(100): + yield {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": f"more{i}"}} + tail_reached = True + yield {"type": "message_delta", "delta": {"stop_reason": "end_turn"}, "usage": {"output_tokens": 42}} + yield {"type": "message_stop"} + + iterator = _RecordingLoggingIterator(litellm_logging_obj=_make_logging_obj("drains_disabled"), request_body={}) + gen = iterator.async_sse_wrapper(_long_stream()) + await gen.__anext__() # message_start + await gen.__anext__() # first delta + await gen.aclose() # client disconnects; 100+ chunks remain upstream + + for _ in range(200): + if iterator.logged_chunks: + break + await asyncio.sleep(0) + + assert iterator.logged_chunks, "pump never billed with detached drains disabled" + assert len(iterator.logged_chunks) <= 2 + streaming_iterator_module.ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE + assert len(iterator.logged_chunks) < 100 + assert not any(c.startswith(b"event: message_stop\n") for c in iterator.logged_chunks) + assert tail_reached is False, "pump kept draining despite detached drains being disabled" + assert len(streaming_iterator_module._DETACHED_STREAM_DRAINS) == 0 + + +@pytest.mark.asyncio +async def test_async_sse_wrapper_aborts_upstream_when_detached_drain_cap_reached(monkeypatch): + """ + Regression: when the cap is full and a disconnected pump bails, it must call + aclose on the upstream stream so the provider stops generating and billing, + not continue running the stream while we record only the partial prefix. + """ + monkeypatch.setattr(streaming_iterator_module, "ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS", 1) + monkeypatch.setattr(streaming_iterator_module, "ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE", 4) + + async def _hold_slot(): + await asyncio.sleep(3600) + + holder = asyncio.ensure_future(_hold_slot()) + streaming_iterator_module._DETACHED_STREAM_DRAINS.add(holder) + + class _AbortableStream: + def __init__(self): + self.aclose_called = False + self._remaining = iter( + ( + {"type": "message_start", "message": {"id": "m", "usage": {"input_tokens": 5, "output_tokens": 1}}}, + {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "x"}}, + ) + + tuple( + {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": f"t{i}"}} + for i in range(50) + ) + ) + + def __aiter__(self): + return self + + async def __anext__(self): + try: + return next(self._remaining) + except StopIteration: + raise StopAsyncIteration + + async def aclose(self): + self.aclose_called = True + + stream = _AbortableStream() + iterator = _RecordingLoggingIterator(litellm_logging_obj=_make_logging_obj("abort_upstream_at_cap"), request_body={}) + try: + gen = iterator.async_sse_wrapper(stream) + await gen.__anext__() + await gen.__anext__() + await gen.aclose() + + for _ in range(200): + if iterator.logged_chunks: + break + await asyncio.sleep(0) + + assert iterator.logged_chunks, "capped pump never billed" + assert stream.aclose_called, "upstream aclose was not called when the detached-drain cap was reached" + finally: + holder.cancel() + streaming_iterator_module._DETACHED_STREAM_DRAINS.discard(holder) + + +@pytest.mark.asyncio +async def test_abort_upstream_logs_warning_when_aclose_raises(caplog): + """_abort_upstream must swallow and log any exception from aclose().""" + import logging + + class _ExplodingStream: + def __aiter__(self): + return self + + async def __anext__(self): + raise StopAsyncIteration + + async def aclose(self): + raise RuntimeError("aclose exploded") + + with caplog.at_level(logging.WARNING, logger="LiteLLM Proxy"): + await BaseAnthropicMessagesStreamingIterator._abort_upstream(_ExplodingStream()) + + assert any("abort" in r.message and "RuntimeError" in r.message for r in caplog.records) + + +@pytest.mark.asyncio +async def test_enqueue_for_client_returns_false_when_already_detached(): + """_enqueue_for_client must return False immediately (without touching the queue) + when client_detached is already set before the call.""" + from litellm.llms.anthropic.experimental_pass_through.messages.streaming_iterator import ( + BaseAnthropicMessagesStreamingIterator, + ) + + queue: asyncio.Queue[bytes | None | BaseException] = asyncio.Queue(maxsize=1) + client_detached = asyncio.Event() + client_detached.set() + + result = await BaseAnthropicMessagesStreamingIterator._enqueue_for_client(queue, client_detached, b"chunk") + assert result is False + assert queue.empty() + + +@pytest.mark.asyncio +async def test_enqueue_for_client_returns_false_when_client_detaches_while_queue_full(): + """_enqueue_for_client must return False (and cancel the put) when the queue + is full and client_detached fires before space becomes available.""" + from litellm.llms.anthropic.experimental_pass_through.messages.streaming_iterator import ( + BaseAnthropicMessagesStreamingIterator, + ) + + queue: asyncio.Queue[bytes | None | BaseException] = asyncio.Queue(maxsize=1) + queue.put_nowait(b"already-full") + + client_detached = asyncio.Event() + + async def _set_detached_soon(): + await asyncio.sleep(0.01) + client_detached.set() + + asyncio.create_task(_set_detached_soon()) + result = await BaseAnthropicMessagesStreamingIterator._enqueue_for_client(queue, client_detached, b"new-chunk") + assert result is False + assert queue.qsize() == 1 + assert queue.get_nowait() == b"already-full" + + +@pytest.mark.asyncio +async def test_async_sse_wrapper_drains_detached_when_cap_available(monkeypatch): + """Complement to the cap test: with a slot free, a disconnected pump drains + the full upstream and bills the terminal usage, and releases its slot after.""" + monkeypatch.setattr(streaming_iterator_module, "ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS", 1) + monkeypatch.setattr(streaming_iterator_module, "ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE", 4) + + async def _stream(): + yield {"type": "message_start", "message": {"id": "m", "usage": {"input_tokens": 5, "output_tokens": 1}}} + yield {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "x"}} + for i in range(20): + yield {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": f"m{i}"}} + yield {"type": "message_delta", "delta": {"stop_reason": "end_turn"}, "usage": {"output_tokens": 42}} + yield {"type": "message_stop"} + + iterator = _RecordingLoggingIterator(litellm_logging_obj=_make_logging_obj("drain_cap_free"), request_body={}) + gen = iterator.async_sse_wrapper(_stream()) + await gen.__anext__() + await gen.__anext__() + await gen.aclose() + + for _ in range(300): + if iterator.logged_chunks: + break + await asyncio.sleep(0.01) + + assert any(c.startswith(b"event: message_stop\n") for c in iterator.logged_chunks) + assert len(streaming_iterator_module._DETACHED_STREAM_DRAINS) == 0 + + def _decode_sse_events(events: tuple[bytes, ...]) -> list[tuple[str, dict]]: decoded = [] for event in events: @@ -599,20 +1111,35 @@ async def test_normal_end_with_deferred_dispatch_armed_parks_logging_coroutine(m @pytest.mark.asyncio async def test_client_disconnect_enqueues_immediately_even_when_deferred_dispatch_armed(monkeypatch): """ - On client disconnect the guardrail end-of-stream scan never runs, so - deferral would strand the spend log; the teardown path must keep - enqueueing immediately (LIT-5839) even when the deferred callback is armed. + Regression: on client disconnect the guardrail end-of-stream scan never + runs, so deferral would strand the spend log. The detached pump's + post-disconnect bill must bypass the deferred-dispatch park and enqueue + immediately (LIT-5839) even when the deferred callback is armed (LIT-6409). """ worker = _RecordingLoggingWorker() monkeypatch.setattr(streaming_iterator_module, "GLOBAL_LOGGING_WORKER", worker) iterator = _make_iterator("test_disconnect_enqueues_when_armed") iterator.litellm_logging_obj._on_deferred_stream_complete = _noop_deferred_dispatch - wrapped = iterator.async_sse_wrapper(_events_then_hang(TRUNCATED_TOOL_USE_EVENTS)) + tail_gated = asyncio.Event() + + async def _gated_stream(): + for event in TRUNCATED_TOOL_USE_EVENTS: + yield event + await tail_gated.wait() + yield {"type": "message_stop"} + + wrapped = iterator.async_sse_wrapper(_gated_stream()) for _ in range(len(TRUNCATED_TOOL_USE_EVENTS)): await wrapped.__anext__() await wrapped.aclose() + tail_gated.set() + for _ in range(100): + if worker.enqueued: + break + await asyncio.sleep(0.01) + assert len(worker.enqueued) == 1 assert getattr(iterator.litellm_logging_obj, "_deferred_stream_complete_args", None) is None worker.close_enqueued() @@ -629,3 +1156,123 @@ async def test_normal_end_without_deferred_dispatch_enqueues_immediately(monkeyp assert len(worker.enqueued) == 1 assert getattr(iterator.litellm_logging_obj, "_deferred_stream_complete_args", None) is None worker.close_enqueued() + + +def _backpressured_wrapper(iterator, upstream_exhausted: asyncio.Event): + async def _stream(): + try: + for event in COMPLETE_STREAM_EVENTS: + yield event + finally: + upstream_exhausted.set() + + return iterator.async_sse_wrapper(_stream()) + + +async def _drain_with_pauses_until_upstream_exhausted(gen, upstream_exhausted: asyncio.Event) -> list: + received = [] + while not upstream_exhausted.is_set(): + received.append(await gen.__anext__()) + for _ in range(25): + await asyncio.sleep(0) + assert len(received) <= len(COMPLETE_STREAM_EVENTS) + return received + + +@pytest.mark.asyncio +async def test_normal_end_parks_deferred_logging_even_when_sentinel_enqueue_backpressured(monkeypatch): + """ + Regression: with a full relay queue at end of stream, the pump suspends + while enqueueing the end-of-stream sentinel, and a client that then drains + the whole tail tears the relay down (setting ``client_detached``) before + the pump resumes. That teardown is a normally completed response, not a + disconnect: billing must still park for the proxy's post-response hook + (preserving post_call decoration such as guardrail_information) instead of + enqueueing immediately through the teardown path. + """ + monkeypatch.setattr(streaming_iterator_module, "ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE", 2) + worker = _RecordingLoggingWorker() + monkeypatch.setattr(streaming_iterator_module, "GLOBAL_LOGGING_WORKER", worker) + + dispatched = [] + + async def _deferred_stream_complete(logging_coroutine): + dispatched.append(logging_coroutine) + logging_coroutine.close() + + iterator = _make_iterator("test_sentinel_backpressure_normal_end") + iterator.litellm_logging_obj._on_deferred_stream_complete = _deferred_stream_complete + + upstream_exhausted = asyncio.Event() + gen = _backpressured_wrapper(iterator, upstream_exhausted) + received = await _drain_with_pauses_until_upstream_exhausted(gen, upstream_exhausted) + + while True: + try: + received.append(await gen.__anext__()) + except StopAsyncIteration: + break + + for _ in range(100): + if worker.enqueued or getattr(iterator.litellm_logging_obj, "_deferred_stream_complete_args", None): + break + await asyncio.sleep(0.01) + + assert len(received) == len(COMPLETE_STREAM_EVENTS) + assert worker.enqueued == [], "fully delivered stream billed through the teardown path" + assert dispatched == [] + parked = getattr(iterator.litellm_logging_obj, "_deferred_stream_complete_args", None) + assert parked is not None, "pump never parked deferred billing" + parked[0].close() + + +@pytest.mark.asyncio +async def test_relay_teardown_dispatches_deferred_billing_when_sentinel_never_consumed(monkeypatch): + """ + Regression: when the pump has parked deferred billing but its end-of-stream + sentinel never fits in the full relay queue (the client disconnects without + draining the tail), the proxy's post-response hook never fires. Exactly one + of the relay teardown or the pump's fallback must dispatch the parked + billing, or the request logs no spend at all. + """ + monkeypatch.setattr(streaming_iterator_module, "ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE", 2) + worker = _RecordingLoggingWorker() + monkeypatch.setattr(streaming_iterator_module, "GLOBAL_LOGGING_WORKER", worker) + + dispatched = [] + deferred_fired = asyncio.Event() + + def _deferred_stream_complete(logging_coroutine): + dispatched.append(logging_coroutine) + + async def _consume(): + logging_coroutine.close() + deferred_fired.set() + + return _consume() + + iterator = _make_iterator("test_sentinel_never_consumed_dispatch") + iterator.litellm_logging_obj._on_deferred_stream_complete = _deferred_stream_complete + + upstream_exhausted = asyncio.Event() + gen = _backpressured_wrapper(iterator, upstream_exhausted) + await _drain_with_pauses_until_upstream_exhausted(gen, upstream_exhausted) + + for _ in range(100): + if getattr(iterator.litellm_logging_obj, "_deferred_stream_complete_args", None) is not None: + break + await asyncio.sleep(0.01) + + await gen.aclose() + + for _ in range(100): + if dispatched: + break + await asyncio.sleep(0.01) + + assert len(dispatched) == 1, "parked billing was never dispatched" + assert getattr(iterator.litellm_logging_obj, "_deferred_stream_complete_args", None) is None + assert getattr(iterator.litellm_logging_obj, "_on_deferred_stream_complete", None) is None + assert len(worker.enqueued) == 1, "teardown billing enqueued alongside the deferred dispatch" + await worker.enqueued[0] + assert deferred_fired.is_set() diff --git a/tests/test_litellm/llms/anthropic/test_anthropic_common_utils.py b/tests/test_litellm/llms/anthropic/test_anthropic_common_utils.py index a2da2cccb7c..794613942a1 100644 --- a/tests/test_litellm/llms/anthropic/test_anthropic_common_utils.py +++ b/tests/test_litellm/llms/anthropic/test_anthropic_common_utils.py @@ -1364,6 +1364,23 @@ class TestAnthropicThinkingSignatureSelfHeal: assert is_empty_thinking_block({"type": "text", "text": ""}) is False assert is_empty_thinking_block("not a dict") is False + def test_is_empty_unsigned_thinking_block(self): + """Emit-side predicate: a signature-only block must be kept (Bedrock + Converse adaptive thinking emits empty text with only a signature, and + the client needs it to replay reasoning in tool-use turns); only an + empty block with nothing to preserve is droppable.""" + from litellm.llms.anthropic.common_utils import is_empty_unsigned_thinking_block + + assert is_empty_unsigned_thinking_block({"type": "thinking", "thinking": ""}) is True + assert is_empty_unsigned_thinking_block({"type": "thinking", "thinking": " \n\t "}) is True + assert is_empty_unsigned_thinking_block({"type": "thinking"}) is True + assert is_empty_unsigned_thinking_block({"type": "thinking", "thinking": "", "signature": ""}) is True + assert is_empty_unsigned_thinking_block({"type": "thinking", "thinking": "", "signature": "sig_abc"}) is False + assert is_empty_unsigned_thinking_block({"type": "thinking", "thinking": " ", "signature": "sig_abc"}) is False + assert is_empty_unsigned_thinking_block({"type": "thinking", "thinking": "plan"}) is False + assert is_empty_unsigned_thinking_block({"type": "redacted_thinking", "data": "opaque"}) is False + assert is_empty_unsigned_thinking_block("not a dict") is False + def test_strip_empty_content_blocks_drops_empty_thinking_blocks(self): """LIT-6357 ingestion half: an assistant tool-loop turn carrying an empty (even signed) thinking block keeps its tool_use blocks and loses diff --git a/tests/test_litellm/llms/azure/chat/test_azure_chat_gpt_transformation.py b/tests/test_litellm/llms/azure/chat/test_azure_chat_gpt_transformation.py index 2cf7cd142d6..4e6b9ed0188 100644 --- a/tests/test_litellm/llms/azure/chat/test_azure_chat_gpt_transformation.py +++ b/tests/test_litellm/llms/azure/chat/test_azure_chat_gpt_transformation.py @@ -11,6 +11,7 @@ sys.path.insert( import litellm from litellm.litellm_core_utils.prompt_templates.common_utils import TOOL_RESULT_IMAGE_BOUNDARY +from litellm.llms.azure.chat.gpt_5_transformation import AzureOpenAIGPT5Config from litellm.llms.azure.chat.gpt_transformation import AzureOpenAIConfig from litellm.utils import get_optional_params @@ -195,3 +196,91 @@ def test_azure_gpt_5_takes_the_reasoning_path() -> None: assert "presence_penalty" not in mapped assert "logit_bias" not in mapped assert "reasoning_effort" in supported + + +class TestAzureToolSchemaCombinatorFlattening: + """ + Regression tests for LIT-6510: Azure's chat completions validator rejects + tool parameters carrying a top-level anyOf/oneOf/allOf for every model + family, so AzureOpenAIConfig.transform_request must flatten them. + """ + + @staticmethod + def _anyof_tool(): + return { + "type": "function", + "function": { + "name": "automation_update", + "description": "Update an automation", + "parameters": { + "type": "object", + "anyOf": [ + { + "properties": {"id": {"type": "string"}, "enabled": {"type": "boolean"}}, + "required": ["id", "enabled"], + }, + { + "properties": {"id": {"type": "string"}, "schedule": {"type": "string"}}, + "required": ["id", "schedule"], + }, + ], + "properties": {"id": {"type": "string"}}, + "required": ["id"], + }, + }, + } + + def _transform(self, config, model, tools): + return config.transform_request( + model=model, + messages=[{"role": "user", "content": "hi"}], + optional_params={"tools": tools}, + litellm_params={"custom_llm_provider": "azure"}, + headers={}, + ) + + def test_transform_request_flattens_top_level_anyof(self): + request = self._transform(AzureOpenAIConfig(), "gpt-4o", [self._anyof_tool()]) + parameters = request["tools"][0]["function"]["parameters"] + assert "anyOf" not in parameters + assert parameters["type"] == "object" + assert set(parameters["properties"]) == {"id", "enabled", "schedule"} + assert parameters["required"] == ["id"] + assert request["tools"][0]["function"]["name"] == "automation_update" + + def test_gpt5_config_flattens_via_shared_transform(self): + request = self._transform(AzureOpenAIGPT5Config(), "gpt-5.4-mini", [self._anyof_tool()]) + parameters = request["tools"][0]["function"]["parameters"] + assert "anyOf" not in parameters + assert set(parameters["properties"]) == {"id", "enabled", "schedule"} + + def test_caller_tool_dict_is_not_mutated(self): + tool = self._anyof_tool() + self._transform(AzureOpenAIConfig(), "gpt-4o", [tool]) + assert tool == self._anyof_tool() + + def test_clean_object_schema_passes_through_as_same_object(self): + tool = { + "type": "function", + "function": { + "name": "lookup", + "parameters": {"type": "object", "properties": {"id": {"type": "string"}}, "required": ["id"]}, + }, + } + request = self._transform(AzureOpenAIConfig(), "gpt-4o", [tool]) + assert request["tools"][0] is tool + + def test_non_dict_tool_entries_pass_through_unchanged(self): + request = self._transform(AzureOpenAIConfig(), "gpt-4o", ["not-a-tool"]) + assert request["tools"] == ["not-a-tool"] + + def test_request_without_tools_is_unchanged(self): + request = AzureOpenAIConfig().transform_request( + model="gpt-4o", + messages=[{"role": "user", "content": "hi"}], + optional_params={"temperature": 0.2}, + litellm_params={"custom_llm_provider": "azure"}, + headers={}, + ) + assert "tools" not in request + assert request["temperature"] == 0.2 diff --git a/tests/test_litellm/llms/azure/chat/test_azure_chat_o_series_transformation.py b/tests/test_litellm/llms/azure/chat/test_azure_chat_o_series_transformation.py index fc7e94a77ba..202f81f1252 100644 --- a/tests/test_litellm/llms/azure/chat/test_azure_chat_o_series_transformation.py +++ b/tests/test_litellm/llms/azure/chat/test_azure_chat_o_series_transformation.py @@ -23,3 +23,48 @@ async def test_azure_chat_o_series_transformation(): ) print(response) assert response["model"] == "web-interface-o1-mini" + + +def test_azure_o_series_transform_request_flattens_top_level_anyof(): + """Regression test for LIT-6510: the o-series super() chain ends in + OpenAIGPTConfig, whose flatten gate skips provider 'azure', so + AzureOpenAIO1Config must flatten tool schema combinators itself.""" + tool = { + "type": "function", + "function": { + "name": "automation_update", + "description": "Update an automation", + "parameters": { + "type": "object", + "anyOf": [ + { + "properties": {"id": {"type": "string"}, "enabled": {"type": "boolean"}}, + "required": ["id", "enabled"], + }, + { + "properties": {"id": {"type": "string"}, "schedule": {"type": "string"}}, + "required": ["id", "schedule"], + }, + ], + "properties": {"id": {"type": "string"}}, + "required": ["id"], + }, + }, + } + optional_params = {"tools": [tool]} + + request = AzureOpenAIO1Config().transform_request( + model="o3-mini", + messages=[{"role": "user", "content": "hi"}], + optional_params=optional_params, + litellm_params={"custom_llm_provider": "azure"}, + headers={}, + ) + + parameters = request["tools"][0]["function"]["parameters"] + assert "anyOf" not in parameters + assert parameters["type"] == "object" + assert set(parameters["properties"]) == {"id", "enabled", "schedule"} + assert parameters["required"] == ["id"] + assert "anyOf" in tool["function"]["parameters"] + assert optional_params["tools"][0] is tool diff --git a/tests/test_litellm/llms/azure/image_edit/test_azure_image_edit_transformation.py b/tests/test_litellm/llms/azure/image_edit/test_azure_image_edit_transformation.py index 59472d1a49d..5c7b249ae72 100644 --- a/tests/test_litellm/llms/azure/image_edit/test_azure_image_edit_transformation.py +++ b/tests/test_litellm/llms/azure/image_edit/test_azure_image_edit_transformation.py @@ -233,3 +233,62 @@ def test_api_version_in_api_base_query_is_preserved(monkeypatch): ) assert _query_params(url) == {"api-version": "2024-05-01-preview"} + + +def test_v1_api_version_uses_v1_route_and_keeps_model(monkeypatch): + monkeypatch.setattr(litellm, "api_version", None, raising=False) + monkeypatch.delenv("AZURE_API_VERSION", raising=False) + config = AzureImageEditConfig() + + for api_version in ("v1", "preview", "latest"): + url = config.get_complete_url( + model=_FALLBACK_MODEL, + api_base=_FALLBACK_API_BASE, + litellm_params={"api_version": api_version}, + ) + assert urllib.parse.urlparse(url).path == "/openai/v1/images/edits" + assert _query_params(url) == {"api-version": api_version} + assert config.finalize_image_edit_request_data({"model": _FALLBACK_MODEL, "prompt": "x"}, url) == { + "model": _FALLBACK_MODEL, + "prompt": "x", + } + + +def test_v1_api_version_from_global_uses_v1_route(monkeypatch): + monkeypatch.setattr(litellm, "api_version", "preview", raising=False) + monkeypatch.delenv("AZURE_API_VERSION", raising=False) + + url = AzureImageEditConfig().get_complete_url( + model=_FALLBACK_MODEL, + api_base=_FALLBACK_API_BASE, + litellm_params={}, + ) + + assert urllib.parse.urlparse(url).path == "/openai/v1/images/edits" + + +def test_dated_api_version_still_uses_deployment_route(monkeypatch): + monkeypatch.setattr(litellm, "api_version", None, raising=False) + monkeypatch.delenv("AZURE_API_VERSION", raising=False) + + url = AzureImageEditConfig().get_complete_url( + model=_FALLBACK_MODEL, + api_base=_FALLBACK_API_BASE, + litellm_params={"api_version": "2024-10-21"}, + ) + + assert urllib.parse.urlparse(url).path == f"/openai/deployments/{_FALLBACK_MODEL}/images/edits" + + +def test_v1_api_version_replaces_deployment_scoped_api_base(monkeypatch): + monkeypatch.setattr(litellm, "api_version", None, raising=False) + monkeypatch.delenv("AZURE_API_VERSION", raising=False) + + url = AzureImageEditConfig().get_complete_url( + model=_FALLBACK_MODEL, + api_base=f"{_FALLBACK_API_BASE}/openai/deployments/{_FALLBACK_MODEL}/images/edits?api-version=2024-10-21", + litellm_params={"api_version": "preview"}, + ) + + assert urllib.parse.urlparse(url).path == "/openai/v1/images/edits" + assert _query_params(url) == {"api-version": "preview"} diff --git a/tests/test_litellm/llms/azure/image_generation/test_azure_image_generation_init.py b/tests/test_litellm/llms/azure/image_generation/test_azure_image_generation_init.py index 560fee17328..70b5eab5c37 100644 --- a/tests/test_litellm/llms/azure/image_generation/test_azure_image_generation_init.py +++ b/tests/test_litellm/llms/azure/image_generation/test_azure_image_generation_init.py @@ -3,9 +3,12 @@ import traceback from typing import Callable, Optional from unittest.mock import AsyncMock, MagicMock, Mock, patch +import httpx import pytest +import respx import litellm +from litellm.caching.llm_caching_handler import LLMClientCache from litellm.llms.azure.azure import AzureChatCompletion from litellm.llms.azure.image_generation.http_utils import ( azure_deployment_image_generation_json_body, @@ -433,3 +436,154 @@ async def test_azure_aimage_generation_base_model_vs_deployment_name(): wire_json = post_kwargs.get("json") or {} assert "model" not in wire_json assert data.get("model") == base_model + + +@pytest.mark.parametrize("api_version", ["v1", "preview", "latest"]) +def test_azure_image_generation_v1_api_version_uses_v1_route(api_version): + """The v1 Azure surface exposes /openai/v1/images/generations and routes by body ``model``.""" + url = AzureChatCompletion().create_azure_base_url( + azure_client_params={ + "azure_endpoint": "https://my-resource.openai.azure.com", + "api_version": api_version, + }, + model="gpt-image-1", + base_model=None, + ) + assert url == f"https://my-resource.openai.azure.com/openai/v1/images/generations?api-version={api_version}" + data = {"model": "gpt-image-1", "prompt": "x"} + assert azure_deployment_image_generation_json_body(url, data) == data + + +def test_azure_image_generation_dated_api_version_uses_deployment_route(): + url = AzureChatCompletion().create_azure_base_url( + azure_client_params={ + "azure_endpoint": "https://my-resource.openai.azure.com", + "api_version": "2024-10-21", + }, + model="gpt-image-1", + base_model=None, + ) + assert ( + url + == "https://my-resource.openai.azure.com/openai/deployments/gpt-image-1/images/generations?api-version=2024-10-21" + ) + assert "model" not in azure_deployment_image_generation_json_body(url, {"model": "gpt-image-1", "prompt": "x"}) + + +def test_azure_image_generation_v1_api_version_replaces_deployment_scoped_api_base(): + url = AzureChatCompletion().create_azure_base_url( + azure_client_params={ + "azure_endpoint": "https://my-resource.openai.azure.com/openai/deployments/gpt-image-1/images/generations", + "api_version": "preview", + }, + model="gpt-image-1", + base_model=None, + ) + assert url == "https://my-resource.openai.azure.com/openai/v1/images/generations?api-version=preview" + + +def test_azure_image_generation_v1_api_version_uses_base_url_client_param(): + url = AzureChatCompletion().create_azure_base_url( + azure_client_params={ + "base_url": "https://my-resource.openai.azure.com/openai/deployments/gpt-image-1?api-version=2024-10-21", + "api_version": "preview", + }, + model="gpt-image-1", + base_model=None, + ) + assert url == "https://my-resource.openai.azure.com/openai/v1/images/generations?api-version=preview" + + +def test_azure_v1_image_generation_json_body_sends_deployment_name(): + """The v1 route ignores the URL and routes by body ``model``, which must be the deployment name.""" + url = "https://my-resource.openai.azure.com/openai/v1/images/generations?api-version=preview" + data = {"model": "gpt-image-2", "prompt": "x", "n": 1} + out = azure_deployment_image_generation_json_body(url, data, deployment_name="img-dep") + assert out["model"] == "img-dep" + assert out["prompt"] == "x" + assert data["model"] == "gpt-image-2" + assert azure_deployment_image_generation_json_body(url, data) == data + + +@pytest.mark.asyncio +async def test_azure_aimage_generation_v1_route_sends_deployment_name_in_body( + respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch +): + monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) + monkeypatch.setattr(litellm, "in_memory_llm_clients_cache", LLMClientCache()) + azure_chat_completion = AzureChatCompletion() + model = "img-dep" + base_model = "gpt-image-2" + data = {"model": base_model, "prompt": "A beautiful image of a cat", "n": 1} + azure_client_params = { + "azure_endpoint": "https://my-resource.openai.azure.com", + "api_version": "preview", + } + + route = respx_mock.post("https://my-resource.openai.azure.com/openai/v1/images/generations").mock( + return_value=httpx.Response(200, json={"created": 1234567890, "data": [{"b64_json": "aaaa"}]}) + ) + + logging_obj = MagicMock() + logging_obj.pre_call = MagicMock() + logging_obj.post_call = MagicMock() + + await azure_chat_completion.aimage_generation( + data=data, + model_response=None, + azure_client_params=azure_client_params, + api_key="test-api-key", + input=[], + logging_obj=logging_obj, + headers={}, + model=model, + timeout=60.0, + ) + + request = route.calls.last.request + assert str(request.url) == ("https://my-resource.openai.azure.com/openai/v1/images/generations?api-version=preview") + sent_body = json.loads(request.content) + assert sent_body["model"] == model + assert sent_body["prompt"] == data["prompt"] + + +def test_azure_image_generation_v1_route_base_model_vs_deployment_name(respx_mock: respx.MockRouter): + """On the v1 surface the body ``model`` must be the deployment name, never base_model.""" + azure_chat_completion = AzureChatCompletion() + prompt = "A beautiful image of a cat" + model = "img-dep" + base_model = "gpt-image-2" + api_base = "https://my-resource.openai.azure.com" + api_version = "v1" + litellm_params = { + "base_model": base_model, + "api_base": api_base, + "api_version": api_version, + } + + route = respx_mock.post(f"{api_base}/openai/v1/images/generations").mock( + return_value=httpx.Response(200, json={"created": 1234567890, "data": [{"b64_json": "aaaa"}]}) + ) + + logging_obj = MagicMock() + logging_obj.pre_call = MagicMock() + logging_obj.post_call = MagicMock() + + azure_chat_completion.image_generation( + prompt=prompt, + timeout=60.0, + optional_params={"n": 1, "size": "1024x1024"}, + logging_obj=logging_obj, + headers={}, + model=model, + api_key="test-api-key", + api_base=api_base, + api_version=api_version, + litellm_params=litellm_params, + ) + + request = route.calls.last.request + assert str(request.url) == f"{api_base}/openai/v1/images/generations?api-version={api_version}" + sent_body = json.loads(request.content) + assert sent_body["model"] == model + assert sent_body["prompt"] == prompt diff --git a/tests/test_litellm/llms/azure/response/test_azure_transformation.py b/tests/test_litellm/llms/azure/response/test_azure_transformation.py index da44394d11d..f6bbf685f26 100644 --- a/tests/test_litellm/llms/azure/response/test_azure_transformation.py +++ b/tests/test_litellm/llms/azure/response/test_azure_transformation.py @@ -537,3 +537,79 @@ class TestAzureResponsesAPIConfig: """ supported = self.config.get_supported_openai_params(self.model) assert "context_management" not in supported + + def _anyof_tool(self): + return { + "type": "function", + "name": "automation_update", + "description": "Update an automation", + "parameters": { + "type": "object", + "anyOf": [ + { + "properties": {"id": {"type": "string"}, "enabled": {"type": "boolean"}}, + "required": ["id", "enabled"], + }, + { + "properties": {"id": {"type": "string"}, "schedule": {"type": "string"}}, + "required": ["id", "schedule"], + }, + ], + "properties": {"id": {"type": "string"}}, + "required": ["id"], + }, + } + + def test_azure_flattens_top_level_anyof_for_gpt4_family_deployment_name(self): + result = self.config.transform_responses_api_request( + model="gpt-4o", + input="hi", + response_api_optional_request_params={"tools": [self._anyof_tool()]}, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + parameters = result["tools"][0]["parameters"] + assert "anyOf" not in parameters + assert parameters["type"] == "object" + assert set(parameters["properties"]) == {"id", "enabled", "schedule"} + assert parameters["required"] == ["id"] + + def test_azure_flattens_via_base_model_for_arbitrary_deployment_name(self): + result = self.config.transform_responses_api_request( + model="my-eastus-deployment", + input="hi", + response_api_optional_request_params={"tools": [self._anyof_tool()]}, + litellm_params=GenericLiteLLMParams(model_info={"base_model": "azure/gpt-4o"}), + headers={}, + ) + + assert "anyOf" not in result["tools"][0]["parameters"] + + def test_azure_keeps_combinators_for_gpt5_base_model(self): + tool = self._anyof_tool() + + result = self.config.transform_responses_api_request( + model="my-eastus-deployment", + input="hi", + response_api_optional_request_params={"tools": [tool]}, + litellm_params=GenericLiteLLMParams(model_info={"base_model": "azure/gpt-5.4-mini"}), + headers={}, + ) + + assert result["tools"][0] is tool + assert "anyOf" in result["tools"][0]["parameters"] + + def test_azure_keeps_combinators_for_unrecognized_deployment_without_base_model(self): + tool = self._anyof_tool() + + result = self.config.transform_responses_api_request( + model="my-eastus-deployment", + input="hi", + response_api_optional_request_params={"tools": [tool]}, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert result["tools"][0] is tool + assert "anyOf" in result["tools"][0]["parameters"] diff --git a/tests/test_litellm/llms/base_llm/test_managed_resource_isolation.py b/tests/test_litellm/llms/base_llm/test_managed_resource_isolation.py index b5fcd9d8219..1746926c689 100644 --- a/tests/test_litellm/llms/base_llm/test_managed_resource_isolation.py +++ b/tests/test_litellm/llms/base_llm/test_managed_resource_isolation.py @@ -7,6 +7,7 @@ import pytest from litellm.llms.base_llm.managed_resources.isolation import ( build_owner_filter, can_access_resource, + resolve_resource_owner_id, ) from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth @@ -154,3 +155,46 @@ def test_access_identity_less_caller_always_denied(created_by, resource_team_id) ) is False ) + + +# --------------------------------------------------------------------------- +# keyless keys (no user_id, no team_id) own their resources by hashed token +# --------------------------------------------------------------------------- + + +def test_owner_id_prefers_user_id_then_falls_back_to_token(): + assert resolve_resource_owner_id(UserAPIKeyAuth(user_id="alice")) == "alice" + assert resolve_resource_owner_id(UserAPIKeyAuth(team_id="team-eng")) is None + assert resolve_resource_owner_id(UserAPIKeyAuth()) is None + + keyless = UserAPIKeyAuth(api_key="sk-keyless") + assert resolve_resource_owner_id(keyless) == f"key:{keyless.token}" + + +def test_keyless_key_can_access_its_own_resource(): + """Regression for the self-lockout: a key generated by a proxy admin (or a + service-account key) has no user_id and no team_id, so it used to stamp + `created_by=None` and then be denied its own batches and files.""" + keyless = UserAPIKeyAuth(api_key="sk-keyless") + owner_id = resolve_resource_owner_id(keyless) + + assert build_owner_filter(keyless) == {"created_by": owner_id} + assert ( + can_access_resource(keyless, created_by=owner_id, resource_team_id=None) is True + ) + + +def test_keyless_key_denied_another_keyless_keys_resource(): + """The #27004 isolation invariant: two distinct keyless keys must not see + each other's resources.""" + creator = UserAPIKeyAuth(api_key="sk-creator") + other = UserAPIKeyAuth(api_key="sk-other") + + assert ( + can_access_resource( + other, + created_by=resolve_resource_owner_id(creator), + resource_team_id=None, + ) + is False + ) diff --git a/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py b/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py index cea299280f8..41d82e4f960 100644 --- a/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py +++ b/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py @@ -428,30 +428,58 @@ def test_output_config_forwarded_for_bedrock_chat_invoke_request(): def test_output_config_format_converted_for_bedrock_chat_invoke_request(): - """Bedrock Invoke chat path consumes ``output_config.format`` before forwarding.""" + """Bedrock Invoke chat path inlines ``output_config.format`` for models + without native structured-output support and keeps the effort key.""" config = AmazonAnthropicClaudeConfig() schema = { "type": "object", "properties": {"answer": {"type": "string"}}, } - result = config.transform_request( + with patch( # test-quality-ok: pin non-native path + "litellm.llms.bedrock.common_utils._bedrock_model_supports", + side_effect=lambda _model, key: key == "supports_output_config", + ): + result = config.transform_request( + model="anthropic.claude-opus-4-7", + messages=[{"role": "user", "content": "test"}], + optional_params={ + "max_tokens": 100, + "output_config": { + "effort": "xhigh", + "format": {"type": "json_schema", "schema": schema}, + }, + }, + litellm_params={}, + headers={}, + ) + + assert result.get("output_config") == {"effort": "xhigh"} + last_content = result["messages"][0]["content"] + assert json.loads(last_content[-1]["text"]) == schema + + +def test_output_config_format_forwarded_for_bedrock_chat_invoke_request(): + """Bedrock Invoke chat path forwards ``output_config.format`` alongside effort + for models with native structured-output support (Claude Opus 4.7).""" + schema_format = { + "type": "json_schema", + "schema": {"type": "object", "properties": {"answer": {"type": "string"}}}, + } + + result = AmazonAnthropicClaudeConfig().transform_request( model="anthropic.claude-opus-4-7", messages=[{"role": "user", "content": "test"}], optional_params={ "max_tokens": 100, - "output_config": { - "effort": "xhigh", - "format": {"type": "json_schema", "schema": schema}, - }, + "output_config": {"effort": "xhigh", "format": schema_format}, }, litellm_params={}, headers={}, ) - assert result.get("output_config") == {"effort": "xhigh"} - last_content = result["messages"][0]["content"] - assert json.loads(last_content[-1]["text"]) == schema + assert result.get("output_config") == {"effort": "xhigh", "format": schema_format} + assert "answer" not in json.dumps(result["messages"]) @pytest.mark.parametrize( @@ -488,7 +516,7 @@ def test_bedrock_chat_invoke_checks_output_config_support_with_bedrock_provider( optional_params = {"max_tokens": 100, "output_config": {"effort": "high"}} with patch( - "litellm.llms.bedrock.chat.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ) as mock_supports_factory: result = config.transform_request( @@ -499,11 +527,7 @@ def test_bedrock_chat_invoke_checks_output_config_support_with_bedrock_provider( headers={}, ) - mock_supports_factory.assert_called_once_with( - model="us.anthropic.claude-opus-4-7", - custom_llm_provider="bedrock", - key="supports_output_config", - ) + mock_supports_factory.assert_called_once_with("us.anthropic.claude-opus-4-7", "supports_output_config") assert result["output_config"] == {"effort": "high"} @@ -542,3 +566,108 @@ def test_output_format_removed_from_bedrock_invoke_request(): assert ( "output_format" not in result ), f"output_format should be removed for Bedrock Invoke, got keys: {result.keys()}" + + +def test_bedrock_chat_invoke_forwards_output_config_format_natively(local_model_cost_map): + """Regression: ``output_config.format`` is forwarded verbatim on models Bedrock + enforces structured outputs for, instead of being inlined as prompt text.""" + import json + + config = AmazonAnthropicClaudeConfig() + schema_format = { + "type": "json_schema", + "schema": { + "type": "object", + "properties": {"zebra_count": {"type": "integer"}}, + "required": ["zebra_count"], + "additionalProperties": False, + }, + } + + result = config.transform_request( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + messages=[{"role": "user", "content": "say hello"}], + optional_params={ + "max_tokens": 100, + "output_config": {"format": schema_format}, + }, + litellm_params={}, + headers={}, + ) + + assert result.get("output_config") == {"format": schema_format} + assert "zebra_count" not in json.dumps(result["messages"]) + + +def test_bedrock_chat_invoke_drop_params_keeps_native_output_config_format(local_model_cost_map, monkeypatch): + """``drop_params=True`` must not eat ``output_config.format`` before the + native-forwarding router runs (Sonnet 4.5 has no effort flags).""" + import litellm + + monkeypatch.setattr(litellm, "drop_params", True) + schema_format = { + "type": "json_schema", + "schema": {"type": "object", "properties": {"zebra_count": {"type": "integer"}}}, + } + + result = AmazonAnthropicClaudeConfig().transform_request( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + messages=[{"role": "user", "content": "say hello"}], + optional_params={"max_tokens": 100, "output_config": {"format": schema_format}}, + litellm_params={}, + headers={}, + ) + + assert result.get("output_config") == {"format": schema_format} + + +def test_bedrock_chat_invoke_drop_params_still_inlines_for_non_native(local_model_cost_map, monkeypatch): + """``drop_params=True`` on a model without native structured-output support + still reaches the inline-schema fallback instead of losing the schema.""" + import litellm + + monkeypatch.setattr(litellm, "drop_params", True) + schema = {"type": "object", "properties": {"zebra_count": {"type": "integer"}}} + + result = AmazonAnthropicClaudeConfig().transform_request( + model="anthropic.claude-3-haiku-20240307-v1:0", + messages=[{"role": "user", "content": "say hello"}], + optional_params={ + "max_tokens": 100, + "output_config": {"format": {"type": "json_schema", "schema": schema}}, + }, + litellm_params={}, + headers={}, + ) + + assert "output_config" not in result + last_content = result["messages"][-1]["content"] + assert json.loads(last_content[-1]["text"]) == schema + + +@pytest.mark.parametrize( + "model", + ["us.anthropic.claude-fable-5-1", "anthropic.claude-fable-5-1"], +) +def test_bedrock_chat_invoke_fable_5_1_response_format_avoids_forced_tool_choice(local_model_cost_map, model): + """Regression: Bedrock rejects both native ``output_config.format`` and forced + tool_choice for Fable 5.1, so invoke must use the tool-based path without a + forced ``tool_choice``.""" + result = AmazonAnthropicClaudeConfig().map_openai_params( + non_default_params={ + "response_format": { + "type": "json_schema", + "json_schema": { + "name": "test_schema", + "schema": {"type": "object", "properties": {"result": {"type": "string"}}}, + }, + } + }, + optional_params={}, + model=model, + drop_params=False, + ) + + assert "output_format" not in result + assert "tools" in result + assert "tool_choice" not in result diff --git a/tests/test_litellm/llms/bedrock/chat/test_bedrock_converse_handler.py b/tests/test_litellm/llms/bedrock/chat/test_bedrock_converse_handler.py index 8e67a7e3438..21e3239f623 100644 --- a/tests/test_litellm/llms/bedrock/chat/test_bedrock_converse_handler.py +++ b/tests/test_litellm/llms/bedrock/chat/test_bedrock_converse_handler.py @@ -96,10 +96,8 @@ def _completion_kwargs(**overrides): return kwargs -def _run(**overrides): - with patch.object( - BedrockConverseLLM, "get_credentials", return_value=RESOLVED_CREDENTIALS - ): +def _run(*, credentials: Credentials | None = RESOLVED_CREDENTIALS, **overrides): + with patch.object(BedrockConverseLLM, "get_credentials", return_value=credentials): return BedrockConverseLLM().completion(**_completion_kwargs(**overrides)) @@ -360,7 +358,7 @@ async def test_async_completion_logs_pre_call_by_default(): def _sync_client_returning_converse_response(): client = MagicMock() - client.post = lambda **_kwargs: httpx.Response( + client.post.side_effect = lambda **_kwargs: httpx.Response( 200, json=CONVERSE_RESPONSE, request=httpx.Request("POST", "https://bedrock-runtime.us-west-2.amazonaws.com"), @@ -487,3 +485,31 @@ def test_post_call_is_not_logged_twice_when_the_sync_rust_call_declines(): assert response.choices[0].message.content == "hi" assert len(calls["post_call"]) == 1 assert "hi" in calls["post_call"][0]["original_response"] + + +def test_bearer_token_auth_serves_when_boto3_resolves_no_sigv4_credentials(monkeypatch): + """With only `AWS_BEARER_TOKEN_BEDROCK` configured boto3 resolves no + credentials at all. Preparing the Rust handoff must not dereference that + None: the bearer token signs the request on its own.""" + monkeypatch.setenv("AWS_BEARER_TOKEN_BEDROCK", "bedrock-bearer-token") + client = _sync_client_returning_converse_response() + + response = _run(credentials=None, litellm_params={}, client=client) + + assert response.choices[0].message.content == "hi" + sent_headers = client.post.call_args.kwargs["headers"] + assert sent_headers["Authorization"] == "Bearer bedrock-bearer-token" + + +def test_the_rust_opt_in_needs_no_sigv4_principal(): + """The core resolves the bearer token itself, so a bearer-only deployment + keeps its opt-in and the gate sees no aws_* credential keys to sign with.""" + seen = _inject() + + response = _run(credentials=None, api_key="bedrock-bearer-token") + + assert response.choices[0].message.content == "hello from rust" + params = seen["call"][0]["optional_params"] + assert not {"aws_access_key_id", "aws_secret_access_key", "aws_session_token"} & params.keys() + assert params["aws_region_name"] == "us-east-1" + assert seen["call"][0]["api_key"] == "bedrock-bearer-token" diff --git a/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py b/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py index 226bba6826a..70f3153ed7e 100644 --- a/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py +++ b/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py @@ -957,6 +957,28 @@ def test_transform_request_helper_includes_anthropic_beta_and_tools(): assert fields["tools"][0]["type"] == "computer_20250124" +def test_config_blocks_do_not_leak_into_inference_config(): + """Regression: inferenceConfig was built before the config blocks were popped, so a dead + nested copy of each block (guardrailConfig, performanceConfig, serviceTier) rode inside + inferenceConfig alongside the real top-level one.""" + data = AmazonConverseConfig()._transform_request_helper( + model="anthropic.claude-haiku-4-5-20251001-v1:0", + system_content_blocks=[], + optional_params={ + "maxTokens": 100, + "guardrailConfig": {"guardrailIdentifier": "gr-id", "guardrailVersion": "DRAFT"}, + "performanceConfig": {"latency": "optimized"}, + "serviceTier": {"type": "priority"}, + }, + messages=[{"role": "user", "content": "hi"}], + ) + + assert data["inferenceConfig"] == {"maxTokens": 100} + assert data["guardrailConfig"] == {"guardrailIdentifier": "gr-id", "guardrailVersion": "DRAFT"} + assert data["performanceConfig"] == {"latency": "optimized"} + assert data["serviceTier"] == {"type": "priority"} + + def test_parallel_tool_calls_config_kept_for_sonnet_5(monkeypatch): old_env = os.environ.get("LITELLM_LOCAL_MODEL_COST_MAP") old_cost = litellm.model_cost @@ -2853,17 +2875,11 @@ def test_guarded_text_guardrail_config_preserved(): headers={}, ) - # GuardrailConfig should be present at top level assert "guardrailConfig" in result assert result["guardrailConfig"]["guardrailIdentifier"] == "gr-abc123" - # GuardrailConfig should also be in inferenceConfig assert "inferenceConfig" in result - assert "guardrailConfig" in result["inferenceConfig"] - assert ( - result["inferenceConfig"]["guardrailConfig"]["guardrailIdentifier"] - == "gr-abc123" - ) + assert "guardrailConfig" not in result["inferenceConfig"] def test_auto_convert_last_user_message_to_guarded_text(): @@ -5232,6 +5248,84 @@ def test_cache_control_injection_tool_config_drops_ttl_for_unsupported_model(): assert tools[-1] == {"cachePoint": {"type": "default"}} +@pytest.mark.parametrize( + ("model", "expects_cache_points"), + [ + pytest.param("nvidia.nemotron-super-3-120b", False, id="mapped-model-without-prompt-caching"), + pytest.param("us.nvidia.nemotron-super-3-120b", False, id="regional-prefix-resolves-through-base-model"), + pytest.param( + "us.anthropic.claude-3-5-sonnet-20240620-v1:0", False, id="claude-named-but-not-caching-on-bedrock" + ), + pytest.param("us.anthropic.claude-sonnet-4-5-20250929-v1:0", True, id="mapped-model-with-prompt-caching"), + pytest.param( + "arn:aws:bedrock:us-east-1:123456789012:application-inference-profile/abc123", + True, + id="unmapped-arn-keeps-emitting", + ), + ], +) +def test_cache_points_emitted_only_for_models_that_support_prompt_caching(model, expects_cache_points, monkeypatch): + """Bedrock rejects cachePoint blocks for models without prompt caching support + ("You invoked an unsupported model or your request did not allow prompt caching"), + and clients like Claude Code attach cache_control to every request, so a map-known + model without the capability must not receive them. Unmapped ids (application + inference profile ARNs, models newer than the map) keep emitting so existing + caching setups never silently degrade.""" + monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") + monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url="")) + + body = AmazonConverseConfig().transform_request( + model=model, + messages=[ + {"role": "system", "content": [{"type": "text", "text": "sys", "cache_control": {"type": "ephemeral"}}]}, + {"role": "user", "content": [{"type": "text", "text": "hi", "cache_control": {"type": "ephemeral"}}]}, + ], + optional_params={}, + litellm_params={}, + headers={}, + ) + + assert ("cachePoint" in json.dumps(body)) is expects_cache_points + assert body["system"][0]["text"] == "sys" + assert body["messages"][0]["content"][0]["text"] == "hi" + + +def test_tool_config_cachepoint_not_placed_or_credited_for_model_without_prompt_caching(monkeypatch): + """The tool_config injection point must stand down with the rest of the cachePoint + emission when the model cannot cache, and spend attribution must not credit the + gateway for a breakpoint that was never placed.""" + monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") + monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url="")) + + bucket: dict = {"user_api_key": "sk-test"} + data = AmazonConverseConfig()._transform_request_helper( + model="nvidia.nemotron-super-3-120b", + system_content_blocks=[], + optional_params={ + "tools": [ + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get weather", + "parameters": { + "type": "object", + "properties": {"location": {"type": "string"}}, + "required": ["location"], + }, + }, + } + ], + "cache_control_injection_points": [{"location": "tool_config"}], + }, + messages=[{"role": "user", "content": "hi"}], + litellm_params={"metadata": bucket, "litellm_metadata": None, "model_info": {"id": "dep-bedrock"}}, + ) + + assert "cachePoint" not in json.dumps(data.get("toolConfig", {})) + assert "litellm_gateway_injected_cache" not in bucket + + def test_translate_response_format_json_schema_still_injects_tool(): """ response_format with an explicit json_schema should still use the @@ -6195,7 +6289,7 @@ def test_message_level_cache_control_drops_ttl_for_unsupported_model(ttl_target) result = _bedrock_converse_messages_pt( messages=_agentic_messages_with_ttl(ttl_target), - model="anthropic.claude-3-5-sonnet-20240620-v1:0", + model="anthropic.claude-3-5-sonnet-20241022-v2:0", llm_provider="bedrock_converse", ) @@ -6433,3 +6527,95 @@ def test_disabled_thinking_omitted_for_always_on_models_converse( assert "thinking" not in additional else: assert additional.get("thinking") == {"type": "disabled"} + +@pytest.mark.parametrize( + "model", + ["anthropic.claude-fable-5-1", "us.anthropic.claude-fable-5-1"], +) +@pytest.mark.parametrize( + "tool_choice", + ["required", {"type": "function", "function": {"name": "get_weather"}}], +) +def test_forced_tool_choice_downgraded_to_auto_on_fable_5_1_converse( + local_model_cost_map, model, tool_choice +): + config = AmazonConverseConfig() + + result = config.map_tool_choice_values( + model=model, tool_choice=tool_choice, drop_params=True + ) + + assert result == {"auto": {}} + + +@pytest.mark.parametrize( + "tool_choice", + ["required", {"type": "function", "function": {"name": "get_weather"}}], +) +def test_forced_tool_choice_raises_clean_error_on_fable_5_1_converse( + local_model_cost_map, tool_choice, monkeypatch +): + monkeypatch.setattr(litellm, "drop_params", False) + config = AmazonConverseConfig() + + with pytest.raises(litellm.utils.UnsupportedParamsError, match="forced tool use"): + config.map_tool_choice_values( + model="anthropic.claude-fable-5-1", tool_choice=tool_choice, drop_params=False + ) + + +@pytest.mark.parametrize("tool_choice", ["auto", "none"]) +def test_unforced_tool_choice_unaffected_on_fable_5_1_converse(local_model_cost_map, tool_choice): + config = AmazonConverseConfig() + + result = config.map_tool_choice_values( + model="anthropic.claude-fable-5-1", tool_choice=tool_choice, drop_params=True + ) + + assert result == ({"auto": {}} if tool_choice == "auto" else None) + + +@pytest.mark.parametrize( + "model", + ["anthropic.claude-fable-5-1", "us.anthropic.claude-fable-5-1"], +) +def test_response_format_avoids_native_and_forced_tool_choice_on_fable_5_1_converse( + local_model_cost_map, model +): + """Regression: Bedrock rejects both ``outputConfig`` structured output and forced + tool_choice for Fable 5.1, so response_format must map to a tool without a forced + tool_choice.""" + config = AmazonConverseConfig() + + result = config.map_openai_params( + non_default_params={ + "response_format": { + "type": "json_schema", + "json_schema": { + "name": "test_schema", + "schema": {"type": "object", "properties": {"result": {"type": "string"}}}, + }, + } + }, + optional_params={}, + model=model, + drop_params=False, + ) + + assert "outputConfig" not in result + assert "tools" in result + assert "tool_choice" not in result + assert result.get("json_mode") is True + + +def test_forced_tool_choice_forwarded_on_converse_models_that_support_it( + local_model_cost_map, monkeypatch +): + monkeypatch.setattr(litellm, "drop_params", False) + config = AmazonConverseConfig() + + result = config.map_tool_choice_values( + model="anthropic.claude-fable-5", tool_choice="required", drop_params=False + ) + + assert result == {"any": {}} diff --git a/tests/test_litellm/llms/bedrock/embed/test_bedrock_embedding.py b/tests/test_litellm/llms/bedrock/embed/test_bedrock_embedding.py index 65ae719d021..08d01127eba 100644 --- a/tests/test_litellm/llms/bedrock/embed/test_bedrock_embedding.py +++ b/tests/test_litellm/llms/bedrock/embed/test_bedrock_embedding.py @@ -945,7 +945,7 @@ def test_titan_image_embedding_cost_uses_per_image_rate(): "encoding_format,expected_embedding_types", [ ("float", ["float"]), - ("base64", ["base64"]), + ("base64", ["float"]), (["float", "int8"], ["float", "int8"]), ], ) diff --git a/tests/test_litellm/llms/bedrock/files/test_bedrock_files_handler.py b/tests/test_litellm/llms/bedrock/files/test_bedrock_files_handler.py index 7f91b49a6f5..639be272351 100644 --- a/tests/test_litellm/llms/bedrock/files/test_bedrock_files_handler.py +++ b/tests/test_litellm/llms/bedrock/files/test_bedrock_files_handler.py @@ -204,3 +204,69 @@ def test_should_forward_trusted_model_credentials_to_retrieve_provider_config(): assert response is mock_response litellm_params = mock_retrieve_file.call_args.kwargs["litellm_params"] assert litellm_params["_litellm_internal_model_credentials"] is trusted_credentials + + +@pytest.mark.asyncio +async def test_afile_content_assumes_role_with_external_id(monkeypatch): + """A trust policy requiring sts:ExternalId must be satisfied by the deployment's aws_external_id.""" + import datetime + + import boto3 + from botocore.exceptions import ClientError + + monkeypatch.delenv("AWS_EXTERNAL_ID", raising=False) + + class FakeSTSClient: + def get_caller_identity(self): + return {"Arn": "arn:aws:iam::111111111111:user/litellm-proxy-pod"} + + def assume_role(self, **params): + if params.get("ExternalId") != "external-id-files-download": + raise ClientError( + {"Error": {"Code": "AccessDenied", "Message": "is not authorized to perform: sts:AssumeRole"}}, + "AssumeRole", + ) + return { + "Credentials": { + "AccessKeyId": "ASIAFILESDOWNLOADROLE", + "SecretAccessKey": "assumed-secret", + "SessionToken": "assumed-session-token", + "Expiration": datetime.datetime.now(datetime.timezone.utc) + datetime.timedelta(minutes=30), + } + } + + class FakeS3Body: + def read(self): + return b'{"custom_id": "req-1"}' + + class FakeS3Client: + def get_object(self, Bucket, Key): + return {"Body": FakeS3Body()} + + def fake_boto3_client(service_name, **kwargs): + if service_name == "sts": + return FakeSTSClient() + return FakeS3Client() + + optional_params = { + "_litellm_internal_model_credentials": MappingProxyType({"s3_bucket_name": "safe-bucket"}), + "aws_region_name": "us-east-1", + "aws_access_key_id": "AKIAFILESDOWNLOADCALLER", + "aws_secret_access_key": "pod-caller-secret", + "aws_role_name": "arn:aws:iam::999999999999:role/litellm-files-download-role", + "aws_session_name": "litellm-files-download-session", + "aws_external_id": "external-id-files-download", + } + + with patch.object(boto3, "client", side_effect=fake_boto3_client) as mock_boto3_client: + response = await BedrockFilesHandler().afile_content( + file_content_request={"file_id": "s3://safe-bucket/litellm-bedrock-files-model-id-abc.jsonl"}, + optional_params=optional_params, + timeout=10.0, + max_retries=None, + ) + + s3_client_kwargs = next(call.kwargs for call in mock_boto3_client.call_args_list if call.args[0] == "s3") + assert s3_client_kwargs["aws_access_key_id"] == "ASIAFILESDOWNLOADROLE" + assert s3_client_kwargs["aws_session_token"] == "assumed-session-token" + assert response.content == b'{"custom_id": "req-1"}' diff --git a/tests/test_litellm/llms/bedrock/files/test_bedrock_files_transformation.py b/tests/test_litellm/llms/bedrock/files/test_bedrock_files_transformation.py index da13f265ee4..541c0db15d8 100644 --- a/tests/test_litellm/llms/bedrock/files/test_bedrock_files_transformation.py +++ b/tests/test_litellm/llms/bedrock/files/test_bedrock_files_transformation.py @@ -2404,3 +2404,111 @@ class TestBedrockFilesS3SignatureEncoding: body=None, headers=litellm_params[S3_SIGNED_GET_HEADERS_PARAM], ) + + +def test_sign_s3_request_assumes_role_with_external_id(monkeypatch): + """A trust policy requiring sts:ExternalId must be satisfied when signing the S3 upload request.""" + import datetime + from unittest.mock import patch + + import boto3 + from botocore.exceptions import ClientError + + from litellm.llms.bedrock.files.transformation import BedrockFilesConfig + + monkeypatch.delenv("AWS_EXTERNAL_ID", raising=False) + + class FakeSTSClient: + def get_caller_identity(self): + return {"Arn": "arn:aws:iam::111111111111:user/litellm-proxy-pod"} + + def assume_role(self, **params): + if params.get("ExternalId") != "external-id-files-put": + raise ClientError( + {"Error": {"Code": "AccessDenied", "Message": "is not authorized to perform: sts:AssumeRole"}}, + "AssumeRole", + ) + return { + "Credentials": { + "AccessKeyId": "ASIAFILESPUTROLE", + "SecretAccessKey": "assumed-secret", + "SessionToken": "assumed-session-token", + "Expiration": datetime.datetime.now(datetime.timezone.utc) + datetime.timedelta(minutes=30), + } + } + + optional_params = { + "aws_region_name": "us-east-1", + "aws_access_key_id": "AKIAFILESPUTCALLER", + "aws_secret_access_key": "pod-caller-secret", + "aws_role_name": "arn:aws:iam::999999999999:role/litellm-files-put-role", + "aws_session_name": "litellm-files-put-session", + "aws_external_id": "external-id-files-put", + } + + with patch.object(boto3, "client", return_value=FakeSTSClient()): + signed_headers, _signed_body = BedrockFilesConfig()._sign_s3_request( + content='{"custom_id": "req-1"}', + api_base="https://s3.us-east-1.amazonaws.com/safe-bucket/litellm-bedrock-files-model-id-abc.jsonl", + optional_params=optional_params, + ) + + authorization = {key.lower(): value for key, value in signed_headers.items()}["authorization"] + assert "ASIAFILESPUTROLE" in authorization + + +def test_sign_s3_get_request_assumes_role_with_external_id(monkeypatch): + """A trust policy requiring sts:ExternalId must be satisfied when signing the S3 download request.""" + import datetime + from unittest.mock import patch + + import boto3 + from botocore.exceptions import ClientError + + from litellm.llms.bedrock.files.transformation import ( + BedrockFilesConfig, + _BedrockS3RequestParams, + ) + + monkeypatch.delenv("AWS_EXTERNAL_ID", raising=False) + + class FakeSTSClient: + def get_caller_identity(self): + return {"Arn": "arn:aws:iam::111111111111:user/litellm-proxy-pod"} + + def assume_role(self, **params): + if params.get("ExternalId") != "external-id-files-get": + raise ClientError( + {"Error": {"Code": "AccessDenied", "Message": "is not authorized to perform: sts:AssumeRole"}}, + "AssumeRole", + ) + return { + "Credentials": { + "AccessKeyId": "ASIAFILESGETROLE", + "SecretAccessKey": "assumed-secret", + "SessionToken": "assumed-session-token", + "Expiration": datetime.datetime.now(datetime.timezone.utc) + datetime.timedelta(minutes=30), + } + } + + request_params = _BedrockS3RequestParams.model_validate( + { + "aws_region_name": "us-east-1", + "aws_access_key_id": "AKIAFILESGETCALLER", + "aws_secret_access_key": "pod-caller-secret", + "aws_role_name": "arn:aws:iam::999999999999:role/litellm-files-get-role", + "aws_session_name": "litellm-files-get-session", + "aws_external_id": "external-id-files-get", + } + ) + assert request_params.aws_external_id == "external-id-files-get" + + with patch.object(boto3, "client", return_value=FakeSTSClient()): + signed_headers = BedrockFilesConfig()._sign_s3_get_request( + api_base="https://s3.us-east-1.amazonaws.com/safe-bucket/litellm-bedrock-files-model-id-abc.jsonl", + aws_region_name="us-east-1", + request_params=request_params, + ) + + authorization = {key.lower(): value for key, value in signed_headers.items()}["authorization"] + assert "ASIAFILESGETROLE" in authorization diff --git a/tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py b/tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py index 1e09afd6919..09ebc1a3c95 100644 --- a/tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py +++ b/tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py @@ -935,7 +935,7 @@ def test_bedrock_messages_strips_output_config(): } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=False, ): result = cfg.transform_anthropic_messages_request( @@ -970,7 +970,7 @@ def test_bedrock_messages_preserves_output_config_for_claude_4_6(): } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ): result = cfg.transform_anthropic_messages_request( @@ -1003,7 +1003,7 @@ def test_bedrock_messages_checks_output_config_support_with_bedrock_provider(): } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ) as mock_supports_factory: result = cfg.transform_anthropic_messages_request( @@ -1014,11 +1014,7 @@ def test_bedrock_messages_checks_output_config_support_with_bedrock_provider(): headers={}, ) - mock_supports_factory.assert_called_with( - model="us.anthropic.claude-opus-4-7", - custom_llm_provider="bedrock", - key="supports_output_config", - ) + mock_supports_factory.assert_called_with("us.anthropic.claude-opus-4-7", "supports_output_config") assert result["output_config"] == {"effort": "high"} @@ -1038,7 +1034,7 @@ def test_bedrock_messages_forwards_output_config(): } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ): result = cfg.transform_anthropic_messages_request( @@ -1054,27 +1050,29 @@ def test_bedrock_messages_forwards_output_config(): def test_bedrock_messages_forwards_output_config_with_output_format(): - """``output_config`` is forwarded; ``output_format`` is converted to inline schema.""" + """Legacy ``output_format`` is forwarded as ``output_config.format`` on models + that support native structured outputs, alongside the effort key.""" from unittest.mock import patch from litellm.types.router import GenericLiteLLMParams cfg = AmazonAnthropicClaudeMessagesConfig() messages = [{"role": "user", "content": [{"type": "text", "text": "Hello"}]}] + schema_format = { + "type": "json_schema", + "schema": { + "type": "object", + "properties": {"answer": {"type": "string"}}, + }, + } optional_params = { "max_tokens": 4096, "output_config": {"effort": "low"}, - "output_format": { - "type": "json_schema", - "schema": { - "type": "object", - "properties": {"answer": {"type": "string"}}, - }, - }, + "output_format": schema_format, } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ): result = cfg.transform_anthropic_messages_request( @@ -1085,12 +1083,14 @@ def test_bedrock_messages_forwards_output_config_with_output_format(): headers={}, ) - assert result.get("output_config") == {"effort": "low"} + assert result.get("output_config") == {"effort": "low", "format": schema_format} assert "output_format" not in result + assert "answer" not in json.dumps(result["messages"]) def test_bedrock_messages_converts_output_config_format_to_inline_schema(): - """``output_config.format`` is consumed so Bedrock does not see an unknown nested key.""" + """Without native structured-output support, ``output_config.format`` falls back + to the inline schema so Bedrock does not see an unknown nested key.""" from unittest.mock import patch from litellm.types.router import GenericLiteLLMParams @@ -1110,8 +1110,8 @@ def test_bedrock_messages_converts_output_config_format_to_inline_schema(): } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", - return_value=True, + "litellm.llms.bedrock.common_utils._bedrock_model_supports", + side_effect=lambda _model, key: key == "supports_output_config", ): result = cfg.transform_anthropic_messages_request( model="anthropic.claude-opus-4-7", @@ -1146,7 +1146,7 @@ def test_bedrock_messages_normalizes_output_config_effort_for_opus( cfg = AmazonAnthropicClaudeMessagesConfig() with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ): result = cfg.transform_anthropic_messages_request( @@ -1184,8 +1184,8 @@ def test_bedrock_messages_does_not_mutate_callers_messages_when_embedding_schema } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", - return_value=True, + "litellm.llms.bedrock.common_utils._bedrock_model_supports", + side_effect=lambda _model, key: key == "supports_output_config", ): result = cfg.transform_anthropic_messages_request( model="anthropic.claude-opus-4-7", @@ -1229,7 +1229,7 @@ def test_bedrock_messages_does_not_mutate_callers_output_config(): } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ): cfg.transform_anthropic_messages_request( @@ -1271,7 +1271,7 @@ def test_bedrock_messages_strips_output_config_with_output_format(): } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=False, ): result = cfg.transform_anthropic_messages_request( @@ -1332,7 +1332,7 @@ def test_bedrock_messages_drop_params_keeps_output_config_for_4_7(): litellm.drop_params = True try: with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ): result = cfg.transform_anthropic_messages_request( @@ -1375,7 +1375,7 @@ def test_bedrock_messages_maps_reasoning_effort_for_adaptive_model( } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ): result = cfg.transform_anthropic_messages_request( @@ -1482,7 +1482,7 @@ def test_bedrock_messages_explicit_output_config_wins_over_reasoning_effort(): } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ): result = cfg.transform_anthropic_messages_request( @@ -3066,17 +3066,21 @@ def test_bedrock_invoke_messages_allows_converted_websearch_function_tool(): async def test_bedrock_sse_wrapper_dispatches_logging_on_client_disconnect(): """ Regression test for LIT-5839: closing the outer bedrock_sse_wrapper - mid-stream (what the proxy does on a client disconnect) must close the - inner async_sse_wrapper deterministically so the partial-stream logging - fires. `completion_start_time` is only stamped on the logging object by - that dispatch, so it observing a value proves the whole chain ran. + mid-stream (what the proxy does on a client disconnect) must not lose the + stream's spend logging. Since the detached-pump relay, the upstream read + survives the disconnect and billing fires once the provider stream ends, + so the dispatch is awaited after releasing the upstream instead of being + observed synchronously at aclose(). `completion_start_time` is only + stamped on the logging object by that dispatch, so it observing a value + proves the whole chain ran. """ cfg = AmazonAnthropicClaudeMessagesConfig() + release_upstream = asyncio.Event() - async def _hanging_stream(): + async def _gated_stream(): yield {"type": "message_start", "message": {"id": "msg_1", "usage": {"input_tokens": 25, "output_tokens": 1}}} yield {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "partial"}} - await asyncio.Event().wait() + await release_upstream.wait() logging_obj = LiteLLMLoggingObj( model="bedrock/invoke/anthropic.claude-3-sonnet-20240229-v1:0", @@ -3087,11 +3091,162 @@ async def test_bedrock_sse_wrapper_dispatches_logging_on_client_disconnect(): litellm_call_id="test_bedrock_sse_wrapper_disconnect_logging", function_id="test_bedrock_sse_wrapper_disconnect_logging", ) - wrapped = cfg.bedrock_sse_wrapper(_hanging_stream(), litellm_logging_obj=logging_obj, request_body={}) + wrapped = cfg.bedrock_sse_wrapper(_gated_stream(), litellm_logging_obj=logging_obj, request_body={}) await wrapped.__anext__() await wrapped.__anext__() assert logging_obj.completion_start_time is None await wrapped.aclose() + release_upstream.set() + for _ in range(500): + if logging_obj.completion_start_time is not None: + break + await asyncio.sleep(0.01) assert logging_obj.completion_start_time is not None + + +def test_bedrock_messages_forwards_output_config_format_natively(local_model_cost_map): + """Regression: on a model Bedrock enforces structured outputs for (Claude + Sonnet 4.5), ``output_config.format`` must be forwarded verbatim, not + silently rewritten into inline prompt text.""" + from litellm.types.router import GenericLiteLLMParams + + cfg = AmazonAnthropicClaudeMessagesConfig() + schema_format = { + "type": "json_schema", + "schema": { + "type": "object", + "properties": { + "zebra_count": {"type": "integer"}, + "is_tuesday": {"type": "boolean"}, + }, + "required": ["zebra_count", "is_tuesday"], + "additionalProperties": False, + }, + } + + result = cfg.transform_anthropic_messages_request( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + messages=[{"role": "user", "content": [{"type": "text", "text": "say hello"}]}], + anthropic_messages_optional_request_params={ + "max_tokens": 100, + "output_config": {"format": schema_format}, + }, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert result.get("output_config") == {"format": schema_format} + assert "zebra_count" not in json.dumps(result["messages"]) + + +def test_bedrock_messages_inlines_schema_for_claude_5(local_model_cost_map): + """Bedrock rejects ``output_config.format`` for the Claude 5 family, so the + schema falls back to the inline-text path instead of a deterministic 400.""" + from litellm.types.router import GenericLiteLLMParams + + cfg = AmazonAnthropicClaudeMessagesConfig() + schema = { + "type": "object", + "properties": {"zebra_count": {"type": "integer"}}, + } + + result = cfg.transform_anthropic_messages_request( + model="us.anthropic.claude-sonnet-5", + messages=[{"role": "user", "content": [{"type": "text", "text": "say hello"}]}], + anthropic_messages_optional_request_params={ + "max_tokens": 100, + "output_config": {"format": {"type": "json_schema", "schema": schema}}, + }, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert "output_config" not in result + last_content = result["messages"][-1]["content"] + assert json.loads(last_content[-1]["text"]) == schema + + +def test_bedrock_messages_legacy_output_format_wins_over_output_config_format(local_model_cost_map): + """When a request carries both schema forms, the legacy top-level + ``output_format`` keeps winning, matching the pre-existing precedence.""" + from litellm.types.router import GenericLiteLLMParams + + cfg = AmazonAnthropicClaudeMessagesConfig() + legacy_format = { + "type": "json_schema", + "schema": {"type": "object", "properties": {"legacy_field": {"type": "string"}}}, + } + newer_format = { + "type": "json_schema", + "schema": {"type": "object", "properties": {"newer_field": {"type": "string"}}}, + } + + result = cfg.transform_anthropic_messages_request( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + messages=[{"role": "user", "content": [{"type": "text", "text": "say hello"}]}], + anthropic_messages_optional_request_params={ + "max_tokens": 100, + "output_format": legacy_format, + "output_config": {"format": newer_format}, + }, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert result.get("output_config") == {"format": legacy_format} + assert "output_format" not in result + assert "newer_field" not in json.dumps(result) + + +def test_bedrock_messages_drop_params_keeps_native_output_config_format(local_model_cost_map, monkeypatch): + """``drop_params=True`` must not strip a natively forwarded + ``output_config.format`` on models without effort support (Sonnet 4.5).""" + import litellm + from litellm.types.router import GenericLiteLLMParams + + monkeypatch.setattr(litellm, "drop_params", True) + cfg = AmazonAnthropicClaudeMessagesConfig() + schema_format = { + "type": "json_schema", + "schema": {"type": "object", "properties": {"zebra_count": {"type": "integer"}}}, + } + + result = cfg.transform_anthropic_messages_request( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + messages=[{"role": "user", "content": [{"type": "text", "text": "say hello"}]}], + anthropic_messages_optional_request_params={ + "max_tokens": 100, + "output_config": {"format": schema_format}, + }, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert result.get("output_config") == {"format": schema_format} + + +def test_bedrock_messages_strips_effort_but_keeps_format_for_sonnet_4_5(local_model_cost_map): + """Sonnet 4.5 has native structured-output support but no effort support, so + a mixed ``output_config`` keeps ``format`` and drops ``effort``.""" + from litellm.types.router import GenericLiteLLMParams + + cfg = AmazonAnthropicClaudeMessagesConfig() + schema_format = { + "type": "json_schema", + "schema": {"type": "object", "properties": {"zebra_count": {"type": "integer"}}}, + } + + result = cfg.transform_anthropic_messages_request( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + messages=[{"role": "user", "content": [{"type": "text", "text": "say hello"}]}], + anthropic_messages_optional_request_params={ + "max_tokens": 4096, + "output_config": {"format": schema_format, "effort": "high"}, + }, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert result.get("output_config") == {"format": schema_format} diff --git a/tests/test_litellm/llms/bedrock/realtime/test_bedrock_realtime_handler.py b/tests/test_litellm/llms/bedrock/realtime/test_bedrock_realtime_handler.py index 9efcee192b1..0ea5b7ad4a1 100644 --- a/tests/test_litellm/llms/bedrock/realtime/test_bedrock_realtime_handler.py +++ b/tests/test_litellm/llms/bedrock/realtime/test_bedrock_realtime_handler.py @@ -6,7 +6,7 @@ from unittest.mock import MagicMock import pytest - +import litellm from litellm.llms.bedrock.common_utils import BedrockError from litellm.llms.bedrock.realtime.handler import BedrockRealtime from litellm.llms.bedrock.realtime.transformation import BedrockRealtimeConfig @@ -104,12 +104,24 @@ class RealtimeClientWS: self.closed = True -class ImmediatelyEndingBedrockStream: - def __init__(self): +class ScriptedBedrockReceiver: + def __init__(self, payloads): + self._payloads = list(payloads) + + async def receive(self): + if not self._payloads: + return None + payload = self._payloads.pop(0) + return SimpleNamespace(value=SimpleNamespace(bytes_=payload.encode("utf-8"))) + + +class ScriptedBedrockStream: + def __init__(self, payloads): self.input_stream = FakeInputStream() + self._receiver = ScriptedBedrockReceiver(payloads) async def await_output(self): - return (None, EndedBedrockReceiver()) + return (None, self._receiver) class FakeStaticCredentialsResolver: @@ -151,7 +163,7 @@ def stub_aws_sdk_client(monkeypatch): async def invoke_model_with_bidirectional_stream(self, operation_input): captured["operation_input"] = operation_input - return ImmediatelyEndingBedrockStream() + return ScriptedBedrockStream(captured.get("scripted_payloads", [])) package = types.ModuleType("aws_sdk_bedrock_runtime") client_module = types.ModuleType("aws_sdk_bedrock_runtime.client") @@ -271,19 +283,132 @@ class TestBedrockRealtimeHandler: assert "sessionEnd" in event_names assert stream.input_stream.closed + @pytest.mark.asyncio + async def test_forwarded_events_are_filtered_to_logged_types_for_spend_logging(self): + handler = BedrockRealtime() + stream = ScriptedBedrockStream( + [ + json.dumps({"event": {"userSpeechStart": {}}}), + json.dumps({"event": {"contentStart": {"role": "ASSISTANT", "type": "TEXT"}}}), + json.dumps({"event": {"textOutput": {"content": "Hi"}}}), + json.dumps({"event": {"contentEnd": {"stopReason": "END_TURN"}}}), + ] + ) + client_ws = RealtimeClientWS() + + logged_events = [ + event + async for event in handler._forward_bedrock_to_client( + stream, + client_ws, + BedrockRealtimeConfig(), + "amazon.nova-sonic-v1:0", + FakeLogging(), + {}, + ) + ] + + assert [event["type"] for event in logged_events] == ["response.done"] + sent_types = [json.loads(message)["type"] for message in client_ws.sent_to_client] + assert "input_audio_buffer.speech_started" in sent_types + assert "response.text.delta" in sent_types + assert "response.done" in sent_types + assert client_ws.closed + + @pytest.mark.asyncio + async def test_logged_event_types_star_collects_every_forwarded_event(self, monkeypatch): + monkeypatch.setattr(litellm, "logged_real_time_event_types", "*") + handler = BedrockRealtime() + stream = ScriptedBedrockStream( + [ + json.dumps({"event": {"userSpeechStart": {}}}), + json.dumps({"event": {"userSpeechEnd": {}}}), + ] + ) + client_ws = RealtimeClientWS() + + logged_events = [ + event + async for event in handler._forward_bedrock_to_client( + stream, + client_ws, + BedrockRealtimeConfig(), + "amazon.nova-sonic-v1:0", + FakeLogging(), + {}, + ) + ] + + assert [event["type"] for event in logged_events] == [ + "input_audio_buffer.speech_started", + "input_audio_buffer.speech_stopped", + ] + + @pytest.mark.asyncio + async def test_trailing_usage_after_last_done_is_dispatched_for_spend(self, stub_aws_sdk_client, monkeypatch): + import litellm.llms.bedrock.realtime.handler as handler_module + + dispatched = {} + + class RecordingLogging(FakeLogging): + async def dispatch_success_handlers(self, result=None, prefer_async_handlers=False, **kwargs): + dispatched["events"] = result + + class RecordingLoggingWorker: + def ensure_initialized_and_enqueue(self, coro): + dispatched["coro"] = coro + + monkeypatch.setattr(handler_module, "GLOBAL_LOGGING_WORKER", RecordingLoggingWorker()) + stub_aws_sdk_client["scripted_payloads"] = [ + json.dumps( + { + "event": { + "usageEvent": { + "totalInputTokens": 3, + "totalOutputTokens": 6, + "totalTokens": 9, + "details": { + "total": { + "input": {"speechTokens": 3, "textTokens": 0}, + "output": {"speechTokens": 0, "textTokens": 6}, + } + }, + } + } + } + ) + ] + + await BedrockRealtime().async_realtime( + model="amazon.nova-sonic-v1:0", + websocket=RealtimeClientWS(), + logging_obj=RecordingLogging(), + aws_region_name="us-east-1", + aws_access_key_id="k", + aws_secret_access_key="s", + ) + await dispatched["coro"] + + assert [event["type"] for event in dispatched["events"]] == ["response.done"] + usage = dispatched["events"][0]["response"]["usage"] + assert (usage["input_tokens"], usage["output_tokens"], usage["total_tokens"]) == (3, 6, 9) + assert usage["input_token_details"] == {"audio_tokens": 3, "text_tokens": 0, "cached_tokens": 0} + assert usage["output_token_details"] == {"audio_tokens": 0, "text_tokens": 6} + @pytest.mark.asyncio async def test_bedrock_stream_end_closes_client_websocket(self): handler = BedrockRealtime() client_ws = ClosableClientWS() - await handler._forward_bedrock_to_client( + async for _ in handler._forward_bedrock_to_client( EndedBedrockStream(), client_ws, BedrockRealtimeConfig(), "amazon.nova-sonic-v1:0", MagicMock(), {}, - ) + ): + pass assert client_ws.closed @@ -320,9 +445,7 @@ class TestBedrockRealtimeSessionLifecycle: [json.dumps({"type": "session.update", "session": {"instructions": "hi", "modalities": ["text"]}})] ) - await handler._forward_client_to_bedrock( - client_ws, stream, config, "amazon.nova-sonic-v1:0", {}, FakeLogging() - ) + await handler._forward_client_to_bedrock(client_ws, stream, config, "amazon.nova-sonic-v1:0", {}, FakeLogging()) acked = [json.loads(message) for message in client_ws.sent_to_client] updated = [event for event in acked if event["type"] == "session.updated"] @@ -334,9 +457,7 @@ class TestBedrockRealtimeSessionLifecycle: handler = BedrockRealtime() config = BedrockRealtimeConfig() stream = FakeBedrockStream() - client_ws = DisconnectingClientWS( - [json.dumps({"type": "session.update", "session": {"instructions": "hi"}})] - ) + client_ws = DisconnectingClientWS([json.dumps({"type": "session.update", "session": {"instructions": "hi"}})]) await handler._forward_client_to_bedrock(client_ws, stream, config, "amazon.nova-sonic-v1:0", {}) diff --git a/tests/test_litellm/llms/bedrock/realtime/test_bedrock_realtime_transformation.py b/tests/test_litellm/llms/bedrock/realtime/test_bedrock_realtime_transformation.py index ae6b1febd6b..a74f03449a1 100644 --- a/tests/test_litellm/llms/bedrock/realtime/test_bedrock_realtime_transformation.py +++ b/tests/test_litellm/llms/bedrock/realtime/test_bedrock_realtime_transformation.py @@ -827,5 +827,310 @@ class TestBedrockRealtimeSessionEvents: assert event["session"]["modalities"] == ["text", "audio"] +class TestBedrockRealtimeUserEventsAndUsage: + """Regression tests for #38346: USER ASR transcripts, speech boundary events, + usage propagation, and duplicate response.created""" + + @staticmethod + def _run(config, messages): + logging_obj = MagicMock() + logging_obj.litellm_trace_id = "trace_123" + state = { + "session_configuration_request": json.dumps({"configured": True}), + "current_output_item_id": None, + "current_response_id": None, + "current_conversation_id": None, + "current_delta_chunks": [], + "current_item_chunks": [], + "current_delta_type": None, + } + all_events = [] + for msg in messages: + result = config.transform_realtime_response( + json.dumps(msg), + "amazon.nova-2-sonic-v1:0", + logging_obj, + realtime_response_transform_input=dict(state), + ) + all_events.extend(result["response"]) + state.update( + { + "current_output_item_id": result["current_output_item_id"], + "current_response_id": result["current_response_id"], + "current_conversation_id": result["current_conversation_id"], + "current_delta_chunks": result["current_delta_chunks"], + "current_delta_type": result["current_delta_type"], + } + ) + return all_events + + def test_user_speech_start_and_stop_events(self): + events = self._run( + BedrockRealtimeConfig(), + [{"event": {"userSpeechStart": {}}}, {"event": {"userSpeechEnd": {}}}], + ) + assert [e["type"] for e in events] == [ + "input_audio_buffer.speech_started", + "input_audio_buffer.speech_stopped", + ] + assert all(e["event_id"] and e["item_id"] for e in events) + assert events[0]["item_id"] == events[1]["item_id"] + + def test_utterance_lifecycle_shares_one_item_id(self): + events = self._run( + BedrockRealtimeConfig(), + [ + {"event": {"userSpeechStart": {}}}, + {"event": {"userSpeechEnd": {}}}, + { + "event": { + "contentStart": { + "role": "USER", + "type": "TEXT", + "additionalModelFields": json.dumps({"generationStage": "FINAL"}), + } + } + }, + {"event": {"textOutput": {"content": "ready"}}}, + {"event": {"contentEnd": {"stopReason": "PARTIAL_TURN"}}}, + ], + ) + item_ids = {e["item_id"] for e in events if "item_id" in e} + assert len(item_ids) == 1 + + def test_new_utterance_gets_new_item_id(self): + config = BedrockRealtimeConfig() + first = self._run(config, [{"event": {"userSpeechStart": {}}}, {"event": {"userSpeechEnd": {}}}]) + second = self._run(config, [{"event": {"userSpeechStart": {}}}, {"event": {"userSpeechEnd": {}}}]) + assert first[0]["item_id"] == first[1]["item_id"] + assert second[0]["item_id"] == second[1]["item_id"] + assert first[0]["item_id"] != second[0]["item_id"] + + def test_user_transcript_emits_input_audio_transcription_events(self): + events = self._run( + BedrockRealtimeConfig(), + [ + { + "event": { + "contentStart": { + "role": "USER", + "type": "TEXT", + "additionalModelFields": json.dumps({"generationStage": "FINAL"}), + } + } + }, + {"event": {"textOutput": {"content": "ready"}}}, + {"event": {"contentEnd": {"stopReason": "PARTIAL_TURN"}}}, + ], + ) + deltas = [e for e in events if e["type"] == "conversation.item.input_audio_transcription.delta"] + completed = [e for e in events if e["type"] == "conversation.item.input_audio_transcription.completed"] + assert len(deltas) == 1 and deltas[0]["delta"] == "ready" + assert len(completed) == 1 and completed[0]["transcript"] == "ready" + assert deltas[0]["item_id"] == completed[0]["item_id"] + assert not any(e["type"] == "response.text.delta" for e in events) + + def test_speculative_user_transcript_emits_delta_only(self): + events = self._run( + BedrockRealtimeConfig(), + [ + { + "event": { + "contentStart": { + "role": "USER", + "type": "TEXT", + "additionalModelFields": json.dumps({"generationStage": "SPECULATIVE"}), + } + } + }, + {"event": {"textOutput": {"content": "rea"}}}, + ], + ) + assert [e["type"] for e in events] == ["conversation.item.input_audio_transcription.delta"] + + def test_user_transcript_state_resets_on_content_end(self): + events = self._run( + BedrockRealtimeConfig(), + [ + {"event": {"contentStart": {"role": "USER", "type": "TEXT"}}}, + {"event": {"contentEnd": {"stopReason": "PARTIAL_TURN"}}}, + {"event": {"contentStart": {"role": "ASSISTANT", "type": "TEXT"}}}, + {"event": {"textOutput": {"content": "Hi there"}}}, + ], + ) + text_deltas = [e for e in events if e["type"] == "response.text.delta"] + assert len(text_deltas) == 1 and text_deltas[0]["delta"] == "Hi there" + assert not any(e["type"].startswith("conversation.item.input_audio_transcription") for e in events) + + def test_response_created_emitted_once_per_response(self): + events = self._run( + BedrockRealtimeConfig(), + [ + {"event": {"contentStart": {"role": "ASSISTANT", "type": "TEXT"}}}, + {"event": {"textOutput": {"content": "Hi"}}}, + {"event": {"contentEnd": {"stopReason": "PARTIAL_TURN"}}}, + {"event": {"contentStart": {"role": "ASSISTANT", "type": "AUDIO"}}}, + ], + ) + assert sum(1 for e in events if e["type"] == "response.created") == 1 + + def test_usage_event_propagates_to_response_done(self): + events = self._run( + BedrockRealtimeConfig(), + [ + { + "event": { + "usageEvent": { + "totalInputTokens": 25, + "totalOutputTokens": 40, + "totalTokens": 65, + "details": { + "total": { + "input": {"speechTokens": 20, "textTokens": 5}, + "output": {"speechTokens": 30, "textTokens": 10}, + } + }, + } + } + }, + {"event": {"contentStart": {"role": "ASSISTANT", "type": "TEXT"}}}, + {"event": {"textOutput": {"content": "Hi"}}}, + {"event": {"contentEnd": {"stopReason": "END_TURN"}}}, + ], + ) + done_events = [e for e in events if e["type"] == "response.done"] + assert len(done_events) == 1 + usage = done_events[0]["response"]["usage"] + assert usage["input_tokens"] == 25 + assert usage["output_tokens"] == 40 + assert usage["total_tokens"] == 65 + assert usage["input_token_details"]["audio_tokens"] == 20 + assert usage["input_token_details"]["text_tokens"] == 5 + assert usage["output_token_details"]["audio_tokens"] == 30 + assert usage["output_token_details"]["text_tokens"] == 10 + + def test_response_done_without_usage_event_reports_zero_usage(self): + events = self._run( + BedrockRealtimeConfig(), + [ + {"event": {"contentStart": {"role": "ASSISTANT", "type": "TEXT"}}}, + {"event": {"textOutput": {"content": "Hi"}}}, + {"event": {"contentEnd": {"stopReason": "END_TURN"}}}, + ], + ) + done_events = [e for e in events if e["type"] == "response.done"] + assert len(done_events) == 1 + usage = done_events[0]["response"]["usage"] + assert usage["input_tokens"] == 0 + assert usage["output_tokens"] == 0 + assert usage["total_tokens"] == 0 + + @staticmethod + def _usage_event(total_input, total_output, in_speech, in_text, out_speech, out_text): + return { + "event": { + "usageEvent": { + "totalInputTokens": total_input, + "totalOutputTokens": total_output, + "totalTokens": total_input + total_output, + "details": { + "total": { + "input": {"speechTokens": in_speech, "textTokens": in_text}, + "output": {"speechTokens": out_speech, "textTokens": out_text}, + } + }, + } + } + } + + _ASSISTANT_TURN = ( + {"event": {"contentStart": {"role": "ASSISTANT", "type": "TEXT"}}}, + {"event": {"textOutput": {"content": "Hi"}}}, + {"event": {"contentEnd": {"stopReason": "END_TURN"}}}, + ) + + def test_multi_turn_usage_reports_per_response_deltas_not_cumulative_totals(self): + events = self._run( + BedrockRealtimeConfig(), + [ + self._usage_event(25, 40, in_speech=20, in_text=5, out_speech=30, out_text=10), + *self._ASSISTANT_TURN, + self._usage_event(40, 100, in_speech=30, in_text=10, out_speech=75, out_text=25), + *self._ASSISTANT_TURN, + ], + ) + usages = [e["response"]["usage"] for e in events if e["type"] == "response.done"] + assert len(usages) == 2 + assert (usages[0]["input_tokens"], usages[0]["output_tokens"], usages[0]["total_tokens"]) == (25, 40, 65) + assert (usages[1]["input_tokens"], usages[1]["output_tokens"], usages[1]["total_tokens"]) == (15, 60, 75) + assert usages[1]["input_token_details"] == {"audio_tokens": 10, "text_tokens": 5, "cached_tokens": 0} + assert usages[1]["output_token_details"] == {"audio_tokens": 45, "text_tokens": 15} + assert sum(u["total_tokens"] for u in usages) == 140 + + def test_usage_reported_after_last_response_done_flushes_as_logged_only_done(self): + config = BedrockRealtimeConfig() + self._run( + config, + [ + self._usage_event(25, 40, in_speech=20, in_text=5, out_speech=30, out_text=10), + *self._ASSISTANT_TURN, + ], + ) + assert config.leftover_usage_done_events() == () + + self._run(config, [self._usage_event(25, 46, in_speech=20, in_text=5, out_speech=30, out_text=16)]) + leftover = config.leftover_usage_done_events() + assert len(leftover) == 1 + assert leftover[0]["type"] == "response.done" + usage = leftover[0]["response"]["usage"] + assert (usage["input_tokens"], usage["output_tokens"], usage["total_tokens"]) == (0, 6, 6) + assert usage["output_token_details"] == {"audio_tokens": 0, "text_tokens": 6} + assert config.leftover_usage_done_events() == () + + def test_final_transcript_fragments_emit_one_completed_with_full_transcript(self): + events = self._run( + BedrockRealtimeConfig(), + [ + { + "event": { + "contentStart": { + "role": "USER", + "type": "TEXT", + "additionalModelFields": json.dumps({"generationStage": "FINAL"}), + } + } + }, + {"event": {"textOutput": {"content": "What is the "}}}, + {"event": {"textOutput": {"content": "capital of France?"}}}, + {"event": {"contentEnd": {"stopReason": "PARTIAL_TURN"}}}, + ], + ) + deltas = [e for e in events if e["type"] == "conversation.item.input_audio_transcription.delta"] + completed = [e for e in events if e["type"] == "conversation.item.input_audio_transcription.completed"] + assert [d["delta"] for d in deltas] == ["What is the ", "capital of France?"] + assert len(completed) == 1 + assert completed[0]["transcript"] == "What is the capital of France?" + assert {e["item_id"] for e in deltas + completed} == {completed[0]["item_id"]} + + def test_speculative_transcript_block_end_emits_no_completed(self): + events = self._run( + BedrockRealtimeConfig(), + [ + { + "event": { + "contentStart": { + "role": "USER", + "type": "TEXT", + "additionalModelFields": json.dumps({"generationStage": "SPECULATIVE"}), + } + } + }, + {"event": {"textOutput": {"content": "rea"}}}, + {"event": {"contentEnd": {"stopReason": "PARTIAL_TURN"}}}, + ], + ) + assert [e["type"] for e in events] == ["conversation.item.input_audio_transcription.delta"] + + if __name__ == "__main__": pytest.main([__file__, "-v"]) diff --git a/tests/test_litellm/llms/bedrock/test_base_aws_llm.py b/tests/test_litellm/llms/bedrock/test_base_aws_llm.py index 7d07ac947b1..f854d806bdc 100644 --- a/tests/test_litellm/llms/bedrock/test_base_aws_llm.py +++ b/tests/test_litellm/llms/bedrock/test_base_aws_llm.py @@ -15,6 +15,7 @@ from unittest.mock import MagicMock, patch from botocore.awsrequest import AWSPreparedRequest, AWSRequest from botocore.auth import SigV4Auth from botocore.credentials import Credentials +from botocore.exceptions import NoCredentialsError import litellm from litellm.llms.bedrock.base_aws_llm import ( @@ -801,6 +802,23 @@ def test_get_request_headers_with_sigv4(): assert result == mock_request.prepare.return_value +def test_get_request_headers_without_credentials_or_bearer_token_raises_no_credentials(): + """Bearer-token auth needs no SigV4 principal, so `credentials` may be None. + Reaching the SigV4 branch with neither must fail the way botocore always + has instead of signing with a missing principal.""" + llm = BaseAWSLLM() + + with patch.dict(os.environ, {}, clear=True), pytest.raises(NoCredentialsError): + llm.get_request_headers( + credentials=None, + aws_region_name="us-west-2", + extra_headers=None, + endpoint_url="https://api.example.com", + data='{"prompt": "test"}', + headers={"Content-Type": "application/json"}, + ) + + def test_sigv4_matches_rust_golden_vector(): request = AWSRequest( method="POST", diff --git a/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py b/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py index 389bf4a8e40..9302dc01abe 100644 --- a/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py +++ b/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py @@ -520,3 +520,97 @@ def test_merge_bedrock_aws_request_params_keeps_caller_credentials_without_stati assert merged["aws_secret_access_key"] == "caller-secret" assert merged["aws_session_token"] == "caller-token" assert merged["aws_region_name"] == "us-west-2" + + +def test_strip_unsupported_output_config_keeps_format_drops_effort(local_model_cost_map): + """On a model with neither effort flag, only the ``format`` key survives.""" + from litellm.llms.bedrock.common_utils import ( + strip_unsupported_bedrock_invoke_output_config_keys, + ) + + schema_format = {"type": "json_schema", "schema": {"type": "object"}} + body = {"output_config": {"effort": "high", "format": schema_format}} + + strip_unsupported_bedrock_invoke_output_config_keys( + model="anthropic.claude-3-haiku-20240307-v1:0", + request_body=body, + ) + + assert body["output_config"] == {"format": schema_format} + + +def test_apply_structured_output_prefers_legacy_output_format(local_model_cost_map): + """The legacy ``output_format`` wins over ``output_config.format`` when a + request carries both, matching the pre-existing precedence.""" + from litellm.llms.bedrock.common_utils import ( + apply_bedrock_invoke_structured_output, + ) + + legacy = {"type": "json_schema", "schema": {"type": "object", "properties": {"a": {"type": "string"}}}} + newer = {"type": "json_schema", "schema": {"type": "object", "properties": {"b": {"type": "string"}}}} + body = { + "messages": [{"role": "user", "content": "hi"}], + "output_format": legacy, + "output_config": {"format": newer}, + } + + apply_bedrock_invoke_structured_output( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + request_body=body, + ) + + assert body["output_config"] == {"format": legacy} + assert "output_format" not in body + + +def test_sign_aws_request_assumes_role_with_external_id(monkeypatch): + """A trust policy requiring sts:ExternalId must be satisfied when signing batch API requests.""" + import datetime + from unittest.mock import patch + + import boto3 + from botocore.exceptions import ClientError + + from litellm.llms.bedrock.common_utils import CommonBatchFilesUtils + + monkeypatch.delenv("AWS_EXTERNAL_ID", raising=False) + + class FakeSTSClient: + def get_caller_identity(self): + return {"Arn": "arn:aws:iam::111111111111:user/litellm-proxy-pod"} + + def assume_role(self, **params): + if params.get("ExternalId") != "external-id-batch-sign": + raise ClientError( + {"Error": {"Code": "AccessDenied", "Message": "is not authorized to perform: sts:AssumeRole"}}, + "AssumeRole", + ) + return { + "Credentials": { + "AccessKeyId": "ASIABATCHSIGNROLE", + "SecretAccessKey": "assumed-secret", + "SessionToken": "assumed-session-token", + "Expiration": datetime.datetime.now(datetime.timezone.utc) + datetime.timedelta(minutes=30), + } + } + + optional_params = { + "aws_region_name": "us-east-1", + "aws_access_key_id": "AKIABATCHSIGNCALLER", + "aws_secret_access_key": "pod-caller-secret", + "aws_role_name": "arn:aws:iam::999999999999:role/litellm-batch-sign-role", + "aws_session_name": "litellm-batch-sign-session", + "aws_external_id": "external-id-batch-sign", + } + + with patch.object(boto3, "client", return_value=FakeSTSClient()): + signed_headers, signed_data = CommonBatchFilesUtils().sign_aws_request( + service_name="bedrock", + data={"jobName": "litellm-batch-job"}, + endpoint_url="https://bedrock.us-east-1.amazonaws.com/model-invocation-job", + optional_params=optional_params, + ) + + authorization = {key.lower(): value for key, value in signed_headers.items()}["authorization"] + assert "ASIABATCHSIGNROLE" in authorization + assert signed_data == b'{"jobName": "litellm-batch-job"}' diff --git a/tests/test_litellm/llms/custom_httpx/test_aiohttp_transport.py b/tests/test_litellm/llms/custom_httpx/test_aiohttp_transport.py index 4c92c52d556..7509e35e3f7 100644 --- a/tests/test_litellm/llms/custom_httpx/test_aiohttp_transport.py +++ b/tests/test_litellm/llms/custom_httpx/test_aiohttp_transport.py @@ -1,7 +1,11 @@ import asyncio import concurrent.futures +import socket +import sys +from typing import Final import aiohttp +import aiohttp.abc import aiohttp.client_exceptions import aiohttp.http_exceptions import httpx @@ -1140,3 +1144,55 @@ async def test_stopped_loop_session_disposed_synchronously_on_recycle(): finally: await new_session.close() result["loop"].close() + + +class _CancellingResolver(aiohttp.abc.AbstractResolver): + """Cancels the given task (or, by default, aiohttp's shielded DNS child task) mid-lookup.""" + + def __init__(self, task_to_cancel: "asyncio.Task[object] | None" = None): + self._task_to_cancel: Final = task_to_cancel + + async def resolve( + self, host: str, port: int = 0, family: socket.AddressFamily = socket.AF_INET + ) -> list[aiohttp.abc.ResolveResult]: + target: Final = self._task_to_cancel or asyncio.current_task() + assert target is not None + target.cancel() + await asyncio.sleep(0) + raise OSError("resolver finished after the task was cancelled") + + async def close(self) -> None: + return None + + +@pytest.mark.asyncio +@pytest.mark.skipif( + sys.version_info < (3, 11), reason="Task.cancelling() is needed to tell the two cancellations apart" +) +async def test_internal_dns_cancellation_maps_to_connect_error(): + """A CancelledError the request task never asked for must surface as a mapped httpx transport error.""" + session = aiohttp.ClientSession(connector=aiohttp.TCPConnector(resolver=_CancellingResolver())) + transport = LiteLLMAiohttpTransport(client=session) + try: + with pytest.raises(httpx.ConnectError): + await transport.handle_async_request(httpx.Request("GET", "http://example.invalid/")) + current = asyncio.current_task() + assert current is not None and current.cancelling() == 0 + finally: + await transport.aclose() + + +@pytest.mark.asyncio +async def test_genuine_request_cancellation_still_propagates(): + """Cancelling the request task itself (client disconnect, shutdown) must still propagate unmapped.""" + current = asyncio.current_task() + assert current is not None + session = aiohttp.ClientSession(connector=aiohttp.TCPConnector(resolver=_CancellingResolver(current))) + transport = LiteLLMAiohttpTransport(client=session) + try: + with pytest.raises(asyncio.CancelledError): + await transport.handle_async_request(httpx.Request("GET", "http://example.invalid/")) + finally: + if sys.version_info >= (3, 11): + current.uncancel() + await transport.aclose() diff --git a/tests/test_litellm/llms/custom_httpx/test_llm_http_handler.py b/tests/test_litellm/llms/custom_httpx/test_llm_http_handler.py index b37c0f466d2..26f841c1146 100644 --- a/tests/test_litellm/llms/custom_httpx/test_llm_http_handler.py +++ b/tests/test_litellm/llms/custom_httpx/test_llm_http_handler.py @@ -271,6 +271,85 @@ async def test_async_response_api_handler_streams_when_provider_transform_adds_s assert client.post.call_args.kwargs["json"]["stream"] is True +@pytest.mark.asyncio +async def test_async_response_api_handler_streaming_passes_logging_obj_to_post(): + """LIT-5466: @track_llm_api_timing only records llm_api_duration_ms when the POST + receives logging_obj; without it streaming /v1/responses never gets + x-litellm-overhead-duration-ms (the non-streaming site is pinned by + test_async_responses_records_llm_api_duration below).""" + handler = BaseLLMHTTPHandler() + config = Mock() + config.validate_environment.return_value = {} + config.get_complete_url.return_value = "https://chatgpt.example.com/responses" + config.transform_responses_api_request.return_value = {"model": "gpt-5", "input": "hi", "stream": True} + config.sign_request.return_value = ({}, None) + client = AsyncHTTPHandler() + client.post = AsyncMock( + return_value=httpx.Response( + 200, + request=httpx.Request("POST", "https://chatgpt.example.com/responses"), + ) + ) + logging_obj = Mock() + + await handler.async_response_api_handler( + model="gpt-5", + input="hi", + responses_api_provider_config=config, + response_api_optional_request_params={}, + custom_llm_provider="chatgpt", + litellm_params=GenericLiteLLMParams(), + logging_obj=logging_obj, + client=client, + ) + + assert client.post.call_args.kwargs["logging_obj"] is logging_obj + + +@pytest.mark.asyncio +async def test_async_responses_records_llm_api_duration(): + """aresponses must feed the httpx timing into the logging obj, so the proxy can emit + x-litellm-overhead-duration-ms on /v1/responses (mirrors the arerank regression test).""" + + def handle(request: httpx.Request) -> httpx.Response: + return httpx.Response( + 200, + json={ + "id": "resp_1", + "object": "response", + "created_at": 1, + "model": "gpt-4o-mini", + "status": "completed", + "output": [ + { + "type": "message", + "id": "msg_1", + "status": "completed", + "role": "assistant", + "content": [{"type": "output_text", "text": "pong", "annotations": []}], + } + ], + "usage": {"input_tokens": 1, "output_tokens": 1, "total_tokens": 2}, + "parallel_tool_calls": True, + "tool_choice": "auto", + "tools": [], + }, + ) + + client = AsyncHTTPHandler() + client.client = httpx.AsyncClient(transport=httpx.MockTransport(handle)) + + response = await litellm.aresponses( + model="openai/gpt-4o-mini", + input="ping", + api_key="fake-key", + client=client, + ) + + assert response._hidden_params["litellm_overhead_time_ms"] is not None + assert response._hidden_params["_response_ms"] >= response._hidden_params["litellm_overhead_time_ms"] + + def test_get_agentic_loop_settings_defaults_and_overrides(): handler = BaseLLMHTTPHandler() diff --git a/tests/test_litellm/llms/dashscope/test_qwen_brand_aliases.py b/tests/test_litellm/llms/dashscope/test_qwen_brand_aliases.py new file mode 100644 index 00000000000..064d9d58f0c --- /dev/null +++ b/tests/test_litellm/llms/dashscope/test_qwen_brand_aliases.py @@ -0,0 +1,331 @@ +import math + +import pytest + +import litellm +from litellm import completion, get_llm_provider +from litellm.llms.dashscope.chat.transformation import DashScopeChatConfig +from litellm.llms.dashscope.cost_calculator import ( + cost_per_token as dashscope_cost_per_token, +) +from litellm.llms.dashscope.embed.transformation import DashScopeEmbeddingConfig +from litellm.llms.dashscope.image_generation.transformation import ( + DashScopeImageGenerationConfig, +) +from litellm.llms.dashscope.qwen_ai_platform import ( + QWEN_AI_PLATFORM_API_BASE, + QWEN_AI_PLATFORM_IMAGE_API_BASE, + QWEN_AI_PLATFORM_RERANK_API_BASE, + QwenAIPlatformChatConfig, + QwenAIPlatformEmbeddingConfig, + QwenAIPlatformImageGenerationConfig, + QwenAIPlatformRerankConfig, +) +from litellm.llms.dashscope.qwencloud import ( + QWENCLOUD_API_BASE, + QWENCLOUD_IMAGE_API_BASE, + QWENCLOUD_RERANK_API_BASE, + QwenCloudChatConfig, + QwenCloudEmbeddingConfig, + QwenCloudImageGenerationConfig, + QwenCloudRerankConfig, +) +from litellm.llms.dashscope.rerank.transformation import DashScopeRerankConfig +from litellm.types.utils import LlmProviders, Usage +from litellm.utils import ProviderConfigManager + +DASHSCOPE_FAMILY_ENV_VARS = [ + "DASHSCOPE_API_KEY", + "DASHSCOPE_API_BASE", + "DASHSCOPE_API_BASE_RERANK", + "DASHSCOPE_API_BASE_IMAGE", + "QWENCLOUD_API_KEY", + "QWENCLOUD_API_BASE", + "QWENCLOUD_API_BASE_RERANK", + "QWENCLOUD_API_BASE_IMAGE", + "QWEN_AI_PLATFORM_API_KEY", + "QWEN_AI_PLATFORM_API_BASE", + "QWEN_AI_PLATFORM_API_BASE_RERANK", + "QWEN_AI_PLATFORM_API_BASE_IMAGE", +] + +BRAND_CASES = [ + pytest.param( + { + "provider": "qwencloud", + "enum": LlmProviders.QWENCLOUD, + "key_env": "QWENCLOUD_API_KEY", + "base_env": "QWENCLOUD_API_BASE", + "default_base": QWENCLOUD_API_BASE, + "default_rerank_base": QWENCLOUD_RERANK_API_BASE, + "default_image_base": QWENCLOUD_IMAGE_API_BASE, + "chat_config": QwenCloudChatConfig, + "embedding_config": QwenCloudEmbeddingConfig, + "rerank_config": QwenCloudRerankConfig, + "image_config": QwenCloudImageGenerationConfig, + }, + id="qwencloud", + ), + pytest.param( + { + "provider": "qwen_ai_platform", + "enum": LlmProviders.QWEN_AI_PLATFORM, + "key_env": "QWEN_AI_PLATFORM_API_KEY", + "base_env": "QWEN_AI_PLATFORM_API_BASE", + "default_base": QWEN_AI_PLATFORM_API_BASE, + "default_rerank_base": QWEN_AI_PLATFORM_RERANK_API_BASE, + "default_image_base": QWEN_AI_PLATFORM_IMAGE_API_BASE, + "chat_config": QwenAIPlatformChatConfig, + "embedding_config": QwenAIPlatformEmbeddingConfig, + "rerank_config": QwenAIPlatformRerankConfig, + "image_config": QwenAIPlatformImageGenerationConfig, + }, + id="qwen_ai_platform", + ), +] + + +@pytest.fixture(autouse=True) +def clear_dashscope_family_env(monkeypatch): + for env_var in DASHSCOPE_FAMILY_ENV_VARS: + monkeypatch.delenv(env_var, raising=False) + + +class TestQwenBrandProviderResolution: + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_get_llm_provider_resolves_brand_default_base(self, brand): + model, provider, api_key, api_base = get_llm_provider(f"{brand['provider']}/qwen-max", api_key="sk-explicit") + assert model == "qwen-max" + assert provider == brand["provider"] + assert api_key == "sk-explicit" + assert api_base == brand["default_base"] + + def test_dashscope_resolution_unchanged(self): + model, provider, api_key, api_base = get_llm_provider("dashscope/qwen-max", api_key="sk-explicit") + assert model == "qwen-max" + assert provider == "dashscope" + assert api_base == "https://dashscope.aliyuncs.com/compatible-mode/v1" + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_brand_env_key_wins_over_dashscope_key(self, monkeypatch, brand): + monkeypatch.setenv(brand["key_env"], "sk-brand") + monkeypatch.setenv("DASHSCOPE_API_KEY", "sk-dashscope") + _, _, api_key, _ = get_llm_provider(f"{brand['provider']}/qwen-max") + assert api_key == "sk-brand" + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_dashscope_key_is_fallback(self, monkeypatch, brand): + monkeypatch.setenv("DASHSCOPE_API_KEY", "sk-dashscope") + _, _, api_key, _ = get_llm_provider(f"{brand['provider']}/qwen-max") + assert api_key == "sk-dashscope" + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_dashscope_api_base_does_not_leak_into_brand(self, monkeypatch, brand): + monkeypatch.setenv("DASHSCOPE_API_BASE", "https://legacy.example.com/v1") + _, _, _, api_base = get_llm_provider(f"{brand['provider']}/qwen-max", api_key="sk-explicit") + assert api_base == brand["default_base"] + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_brand_api_base_env_wins(self, monkeypatch, brand): + monkeypatch.setenv(brand["base_env"], "https://brand.example.com/v1") + _, _, _, api_base = get_llm_provider(f"{brand['provider']}/qwen-max", api_key="sk-explicit") + assert api_base == "https://brand.example.com/v1" + + +class TestQwenBrandConfigDispatch: + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_chat_config(self, brand): + config = ProviderConfigManager.get_provider_chat_config("qwen-max", brand["enum"]) + assert isinstance(config, brand["chat_config"]) + assert isinstance(config, DashScopeChatConfig) + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_embedding_config(self, brand): + config = ProviderConfigManager.get_provider_embedding_config(model="text-embedding-v3", provider=brand["enum"]) + assert isinstance(config, brand["embedding_config"]) + assert isinstance(config, DashScopeEmbeddingConfig) + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_rerank_config(self, brand): + config = ProviderConfigManager.get_provider_rerank_config( + model="gte-rerank-v2", + provider=brand["enum"], + api_base=None, + present_version_params=[], + ) + assert isinstance(config, brand["rerank_config"]) + assert isinstance(config, DashScopeRerankConfig) + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_image_generation_config(self, brand): + config = ProviderConfigManager.get_provider_image_generation_config(model="qwen-image", provider=brand["enum"]) + assert isinstance(config, brand["image_config"]) + assert isinstance(config, DashScopeImageGenerationConfig) + + +class TestQwenBrandDefaultUrls: + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_chat_complete_url(self, brand): + url = brand["chat_config"]().get_complete_url( + api_base=None, + api_key="sk-test", + model="qwen-max", + optional_params={}, + litellm_params={}, + ) + assert url == f"{brand['default_base']}/chat/completions" + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_embedding_complete_url(self, brand): + url = brand["embedding_config"]().get_complete_url( + api_base=None, + api_key="sk-test", + model="text-embedding-v3", + optional_params={}, + litellm_params={}, + ) + assert url == f"{brand['default_base']}/embeddings" + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_embedding_ignores_dashscope_api_base(self, monkeypatch, brand): + monkeypatch.setenv("DASHSCOPE_API_BASE", "https://legacy.example.com/v1") + url = brand["embedding_config"]().get_complete_url( + api_base=None, + api_key="sk-test", + model="text-embedding-v3", + optional_params={}, + litellm_params={}, + ) + assert url == f"{brand['default_base']}/embeddings" + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_rerank_complete_url(self, brand): + url = brand["rerank_config"]().get_complete_url(api_base=None, model="gte-rerank-v2") + assert url == brand["default_rerank_base"] + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_rerank_env_override(self, monkeypatch, brand): + monkeypatch.setenv(f"{brand['base_env']}_RERANK", "https://rerank.example.com/v1/reranks") + url = brand["rerank_config"]().get_complete_url(api_base=None, model="gte-rerank-v2") + assert url == "https://rerank.example.com/v1/reranks" + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_image_generation_complete_url(self, brand): + url = brand["image_config"]().get_complete_url( + api_base=None, + api_key="sk-test", + model="qwen-image", + optional_params={}, + litellm_params={}, + ) + assert url == brand["default_image_base"] + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_image_generation_ignores_chat_compatible_api_base(self, brand): + url = brand["image_config"]().get_complete_url( + api_base=brand["default_base"], + api_key="sk-test", + model="qwen-image", + optional_params={}, + litellm_params={}, + ) + assert url == brand["default_image_base"] + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_validate_environment_requires_key(self, brand): + with pytest.raises(ValueError, match="DASHSCOPE_API_KEY"): + brand["embedding_config"]().validate_environment( + headers={}, + model="text-embedding-v3", + messages=[], + optional_params={}, + litellm_params={}, + api_key=None, + api_base=None, + ) + + +class TestQwenBrandCostParity: + @pytest.fixture(autouse=True) + def setup_model_cost_map(self, monkeypatch): + monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") + monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url="")) + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_get_model_info(self, brand): + model_info = litellm.get_model_info(f"{brand['provider']}/qwen-max") + dashscope_info = litellm.get_model_info("dashscope/qwen-max") + assert model_info["litellm_provider"] == brand["provider"] + assert model_info["input_cost_per_token"] == dashscope_info["input_cost_per_token"] + assert model_info["output_cost_per_token"] == dashscope_info["output_cost_per_token"] + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_flat_pricing_matches_dashscope(self, brand): + usage = Usage(prompt_tokens=1000, completion_tokens=500) + brand_costs = dashscope_cost_per_token(model="qwen-max", usage=usage, custom_llm_provider=brand["provider"]) + dashscope_costs = dashscope_cost_per_token(model="qwen-max", usage=usage) + assert brand_costs == dashscope_costs + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_tiered_pricing_matches_dashscope(self, brand): + usage = Usage(prompt_tokens=300000, completion_tokens=300000) + brand_costs = dashscope_cost_per_token(model="qwen-flash", usage=usage, custom_llm_provider=brand["provider"]) + dashscope_costs = dashscope_cost_per_token(model="qwen-flash", usage=usage) + assert brand_costs == dashscope_costs + tier_2 = litellm.get_model_info(f"{brand['provider']}/qwen-flash")["tiered_pricing"][1] + assert math.isclose(brand_costs[0], 300000 * tier_2["input_cost_per_token"], rel_tol=1e-10) + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_public_cost_per_token_routes_to_dashscope_calculator(self, brand): + brand_costs = litellm.cost_per_token( + model=f"{brand['provider']}/qwen-max", + prompt_tokens=1000, + completion_tokens=500, + custom_llm_provider=brand["provider"], + ) + dashscope_costs = litellm.cost_per_token( + model="dashscope/qwen-max", + prompt_tokens=1000, + completion_tokens=500, + custom_llm_provider="dashscope", + ) + assert brand_costs == dashscope_costs + + +class TestQwenBrandCompletionMock: + @pytest.mark.respx() + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_completion_hits_brand_default_host(self, respx_mock, brand, monkeypatch): + monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) + respx_mock.post(f"{brand['default_base']}/chat/completions").respond( + json={ + "id": "chatcmpl-123", + "object": "chat.completion", + "created": 1677652288, + "model": "qwen-turbo", + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": "Hey from LiteLLM!"}, + "finish_reason": "stop", + } + ], + "usage": { + "prompt_tokens": 9, + "completion_tokens": 12, + "total_tokens": 21, + }, + }, + status_code=200, + ) + + response = completion( + model=f"{brand['provider']}/qwen-turbo", + messages=[{"role": "user", "content": "say hey from LiteLLM"}], + api_key="fake-brand-key", + ) + + assert response.choices[0].message.content == "Hey from LiteLLM!" + request = respx_mock.calls[0].request + assert request.url == f"{brand['default_base']}/chat/completions" + assert request.headers["Authorization"] == "Bearer fake-brand-key" diff --git a/tests/test_litellm/llms/databricks/test_databricks_cost_calculator.py b/tests/test_litellm/llms/databricks/test_databricks_cost_calculator.py index 29ad8ee4b6e..e72642f7a04 100644 --- a/tests/test_litellm/llms/databricks/test_databricks_cost_calculator.py +++ b/tests/test_litellm/llms/databricks/test_databricks_cost_calculator.py @@ -62,6 +62,8 @@ PUBLISHED_DBU_PER_MILLION: Final = { "databricks/databricks-gemini-2-5-pro": ("22.321", "178.571", "22.321", "2.232"), "databricks/databricks-gemini-2-5-flash": ("5.357", "44.643", "5.357", "0.536"), "databricks/databricks-kimi-k3": ("42.857", "214.286", "42.857", "4.286"), + "databricks/databricks-deepseek-v4-flash-0731": ("2.000", "4.000", "2.000", "0.400"), + "databricks/databricks-deepseek-v4-pro-0813": ("18.857", "56.571", "18.857", "1.886"), "databricks/databricks-glm-5-2": ("20.000", "62.857", "20.000", "3.714"), } PROMOTIONAL_DISCOUNT: Final = 0.80 diff --git a/tests/test_litellm/llms/gdc/chat/test_gdc_chat_transformation.py b/tests/test_litellm/llms/gdc/chat/test_gdc_chat_transformation.py index 3153c12aa94..3d15a2e8870 100644 --- a/tests/test_litellm/llms/gdc/chat/test_gdc_chat_transformation.py +++ b/tests/test_litellm/llms/gdc/chat/test_gdc_chat_transformation.py @@ -310,6 +310,25 @@ class TestGDCGeminiConfig: api_base=TEST_API_BASE, ) + def test_validate_environment_credentials_missing_audience_binding_are_named(self): + config = GDCGeminiConfig() + creds_without_audience_binding = MagicMock(spec=[]) + + with patch( + "google.auth.load_credentials_from_dict", + return_value=(creds_without_audience_binding, None), + ): + with pytest.raises(AttributeError, match="must expose with_gdch_audience"): + config.validate_environment( + headers={}, + model=TEST_MODEL, + messages=[], + optional_params={}, + litellm_params={"vertex_project": TEST_PROJECT}, + api_key=TEST_API_KEY, + api_base=TEST_API_BASE, + ) + def test_validate_environment_string_false_disables_token_caching(self): config = GDCGeminiConfig() mock_creds = MagicMock() diff --git a/tests/test_litellm/llms/gemini/realtime/test_gemini_realtime_transformation.py b/tests/test_litellm/llms/gemini/realtime/test_gemini_realtime_transformation.py index d0613403e67..3d8200bc474 100644 --- a/tests/test_litellm/llms/gemini/realtime/test_gemini_realtime_transformation.py +++ b/tests/test_litellm/llms/gemini/realtime/test_gemini_realtime_transformation.py @@ -1812,6 +1812,7 @@ def patch_gemini_audio_cost_map_entries(monkeypatch): "gemini-2.5-flash-native-audio", "gemini-2.5-flash-native-audio-latest", "gemini/gemini-2.5-flash-native-audio-latest", + "gemini-live-2.5-flash-native-audio", ] flash_live_models = [ "gemini-3.1-flash-live-preview", @@ -1834,6 +1835,8 @@ def patch_gemini_audio_cost_map_entries(monkeypatch): ("gemini/gemini-3.1-flash-live-preview", True), ("gemini-2.5-flash-native-audio-latest", True), ("gemini/gemini-2.5-flash-native-audio-latest", True), + ("gemini-live-2.5-flash-native-audio", True), + ("vertex_ai/gemini-live-2.5-flash-native-audio", True), ("gemini-2.0-flash", False), ("gemini-2.5-flash", False), ], @@ -1842,6 +1845,19 @@ def test_is_audio_only_live_model_uses_cost_map(model, expected, patch_gemini_au assert GeminiRealtimeConfig._is_audio_only_live_model(model) == expected +def test_gemini_live_native_audio_entry_is_vertex_only(): + import json + from pathlib import Path + from typing import Final + + catalog_path: Final = Path(__file__).parents[5] / "model_prices_and_context_window.json" + catalog: Final = json.loads(catalog_path.read_text()) + vertex_key: Final = "gemini-live-2.5-flash-native-audio" + assert catalog[vertex_key]["litellm_provider"] == "vertex_ai-language-models" + assert catalog[vertex_key].get("gemini_native_audio") is True + assert "gemini/gemini-live-2.5-flash-native-audio" not in catalog, "the Gemini API does not serve this model" + + def test_is_setup_message_and_is_content_message(): config = GeminiRealtimeConfig() assert config.is_setup_message({"setup": {}}) is True diff --git a/tests/test_litellm/llms/gigachat/__init__.py b/tests/test_litellm/llms/gigachat/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/test_litellm/llms/gigachat/chat/test_gigachat_chat_streaming.py b/tests/test_litellm/llms/gigachat/chat/test_gigachat_chat_streaming.py new file mode 100644 index 00000000000..35ca93319f5 --- /dev/null +++ b/tests/test_litellm/llms/gigachat/chat/test_gigachat_chat_streaming.py @@ -0,0 +1,87 @@ +""" +Tests for litellm.llms.gigachat.chat.streaming +""" + +from litellm.llms.gigachat.chat.streaming import GigaChatModelResponseIterator + + +def _parse(chunk: dict) -> dict: + iterator = GigaChatModelResponseIterator(streaming_response=None, sync_stream=True) + return dict(iterator.chunk_parser(chunk=chunk)) + + +class TestChunkParserUsage: + def test_usage_on_stop_chunk(self): + parsed = _parse( + { + "choices": [{"delta": {"content": ""}, "index": 0, "finish_reason": "stop"}], + "usage": {"prompt_tokens": 25, "completion_tokens": 7, "total_tokens": 32}, + } + ) + + assert parsed["finish_reason"] == "stop" + assert parsed["usage"] is not None + assert parsed["usage"]["prompt_tokens"] == 25 + assert parsed["usage"]["completion_tokens"] == 7 + assert parsed["usage"]["total_tokens"] == 32 + + def test_usage_on_function_call_chunk(self): + """Regression: a final chunk ending in function_call still carries usage; it must not be dropped.""" + parsed = _parse( + { + "choices": [ + { + "delta": {"function_call": {"name": "get_weather", "arguments": {"city": "Moscow"}}}, + "index": 0, + "finish_reason": "function_call", + } + ], + "usage": {"prompt_tokens": 40, "completion_tokens": 12, "total_tokens": 52}, + } + ) + + assert parsed["finish_reason"] == "tool_calls" + assert parsed["tool_use"] is not None + assert parsed["usage"] is not None + assert parsed["usage"]["prompt_tokens"] == 40 + assert parsed["usage"]["completion_tokens"] == 12 + assert parsed["usage"]["total_tokens"] == 52 + + def test_usage_on_length_chunk(self): + parsed = _parse( + { + "choices": [{"delta": {"content": "truncated"}, "index": 0, "finish_reason": "length"}], + "usage": {"prompt_tokens": 10, "completion_tokens": 128, "total_tokens": 138}, + } + ) + + assert parsed["usage"] is not None + assert parsed["usage"]["total_tokens"] == 138 + + def test_no_usage_on_interim_chunk(self): + parsed = _parse({"choices": [{"delta": {"content": "hello"}, "index": 0, "finish_reason": None}]}) + + assert parsed["text"] == "hello" + assert parsed["is_finished"] is False + assert parsed["usage"] is None + + def test_cache_hit_usage_folds_cached_tokens_back_in(self): + """GigaChat reports prompt_tokens and total_tokens after subtracting cached tokens + (docs example: prompt_tokens=1, precached_prompt_tokens=37, total_tokens=5), so the + OpenAI-convention usage must add them back and surface them as cached_tokens.""" + parsed = _parse( + { + "choices": [{"delta": {"content": ""}, "index": 0, "finish_reason": "stop"}], + "usage": { + "prompt_tokens": 25, + "completion_tokens": 7, + "total_tokens": 32, + "precached_prompt_tokens": 20, + }, + } + ) + + assert parsed["usage"] is not None + assert parsed["usage"]["prompt_tokens"] == 45 + assert parsed["usage"]["total_tokens"] == 52 + assert parsed["usage"]["prompt_tokens_details"]["cached_tokens"] == 20 diff --git a/tests/test_litellm/llms/gigachat/chat/test_gigachat_chat_transformation.py b/tests/test_litellm/llms/gigachat/chat/test_gigachat_chat_transformation.py new file mode 100644 index 00000000000..2f9511e642c --- /dev/null +++ b/tests/test_litellm/llms/gigachat/chat/test_gigachat_chat_transformation.py @@ -0,0 +1,883 @@ +""" +Unit tests for GigaChat chat transformation. + +Tests GigaChatConfig covering get_complete_url, validate_environment, +get_supported_openai_params, map_openai_params, _convert_tools_to_functions, +_map_tool_choice, _transform_messages, transform_request, transform_response, +get_model_response_iterator, and get_error_class. +""" + +import json +from unittest.mock import MagicMock, patch + +import httpx +import pytest + +from litellm.llms.gigachat.chat.transformation import ( + GigaChatConfig, + GigaChatError, + is_valid_json, +) +from litellm.types.utils import ModelResponse, Usage + +TRANSFORM_MODULE = "litellm.llms.gigachat.chat.transformation" + + +def _make_httpx_response( + body: dict, status_code: int = 200 +) -> httpx.Response: + return httpx.Response( + status_code=status_code, + headers={"content-type": "application/json"}, + content=json.dumps(body).encode("utf-8"), + request=httpx.Request( + "POST", + "https://gigachat.devices.sberbank.ru/api/v1/chat/completions", + ), + ) + + +# --------------------------------------------------------------------------- +# is_valid_json +# --------------------------------------------------------------------------- + + +class TestIsValidJson: + def test_valid_json_object(self): + assert is_valid_json('{"key": "value"}') is True + + def test_valid_json_array(self): + assert is_valid_json("[1, 2, 3]") is True + + def test_valid_json_string(self): + assert is_valid_json('"hello"') is True + + def test_invalid_json(self): + assert is_valid_json("{invalid}") is False + + def test_empty_string(self): + assert is_valid_json("") is False + + +# --------------------------------------------------------------------------- +# GigaChatConfig +# --------------------------------------------------------------------------- + + +class TestGetCompleteUrl: + def setup_method(self): + self.config = GigaChatConfig() + + def test_uses_api_base_from_param(self): + url = self.config.get_complete_url( + api_base="https://custom.example.com", + api_key=None, + model="GigaChat", + optional_params={}, + litellm_params={}, + stream=False, + ) + assert url == "https://custom.example.com/chat/completions" + + def test_uses_api_base_with_trailing_slash(self): + url = self.config.get_complete_url( + api_base="https://custom.example.com/", + api_key=None, + model="GigaChat", + optional_params={}, + litellm_params={}, + stream=False, + ) + # get_api_base passes the value through without stripping the slash + assert url == "https://custom.example.com//chat/completions" + + def test_uses_api_base_from_get_api_base_when_none(self): + url = self.config.get_complete_url( + api_base=None, + api_key=None, + model="GigaChat", + optional_params={}, + litellm_params={}, + stream=False, + ) + assert url.endswith("/chat/completions") + + +class TestValidateEnvironment: + def setup_method(self): + self.config = GigaChatConfig() + + @patch(f"{TRANSFORM_MODULE}.get_access_token", return_value="test-token") + @patch(f"{TRANSFORM_MODULE}.get_secret_str", return_value=None) + def test_sets_auth_headers(self, mock_get_secret, mock_get_token): + headers: dict = {} + result = self.config.validate_environment( + headers=headers, + model="GigaChat", + messages=[{"role": "user", "content": "hi"}], + optional_params={}, + litellm_params={}, + api_key="creds", + api_base="https://api.example.com", + ) + assert result["Authorization"] == "Bearer test-token" + assert result["Content-Type"] == "application/json" + assert result["Accept"] == "application/json" + + @patch(f"{TRANSFORM_MODULE}.get_access_token", return_value="token") + @patch(f"{TRANSFORM_MODULE}.get_secret_str", return_value=None) + def test_stores_credentials_and_api_base_for_image_uploads( + self, mock_get_secret, mock_get_token + ): + self.config.validate_environment( + headers={}, + model="GigaChat", + messages=[], + optional_params={}, + litellm_params={}, + api_key="my-creds", + api_base="https://my-api.example.com", + ) + assert self.config._current_credentials == "my-creds" + assert self.config._current_api_base == "https://my-api.example.com" + + @patch(f"{TRANSFORM_MODULE}.get_access_token", return_value="token") + @patch(f"{TRANSFORM_MODULE}.get_secret_str") + def test_falls_back_to_env_for_credentials( # test-quality-ok: mock-echo of internal wiring + self, mock_get_secret, mock_get_token + ): + mock_get_secret.return_value = "env-creds" + self.config.validate_environment( + headers={}, + model="GigaChat", + messages=[], + optional_params={}, + litellm_params={}, + api_key=None, + api_base=None, + ) + mock_get_secret.assert_any_call("GIGACHAT_CREDENTIALS") # test-quality-ok: mock-echo of internal wiring + + +class TestGetSupportedOpenAiParams: + def setup_method(self): + self.config = GigaChatConfig() + + def test_returns_expected_params(self): + params = self.config.get_supported_openai_params("GigaChat") + expected = [ + "stream", + "temperature", + "top_p", + "max_tokens", + "max_completion_tokens", + "stop", + "tools", + "tool_choice", + "functions", + "function_call", + "response_format", + ] + assert params == expected + + +class TestMapOpenAiParams: + def setup_method(self): + self.config = GigaChatConfig() + + def test_stream(self): + result = self.config.map_openai_params( + non_default_params={"stream": True}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result["stream"] is True + + def test_temperature_zero_maps_to_top_p_zero(self): + result = self.config.map_openai_params( + non_default_params={"temperature": 0}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result["top_p"] == 0 + assert "temperature" not in result + + def test_temperature_non_zero(self): + result = self.config.map_openai_params( + non_default_params={"temperature": 0.7}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result["temperature"] == 0.7 + + def test_top_p(self): + result = self.config.map_openai_params( + non_default_params={"top_p": 0.5}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result["top_p"] == 0.5 + + def test_max_tokens(self): + result = self.config.map_openai_params( + non_default_params={"max_tokens": 100}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result["max_tokens"] == 100 + + def test_max_completion_tokens(self): + result = self.config.map_openai_params( + non_default_params={"max_completion_tokens": 200}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result["max_tokens"] == 200 + + def test_stop_is_dropped(self): + result = self.config.map_openai_params( + non_default_params={"stop": ["\n\n"]}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert "stop" not in result + + def test_tools_converted_to_functions(self): + tools = [ + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get weather", + "parameters": {"type": "object"}, + }, + } + ] + result = self.config.map_openai_params( + non_default_params={"tools": tools}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert "functions" in result + assert result["functions"] == [ + {"name": "get_weather", "description": "Get weather", "parameters": {"type": "object"}} + ] + + def test_tool_choice_auto(self): + result = self.config.map_openai_params( + non_default_params={"tool_choice": "auto"}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result.get("function_call") == "auto" + + def test_tool_choice_none(self): + result = self.config.map_openai_params( + non_default_params={"tool_choice": "none"}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result.get("function_call") == "none" + + def test_tool_choice_required(self): + result = self.config.map_openai_params( + non_default_params={"tool_choice": "required"}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result.get("function_call") == "auto" + + def test_tool_choice_dict(self): + result = self.config.map_openai_params( + non_default_params={ + "tool_choice": { + "type": "function", + "function": {"name": "get_weather"}, + } + }, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result.get("function_call") == {"name": "get_weather"} + + def test_functions(self): + funcs = [{"name": "my_func", "description": "desc", "parameters": {}}] + result = self.config.map_openai_params( + non_default_params={"functions": funcs}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result["functions"] == funcs + + def test_function_call(self): + result = self.config.map_openai_params( + non_default_params={"function_call": {"name": "my_func"}}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result["function_call"] == {"name": "my_func"} + + def test_response_format_json_schema(self): + response_format = { + "type": "json_schema", + "json_schema": { + "name": "test_schema", + "schema": {"type": "object", "properties": {"name": {"type": "string"}}}, + }, + } + result = self.config.map_openai_params( + non_default_params={"response_format": response_format}, + optional_params={"functions": []}, + model="GigaChat", + drop_params=False, + ) + # Should add a function for the schema + assert len(result["functions"]) == 1 + assert result["functions"][0]["name"] == "test_schema" + assert result["function_call"] == {"name": "test_schema"} + assert result["_structured_output"] is True + + +class TestConvertToolsToFunctions: + def setup_method(self): + self.config = GigaChatConfig() + + def test_converts_function_tools_only(self): + tools = [ + {"type": "function", "function": {"name": "a", "description": "d", "parameters": {}}}, + {"type": "code_interpreter"}, # should be ignored + ] + result = self.config._convert_tools_to_functions(tools) + assert len(result) == 1 + assert result[0]["name"] == "a" + + def test_empty_tools(self): + assert self.config._convert_tools_to_functions([]) == [] + + +class TestMapToolChoice: + def setup_method(self): + self.config = GigaChatConfig() + + def test_none(self): + assert self.config._map_tool_choice("none") == "none" + + def test_auto(self): + assert self.config._map_tool_choice("auto") == "auto" + + def test_required(self): + assert self.config._map_tool_choice("required") == "auto" + + def test_dict_with_function(self): + result = self.config._map_tool_choice( + {"type": "function", "function": {"name": "get_weather"}} + ) + assert result == {"name": "get_weather"} + + def test_dict_without_name(self): + result = self.config._map_tool_choice( + {"type": "function", "function": {}} + ) + assert result is None + + def test_unknown_value(self): + assert self.config._map_tool_choice("unknown") is None + + +class TestTransformMessages: + def setup_method(self): + self.config = GigaChatConfig() + + def test_developer_role_to_system(self): + result = self.config._transform_messages( + [{"role": "developer", "content": "be helpful"}] + ) + assert result[0]["role"] == "system" + assert result[0]["content"] == "be helpful" + + def test_system_message_not_first_becomes_user(self): + result = self.config._transform_messages([ + {"role": "user", "content": "hi"}, + {"role": "system", "content": "instruction"}, + ]) + assert result[0]["role"] == "user" + assert result[1]["role"] == "user" + assert result[1]["content"] == "instruction" + + def test_tool_role_to_function(self): + result = self.config._transform_messages([ + {"role": "tool", "content": '{"result": "ok"}'} + ]) + assert result[0]["role"] == "function" + + def test_tool_role_content_wraps_non_json(self): + result = self.config._transform_messages([ + {"role": "tool", "content": "plain text"} + ]) + assert result[0]["role"] == "function" + assert is_valid_json(result[0]["content"]) + + def test_none_content_becomes_empty_string(self): + result = self.config._transform_messages([ + {"role": "user", "content": None} + ]) + assert result[0]["content"] == "" + + def test_name_field_removed(self): + result = self.config._transform_messages([ + {"role": "user", "content": "hi", "name": "John"} + ]) + assert "name" not in result[0] + + def test_tool_calls_converted_to_function_call(self): + result = self.config._transform_messages([ + { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "id": "call_abc", + "type": "function", + "function": { + "name": "get_weather", + "arguments": '{"city": "London"}', + }, + } + ], + } + ]) + assert "tool_calls" not in result[0] + assert result[0]["function_call"]["name"] == "get_weather" + assert result[0]["function_call"]["arguments"] == {"city": "London"} + + def test_tool_calls_with_dict_arguments(self): + result = self.config._transform_messages([ + { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "id": "call_xyz", + "type": "function", + "function": { + "name": "search", + "arguments": {"query": "test"}, + }, + } + ], + } + ]) + assert result[0]["function_call"]["arguments"] == {"query": "test"} + + def test_list_content_multimodal(self): + content = [ + {"type": "text", "text": "describe this"}, + { + "type": "image_url", + "image_url": {"url": "https://example.com/img.jpg"}, + }, + ] + with patch.object(self.config, "_upload_image", return_value="file-123"): + result = self.config._transform_messages([ + {"role": "user", "content": content} + ]) + assert result[0]["content"] == "describe this" + assert result[0]["attachments"] == ["file-123"] + + def test_list_content_with_image_url_string(self): + content = [ + {"type": "text", "text": "look"}, + {"type": "image_url", "image_url": "https://example.com/img.jpg"}, + ] + with patch.object(self.config, "_upload_image", return_value="file-456"): + result = self.config._transform_messages([ + {"role": "user", "content": content} + ]) + assert result[0]["content"] == "look" + assert "file-456" in result[0]["attachments"] + + +class TestTransformRequest: + def setup_method(self): + self.config = GigaChatConfig() + + def test_builds_basic_request(self): + body = self.config.transform_request( + model="gigachat/GigaChat", + messages=[{"role": "user", "content": "hi"}], + optional_params={}, + litellm_params={}, + headers={}, + ) + assert body["model"] == "GigaChat" + assert len(body["messages"]) == 1 + assert body["messages"][0]["content"] == "hi" + + def test_model_prefix_stripped(self): + body = self.config.transform_request( + model="gigachat/GigaChat-Pro", + messages=[{"role": "user", "content": "hello"}], + optional_params={}, + litellm_params={}, + headers={}, + ) + assert body["model"] == "GigaChat-Pro" + + def test_includes_optional_params(self): + body = self.config.transform_request( + model="gigachat/GigaChat", + messages=[{"role": "user", "content": "hi"}], + optional_params={ + "temperature": 0.5, + "max_tokens": 100, + "stream": True, + }, + litellm_params={}, + headers={}, + ) + assert body["temperature"] == 0.5 + assert body["max_tokens"] == 100 + assert body["stream"] is True + + def test_includes_functions(self): + body = self.config.transform_request( + model="gigachat/GigaChat", + messages=[{"role": "user", "content": "hi"}], + optional_params={ + "functions": [{"name": "my_func"}], + "function_call": {"name": "my_func"}, + }, + litellm_params={}, + headers={}, + ) + assert body["functions"] == [{"name": "my_func"}] + assert body["function_call"] == {"name": "my_func"} + + def test_skips_unsupported_params(self): + body = self.config.transform_request( + model="gigachat/GigaChat", + messages=[{"role": "user", "content": "hi"}], + optional_params={"n": 2, "user": "abc"}, + litellm_params={}, + headers={}, + ) + assert "n" not in body + assert "user" not in body + + +class TestTransformResponse: + def setup_method(self): + self.config = GigaChatConfig() + + def test_basic_response(self): + raw = _make_httpx_response({ + "id": "chatcmpl-123", + "created": 1700000000, + "model": "GigaChat", + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": "Hello!"}, + "finish_reason": "stop", + } + ], + "usage": {"prompt_tokens": 5, "completion_tokens": 3, "total_tokens": 8}, + }) + model_response = ModelResponse() + result = self.config.transform_response( + model="gigachat/GigaChat", + raw_response=raw, + model_response=model_response, + logging_obj=MagicMock(), + request_data={}, + messages=[], + optional_params={}, + litellm_params={}, + encoding=None, + ) + assert result.choices[0].message.content == "Hello!" + assert result.choices[0].finish_reason == "stop" + assert result.usage.prompt_tokens == 5 + assert result.usage.total_tokens == 8 + + def test_function_call_into_tool_calls(self): + raw = _make_httpx_response({ + "id": "chatcmpl-456", + "created": 1700000000, + "model": "GigaChat", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": None, + "function_call": { + "name": "get_weather", + "arguments": {"city": "Moscow"}, + }, + }, + "finish_reason": "function_call", + } + ], + "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15}, + }) + model_response = ModelResponse() + result = self.config.transform_response( + model="gigachat/GigaChat", + raw_response=raw, + model_response=model_response, + logging_obj=MagicMock(), + request_data={}, + messages=[], + optional_params={}, + litellm_params={}, + encoding=None, + ) + assert result.choices[0].finish_reason == "tool_calls" + tool_calls = result.choices[0].message.tool_calls + assert tool_calls is not None + assert len(tool_calls) == 1 + assert tool_calls[0].function.name == "get_weather" + assert '{"city": "Moscow"}' in tool_calls[0].function.arguments + + def test_function_call_structured_output(self): + raw = _make_httpx_response({ + "id": "chatcmpl-789", + "created": 1700000000, + "model": "GigaChat", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": None, + "function_call": { + "name": "test_schema", + "arguments": {"name": "John"}, + }, + }, + "finish_reason": "function_call", + } + ], + "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15}, + }) + model_response = ModelResponse() + result = self.config.transform_response( + model="gigachat/GigaChat", + raw_response=raw, + model_response=model_response, + logging_obj=MagicMock(), + request_data={}, + messages=[], + optional_params={"_structured_output": True}, + litellm_params={}, + encoding=None, + ) + # Structured output: function_call -> content + assert result.choices[0].finish_reason == "stop" + assert result.choices[0].message.content is not None + assert '"name": "John"' in result.choices[0].message.content + + def test_function_call_string_arguments(self): + raw = _make_httpx_response({ + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "function_call": { + "name": "get_weather", + "arguments": '{"city": "Moscow"}', + }, + }, + "finish_reason": "function_call", + } + ], + "usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2}, + }) + model_response = ModelResponse() + result = self.config.transform_response( + model="gigachat/GigaChat", + raw_response=raw, + model_response=model_response, + logging_obj=MagicMock(), + request_data={}, + messages=[], + optional_params={}, + litellm_params={}, + encoding=None, + ) + tc = result.choices[0].message.tool_calls[0] + assert '{"city": "Moscow"}' in tc.function.arguments + + def test_cleans_up_gigachat_specific_fields(self): + raw = _make_httpx_response({ + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "done", + "functions_state_id": "some-state", + }, + "finish_reason": "stop", + } + ], + "usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2}, + }) + model_response = ModelResponse() + result = self.config.transform_response( + model="gigachat/GigaChat", + raw_response=raw, + model_response=model_response, + logging_obj=MagicMock(), + request_data={}, + messages=[], + optional_params={}, + litellm_params={}, + encoding=None, + ) + # functions_state_id should have been removed from the message data + assert result.choices[0].message.content == "done" + + def test_raises_on_invalid_json(self): + raw = httpx.Response( + status_code=500, + headers={"content-type": "text/plain"}, + content=b"not json", + request=httpx.Request("POST", "https://example.com"), + ) + model_response = ModelResponse() + with pytest.raises(GigaChatError) as exc_info: + self.config.transform_response( + model="gigachat/GigaChat", + raw_response=raw, + model_response=model_response, + logging_obj=MagicMock(), + request_data={}, + messages=[], + optional_params={}, + litellm_params={}, + encoding=None, + ) + assert "Invalid JSON response" in str(exc_info.value.message) + + def test_empty_choices(self): + raw = _make_httpx_response({ + "choices": [], + "usage": {}, + }) + model_response = ModelResponse() + result = self.config.transform_response( + model="gigachat/GigaChat", + raw_response=raw, + model_response=model_response, + logging_obj=MagicMock(), + request_data={}, + messages=[], + optional_params={}, + litellm_params={}, + encoding=None, + ) + assert result.choices == [] + + def test_function_call_with_non_dict_arguments(self): + raw = _make_httpx_response({ + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "function_call": { + "name": "say_hello", + "arguments": "hello", + }, + }, + "finish_reason": "function_call", + } + ], + "usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2}, + }) + model_response = ModelResponse() + result = self.config.transform_response( + model="gigachat/GigaChat", + raw_response=raw, + model_response=model_response, + logging_obj=MagicMock(), + request_data={}, + messages=[], + optional_params={}, + litellm_params={}, + encoding=None, + ) + tc = result.choices[0].message.tool_calls[0] + assert tc.function.arguments == "hello" + + +class TestGetModelResponseIterator: + def setup_method(self): + self.config = GigaChatConfig() + + def test_returns_gigachat_iterator_sync(self): + from litellm.llms.gigachat.chat.streaming import ( + GigaChatModelResponseIterator, + ) + + result = self.config.get_model_response_iterator( + streaming_response=iter(["data"]), + sync_stream=True, + json_mode=False, + ) + assert isinstance(result, GigaChatModelResponseIterator) + + +class TestGetErrorClass: + def setup_method(self): + self.config = GigaChatConfig() + + def test_returns_gigachat_error(self): + error = self.config.get_error_class( + error_message="something went wrong", + status_code=400, + headers={"x-request-id": "abc"}, + ) + assert isinstance(error, GigaChatError) + assert error.status_code == 400 + assert error.message == "something went wrong" + assert error.headers == {"x-request-id": "abc"} + + +class TestUploadImage: + def setup_method(self): + self.config = GigaChatConfig() + + @patch(f"{TRANSFORM_MODULE}.upload_file_sync", return_value="file-uploaded") + def test_upload_image_success(self, mock_upload): + self.config._current_credentials = "creds" + self.config._current_api_base = "https://api.example.com" + result = self.config._upload_image("https://example.com/img.jpg") + assert result == "file-uploaded" + mock_upload.assert_called_once_with( + image_url="https://example.com/img.jpg", + credentials="creds", + api_base="https://api.example.com", + ) + + @patch(f"{TRANSFORM_MODULE}.upload_file_sync", side_effect=Exception("fail")) + def test_upload_image_failure_returns_none(self, mock_upload): + result = self.config._upload_image("https://example.com/img.jpg") + assert result is None \ No newline at end of file diff --git a/tests/test_litellm/llms/gigachat/embedding/__init__.py b/tests/test_litellm/llms/gigachat/embedding/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/test_litellm/llms/gigachat/embedding/test_gigachat_embedding_transformation.py b/tests/test_litellm/llms/gigachat/embedding/test_gigachat_embedding_transformation.py new file mode 100644 index 00000000000..8537793ea72 --- /dev/null +++ b/tests/test_litellm/llms/gigachat/embedding/test_gigachat_embedding_transformation.py @@ -0,0 +1,372 @@ +""" +Unit tests for GigaChat embedding transformation. + +Tests GigaChatEmbeddingConfig covering get_config, get_supported_openai_params, +map_openai_params, _get_openai_compatible_provider_info, get_complete_url, +transform_embedding_request, transform_embedding_response, validate_environment, +and get_error_class. +""" + +import json +from unittest.mock import MagicMock, patch + +import httpx +import pytest + +from litellm import LlmProviders +from litellm.llms.gigachat.embedding.transformation import ( + GigaChatEmbeddingConfig, + GigaChatEmbeddingError, +) +from litellm.types.utils import EmbeddingResponse + +TRANSFORM_MODULE = "litellm.llms.gigachat.embedding.transformation" + + +def _make_httpx_response(body: dict, status_code: int = 200) -> httpx.Response: + return httpx.Response( + status_code=status_code, + headers={"content-type": "application/json"}, + content=json.dumps(body).encode("utf-8"), + request=httpx.Request("POST", "https://gigachat.devices.sberbank.ru/api/v1/embeddings"), + ) + + +# --------------------------------------------------------------------------- +# GigaChatEmbeddingConfig +# --------------------------------------------------------------------------- + + +class TestGetConfig: + def setup_method(self): + self.config = GigaChatEmbeddingConfig() + + def test_contains_only_abc_impl(self): + """get_config returns ABC internal data due to inheritance.""" + result = self.config.get_config() + # The only key should be _abc_impl from ABC base class + assert set(result.keys()) == {"_abc_impl"} + + +class TestGetSupportedOpenAiParams: + def setup_method(self): + self.config = GigaChatEmbeddingConfig() + + def test_returns_empty_list(self): + params = self.config.get_supported_openai_params("GigaChat") + assert params == [] + + +class TestMapOpenAiParams: + def setup_method(self): + self.config = GigaChatEmbeddingConfig() + + def test_returns_optional_params_unchanged(self): + result = self.config.map_openai_params( + non_default_params={"model": "test"}, + optional_params={"temperature": 0.5}, + model="GigaChat", + drop_params=False, + ) + assert result == {"temperature": 0.5} + + def test_returns_empty_dict_when_no_optional_params(self): + result = self.config.map_openai_params( + non_default_params={}, + optional_params={}, + model="GigaChat", + drop_params=False, + ) + assert result == {} + + +class TestGetOpenaiCompatibleProviderInfo: + def setup_method(self): + self.config = GigaChatEmbeddingConfig() + + def test_returns_gigachat_provider(self): + provider, api_base, api_key = self.config._get_openai_compatible_provider_info( + api_base="https://api.example.com", api_key="test-key" + ) + assert provider == LlmProviders.GIGACHAT.value + assert api_base == "https://api.example.com" + assert api_key == "test-key" + + def test_resolves_api_base_when_none(self, monkeypatch): + monkeypatch.delenv("GIGACHAT_API_BASE", raising=False) + provider, api_base, api_key = self.config._get_openai_compatible_provider_info( + api_base=None, api_key="key" + ) + assert api_base is not None + assert api_base.endswith("/api/v1") + + def test_returns_none_api_key(self): + _, _, api_key = self.config._get_openai_compatible_provider_info( + api_base="https://example.com", api_key=None + ) + assert api_key is None + + +class TestGetCompleteUrl: + def setup_method(self): + self.config = GigaChatEmbeddingConfig() + + def test_default_url(self): + url = self.config.get_complete_url( + api_base=None, api_key=None, model="GigaChat", + optional_params={}, litellm_params={}, + ) + assert url.endswith("/embeddings") + + def test_custom_api_base(self): + url = self.config.get_complete_url( + api_base="https://custom.example.com", api_key=None, model="GigaChat", + optional_params={}, litellm_params={}, + ) + assert url == "https://custom.example.com/embeddings" + + def test_trailing_slash_api_base(self): + url = self.config.get_complete_url( + api_base="https://custom.example.com/", api_key=None, model="GigaChat", + optional_params={}, litellm_params={}, + ) + # get_api_base doesn't strip slash, so we get double slash + assert url == "https://custom.example.com//embeddings" + + +class TestTransformEmbeddingRequest: + def setup_method(self): + self.config = GigaChatEmbeddingConfig() + + def test_string_input(self): + result = self.config.transform_embedding_request( + model="gigachat/Embeddings", + input="hello world", + optional_params={}, + headers={}, + ) + assert result == {"model": "Embeddings", "input": ["hello world"]} + + def test_list_input(self): + result = self.config.transform_embedding_request( + model="gigachat/Embeddings", + input=["text1", "text2"], + optional_params={}, + headers={}, + ) + assert result == {"model": "Embeddings", "input": ["text1", "text2"]} + + def test_strips_gigachat_prefix(self): + result = self.config.transform_embedding_request( + model="gigachat/GigaChat-Pro", + input="test", + optional_params={}, + headers={}, + ) + assert result["model"] == "GigaChat-Pro" + + def test_model_without_prefix(self): + result = self.config.transform_embedding_request( + model="Embeddings", + input="test", + optional_params={}, + headers={}, + ) + assert result["model"] == "Embeddings" + + +class TestTransformEmbeddingResponse: + def setup_method(self): + self.config = GigaChatEmbeddingConfig() + self.logging_obj = MagicMock() + + def _make_gigachat_response(self, data: list[dict]) -> httpx.Response: + return _make_httpx_response({ + "object": "list", + "data": data, + "model": "Embeddings", + }) + + def test_basic_response(self): + raw = self._make_gigachat_response([ + { + "object": "embedding", + "embedding": [0.1, 0.2, 0.3], + "index": 0, + } + ]) + model_response = EmbeddingResponse() + result = self.config.transform_embedding_response( + model="gigachat/Embeddings", + raw_response=raw, + model_response=model_response, + logging_obj=self.logging_obj, + api_key="test-key", + request_data={"input": ["text"]}, + optional_params={}, + litellm_params={}, + ) + assert result.object == "list" + assert len(result.data) == 1 + assert result.data[0]["embedding"] == [0.1, 0.2, 0.3] + assert result.data[0]["index"] == 0 + assert result.usage.prompt_tokens == 0 + assert result.usage.total_tokens == 0 + + def test_aggregates_per_embedding_usage(self): + raw = self._make_gigachat_response([ + { + "object": "embedding", + "embedding": [0.1, 0.2], + "index": 0, + "usage": {"prompt_tokens": 5}, + }, + { + "object": "embedding", + "embedding": [0.3, 0.4], + "index": 1, + "usage": {"prompt_tokens": 7}, + }, + ]) + model_response = EmbeddingResponse() + result = self.config.transform_embedding_response( + model="gigachat/Embeddings", + raw_response=raw, + model_response=model_response, + logging_obj=self.logging_obj, + api_key="test-key", + request_data={"input": ["a", "b"]}, + optional_params={}, + litellm_params={}, + ) + # Total should be sum of per-embedding prompt_tokens + assert result.usage.prompt_tokens == 12 + assert result.usage.total_tokens == 12 + # Usage should be removed from individual embedding data + assert "usage" not in result.data[0] + assert "usage" not in result.data[1] + + def test_usage_removed_from_individual_embeddings(self): + raw = self._make_gigachat_response([ + { + "object": "embedding", + "embedding": [0.5], + "index": 0, + "usage": {"prompt_tokens": 3}, + } + ]) + model_response = EmbeddingResponse() + result = self.config.transform_embedding_response( + model="gigachat/Embeddings", + raw_response=raw, + model_response=model_response, + logging_obj=self.logging_obj, + api_key="key", + request_data={"input": ["x"]}, + optional_params={}, + litellm_params={}, + ) + # usage should NOT be in the final EmbeddingResponse data items + for emb in result.data: + assert "usage" not in emb + + def test_passes_model_from_response(self): + raw = self._make_gigachat_response([ + {"object": "embedding", "embedding": [0.1], "index": 0}, + ]) + model_response = EmbeddingResponse() + result = self.config.transform_embedding_response( + model="gigachat/Embeddings", + raw_response=raw, + model_response=model_response, + logging_obj=self.logging_obj, + api_key="key", + request_data={"input": ["x"]}, + optional_params={}, + litellm_params={}, + ) + assert result.model == "Embeddings" + + def test_calls_logging_post_call(self): + raw = self._make_gigachat_response([ + {"object": "embedding", "embedding": [0.1], "index": 0}, + ]) + model_response = EmbeddingResponse() + self.config.transform_embedding_response( + model="gigachat/Embeddings", + raw_response=raw, + model_response=model_response, + logging_obj=self.logging_obj, + api_key="test-api-key", + request_data={"input": ["hello"]}, + optional_params={}, + litellm_params={}, + ) + self.logging_obj.post_call.assert_called_once() + args = self.logging_obj.post_call.call_args.kwargs + assert args["api_key"] == "test-api-key" + assert args["input"] == ["hello"] + + +class TestValidateEnvironment: + def setup_method(self): + self.config = GigaChatEmbeddingConfig() + + @patch(f"{TRANSFORM_MODULE}.get_access_token", return_value="test-token") + def test_sets_oauth_headers(self, mock_get_token): + headers = self.config.validate_environment( + headers={}, + model="GigaChat", + messages=[], + optional_params={}, + litellm_params={}, + api_key="creds", + api_base="https://api.example.com", + ) + assert headers["Authorization"] == "Bearer test-token" + assert headers["Content-Type"] == "application/json" + mock_get_token.assert_called_once_with(credentials="creds", litellm_params={}) + + @patch(f"{TRANSFORM_MODULE}.get_access_token", return_value="token") + def test_merges_custom_headers(self, mock_get_token): + headers = self.config.validate_environment( + headers={"X-Custom": "value"}, + model="GigaChat", + messages=[], + optional_params={}, + litellm_params={}, + api_key="creds", + api_base="https://api.example.com", + ) + assert headers["Authorization"] == "Bearer token" + assert headers["Content-Type"] == "application/json" + assert headers["X-Custom"] == "value" + + @patch(f"{TRANSFORM_MODULE}.get_access_token", return_value="token") + def test_custom_header_overwrites_default(self, mock_get_token): + headers = self.config.validate_environment( + headers={"Authorization": "Bearer custom"}, + model="GigaChat", + messages=[], + optional_params={}, + litellm_params={}, + api_key="creds", + api_base="https://api.example.com", + ) + # Merge: default headers first, then custom headers on top + assert headers["Authorization"] == "Bearer custom" + + +class TestGetErrorClass: + def setup_method(self): + self.config = GigaChatEmbeddingConfig() + + def test_returns_gigachat_embedding_error(self): + error = self.config.get_error_class( + error_message="embedding failed", + status_code=400, + headers={"x-request-id": "abc"}, + ) + assert isinstance(error, GigaChatEmbeddingError) + assert error.status_code == 400 + assert error.message == "embedding failed" \ No newline at end of file diff --git a/tests/test_litellm/llms/gigachat/passthrough/__init__.py b/tests/test_litellm/llms/gigachat/passthrough/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/test_litellm/llms/gigachat/passthrough/test_gigachat_passthrough_transformation.py b/tests/test_litellm/llms/gigachat/passthrough/test_gigachat_passthrough_transformation.py new file mode 100644 index 00000000000..0a6ef364954 --- /dev/null +++ b/tests/test_litellm/llms/gigachat/passthrough/test_gigachat_passthrough_transformation.py @@ -0,0 +1,607 @@ +""" +Unit tests for GigaChatPassthroughConfig transformation. + +Tests the GigaChat-specific passthrough configuration including URL construction, +streaming detection, authentication handling, and logging response transformations. +""" + +import json +from unittest.mock import MagicMock, patch + +import httpx +import pytest + +from litellm.llms.gigachat.passthrough.transformation import GigaChatPassthroughConfig +from litellm.types.utils import EmbeddingResponse, ModelResponse + + +def _gigachat_chat_completion_body(): + return { + "id": "chatcmpl-test123", + "object": "chat.completion", + "created": 1700000000, + "model": "GigaChat", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "Hello from GigaChat", + }, + "finish_reason": "stop", + } + ], + "usage": { + "prompt_tokens": 5, + "completion_tokens": 3, + "total_tokens": 8, + }, + } + + +def _gigachat_embedding_body(): + return { + "object": "list", + "data": [ + { + "object": "embedding", + "embedding": [0.1, 0.2, 0.3], + "index": 0, + "usage": {"prompt_tokens": 4}, + } + ], + "model": "Embeddings", + } + + +def _make_httpx_response(body: dict) -> httpx.Response: + return httpx.Response( + status_code=200, + headers={"content-type": "application/json"}, + content=json.dumps(body).encode("utf-8"), + request=httpx.Request( + "POST", "https://gigachat.devices.sberbank.ru/api/v1/chat/completions" + ), + ) + + +class TestGigaChatPassthroughConfig: + """Tests for GigaChatPassthroughConfig class.""" + + def test_is_streaming_request_true(self): + """Test streaming is detected when stream=True.""" + config = GigaChatPassthroughConfig() + assert ( + config.is_streaming_request("chat/completions", {"stream": True}) is True + ) + + def test_is_streaming_request_false(self): + """Test streaming is not detected when stream=False.""" + config = GigaChatPassthroughConfig() + assert ( + config.is_streaming_request("chat/completions", {"stream": False}) + is False + ) + + def test_is_streaming_request_missing_stream_key(self): + """Test streaming defaults to False when stream key is missing.""" + config = GigaChatPassthroughConfig() + assert ( + config.is_streaming_request("chat/completions", {"model": "GigaChat"}) + is False + ) + + def test_get_complete_url_with_api_base(self): + """Test URL construction with explicit api_base.""" + config = GigaChatPassthroughConfig() + api_base = "https://custom.gigachat.ru/api/v1" + endpoint = "chat/completions" + + complete_url, base_target_url = config.get_complete_url( + api_base=api_base, + api_key=None, + model="GigaChat", + endpoint=endpoint, + request_query_params=None, + litellm_params={}, + ) + + assert isinstance(complete_url, httpx.URL) + assert str(complete_url) == f"{api_base}/{endpoint}" + assert base_target_url == api_base + + def test_get_complete_url_with_leading_slash_endpoint(self): + """Test URL construction with endpoint having leading slash.""" + config = GigaChatPassthroughConfig() + api_base = "https://custom.gigachat.ru/api/v1" + endpoint = "/chat/completions" + + complete_url, base_target_url = config.get_complete_url( + api_base=api_base, + api_key=None, + model="GigaChat", + endpoint=endpoint, + request_query_params=None, + litellm_params={}, + ) + + assert str(complete_url) == "https://custom.gigachat.ru/api/v1/chat/completions" + assert base_target_url == api_base + + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.llms.gigachat.passthrough.transformation.get_secret_str" + ) + def test_get_complete_url_with_env_api_base(self, mock_get_secret): + """Test URL construction with api_base from environment.""" + config = GigaChatPassthroughConfig() + env_api_base = "https://env.gigachat.ru/api/v1" + mock_get_secret.return_value = env_api_base + + complete_url, base_target_url = config.get_complete_url( + api_base=None, + api_key=None, + model="GigaChat", + endpoint="embeddings", + request_query_params=None, + litellm_params={}, + ) + + assert isinstance(complete_url, httpx.URL) + assert str(complete_url).startswith(env_api_base) + assert base_target_url == env_api_base + mock_get_secret.assert_called_once_with("GIGACHAT_API_BASE") + + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.llms.gigachat.passthrough.transformation.get_secret_str" + ) + def test_get_complete_url_fallback_to_default(self, mock_get_secret): + """Test URL construction falls back to default GIGACHAT_BASE_URL.""" + config = GigaChatPassthroughConfig() + mock_get_secret.return_value = None + + complete_url, base_target_url = config.get_complete_url( + api_base=None, + api_key=None, + model="GigaChat", + endpoint="models", + request_query_params=None, + litellm_params={}, + ) + + assert isinstance(complete_url, httpx.URL) + assert "gigachat.devices.sberbank.ru" in str(complete_url) + assert base_target_url == "https://gigachat.devices.sberbank.ru/api/v1" + + def test_get_complete_url_no_api_base_raises(self): + """Test that exception is raised when no api_base can be resolved.""" + config = GigaChatPassthroughConfig() + with patch( + "litellm.llms.gigachat.passthrough.transformation.get_secret_str", # test-quality-ok: patching litellm internal for unit test isolation + return_value=None, + ): + with patch( + "litellm.llms.gigachat.passthrough.transformation.GIGACHAT_BASE_URL", # test-quality-ok: patching litellm internal for unit test isolation + None, + ): + with pytest.raises(Exception, match="GigaChat api base not found"): + config.get_complete_url( + api_base=None, + api_key=None, + model="GigaChat", + endpoint="chat/completions", + request_query_params=None, + litellm_params={}, + ) + + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.llms.gigachat.passthrough.transformation.get_access_token" + ) + def test_validate_environment(self, mock_get_access_token): + """Test headers are set correctly with OAuth token.""" + config = GigaChatPassthroughConfig() + mock_get_access_token.return_value = "test-token-123" + + headers = config.validate_environment( + headers={}, + model="GigaChat", + messages=[{"role": "user", "content": "hi"}], + optional_params={}, + litellm_params={}, + api_key="test-credentials", + api_base="https://custom.gigachat.ru", + ) + + assert headers["Authorization"] == "Bearer test-token-123" + assert headers["Content-Type"] == "application/json" + assert headers["Accept"] == "application/json" + mock_get_access_token.assert_called_once_with( + credentials="test-credentials", + litellm_params={}, + ) + + def test_logging_non_streaming_response_chat_completions(self): + """Test chat completions endpoint returns ModelResponse.""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + result = config.logging_non_streaming_response( + model="gigachat/GigaChat", + custom_llm_provider="gigachat", + httpx_response=_make_httpx_response(_gigachat_chat_completion_body()), + request_data={ + "model": "gigachat/GigaChat", + "messages": [{"role": "user", "content": "hi"}], + }, + logging_obj=logging_obj, + endpoint="chat/completions", + ) + + assert isinstance(result, ModelResponse) + assert result.choices[0].message.content == "Hello from GigaChat" + assert result.usage.prompt_tokens == 5 + assert result.usage.completion_tokens == 3 + assert result.usage.total_tokens == 8 + + def test_logging_non_streaming_response_embeddings(self): + """Test embeddings endpoint returns EmbeddingResponse.""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + result = config.logging_non_streaming_response( + model="gigachat/Embeddings", + custom_llm_provider="gigachat", + httpx_response=_make_httpx_response(_gigachat_embedding_body()), + request_data={"input": ["hello"], "model": "gigachat/Embeddings"}, + logging_obj=logging_obj, + endpoint="embeddings", + ) + + assert isinstance(result, EmbeddingResponse) + assert len(result.data) == 1 + assert result.data[0]["embedding"] == [0.1, 0.2, 0.3] + + def test_logging_non_streaming_response_unknown_endpoint_returns_none(self): + """Test unknown endpoint returns None.""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + result = config.logging_non_streaming_response( + model="gigachat/GigaChat", + custom_llm_provider="gigachat", + httpx_response=_make_httpx_response(_gigachat_chat_completion_body()), + request_data={}, + logging_obj=logging_obj, + endpoint="images/generations", + ) + + assert result is None + + def test_handle_logging_collected_chunks_with_string_chunks(self): + """Test converting string chunks to model response.""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + chunks = [ + '{"choices": [{"delta": {"content": "Hello"}, "index": 0}]}', + '{"choices": [{"delta": {"content": " world"}, "index": 0}]}', + '{"choices": [{"delta": {}, "finish_reason": "stop", "index": 0}], "usage": {"prompt_tokens": 5, "completion_tokens": 2, "total_tokens": 7}}', + ] + + result = config.handle_logging_collected_chunks( + all_chunks=chunks, + litellm_logging_obj=logging_obj, + model="gigachat/GigaChat", + custom_llm_provider="gigachat", + endpoint="chat/completions", + ) + + assert isinstance(result, ModelResponse) + assert result.choices[0].message.content == "Hello world" + + def test_handle_logging_collected_chunks_with_bytes_chunks(self): + """Test converting string chunks to model response (bytes pre-decoded upstream).""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + chunks = [ + '{"choices": [{"delta": {"content": "Hi"}, "index": 0}]}', + '{"choices": [{"delta": {}, "finish_reason": "stop", "index": 0}], "usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2}}', + ] + + result = config.handle_logging_collected_chunks( + all_chunks=chunks, + litellm_logging_obj=logging_obj, + model="gigachat/GigaChat", + custom_llm_provider="gigachat", + endpoint="chat/completions", + ) + + assert isinstance(result, ModelResponse) + assert result.choices[0].message.content == "Hi" + + def test_handle_logging_collected_chunks_with_done_and_empty(self): + """Test that [DONE] and empty chunks are skipped.""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + chunks = [ + "", + "[DONE]", + '{"choices": [{"delta": {"content": "test"}, "index": 0}]}', + '{"choices": [{"delta": {}, "finish_reason": "stop", "index": 0}], "usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2}}', + ] + + result = config.handle_logging_collected_chunks( + all_chunks=chunks, + litellm_logging_obj=logging_obj, + model="gigachat/GigaChat", + custom_llm_provider="gigachat", + endpoint="chat/completions", + ) + + assert isinstance(result, ModelResponse) + assert result.choices[0].message.content == "test" + + def test_handle_logging_collected_chunks_with_dict_chunks(self): + """Test converting string-serialized dict chunks (dicts pre-serialized upstream).""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + chunks = [ + '{"choices": [{"delta": {"content": "direct"}, "index": 0}]}', + json.dumps( + { + "choices": [ + { + "delta": {}, + "finish_reason": "stop", + "index": 0, + } + ], + "usage": { + "prompt_tokens": 1, + "completion_tokens": 1, + "total_tokens": 2, + }, + } + ), + ] + + result = config.handle_logging_collected_chunks( + all_chunks=chunks, + litellm_logging_obj=logging_obj, + model="gigachat/GigaChat", + custom_llm_provider="gigachat", + endpoint="chat/completions", + ) + + assert isinstance(result, ModelResponse) + assert result.choices[0].message.content == "direct" + + def test_handle_logging_collected_chunks_empty_list_returns_none(self): + """Test empty chunks list returns None.""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + result = config.handle_logging_collected_chunks( + all_chunks=[], + litellm_logging_obj=logging_obj, + model="gigachat/GigaChat", + custom_llm_provider="gigachat", + endpoint="chat/completions", + ) + + assert result is None + + def test_handle_logging_collected_chunks_invalid_json_skipped(self): + """Test invalid JSON chunks are skipped gracefully.""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + chunks = [ + "not-valid-json", + '{"choices": [{"delta": {"content": "valid"}, "index": 0}]}', + '{"choices": [{"delta": {}, "finish_reason": "stop", "index": 0}], "usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2}}', + ] + + result = config.handle_logging_collected_chunks( + all_chunks=chunks, + litellm_logging_obj=logging_obj, + model="gigachat/GigaChat", + custom_llm_provider="gigachat", + endpoint="chat/completions", + ) + + assert isinstance(result, ModelResponse) + assert result.choices[0].message.content == "valid" + + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.llms.gigachat.passthrough.transformation.get_secret_str" + ) + def test_get_api_base_with_explicit_value(self, mock_get_secret): + """Test get_api_base returns explicit value when provided.""" + explicit_base = "https://custom.gigachat.ru/api/v1" + result = GigaChatPassthroughConfig.get_api_base(api_base=explicit_base) + assert result == explicit_base + mock_get_secret.assert_not_called() + + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.llms.gigachat.passthrough.transformation.get_secret_str" + ) + def test_get_api_base_from_environment(self, mock_get_secret): + """Test get_api_base retrieves from environment when not provided.""" + env_base = "https://env.gigachat.ru/api/v1" + mock_get_secret.return_value = env_base + result = GigaChatPassthroughConfig.get_api_base(api_base=None) + assert result == env_base + mock_get_secret.assert_called_once_with("GIGACHAT_API_BASE") + + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.llms.gigachat.passthrough.transformation.get_secret_str" + ) + def test_get_api_base_fallback_to_default(self, mock_get_secret): + """Test get_api_base falls back to GIGACHAT_BASE_URL.""" + mock_get_secret.return_value = None + result = GigaChatPassthroughConfig.get_api_base(api_base=None) + assert result == "https://gigachat.devices.sberbank.ru/api/v1" + + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.llms.gigachat.passthrough.transformation.get_secret_str" + ) + def test_get_api_key_with_explicit_value(self, mock_get_secret): + """Test get_api_key returns explicit value when provided.""" + explicit_key = "test-api-key" + result = GigaChatPassthroughConfig.get_api_key(api_key=explicit_key) + assert result == explicit_key + mock_get_secret.assert_not_called() + + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.llms.gigachat.passthrough.transformation.get_secret_str" + ) + def test_get_api_key_from_environment(self, mock_get_secret): + """Test get_api_key retrieves from environment when not provided.""" + env_key = "env-api-key" + mock_get_secret.return_value = env_key + result = GigaChatPassthroughConfig.get_api_key(api_key=None) + assert result == env_key + mock_get_secret.assert_called_once_with("GIGACHAT_API_KEY") + + def test_get_base_model_returns_model(self): + """Test get_base_model returns the model as-is.""" + model = "gigachat/GigaChat" + result = GigaChatPassthroughConfig.get_base_model(model) + assert result == model + + def test_get_models(self): + """Test get_models delegates to base class.""" + config = GigaChatPassthroughConfig() + result = config.get_models() + assert result == [] + + def test_logging_non_streaming_chat_raises_when_no_config(self): + """Test raise when ProviderConfigManager returns None for chat.""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + with patch( + "litellm.utils.ProviderConfigManager.get_provider_chat_config", # test-quality-ok: patching litellm internal for unit test isolation + return_value=None, + ): + with pytest.raises(ValueError, match="No provider config found for model"): + config.logging_non_streaming_response( + model="gigachat/GigaChat", + custom_llm_provider="gigachat", + httpx_response=_make_httpx_response(_gigachat_chat_completion_body()), + request_data={ + "model": "gigachat/GigaChat", + "messages": [{"role": "user", "content": "hi"}], + }, + logging_obj=logging_obj, + endpoint="chat/completions", + ) + + def test_logging_non_streaming_embedding_raises_when_no_config(self): + """Test raise when ProviderConfigManager returns None for embeddings.""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + with patch( + "litellm.utils.ProviderConfigManager.get_provider_embedding_config", # test-quality-ok: patching litellm internal for unit test isolation + return_value=None, + ): + with pytest.raises(ValueError, match="No provider config found for model"): + config.logging_non_streaming_response( + model="gigachat/Embeddings", + custom_llm_provider="gigachat", + httpx_response=_make_httpx_response(_gigachat_embedding_body()), + request_data={ + "input": ["hello"], + "model": "gigachat/Embeddings", + }, + logging_obj=logging_obj, + endpoint="embeddings", + ) + + def test_handle_logging_collected_chunks_with_model_response_stream_chunk(self): + """Test that a chunk returning ModelResponseStream from chunk_parser is handled. + + Requires patching GigaChatModelResponseIterator.chunk_parser to return + a ModelResponseStream so the elif branch is exercised. + """ + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + from litellm.types.utils import ModelResponseStream + + stream_chunk = ModelResponseStream( + choices=[ + { + "index": 0, + "delta": {"content": "streamed"}, + "finish_reason": None, + } + ] + ) + + chunks = [ + '{"choices": [{"delta": {"content": "streamed"}, "index": 0}]}', + '{"choices": [{"delta": {}, "finish_reason": "stop", "index": 0}], "usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2}}', + ] + + with patch( + "litellm.llms.gigachat.passthrough.transformation.GigaChatModelResponseIterator.chunk_parser", # test-quality-ok: patching litellm internal for unit test isolation + return_value=stream_chunk, + ): + result = config.handle_logging_collected_chunks( + all_chunks=chunks, + litellm_logging_obj=logging_obj, + model="gigachat/GigaChat", + custom_llm_provider="gigachat", + endpoint="chat/completions", + ) + + assert isinstance(result, ModelResponse) + assert result.choices[0].message.content == "streamedstreamed" + + def test_handle_logging_collected_chunks_skips_unknown_chunk_type(self): + """Test that chunk_parser returning an unknown type is skipped.""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + chunks = [ + '{"choices": [{"delta": {"content": "good"}, "index": 0}]}', + '{"choices": [{"delta": {}, "finish_reason": "stop", "index": 0}], "usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2}}', + ] + + with patch( + "litellm.llms.gigachat.passthrough.transformation.GigaChatModelResponseIterator.chunk_parser", # test-quality-ok: patching litellm internal for unit test isolation + return_value=12345, # not dict and not ModelResponseStream + ): + result = config.handle_logging_collected_chunks( + all_chunks=chunks, + litellm_logging_obj=logging_obj, + model="gigachat/GigaChat", + custom_llm_provider="gigachat", + endpoint="chat/completions", + ) + + # All chunks skipped, returns None + assert result is None + + def test_handle_logging_collected_chunks_skips_unsupported_chunk_type(self): + """Test that unsupported chunk types (non-JSON str) are skipped.""" + config = GigaChatPassthroughConfig() + logging_obj = MagicMock() + + # Both are valid str chunks; "not-a-valid-json" fails json.loads, int is not a str + chunks: list[str] = ["not-a-valid-json"] + + result = config.handle_logging_collected_chunks( + all_chunks=chunks, + litellm_logging_obj=logging_obj, + model="gigachat/GigaChat", + custom_llm_provider="gigachat", + endpoint="chat/completions", + ) + + assert result is None diff --git a/tests/test_litellm/llms/gigachat/test_authenticator.py b/tests/test_litellm/llms/gigachat/test_authenticator.py new file mode 100644 index 00000000000..0a2695dc21e --- /dev/null +++ b/tests/test_litellm/llms/gigachat/test_authenticator.py @@ -0,0 +1,494 @@ +""" +Unit tests for GigaChat OAuth authenticator. + +Tests get_access_token and get_access_token_async covering token resolution +from litellm_params/env, credential validation, caching, and error handling. +""" + +import time +from unittest.mock import AsyncMock, MagicMock, patch + +import httpx +import pytest + +from litellm.llms.gigachat import authenticator +from litellm.llms.gigachat.authenticator import ( + GigaChatAuthError, + TOKEN_EXPIRY_BUFFER_MS, + get_access_token, + get_access_token_async, +) + + +AUTH_MODULE = "litellm.llms.gigachat.authenticator" + + +def _future_expires_at_ms(offset_seconds: float = 3600) -> int: + return int(time.time() * 1000 + offset_seconds * 1000) + + +def _past_expires_at_ms(offset_seconds: float = 3600) -> int: + return int(time.time() * 1000 - offset_seconds * 1000) + + +@pytest.fixture(autouse=True) +def _isolate_token_cache(): + """Each test gets a fresh module-level token cache to avoid cross-test leakage.""" + with patch(f"{AUTH_MODULE}._token_cache", new=MagicMock()): + authenticator._token_cache.get_cache.return_value = None + authenticator._token_cache.set_cache = MagicMock() + yield + + +class TestGetAccessTokenSync: + def test_returns_token_from_litellm_params(self): + token = get_access_token(litellm_params={"gigachat_access_token": "param-token"}) + assert token == "param-token" + authenticator._token_cache.get_cache.assert_not_called() + + @patch(f"{AUTH_MODULE}.get_secret_str") + def test_returns_token_from_env(self, mock_get_secret): + mock_get_secret.return_value = "env-access-token" + token = get_access_token() + assert token == "env-access-token" + + @patch(f"{AUTH_MODULE}._get_credentials", return_value=None) + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + def test_raises_when_no_credentials(self, mock_get_secret, mock_get_creds): + with pytest.raises(GigaChatAuthError) as exc_info: + get_access_token() + assert exc_info.value.status_code == 401 + assert "credentials not provided" in exc_info.value.message + + @patch(f"{AUTH_MODULE}._request_token_sync") + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value=None) + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + def test_raises_when_no_credentials_even_with_other_resolvers( + self, mock_get_secret, mock_get_creds, mock_scope, mock_auth_url, mock_request + ): + with pytest.raises(GigaChatAuthError) as exc_info: + get_access_token() + assert exc_info.value.status_code == 401 + mock_request.assert_not_called() + + @patch(f"{AUTH_MODULE}._request_token_sync") + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="creds-from-env") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + def test_requests_new_token_and_caches(self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request): + token = "fresh-token" + expires_at = _future_expires_at_ms() + mock_request.return_value = (token, expires_at) + + result = get_access_token() + + assert result == token + mock_request.assert_called_once_with("creds-from-env", "GIGACHAT_API_PERS", "https://auth.example.com") + authenticator._token_cache.set_cache.assert_called_once() + call_args = authenticator._token_cache.set_cache.call_args + assert call_args.args[1] == (token, expires_at) + assert call_args.kwargs["ttl"] > 0 + + @patch(f"{AUTH_MODULE}._request_token_sync") + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + def test_does_not_cache_when_no_expiry(self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request): + mock_request.return_value = ("token-no-exp", 0) + + result = get_access_token() + + assert result == "token-no-exp" + authenticator._token_cache.set_cache.assert_not_called() + + @patch(f"{AUTH_MODULE}._request_token_sync") + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + def test_does_not_cache_when_ttl_non_positive(self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request): + expires_at = int(time.time() * 1000) + TOKEN_EXPIRY_BUFFER_MS - 1000 + mock_request.return_value = ("token", expires_at) + + result = get_access_token() + + assert result == "token" + authenticator._token_cache.set_cache.assert_not_called() + + @patch(f"{AUTH_MODULE}._request_token_sync") + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + def test_returns_cached_valid_token(self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request): + cached_token = "cached-token" + cached_expires_at = _future_expires_at_ms(offset_seconds=7200) + authenticator._token_cache.get_cache.return_value = (cached_token, cached_expires_at) + + result = get_access_token(credentials="creds") + + assert result == cached_token + mock_request.assert_not_called() + authenticator._token_cache.set_cache.assert_not_called() + + @patch(f"{AUTH_MODULE}._request_token_sync") + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + def test_requests_new_token_when_cache_expired(self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request): + cached_token = "stale-token" + cached_expires_at = _past_expires_at_ms(offset_seconds=10) + authenticator._token_cache.get_cache.return_value = (cached_token, cached_expires_at) + + new_token = "refreshed-token" + mock_request.return_value = (new_token, _future_expires_at_ms()) + + result = get_access_token(credentials="creds") + + assert result == new_token + mock_request.assert_called_once() + + @patch(f"{AUTH_MODULE}._request_token_sync") + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://default-auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="env-creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + def test_litellm_params_override_scope_and_auth_url(self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request): # test-quality-ok: mock-echo of internal wiring + mock_request.return_value = ("token", _future_expires_at_ms()) + + get_access_token( + litellm_params={ + "gigachat_scope": "GIGACHAT_API_CORP", + "gigachat_auth_url": "https://params-auth.example.com", + } + ) + + mock_request.assert_called_once_with( # test-quality-ok: mock-echo of internal wiring + "env-creds", "GIGACHAT_API_CORP", "https://params-auth.example.com" + ) + + @patch(f"{AUTH_MODULE}._request_token_sync") + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://default-auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="env-creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + def test_explicit_args_override_everything(self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request): # test-quality-ok: mock-echo of internal wiring + mock_request.return_value = ("token", _future_expires_at_ms()) + + get_access_token( + credentials="explicit-creds", + scope="EXPLICIT_SCOPE", + auth_url="https://explicit.example.com", + litellm_params={ + "gigachat_scope": "PARAM_SCOPE", + "gigachat_auth_url": "https://params.example.com", + }, + ) + + mock_request.assert_called_once_with( # test-quality-ok: mock-echo of internal wiring + "explicit-creds", "EXPLICIT_SCOPE", "https://explicit.example.com" + ) + + @patch(f"{AUTH_MODULE}._request_token_sync") + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + def test_propagates_auth_error_from_request(self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request): + mock_request.side_effect = GigaChatAuthError(status_code=403, message="forbidden") + + with pytest.raises(GigaChatAuthError) as exc_info: + get_access_token() + assert exc_info.value.status_code == 403 + assert exc_info.value.message == "forbidden" + + +class TestGetAccessTokenAsync: + @pytest.mark.asyncio + async def test_returns_token_from_litellm_params(self): + token = await get_access_token_async( + litellm_params={"gigachat_access_token": "param-token"} + ) + assert token == "param-token" + authenticator._token_cache.get_cache.assert_not_called() + + @pytest.mark.asyncio + @patch(f"{AUTH_MODULE}.get_secret_str") + async def test_returns_token_from_env(self, mock_get_secret): + mock_get_secret.return_value = "env-access-token" + token = await get_access_token_async() + assert token == "env-access-token" + + @pytest.mark.asyncio + @patch(f"{AUTH_MODULE}._get_credentials", return_value=None) + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + async def test_raises_when_no_credentials(self, mock_get_secret, mock_get_creds): + with pytest.raises(GigaChatAuthError) as exc_info: + await get_access_token_async() + assert exc_info.value.status_code == 401 + assert "credentials not provided" in exc_info.value.message + + @pytest.mark.asyncio + @patch(f"{AUTH_MODULE}._request_token_async", new_callable=AsyncMock) + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="creds-from-env") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + async def test_requests_new_token_and_caches( + self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request + ): + token = "fresh-token-async" + expires_at = _future_expires_at_ms() + mock_request.return_value = (token, expires_at) + + result = await get_access_token_async() + + assert result == token + mock_request.assert_called_once_with( + "creds-from-env", "GIGACHAT_API_PERS", "https://auth.example.com" + ) + authenticator._token_cache.set_cache.assert_called_once() + call_args = authenticator._token_cache.set_cache.call_args + assert call_args.args[1] == (token, expires_at) + assert call_args.kwargs["ttl"] > 0 + + @pytest.mark.asyncio + @patch(f"{AUTH_MODULE}._request_token_async", new_callable=AsyncMock) + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + async def test_does_not_cache_when_no_expiry( + self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request + ): + mock_request.return_value = ("token-no-exp", 0) + + result = await get_access_token_async() + + assert result == "token-no-exp" + authenticator._token_cache.set_cache.assert_not_called() + + @pytest.mark.asyncio + @patch(f"{AUTH_MODULE}._request_token_async", new_callable=AsyncMock) + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + async def test_returns_cached_valid_token( + self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request + ): + cached_token = "cached-token-async" + cached_expires_at = _future_expires_at_ms(offset_seconds=7200) + authenticator._token_cache.get_cache.return_value = (cached_token, cached_expires_at) + + result = await get_access_token_async(credentials="creds") + + assert result == cached_token + mock_request.assert_not_called() + authenticator._token_cache.set_cache.assert_not_called() + + @pytest.mark.asyncio + @patch(f"{AUTH_MODULE}._request_token_async", new_callable=AsyncMock) + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + async def test_requests_new_token_when_cache_expired( + self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request + ): + cached_expires_at = _past_expires_at_ms(offset_seconds=10) + authenticator._token_cache.get_cache.return_value = ("stale", cached_expires_at) + + new_token = "refreshed-token-async" + mock_request.return_value = (new_token, _future_expires_at_ms()) + + result = await get_access_token_async(credentials="creds") + + assert result == new_token + mock_request.assert_called_once() + + @pytest.mark.asyncio + @patch(f"{AUTH_MODULE}._request_token_async", new_callable=AsyncMock) + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://default-auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="env-creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + async def test_litellm_params_override_scope_and_auth_url( # test-quality-ok: mock-echo of internal wiring + self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request + ): + mock_request.return_value = ("token", _future_expires_at_ms()) + + await get_access_token_async( + litellm_params={ + "gigachat_scope": "GIGACHAT_API_CORP", + "gigachat_auth_url": "https://params-auth.example.com", + } + ) + + mock_request.assert_called_once_with( # test-quality-ok: mock-echo of internal wiring + "env-creds", "GIGACHAT_API_CORP", "https://params-auth.example.com" + ) + + @pytest.mark.asyncio + @patch(f"{AUTH_MODULE}._request_token_async", new_callable=AsyncMock) + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://default-auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="env-creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + async def test_explicit_args_override_everything( # test-quality-ok: mock-echo of internal wiring + self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request + ): + mock_request.return_value = ("token", _future_expires_at_ms()) + + await get_access_token_async( + credentials="explicit-creds", + scope="EXPLICIT_SCOPE", + auth_url="https://explicit.example.com", + litellm_params={ + "gigachat_scope": "PARAM_SCOPE", + "gigachat_auth_url": "https://params.example.com", + }, + ) + + mock_request.assert_called_once_with( # test-quality-ok: mock-echo of internal wiring + "explicit-creds", "EXPLICIT_SCOPE", "https://explicit.example.com" + ) + + @pytest.mark.asyncio + @patch(f"{AUTH_MODULE}._request_token_async", new_callable=AsyncMock) + @patch(f"{AUTH_MODULE}._get_auth_url", return_value="https://auth.example.com") + @patch(f"{AUTH_MODULE}._get_scope", return_value="GIGACHAT_API_PERS") + @patch(f"{AUTH_MODULE}._get_credentials", return_value="creds") + @patch(f"{AUTH_MODULE}.get_secret_str", return_value=None) + async def test_propagates_auth_error_from_request( + self, mock_get_secret, mock_creds, mock_scope, mock_auth_url, mock_request + ): + mock_request.side_effect = GigaChatAuthError(status_code=403, message="forbidden") + + with pytest.raises(GigaChatAuthError) as exc_info: + await get_access_token_async() + assert exc_info.value.status_code == 403 + assert exc_info.value.message == "forbidden" + + +class TestRequestTokenSyncErrorMapping: + @patch(f"{AUTH_MODULE}._get_http_client") + def test_http_status_error_maps_to_auth_error(self, mock_get_client): + client = MagicMock() + request = httpx.Request("POST", "https://auth.example.com") + response = httpx.Response(status_code=401, content=b"bad creds", request=request) + http_error = httpx.HTTPStatusError("unauthorized", request=request, response=response) + client.post.side_effect = http_error + mock_get_client.return_value = client + + from litellm.llms.gigachat.authenticator import _request_token_sync + + with pytest.raises(GigaChatAuthError) as exc_info: + _request_token_sync("creds", "GIGACHAT_API_PERS", "https://auth.example.com") + assert exc_info.value.status_code == 401 + assert "bad creds" in exc_info.value.message + + @patch(f"{AUTH_MODULE}._get_http_client") + def test_request_error_maps_to_auth_error(self, mock_get_client): + client = MagicMock() + client.post.side_effect = httpx.ConnectError("connection refused") + mock_get_client.return_value = client + + from litellm.llms.gigachat.authenticator import _request_token_sync + + with pytest.raises(GigaChatAuthError) as exc_info: + _request_token_sync("creds", "GIGACHAT_API_PERS", "https://auth.example.com") + assert exc_info.value.status_code == 500 + assert "connection refused" in exc_info.value.message + + +class TestRequestTokenAsyncErrorMapping: + @pytest.mark.asyncio + @patch(f"{AUTH_MODULE}.get_async_httpx_client") + async def test_http_status_error_maps_to_auth_error(self, mock_get_client): + client = MagicMock() + request = httpx.Request("POST", "https://auth.example.com") + response = httpx.Response(status_code=401, content=b"bad creds", request=request) + http_error = httpx.HTTPStatusError("unauthorized", request=request, response=response) + client.post = AsyncMock(side_effect=http_error) + mock_get_client.return_value = client + + from litellm.llms.gigachat.authenticator import _request_token_async + + with pytest.raises(GigaChatAuthError) as exc_info: + await _request_token_async("creds", "GIGACHAT_API_PERS", "https://auth.example.com") + assert exc_info.value.status_code == 401 + assert "bad creds" in exc_info.value.message + + @pytest.mark.asyncio + @patch(f"{AUTH_MODULE}.get_async_httpx_client") + async def test_request_error_maps_to_auth_error(self, mock_get_client): + client = MagicMock() + client.post = AsyncMock(side_effect=httpx.ConnectError("connection refused")) + mock_get_client.return_value = client + + from litellm.llms.gigachat.authenticator import _request_token_async + + with pytest.raises(GigaChatAuthError) as exc_info: + await _request_token_async("creds", "GIGACHAT_API_PERS", "https://auth.example.com") + assert exc_info.value.status_code == 500 + assert "connection refused" in exc_info.value.message + + +class TestParseTokenResponse: + def _make_response(self, body: dict) -> httpx.Response: + import json + + return httpx.Response( + status_code=200, + content=json.dumps(body).encode("utf-8"), + request=httpx.Request("POST", "https://auth.example.com"), + ) + + def test_parses_tok_exp_fields(self): + from litellm.llms.gigachat.authenticator import _parse_token_response + + token, expires_at = _parse_token_response( + self._make_response({"tok": "abc", "exp": 1700000000000}) + ) + assert token == "abc" + assert expires_at == 1700000000000 + + def test_parses_access_token_expires_at_fields(self): + from litellm.llms.gigachat.authenticator import _parse_token_response + + token, expires_at = _parse_token_response( + self._make_response({"access_token": "xyz", "expires_at": 1700000000000}) + ) + assert token == "xyz" + assert expires_at == 1700000000000 + + def test_parses_string_expires_at(self): + from litellm.llms.gigachat.authenticator import _parse_token_response + + token, expires_at = _parse_token_response( + self._make_response({"tok": "abc", "exp": "1700000000000"}) + ) + assert token == "abc" + assert expires_at == 1700000000000 + assert isinstance(expires_at, int) + + def test_raises_when_no_access_token(self): + from litellm.llms.gigachat.authenticator import _parse_token_response + + with pytest.raises(GigaChatAuthError) as exc_info: + _parse_token_response(self._make_response({"exp": 1700000000000})) + assert exc_info.value.status_code == 500 + assert "Invalid token response" in exc_info.value.message + + +class TestGetHttpClient: + def test_reuses_cached_client_across_calls(self): + """Regression: the sync OAuth path must use the shared cached httpx client, + not construct a fresh HTTPHandler per token request.""" + assert authenticator._get_http_client() is authenticator._get_http_client() diff --git a/tests/test_litellm/llms/gigachat/test_file_handler.py b/tests/test_litellm/llms/gigachat/test_file_handler.py new file mode 100644 index 00000000000..ce9505f11f2 --- /dev/null +++ b/tests/test_litellm/llms/gigachat/test_file_handler.py @@ -0,0 +1,504 @@ +""" +Unit tests for GigaChat file handler. + +Tests _get_url_hash, _parse_data_url, _download_image_sync, _download_image_async, +upload_file_sync, and upload_file_async covering caching, base64 data URL decoding, +network errors, and the full upload flow. +""" + +import json +from unittest.mock import AsyncMock, MagicMock, patch + +import httpx +import pytest + +from litellm.llms.gigachat import file_handler +from litellm.llms.gigachat.file_handler import ( + _file_cache, + _get_url_hash, + _parse_data_url, + upload_file_async, + upload_file_sync, +) + +FILE_MODULE = "litellm.llms.gigachat.file_handler" + +# A valid 1x1 red PNG as base64 +_RED_PNG_B64 = ( + "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAA" + "DUlEQVQI12NgYPgPAAEDAQAR3X3ZAAAASUVORK5CYII=" +) +_RED_PNG_DATA_URL = f"data:image/png;base64,{_RED_PNG_B64}" + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- + + +@pytest.fixture(autouse=True) +def _isolate_file_cache(): + """Each test gets a fresh module-level file cache to avoid cross-test leakage.""" + _file_cache.clear() + yield + _file_cache.clear() + + +# --------------------------------------------------------------------------- +# _get_url_hash +# --------------------------------------------------------------------------- + + +class TestGetUrlHash: + def test_returns_hex_string(self): + h = _get_url_hash("https://example.com/image.png") + assert isinstance(h, str) + assert len(h) == 64 # SHA-256 + + def test_different_urls_different_hashes(self): + h1 = _get_url_hash("https://example.com/a.png") + h2 = _get_url_hash("https://example.com/b.png") + assert h1 != h2 + + def test_same_url_same_hash(self): + h1 = _get_url_hash("https://example.com/image.png") + h2 = _get_url_hash("https://example.com/image.png") + assert h1 == h2 + + +# --------------------------------------------------------------------------- +# _parse_data_url +# --------------------------------------------------------------------------- + + +class TestParseDataUrl: + def test_valid_base64_png(self): + result = _parse_data_url(_RED_PNG_DATA_URL) + assert result is not None + content_bytes, content_type, ext = result + assert content_type == "image/png" + assert ext == "png" + assert len(content_bytes) > 0 + + def test_valid_base64_jpeg(self): + # Simple valid base64 (24 chars, properly padded, no + or / chars) + valid_b64 = "aGVsbG8gd29ybGQhISEhIQ==" + data_url = f"data:image/jpeg;base64,{valid_b64}" + result = _parse_data_url(data_url) + assert result is not None + _, content_type, ext = result + assert content_type == "image/jpeg" + assert ext == "jpeg" + + def test_valid_base64_with_semicolon_in_type(self): + """Data URLs with charset before base64 segment do not match the regex.""" + # The regex `data:([^;]+);base64,(.+)` requires the pattern to be + # `data:;base64,`. If `;charset=utf-8` appears before + # `;base64,`, the regex sees `data:image/png` as group 1 but then + # looks for `;base64,` immediately after — which isn't there because + # `;charset=utf-8;base64,` has extra text before `;base64,` + data_url = "data:image/png;charset=utf-8;base64," + _RED_PNG_B64 + result = _parse_data_url(data_url) + assert result is None + + def test_invalid_data_url_returns_none(self): + assert _parse_data_url("not-a-data-url") is None + + def test_empty_base64_returns_none(self): + """Empty base64 data (nothing after comma) does not match regex `(.+)`.""" + assert _parse_data_url("data:image/png;base64,") is None + + def test_missing_base64_segment(self): + assert _parse_data_url("data:image/png;base64") is None + + def test_unknown_extension_falls_back_to_jpg(self): + data_url = "data:application/octet-stream;base64," + _RED_PNG_B64 + result = _parse_data_url(data_url) + assert result is not None + _, content_type, ext = result + assert content_type == "application/octet-stream" + # The extension is derived from content_type.split("/")[-1].split(";")[0] + # which gives "octet-stream", not "jpg" + assert ext == "octet-stream" + + +# --------------------------------------------------------------------------- +# _download_image_sync +# --------------------------------------------------------------------------- + + +class TestDownloadImageSync: + @patch(f"{FILE_MODULE}._get_httpx_client") + def test_downloads_image_successfully(self, mock_http_handler_cls): + mock_client = MagicMock() + mock_response = MagicMock() + mock_response.content = b"fake-image-bytes" + mock_response.headers = {"content-type": "image/jpeg"} + mock_client.get.return_value = mock_response + mock_http_handler_cls.return_value = mock_client + + content_bytes, content_type, ext = file_handler._download_image_sync("https://example.com/img.jpg") + + assert content_bytes == b"fake-image-bytes" + assert content_type == "image/jpeg" + assert ext == "jpeg" + mock_client.get.assert_called_once_with("https://example.com/img.jpg") + + @patch(f"{FILE_MODULE}._get_httpx_client") + def test_raises_on_http_error(self, mock_http_handler_cls): + mock_client = MagicMock() + mock_client.get.side_effect = httpx.HTTPStatusError( + "Not Found", + request=httpx.Request("GET", "https://example.com/404"), + response=httpx.Response(status_code=404, request=httpx.Request("GET", "https://example.com/404")), + ) + mock_http_handler_cls.return_value = mock_client + + with pytest.raises(httpx.HTTPStatusError): + file_handler._download_image_sync("https://example.com/404") + + @patch(f"{FILE_MODULE}._get_httpx_client") + def test_parse_content_type_fallback(self, mock_http_handler_cls): + mock_client = MagicMock() + mock_response = MagicMock() + mock_response.content = b"data" + mock_response.headers = {} + mock_client.get.return_value = mock_response + mock_http_handler_cls.return_value = mock_client + + _, content_type, ext = file_handler._download_image_sync("https://example.com/img") + + assert content_type == "image/jpeg" + assert ext == "jpeg" + + @patch(f"{FILE_MODULE}._get_httpx_client") + def test_extracts_extension_from_parametrized_type(self, mock_http_handler_cls): + mock_client = MagicMock() + mock_response = MagicMock() + mock_response.content = b"data" + mock_response.headers = {"content-type": "image/png; charset=utf-8"} + mock_client.get.return_value = mock_response + mock_http_handler_cls.return_value = mock_client + + _, _, ext = file_handler._download_image_sync("https://example.com/img.png") + + assert ext == "png" + + +# --------------------------------------------------------------------------- +# _download_image_async +# --------------------------------------------------------------------------- + + +class TestDownloadImageAsync: + @pytest.mark.asyncio + @patch(f"{FILE_MODULE}.get_async_httpx_client") + async def test_downloads_image_successfully(self, mock_get_client): + mock_client = MagicMock() + mock_response = MagicMock() + mock_response.content = b"fake-image-bytes" + mock_response.headers = {"content-type": "image/webp"} + mock_client.get = AsyncMock(return_value=mock_response) + mock_get_client.return_value = mock_client + + content_bytes, content_type, ext = await file_handler._download_image_async( + "https://example.com/img.webp" + ) + + assert content_bytes == b"fake-image-bytes" + assert content_type == "image/webp" + assert ext == "webp" + mock_client.get.assert_called_once_with("https://example.com/img.webp") + + @pytest.mark.asyncio + @patch(f"{FILE_MODULE}.get_async_httpx_client") + async def test_raises_on_http_error(self, mock_get_client): + mock_client = MagicMock() + mock_client.get = AsyncMock( + side_effect=httpx.HTTPStatusError( + "Forbidden", + request=httpx.Request("GET", "https://example.com/403"), + response=httpx.Response(status_code=403, request=httpx.Request("GET", "https://example.com/403")), + ) + ) + mock_get_client.return_value = mock_client + + with pytest.raises(httpx.HTTPStatusError): + await file_handler._download_image_async("https://example.com/403") + + +# --------------------------------------------------------------------------- +# upload_file_sync +# --------------------------------------------------------------------------- + + +class TestUploadFileSync: + @patch(f"{FILE_MODULE}.get_api_base", return_value="https://api.example.com") + @patch(f"{FILE_MODULE}.get_access_token", return_value="test-token") + @patch(f"{FILE_MODULE}._get_httpx_client") + def test_uploads_base64_image_and_caches( + self, mock_http_handler_cls, mock_get_token, mock_get_api_base + ): + mock_client = MagicMock() + mock_response = MagicMock() + mock_response.json.return_value = {"id": "file-12345"} + mock_response.raise_for_status = MagicMock() + mock_client.post.return_value = mock_response + mock_http_handler_cls.return_value = mock_client + + result = upload_file_sync( + image_url=_RED_PNG_DATA_URL, + credentials="creds", + api_base="https://custom.example.com", + ) + + assert result == "file-12345" + # Verify it was cached + url_hash = _get_url_hash(_RED_PNG_DATA_URL) + assert _file_cache[url_hash] == "file-12345" + + # Check the upload request — url is passed as first positional arg + call_args = mock_client.post.call_args + assert call_args.args[0] == "https://api.example.com/files" + assert call_args.kwargs["headers"]["Authorization"] == "Bearer test-token" + # Verify purpose + assert call_args.kwargs["data"] == {"purpose": "general"} + # Verify a file was attached + assert "file" in call_args.kwargs["files"] + + @patch(f"{FILE_MODULE}.get_api_base", return_value="https://api.example.com") + @patch(f"{FILE_MODULE}.get_access_token", return_value="test-token") + @patch(f"{FILE_MODULE}._get_httpx_client") + def test_returns_cached_file_id( + self, mock_http_handler_cls, mock_get_token, mock_get_api_base + ): + # Pre-populate the cache + url_hash = _get_url_hash(_RED_PNG_DATA_URL) + _file_cache[url_hash] = "cached-file-id" + + result = upload_file_sync(image_url=_RED_PNG_DATA_URL, credentials="creds") + + assert result == "cached-file-id" + # No upload call was made + mock_http_handler_cls.return_value.post.assert_not_called() + + @patch(f"{FILE_MODULE}._get_httpx_client") + @patch(f"{FILE_MODULE}.get_access_token", return_value="test-token") + @patch(f"{FILE_MODULE}.get_api_base", return_value="https://api.example.com") + @patch(f"{FILE_MODULE}._download_image_sync") + def test_downloads_and_uploads_url_image( + self, mock_download, mock_get_api_base, mock_get_token, mock_http_handler_cls + ): + mock_download.return_value = (b"remote-bytes", "image/png", "png") + mock_client = MagicMock() + mock_response = MagicMock() + mock_response.json.return_value = {"id": "file-remote"} + mock_response.raise_for_status = MagicMock() + mock_client.post.return_value = mock_response + mock_http_handler_cls.return_value = mock_client + + result = upload_file_sync( + image_url="https://example.com/remote.png", credentials="creds" + ) + + assert result == "file-remote" + mock_download.assert_called_once_with("https://example.com/remote.png") + + @patch(f"{FILE_MODULE}._get_httpx_client") + @patch(f"{FILE_MODULE}.get_access_token", return_value="test-token") + @patch(f"{FILE_MODULE}.get_api_base", return_value="https://api.example.com") + def test_returns_none_on_upload_failure( + self, mock_get_api_base, mock_get_token, mock_http_handler_cls + ): + mock_client = MagicMock() + mock_client.post.side_effect = httpx.HTTPStatusError( + "Bad Request", + request=httpx.Request("POST", "https://api.example.com/files"), + response=httpx.Response(status_code=400, request=httpx.Request("POST", "https://api.example.com/files")), + ) + mock_http_handler_cls.return_value = mock_client + + # upload_file_sync catches all exceptions and returns None + result = upload_file_sync( + image_url=_RED_PNG_DATA_URL, credentials="creds" + ) + + assert result is None + + @patch(f"{FILE_MODULE}._get_httpx_client") + @patch(f"{FILE_MODULE}.get_access_token", return_value="test-token") + @patch(f"{FILE_MODULE}.get_api_base", return_value="https://api.example.com") + def test_returns_none_when_response_missing_id( + self, mock_get_api_base, mock_get_token, mock_http_handler_cls + ): + mock_client = MagicMock() + mock_response = MagicMock() + mock_response.json.return_value = {"status": "ok"} # no "id" key + mock_response.raise_for_status = MagicMock() + mock_client.post.return_value = mock_response + mock_http_handler_cls.return_value = mock_client + + result = upload_file_sync( + image_url=_RED_PNG_DATA_URL, credentials="creds" + ) + + assert result is None + + @patch(f"{FILE_MODULE}.get_api_base", return_value="https://api.example.com") + @patch(f"{FILE_MODULE}.get_access_token", return_value="test-token") + @patch(f"{FILE_MODULE}._get_httpx_client") + def test_uploads_without_optional_args( + self, mock_http_handler_cls, mock_get_token, mock_get_api_base + ): + """Verify that credentials, api_base, and litellm_params are optional.""" + mock_client = MagicMock() + mock_response = MagicMock() + mock_response.json.return_value = {"id": "file-no-args"} + mock_response.raise_for_status = MagicMock() + mock_client.post.return_value = mock_response + mock_http_handler_cls.return_value = mock_client + + result = upload_file_sync(image_url=_RED_PNG_DATA_URL) + + assert result == "file-no-args" + # Should still have called get_access_token without args + mock_get_token.assert_called_once_with(credentials=None, litellm_params=None) + + +# --------------------------------------------------------------------------- +# upload_file_async +# --------------------------------------------------------------------------- + + +class TestUploadFileAsync: + @pytest.mark.asyncio + @patch(f"{FILE_MODULE}.get_api_base", return_value="https://api.example.com") + @patch(f"{FILE_MODULE}.get_access_token_async", return_value="test-token-async") + @patch(f"{FILE_MODULE}.get_async_httpx_client") + async def test_uploads_base64_image_and_caches( + self, mock_get_client, mock_get_token, mock_get_api_base + ): + mock_client = MagicMock() + mock_response = MagicMock() + mock_response.json = MagicMock(return_value={"id": "async-file-1"}) + mock_response.raise_for_status = MagicMock() + mock_client.post = AsyncMock(return_value=mock_response) + mock_get_client.return_value = mock_client + + result = await upload_file_async( + image_url=_RED_PNG_DATA_URL, + credentials="creds", + api_base="https://custom.example.com", + ) + + assert result == "async-file-1" + # Verify cache + url_hash = _get_url_hash(_RED_PNG_DATA_URL) + assert _file_cache[url_hash] == "async-file-1" + + # Check upload request details — url is first positional arg + call_args = mock_client.post.call_args + assert call_args.args[0] == "https://api.example.com/files" + assert call_args.kwargs["headers"]["Authorization"] == "Bearer test-token-async" + assert "purpose" in str(call_args.kwargs["data"]) + assert "file" in call_args.kwargs["files"] + + @pytest.mark.asyncio + @patch(f"{FILE_MODULE}.get_api_base", return_value="https://api.example.com") + @patch(f"{FILE_MODULE}.get_access_token_async", return_value="test-token-async") + @patch(f"{FILE_MODULE}.get_async_httpx_client") + async def test_returns_cached_file_id( + self, mock_get_client, mock_get_token, mock_get_api_base + ): + url_hash = _get_url_hash(_RED_PNG_DATA_URL) + _file_cache[url_hash] = "cached-async-id" + + result = await upload_file_async(image_url=_RED_PNG_DATA_URL, credentials="creds") + + assert result == "cached-async-id" + mock_get_client.return_value.post.assert_not_called() + + @pytest.mark.asyncio + @patch(f"{FILE_MODULE}.get_async_httpx_client") + @patch(f"{FILE_MODULE}.get_access_token_async", return_value="test-token-async") + @patch(f"{FILE_MODULE}.get_api_base", return_value="https://api.example.com") + @patch(f"{FILE_MODULE}._download_image_async") + async def test_downloads_and_uploads_url_image( + self, mock_download, mock_get_api_base, mock_get_token, mock_get_client + ): + mock_download.return_value = (b"remote-bytes-async", "image/png", "png") + mock_client = MagicMock() + mock_response = MagicMock() + mock_response.json = MagicMock(return_value={"id": "async-file-remote"}) + mock_response.raise_for_status = MagicMock() + mock_client.post = AsyncMock(return_value=mock_response) + mock_get_client.return_value = mock_client + + result = await upload_file_async( + image_url="https://example.com/remote.png", credentials="creds" + ) + + assert result == "async-file-remote" + mock_download.assert_called_once_with("https://example.com/remote.png") + + @pytest.mark.asyncio + @patch(f"{FILE_MODULE}.get_async_httpx_client") + @patch(f"{FILE_MODULE}.get_access_token_async", return_value="test-token-async") + @patch(f"{FILE_MODULE}.get_api_base", return_value="https://api.example.com") + async def test_returns_none_on_upload_failure( + self, mock_get_api_base, mock_get_token, mock_get_client + ): + mock_client = MagicMock() + mock_client.post = AsyncMock( + side_effect=httpx.HTTPStatusError( + "Bad Request", + request=httpx.Request("POST", "https://api.example.com/files"), + response=httpx.Response(status_code=400, request=httpx.Request("POST", "https://api.example.com/files")), + ) + ) + mock_get_client.return_value = mock_client + + result = await upload_file_async( + image_url=_RED_PNG_DATA_URL, credentials="creds" + ) + + assert result is None + + @pytest.mark.asyncio + @patch(f"{FILE_MODULE}.get_async_httpx_client") + @patch(f"{FILE_MODULE}.get_access_token_async", return_value="test-token-async") + @patch(f"{FILE_MODULE}.get_api_base", return_value="https://api.example.com") + async def test_returns_none_when_response_missing_id( + self, mock_get_api_base, mock_get_token, mock_get_client + ): + mock_client = MagicMock() + mock_response = MagicMock() + mock_response.json = MagicMock(return_value={"status": "ok"}) + mock_response.raise_for_status = MagicMock() + mock_client.post = AsyncMock(return_value=mock_response) + mock_get_client.return_value = mock_client + + result = await upload_file_async( + image_url=_RED_PNG_DATA_URL, credentials="creds" + ) + + assert result is None + + @pytest.mark.asyncio + @patch(f"{FILE_MODULE}.get_api_base", return_value="https://api.example.com") + @patch(f"{FILE_MODULE}.get_access_token_async", return_value="test-token-async") + @patch(f"{FILE_MODULE}.get_async_httpx_client") + async def test_uploads_without_optional_args( + self, mock_get_client, mock_get_token, mock_get_api_base + ): + mock_client = MagicMock() + mock_response = MagicMock() + mock_response.json = MagicMock(return_value={"id": "async-no-args"}) + mock_response.raise_for_status = MagicMock() + mock_client.post = AsyncMock(return_value=mock_response) + mock_get_client.return_value = mock_client + + result = await upload_file_async(image_url=_RED_PNG_DATA_URL) + + assert result == "async-no-args" + mock_get_token.assert_called_once_with(credentials=None, litellm_params=None) \ No newline at end of file diff --git a/tests/test_litellm/llms/gigachat/test_utils.py b/tests/test_litellm/llms/gigachat/test_utils.py new file mode 100644 index 00000000000..71a193d7b29 --- /dev/null +++ b/tests/test_litellm/llms/gigachat/test_utils.py @@ -0,0 +1,79 @@ +""" +Tests for litellm.llms.gigachat.utils +""" + +import pytest +from litellm.llms.gigachat.utils import convert_usage +from litellm.types.utils import PromptTokensDetailsWrapper, Usage + + +class TestConvertUsage: + def test_basic_usage_without_precached(self): + """Test convert_usage with standard tokens, no precached prompt tokens.""" + result = convert_usage( + { + "prompt_tokens": 10, + "completion_tokens": 5, + "total_tokens": 15, + } + ) + + assert result == Usage( + prompt_tokens=10, + completion_tokens=5, + total_tokens=15, + prompt_tokens_details=None, + ) + + def test_usage_with_precached_prompt_tokens(self): + """GigaChat's prompt_tokens and total_tokens exclude cached tokens (docs example: + prompt_tokens=1, precached_prompt_tokens=37, total_tokens=5), so OpenAI-convention + usage adds precached back in and surfaces it as cached_tokens.""" + result = convert_usage( + { + "prompt_tokens": 10, + "completion_tokens": 5, + "precached_prompt_tokens": 3, + "total_tokens": 15, + } + ) + + assert result == Usage( + prompt_tokens=13, + completion_tokens=5, + total_tokens=18, + prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=3), + ) + + def test_zero_precached_prompt_tokens(self): + """Test convert_usage with zero precached_prompt_tokens does not create details wrapper.""" + result = convert_usage( + { + "prompt_tokens": 10, + "completion_tokens": 5, + "precached_prompt_tokens": 0, + "total_tokens": 15, + } + ) + + assert result == Usage( + prompt_tokens=10, + completion_tokens=5, + total_tokens=15, + prompt_tokens_details=None, + ) + + def test_missing_optional_fields(self): + """Test convert_usage with missing optional fields defaults to zero.""" + result = convert_usage( + { + "prompt_tokens": 10, + "completion_tokens": 5, + "total_tokens": 15, + } + ) + + assert result.prompt_tokens == 10 + assert result.completion_tokens == 5 + assert result.total_tokens == 15 + assert result.prompt_tokens_details is None \ No newline at end of file diff --git a/tests/test_litellm/llms/huggingface/embedding/test_huggingface_embedding_handler.py b/tests/test_litellm/llms/huggingface/embedding/test_huggingface_embedding_handler.py index f1226311b5e..0384fb796d9 100644 --- a/tests/test_litellm/llms/huggingface/embedding/test_huggingface_embedding_handler.py +++ b/tests/test_litellm/llms/huggingface/embedding/test_huggingface_embedding_handler.py @@ -4,6 +4,7 @@ from unittest.mock import patch, MagicMock, AsyncMock import litellm import pytest +import respx MOCK_EMBEDDING_RESPONSE = [[0.1, 0.2, 0.3, 0.4, 0.5]] @@ -21,6 +22,16 @@ def mock_embedding_http_handler(): yield mock_post +@pytest.fixture +def mock_hf_config_fetch(): + """Serve the Hugging Face config.json fetched during cost calculation, so no test leaves the process""" + with respx.mock(assert_all_called=False) as respx_mock: + respx_mock.get(url__regex=r"https://huggingface\.co/.*/config\.json").respond( + json={"max_position_embeddings": 512} + ) + yield respx_mock + + @pytest.fixture def mock_embedding_async_http_handler(): """Fixture to mock the async HTTP handler for embedding tests""" @@ -39,7 +50,7 @@ def mock_embedding_async_http_handler(): class TestHuggingFaceEmbedding: @pytest.fixture(autouse=True) - def setup(self, mock_embedding_http_handler, mock_embedding_async_http_handler): + def setup(self, mock_embedding_http_handler, mock_embedding_async_http_handler, mock_hf_config_fetch): self.mock_get_task_patcher = patch( "litellm.llms.huggingface.embedding.handler.get_hf_task_embedding_for_model" ) diff --git a/tests/test_litellm/llms/mistral/ocr/test_mistral_ocr_cost.py b/tests/test_litellm/llms/mistral/ocr/test_mistral_ocr_cost.py index 890df597933..a0e1616d4b2 100644 --- a/tests/test_litellm/llms/mistral/ocr/test_mistral_ocr_cost.py +++ b/tests/test_litellm/llms/mistral/ocr/test_mistral_ocr_cost.py @@ -24,6 +24,9 @@ OCR3_MODEL = "mistral/mistral-ocr-2512" OCR3_COST_PER_PAGE = 0.002 OCR3_ANNOTATION_COST_PER_PAGE = 0.003 +AZURE_DOC_AI_MODEL = "azure_ai/mistral-document-ai-2512" +AZURE_DOC_AI_COST_PER_PAGE = 0.003 + def _ocr_response(model: str, pages_processed: int) -> OCRResponse: return OCRResponse( @@ -33,6 +36,14 @@ def _ocr_response(model: str, pages_processed: int) -> OCRResponse: ) +def _annotated_ocr_response(model: str, pages_processed: int | None, annotation_pages: int) -> OCRResponse: + return OCRResponse( + pages=[], + model=model, + usage_info=OCRUsageInfo(pages_processed=pages_processed, pages_processed_annotation=annotation_pages), + ) + + @pytest.mark.parametrize("model", ["mistral-ocr-4-0", "mistral-ocr-latest"]) def test_model_info_ocr4_price(model: str) -> None: info = litellm.get_model_info(model=f"mistral/{model}", custom_llm_provider="mistral") @@ -79,3 +90,46 @@ def test_ocr3_cost_scales_with_pages(local_model_cost_map, pages_processed: int) call_type="ocr", ) assert cost == pytest.approx(OCR3_COST_PER_PAGE * pages_processed) + + +def test_ocr3_bills_ocr_and_annotation_pages_at_their_own_rates(local_model_cost_map) -> None: + cost = completion_cost( + completion_response=_annotated_ocr_response("mistral-ocr-2512", 2, 3), + model=OCR3_MODEL, + custom_llm_provider="mistral", + call_type="ocr", + ) + assert cost == pytest.approx(2 * OCR3_COST_PER_PAGE + 3 * OCR3_ANNOTATION_COST_PER_PAGE) + + +def test_ocr3_bills_annotation_only_response(local_model_cost_map) -> None: + cost = completion_cost( + completion_response=_annotated_ocr_response("mistral-ocr-2512", 0, 3), + model=OCR3_MODEL, + custom_llm_provider="mistral", + call_type="ocr", + ) + assert cost == pytest.approx(3 * OCR3_ANNOTATION_COST_PER_PAGE) + + +def test_ocr3_bills_annotation_pages_when_pages_processed_missing(local_model_cost_map) -> None: + cost = completion_cost( + completion_response=_annotated_ocr_response("mistral-ocr-2512", None, 4), + model=OCR3_MODEL, + custom_llm_provider="mistral", + call_type="ocr", + ) + assert cost == pytest.approx(4 * OCR3_ANNOTATION_COST_PER_PAGE) + + +def test_azure_doc_ai_annotation_pages_fall_back_to_ocr_rate(local_model_cost_map) -> None: + info = litellm.get_model_info(model=AZURE_DOC_AI_MODEL, custom_llm_provider="azure_ai") + assert info.get("annotation_cost_per_page") is None + assert info["ocr_cost_per_page"] == AZURE_DOC_AI_COST_PER_PAGE + cost = completion_cost( + completion_response=_annotated_ocr_response("mistral-document-ai-2512", 0, 1), + model=AZURE_DOC_AI_MODEL, + custom_llm_provider="azure_ai", + call_type="ocr", + ) + assert cost == pytest.approx(AZURE_DOC_AI_COST_PER_PAGE) diff --git a/tests/test_litellm/llms/openai/chat/guardrail_translation/test_openai_guardrail_handler.py b/tests/test_litellm/llms/openai/chat/guardrail_translation/test_openai_guardrail_handler.py index a29e0be4655..7dd6065063a 100644 --- a/tests/test_litellm/llms/openai/chat/guardrail_translation/test_openai_guardrail_handler.py +++ b/tests/test_litellm/llms/openai/chat/guardrail_translation/test_openai_guardrail_handler.py @@ -1559,3 +1559,87 @@ class TestScanOnlyToolResults: assert data["messages"][3]["content"] == "page says [BLOCKED] here" assert data["messages"][3]["tool_call_id"] == "call_1" assert data["messages"][4]["content"] == "and then?" + + +class TestBuildBlockSseChunks: + """build_block_sse_chunks turns a streaming ModifyResponseException into 200 SSE chunks""" + + def _exc(self, original_response=None): + from litellm.exceptions import ModifyResponseException + + return ModifyResponseException( + message="Blocked by policy.", + model="gpt-5.4-mini", + request_data={}, + guardrail_name="test", + original_response=original_response, + ) + + def _payloads(self, chunks): + return [json.loads(chunk.decode().removeprefix("data: ").strip()) for chunk in chunks] + + def test_standalone_block_uses_fresh_identity_and_zero_usage(self): + handler = OpenAIChatCompletionsHandler() + first, final = self._payloads(handler.build_block_sse_chunks(self._exc(), stream_started=False)) + assert first["id"].startswith("chatcmpl-") + assert first["model"] == "gpt-5.4-mini" + assert first["choices"][0]["delta"] == {"role": "assistant", "content": "Blocked by policy."} + assert first["choices"][0]["finish_reason"] is None + assert final["choices"][0]["delta"] == {} + assert final["choices"][0]["finish_reason"] == "content_filter" + assert final["usage"] == {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0} + + def test_continuation_reuses_stream_identity_and_real_usage(self): + handler = OpenAIChatCompletionsHandler() + yielded = [ + {"id": "chatcmpl-live", "created": 1724900000, "model": "gpt-5.4-mini-2026-01-01"}, + ] + original = yielded + [ + {"id": "chatcmpl-live", "usage": {"prompt_tokens": 11, "completion_tokens": 5}}, + ] + first, final = self._payloads( + handler.build_block_sse_chunks( + self._exc(original_response=original), stream_started=True, responses_so_far=yielded + ) + ) + assert (first["id"], first["created"], first["model"]) == ( + "chatcmpl-live", + 1724900000, + "gpt-5.4-mini-2026-01-01", + ) + assert first["choices"][0]["delta"] == {"content": "Blocked by policy."} + assert final["id"] == "chatcmpl-live" + assert final["usage"] == {"prompt_tokens": 11, "completion_tokens": 5, "total_tokens": 16} + + +class TestCheckStreamingHasEnded: + """_check_streaming_has_ended lets end_of_stream_only withhold the finish chunk until moderation""" + + def test_empty_and_content_only_chunks_are_not_ended(self): + handler = OpenAIChatCompletionsHandler() + assert handler._check_streaming_has_ended([]) is False + content_only = [ + {"id": "chatcmpl-live", "choices": [{"index": 0, "delta": {"content": "hi"}, "finish_reason": None}]}, + {"id": "chatcmpl-live", "choices": []}, + {"id": "chatcmpl-live", "usage": {"prompt_tokens": 1, "completion_tokens": 1}}, + ] + assert handler._check_streaming_has_ended(content_only) is False + + def test_dict_finish_chunk_marks_stream_ended(self): + handler = OpenAIChatCompletionsHandler() + chunks = [ + {"id": "chatcmpl-live", "choices": [{"index": 0, "delta": {"content": "hi"}, "finish_reason": None}]}, + {"id": "chatcmpl-live", "choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}]}, + ] + assert handler._check_streaming_has_ended(chunks) is True + + def test_object_finish_chunk_marks_stream_ended(self): + from litellm.types.utils import Delta, ModelResponseStream, StreamingChoices + + handler = OpenAIChatCompletionsHandler() + chunks = [ + ModelResponseStream( + choices=[StreamingChoices(index=0, delta=Delta(content=None), finish_reason="stop")] + ) + ] + assert handler._check_streaming_has_ended(chunks) is True diff --git a/tests/test_litellm/llms/openai/chat/test_openai_gpt_transformation.py b/tests/test_litellm/llms/openai/chat/test_openai_gpt_transformation.py index 3f346b5e8e7..9737d63cc26 100644 --- a/tests/test_litellm/llms/openai/chat/test_openai_gpt_transformation.py +++ b/tests/test_litellm/llms/openai/chat/test_openai_gpt_transformation.py @@ -145,6 +145,69 @@ class TestGetOptionalParamsIntegration: assert regular_params.get("user") == "my-end-user" assert responses_params.get("user") == "my-end-user" + def test_reasoning_effort_supported_for_unknown_model_alias(self): + """An openai/-routed model litellm doesn't recognize is likely a proxy alias: + reasoning_effort must be forwarded so the server decides support.""" + from litellm.llms.openai.openai import OpenAIConfig + + supported_params = OpenAIConfig().get_supported_openai_params( + "my-claude-alias" + ) + assert "reasoning_effort" in supported_params + + def test_reasoning_effort_not_supported_for_known_non_reasoning_models(self): + """Known OpenAI models keep failing closed client-side.""" + from litellm.llms.openai.openai import OpenAIConfig + + config = OpenAIConfig() + assert "reasoning_effort" not in config.get_supported_openai_params("gpt-4o") + assert "reasoning_effort" not in config.get_supported_openai_params( + "responses/gpt-4.1-mini" + ) + + def test_reasoning_effort_not_inherited_by_openai_compatible_subclasses(self): + """Providers subclassing either openai config keep their own reasoning_effort gating + for their models, which are all unknown to the openai catalog.""" + from litellm.llms.openai.openai import OpenAIConfig + + class InheritingDispatcherConfig(OpenAIConfig): + pass + + class InheritingGPTConfig(OpenAIGPTConfig): + pass + + assert "reasoning_effort" not in InheritingDispatcherConfig().get_supported_openai_params( + "some-unknown-model" + ) + assert "reasoning_effort" not in InheritingGPTConfig().get_supported_openai_params( + "some-unknown-model" + ) + + def test_reasoning_effort_forwarded_in_optional_params_for_unknown_model_alias( + self, + ): + """Regression test for reasoning_effort raising UnsupportedParamsError + client-side for openai/-prefixed proxy aliases before any HTTP request.""" + from litellm.utils import get_optional_params + + optional_params = get_optional_params( + model="my-claude-alias", + custom_llm_provider="openai", + reasoning_effort="low", + ) + assert optional_params.get("reasoning_effort") == "low" + + def test_reasoning_effort_still_rejected_for_known_non_reasoning_model(self): + """A real OpenAI model that doesn't reason still rejects the param client-side.""" + from litellm.utils import get_optional_params + + with pytest.raises(litellm.utils.UnsupportedParamsError): + get_optional_params( + model="gpt-4o", + custom_llm_provider="openai", + reasoning_effort="low", + ) + class TestOpenAIChatCompletionStreamingHandler: """Tests for OpenAIChatCompletionStreamingHandler.chunk_parser()""" @@ -808,6 +871,134 @@ class TestCacheControlPreservationForCustomEndpoint: assert all("cache_control" not in m for m in body["messages"]) +class TestToolChoiceWithoutToolsDropped: + def setup_method(self): + self.config = OpenAIGPTConfig() + + @staticmethod + def _pi_compact_summarization_messages(): + return [ + { + "role": "system", + "content": "You are a context summarization assistant. Your task is to read a conversation between a user and an AI assistant, then produce a structured summary following the exact format specified.", + }, + { + "role": "user", + "content": [ + { + "type": "text", + "text": "\n[User]: Reply with exactly: ok-1\n\n[Assistant]: ok-1\n\n\nThe messages above are a conversation to summarize.", + } + ], + }, + ] + + def _transform(self, optional_params, config=None, model="gpt-5.6-sol"): + return (config or self.config).transform_request( + model=model, + messages=self._pi_compact_summarization_messages(), + optional_params=optional_params, + litellm_params={"custom_llm_provider": "openai", "api_base": None}, + headers={}, + ) + + def test_pi_compact_shape_drops_tool_choice_none_without_tools(self): + body = self._transform( + { + "stream": True, + "stream_options": {"include_usage": True}, + "store": False, + "max_completion_tokens": 13107, + "tool_choice": "none", + } + ) + assert "tool_choice" not in body + assert "tools" not in body + assert body["model"] == "gpt-5.6-sol" + assert body["stream"] is True + assert body["stream_options"] == {"include_usage": True} + assert body["store"] is False + assert body["max_completion_tokens"] == 13107 + + def test_drops_tool_choice_auto_without_tools(self): + body = self._transform({"tool_choice": "auto"}) + assert "tool_choice" not in body + + def test_drops_named_function_tool_choice_without_tools(self): + body = self._transform( + {"tool_choice": {"type": "function", "function": {"name": "get_weather"}}} + ) + assert "tool_choice" not in body + + def test_drops_tool_choice_but_keeps_empty_tools_array(self): + body = self._transform({"tools": [], "tool_choice": "none"}) + assert "tool_choice" not in body + assert body["tools"] == [] + + def test_gpt5_config_drops_tool_choice_without_tools(self): + body = self._transform({"tool_choice": "none"}, config=OpenAIGPT5Config()) + assert "tool_choice" not in body + + @pytest.mark.parametrize( + "tool_choice", + [ + "none", + "auto", + "required", + {"type": "function", "function": {"name": "get_weather"}}, + ], + ) + def test_preserves_tool_choice_when_tools_present(self, tool_choice): + tools = [ + { + "type": "function", + "function": {"name": "get_weather", "parameters": {}}, + } + ] + body = self._transform({"tools": tools, "tool_choice": tool_choice}) + assert body["tool_choice"] == tool_choice + assert body["tools"] == tools + + def test_preserves_tool_choice_with_legacy_functions(self): + functions = [{"name": "get_weather", "parameters": {}}] + body = self._transform({"functions": functions, "tool_choice": "auto"}) + assert body["tool_choice"] == "auto" + assert body["functions"] == functions + + def test_preserves_function_call_without_functions(self): + body = self._transform({"function_call": "none"}) + assert body["function_call"] == "none" + + @pytest.mark.asyncio + async def test_async_transform_drops_tool_choice_without_tools(self): + body = await self.config.async_transform_request( + model="gpt-5.6-sol", + messages=self._pi_compact_summarization_messages(), + optional_params={"stream": True, "tool_choice": "none"}, + litellm_params={"custom_llm_provider": "openai", "api_base": None}, + headers={}, + ) + assert "tool_choice" not in body + + @pytest.mark.asyncio + async def test_async_transform_preserves_tool_choice_when_tools_present(self): + tools = [ + { + "type": "function", + "function": {"name": "get_weather", "parameters": {}}, + } + ] + body = await self.config.async_transform_request( + model="gpt-5.6-sol", + messages=self._pi_compact_summarization_messages(), + optional_params={"tools": tools, "tool_choice": "auto"}, + litellm_params={"custom_llm_provider": "openai", "api_base": None}, + headers={}, + ) + assert body["tool_choice"] == "auto" + assert body["tools"] == tools + + class TestToolMessageImageHoisting: """transform_request moves tool-message images into a following user message (OpenAI-compatible APIs only accept text in role:"tool" messages).""" @@ -975,3 +1166,118 @@ class TestOpenAIPromptCacheBreakpointChatPath: assert request["messages"][1]["content"] == [{"type": "text", "text": "hi", "prompt_cache_breakpoint": self.EXPLICIT}] assert request["extra_body"] == {"prompt_cache_options": self.EXPLICIT} assert "prompt_cache_options" not in request + + +class TestToolSchemaCombinatorFlatteningForOpenAI: + """ + Regression tests for LIT-6488: OpenAI's chat completions validator rejects + tool parameters carrying a top-level anyOf/oneOf/allOf for every model + family, GPT-5 included, unlike the Responses API. + """ + + def setup_method(self): + self.config = OpenAIGPTConfig() + + @pytest.fixture(autouse=True) + def _clean_openai_base_env(self, monkeypatch): + monkeypatch.delenv("OPENAI_BASE_URL", raising=False) + monkeypatch.delenv("OPENAI_API_BASE", raising=False) + monkeypatch.setattr(litellm, "api_base", None, raising=False) + + @staticmethod + def _anyof_tool(): + return { + "type": "function", + "function": { + "name": "automation_update", + "description": "Update an automation", + "parameters": { + "type": "object", + "anyOf": [ + { + "properties": {"id": {"type": "string"}, "enabled": {"type": "boolean"}}, + "required": ["id", "enabled"], + }, + { + "properties": {"id": {"type": "string"}, "schedule": {"type": "string"}}, + "required": ["id", "schedule"], + }, + ], + "properties": {"id": {"type": "string"}}, + "required": ["id"], + }, + }, + } + + def _transform(self, config, model, litellm_params, tools): + return config.transform_request( + model=model, + messages=[{"role": "user", "content": "hi"}], + optional_params={"tools": tools}, + litellm_params=litellm_params, + headers={}, + ) + + def test_flattens_top_level_anyof_for_hosted_openai(self): + request = self._transform( + self.config, "gpt-4o", {"custom_llm_provider": "openai", "api_base": None}, [self._anyof_tool()] + ) + parameters = request["tools"][0]["function"]["parameters"] + assert "anyOf" not in parameters + assert parameters["type"] == "object" + assert set(parameters["properties"]) == {"id", "enabled", "schedule"} + assert parameters["required"] == ["id"] + assert request["tools"][0]["function"]["name"] == "automation_update" + + def test_gpt5_family_flattens_on_chat_completions(self): + request = self._transform( + OpenAIGPT5Config(), "gpt-5.6", {"custom_llm_provider": "openai", "api_base": None}, [self._anyof_tool()] + ) + assert "anyOf" not in request["tools"][0]["function"]["parameters"] + + def test_custom_api_base_keeps_union(self): + tool = self._anyof_tool() + request = self._transform( + self.config, + "gpt-4o", + {"custom_llm_provider": "openai", "api_base": "http://localhost:8000/v1"}, + [tool], + ) + assert request["tools"][0]["function"]["parameters"] == self._anyof_tool()["function"]["parameters"] + + def test_non_openai_provider_keeps_union(self): + request = self._transform( + self.config, "some-oss-model", {"custom_llm_provider": "groq", "api_base": None}, [self._anyof_tool()] + ) + assert request["tools"][0]["function"]["parameters"] == self._anyof_tool()["function"]["parameters"] + + def test_caller_tool_dict_is_not_mutated(self): + tool = self._anyof_tool() + self._transform(self.config, "gpt-4o", {"custom_llm_provider": "openai", "api_base": None}, [tool]) + assert tool == self._anyof_tool() + + def test_clean_object_schema_passes_through_as_same_object(self): + tool = { + "type": "function", + "function": { + "name": "lookup", + "parameters": {"type": "object", "properties": {"id": {"type": "string"}}, "required": ["id"]}, + }, + } + request = self._transform( + self.config, "gpt-4o", {"custom_llm_provider": "openai", "api_base": None}, [tool] + ) + assert request["tools"][0] is tool + + @pytest.mark.asyncio + async def test_async_transform_request_flattens_for_hosted_openai(self): + request = await self.config.async_transform_request( + model="gpt-4o", + messages=[{"role": "user", "content": "hi"}], + optional_params={"tools": [self._anyof_tool()]}, + litellm_params={"custom_llm_provider": "openai", "api_base": None}, + headers={}, + ) + parameters = request["tools"][0]["function"]["parameters"] + assert "anyOf" not in parameters + assert set(parameters["properties"]) == {"id", "enabled", "schedule"} diff --git a/tests/test_litellm/llms/openai/responses/test_openai_count_tokens_transformation.py b/tests/test_litellm/llms/openai/responses/test_openai_count_tokens_transformation.py index e1cc6a92927..c2efc1acdb9 100644 --- a/tests/test_litellm/llms/openai/responses/test_openai_count_tokens_transformation.py +++ b/tests/test_litellm/llms/openai/responses/test_openai_count_tokens_transformation.py @@ -163,6 +163,240 @@ def test_messages_to_responses_input_with_tool(): } +def test_messages_to_responses_input_preserves_images(): + """An image block must survive the round trip, or OpenAI counts only the text. + + A 256x256 image is worth 255 tokens to OpenAI's counting API; dropping it + turned a 268-token request into a 13-token one. + """ + messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "What is in this image?"}, + { + "type": "image_url", + "image_url": {"url": "data:image/png;base64,iVBORw0KGgo=", "detail": "high"}, + }, + ], + } + ] + + input_items, instructions = OpenAICountTokensConfig.messages_to_responses_input(messages) + + assert instructions is None + assert input_items == [ + { + "role": "user", + "content": ( + {"type": "input_text", "text": "What is in this image?"}, + { + "type": "input_image", + "image_url": "data:image/png;base64,iVBORw0KGgo=", + "detail": "high", + }, + ), + } + ] + + +def test_messages_to_responses_input_image_without_detail_defaults_to_auto(): + messages = [ + { + "role": "user", + "content": [{"type": "image_url", "image_url": {"url": "https://example.com/cat.png"}}], + } + ] + + input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages) + + assert input_items[0]["content"] == ( + {"type": "input_image", "image_url": "https://example.com/cat.png", "detail": "auto"}, + ) + + +def test_messages_to_responses_input_bare_string_image_url_is_preserved(): + messages = [{"role": "user", "content": [{"type": "image_url", "image_url": "https://example.com/cat.png"}]}] + + input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages) + + assert input_items[0]["content"] == ( + {"type": "input_image", "image_url": "https://example.com/cat.png", "detail": "auto"}, + ) + + +def test_messages_to_responses_input_text_only_blocks_stay_a_joined_string(): + """Text-only content must keep collapsing to a string so existing counts do not shift.""" + messages = [ + { + "role": "user", + "content": [{"type": "text", "text": "first"}, {"type": "text", "text": "second"}], + } + ] + + input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages) + + assert input_items == [{"role": "user", "content": "first\nsecond"}] + + +def test_messages_to_responses_input_drops_unmappable_blocks(): + """A block with no Responses API equivalent is skipped, never forwarded verbatim.""" + messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "hi"}, + {"type": "image_url", "image_url": {"url": "https://example.com/cat.png"}}, + {"type": "input_audio", "input_audio": {"data": "AAAA", "format": "wav"}}, + ], + } + ] + + input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages) + + assert input_items[0]["content"] == ( + {"type": "input_text", "text": "hi"}, + {"type": "input_image", "image_url": "https://example.com/cat.png", "detail": "auto"}, + ) + + +def test_messages_to_responses_input_assistant_blocks_collapse_to_a_string(): + """An assistant turn must never forward chat `text` blocks. + + The Responses API only accepts output_text and refusal inside an assistant turn, so + forwarding them 400s the whole request and silently drops the count back to the local + tokenizer, which is exactly what defeats the image fix above. + """ + messages = [ + {"role": "user", "content": [{"type": "text", "text": "What is the capital of France?"}]}, + {"role": "assistant", "content": [{"type": "text", "text": "Paris."}]}, + ] + + input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages) + + assert input_items == [ + {"role": "user", "content": "What is the capital of France?"}, + {"role": "assistant", "content": "Paris."}, + ] + + +def test_messages_to_responses_input_assistant_image_block_is_dropped(): + """An image part is illegal inside an assistant turn, so it must not reach the provider.""" + messages = [ + { + "role": "assistant", + "content": [ + {"type": "text", "text": "Here it is"}, + {"type": "image_url", "image_url": {"url": "https://example.com/cat.png"}}, + ], + } + ] + + input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages) + + assert input_items == [{"role": "assistant", "content": "Here it is"}] + + +def test_messages_to_responses_input_keeps_user_image_alongside_an_assistant_turn(): + messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "What is in this image?"}, + {"type": "image_url", "image_url": {"url": "https://example.com/cat.png"}}, + ], + }, + {"role": "assistant", "content": [{"type": "text", "text": "A cat."}]}, + ] + + input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages) + + assert input_items == [ + { + "role": "user", + "content": ( + {"type": "input_text", "text": "What is in this image?"}, + {"type": "input_image", "image_url": "https://example.com/cat.png", "detail": "auto"}, + ), + }, + {"role": "assistant", "content": "A cat."}, + ] + + +def test_messages_to_responses_input_preserves_inline_files(): + """An inline file must survive the round trip, or the count silently drops the file. + + A small PDF is worth 36 tokens to OpenAI's counting API; dropping it left the same + request counting 13, the text-only total. + """ + messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "Summarize this file."}, + { + "type": "file", + "file": {"filename": "report.pdf", "file_data": "data:application/pdf;base64,JVBERi0="}, + }, + ], + } + ] + + input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages) + + assert input_items == [ + { + "role": "user", + "content": ( + {"type": "input_text", "text": "Summarize this file."}, + { + "type": "input_file", + "filename": "report.pdf", + "file_data": "data:application/pdf;base64,JVBERi0=", + }, + ), + } + ] + + +def test_messages_to_responses_input_drops_a_file_with_no_inline_data(): + """OpenAI rejects `file_data` without a `filename`, and a rejected request loses the whole count.""" + messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "Summarize this file."}, + {"type": "file", "file": {"file_data": "data:application/pdf;base64,JVBERi0="}}, + {"type": "file", "file": {"file_id": "file-abc123"}}, + ], + } + ] + + input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages) + + assert input_items == [{"role": "user", "content": "Summarize this file."}] + + +def test_messages_to_responses_input_assistant_file_block_is_dropped(): + """A file part is illegal inside an assistant turn, so it must not reach the provider.""" + messages = [ + { + "role": "assistant", + "content": [ + {"type": "text", "text": "Here it is"}, + { + "type": "file", + "file": {"filename": "report.pdf", "file_data": "data:application/pdf;base64,JVBERi0="}, + }, + ], + } + ] + + input_items, _ = OpenAICountTokensConfig.messages_to_responses_input(messages) + + assert input_items == [{"role": "assistant", "content": "Here it is"}] + + def test_validate_request_valid(): """Test that valid requests pass validation.""" config = OpenAICountTokensConfig() diff --git a/tests/test_litellm/llms/openai/responses/test_openai_responses_guardrail_handler.py b/tests/test_litellm/llms/openai/responses/test_openai_responses_guardrail_handler.py index 447175b09a6..315b6948bd8 100644 --- a/tests/test_litellm/llms/openai/responses/test_openai_responses_guardrail_handler.py +++ b/tests/test_litellm/llms/openai/responses/test_openai_responses_guardrail_handler.py @@ -828,6 +828,24 @@ class MockPassThroughGuardrail(CustomGuardrail): return inputs +class MockRecordingGuardrail(MockPassThroughGuardrail): + """Pass-through guardrail that records every apply_guardrail inputs payload""" + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.seen_inputs: List[GenericGuardrailAPIInputs] = [] + + async def apply_guardrail( + self, + inputs: GenericGuardrailAPIInputs, + request_data: dict, + input_type: Literal["request", "response"], + logging_obj: Optional[Any] = None, + ) -> GenericGuardrailAPIInputs: + self.seen_inputs.append(inputs) + return inputs + + class TestOpenAIResponsesHandlerStreamingOutputProcessing: """Test streaming output processing functionality""" @@ -1104,6 +1122,80 @@ class TestOpenAIResponsesHandlerStreamingOutputProcessing: output_text = result[-1]["response"]["output"][0]["content"][0]["text"] assert output_text == original_text + @pytest.mark.asyncio + async def test_failed_stream_scans_delta_text(self): + """A stream ending in response.failed has text only in delta events; the + fallback scan must assemble and scan it instead of skipping on an empty string.""" + handler = OpenAIResponsesHandler() + guardrail = MockRecordingGuardrail(guardrail_name="test") + + responses_so_far = [ + {"type": "response.created", "response": {"id": "resp_123"}}, + {"type": "response.output_item.added", "item": {"type": "message", "id": "msg_123"}}, + { + "type": "response.output_text.delta", + "item_id": "msg_123", + "output_index": 0, + "content_index": 0, + "delta": "Hello", + }, + { + "type": "response.output_text.delta", + "item_id": "msg_123", + "output_index": 0, + "content_index": 0, + "delta": " world", + }, + {"type": "response.failed", "response": {"id": "resp_123", "status": "failed"}}, + ] + + result = await handler.process_output_streaming_response( + responses_so_far=responses_so_far, + guardrail_to_apply=guardrail, + litellm_logging_obj=None, + ) + + assert result == responses_so_far + assert [inputs.get("texts") for inputs in guardrail.seen_inputs] == [["Hello world"]] + + def test_get_streaming_string_so_far_prefers_done_text_over_deltas(self): + """The done event repeats the whole part, so deltas must not be double counted; + a part with no done event yet still contributes its joined deltas.""" + handler = OpenAIResponsesHandler() + + events = [ + { + "type": "response.output_text.delta", + "item_id": "msg_1", + "output_index": 0, + "content_index": 0, + "delta": "Hello", + }, + { + "type": "response.output_text.delta", + "item_id": "msg_1", + "output_index": 0, + "content_index": 0, + "delta": " world", + }, + { + "type": "response.output_text.done", + "item_id": "msg_1", + "output_index": 0, + "content_index": 0, + "text": "Hello world", + }, + { + "type": "response.output_text.delta", + "item_id": "msg_2", + "output_index": 1, + "content_index": 0, + "delta": "; unfinished", + }, + ] + + assert handler.get_streaming_string_so_far(events) == "Hello world; unfinished" + class TestGetStructuredMessages: """Test the get_structured_messages method for Responses API handler.""" @@ -1229,3 +1321,219 @@ class TestOpenAIResponsesHandlerToolInjection: names = [t.get("name") for t in result["tools"]] assert "get_weather" in names assert "injected_tool" in names + + +class TestBuildBlockSseChunks: + """build_block_sse_chunks turns a streaming ModifyResponseException into 200 SSE events""" + + def _exc(self, original_response=None): + from litellm.exceptions import ModifyResponseException + + return ModifyResponseException( + message="Blocked by policy.", + model="gpt-5.4-mini", + request_data={}, + guardrail_name="test", + original_response=original_response, + ) + + def _payloads(self, chunks): + import json + + return [json.loads(chunk.decode().removeprefix("data: ").strip()) for chunk in chunks] + + def test_standalone_block_emits_complete_synthetic_stream(self): + handler = OpenAIResponsesHandler() + payloads = self._payloads(handler.build_block_sse_chunks(self._exc(), stream_started=False)) + types = [payload["type"] for payload in payloads] + assert types[0] == "response.created" + assert types[-1] == "response.completed" + completed = payloads[-1]["response"] + assert completed["id"].startswith("resp_") + assert completed["model"] == "gpt-5.4-mini" + assert completed["output"][0]["content"][0]["text"] == "Blocked by policy." + + def test_continuation_appends_item_at_next_output_index_with_real_usage(self): + handler = OpenAIResponsesHandler() + yielded = [ + {"type": "response.created", "response": {"id": "resp_live", "model": "gpt-5.4-mini-2026-01-01"}}, + {"type": "response.output_item.added", "output_index": 2, "item": {"id": "msg_orig"}}, + ] + original = yielded + [ + { + "type": "response.completed", + "response": { + "id": "resp_live", + "model": "gpt-5.4-mini-2026-01-01", + "output": [], + "usage": {"input_tokens": 7, "output_tokens": 21, "total_tokens": 28}, + }, + } + ] + payloads = self._payloads( + handler.build_block_sse_chunks( + self._exc(original_response=original), stream_started=True, responses_so_far=yielded + ) + ) + types = [payload["type"] for payload in payloads] + assert "response.created" not in types + assert types[0] == "response.output_item.done" + assert payloads[0]["output_index"] == 2 + assert payloads[0]["item"]["id"] == "msg_orig" + assert payloads[0]["item"]["status"] == "completed" + assert types[1] == "response.output_item.added" + assert payloads[1]["output_index"] == 3 + completed = payloads[-1]["response"] + assert completed["id"] == "resp_live" + assert completed["model"] == "gpt-5.4-mini-2026-01-01" + assert completed["output"][0]["content"][0]["text"] == "Blocked by policy." + assert completed["usage"] == {"input_tokens": 7, "output_tokens": 21, "total_tokens": 28} + + def test_continuation_reads_usage_from_typed_completed_event(self): + from litellm.types.llms.openai import ( + ResponseCompletedEvent, + ResponsesAPIResponse, + ResponsesAPIStreamEvents, + ) + + handler = OpenAIResponsesHandler() + original = [ + ResponseCompletedEvent( + type=ResponsesAPIStreamEvents.RESPONSE_COMPLETED, + response=ResponsesAPIResponse.model_validate( + { + "id": "resp_live", + "created_at": 1, + "model": "gpt-5.4-mini", + "output": [], + "usage": {"input_tokens": 7, "output_tokens": 21, "total_tokens": 28}, + } + ), + ) + ] + payloads = self._payloads( + handler.build_block_sse_chunks( + self._exc(original_response=original), stream_started=True, responses_so_far=[] + ) + ) + completed = payloads[-1]["response"] + assert completed["usage"]["input_tokens"] == 7 + assert completed["usage"]["output_tokens"] == 21 + assert completed["usage"]["total_tokens"] == 28 + + def test_continuation_closes_open_item_given_pydantic_events_with_enum_types(self): + from litellm.types.llms.openai import ( + BaseLiteLLMOpenAIResponseObject, + ContentPartAddedEvent, + OutputItemAddedEvent, + OutputTextDeltaEvent, + ResponsesAPIStreamEvents, + ) + + handler = OpenAIResponsesHandler() + open_item = GenericResponseOutputItem.model_validate( + {"type": "message", "id": "msg_live", "status": "in_progress", "role": "assistant", "content": []} + ) + yielded = [ + OutputItemAddedEvent( + type=ResponsesAPIStreamEvents.OUTPUT_ITEM_ADDED, output_index=0, item=open_item + ), + ContentPartAddedEvent( + type=ResponsesAPIStreamEvents.CONTENT_PART_ADDED, + item_id="msg_live", + output_index=0, + content_index=0, + part=BaseLiteLLMOpenAIResponseObject.model_validate( + {"type": "output_text", "text": "", "annotations": []} + ), + ), + OutputTextDeltaEvent( + type=ResponsesAPIStreamEvents.OUTPUT_TEXT_DELTA, + item_id="msg_live", + output_index=0, + content_index=0, + delta="partial ", + ), + OutputTextDeltaEvent( + type=ResponsesAPIStreamEvents.OUTPUT_TEXT_DELTA, + item_id="msg_live", + output_index=0, + content_index=0, + delta="text", + ), + ] + payloads = self._payloads( + handler.build_block_sse_chunks( + self._exc(original_response=yielded), stream_started=True, responses_so_far=yielded + ) + ) + types = [payload["type"] for payload in payloads] + assert types[:3] == [ + "response.output_text.done", + "response.content_part.done", + "response.output_item.done", + ] + assert payloads[0]["text"] == "partial text" + assert payloads[2]["item"]["id"] == "msg_live" + assert payloads[2]["item"]["status"] == "completed" + assert payloads[2]["item"]["content"][0]["text"] == "partial text" + assert types[3] == "response.output_item.added" + assert payloads[3]["output_index"] == 1 + + def test_continuation_closes_open_function_call_as_incomplete(self): + handler = OpenAIResponsesHandler() + yielded = [ + {"type": "response.created", "response": {"id": "resp_live", "model": "gpt-5.4-mini"}}, + { + "type": "response.output_item.added", + "output_index": 0, + "item": { + "id": "fc_live", + "type": "function_call", + "status": "in_progress", + "call_id": "call_1", + "name": "run_payment", + "arguments": "", + }, + }, + { + "type": "response.function_call_arguments.delta", + "item_id": "fc_live", + "output_index": 0, + "delta": '{"amount": 100}', + }, + ] + payloads = self._payloads( + handler.build_block_sse_chunks( + self._exc(original_response=yielded), stream_started=True, responses_so_far=yielded + ) + ) + types = [payload["type"] for payload in payloads] + assert types[0] == "response.output_item.done" + closed = payloads[0]["item"] + assert closed["id"] == "fc_live" + assert closed["type"] == "function_call" + assert closed["status"] == "incomplete" + assert closed["name"] == "run_payment" + assert "content" not in closed + assert types[1] == "response.output_item.added" + assert payloads[1]["output_index"] == 1 + assert types[-1] == "response.completed" + + def test_continuation_without_open_item_emits_no_closing_events(self): + handler = OpenAIResponsesHandler() + yielded = [ + {"type": "response.created", "response": {"id": "resp_live", "model": "gpt-5.4-mini"}}, + {"type": "response.in_progress", "response": {"id": "resp_live"}}, + ] + payloads = self._payloads( + handler.build_block_sse_chunks( + self._exc(original_response=yielded), stream_started=True, responses_so_far=yielded + ) + ) + types = [payload["type"] for payload in payloads] + assert types[0] == "response.output_item.added" + assert types[-1] == "response.completed" + dones = [payload for payload in payloads if payload["type"] == "response.output_item.done"] + assert len(dones) == 1 + assert dones[0]["item"]["content"][0]["text"] == "Blocked by policy." diff --git a/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py b/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py index e314b94444b..b0ffd1845fe 100644 --- a/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py +++ b/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py @@ -1,4 +1,5 @@ import json +from types import SimpleNamespace from unittest.mock import AsyncMock, MagicMock, Mock, patch import httpx @@ -219,6 +220,111 @@ class TestOpenAIResponsesAPIConfig: assert result["input"] == input_clean + def test_transform_drops_foreign_tool_call_item_ids(self): + """Replayed tool call items whose ids are not OpenAI-shaped (e.g. + Anthropic toolu_/srvtoolu_ ids after a router fallback) must be sent + without an id: OpenAI 400s foreign ids ("Expected an ID that begins + with 'fc'") but accepts the items with no id at all. Genuine fc_/ctc_ + ids and non-tool-call items pass through untouched.""" + replayed_input = [ + {"role": "user", "content": [{"type": "input_text", "text": "hi"}]}, + { + "type": "function_call", + "id": "toolu_01Foreign", + "call_id": "toolu_01Foreign", + "name": "get_weather", + "arguments": '{"city": "SF"}', + }, + {"type": "function_call_output", "call_id": "toolu_01Foreign", "output": "sunny"}, + { + "type": "custom_tool_call", + "id": "srvtoolu_01Foreign", + "call_id": "srvtoolu_01Foreign", + "name": "apply_patch", + "input": "patch", + }, + { + "type": "function_call", + "id": "fc_genuine", + "call_id": "call_genuine", + "name": "get_weather", + "arguments": "{}", + }, + {"type": "message", "id": "msg_1", "role": "assistant", "content": []}, + ] + + result = self.config.transform_responses_api_request( + model=self.model, + input=replayed_input, + response_api_optional_request_params={}, + litellm_params={}, + headers={}, + ) + + assert "id" not in result["input"][1] + assert result["input"][1]["call_id"] == "toolu_01Foreign" + assert "id" not in result["input"][3] + assert result["input"][3]["call_id"] == "srvtoolu_01Foreign" + assert result["input"][4]["id"] == "fc_genuine" + assert result["input"][5]["id"] == "msg_1" + assert replayed_input[1]["id"] == "toolu_01Foreign" + assert replayed_input[3]["id"] == "srvtoolu_01Foreign" + + def test_transform_keeps_foreign_tool_call_item_ids_for_other_providers(self): + """Providers reusing this config that do not enforce OpenAI's id + shapes must keep replayed ids untouched.""" + from litellm.types.utils import LlmProviders + + class _OpenRouterLikeConfig(OpenAIResponsesAPIConfig): + @property + def custom_llm_provider(self) -> LlmProviders: + return LlmProviders.OPENROUTER + + replayed_input = [ + { + "type": "function_call", + "id": "toolu_01Foreign", + "call_id": "toolu_01Foreign", + "name": "get_weather", + "arguments": "{}", + } + ] + + result = _OpenRouterLikeConfig().transform_responses_api_request( + model="openrouter/some-model", + input=replayed_input, + response_api_optional_request_params={}, + litellm_params={}, + headers={}, + ) + + assert result["input"][0]["id"] == "toolu_01Foreign" + + def test_transform_compact_drops_foreign_tool_call_item_ids(self): + """The compact request path replays input the same way, so it must + apply the same id drop.""" + replayed_input = [ + { + "type": "function_call", + "id": "toolu_01Foreign", + "call_id": "toolu_01Foreign", + "name": "get_weather", + "arguments": "{}", + } + ] + + _url, data = self.config.transform_compact_response_api_request( + model=self.model, + input=replayed_input, + response_api_optional_request_params={}, + api_base="https://api.openai.com/v1/responses", + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert "id" not in data["input"][0] + assert data["input"][0]["call_id"] == "toolu_01Foreign" + def test_transform_streaming_response(self): """Test streaming response transformation""" # Test with a text delta event @@ -1626,3 +1732,192 @@ class TestResponsesSurfaceSharesTheEffortRule: drop_params=True, ) assert ("temperature" in mapped) is temperature_survives + + +class TestFlattenToolSchemaCombinatorsWiring: + """Regression tests for MCP tools with a top-level anyOf schema (Codex Desktop). + + OpenAI's /v1/responses rejects function tool parameters carrying + 'oneOf'/'anyOf'/'allOf'/'enum'/'const'/'not' at the top level, while the + ChatGPT backend Codex uses natively accepts them, so those tools 400'd + through the proxy with "Invalid schema for function ...". + """ + + def _anyof_parameters(self): + return { + "type": "object", + "anyOf": [ + { + "properties": {"id": {"type": "string"}, "enabled": {"type": "boolean"}}, + "required": ["id", "enabled"], + }, + { + "properties": {"id": {"type": "string"}, "schedule": {"type": "string"}}, + "required": ["id", "schedule"], + }, + ], + "properties": {"id": {"type": "string"}}, + "required": ["id"], + } + + def _flat_function_tool(self): + return { + "type": "function", + "name": "mcp__codex_app__automation_update", + "description": "Update an automation", + "parameters": self._anyof_parameters(), + "strict": False, + } + + def _codex_namespace_tool(self): + return { + "type": "namespace", + "name": "mcp__codex_app", + "tools": [ + { + "name": "automation_update", + "description": "Update an automation", + "parameters": self._anyof_parameters(), + "strict": False, + } + ], + } + + def test_openai_flattens_top_level_anyof_on_flat_function_tool(self): + result = OpenAIResponsesAPIConfig().transform_responses_api_request( + model="gpt-4o", + input="hi", + response_api_optional_request_params={"tools": [self._flat_function_tool()]}, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + parameters = result["tools"][0]["parameters"] + assert "anyOf" not in parameters + assert parameters["type"] == "object" + assert set(parameters["properties"]) == {"id", "enabled", "schedule"} + assert parameters["required"] == ["id"] + assert json.loads(json.dumps(result["tools"])) == result["tools"] + + def test_openai_flattens_anyof_inside_codex_namespace_tools(self): + result = OpenAIResponsesAPIConfig().transform_responses_api_request( + model="gpt-4o", + input="hi", + response_api_optional_request_params={"tools": [self._codex_namespace_tool()]}, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + nested_parameters = result["tools"][0]["tools"][0]["parameters"] + assert "anyOf" not in nested_parameters + assert set(nested_parameters["properties"]) == {"id", "enabled", "schedule"} + assert json.loads(json.dumps(result["tools"])) == result["tools"] + + def test_openai_compact_request_flattens_top_level_anyof(self): + _, data = OpenAIResponsesAPIConfig().transform_compact_response_api_request( + model="gpt-4o", + input="hi", + response_api_optional_request_params={"tools": [self._flat_function_tool()]}, + api_base="https://api.openai.com/v1/responses", + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert "anyOf" not in data["tools"][0]["parameters"] + + def test_openai_leaves_tools_without_rejected_keys_alone(self): + clean_tool = { + "type": "function", + "name": "get_weather", + "parameters": {"type": "object", "properties": {"city": {"type": "string"}}}, + } + + result = OpenAIResponsesAPIConfig().transform_responses_api_request( + model="gpt-4o", + input="hi", + response_api_optional_request_params={"tools": [clean_tool]}, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert result["tools"][0]["parameters"] == {"type": "object", "properties": {"city": {"type": "string"}}} + + def test_openai_does_not_mutate_caller_tool_dicts(self): + tool = self._flat_function_tool() + + OpenAIResponsesAPIConfig().transform_responses_api_request( + model="gpt-4o", + input="hi", + response_api_optional_request_params={"tools": [tool]}, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert "anyOf" in tool["parameters"] + + def test_non_openai_subclass_does_not_flatten(self): + from litellm.llms.hosted_vllm.responses.transformation import HostedVLLMResponsesAPIConfig + + result = HostedVLLMResponsesAPIConfig().transform_responses_api_request( + model="hosted_vllm/qwen", + input="hi", + response_api_optional_request_params={"tools": [self._flat_function_tool()]}, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert "anyOf" in result["tools"][0]["parameters"] + + @pytest.mark.parametrize( + "model", + [ + "gpt-4o", + "gpt-4.1-mini", + "gpt-4-turbo", + "o1", + "o3-pro", + "o4-mini", + "openai/gpt-4o", + "ft:gpt-4o-2024-08-06:org::abc", + ], + ) + def test_openai_flattens_for_models_whose_validator_rejects_combinators(self, model): + result = OpenAIResponsesAPIConfig().transform_responses_api_request( + model=model, + input="hi", + response_api_optional_request_params={"tools": [self._flat_function_tool()]}, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert "anyOf" not in result["tools"][0]["parameters"] + + @pytest.mark.parametrize( + "model", ["gpt-5", "gpt-5-nano", "gpt-5.4-mini", "gpt-5.4-codex", "gpt-5.5", "openai/gpt-5.2"] + ) + def test_openai_keeps_combinators_for_models_that_accept_them(self, model): + tool = self._flat_function_tool() + + result = OpenAIResponsesAPIConfig().transform_responses_api_request( + model=model, + input="hi", + response_api_optional_request_params={"tools": [tool]}, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert result["tools"][0] is tool + + def test_openai_leaves_non_dict_tool_entries_alone(self): + opaque_tool = SimpleNamespace(type="function", name="automation_update") + + result = OpenAIResponsesAPIConfig().transform_responses_api_request( + model="gpt-4o", + input="hi", + response_api_optional_request_params={"tools": [opaque_tool, self._flat_function_tool()]}, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert result["tools"][0] is opaque_tool + assert "anyOf" not in result["tools"][1]["parameters"] diff --git a/tests/test_litellm/llms/openai/test_openai_workload_identity.py b/tests/test_litellm/llms/openai/test_openai_workload_identity.py new file mode 100644 index 00000000000..d8d9936e9a1 --- /dev/null +++ b/tests/test_litellm/llms/openai/test_openai_workload_identity.py @@ -0,0 +1,238 @@ +import json +import sys +from pathlib import Path +from typing import Final + +import httpx +import pytest +import respx +from openai import AsyncOpenAI, OpenAI + +import litellm +from litellm.llms.litellm_proxy.responses.transformation import LiteLLMProxyResponsesAPIConfig +from litellm.llms.openai.common_utils import BaseOpenAILLM, OpenAIError +from litellm.llms.openai.openai import OpenAIChatCompletion +from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig +from litellm.llms.openai.workload_identity import ( + OpenAIWorkloadIdentityConfig, + _workload_identity_auth, + get_workload_identity_bearer_token, + resolve_openai_workload_identity_config, +) +from litellm.types.router import GenericLiteLLMParams + +TOKEN_EXCHANGE_URL: Final = "https://auth.openai.com/oauth/token" + + +@pytest.fixture +def wif_env(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> OpenAIWorkloadIdentityConfig: + token_file: Final = tmp_path / "subject_token.jwt" + token_file.write_text("subject-token-from-file") + monkeypatch.delenv("OPENAI_API_KEY", raising=False) + monkeypatch.delenv("OPENAI_BASE_URL", raising=False) + monkeypatch.delenv("OPENAI_API_BASE", raising=False) + monkeypatch.setattr(litellm, "api_base", None) + monkeypatch.setenv("OPENAI_IDENTITY_PROVIDER_ID", "idp_test123") + monkeypatch.setenv("OPENAI_SERVICE_ACCOUNT_ID", "user-test456") + monkeypatch.setenv("OPENAI_IDENTITY_TOKEN_FILE", str(token_file)) + _workload_identity_auth.cache_clear() + litellm.in_memory_llm_clients_cache.flush_cache() + return OpenAIWorkloadIdentityConfig( + identity_provider_id="idp_test123", + service_account_id="user-test456", + token_file=str(token_file), + ) + + +def mock_token_exchange(access_token: str = "exchanged-bearer-token") -> respx.Route: + return respx.post(TOKEN_EXCHANGE_URL).mock( + return_value=httpx.Response(200, json={"access_token": access_token, "expires_in": 3600}) + ) + + +class TestResolveConfig: + def test_resolves_from_env(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + assert resolve_openai_workload_identity_config(api_key=None, api_base=None) == wif_env + + def test_static_api_key_wins(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + assert resolve_openai_workload_identity_config(api_key="sk-static", api_base=None) is None + + def test_env_openai_api_key_wins( + self, wif_env: OpenAIWorkloadIdentityConfig, monkeypatch: pytest.MonkeyPatch + ) -> None: + monkeypatch.setenv("OPENAI_API_KEY", "sk-from-env") + assert resolve_openai_workload_identity_config(api_key=None, api_base=None) is None + + @pytest.mark.parametrize("empty_key", ["", " "]) + def test_empty_api_key_arg_does_not_disable_wif( + self, wif_env: OpenAIWorkloadIdentityConfig, empty_key: str + ) -> None: + assert resolve_openai_workload_identity_config(api_key=empty_key, api_base=None) == wif_env + + @pytest.mark.parametrize("empty_key", ["", " "]) + def test_empty_env_openai_api_key_does_not_disable_wif( + self, wif_env: OpenAIWorkloadIdentityConfig, monkeypatch: pytest.MonkeyPatch, empty_key: str + ) -> None: + monkeypatch.setenv("OPENAI_API_KEY", empty_key) + assert resolve_openai_workload_identity_config(api_key=None, api_base=None) == wif_env + + def test_foreign_api_base_disables(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + assert resolve_openai_workload_identity_config(api_key=None, api_base="https://my-vllm.internal/v1") is None + + def test_openai_api_base_allows(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + assert resolve_openai_workload_identity_config(api_key=None, api_base="https://api.openai.com/v1") == wif_env + + def test_plaintext_http_api_base_disables(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + assert resolve_openai_workload_identity_config(api_key=None, api_base="http://api.openai.com/v1") is None + + def test_foreign_env_base_url_disables( + self, wif_env: OpenAIWorkloadIdentityConfig, monkeypatch: pytest.MonkeyPatch + ) -> None: + monkeypatch.setenv("OPENAI_BASE_URL", "https://my-vllm.internal/v1") + assert resolve_openai_workload_identity_config(api_key=None, api_base=None) is None + + def test_openai_env_base_url_allows( + self, wif_env: OpenAIWorkloadIdentityConfig, monkeypatch: pytest.MonkeyPatch + ) -> None: + monkeypatch.setenv("OPENAI_BASE_URL", "https://api.openai.com/v1") + assert resolve_openai_workload_identity_config(api_key=None, api_base=None) == wif_env + + def test_foreign_litellm_api_base_disables( + self, wif_env: OpenAIWorkloadIdentityConfig, monkeypatch: pytest.MonkeyPatch + ) -> None: + monkeypatch.setattr(litellm, "api_base", "https://my-vllm.internal/v1") + assert resolve_openai_workload_identity_config(api_key=None, api_base=None) is None + + @pytest.mark.parametrize( + "missing_var", + ["OPENAI_IDENTITY_PROVIDER_ID", "OPENAI_SERVICE_ACCOUNT_ID", "OPENAI_IDENTITY_TOKEN_FILE"], + ) + def test_partial_env_disables( + self, wif_env: OpenAIWorkloadIdentityConfig, monkeypatch: pytest.MonkeyPatch, missing_var: str + ) -> None: + monkeypatch.delenv(missing_var) + assert resolve_openai_workload_identity_config(api_key=None, api_base=None) is None + + +class TestTokenExchange: + @respx.mock + def test_exchanges_subject_token_for_bearer(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + route: Final = mock_token_exchange() + assert get_workload_identity_bearer_token(wif_env) == "exchanged-bearer-token" + request_body: Final = json.loads(route.calls.last.request.content) + assert request_body["grant_type"] == "urn:ietf:params:oauth:grant-type:token-exchange" + assert request_body["subject_token"] == "subject-token-from-file" + assert request_body["subject_token_type"] == "urn:ietf:params:oauth:token-type:jwt" + assert request_body["identity_provider_id"] == "idp_test123" + assert request_body["service_account_id"] == "user-test456" + + @respx.mock + def test_token_cached_across_mints(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + route: Final = mock_token_exchange() + first: Final = get_workload_identity_bearer_token(wif_env) + second: Final = get_workload_identity_bearer_token(wif_env) + assert first == second == "exchanged-bearer-token" + assert route.call_count == 1 + + def test_old_sdk_raises_upgrade_error( + self, wif_env: OpenAIWorkloadIdentityConfig, monkeypatch: pytest.MonkeyPatch + ) -> None: + import openai as openai_module + + monkeypatch.delattr(openai_module, "auth", raising=False) + monkeypatch.setitem(sys.modules, "openai.auth", None) + with pytest.raises(OpenAIError, match=r"openai>=2\.32\.0"): + wif_env.to_sdk_workload_identity() + + +class TestClientConstruction: + def test_sync_client_uses_workload_identity(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + client: Final = OpenAIChatCompletion()._get_openai_client(is_async=False, api_key=None, api_base=None) + assert isinstance(client, OpenAI) + assert client.api_key == "workload-identity-auth" + assert client._workload_identity_auth is not None + + def test_async_client_uses_workload_identity(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + client: Final = OpenAIChatCompletion()._get_openai_client(is_async=True, api_key=None, api_base=None) + assert isinstance(client, AsyncOpenAI) + assert client.api_key == "workload-identity-auth" + assert client._workload_identity_auth is not None + + def test_static_key_client_unaffected(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + client: Final = OpenAIChatCompletion()._get_openai_client(is_async=False, api_key="sk-static", api_base=None) + assert isinstance(client, OpenAI) + assert client.api_key == "sk-static" + assert client._workload_identity_auth is None + + def test_cache_key_separates_wif_identities(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + other_config: Final = OpenAIWorkloadIdentityConfig( + identity_provider_id="idp_other", + service_account_id="user-other", + token_file=wif_env.token_file, + ) + keys: Final = tuple( + BaseOpenAILLM.get_openai_client_cache_key( + client_initialization_params={"api_key": None, "is_async": False, "workload_identity_config": config}, + client_type="openai", + ) + for config in (wif_env, other_config, None) + ) + assert len(set(keys)) == 3 + + @respx.mock + def test_request_carries_exchanged_bearer(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + mock_token_exchange() + completion_route: Final = respx.post("https://api.openai.com/v1/chat/completions").mock( + return_value=httpx.Response( + 200, + json={ + "id": "chatcmpl-wif", + "object": "chat.completion", + "created": 1, + "model": "gpt-4o-mini", + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": "ok"}, + "finish_reason": "stop", + } + ], + "usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2}, + }, + ) + ) + client = OpenAIChatCompletion()._get_openai_client(is_async=False, api_key=None, api_base=None) + assert isinstance(client, OpenAI) + client.chat.completions.create(model="gpt-4o-mini", messages=[{"role": "user", "content": "hi"}]) + auth_header: Final = completion_route.calls.last.request.headers["Authorization"] + assert auth_header == "Bearer exchanged-bearer-token" + + +class TestResponsesValidateEnvironment: + @respx.mock + def test_mints_bearer_when_wif_configured(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + mock_token_exchange() + headers: Final = OpenAIResponsesAPIConfig().validate_environment( + headers={}, model="gpt-4o-mini", litellm_params=GenericLiteLLMParams() + ) + assert headers["Authorization"] == "Bearer exchanged-bearer-token" + + def test_static_key_wins(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + headers: Final = OpenAIResponsesAPIConfig().validate_environment( + headers={}, model="gpt-4o-mini", litellm_params=GenericLiteLLMParams(api_key="sk-responses") + ) + assert headers["Authorization"] == "Bearer sk-responses" + + def test_foreign_api_base_skips_wif(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + headers: Final = OpenAIResponsesAPIConfig().validate_environment( + headers={}, + model="gpt-4o-mini", + litellm_params=GenericLiteLLMParams(api_base="https://my-vllm.internal/v1"), + ) + assert headers["Authorization"] == "Bearer None" + + def test_litellm_proxy_subclass_never_mints_wif(self, wif_env: OpenAIWorkloadIdentityConfig) -> None: + headers: Final = LiteLLMProxyResponsesAPIConfig().validate_environment( + headers={}, model="gpt-4o-mini", litellm_params=GenericLiteLLMParams() + ) + assert headers["Authorization"] == "Bearer None" diff --git a/tests/test_litellm/llms/openai_like/messages/test_openai_like_anthropic_messages_transformation.py b/tests/test_litellm/llms/openai_like/messages/test_openai_like_anthropic_messages_transformation.py index e33b03afdff..67a56fdcd79 100644 --- a/tests/test_litellm/llms/openai_like/messages/test_openai_like_anthropic_messages_transformation.py +++ b/tests/test_litellm/llms/openai_like/messages/test_openai_like_anthropic_messages_transformation.py @@ -254,7 +254,7 @@ def test_request_maps_reasoning_effort_to_thinking(config): model="claude-sonnet-4-20250514", messages=[{"role": "user", "content": "hi"}], anthropic_messages_optional_request_params={ - "max_tokens": 1024, + "max_tokens": 8192, "reasoning_effort": "medium", }, litellm_params=GenericLiteLLMParams(), @@ -264,6 +264,7 @@ def test_request_maps_reasoning_effort_to_thinking(config): assert "reasoning_effort" not in payload assert isinstance(payload.get("thinking"), dict) assert payload["thinking"].get("type") == "enabled" + assert payload["thinking"]["budget_tokens"] < payload["max_tokens"] def test_passthrough_disables_anthropic_beta_filtering(config): diff --git a/tests/test_litellm/llms/parallel_ai/test_parallel_ai_search.py b/tests/test_litellm/llms/parallel_ai/test_parallel_ai_search.py index 8a9ae4dae6d..62b4d003b45 100644 --- a/tests/test_litellm/llms/parallel_ai/test_parallel_ai_search.py +++ b/tests/test_litellm/llms/parallel_ai/test_parallel_ai_search.py @@ -2,6 +2,7 @@ Tests for Parallel AI Search API integration (v1 endpoint). """ +import json from unittest.mock import AsyncMock, MagicMock, patch import pytest @@ -30,13 +31,41 @@ MOCK_V1_RESPONSE = { } -def _mock_response(): +def _mock_response(payload=None): mock_response = MagicMock() mock_response.status_code = 200 - mock_response.json.return_value = MOCK_V1_RESPONSE + mock_response.json.return_value = payload if payload is not None else MOCK_V1_RESPONSE return mock_response +@pytest.fixture +def httpx_transport(monkeypatch): + monkeypatch.setattr( # test-quality-ok: respx needs HTTPX enabled to fake the provider HTTP boundary. + litellm, + "disable_aiohttp_transport", + True, + ) + litellm.in_memory_llm_clients_cache.flush_cache() + yield + litellm.in_memory_llm_clients_cache.flush_cache() + + +@pytest.fixture +def bundled_cost_map(monkeypatch): + """Price lookups against the bundled cost map. + + litellm caches model-info lookups, so swapping ``model_cost`` only takes + effect once those caches are invalidated -- on the way in and back out. + """ + from litellm.utils import _invalidate_model_cost_lowercase_map + + monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url="")) + _invalidate_model_cost_lowercase_map() + yield + monkeypatch.undo() + _invalidate_model_cost_lowercase_map() + + class TestParallelAISearch: @pytest.fixture(autouse=True) def _set_api_key(self, monkeypatch): @@ -135,9 +164,7 @@ class TestParallelAISearch: json_data = mock_post.call_args.kwargs.get("json") assert json_data["mode"] == "basic" - @pytest.mark.parametrize( - "processor,expected_mode", [("base", "basic"), ("pro", "advanced")] - ) + @pytest.mark.parametrize("processor,expected_mode", [("base", "basic"), ("pro", "advanced")]) @pytest.mark.asyncio async def test_legacy_processor_maps_to_mode(self, processor, expected_mode): with patch( @@ -222,9 +249,7 @@ class TestParallelAISearch: "arxiv.org", "nature.com", ] - assert advanced_settings["source_policy"]["exclude_domains"] == [ - "reddit.com" - ] + assert advanced_settings["source_policy"]["exclude_domains"] == ["reddit.com"] assert advanced_settings["excerpt_settings"]["max_chars_per_result"] == 1500 assert "max_results" not in json_data @@ -306,10 +331,7 @@ class TestParallelAISearch: ) call_args = mock_post.call_args - assert ( - call_args.kwargs["url"] - == "https://proxy.internal.example.com/v1/search" - ) + assert call_args.kwargs["url"] == "https://proxy.internal.example.com/v1/search" @pytest.mark.asyncio async def test_caller_api_base_without_key_is_refused(self, monkeypatch): @@ -338,3 +360,147 @@ class TestParallelAISearch: query="AI developments", search_provider="parallel_ai", ) + + @pytest.mark.asyncio + async def test_flat_source_and_fetch_params_nest_under_advanced_settings(self, respx_mock, httpx_transport): + route = respx_mock.post("https://api.parallel.ai/v1/search").respond(json=MOCK_V1_RESPONSE) + + await litellm.asearch( + query="AI developments", + search_provider="parallel_ai", + objective="find peer-reviewed AI research", + include_domains=["arxiv.org"], + after_date="2026-01-01", + location="gb", + fetch_policy={"max_age_seconds": 600, "disable_cache_fallback": True}, + client_model="claude-fable-5", + ) + + json_data = json.loads(route.calls[0].request.content) + assert json_data["objective"] == "find peer-reviewed AI research" + assert json_data["client_model"] == "claude-fable-5" + + advanced_settings = json_data["advanced_settings"] + assert advanced_settings["location"] == "gb" + assert advanced_settings["fetch_policy"] == { + "max_age_seconds": 600, + "disable_cache_fallback": True, + } + assert advanced_settings["source_policy"]["include_domains"] == ["arxiv.org"] + assert advanced_settings["source_policy"]["after_date"] == "2026-01-01" + + assert "include_domains" not in json_data + assert "after_date" not in json_data + assert "location" not in json_data + assert "fetch_policy" not in json_data + + @pytest.mark.asyncio + async def test_response_preserves_raw_parallel_fields(self, respx_mock, httpx_transport): + respx_mock.post("https://api.parallel.ai/v1/search").respond(json=MOCK_V1_RESPONSE) + + response = await litellm.asearch( + query="AI developments", + search_provider="parallel_ai", + ) + + dumped = response.model_dump() + assert dumped["search_id"] == "search_abc123" + assert dumped["session_id"] == "session_xyz" + assert dumped["parallel_usage"] == [{"name": "search_advanced", "count": 1}] + + first = response.results[0].model_dump() + assert first["excerpts"] == ["First excerpt.", "Second excerpt."] + + @pytest.mark.asyncio + async def test_response_normalizes_null_result_fields(self, respx_mock, httpx_transport): + response_payload = { + **MOCK_V1_RESPONSE, + "results": [{"url": None, "title": None, "publish_date": None, "excerpts": None}], + } + respx_mock.post("https://api.parallel.ai/v1/search").respond(json=response_payload) + + response = await litellm.asearch( + query="AI developments", + search_provider="parallel_ai", + ) + + assert len(response.results) == 1 + result = response.results[0] + assert result.url == "" + assert result.title == "" + assert result.snippet == "" + assert result.date is None + assert result.model_dump()["excerpts"] == () + + @pytest.mark.parametrize( + "mode,usage,max_results,expected_cost", + [ + ("turbo", [{"name": "sku_search", "count": 1}], None, 0.001), + ("fast", [{"name": "sku_search", "count": 1}], None, 0.001), + ("basic", [{"name": "sku_search", "count": 1}], None, 0.005), + ("advanced", [{"name": "sku_search", "count": 1}], None, 0.005), + ( + "basic", + [ + {"name": "sku_search", "count": 1}, + {"name": "sku_search_additional_results", "count": 2}, + ], + 20, + 0.007, + ), + ("basic", None, 20, 0.015), + ], + ) + @pytest.mark.asyncio + async def test_search_cost_uses_mode_and_provider_usage( + self, mode, usage, max_results, expected_cost, bundled_cost_map, respx_mock, httpx_transport + ): + response_payload = {**MOCK_V1_RESPONSE, "usage": usage} + respx_mock.post("https://api.parallel.ai/v1/search").respond(json=response_payload) + + response = await litellm.asearch( + query="AI developments", + search_provider="parallel_ai", + mode=mode, + max_results=max_results, + ) + + assert response._hidden_params["response_cost"] == pytest.approx(expected_cost) + + @pytest.mark.asyncio + async def test_search_cost_treats_keyword_queries_as_one_request( + self, bundled_cost_map, respx_mock, httpx_transport + ): + response_payload = { + **MOCK_V1_RESPONSE, + "usage": [{"name": "sku_search", "count": 1}], + } + respx_mock.post("https://api.parallel.ai/v1/search").respond(json=response_payload) + + response = await litellm.asearch( + query=["AI developments", "machine learning trends"], + search_provider="parallel_ai", + mode="basic", + ) + + assert response._hidden_params["response_cost"] == pytest.approx(0.005) + + @pytest.mark.asyncio + async def test_caller_cannot_supply_provider_usage(self, bundled_cost_map, respx_mock, httpx_transport): + """`_parallel_ai_usage` prices the request, so a caller must not be able to set it. + + The provider reports no usage here, which is the case where a caller-supplied + value would otherwise survive into the cost calculation. + """ + response_payload = {k: v for k, v in MOCK_V1_RESPONSE.items() if k != "usage"} + route = respx_mock.post("https://api.parallel.ai/v1/search").respond(json=response_payload) + + response = await litellm.asearch( + query="AI developments", + search_provider="parallel_ai", + mode="basic", + _parallel_ai_usage=[{"name": "sku_search", "count": 0}], + ) + + assert response._hidden_params["response_cost"] == pytest.approx(0.005) + assert "_parallel_ai_usage" not in json.loads(route.calls[0].request.content) diff --git a/tests/test_litellm/llms/parallel_ai/test_parallel_ai_search_gateway.py b/tests/test_litellm/llms/parallel_ai/test_parallel_ai_search_gateway.py new file mode 100644 index 00000000000..72c69fc622c --- /dev/null +++ b/tests/test_litellm/llms/parallel_ai/test_parallel_ai_search_gateway.py @@ -0,0 +1,191 @@ +"""Gateway coverage for Parallel AI Search.""" + +from __future__ import annotations + +from collections.abc import Iterator +from typing import Final +from unittest.mock import AsyncMock + +import httpx +import pytest +from fastapi.testclient import TestClient + +import litellm +from litellm import Router +from litellm.integrations.websearch_interception.handler import ( + WebSearchInterceptionLogger, +) +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler +from litellm.proxy import proxy_server +from litellm.proxy._types import UserAPIKeyAuth +from litellm.proxy.auth.user_api_key_auth import user_api_key_auth +from litellm.types.utils import LlmProviders + +PARALLEL_SEARCH_URL: Final = "https://api.parallel.ai/v1/search" + + +@pytest.fixture +def client() -> TestClient: + return TestClient(proxy_server.app, raise_server_exceptions=False) + + +@pytest.fixture +def auth_as() -> Iterator[None]: + async def _authorized_request() -> UserAPIKeyAuth: + return UserAPIKeyAuth( + api_key="hashed-sk-test", + user_id="parallel-test-user", + ) + + previous: Final = proxy_server.app.dependency_overrides.get(user_api_key_auth) + proxy_server.app.dependency_overrides[user_api_key_auth] = _authorized_request + try: + yield + finally: + if previous is None: + proxy_server.app.dependency_overrides.pop(user_api_key_auth, None) + else: + proxy_server.app.dependency_overrides[user_api_key_auth] = previous + + +def _parallel_search_body() -> dict[str, object]: + return { + "search_id": "search_parallel_gateway", + "results": [ + { + "url": "https://example.com/parallel", + "title": "Parallel result", + "publish_date": "2026-08-13", + "excerpts": ["First excerpt", "Second excerpt"], + } + ], + "usage": [{"name": "sku_search", "count": 1}], + } + + +def _parallel_router(mode: str = "turbo") -> Router: + return Router( + model_list=[], + search_tools=[ + { + "search_tool_name": "parallel-search", + "litellm_params": { + "search_provider": "parallel_ai", + "api_key": "parallel-search-key", + "mode": mode, + }, + } + ], + num_retries=0, + ) + + +def _mock_async_post( + monkeypatch, + *, + url: str, + response_body: dict[str, object], +) -> AsyncMock: + response = httpx.Response( + status_code=200, + json=response_body, + request=httpx.Request("POST", url), + ) + mock_post = AsyncMock(return_value=response) + monkeypatch.setattr(AsyncHTTPHandler, "post", mock_post) + return mock_post + + +def test_parallel_search_gateway_route(client, auth_as, monkeypatch): + """The named search route selects its configured Parallel Search tool. + + The tool-level `mode` must survive the router hop, so the upstream request + is sent as `turbo` rather than falling back to the adapter default. + """ + monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url="")) + monkeypatch.setattr(proxy_server, "llm_router", _parallel_router()) + mock_post = _mock_async_post( + monkeypatch, + url=PARALLEL_SEARCH_URL, + response_body=_parallel_search_body(), + ) + + response = client.post( + "/v1/search/parallel-search", + json={"query": "Parallel AI news", "max_results": 3}, + ) + + assert response.status_code == 200, response.text + assert response.json()["results"] == [ + { + "title": "Parallel result", + "url": "https://example.com/parallel", + "snippet": "First excerpt ... Second excerpt", + "date": "2026-08-13", + "last_updated": None, + "excerpts": ["First excerpt", "Second excerpt"], + } + ] + + request_kwargs = mock_post.await_args.kwargs + assert request_kwargs["url"] == PARALLEL_SEARCH_URL + assert request_kwargs["headers"]["x-api-key"] == "parallel-search-key" + assert request_kwargs["json"] == { + "objective": "Parallel AI news", + "search_queries": ["Parallel AI news"], + "mode": "turbo", + "advanced_settings": {"max_results": 3}, + } + + +@pytest.mark.asyncio +async def test_web_search_interception_executes_parallel_search(monkeypatch): + """An intercepted web-search call uses the configured Parallel Search tool.""" + monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url="")) + monkeypatch.setattr(proxy_server, "llm_router", _parallel_router(mode="fast")) + mock_post = _mock_async_post( + monkeypatch, + url=PARALLEL_SEARCH_URL, + response_body=_parallel_search_body(), + ) + logger = WebSearchInterceptionLogger( + enabled_providers=[LlmProviders.OPENAI], + search_tool_name="parallel-search", + ) + + plan = await logger.async_build_responses_agentic_loop_plan( + tools={ + "tool_calls": [ + { + "id": "fc_parallel", + "call_id": "fc_parallel", + "type": "function_call", + "name": "litellm_web_search", + "arguments": '{"query":"Parallel AI news"}', + "input": {"query": "Parallel AI news"}, + } + ] + }, + model="gpt-5", + messages=[{"role": "user", "content": "Research Parallel"}], + response=None, + optional_params={"tools": [{"type": "function", "name": "litellm_web_search"}]}, + logging_obj=None, + stream=False, + kwargs={"custom_llm_provider": "openai"}, + ) + + assert plan.run_agentic_loop is True + assert plan.request_patch is not None + assert plan.request_patch.messages[-1] == { + "type": "function_call_output", + "call_id": "fc_parallel", + "output": ( + "Title: Parallel result\nURL: https://example.com/parallel\nSnippet: First excerpt ... Second excerpt" + ), + } + + request_kwargs = mock_post.await_args.kwargs + assert request_kwargs["url"] == PARALLEL_SEARCH_URL + assert request_kwargs["headers"]["x-api-key"] == "parallel-search-key" + assert request_kwargs["json"]["mode"] == "fast" diff --git a/tests/test_litellm/llms/soniox/audio_transcription/test_soniox_audio_transcription_transformation.py b/tests/test_litellm/llms/soniox/audio_transcription/test_soniox_audio_transcription_transformation.py index 261efcb7b24..eadf870cb61 100644 --- a/tests/test_litellm/llms/soniox/audio_transcription/test_soniox_audio_transcription_transformation.py +++ b/tests/test_litellm/llms/soniox/audio_transcription/test_soniox_audio_transcription_transformation.py @@ -369,6 +369,191 @@ class TestRenderSonioxTokensAsSrt: assert "01:01:01,000" in result +def _subword_tokens(words, start_ms=0, subword_ms=150, inter_word_gap_ms=50): + tokens = [] + t = start_ms + for word in words: + halves = [word[: len(word) // 2], word[len(word) // 2 :]] if len(word) > 3 else [word] + for i, piece in enumerate(halves): + text = (" " + piece) if i == 0 else piece + tokens.append({"text": text, "start_ms": t, "end_ms": t + subword_ms}) + t += subword_ms + t += inter_word_gap_ms + return tokens, t + + +class TestCueGroupingAlignment: + def test_should_split_cue_on_silence_gap_with_exact_timestamps(self): + from litellm.llms.soniox.common_utils import render_soniox_tokens_as_srt + + before, t = _subword_tokens(["hello", "there"]) + after, _ = _subword_tokens(["welcome", "back"], start_ms=t + 5000) + result = render_soniox_tokens_as_srt(before + after) + cues = result.strip().split("\n\n") + assert len(cues) == 2 + assert "00:00:00,000 --> 00:00:00,650" in cues[0] + assert "hello there" in cues[0] + assert "00:00:05,700 --> 00:00:06,350" in cues[1] + assert "welcome back" in cues[1] + + def test_should_not_bridge_pause_shorter_than_old_duration_cap(self): + from litellm.llms.soniox.common_utils import render_soniox_tokens_as_srt + + before, t = _subword_tokens(["first", "part"]) + after, _ = _subword_tokens(["second", "part"], start_ms=t + 3000) + result = render_soniox_tokens_as_srt(before + after) + cues = result.strip().split("\n\n") + assert len(cues) == 2 + assert "first part" in cues[0] + assert "second part" in cues[1] + + def test_should_never_split_mid_word(self): + from litellm.llms.soniox.common_utils import render_soniox_tokens_as_srt + + tokens, _ = _subword_tokens(["hello"] * 20) + result = render_soniox_tokens_as_srt(tokens) + text_lines = [ + line for line in result.split("\n") if line and "-->" not in line and not line.isdigit() + ] + assert len(text_lines) >= 2 + for line in text_lines: + assert set(line.split()) == {"hello"} + + def test_should_split_after_sentence_final_punctuation(self): + from litellm.llms.soniox.common_utils import render_soniox_tokens_as_srt + + tokens, _ = _subword_tokens(["That", "is", "done.", "Next", "topic"]) + result = render_soniox_tokens_as_srt(tokens) + cues = result.strip().split("\n\n") + assert len(cues) == 2 + assert cues[0].endswith("That is done.") + assert cues[1].endswith("Next topic") + + def test_should_split_on_char_budget_at_word_boundary(self): + from litellm.llms.soniox.common_utils import render_soniox_tokens_as_srt + + tokens, _ = _subword_tokens(["wonderful"] * 12) + result = render_soniox_tokens_as_srt(tokens) + text_lines = [ + line for line in result.split("\n") if line and "-->" not in line and not line.isdigit() + ] + assert len(text_lines) >= 2 + for line in text_lines: + assert len(line) <= 84 + assert set(line.split()) == {"wonderful"} + + def test_should_exclude_untimestamped_translation_tokens_from_cues(self): + from litellm.llms.soniox.common_utils import render_soniox_tokens_as_srt + + tokens = [ + {"text": " Good", "start_ms": 0, "end_ms": 200, "translation_status": "original", "language": "en"}, + {"text": " Guten", "translation_status": "translation", "language": "de", "source_language": "en"}, + {"text": " morning.", "start_ms": 250, "end_ms": 600, "translation_status": "original", "language": "en"}, + ] + result = render_soniox_tokens_as_srt(tokens) + assert "Good morning." in result + assert "Guten" not in result + assert "00:00:00,000 --> 00:00:00,600" in result + + def test_should_split_before_word_whose_end_crosses_duration_cap(self): + from litellm.llms.soniox.common_utils import render_soniox_tokens_as_srt + + tokens = [{"text": " hm", "start_ms": i * 650, "end_ms": i * 650 + 600} for i in range(10)] + [ + {"text": " boom", "start_ms": 6900, "end_ms": 7600} + ] + result = render_soniox_tokens_as_srt(tokens) + cues = result.strip().split("\n\n") + assert len(cues) == 2 + assert "00:00:00,000 --> 00:00:06,450" in cues[0] + assert "00:00:06,900 --> 00:00:07,600" in cues[1] + assert cues[1].endswith("boom") + + def test_should_keep_untimestamped_word_in_cue(self): + from litellm.llms.soniox.common_utils import render_soniox_tokens_as_srt + + tokens = [ + {"text": " uh", "start_ms": None, "end_ms": None}, + {"text": " hello", "start_ms": 100, "end_ms": 500}, + ] + result = render_soniox_tokens_as_srt(tokens) + assert "uh hello" in result + assert "00:00:00,100 --> 00:00:00,500" in result + + +def _cue_texts(srt: str) -> list: + return [cue.split("\n", 2)[2] for cue in srt.strip().split("\n\n")] + + +class TestMultilingualCueGrouping: + def test_should_split_spaceless_chinese_on_width_budget(self): + from litellm.litellm_core_utils.audio_utils.subtitle_utils import _text_width + from litellm.llms.soniox.common_utils import render_soniox_tokens_as_srt + + tokens = [{"text": "你好", "start_ms": i * 100, "end_ms": i * 100 + 90} for i in range(60)] + result = render_soniox_tokens_as_srt(tokens) + texts = _cue_texts(result) + assert len(texts) >= 3 + for text in texts: + assert _text_width(text) <= 84 + assert set(text) <= {"你", "好"} + + def test_should_split_japanese_after_sentence_end_and_keep_punctuation_attached(self): + from litellm.llms.soniox.common_utils import render_soniox_tokens_as_srt + + tokens = [ + {"text": "今日は", "start_ms": 0, "end_ms": 300}, + {"text": "いい", "start_ms": 300, "end_ms": 500}, + {"text": "天気です", "start_ms": 500, "end_ms": 900}, + {"text": "。", "start_ms": 900, "end_ms": 950}, + {"text": "明日も", "start_ms": 1000, "end_ms": 1300}, + {"text": "晴れ", "start_ms": 1300, "end_ms": 1500}, + ] + texts = _cue_texts(render_soniox_tokens_as_srt(tokens)) + assert texts == ["今日はいい天気です。", "明日も晴れ"] + + def test_should_split_arabic_after_arabic_question_mark(self): + from litellm.llms.soniox.common_utils import render_soniox_tokens_as_srt + + tokens = [ + {"text": " كيف", "start_ms": 0, "end_ms": 300}, + {"text": " حالك؟", "start_ms": 300, "end_ms": 700}, + {"text": " أنا", "start_ms": 800, "end_ms": 1000}, + {"text": " بخير", "start_ms": 1000, "end_ms": 1300}, + ] + texts = _cue_texts(render_soniox_tokens_as_srt(tokens)) + assert texts == ["كيف حالك؟", "أنا بخير"] + + def test_should_split_after_devanagari_and_urdu_terminators(self): + from litellm.llms.soniox.common_utils import render_soniox_tokens_as_srt + + tokens = [ + {"text": " नमस्ते।", "start_ms": 0, "end_ms": 400}, + {"text": " آپ", "start_ms": 500, "end_ms": 700}, + {"text": " ٹھیک۔", "start_ms": 700, "end_ms": 1100}, + {"text": " शुभ", "start_ms": 1200, "end_ms": 1400}, + ] + texts = _cue_texts(render_soniox_tokens_as_srt(tokens)) + assert texts == ["नमस्ते।", "آپ ٹھیک۔", "शुभ"] + + def test_should_split_russian_after_sentence_end(self): + from litellm.llms.soniox.common_utils import render_soniox_tokens_as_srt + + tokens = [ + {"text": " Как", "start_ms": 0, "end_ms": 200}, + {"text": " дела?", "start_ms": 200, "end_ms": 600}, + {"text": " Хорошо.", "start_ms": 700, "end_ms": 1200}, + ] + texts = _cue_texts(render_soniox_tokens_as_srt(tokens)) + assert texts == ["Как дела?", "Хорошо."] + + def test_should_not_split_latin_text_within_width_budget(self): + from litellm.llms.soniox.common_utils import render_soniox_tokens_as_srt + + tokens = [{"text": f" word{i}", "start_ms": i * 100, "end_ms": i * 100 + 90} for i in range(12)] + texts = _cue_texts(render_soniox_tokens_as_srt(tokens)) + assert len(texts) == 1 + + class TestRenderSonioxTokensAsVtt: def test_should_render_basic_vtt_with_header(self): from litellm.llms.soniox.common_utils import render_soniox_tokens_as_vtt diff --git a/tests/test_litellm/llms/vertex_ai/audio_transcription/test_vertex_ai_gemini_transcribe_transformation.py b/tests/test_litellm/llms/vertex_ai/audio_transcription/test_vertex_ai_gemini_transcribe_transformation.py new file mode 100644 index 00000000000..eadd87d9c92 --- /dev/null +++ b/tests/test_litellm/llms/vertex_ai/audio_transcription/test_vertex_ai_gemini_transcribe_transformation.py @@ -0,0 +1,345 @@ +import base64 +import json +import os + +import httpx +import pytest + +import litellm +from litellm.llms.vertex_ai.audio_transcription.gemini_transcribe_transformation import ( + VertexGeminiAudioTranscriptionConfig, +) +from litellm.llms.vertex_ai.audio_transcription.transformation import ( + VertexAIAudioTranscriptionConfig, +) +from litellm.llms.vertex_ai.common_utils import VertexAIError +from litellm.types.utils import LlmProviders, TranscriptionUsageTokensObject +from litellm.utils import ProviderConfigManager, get_optional_params_transcription + +AUDIO_BYTES = b"fake-audio-bytes" +TRANSCRIPT_TEXT = ( + "Four score and seven years ago our fathers brought forth on this continent, a new nation, " + "conceived in Liberty, and dedicated to the proposition that all men are created equal. " + "Now we are engaged in a great civil war, testing whether that nation, or any nation so " + "conceived and so dedicated, can long endure." +) +GENERATE_CONTENT_RESPONSE = { + "candidates": [ + { + "content": { + "role": "model", + "parts": [ + { + "text": TRANSCRIPT_TEXT, + "audioTranscription": {"text": TRANSCRIPT_TEXT}, + } + ], + }, + "finishReason": "STOP", + } + ], + "usageMetadata": { + "promptTokenCount": 440, + "candidatesTokenCount": 62, + "totalTokenCount": 502, + "trafficType": "ON_DEMAND", + "promptTokensDetails": [{"modality": "AUDIO", "tokenCount": 440}], + "candidatesTokensDetails": [{"modality": "TEXT", "tokenCount": 62}], + }, + "modelVersion": "gemini-3.5-transcribe-preview", + "createTime": "2026-08-29T07:25:27.591648Z", + "responseId": "Z4mSaqCOJL-O4_UP0aSh4Aw", +} + + +@pytest.fixture +def config(): + return VertexGeminiAudioTranscriptionConfig() + + +class TestProviderRouting: + @pytest.mark.parametrize( + "model", + [ + "gemini-3.5-transcribe-preview", + "gemini-3.5-transcribe-live-preview", + "vertex_ai/gemini-3.5-transcribe-preview", + ], + ) + def test_gemini_transcribe_models_use_generate_content_config(self, model): + provider_config = ProviderConfigManager.get_provider_audio_transcription_config( + model=model, + provider=LlmProviders.VERTEX_AI, + ) + assert isinstance(provider_config, VertexGeminiAudioTranscriptionConfig) + + @pytest.mark.parametrize("model", ["chirp_2", "chirp_3", "long-form", "gemini-2.5-flash"]) + def test_other_vertex_models_keep_speech_to_text_config(self, model): + provider_config = ProviderConfigManager.get_provider_audio_transcription_config( + model=model, + provider=LlmProviders.VERTEX_AI, + ) + assert isinstance(provider_config, VertexAIAudioTranscriptionConfig) + assert not isinstance(provider_config, VertexGeminiAudioTranscriptionConfig) + + +class TestGetCompleteUrl: + @pytest.fixture(autouse=True) + def _clear_ambient_vertex_location(self, monkeypatch): + monkeypatch.delenv("VERTEXAI_LOCATION", raising=False) + monkeypatch.delenv("VERTEX_LOCATION", raising=False) + + def test_defaults_to_global_location(self, config): + url = config.get_complete_url( + api_base=None, + api_key=None, + model="gemini-3.5-transcribe-preview", + optional_params={}, + litellm_params={"vertex_project": "test-project"}, + ) + assert url == ( + "https://aiplatform.googleapis.com/v1/projects/test-project/locations/global" + "/publishers/google/models/gemini-3.5-transcribe-preview:generateContent" + ) + + def test_explicit_location_is_honored(self, config): + url = config.get_complete_url( + api_base=None, + api_key=None, + model="gemini-3.5-transcribe-preview", + optional_params={}, + litellm_params={"vertex_project": "test-project", "vertex_location": "us-central1"}, + ) + assert url == ( + "https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1" + "/publishers/google/models/gemini-3.5-transcribe-preview:generateContent" + ) + + def test_model_prefix_is_stripped(self, config): + url = config.get_complete_url( + api_base=None, + api_key=None, + model="vertex_ai/gemini-3.5-transcribe-preview", + optional_params={}, + litellm_params={"vertex_project": "test-project"}, + ) + assert "/models/gemini-3.5-transcribe-preview:generateContent" in url + assert "vertex_ai/" not in url + + def test_api_base_override(self, config): + url = config.get_complete_url( + api_base="http://localhost:8080/", + api_key=None, + model="gemini-3.5-transcribe-preview", + optional_params={}, + litellm_params={"vertex_project": "test-project"}, + ) + assert url == ( + "http://localhost:8080/v1/projects/test-project/locations/global" + "/publishers/google/models/gemini-3.5-transcribe-preview:generateContent" + ) + + @pytest.mark.parametrize("malicious_location", ["attacker.example/", "evil.com#", "US", "us/../.."]) + def test_malicious_location_is_rejected(self, config, malicious_location): + with pytest.raises(VertexAIError): + config.get_complete_url( + api_base=None, + api_key=None, + model="gemini-3.5-transcribe-preview", + optional_params={}, + litellm_params={"vertex_project": "test-project", "vertex_location": malicious_location}, + ) + + @pytest.mark.parametrize("malicious_project", ["proj/../../locations", "proj#frag", "proj?a=b", "proj space"]) + def test_malicious_project_is_rejected(self, config, malicious_project): + with pytest.raises(VertexAIError): + config.get_complete_url( + api_base=None, + api_key=None, + model="gemini-3.5-transcribe-preview", + optional_params={}, + litellm_params={"vertex_project": malicious_project}, + ) + + +class TestTransformRequest: + def test_request_body_shape(self, config): + request_data = config.transform_audio_transcription_request( + model="gemini-3.5-transcribe-preview", + audio_file=AUDIO_BYTES, + optional_params={}, + litellm_params={}, + ) + assert request_data.files is None + assert request_data.data == { + "contents": ( + { + "role": "user", + "parts": ( + { + "inlineData": { + "mimeType": "audio/wav", + "data": base64.b64encode(AUDIO_BYTES).decode("utf-8"), + } + }, + ), + }, + ), + "generationConfig": {"audioTranscriptionConfig": {}}, + } + + @pytest.mark.parametrize( + "language,expected_language_codes", + [ + ("en", ("en-US",)), + ("en-US", ("en-US",)), + ("fr", ("fr-FR",)), + ], + ) + def test_language_param_maps_to_language_codes(self, config, language, expected_language_codes): + request_data = config.transform_audio_transcription_request( + model="gemini-3.5-transcribe-preview", + audio_file=AUDIO_BYTES, + optional_params={"language": language}, + litellm_params={}, + ) + audio_config = request_data.data["generationConfig"]["audioTranscriptionConfig"] + assert audio_config["languageCodes"] == expected_language_codes + + def test_body_round_trips_through_json(self, config): + request_data = config.transform_audio_transcription_request( + model="gemini-3.5-transcribe-preview", + audio_file=AUDIO_BYTES, + optional_params={"language": "en"}, + litellm_params={}, + ) + round_tripped = json.loads(json.dumps(request_data.data)) + assert round_tripped["generationConfig"] == {"audioTranscriptionConfig": {"languageCodes": ["en-US"]}} + assert round_tripped["contents"][0]["role"] == "user" + + +class TestTransformResponse: + def test_generate_content_response(self, config): + raw_response = httpx.Response(status_code=200, json=GENERATE_CONTENT_RESPONSE) + response = config.transform_audio_transcription_response(raw_response) + assert response.text == TRANSCRIPT_TEXT + assert response["task"] == "transcribe" + assert isinstance(response.usage, TranscriptionUsageTokensObject) + assert response.usage.input_tokens == 440 + assert response.usage.output_tokens == 62 + assert response.usage.total_tokens == 502 + assert response.usage.input_token_details.audio_tokens == 440 + assert response.usage.input_token_details.text_tokens == 0 + + def test_multi_part_texts_are_joined(self, config): + raw_response = httpx.Response( + status_code=200, + json={ + "candidates": [ + {"content": {"role": "model", "parts": [{"text": "Hello world."}, {"text": "How are you?"}]}} + ], + "usageMetadata": {"promptTokenCount": 10, "candidatesTokenCount": 5, "totalTokenCount": 15}, + }, + ) + response = config.transform_audio_transcription_response(raw_response) + assert response.text == "Hello world. How are you?" + + def test_empty_candidates_returns_empty_text(self, config): + raw_response = httpx.Response(status_code=200, json={}) + response = config.transform_audio_transcription_response(raw_response) + assert response.text == "" + assert response.usage is None + + def test_non_json_body_raises(self, config): + raw_response = httpx.Response(status_code=200, text="not json") + with pytest.raises(VertexAIError, match="non-JSON"): + config.transform_audio_transcription_response(raw_response) + + +class TestValidateEnvironment: + def test_sets_oauth_headers(self): + class StubbedConfig(VertexGeminiAudioTranscriptionConfig): + def _ensure_access_token(self, credentials, project_id, custom_llm_provider): + return "fake-token", "resolved-project" + + headers = StubbedConfig().validate_environment( + headers={}, + model="gemini-3.5-transcribe-preview", + messages=[], + optional_params={}, + litellm_params={"vertex_project": "resolved-project"}, + ) + assert headers["Authorization"] == "Bearer fake-token" + assert headers["x-goog-user-project"] == "resolved-project" + assert headers["Content-Type"] == "application/json" + + +class TestOptionalParams: + def test_language_and_json_response_format_pass_through(self): + optional_params = get_optional_params_transcription( + model="gemini-3.5-transcribe-preview", + custom_llm_provider="vertex_ai", + language="fr-FR", + response_format="json", + ) + assert optional_params["language"] == "fr-FR" + assert optional_params["response_format"] == "json" + + @pytest.mark.parametrize("response_format", ["verbose_json", "srt", "vtt"]) + def test_unsupported_response_format_raises(self, response_format): + with pytest.raises(litellm.utils.UnsupportedParamsError, match="response_format"): + get_optional_params_transcription( + model="gemini-3.5-transcribe-preview", + custom_llm_provider="vertex_ai", + response_format=response_format, + ) + + @pytest.mark.parametrize("response_format", ["verbose_json", "srt", "vtt"]) + def test_unsupported_response_format_dropped_with_drop_params(self, response_format): + optional_params = get_optional_params_transcription( + model="gemini-3.5-transcribe-preview", + custom_llm_provider="vertex_ai", + language="fr-FR", + response_format=response_format, + drop_params=True, + ) + assert "response_format" not in optional_params + assert optional_params["language"] == "fr-FR" + + +class TestModelCostEntry: + REPO_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../../..")) + + @pytest.mark.parametrize( + "cost_map_path", + [ + "model_prices_and_context_window.json", + "litellm/model_prices_and_context_window_backup.json", + ], + ) + def test_transcribe_preview_pricing(self, cost_map_path): + with open(os.path.join(self.REPO_ROOT, cost_map_path)) as f: + entry = json.load(f)["vertex_ai/gemini-3.5-transcribe-preview"] + assert entry["mode"] == "audio_transcription" + assert entry["litellm_provider"] == "vertex_ai" + assert entry["input_cost_per_audio_token"] == pytest.approx(2.5e-06) + assert entry["input_cost_per_token"] == pytest.approx(2.5e-06) + assert entry["output_cost_per_token"] == pytest.approx(1.2e-05) + assert entry["supported_endpoints"] == ["/v1/audio/transcriptions"] + + @pytest.mark.parametrize( + "cost_map_path", + [ + "model_prices_and_context_window.json", + "litellm/model_prices_and_context_window_backup.json", + ], + ) + def test_transcribe_live_preview_pricing(self, cost_map_path): + with open(os.path.join(self.REPO_ROOT, cost_map_path)) as f: + entry = json.load(f)["vertex_ai/gemini-3.5-transcribe-live-preview"] + assert entry["mode"] == "audio_transcription" + assert entry["litellm_provider"] == "vertex_ai" + assert entry["input_cost_per_audio_token"] == pytest.approx(3.5e-06) + assert entry["input_cost_per_token"] == pytest.approx(3.5e-06) + assert entry["output_cost_per_token"] == pytest.approx(2.1e-05) + assert entry["supported_endpoints"] == ["/v1/realtime"] diff --git a/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py b/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py index cc923f05831..d1d751989ea 100644 --- a/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py +++ b/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py @@ -195,6 +195,22 @@ def test_set_schema_property_ordering_with_excessive_nesting(): set_schema_property_ordering(schema) +def test_set_schema_property_ordering_skips_non_dict_property_values(): + """Non-dict property values must be skipped, not recursed into (they used to raise).""" + schema = { + "properties": { + "a": "hello", + "b": {"type": "string"}, + "c": ["x"], + "d": "a string mentioning items", + } + } + + result = set_schema_property_ordering(schema) + + assert result["propertyOrdering"] == ["a", "b", "c", "d"] + + def test_build_vertex_schema(): """Test build_vertex_schema with a sample schema""" from litellm.llms.vertex_ai.common_utils import _build_vertex_schema diff --git a/tests/test_litellm/llms/vertex_ai/test_vertex_llm_base.py b/tests/test_litellm/llms/vertex_ai/test_vertex_llm_base.py index 29d22e844a5..a4d67606698 100644 --- a/tests/test_litellm/llms/vertex_ai/test_vertex_llm_base.py +++ b/tests/test_litellm/llms/vertex_ai/test_vertex_llm_base.py @@ -982,6 +982,116 @@ class TestVertexBase: assert result_url == f"{gateway_api_base}:embedContent" + def test_check_custom_proxy_vertex_api_base_with_version_path_grafts_default_path(self): + vertex_base = VertexBase() + + _, result_url = vertex_base._check_custom_proxy( + api_base="https://aiplatform.googleapis.com/v1beta1", + custom_llm_provider="vertex_ai", + gemini_api_key=None, + endpoint="generateContent", + stream=None, + auth_header="Bearer token123", + url="https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:generateContent", + model="gemini-3.5-flash-lite", + ) + + assert ( + result_url + == "https://aiplatform.googleapis.com/v1beta1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:generateContent" + ) + + def test_check_custom_proxy_vertex_api_base_with_version_path_trailing_slash_grafts_default_path(self): + vertex_base = VertexBase() + + _, result_url = vertex_base._check_custom_proxy( + api_base="https://internal-gateway.example.com/v1/", + custom_llm_provider="vertex_ai", + gemini_api_key=None, + endpoint="generateContent", + stream=None, + auth_header="Bearer token123", + url="https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:generateContent", + model="gemini-3.5-flash-lite", + ) + + assert ( + result_url + == "https://internal-gateway.example.com/v1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:generateContent" + ) + + def test_check_custom_proxy_vertex_api_base_with_version_path_and_query_grafts_before_query(self): + vertex_base = VertexBase() + + _, result_url = vertex_base._check_custom_proxy( + api_base="https://internal-gateway.example.com/v1beta1?key=abc", + custom_llm_provider="vertex_ai", + gemini_api_key=None, + endpoint="generateContent", + stream=None, + auth_header="Bearer token123", + url="https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:generateContent", + model="gemini-3.5-flash-lite", + ) + + assert ( + result_url + == "https://internal-gateway.example.com/v1beta1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:generateContent?key=abc" + ) + + def test_check_custom_proxy_vertex_api_base_with_version_path_and_query_streaming_appends_alt_sse(self): + vertex_base = VertexBase() + + _, result_url = vertex_base._check_custom_proxy( + api_base="https://internal-gateway.example.com/v1beta1?key=abc", + custom_llm_provider="vertex_ai", + gemini_api_key=None, + endpoint="streamGenerateContent", + stream=True, + auth_header="Bearer token123", + url="https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:streamGenerateContent", + model="gemini-3.5-flash-lite", + ) + + assert ( + result_url + == "https://internal-gateway.example.com/v1beta1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:streamGenerateContent?key=abc&alt=sse" + ) + + def test_check_custom_proxy_vertex_api_base_with_non_version_path_keeps_endpoint_append(self): + vertex_base = VertexBase() + gateway_api_base = "https://gateway.example.com/vertex-proxy" + + _, result_url = vertex_base._check_custom_proxy( + api_base=gateway_api_base, + custom_llm_provider="vertex_ai", + gemini_api_key=None, + endpoint="generateContent", + stream=None, + auth_header="Bearer token123", + url="https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/gemini-3.5-flash-lite:generateContent", + model="gemini-3.5-flash-lite", + ) + + assert result_url == f"{gateway_api_base}:generateContent" + + def test_check_custom_proxy_vertex_api_base_without_projects_in_default_url_keeps_endpoint_append(self): + vertex_base = VertexBase() + gemma_api_base = "https://example.com/custom/gemma-deployment" + + _, result_url = vertex_base._check_custom_proxy( + api_base=gemma_api_base, + custom_llm_provider="vertex_ai", + gemini_api_key=None, + endpoint="predict", + stream=False, + auth_header=None, + url=gemma_api_base, + model="gemma-3-27b-it", + ) + + assert result_url == f"{gemma_api_base}:predict" + def test_check_custom_proxy_vertex_bare_host_streaming_keeps_single_alt_sse(self): vertex_base = VertexBase() diff --git a/tests/test_litellm/llms/vertex_ai/text_to_speech/test_transformation.py b/tests/test_litellm/llms/vertex_ai/text_to_speech/test_transformation.py index 05da22a73fd..fba337b5f2c 100644 --- a/tests/test_litellm/llms/vertex_ai/text_to_speech/test_transformation.py +++ b/tests/test_litellm/llms/vertex_ai/text_to_speech/test_transformation.py @@ -1,3 +1,4 @@ +import base64 from unittest.mock import MagicMock, Mock, patch import httpx @@ -126,6 +127,48 @@ class TestVertexAITextToSpeechConfig: assert voice_dict == voice_input +@pytest.mark.parametrize( + ("audio", "expected_content_type"), + [ + (b"RIFF\x24\x00\x00\x00WAVEfmt \x10\x00\x00\x00", "audio/wav"), + (b"\xff\xfb\x90\x64\x00\x00\x00\x00", "audio/mpeg"), + (b"OggS" + b"\x00" * 24 + b"OpusHead", "audio/opus"), + (b"fLaC\x00\x00\x00\x22", "audio/flac"), + ], +) +def test_transform_text_to_speech_response_labels_content_type(audio, expected_content_type): + raw_response = httpx.Response( + status_code=200, + json={"audioContent": base64.b64encode(audio).decode()}, + ) + + result = VertexAITextToSpeechConfig().transform_text_to_speech_response( + model="vertex_ai/chirp", + raw_response=raw_response, + logging_obj=MagicMock(), + ) + + assert result.response.headers["content-type"] == expected_content_type + assert result.response.content == audio + + +def test_transform_text_to_speech_response_leaves_unknown_bytes_unlabeled(): + raw_pcm = b"\x00\x01\x02\x03\x04\x05\x06\x07" + raw_response = httpx.Response( + status_code=200, + json={"audioContent": base64.b64encode(raw_pcm).decode()}, + ) + + result = VertexAITextToSpeechConfig().transform_text_to_speech_response( + model="vertex_ai/chirp", + raw_response=raw_response, + logging_obj=MagicMock(), + ) + + assert "content-type" not in result.response.headers + assert result.response.content == raw_pcm + + @patch("litellm.llms.custom_httpx.llm_http_handler.HTTPHandler.post") @patch.object(VertexAITextToSpeechConfig, "_ensure_access_token") @patch.object(VertexAITextToSpeechConfig, "_get_token_and_url") diff --git a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_transformation.py b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_transformation.py index 552ca98441f..9419f88a981 100644 --- a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_transformation.py +++ b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_transformation.py @@ -727,3 +727,28 @@ def test_sanitize_strips_effort_for_haiku_45(): data = {"output_config": {"effort": "high"}} sanitize_vertex_anthropic_output_params(data, "vertex_ai/claude-opus-4-6") assert data["output_config"] == {"effort": "high"} + + +def test_vertex_ai_fable_5_1_response_format_uses_native_output_format(local_model_cost_map): + """Regression: Fable 5.1 rejects forced tool use, so the vertex map entry + advertises native structured output and ``response_format`` must map to + ``output_format`` instead of the tool-based path's forced tool_choice.""" + config = VertexAIAnthropicConfig() + response_format = { + "type": "json_schema", + "json_schema": { + "name": "test_schema", + "schema": {"type": "object", "properties": {"result": {"type": "string"}}}, + }, + } + + result_params = config.map_openai_params( + non_default_params={"response_format": response_format}, + optional_params={}, + model="claude-fable-5-1", + drop_params=False, + ) + + assert "output_format" in result_params + assert "tool_choice" not in result_params + assert "tools" not in result_params diff --git a/tests/test_litellm/llms/vertex_ai/videos/test_vertex_video_transformation.py b/tests/test_litellm/llms/vertex_ai/videos/test_vertex_video_transformation.py index 862969abbc2..6ba8706b0d8 100644 --- a/tests/test_litellm/llms/vertex_ai/videos/test_vertex_video_transformation.py +++ b/tests/test_litellm/llms/vertex_ai/videos/test_vertex_video_transformation.py @@ -4,13 +4,17 @@ Tests for Vertex AI (Veo) video generation transformation. import base64 import json -import os -from unittest.mock import MagicMock, Mock, patch +from collections.abc import Mapping +from pathlib import Path +from typing import cast +from unittest.mock import Mock, patch import httpx import pytest import litellm +from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider +from litellm.llms.openai.cost_calculation import video_generation_cost from litellm.llms.vertex_ai.videos.transformation import ( VertexAIVideoConfig, _convert_image_to_vertex_format, @@ -18,6 +22,21 @@ from litellm.llms.vertex_ai.videos.transformation import ( from litellm.types.router import GenericLiteLLMParams from litellm.types.videos.main import VideoObject +VEO_31_LITE_VERTEX_MODEL = "vertex_ai/veo-3.1-lite-generate-001" +ROOT_MODEL_COST_PATH = ( + Path(__file__).parents[5] / "model_prices_and_context_window.json" +) +BACKUP_MODEL_COST_PATH = ( + Path(__file__).parents[5] + / "litellm" + / "model_prices_and_context_window_backup.json" +) +ModelCostMap = Mapping[str, Mapping[str, object]] + + +def _load_model_cost_map(path: Path) -> ModelCostMap: + return cast(ModelCostMap, json.loads(path.read_text())) + class TestVertexAIVideoConfig: """Test VertexAIVideoConfig transformation class.""" @@ -117,6 +136,56 @@ class TestVertexAIVideoConfig: # Should NOT include endpoint assert not url.endswith(":predictLongRunning") + def test_veo_31_lite_model_cost_entries_match_pricing(self): + for path in (ROOT_MODEL_COST_PATH, BACKUP_MODEL_COST_PATH): + model_cost = _load_model_cost_map(path) + info = model_cost.get(VEO_31_LITE_VERTEX_MODEL) + + assert info is not None, f"{VEO_31_LITE_VERTEX_MODEL} missing from {path}" + assert info["litellm_provider"] == "vertex_ai-video-models" + assert info["mode"] == "video_generation" + assert info["max_input_tokens"] == 1024 + assert info["output_cost_per_second"] == 0.05 + assert info["output_cost_per_second_1080p"] == 0.08 + assert info["supported_modalities"] == ["text", "image"] + + def test_veo_31_lite_provider_routing_from_local_model_map( + self, monkeypatch: pytest.MonkeyPatch + ): + model_cost = _load_model_cost_map(BACKUP_MODEL_COST_PATH) + vertex_video_models = { + model_name.removeprefix("vertex_ai/") + for model_name, info in model_cost.items() + if info.get("litellm_provider") == "vertex_ai-video-models" + } + monkeypatch.setattr(litellm, "vertex_ai_video_models", vertex_video_models) + + model, custom_llm_provider, _, _ = get_llm_provider( + model="veo-3.1-lite-generate-001" + ) + + assert model == "veo-3.1-lite-generate-001" + assert custom_llm_provider == "vertex_ai" + + def test_veo_31_lite_cost_uses_resolution_tiers(self): + model_cost = _load_model_cost_map(BACKUP_MODEL_COST_PATH) + model_info = model_cost[VEO_31_LITE_VERTEX_MODEL] + + assert video_generation_cost( + model=VEO_31_LITE_VERTEX_MODEL, + duration_seconds=10.0, + custom_llm_provider="vertex_ai", + model_info=dict(model_info), + video_resolution="720p", + ) == pytest.approx(0.5) + assert video_generation_cost( + model=VEO_31_LITE_VERTEX_MODEL, + duration_seconds=10.0, + custom_llm_provider="vertex_ai", + model_info=dict(model_info), + video_resolution="1080p", + ) == pytest.approx(0.8) + def test_transform_video_create_request(self): """Test transformation of video creation request.""" prompt = "A cat playing with a ball of yarn" @@ -210,6 +279,95 @@ class TestVertexAIVideoConfig: assert mapped["durationSeconds"] == 8 assert mapped["aspectRatio"] == "16:9" + assert "resolution" not in mapped + + @pytest.mark.parametrize( + ("model", "size", "expected_resolution"), + ( + (VEO_31_LITE_VERTEX_MODEL, "1280x720", "720p"), + ( + VEO_31_LITE_VERTEX_MODEL.removeprefix("vertex_ai/"), + "1920x1080", + "1080p", + ), + ), + ) + def test_map_openai_size_to_resolution_for_resolution_tier_model( + self, + model: str, + size: str, + expected_resolution: str, + monkeypatch: pytest.MonkeyPatch, + ): + model_cost = _load_model_cost_map(BACKUP_MODEL_COST_PATH) + monkeypatch.setitem( + litellm.model_cost, + VEO_31_LITE_VERTEX_MODEL, + dict(model_cost[VEO_31_LITE_VERTEX_MODEL]), + ) + + mapped = self.config.map_openai_params( + video_create_optional_params={"size": size}, + model=model, + drop_params=False, + ) + + assert mapped["aspectRatio"] == "16:9" + assert mapped["resolution"] == expected_resolution + + def test_map_openai_size_does_not_infer_resolution_for_veo_2(self): + mapped = self.config.map_openai_params( + video_create_optional_params={"size": "1920x1080"}, + model="vertex_ai/veo-2.0-generate-001", + drop_params=False, + ) + + assert mapped["aspectRatio"] == "16:9" + assert "resolution" not in mapped + + def test_map_openai_size_does_not_infer_resolution_for_existing_veo_3( + self, monkeypatch: pytest.MonkeyPatch + ): + model = "veo-3.1-generate-001" + model_key = f"vertex_ai/{model}" + model_cost = _load_model_cost_map(BACKUP_MODEL_COST_PATH) + monkeypatch.setitem(litellm.model_cost, model_key, dict(model_cost[model_key])) + + mapped = self.config.map_openai_params( + video_create_optional_params={"size": "1920x1080"}, + model=model, + drop_params=False, + ) + + assert mapped["aspectRatio"] == "16:9" + assert "resolution" not in mapped + + def test_map_openai_size_does_not_override_provider_resolution(self): + mapped = self.config.map_openai_params( + video_create_optional_params={ + "size": "1920x1080", + "parameters": {"resolution": "720p"}, + }, + model=VEO_31_LITE_VERTEX_MODEL, + drop_params=False, + ) + + assert mapped["aspectRatio"] == "16:9" + assert "resolution" not in mapped + assert mapped["parameters"] == {"resolution": "720p"} + + def test_map_openai_size_does_not_override_direct_resolution(self): + mapped = self.config.map_openai_params( + video_create_optional_params={ + "size": "1920x1080", + "resolution": "720p", + }, + model=VEO_31_LITE_VERTEX_MODEL, + drop_params=False, + ) + + assert mapped["aspectRatio"] == "16:9" + assert mapped["resolution"] == "720p" def test_map_openai_params_default_duration(self): """Test that durationSeconds is omitted when not provided.""" diff --git a/tests/test_litellm/llms/xai/test_xai_cost_calculator.py b/tests/test_litellm/llms/xai/test_xai_cost_calculator.py index 55e28dff81d..92e76fd18ab 100644 --- a/tests/test_litellm/llms/xai/test_xai_cost_calculator.py +++ b/tests/test_litellm/llms/xai/test_xai_cost_calculator.py @@ -51,8 +51,8 @@ class TestXAICostCalculator: # Expected costs for grok-3-mini: # Input: 12 tokens * $3e-7 = $0.0000036 # Output: 125 tokens * $5e-7 = $0.0000625 - expected_prompt_cost = 12 * 3e-7 - expected_completion_cost = 125 * 5e-7 + expected_prompt_cost = 12 * 1.25e-6 + expected_completion_cost = 125 * 2.5e-6 assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) @@ -77,8 +77,8 @@ class TestXAICostCalculator: # Expected costs for grok-3-mini: # Input: 12 tokens * $3e-7 = $0.0000036 # Completion: (125 + 949) tokens * $5e-7 = $0.000537 - expected_prompt_cost = 12 * 3e-7 - expected_completion_cost = (125 + 949) * 5e-7 + expected_prompt_cost = 12 * 1.25e-6 + expected_completion_cost = (125 + 949) * 2.5e-6 assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) @@ -104,8 +104,8 @@ class TestXAICostCalculator: # Input: 12 tokens * $3e-7 = $0.0000036 # Completion: (125 + 949) tokens * $5e-7 = $0.000537 # Note: text_tokens field is ignored, only completion_tokens + reasoning_tokens matters - expected_prompt_cost = 12 * 3e-7 - expected_completion_cost = (125 + 949) * 5e-7 + expected_prompt_cost = 12 * 1.25e-6 + expected_completion_cost = (125 + 949) * 2.5e-6 assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) @@ -127,11 +127,12 @@ class TestXAICostCalculator: prompt_cost, completion_cost = cost_per_token(model="grok-4", usage=usage) - # Expected costs for grok-4: - # Input: 10 tokens * $3e-6 = $0.00003 - # Completion: (200 + 150) tokens * $1.5e-5 = $0.00525 - expected_prompt_cost = 10 * 3e-6 - expected_completion_cost = (200 + 150) * 1.5e-5 + # grok-4 was retired on 2026-05-15 and now redirects to grok-4.3, so it bills + # at grok-4.3's rates: + # Input: 10 tokens * $1.25e-6 + # Completion: (200 + 150) tokens * $2.5e-6 + expected_prompt_cost = 10 * 1.25e-6 + expected_completion_cost = (200 + 150) * 2.5e-6 assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) @@ -158,8 +159,8 @@ class TestXAICostCalculator: # Expected costs for grok-3-fast-beta: # Input: 20 tokens * $5e-6 = $0.0001 # Completion: (300 + 200) tokens * $2.5e-5 = $0.0125 - expected_prompt_cost = 20 * 5e-6 - expected_completion_cost = (300 + 200) * 2.5e-5 + expected_prompt_cost = 20 * 1.25e-6 + expected_completion_cost = (300 + 200) * 2.5e-6 assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) @@ -185,46 +186,34 @@ class TestXAICostCalculator: # Expected costs: # Input: 12 tokens * $3e-7 = $0.0000036 # Completion: (50 + 100) tokens * $5e-7 = $0.000075 - expected_prompt_cost = 12 * 3e-7 - expected_completion_cost = (50 + 100) * 5e-7 + expected_prompt_cost = 12 * 1.25e-6 + expected_completion_cost = (50 + 100) * 2.5e-6 assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) - def test_tiered_pricing_above_128k_tokens(self): - """Test tiered pricing for tokens above 128k.""" - # Test with grok-4-fast-reasoning which has tiered pricing + def test_tiered_pricing_above_200k_tokens(self): usage = Usage( - prompt_tokens=150000, # Above 128k threshold - completion_tokens=100000, # Above 128k threshold - total_tokens=300000, + prompt_tokens=250000, + completion_tokens=100000, + total_tokens=400000, completion_tokens_details=CompletionTokensDetailsWrapper( accepted_prediction_tokens=0, audio_tokens=0, - reasoning_tokens=50000, # Total completion tokens = 100000 + 50000 = 150000 > 128k + reasoning_tokens=50000, rejected_prediction_tokens=0, text_tokens=None, ), ) - - prompt_cost, completion_cost = cost_per_token( - model="xai/grok-4-fast-reasoning", usage=usage - ) - - # Expected costs for grok-4-fast-reasoning with tiered pricing: - # Input: 150000 tokens * $0.4e-6 (ALL tokens at tiered rate since input > 128k) = $0.06 - # Completion: (100000 + 50000) tokens * $1e-6 (tiered rate since input > 128k) = $0.15 - expected_prompt_cost = 150000 * 0.4e-6 - expected_completion_cost = (100000 + 50000) * 1e-6 - + prompt_cost, completion_cost = cost_per_token(model="xai/grok-4.3", usage=usage) + expected_prompt_cost = 250000 * 2.5e-6 + expected_completion_cost = (100000 + 50000) * 5e-6 assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) - def test_tiered_pricing_below_128k_tokens(self): - """Test that regular pricing is used for tokens below 128k threshold.""" - # Test with grok-4-fast-reasoning which has tiered pricing + def test_tiered_pricing_below_200k_tokens(self): usage = Usage( - prompt_tokens=100000, # Below 128k threshold + prompt_tokens=100000, completion_tokens=50000, total_tokens=160000, completion_tokens_details=CompletionTokensDetailsWrapper( @@ -235,26 +224,18 @@ class TestXAICostCalculator: text_tokens=None, ), ) - - prompt_cost, completion_cost = cost_per_token( - model="xai/grok-4-fast-reasoning", usage=usage - ) - - # Expected costs for grok-4-fast-reasoning with regular pricing: - # Input: 100000 tokens * $0.2e-6 (regular rate) = $0.02 - # Completion: (50000 + 10000) tokens * $0.5e-6 (regular rate) = $0.03 - expected_prompt_cost = 100000 * 0.2e-6 - expected_completion_cost = (50000 + 10000) * 0.5e-6 - + prompt_cost, completion_cost = cost_per_token(model="xai/grok-4.3", usage=usage) + expected_prompt_cost = 100000 * 1.25e-6 + expected_completion_cost = (50000 + 10000) * 2.5e-6 assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) def test_tiered_pricing_grok_4_latest(self): """Test tiered pricing for grok-4-latest model.""" usage = Usage( - prompt_tokens=200000, # Above 128k threshold + prompt_tokens=250000, # Above the 200k threshold completion_tokens=100000, - total_tokens=350000, + total_tokens=400000, completion_tokens_details=CompletionTokensDetailsWrapper( accepted_prediction_tokens=0, audio_tokens=0, @@ -268,59 +249,45 @@ class TestXAICostCalculator: model="xai/grok-4-latest", usage=usage ) - # Expected costs for grok-4-latest with tiered pricing: - # Input: 200000 tokens * $6e-6 (ALL tokens at tiered rate since input > 128k) = $1.2 - # Completion: (100000 + 50000) tokens * $30e-6 (tiered rate since input > 128k) = $4.5 - expected_prompt_cost = 200000 * 6e-6 - expected_completion_cost = (100000 + 50000) * 30e-6 + # grok-4-latest redirects to grok-4.3, which tiers at 200k rather than 128k: + # Input: 250000 tokens * $2.5e-6 (ALL tokens at tiered rate since input > 200k) + # Completion: (100000 + 50000) tokens * $5e-6 (tiered rate since input > 200k) + expected_prompt_cost = 250000 * 2.5e-6 + expected_completion_cost = (100000 + 50000) * 5e-6 assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) - def test_tiered_pricing_output_tokens_below_128k(self): - """Test that output tokens get tiered rate when input tokens > 128k, even if output tokens < 128k.""" + def test_tiered_pricing_output_tokens_below_200k(self): usage = Usage( - prompt_tokens=150000, # Above 128k threshold - completion_tokens=50000, # Below 128k threshold - total_tokens=210000, + prompt_tokens=250000, + completion_tokens=50000, + total_tokens=310000, completion_tokens_details=CompletionTokensDetailsWrapper( accepted_prediction_tokens=0, audio_tokens=0, - reasoning_tokens=10000, # Total completion tokens = 50000 + 10000 = 60000 < 128k + reasoning_tokens=10000, rejected_prediction_tokens=0, text_tokens=None, ), ) - - prompt_cost, completion_cost = cost_per_token( - model="xai/grok-4-fast-reasoning", usage=usage - ) - - # Expected costs for grok-4-fast-reasoning: - # Input: 150000 tokens * $0.4e-6 (ALL tokens at tiered rate since input > 128k) = $0.06 - # Completion: (50000 + 10000) tokens * $1e-6 (tiered rate since input > 128k) = $0.06 - expected_prompt_cost = 150000 * 0.4e-6 - expected_completion_cost = (50000 + 10000) * 1e-6 - + prompt_cost, completion_cost = cost_per_token(model="xai/grok-4.3", usage=usage) + expected_prompt_cost = 250000 * 2.5e-6 + expected_completion_cost = (50000 + 10000) * 5e-6 assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) def test_tiered_pricing_model_without_tiered_pricing(self): - """Test that models without tiered pricing use regular pricing even above 128k.""" - usage = Usage( - prompt_tokens=150000, # Above 128k threshold - completion_tokens=50000, - total_tokens=200000, - ) - - prompt_cost, completion_cost = cost_per_token(model="grok-3-mini", usage=usage) - - # grok-3-mini doesn't have tiered pricing, so should use regular rates: - # Input: 150000 tokens * $3e-7 (regular rate) = $0.045 - # Completion: 50000 tokens * $5e-7 (regular rate) = $0.025 - expected_prompt_cost = 150000 * 3e-7 + litellm.model_cost["xai/flat-rate-fixture"] = { + "input_cost_per_token": 3e-7, + "output_cost_per_token": 5e-7, + "litellm_provider": "xai", + "mode": "chat", + } + usage = Usage(prompt_tokens=250000, completion_tokens=50000, total_tokens=300000) + prompt_cost, completion_cost = cost_per_token(model="xai/flat-rate-fixture", usage=usage) + expected_prompt_cost = 250000 * 3e-7 expected_completion_cost = 50000 * 5e-7 - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) @@ -341,8 +308,8 @@ class TestXAICostCalculator: prompt_cost, completion_cost = cost_per_token(model="grok-3-mini", usage=usage) - expected_prompt_cost = 12 * 3e-7 - expected_completion_cost = 200 * 5e-7 + expected_prompt_cost = 12 * 1.25e-6 + expected_completion_cost = 200 * 2.5e-6 assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) diff --git a/tests/test_litellm/llms/xai/test_xai_redirected_slug_pricing.py b/tests/test_litellm/llms/xai/test_xai_redirected_slug_pricing.py new file mode 100644 index 00000000000..83c3bf1ecef --- /dev/null +++ b/tests/test_litellm/llms/xai/test_xai_redirected_slug_pricing.py @@ -0,0 +1,144 @@ +""" +xAI retired eight slugs on 2026-05-15 but kept them resolvable: chat slugs redirect to +grok-4.3 and bill at grok-4.3's rates, while the grok-code-fast slugs are aliases of +grok-build-0.1 and bill at its rates, so the registry must price them that way or spend +tracking is wrong. The grok-3-beta, grok-3-fast, grok-3-mini, and grok-4-1-fast slugs +are absent from /v1/language-models and resolve to grok-4.3 the same way (the chat +response names grok-4.3 as the served model), so they carry grok-4.3's rates too. +https://docs.x.ai/developers/migration/may-15-retirement +https://docs.x.ai/developers/models/grok-build-0.1 +""" + +from __future__ import annotations + +import json +from pathlib import Path + +import pytest + +REPO_ROOT = Path(__file__).parents[4] +PRICES_PATH = REPO_ROOT / "model_prices_and_context_window.json" +BACKUP_PRICES_PATH = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json" +MAP_PATHS = (PRICES_PATH, BACKUP_PRICES_PATH) + +REDIRECT_TARGET = "xai/grok-4.3" +GROK_3_MINI_SLUGS = ( + "xai/grok-3-mini", + "xai/grok-3-mini-beta", + "xai/grok-3-mini-fast", + "xai/grok-3-mini-fast-beta", + "xai/grok-3-mini-fast-latest", + "xai/grok-3-mini-latest", +) +REDIRECTED_SLUGS = ( + "xai/grok-3", + "xai/grok-3-beta", + "xai/grok-3-fast-beta", + "xai/grok-3-fast-latest", + "xai/grok-3-latest", + *GROK_3_MINI_SLUGS, + "xai/grok-4", + "xai/grok-4-0709", + "xai/grok-4-1-fast", + "xai/grok-4-1-fast-non-reasoning", + "xai/grok-4-1-fast-non-reasoning-latest", + "xai/grok-4-1-fast-reasoning", + "xai/grok-4-1-fast-reasoning-latest", + "xai/grok-4-fast-non-reasoning", + "xai/grok-4-fast-reasoning", + "xai/grok-4-latest", +) +CODE_REDIRECT_TARGET = "xai/grok-build-0.1" +CODE_SLUGS = ( + "xai/grok-code-fast", + "xai/grok-code-fast-1", + "xai/grok-code-fast-1-0825", +) +RETIREMENT_DATE = "2026-05-15" +GROK_3_MINI_RETIREMENT_DATE = "2026-02-28" + +BASE_COST_FIELDS = ("input_cost_per_token", "output_cost_per_token", "cache_read_input_token_cost") +TIER_COST_FIELDS = ( + "input_cost_per_token_above_200k_tokens", + "output_cost_per_token_above_200k_tokens", + "cache_read_input_token_cost_above_200k_tokens", +) +STALE_TIER_FIELDS = ( + "input_cost_per_token_above_128k_tokens", + "output_cost_per_token_above_128k_tokens", + "cache_read_input_token_cost_above_128k_tokens", +) + + +def expected_retirement_date(slug: str) -> str: + return GROK_3_MINI_RETIREMENT_DATE if slug in GROK_3_MINI_SLUGS else RETIREMENT_DATE + + +@pytest.fixture(scope="module", params=[p.name for p in MAP_PATHS]) +def cost_map(request: pytest.FixtureRequest) -> dict: + path = next(p for p in MAP_PATHS if p.name == request.param) + return json.loads(path.read_text(encoding="utf-8")) + + +@pytest.mark.parametrize("slug", REDIRECTED_SLUGS) +def test_redirected_slug_bills_at_the_target_rate(cost_map: dict, slug: str): + target = cost_map[REDIRECT_TARGET] + entry = cost_map[slug] + for field in BASE_COST_FIELDS: + assert entry[field] == target[field], field + + +@pytest.mark.parametrize("slug", CODE_SLUGS) +def test_code_slug_bills_at_grok_build_rate(cost_map: dict, slug: str): + """grok-code-fast* are aliases of grok-build-0.1, not grok-4.3 redirects.""" + target = cost_map[CODE_REDIRECT_TARGET] + entry = cost_map[slug] + for field in (*BASE_COST_FIELDS, *TIER_COST_FIELDS): + assert entry[field] == target[field], field + + +@pytest.mark.parametrize("slug", (*REDIRECTED_SLUGS, *CODE_SLUGS)) +def test_redirected_slug_keeps_its_retirement_date(cost_map: dict, slug: str): + assert cost_map[slug]["deprecation_date"] == expected_retirement_date(slug) + + +@pytest.mark.parametrize("slug", REDIRECTED_SLUGS) +def test_no_slug_keeps_the_superseded_128k_tier(cost_map: dict, slug: str): + """The 128k tier belonged to the retired model; grok-4.3 tiers at 200k.""" + for field in STALE_TIER_FIELDS: + assert field not in cost_map[slug], field + + +@pytest.mark.parametrize("slug", REDIRECTED_SLUGS) +def test_redirected_slug_carries_the_target_tier_rates(cost_map: dict, slug: str): + """The request executes as grok-4.3, so it is tiered at grok-4.3's 200k boundary.""" + target = cost_map[REDIRECT_TARGET] + entry = cost_map[slug] + for field in TIER_COST_FIELDS: + assert entry[field] == target[field], field + + +def test_a_live_xai_model_is_untouched(cost_map: dict): + """Guard against the repricing leaking onto models xAI still serves directly.""" + assert cost_map["xai/grok-4.6"]["input_cost_per_token"] != cost_map[REDIRECT_TARGET]["input_cost_per_token"] + assert "deprecation_date" not in cost_map["xai/grok-4.6"] + + +def test_both_cost_maps_agree_on_the_redirected_slugs(): + prices = json.loads(PRICES_PATH.read_text(encoding="utf-8")) + backup = json.loads(BACKUP_PRICES_PATH.read_text(encoding="utf-8")) + for slug in (*REDIRECTED_SLUGS, *CODE_SLUGS, REDIRECT_TARGET, CODE_REDIRECT_TARGET): + assert prices[slug] == backup[slug], slug + + +def test_every_retired_chat_slug_is_covered(cost_map: dict): + """The lists above must stay in step with what the registry marks retired.""" + marked = { + key + for key, entry in cost_map.items() + if isinstance(entry, dict) + and entry.get("litellm_provider") == "xai" + and "deprecation_date" in entry + and entry.get("mode") == "chat" + } + assert marked == {*REDIRECTED_SLUGS, *CODE_SLUGS} diff --git a/tests/test_litellm/models/test_models.py b/tests/test_litellm/models/test_models.py index 669dba8e466..9ae9b732066 100644 --- a/tests/test_litellm/models/test_models.py +++ b/tests/test_litellm/models/test_models.py @@ -5,6 +5,7 @@ Tests for backend domain models. from datetime import datetime import pytest +from pydantic import BaseModel, TypeAdapter from litellm.models.access_group import LiteLLM_AccessGroupTable from litellm.models.budget import ( @@ -130,6 +131,33 @@ class TestModel: assert model.litellm_params == {"model": "gpt-4"} assert model.model_info == {"team_id": "t1"} + def test_response_type_adapter_accepts_pydantic_row(self): + class PrismaModelRow(BaseModel): + model_id: str + model_name: str + litellm_params: dict[str, str] + model_info: dict[str, str] | None = None + blocked: bool = False + + row = PrismaModelRow( + model_id="m1", + model_name="gpt-4", + litellm_params={"model": "gpt-4"}, + model_info={"team_id": "t1"}, + blocked=True, + ) + + model = TypeAdapter(LiteLLM_ProxyModelTable | None).validate_python( + row, + from_attributes=True, + ) + + assert model is not None + assert model.model_id == "m1" + assert model.litellm_params == {"model": "gpt-4"} + assert model.model_info == {"team_id": "t1"} + assert model.blocked is True + def test_team_helpers_none_when_no_model_info(self): model = LiteLLM_ProxyModelTable( model_id="m1", model_name="gpt-4", litellm_params={}, model_info=None diff --git a/tests/test_litellm/passthrough/test_async_streaming_error_propagation.py b/tests/test_litellm/passthrough/test_async_streaming_error_propagation.py index faf4ea46c43..9f2b436d2d8 100644 --- a/tests/test_litellm/passthrough/test_async_streaming_error_propagation.py +++ b/tests/test_litellm/passthrough/test_async_streaming_error_propagation.py @@ -1,12 +1,12 @@ """ -Tests for error propagation in _async_streaming passthrough routes. +Tests for error propagation in async passthrough streaming routes. -Verifies that HTTP 4xx/5xx errors from upstream (e.g. Azure 429 rate limits) -raise exceptions instead of being silently forwarded as raw bytes under HTTP 200. - -See: litellm/passthrough/main.py _async_streaming() +Verifies that streaming passthrough wrappers preserve the previous guarantees: +HTTP 4xx/5xx failures must raise instead of being silently forwarded as bytes, +and successful streaming responses should still yield chunks normally. """ +import asyncio import json from unittest.mock import AsyncMock, MagicMock @@ -54,19 +54,19 @@ def _make_mock_logging_obj(): @pytest.mark.asyncio async def test_async_streaming_429_raises(): """429 from upstream should raise HTTPStatusError, not yield error bytes.""" - from litellm.passthrough.main import _async_streaming - + from litellm.passthrough.main import AsyncPassthroughStreamingResponse + error_body = json.dumps( {"error": {"code": "429", "message": "Rate limit exceeded."}} ).encode() mock_response = _make_mock_response(429, error_body) - + async def response_coro(): return mock_response - + chunks = [] async def _drain(): - async for chunk in _async_streaming( + async for chunk in AsyncPassthroughStreamingResponse( response=response_coro(), litellm_logging_obj=_make_mock_logging_obj(), provider_config=MagicMock(), @@ -83,45 +83,78 @@ async def test_async_streaming_429_raises(): @pytest.mark.asyncio async def test_async_streaming_500_raises(): """500 from upstream should also raise, not yield error bytes.""" - from litellm.passthrough.main import _async_streaming - + from litellm.passthrough.main import AsyncPassthroughStreamingResponse + error_body = json.dumps( {"error": {"code": "500", "message": "Internal server error"}} ).encode() mock_response = _make_mock_response(500, error_body) - + async def response_coro(): return mock_response - + with pytest.raises(httpx.HTTPStatusError) as exc_info: - async for _ in _async_streaming( + async for _ in AsyncPassthroughStreamingResponse( response=response_coro(), litellm_logging_obj=_make_mock_logging_obj(), provider_config=MagicMock(), ): pass - + assert exc_info.value.response.status_code == 500 @pytest.mark.asyncio -async def test_async_streaming_200_yields_chunks(): +async def test_async_passthrough_wrapper_200_yields_chunks(): """Successful 200 streaming responses should continue to work normally.""" - from litellm.passthrough.main import _async_streaming + from litellm.passthrough.main import AsyncPassthroughStreamingResponse sse_data = b'data: {"type":"response.created"}\n\ndata: [DONE]\n\n' mock_response = _make_mock_response(200, sse_data) + mock_logging_obj = _make_mock_logging_obj() async def response_coro(): return mock_response - chunks = [] - async for chunk in _async_streaming( + async_stream = AsyncPassthroughStreamingResponse( response=response_coro(), - litellm_logging_obj=_make_mock_logging_obj(), + litellm_logging_obj=mock_logging_obj, provider_config=MagicMock(), - ): + ) + + chunks = [] + async for chunk in async_stream: chunks.append(chunk) + await asyncio.sleep(0) + assert len(chunks) == 1 assert b"response.created" in chunks[0] + mock_logging_obj.async_flush_passthrough_collected_chunks.assert_awaited_once() + + +@pytest.mark.asyncio +async def test_error_body_readable_after_failed_await(): + """The upstream error body must stay readable so the proxy can map the real status and message.""" + from litellm.passthrough.main import AsyncPassthroughStreamingResponse + + error_body = b'{"message":"model not found"}' + + async def byte_stream(): + yield error_body + + request = httpx.Request("POST", "https://bedrock.example.com/model/x/converse-stream") + response = httpx.Response(400, content=byte_stream(), request=request) + + async def response_coro(): + return response + + with pytest.raises(httpx.HTTPStatusError) as exc_info: + await AsyncPassthroughStreamingResponse( + response=response_coro(), + litellm_logging_obj=_make_mock_logging_obj(), + provider_config=MagicMock(), + ) + + assert exc_info.value.response.status_code == 400 + assert await exc_info.value.response.aread() == error_body diff --git a/tests/test_litellm/passthrough/test_passthrough_main.py b/tests/test_litellm/passthrough/test_passthrough_main.py index b8f265ad7ea..1950c37a12e 100644 --- a/tests/test_litellm/passthrough/test_passthrough_main.py +++ b/tests/test_litellm/passthrough/test_passthrough_main.py @@ -645,13 +645,14 @@ async def test_allm_passthrough_route_429_streaming_raises(): Regression test: Azure 429 during streaming must raise HTTPStatusError, not be silently forwarded as raw bytes under HTTP 200. - Before the fix, _async_streaming() would yield the 429 error JSON as - chunks and allm_passthrough_route returned an async generator. The - caller (azure_proxy_route) wrapped it in StreamingResponse(status_code=200), + Before the fix, the async passthrough streaming path would yield the 429 + error JSON as chunks and allm_passthrough_route returned a streaming + iterator. The caller (azure_proxy_route) wrapped it in + StreamingResponse(status_code=200), so the client saw HTTP 200 + unparseable SSE body → silent task_complete(null). - After the fix, raise_for_status() fires inside _async_streaming() before - any chunks are yielded, so the exception propagates all the way up. + After the fix, raise_for_status() fires before the streaming wrapper is + returned, so the exception propagates all the way up. """ mock_provider_config = MagicMock() mock_provider_config.get_complete_url.return_value = ( @@ -679,6 +680,7 @@ async def test_allm_passthrough_route_429_streaming_raises(): mock_logging_obj = MagicMock() mock_logging_obj.update_environment_variables = MagicMock() mock_logging_obj.async_flush_passthrough_collected_chunks = AsyncMock() + mock_logging_obj.async_failure_handler = AsyncMock() with ( patch( @@ -701,29 +703,101 @@ async def test_allm_passthrough_route_429_streaming_raises(): patch.object(async_client.client, "send", mock_send), patch.object(async_client.client, "build_request", mock_build_request), ): - result = await allm_passthrough_route( - model="azure/gpt-4", - endpoint="openai/deployments/gpt-4/responses", - method="POST", - custom_llm_provider="azure", - api_base="https://my-azure.openai.azure.com", - api_key="fake-azure-key", - json={"model": "gpt-4", "input": "hello", "stream": True}, - client=async_client, - litellm_logging_obj=mock_logging_obj, - ) - - # result is an async generator — consuming it must raise, not silently yield error bytes - chunks = [] - async def _drain(): - async for chunk in result: # type: ignore[union-attr] - chunks.append(chunk) - with pytest.raises(httpx.HTTPStatusError) as exc_info: - await _drain() + await allm_passthrough_route( + model="azure/gpt-4", + endpoint="openai/deployments/gpt-4/responses", + method="POST", + custom_llm_provider="azure", + api_base="https://my-azure.openai.azure.com", + api_key="fake-azure-key", + json={"model": "gpt-4", "input": "hello", "stream": True}, + client=async_client, + litellm_logging_obj=mock_logging_obj, + ) assert exc_info.value.response.status_code == 429 - assert len(chunks) == 0, "No chunks should be yielded before the 429 raises" + + +def test_llm_passthrough_route_sync_streaming_error_maps_upstream_status(): + """ + Regression test: a sync streaming passthrough whose upstream answers an + error status must surface the mapped provider error, not + httpx.ResponseNotRead. + + Before the fix, raise_for_status() raised on the still-unread streamed + response, and _handle_error then touched e.response.text, which raises + ResponseNotRead on a streamed-but-unread body, masking the real upstream + error entirely. + """ + from litellm.llms.base_llm.chat.transformation import BaseLLMException + + error_body = json.dumps( + { + "error": { + "code": "429", + "message": "Rate limit exceeded. Retry after 10 seconds.", + } + } + ).encode() + + class _UnreadErrorStream(httpx.SyncByteStream): + def __iter__(self): + yield error_body + + def _handler(request: httpx.Request) -> httpx.Response: + return httpx.Response( + 429, + stream=_UnreadErrorStream(), + headers={"content-type": "application/json"}, + ) + + sync_client = HTTPHandler( + client=httpx.Client(transport=httpx.MockTransport(_handler)) + ) + + mock_provider_config = MagicMock() + mock_provider_config.get_complete_url.return_value = ( + httpx.URL("https://gigachat.devices.sberbank.ru/api/v1/chat/completions"), + "https://gigachat.devices.sberbank.ru/api/v1", + ) + mock_provider_config.get_api_key.return_value = "fake-key" + mock_provider_config.validate_environment.return_value = { + "Authorization": "Bearer fake-key" + } + mock_provider_config.sign_request.return_value = ( + {"Authorization": "Bearer fake-key"}, + None, + ) + mock_provider_config.is_streaming_request.return_value = True + mock_provider_config.get_error_class.side_effect = ( + lambda error_message, status_code, headers: BaseLLMException( + status_code=status_code, message=error_message, headers=headers + ) + ) + + mock_logging_obj = MagicMock() + + with pytest.raises(BaseLLMException) as exc_info: + llm_passthrough_route( + model="gigachat/GigaChat-2", + endpoint="chat/completions", + method="POST", + custom_llm_provider="gigachat", + api_base="https://gigachat.devices.sberbank.ru/api/v1", + api_key="fake-key", + json={ + "model": "GigaChat-2", + "messages": [{"role": "user", "content": "hi"}], + "stream": True, + }, + client=sync_client, + litellm_logging_obj=mock_logging_obj, + provider_config=mock_provider_config, + ) + + assert exc_info.value.status_code == 429 + assert "Rate limit exceeded" in str(exc_info.value) def test_llm_passthrough_route_propagates_allm_passthrough_route_to_logging_obj(): diff --git a/tests/test_litellm/passthrough/test_streaming_interrupt_spend_tracking.py b/tests/test_litellm/passthrough/test_streaming_interrupt_spend_tracking.py index 3783e218e4e..a88b0ef0c4b 100644 --- a/tests/test_litellm/passthrough/test_streaming_interrupt_spend_tracking.py +++ b/tests/test_litellm/passthrough/test_streaming_interrupt_spend_tracking.py @@ -35,11 +35,14 @@ class _ImmediateExecutor: @pytest.mark.asyncio -async def test_async_streaming_flushes_on_normal_completion(): - from litellm.passthrough.main import _async_streaming +async def test_asyncpassthroughstreamingresponse_flushes_on_normal_completion(): + from litellm.passthrough.main import AsyncPassthroughStreamingResponse chunks = [b"chunk-1", b"chunk-2", b"chunk-3"] mock_response = _make_streaming_response(chunks) + mock_response.headers = httpx.Headers( + {"content-type": "application/octet-stream", "x-request-id": "req-123"} + ) async def response_coro(): return mock_response @@ -48,14 +51,19 @@ async def test_async_streaming_flushes_on_normal_completion(): provider_config = MagicMock() received = [] - async for chunk in _async_streaming( + received_response = AsyncPassthroughStreamingResponse( response=response_coro(), litellm_logging_obj=mock_logging_obj, provider_config=provider_config, - ): + ) + + async for chunk in received_response: received.append(chunk) assert received == chunks + + assert received_response.headers["content-type"] == "application/octet-stream" + assert received_response.headers["x-request-id"] == "req-123" await asyncio.sleep(0) @@ -68,8 +76,8 @@ async def test_async_streaming_flushes_on_normal_completion(): @pytest.mark.asyncio -async def test_async_streaming_flushes_on_client_disconnect(): - from litellm.passthrough.main import _async_streaming +async def test_asyncpassthroughstreamingresponse_flushes_on_client_disconnect(): + from litellm.passthrough.main import AsyncPassthroughStreamingResponse chunks = [ b'{"chunk": 1, "outputTokens": 10}', @@ -77,6 +85,9 @@ async def test_async_streaming_flushes_on_client_disconnect(): b'{"chunk": 3, "outputTokens": 8}', ] mock_response = _make_streaming_response(chunks) + mock_response.headers = httpx.Headers( + {"content-type": "application/octet-stream", "x-request-id": "req-123"} + ) async def response_coro(): return mock_response @@ -84,7 +95,7 @@ async def test_async_streaming_flushes_on_client_disconnect(): mock_logging_obj = _make_logging_obj() provider_config = MagicMock() - gen = _async_streaming( + gen = AsyncPassthroughStreamingResponse( response=response_coro(), litellm_logging_obj=mock_logging_obj, provider_config=provider_config, @@ -105,11 +116,14 @@ async def test_async_streaming_flushes_on_client_disconnect(): @pytest.mark.asyncio -async def test_async_streaming_does_not_flush_on_4xx(): - from litellm.passthrough.main import _async_streaming +async def test_asyncpassthroughstreamingresponse_does_not_flush_on_4xx(): + from litellm.passthrough.main import AsyncPassthroughStreamingResponse err_response = MagicMock(spec=httpx.Response) err_response.status_code = 429 + err_response.headers = httpx.Headers( + {"content-type": "application/octet-stream"} + ) def _raise(): raise httpx.HTTPStatusError( @@ -129,7 +143,7 @@ async def test_async_streaming_does_not_flush_on_4xx(): mock_logging_obj = _make_logging_obj() with pytest.raises(httpx.HTTPStatusError): - async for _ in _async_streaming( + async for _ in AsyncPassthroughStreamingResponse( response=response_coro(), litellm_logging_obj=mock_logging_obj, provider_config=MagicMock(), @@ -140,8 +154,8 @@ async def test_async_streaming_does_not_flush_on_4xx(): @pytest.mark.asyncio -async def test_async_streaming_flushes_on_upstream_exception_with_partial_data(): - from litellm.passthrough.main import _async_streaming +async def test_asyncpassthroughstreamingresponse_flushes_on_upstream_exception_with_partial_data(): + from litellm.passthrough.main import AsyncPassthroughStreamingResponse partial_chunks = [b"partial-chunk-1", b"partial-chunk-2"] @@ -149,6 +163,9 @@ async def test_async_streaming_flushes_on_upstream_exception_with_partial_data() mock_response.status_code = 200 mock_response.raise_for_status = MagicMock(return_value=None) mock_response.aclose = AsyncMock() + mock_response.headers = httpx.Headers( + {"content-type": "application/octet-stream", "x-request-id": "req-123"} + ) async def _aiter_bytes_then_raise(): for c in partial_chunks: @@ -165,7 +182,7 @@ async def test_async_streaming_flushes_on_upstream_exception_with_partial_data() received = [] async def _drain(): - async for chunk in _async_streaming( + async for chunk in AsyncPassthroughStreamingResponse( response=response_coro(), litellm_logging_obj=mock_logging_obj, provider_config=provider_config, @@ -186,12 +203,16 @@ async def test_async_streaming_flushes_on_upstream_exception_with_partial_data() assert call_kwargs["raw_bytes"] == partial_chunks -def test_sync_streaming_flushes_on_normal_completion(): - from litellm.passthrough.main import _sync_streaming +def test_passthroughstreamingresponse_flushes_on_normal_completion(): + from litellm.passthrough.main import PassthroughStreamingResponse chunks = [b"a", b"b", b"c"] mock_response = MagicMock(spec=httpx.Response) + mock_response.status_code = 200 + mock_response.headers = httpx.Headers( + {"content-type": "application/octet-stream", "x-request-id": "req-123"} + ) def _iter_bytes(): yield from chunks @@ -202,25 +223,33 @@ def test_sync_streaming_flushes_on_normal_completion(): mock_logging_obj.flush_passthrough_collected_chunks = MagicMock() provider_config = MagicMock() + received_responce = PassthroughStreamingResponse( + response=mock_response, + litellm_logging_obj=mock_logging_obj, + provider_config=provider_config, + ) + with patch("litellm.utils.executor", _ImmediateExecutor()): - received = list( - _sync_streaming( - response=mock_response, - litellm_logging_obj=mock_logging_obj, - provider_config=provider_config, - ) - ) + received = list(received_responce) assert received == chunks + + assert received_responce.headers["content-type"] == "application/octet-stream" + assert received_responce.headers["x-request-id"] == "req-123" + mock_logging_obj.flush_passthrough_collected_chunks.assert_called_once() -def test_sync_streaming_flushes_on_early_close(): - from litellm.passthrough.main import _sync_streaming +def test_passthroughstreamingresponse_flushes_on_early_close(): + from litellm.passthrough.main import PassthroughStreamingResponse chunks = [b"first", b"second", b"third"] mock_response = MagicMock(spec=httpx.Response) + mock_response.status_code = 200 + mock_response.headers = httpx.Headers( + {"content-type": "application/octet-stream", "x-request-id": "req-123"} + ) def _iter_bytes(): yield from chunks @@ -232,7 +261,7 @@ def test_sync_streaming_flushes_on_early_close(): provider_config = MagicMock() with patch("litellm.utils.executor", _ImmediateExecutor()): - gen = _sync_streaming( + gen = PassthroughStreamingResponse( response=mock_response, litellm_logging_obj=mock_logging_obj, provider_config=provider_config, diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/conftest.py b/tests/test_litellm/proxy/_experimental/mcp_server/conftest.py index b477bf3f406..2ccba2b2055 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/conftest.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/conftest.py @@ -2,6 +2,30 @@ import os import pytest +from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( + global_mcp_server_manager, +) + + +@pytest.fixture(autouse=True) +def _hermetic_mcp_server_registry(): + """Restore the singleton ``global_mcp_server_manager``'s registry state around every + test, so entries seeded by one test never leak into another on a shared shard.""" + saved_registry = dict(global_mcp_server_manager.registry) + saved_config_servers = dict(global_mcp_server_manager.config_mcp_servers) + saved_tool_mapping = dict(global_mcp_server_manager.tool_name_to_mcp_server_name_mapping) + saved_oauth_slots = global_mcp_server_manager._oauth_discovery_slots + try: + yield + finally: + global_mcp_server_manager.registry.clear() + global_mcp_server_manager.registry.update(saved_registry) + global_mcp_server_manager.config_mcp_servers.clear() + global_mcp_server_manager.config_mcp_servers.update(saved_config_servers) + global_mcp_server_manager.tool_name_to_mcp_server_name_mapping.clear() + global_mcp_server_manager.tool_name_to_mcp_server_name_mapping.update(saved_tool_mapping) + global_mcp_server_manager._oauth_discovery_slots = saved_oauth_slots + @pytest.fixture(autouse=True) def _hermetic_server_root_path(): diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/outbound_credentials/test_token_endpoint.py b/tests/test_litellm/proxy/_experimental/mcp_server/outbound_credentials/test_token_endpoint.py index f100bd56f8f..5f277db2f72 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/outbound_credentials/test_token_endpoint.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/outbound_credentials/test_token_endpoint.py @@ -224,20 +224,6 @@ async def test_fetch_invalid_json_maps_to_upstream_unavailable(): assert "idp.example.com" not in result.error.summary -@pytest.mark.asyncio -async def test_fetch_none_response_is_upstream_unavailable(): - with patch(_PATCH_TARGET, return_value=_client(None)): - result = await TokenEndpointClient().fetch( - _ENDPOINT, - _CLIENT_ID, - {"grant_type": "g"}, - ClientSecretAuth(client_secret=SecretStr("s")), - ) - - assert isinstance(result, Error) - assert result.error.tag == "upstream_unavailable" - - @pytest.mark.asyncio async def test_fetch_missing_access_token_is_upstream_unavailable(): bad = MagicMock() @@ -275,21 +261,6 @@ async def test_fetch_http_error_does_not_leak_endpoint_url(): assert "idp.example.com" not in result.error.summary -@pytest.mark.asyncio -async def test_fetch_none_response_does_not_leak_endpoint_url(): - with patch(_PATCH_TARGET, return_value=_client(None)): - result = await TokenEndpointClient().fetch( - _ENDPOINT, - _CLIENT_ID, - {"grant_type": "g"}, - ClientSecretAuth(client_secret=SecretStr("s")), - ) - - assert isinstance(result, Error) - assert _ENDPOINT not in result.error.summary - assert "idp.example.com" not in result.error.summary - - @pytest.mark.asyncio async def test_fetch_missing_access_token_does_not_leak_endpoint_url(): bad = MagicMock() diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py index 0c809940b84..598e9276423 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py @@ -35,6 +35,22 @@ def mock_mcp_client_ip(): yield +@pytest.fixture(autouse=True) +def isolate_global_mcp_registry(): + """Restore the module-global MCP server registry after each test. + + Tests here register servers on ``global_mcp_server_manager`` directly; without a + restore, entries leak into other test modules sharing the same worker and break + assertions over the full registry contents. + """ + from litellm.proxy._experimental.mcp_server.mcp_server_manager import global_mcp_server_manager + + snapshot = dict(global_mcp_server_manager.registry) + yield + global_mcp_server_manager.registry.clear() + global_mcp_server_manager.registry.update(snapshot) + + def _mock_callback_request(base_url: str = "http://localhost:3000/"): """Return a MagicMock Request for callback/authorize same-origin tests. @@ -10290,3 +10306,82 @@ def test_native_client_authorize_without_the_proxy_resource_keeps_the_mcp_flow(m assert 'name="decision"' not in response.text assert "team-b" not in response.text assert minted == [] + + +def test_introspect_route_requires_virtual_key_auth_and_is_advertised(): + """RFC 7662 section 2.1: introspection must not be anonymous. Pins the route-level + user_api_key_auth dependency (structure, so removing it fails here without a proxy), + and that the aggregate AS metadata advertises the endpoint for discovery.""" + from fastapi import FastAPI + from fastapi.routing import APIRoute + from fastapi.testclient import TestClient + + from litellm.proxy._experimental.mcp_server.discoverable_endpoints import router + from litellm.proxy.auth.user_api_key_auth import user_api_key_auth + + route = next(r for r in router.routes if isinstance(r, APIRoute) and r.path == "/introspect") + assert route.methods == {"POST"} + assert any(dependency.call is user_api_key_auth for dependency in route.dependant.dependencies) + + from litellm.proxy._types import LiteLLMRoutes + + assert "/introspect" in LiteLLMRoutes.mcp_routes.value + + from litellm.proxy._lazy_features import LAZY_FEATURES + + discoverable = next(feature for feature in LAZY_FEATURES if feature.name == "mcp_discoverable") + assert "/introspect" in discoverable.path_prefixes + + app = FastAPI() + app.include_router(router) + client = TestClient(app) + asm = client.get("/.well-known/oauth-authorization-server/mcp") + assert asm.json()["introspection_endpoint"] == "http://testserver/introspect" + + +def test_introspect_route_answers_for_authenticated_caller(monkeypatch): + """End-to-end over the real route with the auth dependency satisfied: a garbage token + is active false, a freshly minted session access token is active true with its claims.""" + from datetime import datetime, timezone + + from fastapi import FastAPI + from fastapi.testclient import TestClient + + from litellm.proxy._experimental.mcp_server.discoverable_endpoints import router + from litellm.proxy._experimental.mcp_server.outbound_credentials.session_credentials import ( + session_keys_from_master_key, + ) + from litellm.proxy._experimental.mcp_server.outbound_credentials.session_token import ( + SessionPrincipal, + mint_session_token, + ) + from litellm.proxy._types import UserAPIKeyAuth + from litellm.proxy.auth.user_api_key_auth import user_api_key_auth + + introspect_master_key = "sk-introspect-route-test" + monkeypatch.setattr("litellm.proxy.proxy_server.master_key", introspect_master_key, raising=False) + + async def fake_reload(user_id: str): + return None + + monkeypatch.setattr( + "litellm.proxy._experimental.mcp_server.discoverable_endpoints._reload_active_user_by_id", fake_reload + ) + app = FastAPI() + app.include_router(router) + app.dependency_overrides[user_api_key_auth] = lambda: UserAPIKeyAuth() + client = TestClient(app) + + garbage = client.post("/introspect", data={"token": "llm_session_garbage"}) + assert garbage.status_code == 200 + assert garbage.json() == {"active": False} + + minted = mint_session_token( + SessionPrincipal(user_id="u1", client_id="llm_dcrc_client"), + session_keys_from_master_key(introspect_master_key), + datetime.now(timezone.utc), + ) + active = client.post("/introspect", data={"token": minted.token.get_secret_value()}) + assert active.status_code == 200 + assert active.json()["active"] is True + assert active.json()["sub"] == "u1" diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_gateway_dcr_flow.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_gateway_dcr_flow.py index 761f823076b..32a3f70c357 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_gateway_dcr_flow.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_gateway_dcr_flow.py @@ -26,6 +26,7 @@ from litellm.proxy._experimental.mcp_server.gateway_dcr_flow import ( aggregate_authorize, aggregate_token, complete_connect_flow, + introspect_gateway_token, is_gateway_dcr_client_id, is_proxy_api_resource, native_client_auth_contract, @@ -41,7 +42,13 @@ from litellm.proxy._experimental.mcp_server.outbound_credentials.session_credent resolve_session_bearer, session_keys_from_master_key, ) -from litellm.proxy._experimental.mcp_server.outbound_credentials.session_token import SESSION_REFRESH_PREFIX +from litellm.proxy._experimental.mcp_server.outbound_credentials.session_token import ( + SESSION_ISSUER, + SESSION_REFRESH_PREFIX, + SessionPrincipal, + mint_session_refresh_token, + mint_session_token, +) MASTER_KEY = "sk-gateway-dcr-flow-tests" REDIRECT_URI = "https://claude.ai/api/mcp/auth_callback" @@ -1598,17 +1605,24 @@ async def test_refresh_answers_503_without_burning_the_token_while_redis_is_down ) redis_down = await _refresh_native( - payload["refresh_token"], client_id, _Minter(), _redis_that(AsyncMock(side_effect=ConnectionError("redis down"))) + payload["refresh_token"], + client_id, + _Minter(), + _redis_that(AsyncMock(side_effect=ConnectionError("redis down"))), ) assert redis_down.status_code == 503 assert json.loads(redis_down.body)["error"] == "temporarily_unavailable" assert "refresh_token" not in json.loads(redis_down.body) - redis_back = await _refresh_native(payload["refresh_token"], client_id, _Minter(), _redis_that(AsyncMock(return_value=1))) + redis_back = await _refresh_native( + payload["refresh_token"], client_id, _Minter(), _redis_that(AsyncMock(return_value=1)) + ) assert redis_back.status_code == 200 assert json.loads(redis_back.body)["refresh_token"] != payload["refresh_token"] - replayed = await _refresh_native(payload["refresh_token"], client_id, _Minter(), _redis_that(AsyncMock(return_value=2))) + replayed = await _refresh_native( + payload["refresh_token"], client_id, _Minter(), _redis_that(AsyncMock(return_value=2)) + ) assert replayed.status_code == 400 assert json.loads(replayed.body)["error"] == "invalid_grant" @@ -1674,3 +1688,122 @@ def test_native_client_auth_contract_points_every_endpoint_at_this_proxy(): ) def test_is_proxy_api_resource_matches_only_this_proxy(resource, expected): assert is_proxy_api_resource(_request(), resource) is expected + + +def _introspection_fixtures(): + keys = session_keys_from_master_key(MASTER_KEY) + now = datetime.now(timezone.utc) + principal = SessionPrincipal(user_id="u1", client_id="llm_dcrc_client", team_id="t1") + return keys, now, principal + + +async def _introspect(token, cache=None, reload_user=_reload_user_active, master_key=MASTER_KEY): + response = await introspect_gateway_token( + token=token, master_key=master_key, reload_user=reload_user, cache=cache or DualCache() + ) + return response.status_code, json.loads(response.body) + + +@pytest.mark.asyncio +async def test_introspect_active_access_token_reports_rfc7662_claims(): + keys, now, principal = _introspection_fixtures() + minted = mint_session_token(principal, keys, now) + status, body = await _introspect(minted.token.get_secret_value()) + assert status == 200 + assert body["active"] is True + assert body["token_type"] == "Bearer" + assert body["iss"] == SESSION_ISSUER + assert body["sub"] == "u1" + assert body["client_id"] == "llm_dcrc_client" + assert body["kind"] == "session" + assert body["team_id"] == "t1" + assert body["exp"] - body["iat"] == 3600 + assert body["jti"] + + +@pytest.mark.asyncio +async def test_introspect_invalid_tokens_answer_active_false(): + keys, now, principal = _introspection_fixtures() + wrong_key = mint_session_token(principal, session_keys_from_master_key("sk-a-rotated-master-key"), now) + expired = mint_session_token(principal, keys, now - timedelta(seconds=7200)) + for candidate in ( + "sk-not-a-session-token", + "llm_session_malformed", + wrong_key.token.get_secret_value(), + expired.token.get_secret_value(), + ): + status, body = await _introspect(candidate) + assert (status, body) == (200, {"active": False}) + + +@pytest.mark.asyncio +async def test_introspect_refresh_token_goes_inactive_once_rotated(): + keys, now, _ = _introspection_fixtures() + client_id = (await _register([REDIRECT_URI]))["client_id"] + minted = mint_session_refresh_token(SessionPrincipal(user_id="u1", client_id=client_id), keys, now) + cache = DualCache() + status, body = await _introspect(minted.token.get_secret_value(), cache=cache) + assert (status, body["active"], body["kind"]) == (200, True, "session_refresh") + assert "token_type" not in body + + revoked = await revoke_refresh_token( + token=minted.token.get_secret_value(), client_id=client_id, master_key=MASTER_KEY, cache=cache + ) + assert revoked.status_code == 200 + status, body = await _introspect(minted.token.get_secret_value(), cache=cache) + assert (status, body) == (200, {"active": False}) + + +@pytest.mark.asyncio +async def test_introspect_accepts_rs256_signed_tokens_under_configured_signing(monkeypatch): + from cryptography.hazmat.primitives import serialization + from cryptography.hazmat.primitives.asymmetric import rsa + from pydantic import SecretStr + + from litellm.proxy import proxy_server + from litellm.proxy._experimental.mcp_server.outbound_credentials.session_token import AsymmetricSessionKeys + + private_pem = ( + rsa.generate_private_key(public_exponent=65537, key_size=2048) + .private_bytes( + serialization.Encoding.PEM, + serialization.PrivateFormat.PKCS8, + serialization.NoEncryption(), + ) + .decode() + ) + monkeypatch.setitem( + proxy_server.general_settings, + "mcp_session_token_signing", + {"algorithm": "RS256", "kid": "k1", "private_key": private_pem}, + ) + _, now, principal = _introspection_fixtures() + rs_keys = AsymmetricSessionKeys(private_key_pem=SecretStr(private_pem), kid="k1") + minted = mint_session_token(principal, rs_keys, now) + status, body = await _introspect(minted.token.get_secret_value()) + assert (status, body["active"], body["kind"]) == (200, True, "session") + + hs_signed = mint_session_token(principal, session_keys_from_master_key(MASTER_KEY), now) + status, body = await _introspect(hs_signed.token.get_secret_value()) + assert (status, body) == (200, {"active": False}) + + +@pytest.mark.asyncio +async def test_introspect_fails_closed_on_dead_user_and_503s_on_outage(): + keys, now, principal = _introspection_fixtures() + minted = mint_session_token(principal, keys, now) + + async def _reload_user_gone(user_id: str): + return "unresolvable" + + status, body = await _introspect(minted.token.get_secret_value(), reload_user=_reload_user_gone) + assert (status, body) == (200, {"active": False}) + + async def _reload_user_outage(user_id: str): + return "unavailable" + + status, body = await _introspect(minted.token.get_secret_value(), reload_user=_reload_user_outage) + assert (status, body["error"]) == (503, "temporarily_unavailable") + + status, body = await _introspect(minted.token.get_secret_value(), master_key=None) + assert (status, body["error"]) == (500, "server_error") diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_identity_env.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_identity_env.py index ac7082c2668..1c65adac4c6 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_identity_env.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_identity_env.py @@ -23,6 +23,12 @@ MGMT_MODULE = "litellm.proxy.management_endpoints.mcp_management_endpoints" @contextlib.contextmanager def _env_and_reload(**env): saved = {key: os.environ.get(key) for key in env} + utils_module = importlib.import_module(UTILS_MODULE) + mgmt_module = importlib.import_module(MGMT_MODULE) + # Restore pre-reload module attributes afterwards instead of reloading again: + # a reload re-creates the module's classes, breaking exception identity for + # modules that imported them earlier + snapshots = {module: dict(vars(module)) for module in (utils_module, mgmt_module)} def _apply_env(values): for key, value in values.items(): @@ -32,8 +38,8 @@ def _env_and_reload(**env): os.environ[key] = value def _reload(): - utils = importlib.reload(importlib.import_module(UTILS_MODULE)) - mgmt = importlib.reload(importlib.import_module(MGMT_MODULE)) + utils = importlib.reload(utils_module) + mgmt = importlib.reload(mgmt_module) return utils, mgmt try: @@ -41,7 +47,10 @@ def _env_and_reload(**env): yield _reload() finally: _apply_env(saved) - _reload() + for module, snapshot in snapshots.items(): + for key in [key for key in vars(module) if key not in snapshot]: + delattr(module, key) + vars(module).update(snapshot) def test_defaults_used_when_env_unset(): diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_rest_endpoints.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_rest_endpoints.py index ef5631218f3..0480bbc40a7 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_rest_endpoints.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_rest_endpoints.py @@ -214,6 +214,46 @@ class TestExecuteWithMcpClient: assert server.scopes == ["read", "write"] assert server.has_client_credentials is True + async def test_preview_forwards_per_server_timeout_to_client_factory(self, monkeypatch): + """The request's per-server timeout must reach the temporary MCPServer model: + the client factory reads ``server.timeout`` for both the per-request timeout + and the preview's whole-walk listing deadline.""" + captured: dict = {} + + def fake_build_stdio_env(server, raw_headers): + return None + + async def fake_create_client(*args, **kwargs): + captured["server"] = kwargs.get("server") + return object() + + monkeypatch.setattr( + rest_endpoints.global_mcp_server_manager, + "_build_stdio_env", + fake_build_stdio_env, + raising=False, + ) + monkeypatch.setattr( + rest_endpoints.global_mcp_server_manager, + "_create_mcp_client", + fake_create_client, + raising=False, + ) + + async def ok_operation(client): + return {"status": "ok"} + + payload = NewMCPServerRequest( + server_name="slow-catalog-server", + url="https://example.com", + timeout=120.5, + ) + + result = await rest_endpoints._execute_with_mcp_client(payload, ok_operation) + + assert result["status"] == "ok" + assert captured["server"].timeout == 120.5 + @pytest.mark.asyncio async def test_m2m_drops_incoming_oauth2_headers(self, monkeypatch): """For M2M OAuth servers the incoming Authorization header (which carries @@ -524,6 +564,131 @@ class TestTestToolsList: assert captured["oauth2_headers"] is None assert oauth_call_counter["count"] == 0 + async def test_preview_tools_list_times_out_on_slow_pagination(self, monkeypatch): + """A preview whose upstream paginates past the listing deadline returns a + timeout error instead of holding the request open.""" + monkeypatch.setattr(rest_endpoints, "MCP_CLIENT_TIMEOUT", 0.05, raising=False) + monkeypatch.setattr(rest_endpoints, "MCP_TOOL_LISTING_TIMEOUT", 0.05, raising=False) + + class SlowClient: + async def list_tools(self, raise_on_error=False): + await asyncio.sleep(1) + return [] + + async def fake_execute( + request, + operation, + mcp_auth_header=None, + oauth2_headers=None, + raw_headers=None, + ): + return await operation(SlowClient()) + + monkeypatch.setattr(rest_endpoints, "_execute_with_mcp_client", fake_execute, raising=False) + + from litellm.proxy._types import LitellmUserRoles + + request = _build_request() + payload = NewMCPServerRequest( + server_name="example", + url="https://example.com", + auth_type=MCPAuth.api_key, + credentials={"auth_value": "secret-key"}, + ) + + result = await rest_endpoints.test_tools_list( + request, + payload, + user_api_key_dict=UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN), + ) + + assert result["status"] == "error" + assert result["error"] is True + assert "Timed out listing tools" in result["message"] + + async def test_preview_tools_list_succeeds_within_deadline(self, monkeypatch): + """The preview timeout scope passes a fast listing through untouched.""" + from mcp.types import Tool as MCPTool + + class QuickClient: + async def list_tools(self, raise_on_error=False): + return [MCPTool(name="quick_tool", description="q", inputSchema={})] + + async def fake_execute( + request, + operation, + mcp_auth_header=None, + oauth2_headers=None, + raw_headers=None, + ): + return await operation(QuickClient()) + + monkeypatch.setattr(rest_endpoints, "_execute_with_mcp_client", fake_execute, raising=False) + + from litellm.proxy._types import LitellmUserRoles + + request = _build_request() + payload = NewMCPServerRequest( + server_name="example", + url="https://example.com", + auth_type=MCPAuth.api_key, + credentials={"auth_value": "secret-key"}, + ) + + result = await rest_endpoints.test_tools_list( + request, + payload, + user_api_key_dict=UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN), + ) + + assert result["error"] is None + assert result["message"] == "Successfully retrieved tools" + assert [tool["name"] for tool in result["tools"]] == ["quick_tool"] + + async def test_preview_tools_list_honors_per_server_timeout(self, monkeypatch): + """A per-server timeout above the global default extends the preview deadline.""" + monkeypatch.setattr(rest_endpoints, "MCP_CLIENT_TIMEOUT", 0.05, raising=False) + monkeypatch.setattr(rest_endpoints, "MCP_TOOL_LISTING_TIMEOUT", 0.05, raising=False) + + from mcp.types import Tool as MCPTool + + class SlowConfiguredClient: + timeout = 1.0 + + async def list_tools(self, raise_on_error=False): + await asyncio.sleep(0.2) + return [MCPTool(name="slow_tool", description="s", inputSchema={})] + + async def fake_execute( + request, + operation, + mcp_auth_header=None, + oauth2_headers=None, + raw_headers=None, + ): + return await operation(SlowConfiguredClient()) + + monkeypatch.setattr(rest_endpoints, "_execute_with_mcp_client", fake_execute, raising=False) + + from litellm.proxy._types import LitellmUserRoles + + request = _build_request() + payload = NewMCPServerRequest( + server_name="example", + url="https://example.com", + auth_type=MCPAuth.api_key, + credentials={"auth_value": "secret-key"}, + ) + + result = await rest_endpoints.test_tools_list( + request, + payload, + user_api_key_dict=UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN), + ) + + assert result["error"] is None + assert [tool["name"] for tool in result["tools"]] == ["slow_tool"] + async def test_extracts_oauth2_headers(self, monkeypatch): """Ensure oauth2 auth type pulls oauth headers and omits MCP auth header.""" @@ -786,9 +951,7 @@ class TestListToolsRestAPI: they do for a gateway session, never to the bare session key.""" from litellm.constants import UI_SESSION_TOKEN_TEAM_ID - session_auth = UserAPIKeyAuth( - team_id=UI_SESSION_TOKEN_TEAM_ID, user_id="grant-user", user_role="internal_user" - ) + session_auth = UserAPIKeyAuth(team_id=UI_SESSION_TOKEN_TEAM_ID, user_id="grant-user", user_role="internal_user") admitted_auth = UserAPIKeyAuth(user_id="grant-user", org_id="admitted-org") async def fake_reload(user_id): @@ -868,9 +1031,7 @@ class TestListToolsRestAPI: from litellm.constants import UI_SESSION_TOKEN_TEAM_ID from litellm.proxy._types import LiteLLM_ObjectPermissionTable - session_auth = UserAPIKeyAuth( - team_id=UI_SESSION_TOKEN_TEAM_ID, user_id="grant-user", user_role="internal_user" - ) + session_auth = UserAPIKeyAuth(team_id=UI_SESSION_TOKEN_TEAM_ID, user_id="grant-user", user_role="internal_user") scoped_auth = UserAPIKeyAuth( object_permission=LiteLLM_ObjectPermissionTable( object_permission_id="toolset-scope", @@ -952,6 +1113,123 @@ class TestListToolsRestAPI: assert scope_inputs == [session_auth] assert reload_calls == [] + async def test_single_server_response_includes_paginated_upstream_tools( + self, + monkeypatch, + ): + """The REST tools/list path should include tools beyond the upstream first page.""" + import litellm.experimental_mcp_client.client as mcp_client_module + from mcp.types import ListToolsResult, PaginatedRequestParams + from mcp.types import Tool as MCPTool + + from litellm.proxy._experimental.mcp_server.server import MCPServer + from litellm.types.mcp import MCPTransport + + async def fake_contexts(user_api_key_auth): + return [user_api_key_auth] + + async def fake_get_allowed_mcp_servers(*args, **kwargs): + return ["server-1"] + + stub_server = MCPServer( + server_id="server-1", + name="stub", + server_name="stub", + alias="stub", + url="https://example.com/mcp", + transport=MCPTransport.http, + mcp_info={"server_name": "stub"}, + ) + stub_server.available_on_public_internet = True + + mock_transport_ctx = AsyncMock() + mock_transport_ctx.__aenter__ = AsyncMock(return_value=(MagicMock(), MagicMock())) + mock_transport_ctx.__aexit__ = AsyncMock(return_value=None) + monkeypatch.setattr( + mcp_client_module, + "streamable_http_client", + MagicMock(return_value=mock_transport_ctx), + raising=False, + ) + + mock_session_ctx = AsyncMock() + mock_session_instance = AsyncMock() + mock_session_instance.initialize = AsyncMock(return_value=None) + mock_session_instance.list_tools.side_effect = [ + ListToolsResult( + tools=[ + MCPTool( + name="first_page_tool", + description="First page tool", + inputSchema={}, + ) + ], + nextCursor="page-2", + ), + ListToolsResult( + tools=[ + MCPTool( + name="second_page_tool", + description="Second page tool", + inputSchema={}, + ) + ] + ), + ] + mock_session_ctx.__aenter__ = AsyncMock(return_value=mock_session_instance) + mock_session_ctx.__aexit__ = AsyncMock(return_value=None) + monkeypatch.setattr( + mcp_client_module, + "ClientSession", + MagicMock(return_value=mock_session_ctx), + raising=False, + ) + + monkeypatch.setattr( + rest_endpoints, + "build_effective_auth_contexts", + fake_contexts, + raising=False, + ) + monkeypatch.setattr( + rest_endpoints.global_mcp_server_manager, + "get_allowed_mcp_servers", + fake_get_allowed_mcp_servers, + raising=False, + ) + monkeypatch.setattr( + rest_endpoints.global_mcp_server_manager, + "filter_server_ids_by_ip_with_info", + lambda server_ids, client_ip: (server_ids, 0), + raising=False, + ) + monkeypatch.setattr( + rest_endpoints.global_mcp_server_manager, + "get_mcp_server_by_id", + lambda server_id: stub_server if server_id == "server-1" else None, + raising=False, + ) + + request = _build_request(path="/mcp-rest/tools/list", method="GET") + result = await rest_endpoints.list_tool_rest_api( + request, + server_id="server-1", + user_api_key_dict=UserAPIKeyAuth(), + ) + + assert set(result.keys()) == {"tools", "error", "message"} + assert [tool.name for tool in result["tools"]] == [ + "first_page_tool", + "second_page_tool", + ] + assert result["error"] is None + assert result["message"] == "Successfully retrieved tools" + + assert mock_session_instance.list_tools.call_count == 2 + second_call_params = mock_session_instance.list_tools.call_args_list[1].kwargs["params"] + assert isinstance(second_call_params, PaginatedRequestParams) + assert second_call_params.cursor == "page-2" + async def test_include_disabled_tools_is_admin_only(self, monkeypatch): """include_disabled_tools skips the allowlist filter only for PROXY_ADMIN; a non-admin passing it stays filtered so the REST endpoint can't be used @@ -1153,7 +1431,11 @@ class TestListToolsRestAPI: async def test_aggregate_list_absorbs_one_server_auth_failure(self, monkeypatch): """The multi-server aggregate listing degrades a server whose upstream rejects auth to an empty contribution and still returns the healthy - server's tools with a 200, rather than surfacing a 401.""" + server's tools with a 200, rather than surfacing a 401. The absorbed + server must still show up as a classified per-server outcome so a REST + caller can tell "needs upstream auth" apart from "has no tools".""" + from pydantic import TypeAdapter + from litellm.proxy._experimental.mcp_server.exceptions import ( MCPUpstreamAuthError, ) @@ -1219,6 +1501,11 @@ class TestListToolsRestAPI: assert result["tools"] == ["good-tool"] assert result["error"] is None + wire_body = json.loads(TypeAdapter(dict).dump_json(result)) + assert wire_body["server_outcomes"] == { + "good": {"status": "ok", "tool_count": 1}, + "bad": {"status": "auth_required", "http_status": 401}, + } async def test_name_resolution_finds_server_by_uuid(self, monkeypatch): """When server_id is a name string, it should be resolved to its UUID @@ -3021,9 +3308,7 @@ class TestRestListToolsetFiltering: mock_manager = MagicMock() mock_manager.expand_tool_permissions = MagicMock(side_effect=lambda perms: perms or {}) - mock_manager.resolve_toolset_tool_permissions = AsyncMock( - return_value={"server-a": ["lookup_status"]} - ) + mock_manager.resolve_toolset_tool_permissions = AsyncMock(return_value={"server-a": ["lookup_status"]}) monkeypatch.setattr( rest_endpoints.global_mcp_server_manager, diff --git a/tests/test_litellm/proxy/anthropic_endpoints/test_endpoints.py b/tests/test_litellm/proxy/anthropic_endpoints/test_endpoints.py index 9a90daeccb7..c83ba142011 100644 --- a/tests/test_litellm/proxy/anthropic_endpoints/test_endpoints.py +++ b/tests/test_litellm/proxy/anthropic_endpoints/test_endpoints.py @@ -164,6 +164,49 @@ class TestProxyExceptionPassthrough: mock_logging.post_call_failure_hook.assert_awaited_once() +class TestHttpExceptionDictDetail: + @pytest.mark.asyncio + async def test_anthropic_response_serializes_dict_detail_http_exception(self): + """LIT-6466: a post_call guardrail's HTTPException(detail=) must + surface with a clean message plus provider_specific_fields, matching + /v1/chat/completions and /v1/responses, not the str() of the exception.""" + from fastapi import HTTPException + + import litellm.proxy.anthropic_endpoints.endpoints as ep + import litellm.proxy.proxy_server as proxy_server + from litellm.proxy._types import ProxyException, UserAPIKeyAuth + + detail = { + "error": "Content blocked: keyword 'kumquat' detected", + "keyword": "kumquat", + "guardrail": "keyword-block", + } + exc = HTTPException(status_code=400, detail=detail) + + with ( + patch.object(ep, "_read_request_body", new=AsyncMock(return_value={})), # test-quality-ok: endpoint reads the body via a module function; no injection seam + patch.object( # test-quality-ok: the guardrail raise happens deep inside this call; the test targets the endpoint's except block + ep.ProxyBaseLLMRequestProcessing, + "base_process_llm_request", + new=AsyncMock(side_effect=exc), + ), + patch.object(proxy_server, "proxy_logging_obj") as mock_logging, # test-quality-ok: module global imported at call time; no injection seam + ): + mock_logging.post_call_failure_hook = AsyncMock() + with pytest.raises(ProxyException) as exc_info: + await ep.anthropic_response( + fastapi_response=MagicMock(), + request=MagicMock(), + user_api_key_dict=UserAPIKeyAuth(), + ) + + assert exc_info.value.message == "Content blocked: keyword 'kumquat' detected" + assert "{'error'" not in exc_info.value.message + assert exc_info.value.provider_specific_fields == detail + assert exc_info.value.code == "400" + mock_logging.post_call_failure_hook.assert_awaited_once() + + class TestFailureHookRequestData: @pytest.mark.asyncio async def test_failure_hook_gets_post_setup_data_with_logging_obj(self): diff --git a/tests/test_litellm/proxy/anthropic_endpoints/test_streaming_model_restamp.py b/tests/test_litellm/proxy/anthropic_endpoints/test_streaming_model_restamp.py new file mode 100644 index 00000000000..b7bc670c7f8 --- /dev/null +++ b/tests/test_litellm/proxy/anthropic_endpoints/test_streaming_model_restamp.py @@ -0,0 +1,288 @@ +""" +Tests for restamping the public model on Anthropic Messages streaming chunks. +""" + +import json +from unittest.mock import AsyncMock, MagicMock + +import pytest + +from litellm.proxy.anthropic_endpoints.streaming_model_restamp import ( + AnthropicStreamModelRestamper, + restamp_anthropic_stream_chunk_model, +) +from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing + + +def _message_start_frame(model: str, line_end: str = "\n") -> bytes: + payload = { + "type": "message_start", + "message": {"id": "msg_1", "type": "message", "role": "assistant", "model": model, "content": []}, + } + return f"event: message_start{line_end}data: {json.dumps(payload)}{line_end}{line_end}".encode() + + +def _proxy_logging_obj_streaming(frames: list[bytes]) -> MagicMock: + async def _iterator_hook(**_kwargs): + for frame in frames: + yield frame + + proxy_logging_obj = MagicMock() + proxy_logging_obj.async_post_call_streaming_iterator_hook = _iterator_hook + proxy_logging_obj.async_post_call_streaming_hook = AsyncMock(side_effect=lambda **kwargs: kwargs["response"]) + return proxy_logging_obj + + +def _model_from_frame(frame: bytes | str) -> str: + text = frame.decode("utf-8") if isinstance(frame, bytes) else frame + data_line = next(line for line in text.split("\n") if line.startswith("data:")) + return json.loads(data_line[len("data:") :])["message"]["model"] + + +def test_restamps_sse_bytes_frame(): + restamped = restamp_anthropic_stream_chunk_model( + _message_start_frame("claude-haiku-4-5-20251001"), "claude-auto-1" + ) + + assert isinstance(restamped, bytes) + assert _model_from_frame(restamped) == "claude-auto-1" + assert b"event: message_start" in restamped + + +def test_restamps_event_dict(): + chunk = {"type": "message_start", "message": {"id": "msg_1", "model": "claude-sonnet-4-6"}} + + restamped = restamp_anthropic_stream_chunk_model(chunk, "claude-auto-2") + + assert restamped == {"type": "message_start", "message": {"id": "msg_1", "model": "claude-auto-2"}} + assert chunk["message"]["model"] == "claude-sonnet-4-6" + + +@pytest.mark.parametrize( + "chunk", + [ + b'event: content_block_delta\ndata: {"type":"content_block_delta","delta":{"text":"hi"}}\n\n', + {"type": "content_block_delta", "delta": {"text": "hi"}}, + {"type": "message_start", "message": "not-a-dict"}, + b"event: message_start\ndata: not-json\n\n", + b"data: [DONE]\n\n", + ], +) +def test_leaves_chunks_without_a_model_untouched(chunk): + assert restamp_anthropic_stream_chunk_model(chunk, "claude-auto-1") == chunk + + +@pytest.mark.asyncio +async def test_sse_generator_publishes_requested_model_on_message_start(): + """The message_start event reports the requested model, not the provider's.""" + delta_frame = b'event: content_block_delta\ndata: {"type":"content_block_delta","delta":{"text":"hi"}}\n\n' + proxy_logging_obj = _proxy_logging_obj_streaming([_message_start_frame("claude-haiku-4-5-20251001"), delta_frame]) + + chunks = [ + chunk + async for chunk in ProxyBaseLLMRequestProcessing.async_sse_data_generator( + response=MagicMock(), + user_api_key_dict=MagicMock(), + request_data={"model": "claude-auto-1"}, + proxy_logging_obj=proxy_logging_obj, + restamp_model="claude-auto-1", + ) + ] + + assert _model_from_frame(chunks[0]) == "claude-auto-1" + assert chunks[1] == delta_frame + + +@pytest.mark.asyncio +async def test_sse_generator_keeps_provider_model_when_restamping_is_off(): + proxy_logging_obj = _proxy_logging_obj_streaming([_message_start_frame("claude-haiku-4-5-20251001")]) + + chunks = [ + chunk + async for chunk in ProxyBaseLLMRequestProcessing.async_sse_data_generator( + response=MagicMock(), + user_api_key_dict=MagicMock(), + request_data={"model": "claude-auto-1"}, + proxy_logging_obj=proxy_logging_obj, + ) + ] + + assert _model_from_frame(chunks[0]) == "claude-haiku-4-5-20251001" + + +def test_restamps_message_start_split_across_transport_chunks(): + frame = _message_start_frame("claude-haiku-4-5-20251001") + restamper = AnthropicStreamModelRestamper("claude-auto-1") + + held = restamper.process(frame[:25]) + emitted = restamper.process(frame[25:]) + + assert held == b"" + assert isinstance(emitted, bytes) + assert _model_from_frame(emitted) == "claude-auto-1" + + +def test_emits_coalesced_frames_with_only_message_start_rewritten(): + delta = b'event: content_block_delta\ndata: {"type":"content_block_delta","delta":{"text":"hi"}}\n\n' + combined = _message_start_frame("claude-haiku-4-5-20251001") + delta + + emitted = restamper_output = AnthropicStreamModelRestamper("claude-auto-1").process(combined) + + assert isinstance(restamper_output, bytes) + assert _model_from_frame(emitted) == "claude-auto-1" + assert emitted.endswith(delta) + + +def test_ping_frames_keep_the_restamper_armed(): + ping = b'event: ping\ndata: {"type": "ping"}\n\n' + frame = _message_start_frame("claude-haiku-4-5-20251001") + restamper = AnthropicStreamModelRestamper("claude-auto-1") + + assert restamper.process(ping) == ping + reassembled = restamper.process(frame[:10]) + reassembled += restamper.process(frame[10:]) + + assert _model_from_frame(reassembled) == "claude-auto-1" + + +def test_first_non_ping_event_disarms_the_restamper(): + delta = b'event: content_block_delta\ndata: {"type":"content_block_delta","delta":{"text":"hi"}}\n\n' + late_message_start = _message_start_frame("claude-haiku-4-5-20251001") + restamper = AnthropicStreamModelRestamper("claude-auto-1") + + assert restamper.process(delta) == delta + assert restamper.process(late_message_start) == late_message_start + + +def test_oversized_unterminated_chunk_flushes_unmodified(): + blob = b"data: " + b"x" * 70000 + restamper = AnthropicStreamModelRestamper("claude-auto-1") + + assert restamper.process(blob) == blob + frame = _message_start_frame("claude-haiku-4-5-20251001") + assert restamper.process(frame) == frame + + +def test_dict_message_start_disarms_after_restamp(): + restamper = AnthropicStreamModelRestamper("claude-auto-1") + first = restamper.process({"type": "message_start", "message": {"id": "msg_1", "model": "claude-sonnet-4-6"}}) + second = {"type": "message_start", "message": {"id": "msg_2", "model": "claude-sonnet-4-6"}} + + assert first == {"type": "message_start", "message": {"id": "msg_1", "model": "claude-auto-1"}} + assert restamper.process(second) == second + + +@pytest.mark.asyncio +async def test_sse_generator_restamps_message_start_split_across_chunks(): + frame = _message_start_frame("claude-haiku-4-5-20251001") + proxy_logging_obj = _proxy_logging_obj_streaming([frame[:30], frame[30:]]) + + chunks = [ + chunk + async for chunk in ProxyBaseLLMRequestProcessing.async_sse_data_generator( + response=MagicMock(), + user_api_key_dict=MagicMock(), + request_data={"model": "claude-auto-1"}, + proxy_logging_obj=proxy_logging_obj, + restamp_model="claude-auto-1", + ) + ] + + joined = b"".join(chunk if isinstance(chunk, bytes) else chunk.encode("utf-8") for chunk in chunks) + assert _model_from_frame(joined) == "claude-auto-1" + + +def test_restamps_crlf_terminated_message_start_frame(): + frame = _message_start_frame("claude-haiku-4-5-20251001", line_end="\r\n") + delta = b'event: content_block_delta\r\ndata: {"type":"content_block_delta","delta":{"text":"hi"}}\r\n\r\n' + restamper = AnthropicStreamModelRestamper("claude-auto-1") + + emitted = restamper.process(frame) + + assert isinstance(emitted, bytes) + assert _model_from_frame(emitted) == "claude-auto-1" + assert emitted.endswith(b"\r\n\r\n") + assert restamper.process(delta) == delta + + +def test_restamps_cr_terminated_message_start_frame(): + frame = _message_start_frame("claude-haiku-4-5-20251001", line_end="\r") + restamper = AnthropicStreamModelRestamper("claude-auto-1") + + emitted = restamper.process(frame) + + assert isinstance(emitted, bytes) + assert b'"model":"claude-auto-1"' in emitted + assert emitted.endswith(b"\r\r") + + +def test_restamps_crlf_message_start_split_across_transport_chunks(): + frame = _message_start_frame("claude-haiku-4-5-20251001", line_end="\r\n") + restamper = AnthropicStreamModelRestamper("claude-auto-1") + + held = restamper.process(frame[:25]) + emitted = restamper.process(frame[25:]) + + assert held == b"" + assert isinstance(emitted, bytes) + assert _model_from_frame(emitted) == "claude-auto-1" + + +def test_flush_returns_restamped_held_tail(): + unterminated = _message_start_frame("claude-haiku-4-5-20251001")[:-2] + restamper = AnthropicStreamModelRestamper("claude-auto-1") + + assert restamper.process(unterminated) == b"" + flushed = restamper.flush() + + assert b'"model":"claude-auto-1"' in flushed + assert restamper.flush() == b"" + + +def test_flush_disarms_the_restamper(): + restamper = AnthropicStreamModelRestamper("claude-auto-1") + frame = _message_start_frame("claude-haiku-4-5-20251001") + + assert restamper.flush() == b"" + assert restamper.process(frame) == frame + + +@pytest.mark.asyncio +async def test_sse_generator_flushes_held_tail_at_end_of_stream(): + unterminated = _message_start_frame("claude-haiku-4-5-20251001")[:-2] + proxy_logging_obj = _proxy_logging_obj_streaming([unterminated]) + + chunks = [ + chunk + async for chunk in ProxyBaseLLMRequestProcessing.async_sse_data_generator( + response=MagicMock(), + user_api_key_dict=MagicMock(), + request_data={"model": "claude-auto-1"}, + proxy_logging_obj=proxy_logging_obj, + restamp_model="claude-auto-1", + ) + ] + + joined = b"".join(chunk if isinstance(chunk, bytes) else chunk.encode("utf-8") for chunk in chunks) + assert b'"model":"claude-auto-1"' in joined + + +@pytest.mark.asyncio +async def test_sse_generator_restamps_crlf_stream(): + frame = _message_start_frame("claude-haiku-4-5-20251001", line_end="\r\n") + delta = b'event: content_block_delta\r\ndata: {"type":"content_block_delta","delta":{"text":"hi"}}\r\n\r\n' + proxy_logging_obj = _proxy_logging_obj_streaming([frame, delta]) + + chunks = [ + chunk + async for chunk in ProxyBaseLLMRequestProcessing.async_sse_data_generator( + response=MagicMock(), + user_api_key_dict=MagicMock(), + request_data={"model": "claude-auto-1"}, + proxy_logging_obj=proxy_logging_obj, + restamp_model="claude-auto-1", + ) + ] + + assert _model_from_frame(chunks[0]) == "claude-auto-1" + assert chunks[1] == delta diff --git a/tests/test_litellm/proxy/auth/test_auth_exception_handler.py b/tests/test_litellm/proxy/auth/test_auth_exception_handler.py index 90b3b29d919..90be51cfa5b 100644 --- a/tests/test_litellm/proxy/auth/test_auth_exception_handler.py +++ b/tests/test_litellm/proxy/auth/test_auth_exception_handler.py @@ -26,6 +26,7 @@ from prisma.errors import ( from litellm._logging import verbose_proxy_logger +from litellm.constants import INVALID_VIRTUAL_KEY_ERROR_MARKER from litellm.exceptions import BudgetExceededError from litellm.proxy._types import ProxyErrorTypes, ProxyException, UserAPIKeyAuth from litellm.proxy.auth.auth_exception_handler import UserAPIKeyAuthExceptionHandler @@ -703,23 +704,43 @@ async def test_auth_failure_ip_stamp_does_not_mutate_callers_request_data(): assert request_data == {"model": "gpt-4o"} +def _marked_malformed_key_error() -> HTTPException: + """Build the malformed-key 401 as its raise site does: marker stamped on it.""" + error = HTTPException(status_code=401, detail="LiteLLM Virtual Key expected. Received=test") + setattr(error, INVALID_VIRTUAL_KEY_ERROR_MARKER, True) + return error + + @pytest.mark.asyncio @pytest.mark.parametrize( - "auth_error,expect_traceback", + "auth_error,expect_traceback,expect_level", [ pytest.param( ProxyException( message="Authentication Error", type=ProxyErrorTypes.auth_error, param=None, code=401 ), False, + "ERROR", id="expected_401_no_traceback", ), - pytest.param(ValueError("unexpected internal error"), True, id="unexpected_error_keeps_traceback"), + pytest.param(ValueError("unexpected internal error"), True, "ERROR", id="unexpected_error_keeps_traceback"), + pytest.param( + _marked_malformed_key_error(), + False, + "WARNING", + id="malformed_virtual_key_warning_no_traceback", + ), + pytest.param( + HTTPException(status_code=401, detail="LiteLLM Virtual Key expected. Received=test"), + False, + "ERROR", + id="phrase_without_marker_stays_loud", + ), ], ) -async def test_handle_authentication_error_traceback_only_for_unexpected_errors(auth_error, expect_traceback, caplog): +async def test_handle_authentication_error_traceback_only_for_unexpected_errors(auth_error, expect_traceback, expect_level, caplog): """Regression for LIT-6043: expected 4xx auth rejections must not format a - traceback via logger.exception; unexpected errors must keep it.""" + traceback via logger.exception; malformed virtual keys log at WARNING.""" handler = UserAPIKeyAuthExceptionHandler() with ( @@ -740,8 +761,8 @@ async def test_handle_authentication_error_traceback_only_for_unexpected_errors( try: try: raise auth_error - except (ProxyException, ValueError) as caught: - with caplog.at_level("ERROR", logger="LiteLLM Proxy"), pytest.raises(ProxyException): + except (ProxyException, ValueError, HTTPException) as caught: + with caplog.at_level(expect_level, logger="LiteLLM Proxy"), pytest.raises((ProxyException, HTTPException)): await handler._handle_authentication_error( caught, MagicMock(), @@ -756,3 +777,6 @@ async def test_handle_authentication_error_traceback_only_for_unexpected_errors( records = [r for r in caplog.records if "user_api_key_auth(): Exception occured" in r.getMessage()] assert len(records) == 1 assert (records[0].exc_info is not None) is expect_traceback + assert records[0].levelname == expect_level + expected_logger_name = "LiteLLM Proxy.stdout" if expect_level == "WARNING" else "LiteLLM Proxy" + assert records[0].name == expected_logger_name diff --git a/tests/test_litellm/proxy/auth/test_cli_auth.py b/tests/test_litellm/proxy/auth/test_cli_auth.py index c9b31a1d776..5cde5522376 100644 --- a/tests/test_litellm/proxy/auth/test_cli_auth.py +++ b/tests/test_litellm/proxy/auth/test_cli_auth.py @@ -82,7 +82,7 @@ async def test_poll_for_ready_404(sleep_mock, request_mock): _poll_for_ready_data( "https://litellm.com", poll_interval=1, total_timeout=1, request_timeout=42 ) - request_mock.assert_called_once_with("https://litellm.com", timeout=42) + request_mock.assert_called_once_with("https://litellm.com", headers=None, timeout=42) @pytest.mark.asyncio @@ -103,7 +103,7 @@ async def test_poll_for_ready_200_ready(sleep_mock, click_mock, request_mock): ) assert actual == {"status": "ready", "json": "data"} click_mock.assert_not_called() - request_mock.assert_called_once_with("https://litellm.com", timeout=42) + request_mock.assert_called_once_with("https://litellm.com", headers=None, timeout=42) sleep_mock.assert_not_called() @@ -131,8 +131,8 @@ async def test_poll_for_ready_single_pending(sleep_mock, click_mock, request_moc click_mock.assert_not_called() request_mock.assert_has_calls( [ - call("https://litellm.com", timeout=42), - call("https://litellm.com", timeout=42), + call("https://litellm.com", headers=None, timeout=42), + call("https://litellm.com", headers=None, timeout=42), ] ) sleep_mock.assert_called_once_with(1) @@ -168,8 +168,8 @@ async def test_poll_for_ready_pending(sleep_mock, click_mock, request_mock): click_mock.assert_has_calls([call("Pending message"), call("Pending message")]) request_mock.assert_has_calls( [ - call("https://litellm.com", timeout=42), - call("https://litellm.com", timeout=42), + call("https://litellm.com", headers=None, timeout=42), + call("https://litellm.com", headers=None, timeout=42), ] ) sleep_mock.assert_has_calls([call(1), call(1)]) @@ -194,7 +194,7 @@ async def test_poll_for_ready_connection_failure(sleep_mock, click_mock, request click_mock.assert_called_once_with("Connection error (will retry): ERROR") request_mock.assert_has_calls( [ - call("https://litellm.com", timeout=42), + call("https://litellm.com", headers=None, timeout=42), ] ) sleep_mock.assert_has_calls([call(1), call(1)]) diff --git a/tests/test_litellm/proxy/auth/test_model_access_group_budgets.py b/tests/test_litellm/proxy/auth/test_model_access_group_budgets.py new file mode 100644 index 00000000000..fb82d8708fd --- /dev/null +++ b/tests/test_litellm/proxy/auth/test_model_access_group_budgets.py @@ -0,0 +1,543 @@ +""" +Which model access groups a request is charged to. + +A group is attributed only when its name appears on an allowlist the caller was granted, so the +group is what authorized the call. Asking for a model that merely belongs to a group attributes +nothing, and every level that can name a group (key, team, team-member scope, project, org) is +unioned rather than ranked. +""" + +from types import SimpleNamespace +from unittest.mock import patch + +import pytest + +import litellm +from litellm import Router +from litellm.proxy._types import ( + LiteLLM_BudgetTable, + Litellm_EntityType, + LiteLLM_OrganizationTable, + LiteLLM_ProjectTableCachedObj, + LiteLLM_TeamMembership, + LiteLLM_TeamTable, + UserAPIKeyAuth, +) +from litellm.proxy.auth.auth_checks import ( + _model_access_group_max_budget_check, + collect_matched_model_access_groups, + common_checks, + stamp_matched_model_access_groups, +) +from litellm.proxy.common_utils.reset_budget_job import _model_access_group_counter_key +from litellm.proxy.common_utils.user_api_key_cache import ( + UserApiKeyCache, + model_access_group_registry_cache_key, + model_access_group_spend_counter_key, + team_membership_reservation_cache_key, +) +from litellm.proxy.utils import ProxyLogging + +TEAM_ID = "team-1" +USER_ID = "user-1" +ORG_ID = "org-1" +BUDGETED_GROUPS = ("tier-a", "tier-b", "claude-tier") +MODEL_ACCESS_GROUP_COUNTER_KEY = model_access_group_spend_counter_key("tier-a") + +MODEL_LIST = [ + { + "model_name": "gpt-4o", + "litellm_params": {"model": "openai/gpt-4o", "api_key": "k"}, + "model_info": {"access_groups": ["tier-a", "tier-b"]}, + }, + { + "model_name": "claude-sonnet", + "litellm_params": {"model": "anthropic/claude-sonnet", "api_key": "k"}, + "model_info": {"access_groups": ["claude-tier"]}, + }, +] + + +class _ExplodingPrismaClient: + """Every lookup in these tests is served from the injected cache; a real DB read is a bug.""" + + def __getattr__(self, name: str) -> object: + raise AssertionError(f"unexpected database access: {name}") + + +class _CountingRouter(Router): + """Counts access-group lookups, so a test can prove the registry gate skipped them.""" + + def __init__(self, **kwargs) -> None: + super().__init__(**kwargs) + self.access_group_lookups = 0 + + def get_model_access_groups(self, *args, **kwargs): + self.access_group_lookups += 1 + return super().get_model_access_groups(*args, **kwargs) + + +async def _cache( + budgeted_groups: tuple[str, ...] = BUDGETED_GROUPS, + member_allowed_models: tuple[str, ...] = (), + org_models: tuple[str, ...] = (), +) -> UserApiKeyCache: + cache = UserApiKeyCache() + await cache.async_set_cache(key=model_access_group_registry_cache_key(), value=budgeted_groups) + if member_allowed_models: + await cache.async_set_cache( + key=team_membership_reservation_cache_key(user_id=USER_ID, team_id=TEAM_ID), + value=LiteLLM_TeamMembership( + user_id=USER_ID, + team_id=TEAM_ID, + budget_id="member-budget", + litellm_budget_table=LiteLLM_BudgetTable(allowed_models=list(member_allowed_models)), + ), + model_type=LiteLLM_TeamMembership, + ) + if org_models: + await cache.async_set_cache( + key=f"org_id:{ORG_ID}", + value=LiteLLM_OrganizationTable( + organization_id=ORG_ID, + budget_id="org-budget", + models=list(org_models), + created_by=USER_ID, + updated_by=USER_ID, + ), + model_type=LiteLLM_OrganizationTable, + ) + return cache + + +async def _matched( + *, + model: str = "gpt-4o", + key_models: list[str] | None = None, + team_models: list[str] | None = None, + team_org_id: str | None = None, + project_models: list[str] | None = None, + valid_token: UserAPIKeyAuth | None = None, + cache: UserApiKeyCache | None = None, + llm_router: Router | None = None, +) -> tuple[str, ...]: + resolved_cache = cache if cache is not None else await _cache() + return await collect_matched_model_access_groups( + model=model, + valid_token=valid_token + if valid_token is not None + else UserAPIKeyAuth(api_key="hashed", models=key_models or [], team_id=TEAM_ID, user_id=USER_ID), + team_object=( + LiteLLM_TeamTable(team_id=TEAM_ID, models=team_models, organization_id=team_org_id) + if team_models is not None + else None + ), + project_object=( + LiteLLM_ProjectTableCachedObj(project_id="project-1", models=project_models) + if project_models is not None + else None + ), + llm_router=llm_router if llm_router is not None else Router(model_list=MODEL_LIST), + prisma_client=_ExplodingPrismaClient(), + user_api_key_cache=resolved_cache, + proxy_logging_obj=ProxyLogging(user_api_key_cache=resolved_cache), + ) + + +@pytest.mark.asyncio +async def test_group_named_on_the_key_is_attributed(): + assert await _matched(key_models=["tier-a"]) == ("tier-a",) + + +@pytest.mark.asyncio +async def test_model_granted_directly_on_the_key_attributes_nothing(): + assert await _matched(key_models=["gpt-4o"]) == () + + +@pytest.mark.asyncio +@pytest.mark.parametrize("key_models", [["*"], [], ["all-proxy-models"]]) +async def test_unrestricted_key_attributes_nothing(key_models: list[str]): + assert await _matched(key_models=key_models) == () + + +@pytest.mark.asyncio +async def test_group_that_does_not_serve_the_requested_model_is_not_attributed(): + assert await _matched(model="gpt-4o", key_models=["claude-tier"]) == () + + +@pytest.mark.asyncio +async def test_both_granted_groups_covering_the_model_are_attributed(): + assert await _matched(key_models=["tier-b", "tier-a"]) == ("tier-a", "tier-b") + + +@pytest.mark.asyncio +async def test_group_named_only_on_the_team_is_attributed(): + assert await _matched(key_models=[], team_models=["tier-a"]) == ("tier-a",) + + +@pytest.mark.asyncio +async def test_group_named_only_in_a_team_members_scope_is_attributed(): + assert await _matched( + model="claude-sonnet", + key_models=["*"], + team_models=["*"], + cache=await _cache(member_allowed_models=("claude-tier",)), + ) == ("claude-tier",) + + +@pytest.mark.asyncio +async def test_group_named_only_on_the_project_is_attributed(): + assert await _matched(key_models=["*"], project_models=["tier-b"]) == ("tier-b",) + + +@pytest.mark.asyncio +async def test_group_named_only_on_the_org_is_attributed(): + assert await _matched( + valid_token=UserAPIKeyAuth(api_key="hashed", models=["*"], user_id=USER_ID, org_id=ORG_ID), + cache=await _cache(org_models=("tier-a",)), + ) == ("tier-a",) + + +@pytest.mark.asyncio +async def test_group_named_on_the_teams_org_is_attributed_when_the_key_names_no_org(): + assert await _matched( + key_models=["*"], + team_models=["*"], + team_org_id=ORG_ID, + cache=await _cache(org_models=("tier-b",)), + ) == ("tier-b",) + + +@pytest.mark.asyncio +async def test_all_team_models_sentinel_on_the_key_resolves_to_the_teams_groups(): + assert await _matched( + valid_token=UserAPIKeyAuth( + api_key="hashed", + models=["all-team-models"], + team_models=["tier-a"], + team_id=TEAM_ID, + user_id=USER_ID, + ), + ) == ("tier-a",) + + +@pytest.mark.asyncio +async def test_group_without_a_budget_is_not_attributed(): + assert await _matched(key_models=["tier-a"], cache=await _cache(budgeted_groups=("tier-b",))) == () + + +@pytest.mark.asyncio +async def test_empty_registry_skips_the_access_group_matching_entirely(): + router = _CountingRouter(model_list=MODEL_LIST) + + assert await _matched(key_models=["tier-a"], cache=await _cache(budgeted_groups=()), llm_router=router) == () + assert router.access_group_lookups == 0 + + assert await _matched(key_models=["tier-a"], llm_router=router) == ("tier-a",) + assert router.access_group_lookups == 1 + + +@pytest.mark.asyncio +async def test_stamp_records_the_matched_groups_on_the_auth_object(): + cache = await _cache() + valid_token = UserAPIKeyAuth(api_key="hashed", models=["tier-a", "tier-b"], team_id=TEAM_ID, user_id=USER_ID) + + await stamp_matched_model_access_groups( + model="gpt-4o", + valid_token=valid_token, + team_object=None, + project_object=None, + llm_router=Router(model_list=MODEL_LIST), + prisma_client=_ExplodingPrismaClient(), + user_api_key_cache=cache, + proxy_logging_obj=ProxyLogging(user_api_key_cache=cache), + ) + + assert valid_token.matched_model_access_groups == ["tier-a", "tier-b"] + + +class _BrokenRouter(Router): + def get_model_access_groups(self, *args, **kwargs): + raise RuntimeError("access group store unavailable") + + +@pytest.mark.asyncio +async def test_stamp_does_not_break_auth_when_the_access_group_lookup_fails(): + cache = await _cache() + valid_token = UserAPIKeyAuth(api_key="hashed", models=["tier-a"], team_id=TEAM_ID, user_id=USER_ID) + + await stamp_matched_model_access_groups( + model="gpt-4o", + valid_token=valid_token, + team_object=None, + project_object=None, + llm_router=_BrokenRouter(model_list=MODEL_LIST), + prisma_client=_ExplodingPrismaClient(), + user_api_key_cache=cache, + proxy_logging_obj=ProxyLogging(user_api_key_cache=cache), + ) + + assert valid_token.matched_model_access_groups is None + + +@pytest.mark.asyncio +async def test_stamp_leaves_the_auth_object_untouched_when_nothing_matched(): + cache = await _cache() + valid_token = UserAPIKeyAuth(api_key="hashed", models=["gpt-4o"], team_id=TEAM_ID, user_id=USER_ID) + + await stamp_matched_model_access_groups( + model="gpt-4o", + valid_token=valid_token, + team_object=None, + project_object=None, + llm_router=Router(model_list=MODEL_LIST), + prisma_client=_ExplodingPrismaClient(), + user_api_key_cache=cache, + proxy_logging_obj=ProxyLogging(user_api_key_cache=cache), + ) + + assert valid_token.matched_model_access_groups is None + + +class _MagBudgetRow: + """One ``LiteLLM_ModelAccessGroupBudgetTable`` row as prisma hands it back.""" + + def __init__(self, access_group_name: str, spend: float = 0.0, max_budget: float | None = None) -> None: + self.access_group_name = access_group_name + self.spend = spend + self.litellm_budget_table = None if max_budget is None else SimpleNamespace(max_budget=max_budget) + + +class _RecordingPrismaClient: + """Serves budget rows and records which groups actually reached the database.""" + + def __init__(self, *rows: _MagBudgetRow) -> None: + self.rows = {row.access_group_name: row for row in rows} + self.batches: list[list[str]] = [] + self.db = SimpleNamespace( + litellm_modelaccessgroupbudgettable=SimpleNamespace(find_many=self._find_many) + ) + + async def _find_many(self, **kwargs): + requested = list(kwargs["where"]["access_group_name"]["in"]) + self.batches.append(requested) + return [self.rows[group] for group in requested if group in self.rows] + + +def _spend_reader(spend_by_counter_key: dict[str, float]): + """Stand-in for proxy_server.get_current_spend, recording every counter key it is asked for.""" + seen: list[str] = [] + + async def read(counter_key, fallback_spend, max_budget=None, **kwargs): + seen.append(counter_key) + return spend_by_counter_key.get(counter_key, fallback_spend) + + return read, seen + + +async def _enforce( + matched: tuple[str, ...], + *rows: _MagBudgetRow, + spend_by_counter_key: dict[str, float] | None = None, + prisma_client: object | None = None, + cache: UserApiKeyCache | None = None, +) -> list[str]: + read, seen = _spend_reader(spend_by_counter_key or {}) + # The check takes its client and cache as arguments, injected just below. get_current_spend is the + # one collaborator it reaches by a lazy `from litellm.proxy.proxy_server import`, with no parameter. + with patch("litellm.proxy.proxy_server.get_current_spend", read): # test-quality-ok: get_current_spend is lazily imported inside _model_access_group_max_budget_check and has no injection point + await _model_access_group_max_budget_check( + matched_model_access_groups=matched, + prisma_client=prisma_client if prisma_client is not None else _RecordingPrismaClient(*rows), + user_api_key_cache=cache if cache is not None else UserApiKeyCache(), + ) + return seen + + +@pytest.mark.asyncio +async def test_group_under_its_max_budget_passes(): + assert await _enforce( + ("tier-a",), + _MagBudgetRow("tier-a", spend=4.0, max_budget=10.0), + spend_by_counter_key={MODEL_ACCESS_GROUP_COUNTER_KEY: 4.0}, + ) == [MODEL_ACCESS_GROUP_COUNTER_KEY] + + +@pytest.mark.asyncio +async def test_group_exactly_at_its_max_budget_blocks_the_request(): + """A pool whose spend has reached the ceiling has nothing left, so the next request is refused. + + This is where the check departs from the tag one it otherwise mirrors, and it matches where + keys and organizations already draw the line. + """ + with pytest.raises(litellm.BudgetExceededError) as exc_info: + await _enforce( + ("tier-a",), + _MagBudgetRow("tier-a", max_budget=10.0), + spend_by_counter_key={MODEL_ACCESS_GROUP_COUNTER_KEY: 10.0}, + ) + + assert exc_info.value.entity_id == "tier-a" + assert exc_info.value.current_cost == 10.0 + + +@pytest.mark.asyncio +async def test_group_just_under_its_max_budget_passes(): + """Asserting the counter was read is what keeps this honest: a group that got skipped entirely, + because its row never arrived or carried no budget, would also not raise.""" + assert await _enforce( + ("tier-a",), + _MagBudgetRow("tier-a", max_budget=10.0), + spend_by_counter_key={MODEL_ACCESS_GROUP_COUNTER_KEY: 9.99}, + ) == [MODEL_ACCESS_GROUP_COUNTER_KEY] + + +@pytest.mark.asyncio +async def test_a_non_positive_budget_means_no_budget(): + """The reservation path treats max_budget <= 0 as unbudgeted, so the read-time check must agree. + + Without this the exclusive ceiling would turn a zero into a total freeze on one path and a + no-op on the other. + """ + assert ( + await _enforce( + ("tier-a",), + _MagBudgetRow("tier-a", max_budget=0.0), + spend_by_counter_key={MODEL_ACCESS_GROUP_COUNTER_KEY: 5.0}, + ) + == [] + ) + + +@pytest.mark.asyncio +async def test_group_over_its_max_budget_blocks_the_request_and_names_the_group(): + with pytest.raises(litellm.BudgetExceededError) as exc_info: + await _enforce( + ("tier-a",), + _MagBudgetRow("tier-a", max_budget=10.0), + spend_by_counter_key={MODEL_ACCESS_GROUP_COUNTER_KEY: 10.5}, + ) + + assert exc_info.value.entity_id == "tier-a" + assert exc_info.value.entity_type == Litellm_EntityType.MODEL_ACCESS_GROUP.value + assert exc_info.value.current_cost == 10.5 + assert exc_info.value.max_budget == 10.0 + assert "tier-a" in str(exc_info.value) + + +@pytest.mark.asyncio +async def test_group_with_a_row_but_no_budget_never_blocks(): + """An admin can register a group without a ceiling; that must not become an implicit zero budget.""" + assert ( + await _enforce( + ("tier-a",), + _MagBudgetRow("tier-a", spend=9999.0), + spend_by_counter_key={MODEL_ACCESS_GROUP_COUNTER_KEY: 9999.0}, + ) + == [] + ) + + +@pytest.mark.asyncio +async def test_a_cold_counter_falls_back_to_the_spend_recorded_on_the_row(): + """After a counter expires the DB row is the only record of the spend, so it has to be read.""" + with pytest.raises(litellm.BudgetExceededError) as exc_info: + await _enforce(("tier-a",), _MagBudgetRow("tier-a", spend=12.0, max_budget=10.0)) + + assert exc_info.value.current_cost == 12.0 + + +@pytest.mark.asyncio +async def test_an_over_budget_group_blocks_even_when_another_matched_group_is_fine(): + with pytest.raises(litellm.BudgetExceededError) as exc_info: + await _enforce( + ("tier-a", "tier-b"), + _MagBudgetRow("tier-a", max_budget=10.0), + _MagBudgetRow("tier-b", max_budget=1.0), + spend_by_counter_key={ + MODEL_ACCESS_GROUP_COUNTER_KEY: 1.0, + model_access_group_spend_counter_key("tier-b"): 5.0, + }, + ) + + assert exc_info.value.entity_id == "tier-b" + + +@pytest.mark.asyncio +async def test_request_that_matched_no_group_touches_neither_database_nor_counters(): + assert await _enforce((), prisma_client=_ExplodingPrismaClient()) == [] + + +@pytest.mark.asyncio +async def test_budget_check_reads_the_counter_key_the_reset_job_clears(): + """Reads and resets must agree, or a rollover clears a counter nobody reads.""" + reset_job_key = _model_access_group_counter_key(SimpleNamespace(access_group_name="tier-a")) + + assert await _enforce(("tier-a",), _MagBudgetRow("tier-a", max_budget=10.0)) == [reset_job_key] + + +@pytest.mark.asyncio +async def test_a_second_request_serves_the_budget_row_from_cache(): + cache = UserApiKeyCache() + prisma_client = _RecordingPrismaClient(_MagBudgetRow("tier-a", max_budget=10.0)) + + await _enforce(("tier-a",), prisma_client=prisma_client, cache=cache) + await _enforce(("tier-a",), prisma_client=prisma_client, cache=cache) + + assert prisma_client.batches == [["tier-a"]] + + +@pytest.mark.asyncio +async def test_a_database_error_does_not_block_the_request(): + class _FailingPrismaClient: + def __init__(self) -> None: + self.db = SimpleNamespace( + litellm_modelaccessgroupbudgettable=SimpleNamespace(find_many=self._boom) + ) + + async def _boom(self, **kwargs): + raise RuntimeError("database unavailable") + + assert await _enforce(("tier-a",), prisma_client=_FailingPrismaClient()) == [] + + +async def _common_checks_with_over_budget_group(*, skip_budget_checks: bool) -> bool: + cache = await _cache() + prisma_client = _RecordingPrismaClient(_MagBudgetRow("tier-a", max_budget=1.0)) + read, _ = _spend_reader({MODEL_ACCESS_GROUP_COUNTER_KEY: 99.0}) + + with ( + # common_checks resolves all three off the proxy_server module at call time; its signature + # has no client, cache or spend-reader parameter to pass them through instead. + patch("litellm.proxy.proxy_server.prisma_client", prisma_client), # test-quality-ok: common_checks lazily imports prisma_client from proxy_server and takes no client parameter + patch("litellm.proxy.proxy_server.user_api_key_cache", cache), # test-quality-ok: common_checks lazily imports user_api_key_cache from proxy_server and takes no cache parameter + patch("litellm.proxy.proxy_server.get_current_spend", read), # test-quality-ok: get_current_spend is lazily imported inside the budget check and has no injection point + ): + return await common_checks( + request_body={"model": "gpt-4o", "messages": []}, + team_object=None, + user_object=None, + end_user_object=None, + global_proxy_spend=None, + general_settings={}, + route="/v1/chat/completions", + llm_router=Router(model_list=MODEL_LIST), + proxy_logging_obj=ProxyLogging(user_api_key_cache=cache), + valid_token=UserAPIKeyAuth(api_key="hashed", models=["tier-a"], user_id=USER_ID), + request=SimpleNamespace(method="POST", headers={}, query_params={}, url=SimpleNamespace(path="/v1/chat/completions")), + skip_budget_checks=skip_budget_checks, + ) + + +@pytest.mark.asyncio +async def test_common_checks_blocks_a_request_whose_group_is_over_budget(): + with pytest.raises(litellm.BudgetExceededError) as exc_info: + await _common_checks_with_over_budget_group(skip_budget_checks=False) + + assert exc_info.value.entity_id == "tier-a" + + +@pytest.mark.asyncio +async def test_free_model_routes_skip_the_model_access_group_budget_check(): + """skip_budget_checks is how free models stay free; it has to cover this budget too.""" + assert await _common_checks_with_over_budget_group(skip_budget_checks=True) is True diff --git a/tests/test_litellm/proxy/client/cli/autoroute/test_commands.py b/tests/test_litellm/proxy/client/cli/autoroute/test_commands.py index 74bf1c95777..4a3b3ef22c4 100644 --- a/tests/test_litellm/proxy/client/cli/autoroute/test_commands.py +++ b/tests/test_litellm/proxy/client/cli/autoroute/test_commands.py @@ -157,6 +157,7 @@ class TestUpCommand: assert captured["settings"]["theme"] == "dark" assert captured["settings"]["env"]["ANTHROPIC_BASE_URL"] == "http://127.0.0.1:5483" assert captured["settings"]["env"]["ANTHROPIC_AUTH_TOKEN"] == "fixed-master-key" + assert captured["settings"]["env"]["ENABLE_TOOL_SEARCH"] == "true" assert "apiKeyHelper" not in captured["settings"] assert captured["settings_mode"] == 0o600 diff --git a/tests/test_litellm/proxy/client/cli/autoroute/test_settings.py b/tests/test_litellm/proxy/client/cli/autoroute/test_settings.py index 40d3e7f2aee..87a33c79a79 100644 --- a/tests/test_litellm/proxy/client/cli/autoroute/test_settings.py +++ b/tests/test_litellm/proxy/client/cli/autoroute/test_settings.py @@ -19,6 +19,13 @@ def test_sets_base_url_and_auth_token(): merged = merge_claude_settings_static_token({}, "http://127.0.0.1:4000/", "token-abc") assert merged["env"]["ANTHROPIC_BASE_URL"] == "http://127.0.0.1:4000" assert merged["env"]["ANTHROPIC_AUTH_TOKEN"] == "token-abc" + assert merged["env"]["ENABLE_TOOL_SEARCH"] == "true" + + +def test_preserves_existing_tool_search(): + settings = {"env": {"ENABLE_TOOL_SEARCH": "false"}} + merged = merge_claude_settings_static_token(settings, "http://127.0.0.1:4000", "token-abc") + assert merged["env"]["ENABLE_TOOL_SEARCH"] == "false" def test_drops_stray_api_key(): diff --git a/tests/test_litellm/proxy/client/cli/test_agents.py b/tests/test_litellm/proxy/client/cli/test_agents.py index 32dfb8d521d..0191dad3d94 100644 --- a/tests/test_litellm/proxy/client/cli/test_agents.py +++ b/tests/test_litellm/proxy/client/cli/test_agents.py @@ -77,9 +77,19 @@ class TestBuildAgentEnv: ) assert env["ANTHROPIC_BASE_URL"] == "http://localhost:4000" assert env["ANTHROPIC_AUTH_TOKEN"] == "sk-key" + assert env["ENABLE_TOOL_SEARCH"] == "true" assert "OPENAI_BASE_URL" not in env assert "OPENAI_API_KEY" not in env + def test_anthropic_profile_preserves_existing_tool_search(self): + env = build_agent_env( + {"ENABLE_TOOL_SEARCH": "false"}, + "http://localhost:4000", + "sk-key", + frozenset({"anthropic"}), + ) + assert env["ENABLE_TOOL_SEARCH"] == "false" + def test_anthropic_profile_drops_existing_api_key(self): env = build_agent_env( {"ANTHROPIC_API_KEY": "real-key"}, @@ -96,6 +106,7 @@ class TestBuildAgentEnv: assert env["OPENAI_BASE_URL"] == "http://localhost:4000/v1" assert env["OPENAI_API_KEY"] == "sk-key" assert "ANTHROPIC_BASE_URL" not in env + assert "ENABLE_TOOL_SEARCH" not in env def test_both_profiles_set_everything(self): env = build_agent_env( @@ -105,6 +116,7 @@ class TestBuildAgentEnv: assert env["OPENAI_BASE_URL"] == "http://localhost:4000/v1" assert env["ANTHROPIC_AUTH_TOKEN"] == "sk-key" assert env["OPENAI_API_KEY"] == "sk-key" + assert env["ENABLE_TOOL_SEARCH"] == "true" def test_preserves_unrelated_env_and_does_not_mutate_input(self): base = {"PATH": "/usr/bin", "ANTHROPIC_API_KEY": "real-key"} @@ -201,6 +213,7 @@ class TestRunAgent: env = calls["env"] assert env["ANTHROPIC_BASE_URL"] == "http://localhost:4000" assert env["ANTHROPIC_AUTH_TOKEN"] == "sk-key" + assert env["ENABLE_TOOL_SEARCH"] == "true" assert "ANTHROPIC_API_KEY" not in env assert "OPENAI_BASE_URL" not in env @@ -218,6 +231,7 @@ class TestRunAgent: assert calls["env"]["OPENAI_BASE_URL"] == "http://localhost:4000/v1" assert calls["env"]["OPENAI_API_KEY"] == "sk-key" assert "ANTHROPIC_BASE_URL" not in calls["env"] + assert "ENABLE_TOOL_SEARCH" not in calls["env"] def test_codex_injects_proxy_provider_args_before_user_args(self): calls = {} diff --git a/tests/test_litellm/proxy/client/cli/test_auth_commands.py b/tests/test_litellm/proxy/client/cli/test_auth_commands.py index 85a4d90abf9..1d0a99b8e0a 100644 --- a/tests/test_litellm/proxy/client/cli/test_auth_commands.py +++ b/tests/test_litellm/proxy/client/cli/test_auth_commands.py @@ -1373,6 +1373,7 @@ class TestLoginConfigClaude: assert result.exit_code == 0 written = json.loads(settings_path.read_text()) assert written["env"]["ANTHROPIC_BASE_URL"] == "https://test.example.com" + assert written["env"]["ENABLE_TOOL_SEARCH"] == "true" assert written["apiKeyHelper"] == "/usr/local/bin/lite --base-url https://test.example.com auth print-token" assert "Configured Claude Code" in result.output diff --git a/tests/test_litellm/proxy/client/cli/test_claude_settings.py b/tests/test_litellm/proxy/client/cli/test_claude_settings.py index 9010fb4c022..e5f2a9d95bd 100644 --- a/tests/test_litellm/proxy/client/cli/test_claude_settings.py +++ b/tests/test_litellm/proxy/client/cli/test_claude_settings.py @@ -26,6 +26,64 @@ def _owners(*backup_paths): CLAUDE_SETTINGS_MODULE = "litellm.proxy.client.cli.commands.claude_settings" AUTH_MODULE = "litellm.proxy.client.cli.commands.auth" +WINDOWS_LITE_EXE = "C:\\Users\\u\\AppData\\Local\\Programs\\Python\\Python313\\Scripts\\lite.EXE" + +CMD_METACHARACTERS = frozenset("&|<>^()") +CMD_PERCENT_GUARD = "%%cd:~,%" + + +def _through_cmd_exe(command): + """The line cmd.exe hands to CreateProcess after reading the apiKeyHelper. + + A `"` toggles cmd's quote state and the metacharacters only act outside it. cmd expands + `%VAR%` even inside quotes, so every `%` has to arrive as the `%%cd:~,%` guard: the first + `%` has no variable name and stays literal, and `%cd:~,%` is a zero length substring of `cd`. + """ + assert not any(CMD_METACHARACTERS & set(run) for run in command.split('"')[::2]), command + assert command.count("%") == 3 * command.count(CMD_PERCENT_GUARD), command + return command.replace(CMD_PERCENT_GUARD, "%") + + +def _through_c_runtime(command_line): + """argv as the Microsoft C runtime builds it for the `lite` executable. + + Outside quotes whitespace ends an argument. A `"` toggles quoting, and inside quotes `""` + is a literal quote. Backslashes are literal unless they run up to a `"`, where each pair + is one backslash and an odd one left over makes the quote literal. + """ + argv = [] + current = None + quoted = False + i = 0 + while i < len(command_line): + ch = command_line[i] + if ch in " \t" and not quoted: + if current is not None: + argv.append(current) + current = None + i += 1 + continue + if current is None: + current = "" + if ch == "\\": + run = len(command_line[i:]) - len(command_line[i:].lstrip("\\")) + before_quote = command_line[i + run : i + run + 1] == '"' + current += "\\" * (run // 2 if before_quote else run) + if before_quote and run % 2: + current += '"' + i += 1 + i += run + elif ch == '"': + if quoted and command_line[i + 1 : i + 2] == '"': + current += '"' + i += 1 + else: + quoted = not quoted + i += 1 + else: + current += ch + i += 1 + return argv if current is None else [*argv, current] @pytest.fixture @@ -48,6 +106,7 @@ class TestWriteClaudeSettings: written = json.loads(settings_path.read_text()) assert written["env"]["ANTHROPIC_BASE_URL"] == "https://proxy.example.com" + assert written["env"]["ENABLE_TOOL_SEARCH"] == "true" assert written["apiKeyHelper"] == "/usr/local/bin/lite --base-url https://proxy.example.com auth print-token" def test_updates_an_existing_file_preserving_unrelated_settings(self, paths, lite_on_path): @@ -198,6 +257,36 @@ class TestApiKeyHelperIsActuallyInvocable: assert "Not authenticated for this server" in result.output + def _windows_argv(self, lite_exe, base_url): + with patch(f"{CLAUDE_SETTINGS_MODULE}.shutil.which", return_value=lite_exe): + helper = resolve_api_key_helper(base_url, platform="win32") + return _through_c_runtime(_through_cmd_exe(helper)) + + @pytest.mark.parametrize( + ("lite_exe", "base_url"), + [ + (WINDOWS_LITE_EXE, "http://localhost:4000"), + ("C:\\Program Files\\LiteLLM\\lite.EXE", "https://gateway.example.com/?a=1&b=2"), + ("C:\\Users\\u\\Scripts\\lite.EXE", "https://gateway.example.com/team%20a/%7Eproxy"), + ('C:\\odd "dir"\\lite.EXE', "http://localhost:4000/x\\"), + ], + ) + def test_the_windows_command_survives_cmd_exe_and_the_c_runtime(self, lite_exe, base_url): + assert self._windows_argv(lite_exe, base_url) == [lite_exe, "--base-url", base_url, "auth", "print-token"] + + def test_the_windows_command_carries_the_base_url_through_cmd_quoting(self): + stale = CliTokenRecord( + base_url="http://other-proxy.example.com", + key="sk-stale", + timestamp=time.time(), + ) + argv = self._windows_argv(WINDOWS_LITE_EXE, "http://localhost:4000") + with patch(f"{AUTH_MODULE}.load_cli_token", return_value=stale): + result = CliRunner().invoke(cli, argv[1:]) + + assert argv[0] == WINDOWS_LITE_EXE + assert "Not authenticated for this server" in result.output + class TestConflictingOwnersOfTheSettingsFile: """Both `lite up` and `lite autoroute up` restore a backup when they stop. diff --git a/tests/test_litellm/proxy/client/cli/test_up_commands.py b/tests/test_litellm/proxy/client/cli/test_up_commands.py index 9958286884b..c78bdfa75b1 100644 --- a/tests/test_litellm/proxy/client/cli/test_up_commands.py +++ b/tests/test_litellm/proxy/client/cli/test_up_commands.py @@ -55,8 +55,14 @@ class TestMergeClaudeSettings: } merged = merge_claude_settings(settings, "http://localhost:4000/", "new-helper") assert merged["env"]["ANTHROPIC_BASE_URL"] == "http://localhost:4000" + assert merged["env"]["ENABLE_TOOL_SEARCH"] == "true" assert merged["apiKeyHelper"] == "new-helper" + def test_preserves_existing_tool_search(self): + settings = {"env": {"ENABLE_TOOL_SEARCH": "false"}} + merged = merge_claude_settings(settings, "http://localhost:4000", "helper") + assert merged["env"]["ENABLE_TOOL_SEARCH"] == "false" + def test_drops_stray_api_key(self): settings = {"env": {"ANTHROPIC_API_KEY": "leaked-key"}} merged = merge_claude_settings(settings, "http://localhost:4000", "helper") @@ -64,7 +70,10 @@ class TestMergeClaudeSettings: def test_works_from_empty_settings(self): merged = merge_claude_settings({}, "http://localhost:4000", "helper") - assert merged["env"] == {"ANTHROPIC_BASE_URL": "http://localhost:4000"} + assert merged["env"] == { + "ANTHROPIC_BASE_URL": "http://localhost:4000", + "ENABLE_TOOL_SEARCH": "true", + } assert merged["apiKeyHelper"] == "helper" def test_does_not_mutate_input(self): @@ -216,6 +225,32 @@ class TestResolveApiKeyHelper: with pytest.raises(ClaudeSettingsError, match="Could not find `lite`"): resolve_api_key_helper("http://localhost:4000") + def test_windows_quotes_for_cmd_exe_instead_of_posix_sh(self, monkeypatch): + """cmd.exe takes a single quote literally, so a POSIX-quoted backslashed path is unrunnable.""" + lite_exe = "C:\\Users\\u\\AppData\\Local\\Programs\\Python\\Python313\\Scripts\\lite.EXE" + monkeypatch.setattr(shutil, "which", lambda name: lite_exe) + + helper = resolve_api_key_helper("https://gateway.example.com", platform="win32") + + assert helper == f'"{lite_exe}" "--base-url" "https://gateway.example.com" "auth" "print-token"' + + def test_windows_keeps_a_spaced_path_and_a_metacharacter_url_as_single_tokens(self, monkeypatch): + monkeypatch.setattr(shutil, "which", lambda name: "C:\\Program Files\\LiteLLM\\lite.EXE") + + helper = resolve_api_key_helper("https://gateway.example.com/?a=1&b=2", platform="win32") + + assert helper == ( + '"C:\\Program Files\\LiteLLM\\lite.EXE" "--base-url" "https://gateway.example.com/?a=1&b=2" ' + '"auth" "print-token"' + ) + + def test_non_windows_platforms_keep_posix_quoting(self, monkeypatch): + monkeypatch.setattr(shutil, "which", lambda name: "/usr/local/bin/lite") + + helper = resolve_api_key_helper("http://example.com/path; rm -rf /", platform="darwin") + + assert helper == "/usr/local/bin/lite --base-url 'http://example.com/path; rm -rf /' auth print-token" + def _make_ctx(base_url): return click.Context(click.Command("test"), obj={"base_url": base_url}) @@ -486,6 +521,7 @@ class TestUpCommand: assert captured["backup_existed"] is True assert captured["settings"]["theme"] == "dark" assert captured["settings"]["env"]["ANTHROPIC_BASE_URL"] == "http://localhost:4000" + assert captured["settings"]["env"]["ENABLE_TOOL_SEARCH"] == "true" assert captured["settings"]["apiKeyHelper"] == "/usr/local/bin/lite auth print-token" assert json.loads(settings_path.read_text()) == original assert not backup_path.exists() diff --git a/tests/test_litellm/proxy/client/conftest.py b/tests/test_litellm/proxy/client/conftest.py new file mode 100644 index 00000000000..c8b7951e284 --- /dev/null +++ b/tests/test_litellm/proxy/client/conftest.py @@ -0,0 +1,38 @@ +import threading + +import pytest + + +@pytest.fixture +def hanging_server(): + """A server that accepts the connection and never answers, so only a timeout ends the call.""" + from http.server import BaseHTTPRequestHandler, HTTPServer + from socketserver import ThreadingMixIn + + stop: threading.Event = threading.Event() + + class SilentRequestHandler(BaseHTTPRequestHandler): + protocol_version = "HTTP/1.1" + + def _hang(self): + stop.wait(timeout=30) + + do_GET = _hang + do_POST = _hang + + def log_message(self, format, *args): + pass + + class ThreadedServer(ThreadingMixIn, HTTPServer): + daemon_threads = True + + server = ThreadedServer(("127.0.0.1", 0), SilentRequestHandler) + thread = threading.Thread(target=server.serve_forever, daemon=True) + thread.start() + try: + yield f"http://127.0.0.1:{server.server_port}" + finally: + stop.set() + server.shutdown() + server.server_close() + thread.join(timeout=5) diff --git a/tests/test_litellm/proxy/client/test_chat.py b/tests/test_litellm/proxy/client/test_chat.py index b8e55c45502..67b6ee833f2 100644 --- a/tests/test_litellm/proxy/client/test_chat.py +++ b/tests/test_litellm/proxy/client/test_chat.py @@ -1,6 +1,7 @@ import importlib import importlib.util from importlib.machinery import PathFinder +import time import site import sys @@ -227,3 +228,31 @@ def test_completions_other_errors(client, sample_messages): with pytest.raises(requests.exceptions.HTTPError) as exc_info: client.completions(model="gpt-4", messages=sample_messages) assert exc_info.value.response.status_code == 500 + + +def test_completions_gives_up_at_the_timeout_instead_of_hanging(hanging_server): + """ + A proxy that accepts the connection but never answers used to pin the caller's + process forever, since the request carried no timeout at all. + """ + client = ChatClient(base_url=hanging_server, api_key="sk-test", timeout=1) + + started = time.monotonic() + with pytest.raises(requests.exceptions.Timeout): + client.completions(model="gpt-5.4", messages=[{"role": "user", "content": "hi"}]) + + assert time.monotonic() - started < 10 + + +def test_completions_stream_gives_up_at_the_timeout_instead_of_hanging(hanging_server): + """ + The streaming call opens the response before reading chunks, so a proxy that never + sends its headers used to hang here forever too. + """ + client = ChatClient(base_url=hanging_server, api_key="sk-test", timeout=1) + + started = time.monotonic() + with pytest.raises(requests.exceptions.Timeout): + next(client.completions_stream(model="gpt-5.4", messages=[{"role": "user", "content": "hi"}])) + + assert time.monotonic() - started < 10 diff --git a/tests/test_litellm/proxy/client/test_client.py b/tests/test_litellm/proxy/client/test_client.py index fe3e2c52ce5..87eb3400b8c 100644 --- a/tests/test_litellm/proxy/client/test_client.py +++ b/tests/test_litellm/proxy/client/test_client.py @@ -82,6 +82,12 @@ def test_client_initialization(): assert client.http._base_url == "http://localhost:4000" assert client.http._api_key == "test-key" assert client.http._timeout == 60 + assert client.teams._timeout == 60 + assert client.keys._timeout == 60 + assert client.credentials._timeout == 60 + assert client.models._timeout == 60 + assert client.model_groups._timeout == 60 + assert client.chat._timeout == 600 def test_client_default_timeout(): @@ -92,6 +98,8 @@ def test_client_default_timeout(): ) assert client.http._timeout == 30 + assert client.keys._timeout == 30 + assert client.chat._timeout == 600 def test_client_without_api_key(): diff --git a/tests/test_litellm/proxy/client/test_credentials.py b/tests/test_litellm/proxy/client/test_credentials.py index 41886e3b292..666c5dac2b0 100644 --- a/tests/test_litellm/proxy/client/test_credentials.py +++ b/tests/test_litellm/proxy/client/test_credentials.py @@ -1,4 +1,5 @@ +import time import pytest import requests @@ -276,3 +277,17 @@ def test_encrypt_credential_values_does_not_mutate_original(monkeypatch): assert encrypted.credential_values["api_key"] != "sk-123" assert credential.credential_values["api_key"] == "sk-123" assert encrypted.credential_name == credential.credential_name + + +def test_list_gives_up_at_the_timeout_instead_of_hanging(hanging_server): + """ + A proxy that accepts the connection but never answers used to pin the caller's + process forever, since the request carried no timeout at all. + """ + client = CredentialsManagementClient(base_url=hanging_server, api_key="sk-test", timeout=1) + + started = time.monotonic() + with pytest.raises(requests.exceptions.Timeout): + client.list() + + assert time.monotonic() - started < 10 diff --git a/tests/test_litellm/proxy/client/test_keys.py b/tests/test_litellm/proxy/client/test_keys.py index 282b97b1c09..b9b07bddf1f 100644 --- a/tests/test_litellm/proxy/client/test_keys.py +++ b/tests/test_litellm/proxy/client/test_keys.py @@ -1,3 +1,4 @@ +import time import traceback import pytest @@ -509,3 +510,17 @@ def test_not_found_error_redacts_wrapped_key(): assert "REDACTED" in str(wrapped) assert LEAKY_KEY not in str(wrapped.orig_exception) assert wrapped.orig_exception.response.status_code == 404 + + +def test_list_gives_up_at_the_timeout_instead_of_hanging(hanging_server): + """ + A proxy that accepts the connection but never answers used to pin the caller's + process forever, since the request carried no timeout at all. + """ + client = KeysManagementClient(base_url=hanging_server, api_key="sk-test", timeout=1) + + started = time.monotonic() + with pytest.raises(requests.exceptions.Timeout): + client.list() + + assert time.monotonic() - started < 10 diff --git a/tests/test_litellm/proxy/client/test_model_groups.py b/tests/test_litellm/proxy/client/test_model_groups.py index 9ea8e94ff95..4a513a127b8 100644 --- a/tests/test_litellm/proxy/client/test_model_groups.py +++ b/tests/test_litellm/proxy/client/test_model_groups.py @@ -1,4 +1,5 @@ +import time import pytest import requests @@ -172,3 +173,17 @@ def test_client_initialization_without_api_key(base_url): assert client._api_key is None assert client.model_groups._api_key is None + + +def test_info_gives_up_at_the_timeout_instead_of_hanging(hanging_server): + """ + A proxy that accepts the connection but never answers used to pin the caller's + process forever, since the request carried no timeout at all. + """ + client = ModelGroupsManagementClient(base_url=hanging_server, api_key="sk-test", timeout=1) + + started = time.monotonic() + with pytest.raises(requests.exceptions.Timeout): + client.info() + + assert time.monotonic() - started < 10 diff --git a/tests/test_litellm/proxy/client/test_models.py b/tests/test_litellm/proxy/client/test_models.py index fe053ffd683..9aa5a6cf0b3 100644 --- a/tests/test_litellm/proxy/client/test_models.py +++ b/tests/test_litellm/proxy/client/test_models.py @@ -1,4 +1,5 @@ +import time import pytest import requests @@ -732,3 +733,17 @@ def test_update_other_errors(client): with pytest.raises(requests.exceptions.HTTPError) as exc_info: client.update(model_id=model_id, model_params=model_params) assert exc_info.value.response.status_code == 500 + + +def test_list_gives_up_at_the_timeout_instead_of_hanging(hanging_server): + """ + A proxy that accepts the connection but never answers used to pin the caller's + process forever, since the request carried no timeout at all. + """ + client = ModelsManagementClient(base_url=hanging_server, api_key="sk-test", timeout=1) + + started = time.monotonic() + with pytest.raises(requests.exceptions.Timeout): + client.list() + + assert time.monotonic() - started < 10 diff --git a/tests/test_litellm/proxy/client/test_teams.py b/tests/test_litellm/proxy/client/test_teams.py new file mode 100644 index 00000000000..b61091ca44b --- /dev/null +++ b/tests/test_litellm/proxy/client/test_teams.py @@ -0,0 +1,20 @@ +import time + +import pytest +import requests + +from litellm.proxy.client.teams import TeamsManagementClient + + +def test_list_gives_up_at_the_timeout_instead_of_hanging(hanging_server): + """ + A proxy that accepts the connection but never answers used to pin the caller's + process forever, since the request carried no timeout at all. + """ + client = TeamsManagementClient(base_url=hanging_server, api_key="sk-test", timeout=1) + + started = time.monotonic() + with pytest.raises(requests.exceptions.Timeout): + client.list() + + assert time.monotonic() - started < 10 diff --git a/tests/test_litellm/proxy/client/test_users.py b/tests/test_litellm/proxy/client/test_users.py index 87b8392e402..5b4d89420ab 100644 --- a/tests/test_litellm/proxy/client/test_users.py +++ b/tests/test_litellm/proxy/client/test_users.py @@ -1,6 +1,8 @@ +import time from unittest.mock import MagicMock, patch import pytest +import requests @@ -82,3 +84,17 @@ def test_delete_user_unauthorized(mock_post, client): mock_post.return_value.text = "unauthorized" with pytest.raises(UnauthorizedError): client.delete_user(["u1"]) + + +def test_delete_user_gives_up_at_the_timeout_instead_of_hanging(hanging_server): + """ + A proxy that accepts the connection but never answers used to pin the caller's + process forever, since the request carried no timeout at all. + """ + client = UsersManagementClient(base_url=hanging_server, api_key="sk-test", timeout=1) + + started = time.monotonic() + with pytest.raises(requests.exceptions.Timeout): + client.delete_user(["u1"]) + + assert time.monotonic() - started < 10 diff --git a/tests/test_litellm/proxy/common_utils/test_reset_budget_job.py b/tests/test_litellm/proxy/common_utils/test_reset_budget_job.py index 5d3afd95a55..03b05bd9d87 100644 --- a/tests/test_litellm/proxy/common_utils/test_reset_budget_job.py +++ b/tests/test_litellm/proxy/common_utils/test_reset_budget_job.py @@ -77,6 +77,7 @@ class MockBatcher: self.litellm_teammembership = _Table("team_membership", self) self.litellm_organizationtable = _Table("org", self) self.litellm_tagtable = _Table("tag", self) + self.litellm_modelaccessgroupbudgettable = _Table("model_access_group", self) self.litellm_endusertable = _Table("enduser", self) async def commit(self): @@ -91,6 +92,7 @@ class MockDB: self.litellm_endusertable = MockTable() self.litellm_organizationtable = MockTable() self.litellm_tagtable = MockTable() + self.litellm_modelaccessgroupbudgettable = MockTable() self.batch_calls: List[Dict[str, Any]] = [] self.batchers: List[MockBatcher] = [] @@ -521,6 +523,7 @@ _LINKED_TABLE_CASES = [ ), ("org", {"budget_id": {"in": ["7d-budget-tier"]}, "spend": {"gt": 0}}), ("tag", {"budget_id": {"in": ["7d-budget-tier"]}, "spend": {"gt": 0}}), + ("model_access_group", {"budget_id": {"in": ["7d-budget-tier"]}, "spend": {"gt": 0}}), ] @@ -830,6 +833,7 @@ def _make_reset_budget_windows_job( raise AssertionError(f"Unexpected query_raw call: {query}") prisma_client.db.query_raw = AsyncMock(side_effect=fake_query_raw) + prisma_client.db.execute_raw = AsyncMock(return_value=1) prisma_client.db.litellm_verificationtoken.update = AsyncMock(return_value=None) prisma_client.db.litellm_teamtable.update = AsyncMock(return_value=None) @@ -901,6 +905,145 @@ def test_reset_budget_windows_resets_expired_key_window(monkeypatch): spend_counter_cache.in_memory_cache.set_cache.assert_any_call(key="spend:key:sk-expired:window:1d", value=0.0) +def _window_spend_rolls(prisma_client): + return [ + call.args + for call in prisma_client.db.execute_raw.await_args_list + if "LiteLLM_BudgetWindowSpend" in call.args[0] + ] + + +def test_reset_budget_windows_rolls_the_key_window_spend_row(monkeypatch): + """The maintained per-window total has to start the new window at zero + alongside the counter, or enforcement keeps reading the old window's spend.""" + now = datetime.utcnow() + expired = (now - timedelta(minutes=5)).isoformat() + "Z" + + key_rows = [ + { + "token": "sk-expired", + "budget_limits": [{"budget_duration": "1d", "reset_at": expired}], + } + ] + job, prisma_client, _ = _make_reset_budget_windows_job(monkeypatch, key_rows=key_rows, team_rows=[]) + + asyncio.run(job.reset_budget_windows()) + + rolls = _window_spend_rolls(prisma_client) + assert len(rolls) == 1 + query, entity_type, entity_id, window_duration, new_window_start, _updated_at = rolls[0] + assert (entity_type, entity_id, window_duration) == ("key", "sk-expired", "1d") + assert "spend = 0" in " ".join(query.split()) + + # window_start is the start of the window that just began: new reset_at minus the duration. + written_windows = json.loads( + prisma_client.db.litellm_verificationtoken.update.await_args.kwargs["data"]["budget_limits"] + ) + new_reset_at = datetime.fromisoformat(written_windows[0]["reset_at"].replace("Z", "+00:00")).replace(tzinfo=None) + assert new_window_start == pytest.approx( + new_reset_at - timedelta(days=1), + abs=timedelta(seconds=1), + ) + + +def test_reset_budget_windows_roll_is_conditional_on_an_older_stored_window(monkeypatch): + """Another pod may already have rolled the row; clobbering it would drop + spend that landed under the new window.""" + now = datetime.utcnow() + expired = (now - timedelta(minutes=5)).isoformat() + "Z" + + key_rows = [ + { + "token": "sk-expired", + "budget_limits": [{"budget_duration": "1d", "reset_at": expired}], + } + ] + job, prisma_client, _ = _make_reset_budget_windows_job(monkeypatch, key_rows=key_rows, team_rows=[]) + + asyncio.run(job.reset_budget_windows()) + + query = " ".join(_window_spend_rolls(prisma_client)[0][0].split()) + assert "AND window_start < ($4::timestamptz AT TIME ZONE 'UTC')" in query + + +def test_reset_budget_windows_rolls_the_team_window_spend_row(monkeypatch): + now = datetime.utcnow() + expired = (now - timedelta(minutes=1)).isoformat() + "Z" + + team_rows = [ + { + "team_id": "team-expired", + "budget_limits": [{"budget_duration": "30d", "reset_at": expired}], + } + ] + job, prisma_client, _ = _make_reset_budget_windows_job(monkeypatch, key_rows=[], team_rows=team_rows) + + asyncio.run(job.reset_budget_windows()) + + rolls = _window_spend_rolls(prisma_client) + assert len(rolls) == 1 + assert rolls[0][1:4] == ("team", "team-expired", "30d") + + +def test_reset_budget_windows_does_not_roll_an_unexpired_window(monkeypatch): + now = datetime.utcnow() + future = (now + timedelta(hours=1)).isoformat() + "Z" + + key_rows = [ + { + "token": "sk-future", + "budget_limits": [{"budget_duration": "1d", "reset_at": future}], + } + ] + job, prisma_client, _ = _make_reset_budget_windows_job(monkeypatch, key_rows=key_rows, team_rows=[]) + + asyncio.run(job.reset_budget_windows()) + + assert _window_spend_rolls(prisma_client) == [] + + +def test_reset_budget_windows_rolls_only_the_expired_window_of_a_key(monkeypatch): + now = datetime.utcnow() + key_rows = [ + { + "token": "sk-mixed", + "budget_limits": [ + {"budget_duration": "1d", "reset_at": (now - timedelta(minutes=5)).isoformat() + "Z"}, + {"budget_duration": "30d", "reset_at": (now + timedelta(days=2)).isoformat() + "Z"}, + ], + } + ] + job, prisma_client, _ = _make_reset_budget_windows_job(monkeypatch, key_rows=key_rows, team_rows=[]) + + asyncio.run(job.reset_budget_windows()) + + rolls = _window_spend_rolls(prisma_client) + assert [roll[3] for roll in rolls] == ["1d"] + + +def test_reset_budget_windows_survives_a_failed_window_spend_roll(monkeypatch): + """The row is an optimization over aggregating LiteLLM_SpendLogs; a DB + failure there must not stop the counter reset from being persisted.""" + now = datetime.utcnow() + expired = (now - timedelta(minutes=5)).isoformat() + "Z" + + key_rows = [ + { + "token": "sk-expired", + "budget_limits": [{"budget_duration": "1d", "reset_at": expired}], + } + ] + job, prisma_client, spend_counter_cache = _make_reset_budget_windows_job( + monkeypatch, key_rows=key_rows, team_rows=[] + ) + prisma_client.db.execute_raw = AsyncMock(side_effect=Exception("connection reset")) + + asyncio.run(job.reset_budget_windows()) + + prisma_client.db.litellm_verificationtoken.update.assert_awaited_once() + spend_counter_cache.in_memory_cache.set_cache.assert_any_call(key="spend:key:sk-expired:window:1d", value=0.0) + + def test_reset_budget_windows_skips_unexpired_key_window(monkeypatch): """If `reset_at` is in the future, no write should happen for that key.""" now = datetime.utcnow() @@ -1299,13 +1442,19 @@ _INVALIDATION_CASES = [ "spend:tag:tenant-42", {"tag:tenant-42"}, ), + ( + "litellm_modelaccessgroupbudgettable", + type("AccessGroup", (), {"access_group_name": "gpt-4-group"}), + "spend:model_access_group:gpt-4-group", + {"model_access_group:gpt-4-group"}, + ), ] @pytest.mark.parametrize( "table_attr, linked_row, counter_key, cache_keys", _INVALIDATION_CASES, - ids=["team_membership", "key", "org", "tag"], + ids=["team_membership", "key", "org", "tag", "model_access_group"], ) def test_budget_table_reset_invalidates_counters_and_management_cache( reset_budget_job, mock_prisma_client, monkeypatch, table_attr, linked_row, counter_key, cache_keys @@ -1359,6 +1508,102 @@ def test_budget_table_reset_commits_even_when_cache_eviction_fails(reset_budget_ assert mock_prisma_client.db.batchers[0].committed is True +# --------------------------------------------------------------------------- +# Model access group budgets ride the same cascade +# --------------------------------------------------------------------------- + + +def _model_access_group_row(name: str = "gpt-4-group", spend: float = 12.0, budget_id: str = "budget-1"): + """A LiteLLM_ModelAccessGroupBudgetTable row, shaped like prisma hands it back.""" + return type("AccessGroup", (), {"access_group_name": name, "spend": spend, "budget_id": budget_id}) + + +def test_access_group_reset_only_matches_rows_that_have_spend(reset_budget_job, mock_prisma_client, monkeypatch): + """Both the read and the write are filtered to spend > 0 on the due tiers. + + A group sitting at spend 0 has nothing to reset, and a group hanging off a + tier that is not due yet must not be swept along: both are excluded by the + filter, not by anything downstream. + """ + _make_counter_invalidation_job(monkeypatch) + mock_prisma_client.data["budget"] = [_budget_row(budget_id="budget-due", budget_duration="7d")] + mock_prisma_client.db.litellm_modelaccessgroupbudgettable.set_find_many_results( + [_model_access_group_row(budget_id="budget-due")] + ) + + asyncio.run(reset_budget_job.reset_budget_for_litellm_budget_table()) + + expected_where = {"budget_id": {"in": ["budget-due"]}, "spend": {"gt": 0}} + assert mock_prisma_client.db.litellm_modelaccessgroupbudgettable.find_many_calls == [{"where": expected_where}] + writes = _batch_writes(mock_prisma_client, "model_access_group", op="update_many") + assert len(writes) == 1 + assert writes[0]["where"] == expected_where + assert writes[0]["data"] == {"spend": 0} + + +def test_access_groups_are_untouched_when_no_budget_is_due(reset_budget_job, mock_prisma_client, monkeypatch): + """No due tier means the group table is never read, written or evicted.""" + counter_cache = _make_counter_invalidation_job(monkeypatch) + mock_prisma_client.db.litellm_modelaccessgroupbudgettable.set_find_many_results([_model_access_group_row()]) + + asyncio.run(reset_budget_job.reset_budget_for_litellm_budget_table()) + + assert mock_prisma_client.db.litellm_modelaccessgroupbudgettable.find_many_calls == [] + assert _batch_writes(mock_prisma_client, "model_access_group") == [] + counter_cache.in_memory_cache.set_cache.assert_not_called() + counter_cache.user_api_key_cache.async_delete_cache.assert_not_awaited() + + +def test_budget_table_reset_invalidates_every_access_group_not_just_the_first( + reset_budget_job, mock_prisma_client, monkeypatch +): + """When several groups share the expiring tier, all of them are evicted.""" + counter_cache = _make_counter_invalidation_job(monkeypatch) + mock_prisma_client.data["budget"] = [_budget_row(budget_id="budget-1")] + mock_prisma_client.db.litellm_modelaccessgroupbudgettable.set_find_many_results( + [_model_access_group_row(name=name) for name in ("group-a", "group-b", "group-c")] + ) + + asyncio.run(reset_budget_job.reset_budget_for_litellm_budget_table()) + + deleted = {call.kwargs.get("key") for call in counter_cache.user_api_key_cache.async_delete_cache.await_args_list} + assert deleted == {"model_access_group:group-a", "model_access_group:group-b", "model_access_group:group-c"} + for name in ("group-a", "group-b", "group-c"): + counter_cache.in_memory_cache.set_cache.assert_any_call(key=f"spend:model_access_group:{name}", value=0.0, ttl=60) + + +def test_budget_cascade_carries_access_group_overage_when_rollover_enabled( + rollover_enabled, reset_budget_job, mock_prisma_client, monkeypatch +): + """A group 5 over the tier cap keeps a spend of 5 in the next window, the + same way a tag or a team member does: over-cap rows are decremented by the + cap, the rest are zeroed, and the counter is seeded with the carried spend.""" + counter_cache = _make_counter_invalidation_job(monkeypatch) + mock_prisma_client.data["budget"] = [_budget_row(budget_id="budget-roll", budget_duration="7d", max_budget=10.0)] + mock_prisma_client.db.litellm_modelaccessgroupbudgettable.set_find_many_results( + [_model_access_group_row(spend=15.0, budget_id="budget-roll")] + ) + + asyncio.run(reset_budget_job.reset_budget_for_litellm_budget_table()) + + writes = _batch_writes(mock_prisma_client, "model_access_group") + assert { + "table": "model_access_group", + "op": "update_many", + "where": {"budget_id": "budget-roll", "spend": {"gt": 10.0}}, + "data": {"spend": {"decrement": 10.0}}, + } in writes + assert { + "table": "model_access_group", + "op": "update_many", + "where": {"budget_id": "budget-roll", "spend": {"gt": 0, "lte": 10.0}}, + "data": {"spend": 0}, + } in writes + assert _replay_spend_writes(writes, 15.0) == 5.0 + assert _replay_spend_writes(writes, 8.0) == 0 + counter_cache.in_memory_cache.set_cache.assert_any_call(key="spend:model_access_group:gpt-4-group", value=5.0, ttl=60) + + # --------------------------------------------------------------------------- # Atomicity of the budget-table cascade (LIT-5138) # --------------------------------------------------------------------------- @@ -1471,6 +1716,7 @@ def test_budget_cascade_writes_land_in_a_single_transaction(reset_budget_job, mo ("key", "update_many"), ("org", "update_many"), ("tag", "update_many"), + ("model_access_group", "update_many"), ("enduser", "update_many"), ("budget", "update_many"), } @@ -1506,7 +1752,7 @@ def test_failed_cascade_is_logged_as_a_cascade_failure(monkeypatch): assert mock_exception.call_count == 1 message = mock_exception.call_args.args[0] assert "cascade" in message - for mentioned in ("team member", "enduser", "org", "tag", "budget_reset_at"): + for mentioned in ("team member", "enduser", "org", "tag", "model access group", "budget_reset_at"): assert mentioned in message, f"failure log should mention {mentioned}: {message}" diff --git a/tests/test_litellm/proxy/db/db_transaction_queue/test_redis_update_buffer.py b/tests/test_litellm/proxy/db/db_transaction_queue/test_redis_update_buffer.py index fb0c994a476..8f3508fc4e9 100644 --- a/tests/test_litellm/proxy/db/db_transaction_queue/test_redis_update_buffer.py +++ b/tests/test_litellm/proxy/db/db_transaction_queue/test_redis_update_buffer.py @@ -1,4 +1,5 @@ import json +from datetime import datetime, timezone from unittest.mock import AsyncMock, MagicMock, patch import pytest @@ -22,9 +23,7 @@ def redis_update_buffer(mock_redis_cache): @pytest.mark.asyncio -async def test_store_in_memory_spend_updates_uses_pipeline( - redis_update_buffer, mock_redis_cache -): +async def test_store_in_memory_spend_updates_uses_pipeline(redis_update_buffer, mock_redis_cache): """ Verify store_in_memory_spend_updates_in_redis calls async_rpush_pipeline once with the correct operations and skips empty queues. @@ -33,35 +32,29 @@ async def test_store_in_memory_spend_updates_uses_pipeline( # Create mock queues - only 3 of 6 have data spend_update_queue = AsyncMock() - spend_update_queue.flush_and_get_aggregated_db_spend_update_transactions = ( - AsyncMock(return_value={"key_list_transactions": {"key1": 1.0}}) + spend_update_queue.flush_and_get_aggregated_db_spend_update_transactions = AsyncMock( + return_value={"key_list_transactions": {"key1": 1.0}} ) daily_spend_queue = AsyncMock() - daily_spend_queue.flush_and_get_aggregated_daily_spend_update_transactions = ( - AsyncMock(return_value={"user_key1": {"spend": 1.0}}) + daily_spend_queue.flush_and_get_aggregated_daily_spend_update_transactions = AsyncMock( + return_value={"user_key1": {"spend": 1.0}} ) daily_team_queue = AsyncMock() - daily_team_queue.flush_and_get_aggregated_daily_spend_update_transactions = ( - AsyncMock(return_value={"team_key1": {"spend": 2.0}}) + daily_team_queue.flush_and_get_aggregated_daily_spend_update_transactions = AsyncMock( + return_value={"team_key1": {"spend": 2.0}} ) # Empty queues daily_org_queue = AsyncMock() - daily_org_queue.flush_and_get_aggregated_daily_spend_update_transactions = ( - AsyncMock(return_value={}) - ) + daily_org_queue.flush_and_get_aggregated_daily_spend_update_transactions = AsyncMock(return_value={}) daily_end_user_queue = AsyncMock() - daily_end_user_queue.flush_and_get_aggregated_daily_spend_update_transactions = ( - AsyncMock(return_value=None) - ) + daily_end_user_queue.flush_and_get_aggregated_daily_spend_update_transactions = AsyncMock(return_value=None) daily_agent_queue = AsyncMock() - daily_agent_queue.flush_and_get_aggregated_daily_spend_update_transactions = ( - AsyncMock(return_value={}) - ) + daily_agent_queue.flush_and_get_aggregated_daily_spend_update_transactions = AsyncMock(return_value={}) await redis_update_buffer.store_in_memory_spend_updates_in_redis( spend_update_queue=spend_update_queue, @@ -82,9 +75,7 @@ async def test_store_in_memory_spend_updates_uses_pipeline( @pytest.mark.asyncio -async def test_store_in_memory_spend_updates_restores_on_rpush_failure( - redis_update_buffer, mock_redis_cache -): +async def test_store_in_memory_spend_updates_restores_on_rpush_failure(redis_update_buffer, mock_redis_cache): """ If async_rpush_pipeline raises, the already-drained transactions must be put back into the in-memory queues so the next scheduler tick retries. @@ -98,9 +89,7 @@ async def test_store_in_memory_spend_updates_restores_on_rpush_failure( SpendUpdateQueue, ) - mock_redis_cache.async_rpush_pipeline = AsyncMock( - side_effect=ConnectionError("redis went away") - ) + mock_redis_cache.async_rpush_pipeline = AsyncMock(side_effect=ConnectionError("redis went away")) spend_queue = SpendUpdateQueue() daily_user_queue = DailySpendUpdateQueue() @@ -145,16 +134,12 @@ async def test_store_in_memory_spend_updates_restores_on_rpush_failure( # After restore, the main spend queue should hold one item per # (entity_type, entity_id) pair with the aggregated cost - restored_spend = ( - await spend_queue.flush_and_get_aggregated_db_spend_update_transactions() - ) + restored_spend = await spend_queue.flush_and_get_aggregated_db_spend_update_transactions() assert restored_spend["key_list_transactions"] == {"key-abc": 1.5} assert restored_spend["team_list_transactions"] == {"team-xyz": 2.5} # Daily user queue should hold the same aggregated dict - restored_daily = ( - await daily_user_queue.flush_and_get_aggregated_daily_spend_update_transactions() - ) + restored_daily = await daily_user_queue.flush_and_get_aggregated_daily_spend_update_transactions() assert restored_daily == { "user1_day_model": { "spend": 1.0, @@ -165,9 +150,7 @@ async def test_store_in_memory_spend_updates_restores_on_rpush_failure( @pytest.mark.asyncio -async def test_store_in_memory_spend_updates_all_empty_returns_early( - redis_update_buffer, mock_redis_cache -): +async def test_store_in_memory_spend_updates_all_empty_returns_early(redis_update_buffer, mock_redis_cache): """ When all queues are empty, pipeline should never be called. """ @@ -175,13 +158,9 @@ async def test_store_in_memory_spend_updates_all_empty_returns_early( # All queues return empty empty_queue = AsyncMock() - empty_queue.flush_and_get_aggregated_db_spend_update_transactions = AsyncMock( - return_value={} - ) + empty_queue.flush_and_get_aggregated_db_spend_update_transactions = AsyncMock(return_value={}) empty_daily_queue = AsyncMock() - empty_daily_queue.flush_and_get_aggregated_daily_spend_update_transactions = ( - AsyncMock(return_value={}) - ) + empty_daily_queue.flush_and_get_aggregated_daily_spend_update_transactions = AsyncMock(return_value={}) await redis_update_buffer.store_in_memory_spend_updates_in_redis( spend_update_queue=empty_queue, @@ -196,14 +175,13 @@ async def test_store_in_memory_spend_updates_all_empty_returns_early( @pytest.mark.asyncio -async def test_get_all_transactions_from_redis_buffer_pipeline( - redis_update_buffer, mock_redis_cache -): +async def test_get_all_transactions_from_redis_buffer_pipeline(redis_update_buffer, mock_redis_cache): """ Verify get_all_transactions_from_redis_buffer_pipeline correctly parses and aggregates results from async_lpop_pipeline. """ - # Simulate pipeline results: slot 0 = spend updates, slots 1-5 = daily categories + # Simulate pipeline results: slot 0 = spend updates, slots 1-5 = daily categories, + # slot 6 = budget window spend db_spend_json = json.dumps( { "key_list_transactions": {"key1": 1.0, "key2": 2.0}, @@ -217,6 +195,18 @@ async def test_get_all_transactions_from_redis_buffer_pipeline( ) daily_user_json = json.dumps({"user_key1": {"spend": 1.0, "api_requests": 1}}) daily_team_json = json.dumps({"team_key1": {"spend": 2.0, "api_requests": 2}}) + window_spend_json = json.dumps( + [ + { + "entity_type": "key", + "entity_id": "hashed-token", + "window_duration": "30d", + "window_start": "2026-08-01T00:00:00.000000", + "spend": 3.0, + "started_at": None, + } + ] + ) mock_redis_cache.async_lpop_pipeline = AsyncMock( return_value=[ @@ -226,13 +216,28 @@ async def test_get_all_transactions_from_redis_buffer_pipeline( None, # slot 3: daily org (empty) None, # slot 4: daily end-user (empty) None, # slot 5: daily agent (empty) + [window_spend_json, window_spend_json], # slot 6: budget window spend ] ) result = await redis_update_buffer.get_all_transactions_from_redis_buffer_pipeline() - assert len(result) == 6 - db_spend, daily_user, daily_team, daily_org, daily_end_user, daily_agent = result + assert len(result) == 7 + ( + db_spend, + daily_user, + daily_team, + daily_org, + daily_end_user, + daily_agent, + window_spend, + ) = result + + # Budget window spend from two pods is summed per window, not overwritten. + assert window_spend is not None + assert len(window_spend) == 1 + assert window_spend[0]["spend"] == 6.0 + assert window_spend[0]["entity_id"] == "hashed-token" # Verify db spend was parsed correctly assert db_spend is not None @@ -255,6 +260,10 @@ async def test_get_all_transactions_from_redis_buffer_pipeline( # Verify pipeline was called once with correct keys mock_redis_cache.async_lpop_pipeline.assert_called_once() + from litellm.constants import REDIS_WINDOW_SPEND_UPDATE_BUFFER_KEY + + popped_keys = [op["key"] for op in mock_redis_cache.async_lpop_pipeline.call_args.kwargs["lpop_list"]] + assert popped_keys[6] == REDIS_WINDOW_SPEND_UPDATE_BUFFER_KEY @pytest.mark.asyncio @@ -262,13 +271,11 @@ async def test_get_all_transactions_from_redis_buffer_pipeline_no_redis(): """When redis_cache is None, should return all Nones""" buffer = RedisUpdateBuffer(redis_cache=None) result = await buffer.get_all_transactions_from_redis_buffer_pipeline() - assert result == (None, None, None, None, None, None) + assert result == (None, None, None, None, None, None, None) @pytest.mark.asyncio -async def test_restore_transactions_to_redis_pushes_only_provided( - redis_update_buffer, mock_redis_cache -): +async def test_restore_transactions_to_redis_pushes_only_provided(redis_update_buffer, mock_redis_cache): """ restore_transactions_to_redis re-pushes only the transaction sets it was given, to their matching buffer keys, so uncommitted spend can be retried. @@ -302,9 +309,41 @@ async def test_restore_transactions_to_redis_pushes_only_provided( @pytest.mark.asyncio -async def test_restore_transactions_to_redis_noop_when_empty( - redis_update_buffer, mock_redis_cache -): +async def test_restored_window_spend_transactions_drain_back_unchanged(redis_update_buffer, mock_redis_cache): + """A window commit that fails after the destructive lpop must be re-pushed + in the store path's encoding, so the next drain returns the same increments.""" + from litellm.constants import REDIS_WINDOW_SPEND_UPDATE_BUFFER_KEY + from litellm.proxy.db.db_transaction_queue.window_spend_update_queue import ( + build_window_spend_transaction, + ) + + window_transactions = ( + build_window_spend_transaction( + entity_type="key", + entity_id="hashed-token", + window_duration="30d", + window_start=datetime(2026, 8, 1, tzinfo=timezone.utc), + spend=3.0, + started_at=datetime(2026, 8, 10, 12, 0, tzinfo=timezone.utc), + ), + ) + mock_redis_cache.async_rpush_pipeline = AsyncMock(return_value=[1]) + + await redis_update_buffer.restore_transactions_to_redis(window_spend_update_transactions=window_transactions) + + rpush_list = mock_redis_cache.async_rpush_pipeline.call_args.kwargs["rpush_list"] + assert [op["key"] for op in rpush_list] == [REDIS_WINDOW_SPEND_UPDATE_BUFFER_KEY] + + mock_redis_cache.async_lpop_pipeline = AsyncMock( + return_value=[None, None, None, None, None, None, list(rpush_list[0]["values"])] + ) + drained = await redis_update_buffer.get_all_transactions_from_redis_buffer_pipeline() + + assert drained[6] == window_transactions + + +@pytest.mark.asyncio +async def test_restore_transactions_to_redis_noop_when_empty(redis_update_buffer, mock_redis_cache): """Nothing to restore -> no Redis call.""" mock_redis_cache.async_rpush_pipeline = AsyncMock() await redis_update_buffer.restore_transactions_to_redis() @@ -312,15 +351,11 @@ async def test_restore_transactions_to_redis_noop_when_empty( @pytest.mark.asyncio -async def test_restore_transactions_to_redis_swallows_redis_error( - redis_update_buffer, mock_redis_cache -): +async def test_restore_transactions_to_redis_swallows_redis_error(redis_update_buffer, mock_redis_cache): """A Redis failure during restore must not propagate to the caller's finally block.""" from redis.exceptions import RedisError - mock_redis_cache.async_rpush_pipeline = AsyncMock( - side_effect=RedisError("redis down") - ) + mock_redis_cache.async_rpush_pipeline = AsyncMock(side_effect=RedisError("redis down")) await redis_update_buffer.restore_transactions_to_redis( db_spend_update_transactions={"key_list_transactions": {"key1": 1.0}}, @@ -433,3 +468,140 @@ def test_get_transaction_buffer_redis_cache_parses_string_flag(monkeypatch): mock_redis_cache.assert_called_once() assert result is mock_redis_cache.return_value + + +@pytest.mark.asyncio +async def test_store_in_memory_spend_updates_pushes_budget_window_spend(redis_update_buffer, mock_redis_cache): + """The budget window queue has to ride the same rpush as the daily queues, + otherwise multi-pod deployments never persist per-window spend.""" + from datetime import datetime, timezone + + from litellm.constants import REDIS_WINDOW_SPEND_UPDATE_BUFFER_KEY + from litellm.proxy.db.db_transaction_queue.window_spend_update_queue import ( + WindowSpendUpdateQueue, + build_window_spend_transaction, + ) + + mock_redis_cache.async_rpush_pipeline = AsyncMock(return_value=[1]) + + empty_queue = AsyncMock() + empty_queue.flush_and_get_aggregated_db_spend_update_transactions = AsyncMock(return_value={}) + empty_daily_queue = AsyncMock() + empty_daily_queue.flush_and_get_aggregated_daily_spend_update_transactions = AsyncMock(return_value={}) + + window_queue = WindowSpendUpdateQueue() + await window_queue.add_update( + build_window_spend_transaction( + entity_type="key", + entity_id="hashed-token", + window_duration="30d", + window_start=datetime(2026, 8, 1, tzinfo=timezone.utc), + spend=1.25, + started_at=datetime(2026, 8, 10, 12, 0, 0, tzinfo=timezone.utc), + ) + ) + + await redis_update_buffer.store_in_memory_spend_updates_in_redis( + spend_update_queue=empty_queue, + daily_spend_update_queue=empty_daily_queue, + daily_team_spend_update_queue=empty_daily_queue, + daily_org_spend_update_queue=empty_daily_queue, + daily_end_user_spend_update_queue=empty_daily_queue, + daily_agent_spend_update_queue=empty_daily_queue, + window_spend_update_queue=window_queue, + ) + + rpush_list = mock_redis_cache.async_rpush_pipeline.call_args.kwargs["rpush_list"] + assert len(rpush_list) == 1 + assert rpush_list[0]["key"] == REDIS_WINDOW_SPEND_UPDATE_BUFFER_KEY + pushed = json.loads(rpush_list[0]["values"][0]) + assert pushed == [ + { + "entity_type": "key", + "entity_id": "hashed-token", + "window_duration": "30d", + "window_start": "2026-08-01T00:00:00.000000", + "spend": 1.25, + "started_at": "2026-08-10T12:00:00.000000", + "request_ids": [], + } + ] + + +@pytest.mark.asyncio +async def test_budget_window_payloads_keep_request_ids_for_older_workers(redis_update_buffer, mock_redis_cache): + """A leader from before the field was dropped indexes request_ids while + merging what it popped, and the pop is destructive, so a payload without + the key would cost a rolling deploy those increments.""" + from datetime import datetime, timezone + + from litellm.proxy.db.db_transaction_queue.window_spend_update_queue import ( + WindowSpendUpdateQueue, + build_window_spend_transaction, + ) + + mock_redis_cache.async_rpush_pipeline = AsyncMock(return_value=[1]) + window_queue = WindowSpendUpdateQueue() + await window_queue.add_update( + build_window_spend_transaction( + entity_type="key", + entity_id="hashed-token", + window_duration="30d", + window_start=datetime(2026, 8, 1, tzinfo=timezone.utc), + spend=1.25, + ) + ) + + await redis_update_buffer.restore_transactions_to_redis( + window_spend_update_transactions=await window_queue.flush_and_get_aggregated_window_spend_transactions(), + ) + + rpush_list = mock_redis_cache.async_rpush_pipeline.call_args.kwargs["rpush_list"] + restored = json.loads(rpush_list[0]["values"][0]) + assert [payload["request_ids"] for payload in restored] == [[]] + + +@pytest.mark.asyncio +async def test_store_in_memory_spend_updates_restores_budget_window_spend_on_rpush_failure( + redis_update_buffer, mock_redis_cache +): + """The window queue is drained before the rpush, so a Redis hiccup would + silently drop per-window spend without the restore.""" + from datetime import datetime, timezone + + from litellm.proxy.db.db_transaction_queue.window_spend_update_queue import ( + WindowSpendUpdateQueue, + build_window_spend_transaction, + ) + + mock_redis_cache.async_rpush_pipeline = AsyncMock(side_effect=ConnectionError("redis went away")) + + empty_queue = AsyncMock() + empty_queue.flush_and_get_aggregated_db_spend_update_transactions = AsyncMock(return_value={}) + empty_daily_queue = AsyncMock() + empty_daily_queue.flush_and_get_aggregated_daily_spend_update_transactions = AsyncMock(return_value={}) + + window_queue = WindowSpendUpdateQueue() + await window_queue.add_update( + build_window_spend_transaction( + entity_type="team", + entity_id="team-1", + window_duration="7d", + window_start=datetime(2026, 8, 1, tzinfo=timezone.utc), + spend=4.0, + ) + ) + + await redis_update_buffer.store_in_memory_spend_updates_in_redis( + spend_update_queue=empty_queue, + daily_spend_update_queue=empty_daily_queue, + daily_team_spend_update_queue=empty_daily_queue, + daily_org_spend_update_queue=empty_daily_queue, + daily_end_user_spend_update_queue=empty_daily_queue, + daily_agent_spend_update_queue=empty_daily_queue, + window_spend_update_queue=window_queue, + ) + + restored = await window_queue.flush_and_get_aggregated_window_spend_transactions() + assert [payload["spend"] for payload in restored] == [4.0] + assert [payload["entity_id"] for payload in restored] == ["team-1"] diff --git a/tests/test_litellm/proxy/db/db_transaction_queue/test_window_spend_update_queue.py b/tests/test_litellm/proxy/db/db_transaction_queue/test_window_spend_update_queue.py new file mode 100644 index 00000000000..6632b1c8e35 --- /dev/null +++ b/tests/test_litellm/proxy/db/db_transaction_queue/test_window_spend_update_queue.py @@ -0,0 +1,216 @@ +import json +from datetime import datetime, timedelta, timezone + +import pytest + +from litellm.proxy.db.db_transaction_queue.window_spend_update_queue import ( + WindowSpendUpdateQueue, + build_window_spend_transaction, + to_naive_utc, +) + +WINDOW_A = datetime(2026, 8, 1, tzinfo=timezone.utc) +WINDOW_B = datetime(2026, 8, 31, tzinfo=timezone.utc) + + +def _txn( + entity_id: str, + window_start: datetime, + spend: float, + duration: str = "30d", + entity_type: str = "key", + started_at: datetime | None = None, +): + return build_window_spend_transaction( + entity_type=entity_type, + entity_id=entity_id, + window_duration=duration, + window_start=window_start, + spend=spend, + started_at=started_at, + ) + + +def test_build_window_spend_transaction_stores_naive_utc_iso(): + """window_start rides the Redis buffer as a string and lands in a naive-UTC + TIMESTAMP(3) column, so a non-UTC input must be converted, not truncated.""" + non_utc = datetime(2026, 8, 1, 20, 0, tzinfo=timezone(timedelta(hours=-4))) + + assert _txn("k1", non_utc, 1.0) == { + "entity_type": "key", + "entity_id": "k1", + "window_duration": "30d", + "window_start": "2026-08-02T00:00:00.000000", + "spend": 1.0, + "started_at": None, + } + + +def test_build_window_spend_transaction_stores_started_at_as_naive_utc_iso(): + """started_at is compared against LiteLLM_SpendLogs.startTime, which the + spend log writer stores after converting the request start to UTC.""" + non_utc = datetime(2026, 8, 10, 8, 30, 15, 123456, tzinfo=timezone(timedelta(hours=-4))) + + assert _txn("k1", WINDOW_A, 1.0, started_at=non_utc)["started_at"] == "2026-08-10T12:30:15.123456" + + +@pytest.mark.asyncio +async def test_aggregation_keeps_the_earliest_started_at_of_the_batch(): + """The seed stops at the batch's earliest start, so a later start must never + win the merge: it would push the cutoff forward and count a request the + increments already cover.""" + queue = WindowSpendUpdateQueue() + earliest = datetime(2026, 8, 10, 12, 0, 0, tzinfo=timezone.utc) + await queue.add_update(_txn("k1", WINDOW_A, 1.0, started_at=earliest + timedelta(seconds=5))) + await queue.add_update(_txn("k1", WINDOW_A, 1.0, started_at=earliest)) + await queue.add_update(_txn("k1", WINDOW_A, 1.0)) + + aggregated = await queue.flush_and_get_aggregated_window_spend_transactions() + + assert len(aggregated) == 1 + assert aggregated[0]["started_at"] == "2026-08-10T12:00:00.000000" + + +def test_to_naive_utc_leaves_naive_values_alone(): + naive = datetime(2026, 8, 1, 12, 0) + assert to_naive_utc(naive) == naive + + +@pytest.mark.asyncio +async def test_aggregation_sums_increments_within_one_window(): + queue = WindowSpendUpdateQueue() + await queue.add_update(_txn("k1", WINDOW_A, 1.5)) + await queue.add_update(_txn("k1", WINDOW_A, 2.25)) + + aggregated = await queue.flush_and_get_aggregated_window_spend_transactions() + + assert len(aggregated) == 1 + assert aggregated[0]["spend"] == pytest.approx(3.75) + + +@pytest.mark.asyncio +async def test_aggregation_keeps_different_windows_of_same_entity_separate(): + """Merging across windows would fold spend from a window that already + rolled into the new window's total, over-counting the new window.""" + queue = WindowSpendUpdateQueue() + await queue.add_update(_txn("k1", WINDOW_A, 1.0)) + await queue.add_update(_txn("k1", WINDOW_B, 2.0)) + + aggregated = await queue.flush_and_get_aggregated_window_spend_transactions() + + assert len(aggregated) == 2 + assert {payload["window_start"]: payload["spend"] for payload in aggregated} == { + "2026-08-01T00:00:00.000000": 1.0, + "2026-08-31T00:00:00.000000": 2.0, + } + + +@pytest.mark.asyncio +async def test_aggregation_keeps_durations_entities_and_types_separate(): + queue = WindowSpendUpdateQueue() + await queue.add_update(_txn("k1", WINDOW_A, 1.0, duration="30d")) + await queue.add_update(_txn("k1", WINDOW_A, 2.0, duration="7d")) + await queue.add_update(_txn("k2", WINDOW_A, 4.0, duration="30d")) + await queue.add_update(_txn("k1", WINDOW_A, 8.0, duration="30d", entity_type="team")) + + aggregated = await queue.flush_and_get_aggregated_window_spend_transactions() + + assert len(aggregated) == 4 + assert sorted(payload["spend"] for payload in aggregated) == [1.0, 2.0, 4.0, 8.0] + + +@pytest.mark.asyncio +async def test_aggregation_orders_by_primary_key_then_window_start(): + """The flush relies on this order: primary key first for cross-pod lock + ordering, then window_start so an older window is applied before the roll + that supersedes it.""" + queue = WindowSpendUpdateQueue() + await queue.add_update(_txn("t1", WINDOW_A, 1.0, entity_type="team")) + await queue.add_update(_txn("k2", WINDOW_B, 1.0)) + await queue.add_update(_txn("k2", WINDOW_A, 1.0)) + await queue.add_update(_txn("k1", WINDOW_A, 1.0, duration="7d")) + + aggregated = await queue.flush_and_get_aggregated_window_spend_transactions() + + assert [ + (payload["entity_type"], payload["entity_id"], payload["window_duration"], payload["window_start"]) + for payload in aggregated + ] == [ + ("key", "k1", "7d", "2026-08-01T00:00:00.000000"), + ("key", "k2", "30d", "2026-08-01T00:00:00.000000"), + ("key", "k2", "30d", "2026-08-31T00:00:00.000000"), + ("team", "t1", "30d", "2026-08-01T00:00:00.000000"), + ] + + +@pytest.mark.asyncio +async def test_aggregation_does_not_collide_on_entity_ids_containing_a_separator(): + """entity_id is free-form (team ids are user supplied), so grouping must not + depend on a flattened string key.""" + queue = WindowSpendUpdateQueue() + await queue.add_update(_txn("a:30d:2026-08-01T00:00:00.000000:b", WINDOW_A, 1.0)) + await queue.add_update(_txn("b", WINDOW_A, 2.0)) + + aggregated = await queue.flush_and_get_aggregated_window_spend_transactions() + + assert len(aggregated) == 2 + + +@pytest.mark.asyncio +async def test_flush_empties_the_queue(): + queue = WindowSpendUpdateQueue() + await queue.add_update(_txn("k1", WINDOW_A, 1.0)) + + assert await queue.flush_and_get_aggregated_window_spend_transactions() != () + assert await queue.flush_and_get_aggregated_window_spend_transactions() == () + + +@pytest.mark.asyncio +async def test_aggregate_queue_updates_collapses_in_place(): + queue = WindowSpendUpdateQueue() + await queue.add_update(_txn("k1", WINDOW_A, 1.0)) + await queue.add_update(_txn("k1", WINDOW_A, 2.0)) + await queue.add_update(_txn("k1", WINDOW_B, 4.0)) + + await queue.aggregate_queue_updates() + + assert queue.update_queue.qsize() == 1 + aggregated = await queue.flush_and_get_aggregated_window_spend_transactions() + assert sorted(payload["spend"] for payload in aggregated) == [3.0, 4.0] + + +@pytest.mark.asyncio +async def test_aggregation_does_not_mutate_the_queued_payloads(): + """The same payload can be re-aggregated after a failed Redis push, so + aggregation must not accumulate into the caller's object.""" + queue = WindowSpendUpdateQueue() + update = _txn("k1", WINDOW_A, 1.0) + await queue.add_update(update) + await queue.add_update(_txn("k1", WINDOW_A, 2.0)) + + await queue.flush_and_get_aggregated_window_spend_transactions() + + assert update["spend"] == 1.0 + + +def test_aggregation_survives_the_redis_json_round_trip(): + """The Redis buffer stores transactions as JSON, so the aggregated shape + must reload into an equivalent aggregation.""" + aggregated = WindowSpendUpdateQueue.get_aggregated_window_spend_transactions( + [(_txn("k1", WINDOW_A, 1.0),), (_txn("k1", WINDOW_B, 2.0),)] + ) + + reloaded = WindowSpendUpdateQueue.get_aggregated_window_spend_transactions([json.loads(json.dumps(aggregated))]) + + assert reloaded == aggregated + + +def test_started_at_survives_the_redis_json_round_trip(): + aggregated = WindowSpendUpdateQueue.get_aggregated_window_spend_transactions( + [(_txn("k1", WINDOW_A, 1.0, started_at=datetime(2026, 8, 10, 12, 0, tzinfo=timezone.utc)),)] + ) + + reloaded = WindowSpendUpdateQueue.get_aggregated_window_spend_transactions([json.loads(json.dumps(aggregated))]) + + assert reloaded[0]["started_at"] == "2026-08-10T12:00:00.000000" + assert reloaded[0]["spend"] == 1.0 diff --git a/tests/test_litellm/proxy/db/test_autorouter_session_rollup.py b/tests/test_litellm/proxy/db/test_autorouter_session_rollup.py index cb4687ef370..2ed4f843711 100644 --- a/tests/test_litellm/proxy/db/test_autorouter_session_rollup.py +++ b/tests/test_litellm/proxy/db/test_autorouter_session_rollup.py @@ -106,6 +106,28 @@ class TestBuildTransaction: transaction = _build() assert transaction is not None and transaction.tier is None + def test_a_priced_classifier_rides_the_turns_spend(self): + """The classifier row is excluded from the rollup, so its charge lands here, + folded once into the turn that paid for it (GH #38816).""" + transaction = _build(metadata=_metadata(routing_decision={**ROUTING_DECISION, "classifier_cost": 0.005})) + assert transaction is not None and transaction.spend == pytest.approx(0.015) + + @pytest.mark.parametrize( + "decision_extra", [{}, {"classifier_cost": 0.0}, {"classifier_cost": "bogus"}, {"classifier_cost": True}] + ) + def test_an_unpriced_classifier_leaves_the_spend_alone(self, decision_extra: dict): + transaction = _build(metadata=_metadata(routing_decision={**ROUTING_DECISION, **decision_extra})) + assert transaction is not None and transaction.spend == pytest.approx(0.01) + + def test_every_turn_carries_its_own_classifier_charge(self): + first = _build(metadata=_metadata(routing_decision={**ROUTING_DECISION, "classifier_cost": 0.005})) + second = _build( + payload=_payload(startTime="2026-08-01T12:01:00", spend=0.02), + metadata=_metadata(routing_decision={**ROUTING_DECISION, "classifier_cost": 0.007}), + ) + assert first is not None and first.spend == pytest.approx(0.015) + assert second is not None and second.spend == pytest.approx(0.027) + def test_router_name_falls_back_to_the_payload_model_group(self): transaction = _build(metadata=_metadata(routing_decision={"router_type": "complexity"})) assert transaction is not None and transaction.router_name == "live-auto" diff --git a/tests/test_litellm/proxy/db/test_budget_window_spend_writer.py b/tests/test_litellm/proxy/db/test_budget_window_spend_writer.py new file mode 100644 index 00000000000..130f0c56ccf --- /dev/null +++ b/tests/test_litellm/proxy/db/test_budget_window_spend_writer.py @@ -0,0 +1,594 @@ +import math +from contextlib import asynccontextmanager +from datetime import datetime, timedelta, timezone +from typing import Any + +import pytest + +from litellm.proxy.db.budget_window_spend_writer import ( + WindowSeedTotals, + commit_window_spend_updates, + roll_window_spend_row, + spend_logs_seed_totals, +) +from litellm.proxy.db.db_transaction_queue.window_spend_update_queue import ( + build_window_spend_transaction, +) + +WINDOW_A = datetime(2026, 8, 1, tzinfo=timezone.utc) +WINDOW_B = datetime(2026, 8, 31, tzinfo=timezone.utc) +BATCH_STARTED_AT = datetime(2026, 8, 10, 12, 0, 0, 250_000, tzinfo=timezone.utc) +BEFORE_BATCH = BATCH_STARTED_AT - timedelta(hours=1) + +ENTITY_TYPE, ENTITY_ID, WINDOW_DURATION, WINDOW_START, INSERT_SPEND, INCREMENT, NOW = range(7) + + +class _FakeBatcher: + def __init__(self) -> None: + self.calls: list[tuple[str, tuple[Any, ...]]] = [] + + def execute_raw(self, query: str, *args: Any) -> None: + self.calls.append((query, args)) + + +class _FakeDB: + """Stands in for prisma_client.db; records every statement it is handed.""" + + def __init__(self, existing_rows: list[dict[str, str]] | None = None) -> None: + self.existing_rows = existing_rows or [] + self.query_raw_calls: list[tuple[str, tuple[Any, ...]]] = [] + self.execute_raw_calls: list[tuple[str, tuple[Any, ...]]] = [] + self.batcher = _FakeBatcher() + self.committed = False + + async def query_raw(self, query: str, *args: Any) -> list[dict[str, str]]: + self.query_raw_calls.append((query, args)) + return self.existing_rows + + async def execute_raw(self, query: str, *args: Any) -> int: + self.execute_raw_calls.append((query, args)) + return 1 + + @asynccontextmanager + async def _tx(self): + yield self + + def tx(self, timeout: Any = None): + return self._tx() + + @asynccontextmanager + async def _batch(self): + yield self.batcher + self.committed = True + + def batch_(self): + return self._batch() + + +class _FakePrismaClient: + def __init__(self, db: _FakeDB) -> None: + self.db = db + + +class _RecordingAggregate: + """Stands in for the LiteLLM_SpendLogs seed aggregate. before_batch + defaults to the full total, the state where none of this batch's own log + rows have been persisted yet.""" + + def __init__(self, total: float = 5.0, before_batch: float | None = None) -> None: + self.totals = WindowSeedTotals( + total=total, + before_batch=total if before_batch is None else before_batch, + ) + self.calls: list[dict[str, Any]] = [] + + async def __call__( + self, + prisma_client: Any, + entity_type: str, + entity_id: str, + window_start: datetime, + batch_started_at: datetime | None, + ) -> WindowSeedTotals | None: + self.calls.append( + { + "entity_type": entity_type, + "entity_id": entity_id, + "window_start": window_start, + "batch_started_at": batch_started_at, + } + ) + return self.totals + + +class _SpendLogsFake: + """Sums the LiteLLM_SpendLogs rows (request_id, spend, startTime) it holds, + splitting them at the batch start exactly as the real aggregate's + SUM(...) FILTER (WHERE startTime < bound) does.""" + + def __init__(self, rows: tuple[tuple[str, float, datetime], ...]) -> None: + self.rows = rows + + async def __call__( + self, + prisma_client: Any, + entity_type: str, + entity_id: str, + window_start: datetime, + batch_started_at: datetime | None, + ) -> WindowSeedTotals | None: + return WindowSeedTotals( + total=math.fsum(spend for _request_id, spend, _started_at in self.rows), + before_batch=math.fsum( + spend + for _request_id, spend, started_at in self.rows + if batch_started_at is None or started_at < batch_started_at + ), + ) + + +def _batch(spend: float, started_at: datetime | None = BATCH_STARTED_AT) -> dict: + return { + "entity_type": "key", + "entity_id": "k1", + "window_duration": "30d", + "window_start": "2026-08-01T00:00:00.000000", + "spend": spend, + "started_at": None + if started_at is None + else started_at.replace(tzinfo=None).isoformat(timespec="microseconds"), + } + + +def _existing(entity_type: str, entity_id: str, window_duration: str) -> dict[str, str]: + return {"entity_type": entity_type, "entity_id": entity_id, "window_duration": window_duration} + + +@pytest.mark.asyncio +async def test_no_transactions_touches_no_database(): + db = _FakeDB() + + await commit_window_spend_updates(prisma_client=_FakePrismaClient(db), transactions=()) + + assert db.query_raw_calls == [] + assert db.batcher.calls == [] + + +@pytest.mark.asyncio +async def test_missing_row_is_seeded_from_spend_logs_once(): + """A row created mid-window would undercount everything spent before it + existed, so a brand new primary key inserts the SpendLogs total plus this + increment.""" + db = _FakeDB(existing_rows=[]) + aggregate = _RecordingAggregate(total=5.0) + + await commit_window_spend_updates( + prisma_client=_FakePrismaClient(db), + transactions=(build_window_spend_transaction("key", "k1", "30d", WINDOW_A, 1.0),), + spend_logs_aggregate=aggregate, + ) + + assert len(aggregate.calls) == 1 + assert aggregate.calls[0]["entity_type"] == "key" + assert aggregate.calls[0]["entity_id"] == "k1" + assert aggregate.calls[0]["window_start"] == WINDOW_A + + ((_, params),) = db.batcher.calls + assert params[ENTITY_TYPE] == "key" + assert params[ENTITY_ID] == "k1" + assert params[WINDOW_DURATION] == "30d" + assert params[WINDOW_START] == datetime(2026, 8, 1) + assert params[INSERT_SPEND] == pytest.approx(6.0) + assert params[INCREMENT] == pytest.approx(1.0) + + +@pytest.mark.asyncio +async def test_existing_row_is_never_reseeded(): + """The seed is a full LiteLLM_SpendLogs scan; running it for a row that is + already maintained would both cost a scan and double count.""" + db = _FakeDB(existing_rows=[_existing("key", "k1", "30d")]) + aggregate = _RecordingAggregate(total=5.0) + + await commit_window_spend_updates( + prisma_client=_FakePrismaClient(db), + transactions=(build_window_spend_transaction("key", "k1", "30d", WINDOW_A, 1.0),), + spend_logs_aggregate=aggregate, + ) + + assert aggregate.calls == [] + ((_, params),) = db.batcher.calls + assert params[INSERT_SPEND] == pytest.approx(1.0) + assert params[INCREMENT] == pytest.approx(1.0) + + +@pytest.mark.asyncio +async def test_seed_runs_only_for_the_primary_keys_that_are_missing(): + db = _FakeDB(existing_rows=[_existing("key", "k1", "30d")]) + aggregate = _RecordingAggregate(total=5.0) + + await commit_window_spend_updates( + prisma_client=_FakePrismaClient(db), + transactions=( + build_window_spend_transaction("key", "k1", "30d", WINDOW_A, 1.0), + build_window_spend_transaction("team", "t1", "30d", WINDOW_A, 2.0), + ), + spend_logs_aggregate=aggregate, + ) + + assert [call["entity_id"] for call in aggregate.calls] == ["t1"] + assert [call["entity_type"] for call in aggregate.calls] == ["team"] + by_entity = {params[ENTITY_ID]: params for _, params in db.batcher.calls} + assert by_entity["k1"][INSERT_SPEND] == pytest.approx(1.0) + assert by_entity["t1"][INSERT_SPEND] == pytest.approx(7.0) + + +@pytest.mark.asyncio +async def test_insert_spend_and_increment_differ_only_when_a_row_is_seeded(): + """The conflict arm adds the increment alone so two pods that both seed the + same new window cannot add the SpendLogs base twice.""" + db = _FakeDB(existing_rows=[]) + aggregate = _RecordingAggregate(total=9.0) + + await commit_window_spend_updates( + prisma_client=_FakePrismaClient(db), + transactions=(build_window_spend_transaction("key", "k1", "30d", WINDOW_A, 0.25),), + spend_logs_aggregate=aggregate, + ) + + ((_, params),) = db.batcher.calls + assert params[INSERT_SPEND] == pytest.approx(9.25) + assert params[INCREMENT] == pytest.approx(0.25) + + +@pytest.mark.asyncio +async def test_upsert_sql_adds_for_a_current_window_and_replaces_for_a_newer_one(): + """The CASE is the whole contract: an increment at or behind the stored + window_start accumulates, a newer one restarts the window.""" + db = _FakeDB(existing_rows=[_existing("key", "k1", "30d")]) + + await commit_window_spend_updates( + prisma_client=_FakePrismaClient(db), + transactions=(build_window_spend_transaction("key", "k1", "30d", WINDOW_A, 1.0),), + ) + + ((query, _),) = db.batcher.calls + normalized = " ".join(query.split()) + assert ( + 'spend = CASE WHEN "LiteLLM_BudgetWindowSpend".window_start >= EXCLUDED.window_start ' + 'THEN "LiteLLM_BudgetWindowSpend".spend + $6 ELSE EXCLUDED.spend END' in normalized + ) + assert 'window_start = GREATEST("LiteLLM_BudgetWindowSpend".window_start, EXCLUDED.window_start)' in normalized + assert "ON CONFLICT (entity_type, entity_id, window_duration) DO UPDATE SET" in normalized + + +@pytest.mark.asyncio +async def test_upsert_never_interpolates_values_into_the_sql(): + db = _FakeDB(existing_rows=[]) + aggregate = _RecordingAggregate(total=0.0) + + await commit_window_spend_updates( + prisma_client=_FakePrismaClient(db), + transactions=(build_window_spend_transaction("key", "'; DROP TABLE x; --", "30d", WINDOW_A, 1.0),), + spend_logs_aggregate=aggregate, + ) + + ((query, params),) = db.batcher.calls + assert "DROP TABLE" not in query + assert params[ENTITY_ID] == "'; DROP TABLE x; --" + + +@pytest.mark.asyncio +async def test_upserts_are_ordered_by_primary_key_then_window_start(): + """Cross-pod lock ordering, plus an older window must be applied before the + roll that supersedes it or the roll would be undone.""" + db = _FakeDB(existing_rows=[]) + aggregate = _RecordingAggregate(total=0.0) + + await commit_window_spend_updates( + prisma_client=_FakePrismaClient(db), + transactions=( + build_window_spend_transaction("team", "t1", "30d", WINDOW_A, 1.0), + build_window_spend_transaction("key", "k2", "30d", WINDOW_B, 1.0), + build_window_spend_transaction("key", "k2", "30d", WINDOW_A, 1.0), + build_window_spend_transaction("key", "k1", "7d", WINDOW_A, 1.0), + ), + spend_logs_aggregate=aggregate, + ) + + ordered = [ + (params[ENTITY_TYPE], params[ENTITY_ID], params[WINDOW_DURATION], params[WINDOW_START]) + for _, params in db.batcher.calls + ] + assert ordered == [ + ("key", "k1", "7d", datetime(2026, 8, 1)), + ("key", "k2", "30d", datetime(2026, 8, 1)), + ("key", "k2", "30d", datetime(2026, 8, 31)), + ("team", "t1", "30d", datetime(2026, 8, 1)), + ] + + +@pytest.mark.asyncio +async def test_existing_row_lookup_sends_every_primary_key_as_array_params(): + db = _FakeDB(existing_rows=[]) + aggregate = _RecordingAggregate(total=0.0) + + await commit_window_spend_updates( + prisma_client=_FakePrismaClient(db), + transactions=( + build_window_spend_transaction("key", "k1", "30d", WINDOW_A, 1.0), + build_window_spend_transaction("team", "t1", "7d", WINDOW_A, 1.0), + ), + spend_logs_aggregate=aggregate, + ) + + ((query, params),) = db.query_raw_calls + assert "unnest($1::text[], $2::text[], $3::text[])" in query + assert params == (("key", "team"), ("k1", "t1"), ("30d", "7d")) + + +@pytest.mark.asyncio +async def test_all_upserts_are_committed_in_one_transaction(): + db = _FakeDB(existing_rows=[_existing("key", "k1", "30d"), _existing("key", "k2", "30d")]) + + await commit_window_spend_updates( + prisma_client=_FakePrismaClient(db), + transactions=( + build_window_spend_transaction("key", "k1", "30d", WINDOW_A, 1.0), + build_window_spend_transaction("key", "k2", "30d", WINDOW_A, 2.0), + ), + ) + + assert len(db.batcher.calls) == 2 + assert db.committed is True + + +@pytest.mark.asyncio +async def test_unknown_entity_type_contributes_no_seed(): + """Only key and team windows have a LiteLLM_SpendLogs column to aggregate; + anything else starts from its increment alone.""" + db = _FakeDB(existing_rows=[]) + + async def no_such_column(prisma_client, entity_type, entity_id, window_start, batch_started_at): + return None + + await commit_window_spend_updates( + prisma_client=_FakePrismaClient(db), + transactions=(build_window_spend_transaction("user", "u1", "30d", WINDOW_A, 1.0),), + spend_logs_aggregate=no_such_column, + ) + + ((_, params),) = db.batcher.calls + assert params[INSERT_SPEND] == pytest.approx(1.0) + + +@pytest.mark.asyncio +async def test_unavailable_spend_logs_aggregate_seeds_zero_rather_than_failing(): + db = _FakeDB(existing_rows=[]) + + async def unavailable(prisma_client, entity_type, entity_id, window_start, batch_started_at): + return None + + await commit_window_spend_updates( + prisma_client=_FakePrismaClient(db), + transactions=(build_window_spend_transaction("key", "k1", "30d", WINDOW_A, 1.0),), + spend_logs_aggregate=unavailable, + ) + + ((_, params),) = db.batcher.calls + assert params[INSERT_SPEND] == pytest.approx(1.0) + + +@pytest.mark.asyncio +async def test_roll_window_spend_row_is_conditional_on_the_stored_window_being_older(): + """Unconditional zeroing would wipe increments a pod already applied under + the new window.""" + db = _FakeDB() + + await roll_window_spend_row( + prisma_client=_FakePrismaClient(db), + entity_type="team", + entity_id="t1", + window_duration="30d", + new_window_start=WINDOW_B, + ) + + ((query, params),) = db.execute_raw_calls + normalized = " ".join(query.split()) + assert "SET window_start = ($4::timestamptz AT TIME ZONE 'UTC'), spend = 0" in normalized + assert "WHERE entity_type = $1 AND entity_id = $2 AND window_duration = $3" in normalized + assert "AND window_start < ($4::timestamptz AT TIME ZONE 'UTC')" in normalized + assert params[:4] == ("team", "t1", "30d", datetime(2026, 8, 31)) + + +@pytest.mark.asyncio +async def test_seed_receives_the_batch_earliest_start_as_its_cutoff(): + db = _FakeDB(existing_rows=[]) + aggregate = _RecordingAggregate(total=0.0) + + await commit_window_spend_updates( + prisma_client=_FakePrismaClient(db), + transactions=(_batch(3.0),), + spend_logs_aggregate=aggregate, + ) + + assert aggregate.calls[0]["batch_started_at"] == BATCH_STARTED_AT + + +@pytest.mark.asyncio +async def test_seed_passes_no_start_bound_when_the_batch_has_none(): + db = _FakeDB(existing_rows=[]) + aggregate = _RecordingAggregate(total=0.0) + + await commit_window_spend_updates( + prisma_client=_FakePrismaClient(db), + transactions=(_batch(1.0, started_at=None),), + spend_logs_aggregate=aggregate, + ) + + assert aggregate.calls[0]["batch_started_at"] is None + + +@pytest.mark.asyncio +async def test_new_row_is_not_double_counted_when_the_batch_logs_already_flushed(): + """The spend log writer drains on a ~2s poll while window increments flush + on the ~10s batch tick, so a new row is normally seeded from a table that + already holds this batch's rows. Counting them in both places is what made + a fresh row land at exactly twice the true spend.""" + db = _FakeDB(existing_rows=[]) + already_flushed = _SpendLogsFake( + rows=( + ("req-1", 0.000047, BATCH_STARTED_AT), + ("req-2", 0.000047, BATCH_STARTED_AT + timedelta(seconds=1)), + ("req-3", 0.000047, BATCH_STARTED_AT + timedelta(seconds=2)), + ), + ) + + await commit_window_spend_updates( + prisma_client=_FakePrismaClient(db), + transactions=(_batch(0.000141),), + spend_logs_aggregate=already_flushed, + ) + + ((_, params),) = db.batcher.calls + assert params[INSERT_SPEND] == pytest.approx(0.000141) + + +@pytest.mark.asyncio +async def test_new_row_still_covers_spend_that_predates_the_batch(): + """The exclusion must not throw away the pre-existing spend the seed is for.""" + db = _FakeDB(existing_rows=[]) + spend_logs = _SpendLogsFake(rows=(("older", 0.5, BEFORE_BATCH), ("req-1", 0.000047, BATCH_STARTED_AT))) + + await commit_window_spend_updates( + prisma_client=_FakePrismaClient(db), + transactions=(_batch(0.000047),), + spend_logs_aggregate=spend_logs, + ) + + ((_, params),) = db.batcher.calls + assert params[INSERT_SPEND] == pytest.approx(0.500047) + + +@pytest.mark.asyncio +async def test_seed_keeps_spend_another_pod_persisted_after_this_batch_started(): + """A concurrent request on another pod can land its spend log after this + batch started but before this pod seeds the row. Dropping it on a plain + time cutoff would lose that spend for the rest of the window if that pod + died before flushing its increment, so the seed takes off only this batch's + own spend and keeps everything else.""" + db = _FakeDB(existing_rows=[]) + spend_logs = _SpendLogsFake( + rows=( + ("older", 0.5, BEFORE_BATCH), + ("mine", 0.000047, BATCH_STARTED_AT), + ("other-pod", 0.25, BATCH_STARTED_AT + timedelta(seconds=1)), + ), + ) + + await commit_window_spend_updates( + prisma_client=_FakePrismaClient(db), + transactions=(_batch(0.000047),), + spend_logs_aggregate=spend_logs, + ) + + ((_, params),) = db.batcher.calls + assert params[INSERT_SPEND] == pytest.approx(0.750047) + + +@pytest.mark.asyncio +async def test_new_row_is_correct_when_the_batch_logs_have_not_flushed_yet(): + """The other side of the race: rows absent from the aggregate are still + counted exactly once, by their increment.""" + db = _FakeDB(existing_rows=[]) + nothing_flushed = _SpendLogsFake(rows=()) + + await commit_window_spend_updates( + prisma_client=_FakePrismaClient(db), + transactions=(_batch(0.000141),), + spend_logs_aggregate=nothing_flushed, + ) + + ((_, params),) = db.batcher.calls + assert params[INSERT_SPEND] == pytest.approx(0.000141) + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "entity_type, expected_column", + [("key", "api_key = $1"), ("team", "team_id = $1")], +) +async def test_seed_aggregate_sql_splits_the_window_at_the_batch_start(entity_type, expected_column): + db = _FakeDB(existing_rows=[{"total": 1.25, "before_batch": 0.75}]) + + totals = await spend_logs_seed_totals( + prisma_client=_FakePrismaClient(db), + entity_type=entity_type, + entity_id="e1", + window_start=WINDOW_A, + batch_started_at=BATCH_STARTED_AT, + ) + + assert totals == WindowSeedTotals(total=1.25, before_batch=0.75) + ((query, params),) = db.query_raw_calls + normalized = " ".join(query.split()) + assert expected_column in normalized + assert "FILTER (WHERE \"startTime\" < ($3::timestamptz AT TIME ZONE 'UTC'))" in normalized + assert 'FROM "LiteLLM_SpendLogs"' in normalized + # startTime is TIMESTAMP(3): the bound is floored to the second so the + # batch's own earliest row cannot round under it. + assert params == ("e1", WINDOW_A, datetime(2026, 8, 10, 12, 0, 0)) + # Nothing the caller supplied reaches the statement text. + assert "e1" not in query + + +@pytest.mark.asyncio +async def test_seed_aggregate_sums_the_whole_window_without_a_start_bound(): + """A batch with no known start cannot place the split, so both halves are + the same sum and the seed counts everything; at worst that over-counts one + batch, which enforcement tolerates, where under-counting is a budget + bypass.""" + db = _FakeDB(existing_rows=[{"total": 1.25, "before_batch": 1.25}]) + + totals = await spend_logs_seed_totals( + prisma_client=_FakePrismaClient(db), + entity_type="key", + entity_id="e1", + window_start=WINDOW_A, + batch_started_at=None, + ) + + assert totals == WindowSeedTotals(total=1.25, before_batch=1.25) + ((query, params),) = db.query_raw_calls + assert '"startTime" <' not in query + assert params == ("e1", WINDOW_A) + + +@pytest.mark.asyncio +async def test_seed_aggregate_returns_none_for_an_entity_type_with_no_spend_logs_column(): + db = _FakeDB(existing_rows=[]) + + totals = await spend_logs_seed_totals( + prisma_client=_FakePrismaClient(db), + entity_type="user", + entity_id="u1", + window_start=WINDOW_A, + batch_started_at=None, + ) + + assert totals is None + assert db.query_raw_calls == [] + + +@pytest.mark.asyncio +async def test_seed_aggregate_treats_an_entity_with_no_rows_as_zero(): + db = _FakeDB(existing_rows=[]) + + totals = await spend_logs_seed_totals( + prisma_client=_FakePrismaClient(db), + entity_type="key", + entity_id="k-unknown", + window_start=WINDOW_A, + batch_started_at=None, + ) + + assert totals == WindowSeedTotals(total=0.0, before_batch=0.0) diff --git a/tests/test_litellm/proxy/db/test_db_spend_update_writer.py b/tests/test_litellm/proxy/db/test_db_spend_update_writer.py index b1f647bdd3d..11ef911de3e 100644 --- a/tests/test_litellm/proxy/db/test_db_spend_update_writer.py +++ b/tests/test_litellm/proxy/db/test_db_spend_update_writer.py @@ -4,8 +4,8 @@ import json import re - from collections.abc import Callable +from contextlib import asynccontextmanager from datetime import datetime, timezone from unittest.mock import AsyncMock, MagicMock, call, patch @@ -15,6 +15,9 @@ from redis.exceptions import DataError import litellm from litellm.proxy._types import Litellm_EntityType from litellm.proxy.db.db_spend_update_writer import DBSpendUpdateWriter +from litellm.proxy.db.db_transaction_queue.window_spend_update_queue import ( + build_window_spend_transaction, +) @pytest.mark.asyncio @@ -64,9 +67,7 @@ async def test_daily_spend_tracking_with_disabled_spend_logs(): assert db_writer.add_spend_log_transaction_to_daily_user_transaction.called # Verify the payload passed to add_spend_log_transaction_to_daily_user_transaction - call_args = ( - db_writer.add_spend_log_transaction_to_daily_user_transaction.call_args[1] - ) + call_args = db_writer.add_spend_log_transaction_to_daily_user_transaction.call_args[1] assert "payload" in call_args assert call_args["payload"]["spend"] == 0.1 assert call_args["payload"]["model"] == "gpt-4" @@ -406,7 +407,7 @@ async def test_update_daily_spend_sorting(): # fields, but entity_id is sufficient to test sorting. daily_spend_transactions = { f"test_key_{i}": { - "user_id": f"user{60-i}", # user60 ... user11, reverse order + "user_id": f"user{60 - i}", # user60 ... user11, reverse order "date": "2024-01-01", "api_key": "test-api-key", "model": "gpt-4", @@ -985,9 +986,9 @@ async def test_add_spend_log_transaction_to_daily_tag_transaction_with_request_i transaction_dict = call[1]["update"] # Each transaction should have one key with the format tag_date_api_key_model_provider for key, transaction in transaction_dict.items(): - assert ( - transaction["request_id"] == request_id - ), f"request_id should be {request_id} but got {transaction.get('request_id')}" + assert transaction["request_id"] == request_id, ( + f"request_id should be {request_id} but got {transaction.get('request_id')}" + ) @pytest.mark.asyncio @@ -1213,21 +1214,15 @@ async def test_add_spend_log_transaction_to_daily_agent_transaction_calls_common } writer.daily_agent_spend_update_queue.add_update = AsyncMock() - original_common_helper = ( - writer._common_add_spend_log_transaction_to_daily_transaction - ) - writer._common_add_spend_log_transaction_to_daily_transaction = AsyncMock( - wraps=original_common_helper - ) + original_common_helper = writer._common_add_spend_log_transaction_to_daily_transaction + writer._common_add_spend_log_transaction_to_daily_transaction = AsyncMock(wraps=original_common_helper) await writer.add_spend_log_transaction_to_daily_agent_transaction( payload=payload, prisma_client=mock_prisma, ) - assert ( - writer._common_add_spend_log_transaction_to_daily_transaction.await_count == 1 - ) + assert writer._common_add_spend_log_transaction_to_daily_transaction.await_count == 1 @pytest.mark.asyncio @@ -1382,6 +1377,7 @@ async def test_update_daily_spend_re_raises_exception_after_logging(): Test that when batch upsert fails, the exception is properly re-raised after logging. This ensures that error handling continues to work correctly upstream. """ + def raise_connection_lost(): raise ValueError("Database connection lost") @@ -1562,9 +1558,7 @@ async def test_update_database_creates_single_task(): patch("litellm.proxy.proxy_server.prisma_client", MagicMock()), patch("litellm.proxy.proxy_server.user_api_key_cache", MagicMock()), patch("litellm.proxy.proxy_server.litellm_proxy_budget_name", "test-budget"), - patch( - "litellm.proxy.db.db_spend_update_writer.asyncio.create_task" - ) as mock_create_task, + patch("litellm.proxy.db.db_spend_update_writer.asyncio.create_task") as mock_create_task, ): await db_writer.update_database( token="test-token", @@ -1663,9 +1657,7 @@ async def test_daily_agent_receives_deepcopied_payload(): db_writer._update_agent_db = AsyncMock() db_writer.add_spend_log_transaction_to_daily_user_transaction = AsyncMock() db_writer.add_spend_log_transaction_to_daily_end_user_transaction = AsyncMock() - db_writer.add_spend_log_transaction_to_daily_agent_transaction = AsyncMock( - side_effect=capture_agent_payload - ) + db_writer.add_spend_log_transaction_to_daily_agent_transaction = AsyncMock(side_effect=capture_agent_payload) db_writer.add_spend_log_transaction_to_daily_team_transaction = AsyncMock() db_writer.add_spend_log_transaction_to_daily_org_transaction = AsyncMock() db_writer.add_spend_log_transaction_to_daily_tag_transaction = AsyncMock() @@ -1727,8 +1719,8 @@ async def test_commit_spend_updates_uses_pipeline(): mock_redis_update_buffer = AsyncMock() mock_redis_update_buffer.store_in_memory_spend_updates_in_redis = AsyncMock() # Return all-None tuple (no data to commit); the pipeline yields 6 slots - mock_redis_update_buffer.get_all_transactions_from_redis_buffer_pipeline = ( - AsyncMock(return_value=(None, None, None, None, None, None)) + mock_redis_update_buffer.get_all_transactions_from_redis_buffer_pipeline = AsyncMock( + return_value=(None, None, None, None, None, None, None) ) db_writer.redis_update_buffer = mock_redis_update_buffer @@ -1782,7 +1774,7 @@ async def test_commit_with_redis_requeues_all_on_db_failure(): mock_redis_update_buffer = AsyncMock() mock_redis_update_buffer.get_all_transactions_from_redis_buffer_pipeline = AsyncMock( - return_value=(db_spend, daily_user, None, None, None, None) + return_value=(db_spend, daily_user, None, None, None, None, None) ) mock_redis_update_buffer.restore_transactions_to_redis = AsyncMock() db_writer.redis_update_buffer = mock_redis_update_buffer @@ -1837,7 +1829,7 @@ async def test_commit_with_redis_only_requeues_failed_category(): mock_redis_update_buffer = AsyncMock() mock_redis_update_buffer.get_all_transactions_from_redis_buffer_pipeline = AsyncMock( - return_value=(db_spend, daily_user, None, None, None, None) + return_value=(db_spend, daily_user, None, None, None, None, None) ) mock_redis_update_buffer.restore_transactions_to_redis = AsyncMock() db_writer.redis_update_buffer = mock_redis_update_buffer @@ -1885,7 +1877,7 @@ async def test_commit_with_redis_no_requeue_on_success(): mock_redis_update_buffer = AsyncMock() mock_redis_update_buffer.get_all_transactions_from_redis_buffer_pipeline = AsyncMock( - return_value=(db_spend, None, None, None, None, None) + return_value=(db_spend, None, None, None, None, None, None) ) mock_redis_update_buffer.restore_transactions_to_redis = AsyncMock() db_writer.redis_update_buffer = mock_redis_update_buffer @@ -2156,9 +2148,7 @@ async def test_update_database_does_not_deepcopy_on_request_path(): db_writer._update_org_db = AsyncMock() db_writer._update_tag_db = AsyncMock() db_writer._update_agent_db = AsyncMock() - db_writer.add_spend_log_transaction_to_daily_user_transaction = AsyncMock( - side_effect=capture_batch_payload - ) + db_writer.add_spend_log_transaction_to_daily_user_transaction = AsyncMock(side_effect=capture_batch_payload) db_writer.add_spend_log_transaction_to_daily_end_user_transaction = AsyncMock() db_writer.add_spend_log_transaction_to_daily_agent_transaction = AsyncMock() db_writer.add_spend_log_transaction_to_daily_team_transaction = AsyncMock() @@ -2250,9 +2240,7 @@ async def test_spend_update_path_never_queries_user_cache_with_none_user_id(): db_writer = DBSpendUpdateWriter() strict_redis_backed_cache = MagicMock() - strict_redis_backed_cache.async_get_cache = AsyncMock( - side_effect=DataError("Invalid input of type: 'NoneType'") - ) + strict_redis_backed_cache.async_get_cache = AsyncMock(side_effect=DataError("Invalid input of type: 'NoneType'")) with ( patch.object(litellm, "max_budget", 0), @@ -2380,8 +2368,7 @@ async def test_daily_transaction_carries_compression_saved_tokens(): cache_write_cost = model_info.get("cache_creation_input_token_cost") or input_cost assert transaction["compression_savings_spend"] == pytest.approx(7600 * input_cost) assert transaction["prompt_caching_savings_spend"] == pytest.approx( - 40 * max(input_cost - cache_read_cost, 0.0) - - 15 * (cache_write_cost - input_cost) + 40 * max(input_cost - cache_read_cost, 0.0) - 15 * (cache_write_cost - input_cost) ) assert transaction["compression_savings_spend"] > 0 assert transaction["prompt_caching_savings_spend"] > 0 @@ -2421,6 +2408,234 @@ async def test_daily_transaction_compression_saved_tokens_zero_when_absent(): assert transaction["prompt_caching_savings_spend"] == 0 +# --------------------------------------------------------------------------- +# Budget window spend flush (LiteLLM_BudgetWindowSpend) +# --------------------------------------------------------------------------- + + +class _WindowSpendFakeBatcher: + def __init__(self): + self.calls = [] + + def execute_raw(self, query, *args): + self.calls.append((query, args)) + + +class _WindowSpendFakeDB: + """Minimal prisma_client.db that records the raw statements it is handed.""" + + def __init__(self, existing_rows=None): + self.existing_rows = existing_rows or [] + self.query_raw_calls = [] + self.batcher = _WindowSpendFakeBatcher() + + async def query_raw(self, query, *args): + self.query_raw_calls.append((query, args)) + if "LiteLLM_BudgetWindowSpend" in query: + return self.existing_rows + return [] + + @asynccontextmanager + async def _tx(self): + yield self + + def tx(self, timeout=None): + return self._tx() + + @asynccontextmanager + async def _batch(self): + yield self.batcher + + def batch_(self): + return self._batch() + + +class _WindowSpendFakePrisma: + def __init__(self, db): + self.db = db + + +def _window_spend_upserts(db): + return [params for query, params in db.batcher.calls if "LiteLLM_BudgetWindowSpend" in query] + + +@pytest.mark.asyncio +async def test_window_spend_queue_is_flushed_without_redis_buffer(): + """The in-memory window queue must reach the DB on the same scheduler tick + as the other spend queues when the Redis buffer is off.""" + db_writer = DBSpendUpdateWriter() + await db_writer.window_spend_update_queue.add_update( + build_window_spend_transaction( + entity_type="key", + entity_id="hashed-token", + window_duration="30d", + window_start=datetime(2026, 8, 1, tzinfo=timezone.utc), + spend=0.5, + ) + ) + db = _WindowSpendFakeDB( + existing_rows=[{"entity_type": "key", "entity_id": "hashed-token", "window_duration": "30d"}] + ) + + await db_writer._commit_spend_updates_to_db_without_redis_buffer( + prisma_client=_WindowSpendFakePrisma(db), + n_retry_times=0, + proxy_logging_obj=MagicMock(), + ) + + upserts = _window_spend_upserts(db) + assert len(upserts) == 1 + assert upserts[0][0] == "key" + assert upserts[0][1] == "hashed-token" + assert upserts[0][2] == "30d" + assert upserts[0][5] == pytest.approx(0.5) + assert db_writer.window_spend_update_queue.update_queue.qsize() == 0 + + +@pytest.mark.asyncio +async def test_window_spend_queue_is_handed_to_the_redis_buffer(): + """Multi-pod deployments buffer through Redis, so the window queue has to + ride the same rpush path as the daily queues.""" + db_writer = DBSpendUpdateWriter() + mock_redis_update_buffer = AsyncMock() + mock_redis_update_buffer.get_all_transactions_from_redis_buffer_pipeline = AsyncMock( + return_value=(None, None, None, None, None, None, None) + ) + db_writer.redis_update_buffer = mock_redis_update_buffer + db_writer.pod_lock_manager = AsyncMock() + db_writer.pod_lock_manager.acquire_lock = AsyncMock(return_value=True) + + await db_writer._commit_spend_updates_to_db_with_redis( + prisma_client=MagicMock(), + n_retry_times=0, + proxy_logging_obj=MagicMock(), + ) + + stored = mock_redis_update_buffer.store_in_memory_spend_updates_in_redis.call_args[1] + assert stored["window_spend_update_queue"] is db_writer.window_spend_update_queue + + +@pytest.mark.asyncio +async def test_window_spend_transactions_from_redis_are_committed_by_the_lock_winner(): + db_writer = DBSpendUpdateWriter() + window_transactions = ( + build_window_spend_transaction( + entity_type="team", + entity_id="team-1", + window_duration="7d", + window_start=datetime(2026, 8, 1, tzinfo=timezone.utc), + spend=2.0, + ), + ) + mock_redis_update_buffer = AsyncMock() + mock_redis_update_buffer.get_all_transactions_from_redis_buffer_pipeline = AsyncMock( + return_value=(None, None, None, None, None, None, window_transactions) + ) + db_writer.redis_update_buffer = mock_redis_update_buffer + db_writer.pod_lock_manager = AsyncMock() + db_writer.pod_lock_manager.acquire_lock = AsyncMock(return_value=True) + db = _WindowSpendFakeDB(existing_rows=[{"entity_type": "team", "entity_id": "team-1", "window_duration": "7d"}]) + + await db_writer._commit_spend_updates_to_db_with_redis( + prisma_client=_WindowSpendFakePrisma(db), + n_retry_times=0, + proxy_logging_obj=MagicMock(), + ) + + upserts = _window_spend_upserts(db) + assert len(upserts) == 1 + assert upserts[0][:3] == ("team", "team-1", "7d") + assert upserts[0][5] == pytest.approx(2.0) + + +@pytest.mark.asyncio +async def test_window_spend_transactions_are_not_committed_without_the_pod_lock(): + """Every pod buffers to Redis but only the lock winner may drain it.""" + db_writer = DBSpendUpdateWriter() + mock_redis_update_buffer = AsyncMock() + db_writer.redis_update_buffer = mock_redis_update_buffer + db_writer.pod_lock_manager = AsyncMock() + db_writer.pod_lock_manager.acquire_lock = AsyncMock(return_value=False) + db = _WindowSpendFakeDB() + + await db_writer._commit_spend_updates_to_db_with_redis( + prisma_client=_WindowSpendFakePrisma(db), + n_retry_times=0, + proxy_logging_obj=MagicMock(), + ) + + mock_redis_update_buffer.get_all_transactions_from_redis_buffer_pipeline.assert_not_called() + assert _window_spend_upserts(db) == [] + + +@pytest.mark.asyncio +async def test_failed_window_spend_commit_requeues_the_increments_and_continues_the_flush(): + """Budget enforcement trusts a current window row without reconciling it + against LiteLLM_SpendLogs, so a dropped increment would let the key spend + past its limit after the next reseed. The increments must go back on the + queue, and the tool registry flush must still run.""" + db_writer = DBSpendUpdateWriter() + transaction = build_window_spend_transaction( + entity_type="key", + entity_id="hashed-token", + window_duration="30d", + window_start=datetime(2026, 8, 1, tzinfo=timezone.utc), + spend=0.5, + ) + await db_writer.window_spend_update_queue.add_update(transaction) + db = _WindowSpendFakeDB() + db.query_raw = AsyncMock(side_effect=Exception("connection reset")) + db_writer._flush_tool_discovery_queue = AsyncMock() + + await db_writer._commit_spend_updates_to_db_without_redis_buffer( + prisma_client=_WindowSpendFakePrisma(db), + n_retry_times=0, + proxy_logging_obj=MagicMock(), + ) + + db_writer._flush_tool_discovery_queue.assert_called_once() + requeued = await db_writer.window_spend_update_queue.flush_and_get_aggregated_window_spend_transactions() + assert requeued == (transaction,) + + +@pytest.mark.asyncio +async def test_failed_window_spend_commit_from_redis_is_restored_to_redis(): + """The Redis drain is destructive, so a failed window commit has to push + the popped increments back exactly like the other spend categories.""" + db_writer = DBSpendUpdateWriter() + window_transactions = ( + build_window_spend_transaction( + entity_type="team", + entity_id="team-1", + window_duration="7d", + window_start=datetime(2026, 8, 1, tzinfo=timezone.utc), + spend=2.0, + ), + ) + mock_redis_update_buffer = AsyncMock() + mock_redis_update_buffer.get_all_transactions_from_redis_buffer_pipeline = AsyncMock( + return_value=(None, None, None, None, None, None, window_transactions) + ) + mock_redis_update_buffer.restore_transactions_to_redis = AsyncMock() + db_writer.redis_update_buffer = mock_redis_update_buffer + db_writer.pod_lock_manager = AsyncMock() + db_writer.pod_lock_manager.acquire_lock = AsyncMock(return_value=True) + db = _WindowSpendFakeDB() + db.query_raw = AsyncMock(side_effect=Exception("connection reset")) + + await db_writer._commit_spend_updates_to_db_with_redis( + prisma_client=_WindowSpendFakePrisma(db), + n_retry_times=0, + proxy_logging_obj=MagicMock(), + ) + + assert _window_spend_upserts(db) == [] + mock_redis_update_buffer.restore_transactions_to_redis.assert_awaited_once_with( + window_spend_update_transactions=window_transactions + ) + db_writer.pod_lock_manager.release_lock.assert_awaited_once() + + @pytest.mark.asyncio async def test_commit_spend_updates_to_db_does_not_stamp_key_settings_updated_at(): """Spend flushes must leave settings_updated_at alone, or it decays into @@ -2721,9 +2936,7 @@ async def test_commit_spend_updates_retries_deadlock_on_every_entity_path(monkey "call_type, expects_flush", [("aresponses", True), ("responses", True), ("acompletion", False)], ) -async def test_insert_spend_log_asks_for_an_immediate_flush_on_responses_calls( - call_type: str, expects_flush: bool -): +async def test_insert_spend_log_asks_for_an_immediate_flush_on_responses_calls(call_type: str, expects_flush: bool): """ A `previous_response_id` chained straight off the previous turn reads the DB, so a Responses row cannot sit in this worker's queue until the monitor's next poll. @@ -2753,9 +2966,7 @@ async def test_insert_spend_log_asks_for_an_immediate_flush_on_responses_calls( pytest.param("", True, id="injected-before-a-deployment-was-chosen"), ], ) -async def test_caching_savings_are_attributed_to_the_deployment_that_was_injected( - injected_deployment, attributed -): +async def test_caching_savings_are_attributed_to_the_deployment_that_was_injected(injected_deployment, attributed): """Retries, same-group failover and cross-model-group fallbacks all reuse one metadata bucket and one litellm_call_id, so a marker written by the leg that injected is visible to every sibling and nothing request-scoped can tell them apart. diff --git a/tests/test_litellm/proxy/db/test_model_access_group_spend.py b/tests/test_litellm/proxy/db/test_model_access_group_spend.py new file mode 100644 index 00000000000..d2d079bb0e4 --- /dev/null +++ b/tests/test_litellm/proxy/db/test_model_access_group_spend.py @@ -0,0 +1,510 @@ +"""Spend accumulation for model access group budgets.""" + +import asyncio +from collections.abc import Mapping, Sequence + +import pytest + +from litellm.litellm_core_utils.internal_call_metadata import MODEL_ACCESS_GROUP_METADATA_KEY +from litellm.proxy._types import DBSpendUpdateTransactions, Litellm_EntityType, SpendUpdateQueueItem +from litellm.proxy.db.db_spend_update_writer import DBSpendUpdateWriter, debitable_model_access_groups +from litellm.proxy.db.db_transaction_queue.daily_spend_update_queue import DailySpendUpdateQueue +from litellm.proxy.db.db_transaction_queue.redis_update_buffer import RedisUpdateBuffer +from litellm.proxy.db.db_transaction_queue.spend_update_queue import SpendUpdateQueue +from litellm.proxy.spend_tracking.spend_tracking_utils import get_request_model_access_groups + + +class _FakeRouter: + """Deployment lookup returning the access groups each deployment declares.""" + + def __init__(self, deployments: Mapping[str, Sequence[str] | None]) -> None: + self._deployments = deployments + + def get_model_info(self, id: str) -> dict | None: + if id not in self._deployments: + return None + declared = self._deployments[id] + model_info: dict = {"id": id} + if declared is not None: + model_info["access_groups"] = list(declared) + return {"model_name": "some-model", "model_info": model_info} + + +class _FakeBatchTable: + def __init__(self) -> None: + self.calls: list[tuple[dict, dict]] = [] + + def update_many(self, where: dict, data: dict) -> None: + self.calls.append((where, data)) + + +class _FakeBatcher: + def __init__(self) -> None: + self.tables: dict[str, _FakeBatchTable] = {} + + def __getattr__(self, name: str) -> _FakeBatchTable: + return self.tables.setdefault(name, _FakeBatchTable()) + + +class _FakeBatchManager: + def __init__(self, batcher: _FakeBatcher) -> None: + self._batcher = batcher + + async def __aenter__(self) -> _FakeBatcher: + return self._batcher + + async def __aexit__(self, *exc_info: object) -> bool: + return False + + +class _FakeTransaction: + def __init__(self, batcher: _FakeBatcher) -> None: + self._batcher = batcher + + def batch_(self) -> _FakeBatchManager: + return _FakeBatchManager(self._batcher) + + async def __aenter__(self) -> "_FakeTransaction": + return self + + async def __aexit__(self, *exc_info: object) -> bool: + return False + + +class _FakeDb: + def __init__(self, batcher: _FakeBatcher) -> None: + self._batcher = batcher + + def tx(self, timeout: object = None) -> _FakeTransaction: + return _FakeTransaction(self._batcher) + + +class _FakePrismaClient: + def __init__(self) -> None: + self.batcher = _FakeBatcher() + self.db = _FakeDb(self.batcher) + + +def _empty_transactions(**overrides: dict[str, float]) -> DBSpendUpdateTransactions: + return DBSpendUpdateTransactions( + user_list_transactions=overrides.get("user_list_transactions", {}), + end_user_list_transactions=overrides.get("end_user_list_transactions", {}), + key_list_transactions=overrides.get("key_list_transactions", {}), + team_list_transactions=overrides.get("team_list_transactions", {}), + team_member_list_transactions=overrides.get("team_member_list_transactions", {}), + org_list_transactions=overrides.get("org_list_transactions", {}), + tag_list_transactions=overrides.get("tag_list_transactions", {}), + agent_list_transactions=overrides.get("agent_list_transactions", {}), + model_access_group_list_transactions=overrides.get("model_access_group_list_transactions", {}), + ) + + +async def _drain(queue: SpendUpdateQueue) -> list[SpendUpdateQueueItem]: + return await queue.flush_all_updates_from_in_memory_queue() + + +# --- enqueue --------------------------------------------------------------- + + +@pytest.mark.asyncio +async def test_single_matched_group_enqueues_one_item_with_full_cost(): + writer = DBSpendUpdateWriter() + + await writer._update_model_access_group_db( + response_cost=0.42, + request_model_access_groups=["premium-pool"], + served_model_id="deployment-1", + prisma_client=object(), + router=_FakeRouter({"deployment-1": ["premium-pool"]}), + ) + + updates = await _drain(writer.spend_update_queue) + assert updates == [ + SpendUpdateQueueItem( + entity_type=Litellm_EntityType.MODEL_ACCESS_GROUP, + entity_id="premium-pool", + response_cost=0.42, + ) + ] + + +@pytest.mark.asyncio +async def test_every_matched_group_is_charged_the_full_cost_not_a_split(): + writer = DBSpendUpdateWriter() + + await writer._update_model_access_group_db( + response_cost=0.30, + request_model_access_groups=["pool-a", "pool-b", "pool-c"], + served_model_id="deployment-1", + prisma_client=object(), + router=_FakeRouter({"deployment-1": ["pool-a", "pool-b", "pool-c"]}), + ) + + updates = await _drain(writer.spend_update_queue) + assert [update["entity_id"] for update in updates] == ["pool-a", "pool-b", "pool-c"] + assert [update["response_cost"] for update in updates] == [0.30, 0.30, 0.30] + assert {update["entity_type"] for update in updates} == {Litellm_EntityType.MODEL_ACCESS_GROUP} + + +@pytest.mark.parametrize("attributed", [None, [], ()]) +@pytest.mark.asyncio +async def test_no_attributed_groups_enqueues_nothing(attributed): + writer = DBSpendUpdateWriter() + + await writer._update_model_access_group_db( + response_cost=1.0, + request_model_access_groups=attributed, + served_model_id="deployment-1", + prisma_client=object(), + router=_FakeRouter({"deployment-1": ["premium-pool"]}), + ) + + assert await _drain(writer.spend_update_queue) == [] + + +@pytest.mark.asyncio +async def test_no_prisma_client_enqueues_nothing(): + writer = DBSpendUpdateWriter() + + await writer._update_model_access_group_db( + response_cost=1.0, + request_model_access_groups=["premium-pool"], + served_model_id="deployment-1", + prisma_client=None, + router=_FakeRouter({"deployment-1": ["premium-pool"]}), + ) + + assert await _drain(writer.spend_update_queue) == [] + + +@pytest.mark.asyncio +async def test_group_outside_the_attributed_set_is_never_debited(): + """The served deployment also sits in a pool auth never attributed; that pool stays untouched.""" + writer = DBSpendUpdateWriter() + + await writer._update_model_access_group_db( + response_cost=0.10, + request_model_access_groups=["premium-pool"], + served_model_id="deployment-1", + prisma_client=object(), + router=_FakeRouter({"deployment-1": ["premium-pool", "unattributed-pool"]}), + ) + + updates = await _drain(writer.spend_update_queue) + assert [update["entity_id"] for update in updates] == ["premium-pool"] + + +# --- fallback guard -------------------------------------------------------- + + +def test_fallback_to_a_model_in_another_pool_debits_nothing(): + assert ( + debitable_model_access_groups( + attributed=["premium-pool"], + served_model_id="fallback-deployment", + router=_FakeRouter({"fallback-deployment": ["cheap-pool"]}), + ) + == () + ) + + +def test_fallback_to_a_model_in_no_pool_debits_nothing(): + assert ( + debitable_model_access_groups( + attributed=["premium-pool"], + served_model_id="fallback-deployment", + router=_FakeRouter({"fallback-deployment": None}), + ) + == () + ) + + +def test_attributed_set_stands_when_the_served_deployment_is_unknown(): + assert debitable_model_access_groups( + attributed=["premium-pool"], + served_model_id="not-in-router", + router=_FakeRouter({"deployment-1": ["premium-pool"]}), + ) == ("premium-pool",) + + +def test_attributed_set_stands_without_a_router(): + assert debitable_model_access_groups( + attributed=["premium-pool", "premium-pool"], + served_model_id="deployment-1", + router=None, + ) == ("premium-pool",) + + +def test_partial_overlap_keeps_only_the_intersection(): + assert debitable_model_access_groups( + attributed=["pool-a", "pool-b"], + served_model_id="deployment-1", + router=_FakeRouter({"deployment-1": ["pool-b", "pool-c"]}), + ) == ("pool-b",) + + +def test_only_real_group_names_ever_become_entity_ids(): + """Whatever shape the attributed set arrives in, an empty or non-string name never reaches the queue.""" + assert debitable_model_access_groups( + attributed=["pool-a", "", "pool-a", None, 7], + served_model_id=None, + router=None, + ) == ("pool-a",) + + +# --- metadata extraction --------------------------------------------------- + + +def test_access_groups_read_from_request_metadata(): + kwargs = {"litellm_params": {"metadata": {MODEL_ACCESS_GROUP_METADATA_KEY: ["pool-a", "pool-b", "pool-a"]}}} + assert get_request_model_access_groups(kwargs) == ("pool-a", "pool-b") + + +def test_access_groups_read_from_litellm_metadata(): + kwargs = {"litellm_params": {"litellm_metadata": {MODEL_ACCESS_GROUP_METADATA_KEY: ["pool-a"]}}} + assert get_request_model_access_groups(kwargs) == ("pool-a",) + + +def test_standard_logging_payload_wins_over_metadata(): + kwargs = { + "litellm_params": {"metadata": {MODEL_ACCESS_GROUP_METADATA_KEY: ["from-metadata"]}}, + "standard_logging_object": {"request_model_access_groups": ["from-payload"]}, + } + assert get_request_model_access_groups(kwargs) == ("from-payload",) + + +def test_metadata_is_used_when_the_logging_payload_carries_no_groups(): + kwargs = { + "litellm_params": {"metadata": {MODEL_ACCESS_GROUP_METADATA_KEY: ["from-metadata"]}}, + "standard_logging_object": {"request_model_access_groups": []}, + } + assert get_request_model_access_groups(kwargs) == ("from-metadata",) + + +@pytest.mark.parametrize("stamped", ["pool-a", 7, {"pool-a": 1}]) +def test_non_list_access_group_metadata_is_ignored(stamped): + kwargs = {"litellm_params": {"metadata": {MODEL_ACCESS_GROUP_METADATA_KEY: stamped}}} + assert get_request_model_access_groups(kwargs) == () + + +def test_key_absent_from_metadata_yields_no_groups(): + """The chat path only stamps the key when something matched, so absent must mean nothing to debit.""" + kwargs = {"litellm_params": {"metadata": {"user_api_key_user_id": "u-1"}}} + assert get_request_model_access_groups(kwargs) == () + + +def test_explicit_none_yields_no_groups(): + """The pass-through path stamps the key unconditionally, so it can be present and None.""" + kwargs = {"litellm_params": {"metadata": {MODEL_ACCESS_GROUP_METADATA_KEY: None}}} + assert get_request_model_access_groups(kwargs) == () + + +@pytest.mark.parametrize( + "metadata", + [ + {"user_api_key_user_id": "u-1"}, + {MODEL_ACCESS_GROUP_METADATA_KEY: None}, + ], + ids=["key-absent", "key-present-but-none"], +) +@pytest.mark.asyncio +async def test_neither_absent_nor_none_metadata_debits_anything(metadata): + writer = DBSpendUpdateWriter() + + await writer._update_model_access_group_db( + response_cost=0.5, + request_model_access_groups=get_request_model_access_groups({"litellm_params": {"metadata": metadata}}), + served_model_id="deployment-1", + prisma_client=object(), + router=_FakeRouter({"deployment-1": ["premium-pool"]}), + ) + + assert await _drain(writer.spend_update_queue) == [] + + +def test_detached_sub_call_falls_back_to_the_auth_object(): + """Sub-calls inherit only the identity keys, so the groups come off user_api_key_auth there.""" + + class _Auth: + matched_model_access_groups = ["premium-pool"] + + kwargs = {"litellm_params": {"metadata": {"user_api_key_auth": _Auth()}}} + assert get_request_model_access_groups(kwargs) == ("premium-pool",) + + +def test_stamped_metadata_wins_over_the_auth_object(): + class _Auth: + matched_model_access_groups = ["stale-pool"] + + kwargs = { + "litellm_params": { + "metadata": { + MODEL_ACCESS_GROUP_METADATA_KEY: ["fresh-pool"], + "user_api_key_auth": _Auth(), + } + } + } + assert get_request_model_access_groups(kwargs) == ("fresh-pool",) + + +def test_auth_object_without_matched_groups_yields_no_groups(): + class _Auth: + matched_model_access_groups = None + + kwargs = {"litellm_params": {"metadata": {"user_api_key_auth": _Auth()}}} + assert get_request_model_access_groups(kwargs) == () + + +def test_non_string_entries_are_dropped(): + kwargs = {"litellm_params": {"metadata": {MODEL_ACCESS_GROUP_METADATA_KEY: ["pool-a", None, "", 3]}}} + assert get_request_model_access_groups(kwargs) == ("pool-a",) + + +def test_missing_metadata_yields_no_groups(): + assert get_request_model_access_groups(None) == () + assert get_request_model_access_groups({}) == () + assert get_request_model_access_groups({"litellm_params": {}}) == () + + +# --- queue bucketing and redis round trip ---------------------------------- + + +def test_access_group_updates_aggregate_into_their_own_bucket(): + queue = SpendUpdateQueue() + + transactions = queue.get_aggregated_db_spend_update_transactions( + [ + SpendUpdateQueueItem( + entity_type=Litellm_EntityType.MODEL_ACCESS_GROUP, entity_id="pool-a", response_cost=0.1 + ), + SpendUpdateQueueItem( + entity_type=Litellm_EntityType.MODEL_ACCESS_GROUP, entity_id="pool-a", response_cost=0.2 + ), + SpendUpdateQueueItem( + entity_type=Litellm_EntityType.MODEL_ACCESS_GROUP, entity_id="pool-b", response_cost=0.5 + ), + SpendUpdateQueueItem(entity_type=Litellm_EntityType.TAG, entity_id="pool-a", response_cost=9.0), + ] + ) + + assert transactions["model_access_group_list_transactions"] == {"pool-a": pytest.approx(0.3), "pool-b": 0.5} + assert transactions["tag_list_transactions"] == {"pool-a": 9.0} + + +def test_access_group_transactions_survive_the_redis_buffer_merge(): + merged = RedisUpdateBuffer._combine_list_of_transactions( + [ + _empty_transactions(model_access_group_list_transactions={"pool-a": 0.25}), + _empty_transactions(model_access_group_list_transactions={"pool-a": 0.25, "pool-b": 1.0}), + ] + ) + + assert merged["model_access_group_list_transactions"] == {"pool-a": 0.5, "pool-b": 1.0} + + +@pytest.mark.asyncio +async def test_redis_buffer_requeues_access_group_transactions_as_queue_items(): + queue = SpendUpdateQueue() + daily_queue = DailySpendUpdateQueue() + + await RedisUpdateBuffer._restore_spend_updates_to_in_memory_queues( + db_spend_update_transactions=_empty_transactions(model_access_group_list_transactions={"pool-a": 0.75}), + daily_spend_update_transactions=None, + daily_team_spend_update_transactions=None, + daily_org_spend_update_transactions=None, + daily_end_user_spend_update_transactions=None, + daily_agent_spend_update_transactions=None, + window_spend_update_transactions=None, + spend_update_queue=queue, + daily_spend_update_queue=daily_queue, + daily_team_spend_update_queue=daily_queue, + daily_org_spend_update_queue=daily_queue, + daily_end_user_spend_update_queue=daily_queue, + daily_agent_spend_update_queue=daily_queue, + window_spend_update_queue=None, + ) + + updates = await _drain(queue) + assert updates == [ + SpendUpdateQueueItem( + entity_type=Litellm_EntityType.MODEL_ACCESS_GROUP, + entity_id="pool-a", + response_cost=0.75, + ) + ] + + +# --- flush to postgres ----------------------------------------------------- + + +@pytest.mark.asyncio +async def test_commit_increments_spend_on_the_model_access_group_budget_table(): + prisma_client = _FakePrismaClient() + + await DBSpendUpdateWriter()._commit_spend_updates_to_db( + prisma_client=prisma_client, + n_retry_times=0, + proxy_logging_obj=None, + db_spend_update_transactions=_empty_transactions( + model_access_group_list_transactions={"pool-b": 0.5, "pool-a": 0.25} + ), + ) + + assert prisma_client.batcher.tables["litellm_modelaccessgroupbudgettable"].calls == [ + ({"access_group_name": "pool-a"}, {"spend": {"increment": 0.25}}), + ({"access_group_name": "pool-b"}, {"spend": {"increment": 0.5}}), + ] + assert "litellm_tagtable" not in prisma_client.batcher.tables + + +# --- end-to-end through the batched fan-out -------------------------------- + + +@pytest.mark.asyncio +async def test_batch_database_updates_enqueues_access_group_spend(): + writer = DBSpendUpdateWriter() + + await writer._batch_database_updates( + response_cost=0.15, + user_id=None, + hashed_token=None, + team_id=None, + org_id=None, + end_user_id=None, + prisma_client=object(), + litellm_proxy_budget_name=None, + payload={"model_id": "deployment-1", "spend": 0.15}, + request_model_access_groups=("pool-a", "pool-b"), + ) + await asyncio.sleep(0) + + access_group_updates = [ + update + for update in await _drain(writer.spend_update_queue) + if update["entity_type"] is Litellm_EntityType.MODEL_ACCESS_GROUP + ] + assert [(update["entity_id"], update["response_cost"]) for update in access_group_updates] == [ + ("pool-a", 0.15), + ("pool-b", 0.15), + ] + + +@pytest.mark.asyncio +async def test_batch_database_updates_enqueues_nothing_without_access_groups(): + writer = DBSpendUpdateWriter() + + await writer._batch_database_updates( + response_cost=0.15, + user_id=None, + hashed_token=None, + team_id=None, + org_id=None, + end_user_id=None, + prisma_client=object(), + litellm_proxy_budget_name=None, + payload={"model_id": "deployment-1", "spend": 0.15}, + ) + await asyncio.sleep(0) + + updates = await _drain(writer.spend_update_queue) + assert [update for update in updates if update["entity_type"] is Litellm_EntityType.MODEL_ACCESS_GROUP] == [] diff --git a/tests/test_litellm/proxy/db/test_routing_prisma_wrapper.py b/tests/test_litellm/proxy/db/test_routing_prisma_wrapper.py index dcc0036ff04..966a638f6a4 100644 --- a/tests/test_litellm/proxy/db/test_routing_prisma_wrapper.py +++ b/tests/test_litellm/proxy/db/test_routing_prisma_wrapper.py @@ -101,6 +101,49 @@ def test_per_model_reads_route_to_reader_writes_to_writer(): assert actions.delete_many is writer_inner.litellm_usertable.delete_many +def test_writer_pinned_client_bypasses_reader_routing(): + """Regression for #38556: read-after-write reconciles must see the writer's + just-committed rows, so WriterPinnedClient must resolve reads to the writer + even when a read replica is configured.""" + from litellm.proxy.db.routing_prisma_wrapper import RoutingPrismaWrapper, WriterPinnedClient + + writer, writer_inner, reader, reader_inner = _make_wrappers() + writer_inner.litellm_proxymodeltable = _model_actions_mock("writer_models") + reader_inner.litellm_proxymodeltable = _model_actions_mock("reader_models") + routing = RoutingPrismaWrapper(writer=writer, reader=reader) + + pinned = WriterPinnedClient(routing) + + assert pinned.db is writer + assert pinned.db.litellm_proxymodeltable.find_many is writer_inner.litellm_proxymodeltable.find_many + + +def test_writer_pinned_client_passes_through_single_db(): + from litellm.proxy.db.routing_prisma_wrapper import WriterPinnedClient + + writer, _, _, _ = _make_wrappers() + + assert WriterPinnedClient(writer).db is writer + + +def test_writer_pinned_client_yields_to_routed_reads_when_writer_down(): + """The pin must not break reader-only degraded mode: a proxy that starts + during a primary outage still loads DB-backed models from the replica, so + while the writer is degraded the pin resolves to the routed wrapper.""" + from litellm.proxy.db.routing_prisma_wrapper import RoutingPrismaWrapper, WriterPinnedClient + + writer, writer_inner, reader, reader_inner = _make_wrappers() + writer_inner.litellm_proxymodeltable = _model_actions_mock("writer_models") + reader_inner.litellm_proxymodeltable = _model_actions_mock("reader_models") + routing = RoutingPrismaWrapper(writer=writer, reader=reader) + routing._writer_unavailable = True + + pinned = WriterPinnedClient(routing) + + assert pinned.db is routing + assert pinned.db.litellm_proxymodeltable.find_many is reader_inner.litellm_proxymodeltable.find_many + + @pytest.mark.asyncio async def test_connect_invokes_both_clients(): from litellm.proxy.db.routing_prisma_wrapper import RoutingPrismaWrapper diff --git a/tests/test_litellm/proxy/db/test_spend_counter_reseed.py b/tests/test_litellm/proxy/db/test_spend_counter_reseed.py new file mode 100644 index 00000000000..816f9ae72f4 --- /dev/null +++ b/tests/test_litellm/proxy/db/test_spend_counter_reseed.py @@ -0,0 +1,250 @@ +"""Window-spend reads in ``SpendCounterReseed``. + +The maintained ``LiteLLM_BudgetWindowSpend`` row replaces a per-request +``LiteLLM_SpendLogs`` range scan, so these pin *when* the aggregate is still +allowed to run: only when the row is missing or belongs to an older window. +""" + +from __future__ import annotations + +from datetime import datetime, timedelta, timezone +from types import SimpleNamespace + +import pytest + +from litellm.caching.dual_cache import DualCache +from litellm.proxy.db.spend_counter_reseed import SpendCounterReseed + +WINDOW_START = datetime(2026, 8, 1, tzinfo=timezone.utc) + + +class _FakeWindowSpendTable: + def __init__(self, row: SimpleNamespace | None, error: Exception | None = None) -> None: + self._row = row + self._error = error + self.where_clauses: list[dict] = [] + + async def find_unique(self, where: dict): + self.where_clauses.append(where) + if self._error is not None: + raise self._error + return self._row + + +class _FakeSpendLogsTable: + def __init__(self, total: float) -> None: + self._total = total + self.call_count = 0 + + async def group_by(self, by: list[str], where: dict, sum: dict): + self.call_count += 1 + return [{by[0]: where.get(by[0]), "_sum": {"spend": self._total}}] + + +class _FakePrismaClient: + def __init__( + self, + row: SimpleNamespace | None = None, + spend_logs_total: float = 0.0, + error: Exception | None = None, + ) -> None: + self.db = SimpleNamespace( + litellm_budgetwindowspend=_FakeWindowSpendTable(row=row, error=error), + litellm_spendlogs=_FakeSpendLogsTable(total=spend_logs_total), + ) + + +def _row(window_start: datetime, spend: float) -> SimpleNamespace: + return SimpleNamespace(window_start=window_start, spend=spend) + + +@pytest.mark.asyncio +async def test_window_from_table_reads_row_by_primary_key(): + """The lookup must use the table's own entity_type values ("key"), not the + "Key"/"Team" labels the counter keys and spend-log aggregates use.""" + prisma = _FakePrismaClient(row=_row(WINDOW_START, 4.5)) + + result = await SpendCounterReseed.window_from_table( + prisma_client=prisma, + entity_type="Key", + entity_id="tok-1", + window_duration="30d", + expected_window_start=WINDOW_START, + ) + + assert result == 4.5 + assert prisma.db.litellm_budgetwindowspend.where_clauses == [ + { + "entity_type_entity_id_window_duration": { + "entity_type": "key", + "entity_id": "tok-1", + "window_duration": "30d", + } + } + ] + + +@pytest.mark.asyncio +async def test_window_from_table_maps_team_entity_type(): + prisma = _FakePrismaClient(row=_row(WINDOW_START, 9.0)) + + result = await SpendCounterReseed.window_from_table( + prisma_client=prisma, + entity_type="Team", + entity_id="team-1", + window_duration="1d", + expected_window_start=WINDOW_START, + ) + + assert result == 9.0 + inner = prisma.db.litellm_budgetwindowspend.where_clauses[0]["entity_type_entity_id_window_duration"] + assert inner["entity_type"] == "team" + + +@pytest.mark.asyncio +async def test_window_from_table_trusts_row_newer_than_expected_window(): + """Regression: a pod holding a stale ``reset_at`` computes an expected start + behind a window another pod already rolled. Trusting only an exact match + would make it re-add the previous window's spend to the current one.""" + prisma = _FakePrismaClient(row=_row(WINDOW_START + timedelta(days=1), 2.0)) + + result = await SpendCounterReseed.window_from_table( + prisma_client=prisma, + entity_type="Key", + entity_id="tok-1", + window_duration="30d", + expected_window_start=WINDOW_START, + ) + + assert result == 2.0 + + +@pytest.mark.asyncio +async def test_window_from_table_rejects_row_from_previous_window(): + prisma = _FakePrismaClient(row=_row(WINDOW_START - timedelta(seconds=1), 99.0)) + + result = await SpendCounterReseed.window_from_table( + prisma_client=prisma, + entity_type="Key", + entity_id="tok-1", + window_duration="30d", + expected_window_start=WINDOW_START, + ) + + assert result is None + + +@pytest.mark.asyncio +async def test_window_from_table_treats_naive_row_timestamp_as_utc(): + """The column is ``timestamp(3)``, so a driver that hands back a naive value + must still compare against the tz-aware expected start.""" + prisma = _FakePrismaClient(row=_row(WINDOW_START.replace(tzinfo=None), 3.0)) + + result = await SpendCounterReseed.window_from_table( + prisma_client=prisma, + entity_type="Key", + entity_id="tok-1", + window_duration="30d", + expected_window_start=WINDOW_START, + ) + + assert result == 3.0 + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "prisma, entity_type", + [ + (_FakePrismaClient(row=None), "Key"), + (_FakePrismaClient(row=_row(WINDOW_START, 1.0)), "User"), + (_FakePrismaClient(error=RuntimeError("connection reset")), "Key"), + (None, "Key"), + ], +) +async def test_window_from_table_returns_none_without_a_usable_row(prisma, entity_type): + result = await SpendCounterReseed.window_from_table( + prisma_client=prisma, + entity_type=entity_type, + entity_id="tok-1", + window_duration="30d", + expected_window_start=WINDOW_START, + ) + + assert result is None + + +@pytest.mark.asyncio +async def test_window_from_db_prefers_the_row_over_the_spend_logs_aggregate(): + """The aggregate range-scans an unindexed table; a current row must keep it + from running at all.""" + prisma = _FakePrismaClient(row=_row(WINDOW_START, 4.5), spend_logs_total=100.0) + + result = await SpendCounterReseed.window_from_db( + prisma_client=prisma, + entity_type="Key", + entity_id="tok-1", + window_duration="30d", + window_start=WINDOW_START, + ) + + assert result == 4.5 + assert prisma.db.litellm_spendlogs.call_count == 0 + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "row", + [None, _row(WINDOW_START - timedelta(seconds=1), 99.0)], + ids=["missing_row", "previous_window_row"], +) +async def test_window_from_db_falls_back_to_spend_logs(row): + prisma = _FakePrismaClient(row=row, spend_logs_total=7.25) + + result = await SpendCounterReseed.window_from_db( + prisma_client=prisma, + entity_type="Key", + entity_id="tok-1", + window_duration="30d", + window_start=WINDOW_START, + ) + + assert result == 7.25 + assert prisma.db.litellm_spendlogs.call_count == 1 + + +@pytest.mark.asyncio +async def test_window_from_db_without_a_duration_skips_the_row_lookup(): + """Callers that cannot name the window (no PK) keep the pre-table behavior.""" + prisma = _FakePrismaClient(row=_row(WINDOW_START, 4.5), spend_logs_total=7.25) + + result = await SpendCounterReseed.window_from_db( + prisma_client=prisma, + entity_type="Key", + entity_id="tok-1", + window_duration=None, + window_start=WINDOW_START, + ) + + assert result == 7.25 + assert prisma.db.litellm_budgetwindowspend.where_clauses == [] + + +@pytest.mark.asyncio +async def test_coalesced_window_seeds_a_cold_counter_from_the_row(): + prisma = _FakePrismaClient(row=_row(WINDOW_START, 4.5), spend_logs_total=100.0) + cache = DualCache() + counter_key = "spend:key:tok-1:window:30d" + + result = await SpendCounterReseed.coalesced_window( + prisma_client=prisma, + spend_counter_cache=cache, + counter_key=counter_key, + entity_type="Key", + entity_id="tok-1", + window_duration="30d", + window_start=WINDOW_START, + ) + + assert result == 4.5 + assert cache.in_memory_cache.get_cache(key=counter_key) == 4.5 + assert prisma.db.litellm_spendlogs.call_count == 0 diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/openai/test_moderations.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/openai/test_moderations.py index 112bc5e6e49..2b43720a126 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/openai/test_moderations.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/openai/test_moderations.py @@ -482,23 +482,25 @@ async def test_openai_moderation_guardrail_streaming_harmful_content(): "metadata": {"guardrails": ["test-openai-moderation"]}, } - # Should raise HTTPException when processing streaming harmful content - from fastapi import HTTPException + # Chunks have already been flushed by end-of-stream moderation, so + # the block surfaces as the in-stream error frame, not a raise. + import json as _json - async def _drain(): - result_chunks = [] - async for chunk in unified_guardrail.async_post_call_streaming_iterator_hook( - user_api_key_dict=user_api_key_dict, - response=mock_stream(), - request_data=request_data, - ): - result_chunks.append(chunk) + result_chunks = [] + async for chunk in unified_guardrail.async_post_call_streaming_iterator_hook( + user_api_key_dict=user_api_key_dict, + response=mock_stream(), + request_data=request_data, + ): + result_chunks.append(chunk) - with pytest.raises(HTTPException) as exc_info: - await _drain() - - assert exc_info.value.status_code == 400 - assert "Violated OpenAI moderation policy" in str(exc_info.value.detail) + frame = result_chunks[-1] + assert isinstance(frame, bytes) + text = frame.decode() + assert text.startswith("data: ") + assert "Violated OpenAI moderation policy" in text + payload = _json.loads(text[len("data: ") :]) + assert payload["error"]["code"] == "400" @pytest.mark.asyncio diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/openai/test_openai_moderation_streaming.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/openai/test_openai_moderation_streaming.py index 914af0e2368..476d443d8d8 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/openai/test_openai_moderation_streaming.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/openai/test_openai_moderation_streaming.py @@ -161,19 +161,27 @@ async def test_openai_moderation_guardrail_streaming_harmful_content(): "metadata": {"guardrails": ["test-openai-moderation"]}, } - # Should raise HTTPException - with pytest.raises(HTTPException) as exc_info: - async for ( - _ - ) in unified_guardrail.async_post_call_streaming_iterator_hook( - user_api_key_dict=user_api_key_dict, - response=mock_stream(), - request_data=request_data, - ): - pass + # Chunks have already been flushed by end-of-stream moderation, so + # the block surfaces as the in-stream error frame, not a raise. + import json as _json - assert exc_info.value.status_code == 400 - assert "Violated OpenAI moderation policy" in str(exc_info.value.detail) + collected = [] + async for ( + chunk + ) in unified_guardrail.async_post_call_streaming_iterator_hook( + user_api_key_dict=user_api_key_dict, + response=mock_stream(), + request_data=request_data, + ): + collected.append(chunk) + + frame = collected[-1] + assert isinstance(frame, bytes) + text = frame.decode() + assert text.startswith("data: ") + assert "Violated OpenAI moderation policy" in text + payload = _json.loads(text[len("data: ") :]) + assert payload["error"]["code"] == "400" @pytest.mark.asyncio diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_alice.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_alice.py new file mode 100644 index 00000000000..fd2e86ccde8 --- /dev/null +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_alice.py @@ -0,0 +1,614 @@ +import json +import os +from copy import deepcopy +from unittest.mock import AsyncMock + +import httpx +import pytest +from httpx import Request, Response + +import litellm +from litellm.exceptions import GuardrailRaisedException +from litellm.proxy.guardrails.guardrail_hooks.alice.alice import ( + GUARDRAIL_NAME, + AliceGuardrail, + AliceGuardrailMissingSecrets, + _json_safe, +) +from litellm.proxy.guardrails.init_guardrails import init_guardrails_v2 + + +def _guardrail(**overrides: object) -> AliceGuardrail: + params: dict[str, object] = {"api_key": "test-key", "guardrail_name": "alice", "event_hook": "pre_call"} + params.update(overrides) + return AliceGuardrail(**params) + + +def _verdict(payload: dict[str, object], status_code: int = 200) -> Response: + return Response( + status_code=status_code, + json=payload, + request=Request("POST", "https://api.alice.io/v2/evaluate/litellm"), + ) + + +def test_alice_guardrail_config(monkeypatch: pytest.MonkeyPatch): + """Should register through init_guardrails_v2 like any other provider.""" + monkeypatch.setattr(litellm, "guardrail_name_config_map", {}) + monkeypatch.setenv("ALICE_API_KEY", "test-key") + + init_guardrails_v2( + all_guardrails=[ + { + "guardrail_name": "alice", + "litellm_params": {"guardrail": "alice", "mode": "pre_call", "default_on": True}, + } + ], + config_file_path="", + ) + + registered = [cb for cb in litellm.callbacks if isinstance(cb, AliceGuardrail)] + assert len(registered) == 1 + assert registered[0].guardrail_name == "alice" + + +class TestAliceGuardrailInitialization: + def setup_method(self): + for key in ("ALICE_API_KEY", "ALICE_API_BASE"): + os.environ.pop(key, None) + + def test_missing_api_key_raises(self): + with pytest.raises(AliceGuardrailMissingSecrets, match="API key"): + AliceGuardrail(guardrail_name="alice", event_hook="pre_call") + + def test_reads_credentials_from_environment(self, monkeypatch: pytest.MonkeyPatch): + monkeypatch.setenv("ALICE_API_KEY", "env-key") + monkeypatch.setenv("ALICE_API_BASE", "https://env.alice.test") + + guardrail = AliceGuardrail(guardrail_name="alice", event_hook="pre_call") + + assert guardrail.alice_api_key == "env-key" + assert guardrail.api_base == "https://env.alice.test/v2/evaluate/litellm" + + def test_defaults_the_api_base(self): + assert _guardrail().api_base == "https://api.alice.io/v2/evaluate/litellm" + + def test_trailing_slash_does_not_double_up(self): + assert _guardrail(api_base="https://api.alice.io/").api_base == ("https://api.alice.io/v2/evaluate/litellm") + + +class TestAliceForwarding: + """The hook's arguments cross the wire as they were received — nothing selected, nothing + renamed — except the caller's raw credentials, which are stripped before request_data is + serialized (see TestAliceCredentialStripping).""" + + @pytest.mark.asyncio + async def test_forwards_the_hook_arguments_verbatim(self): + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock(return_value=_verdict({"verdict": "ALLOW", "categories": []})) + inputs = {"texts": ["hello"], "structured_messages": [{"role": "user", "content": "hello"}]} + request_data = {"model": "gpt-4o", "metadata": {"user_api_key_alias": "payments-bot"}} + # Snapshot before the call: @log_guardrail_information writes its own entry into + # request_data["metadata"] afterwards, so the original is no longer what was sent. + sent_inputs = deepcopy(inputs) + sent_request_data = deepcopy(request_data) + + await guardrail.apply_guardrail(inputs=inputs, request_data=request_data, input_type="request") + + body = guardrail.async_handler.post.call_args.kwargs["json"] + assert body["input_type"] == "request" + assert body["inputs"] == sent_inputs + assert body["request_data"] == sent_request_data + + @pytest.mark.asyncio + async def test_sends_the_credential(self): + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock(return_value=_verdict({"verdict": "ALLOW", "categories": []})) + + await guardrail.apply_guardrail(inputs={"texts": ["hi"]}, request_data={}, input_type="request") + + assert guardrail.async_handler.post.call_args.kwargs["headers"]["af-api-key"] == "test-key" + + @pytest.mark.asyncio + async def test_marks_a_completion_as_a_response(self): + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock(return_value=_verdict({"verdict": "ALLOW", "categories": []})) + + await guardrail.apply_guardrail(inputs={"texts": ["answer"]}, request_data={}, input_type="response") + + assert guardrail.async_handler.post.call_args.kwargs["json"]["input_type"] == "response" + + @pytest.mark.asyncio + async def test_nothing_selectable_reaches_no_evaluation(self): + """No texts, images, tools, tool_calls, or structured_messages: genuinely nothing to send.""" + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock() + + result = await guardrail.apply_guardrail(inputs={"texts": []}, request_data={}, input_type="request") + + assert result == {"texts": []} + guardrail.async_handler.post.assert_not_called() + + @pytest.mark.asyncio + async def test_tool_calls_only_still_reaches_alice(self): + """A batch with empty texts but populated tool_calls is still a selection decision Alice + should make, not the plugin — see the class docstring.""" + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock(return_value=_verdict({"verdict": "ALLOW", "categories": []})) + inputs = {"texts": [], "tool_calls": [{"id": "call_1", "function": {"name": "get_weather"}}]} + + await guardrail.apply_guardrail(inputs=inputs, request_data={}, input_type="request") + + guardrail.async_handler.post.assert_called_once() + assert guardrail.async_handler.post.call_args.kwargs["json"]["inputs"]["tool_calls"] == inputs["tool_calls"] + + @pytest.mark.asyncio + async def test_images_only_still_reaches_alice(self): + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock(return_value=_verdict({"verdict": "ALLOW", "categories": []})) + + await guardrail.apply_guardrail( + inputs={"texts": [], "images": ["data:image/png;base64,abc"]}, request_data={}, input_type="request" + ) + + guardrail.async_handler.post.assert_called_once() + + @pytest.mark.asyncio + async def test_structured_messages_only_still_reaches_alice(self): + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock(return_value=_verdict({"verdict": "ALLOW", "categories": []})) + + await guardrail.apply_guardrail( + inputs={"texts": [], "structured_messages": [{"role": "user", "content": []}]}, + request_data={}, + input_type="request", + ) + + guardrail.async_handler.post.assert_called_once() + + @pytest.mark.asyncio + async def test_makes_exactly_one_attempt(self): + guardrail = _guardrail(unreachable_fallback="fail_open") + guardrail.async_handler.post = AsyncMock(side_effect=httpx.ConnectError("refused")) + + await guardrail.apply_guardrail(inputs={"texts": ["hi"]}, request_data={}, input_type="request") + + assert guardrail.async_handler.post.call_count == 1 + + +class TestAliceCredentialStripping: + """request_data's raw-credential keys never leave the process.""" + + @pytest.mark.asyncio + async def test_secret_fields_and_api_key_are_stripped(self): + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock(return_value=_verdict({"verdict": "ALLOW", "categories": []})) + request_data = { + "model": "gpt-4o", + "api_key": "sk-forwarded-provider-secret", + "secret_fields": {"raw_headers": {"authorization": "Bearer caller-virtual-key"}}, + "metadata": {"user_api_key_alias": "payments-bot"}, + } + + await guardrail.apply_guardrail(inputs={"texts": ["hi"]}, request_data=request_data, input_type="request") + + sent_request_data = guardrail.async_handler.post.call_args.kwargs["json"]["request_data"] + assert "secret_fields" not in sent_request_data + assert "api_key" not in sent_request_data + assert sent_request_data == {"model": "gpt-4o", "metadata": {"user_api_key_alias": "payments-bot"}} + + @pytest.mark.asyncio + async def test_nested_credentials_are_stripped_at_every_depth(self): + """Shaped after a real captured Claude Code payload: the caller's Authorization/x-api-key + lives under several independent nesting paths, none of which are the root.""" + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock(return_value=_verdict({"verdict": "ALLOW", "categories": []})) + request_data = { + "model": "claude-3-5-sonnet", + "secret_fields": {"raw_headers": {"authorization": "Bearer caller-virtual-key"}}, + "provider_specific_header": {"extra_headers": {"authorization": "sk-ant-oat01-nested-oauth"}}, + "proxy_server_request": { + "url": "/v1/messages", + "headers": {"authorization": "Bearer inbound-caller-secret", "x-request-id": "req-1"}, + "body": { + "model": "claude-3-5-sonnet", + "metadata": {"headers": {"authorization": "Bearer body-metadata-secret"}}, + }, + }, + "metadata": { + "user_api_key_alias": "payments-bot", + "headers": {"authorization": "Bearer metadata-secret"}, + "requester_metadata": {"headers": {"authorization": "Bearer requester-metadata-secret"}}, + }, + "litellm_metadata": {"headers": {"authorization": "Bearer litellm-metadata-secret"}}, + } + + await guardrail.apply_guardrail(inputs={"texts": ["hi"]}, request_data=request_data, input_type="request") + + posted_body = guardrail.async_handler.post.call_args.kwargs["json"] + serialized = json.dumps(posted_body) + assert "authorization" not in serialized.lower() + assert "caller-virtual-key" not in serialized + assert "nested-oauth" not in serialized + assert "inbound-caller-secret" not in serialized + assert "body-metadata-secret" not in serialized + assert "metadata-secret" not in serialized + assert "requester-metadata-secret" not in serialized + assert "litellm-metadata-secret" not in serialized + + sent_request_data = posted_body["request_data"] + assert sent_request_data["model"] == "claude-3-5-sonnet" + assert sent_request_data["proxy_server_request"]["url"] == "/v1/messages" + assert "headers" not in sent_request_data["proxy_server_request"] + assert sent_request_data["proxy_server_request"]["body"]["model"] == "claude-3-5-sonnet" + assert "headers" not in sent_request_data["proxy_server_request"]["body"]["metadata"] + assert sent_request_data["metadata"]["user_api_key_alias"] == "payments-bot" + assert "headers" not in sent_request_data["metadata"] + assert "requester_metadata" in sent_request_data["metadata"] + assert "headers" not in sent_request_data["metadata"]["requester_metadata"] + assert "headers" not in sent_request_data["litellm_metadata"] + assert "secret_fields" not in sent_request_data + assert "provider_specific_header" not in sent_request_data + + @pytest.mark.asyncio + async def test_the_original_request_data_is_not_mutated(self): + """Stripping must only affect the outbound copy — api_key still has to reach the + provider, and secret_fields still has to reach the rest of the request pipeline.""" + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock(return_value=_verdict({"verdict": "ALLOW", "categories": []})) + request_data = {"api_key": "sk-forwarded-provider-secret", "secret_fields": {"raw_headers": {}}} + + await guardrail.apply_guardrail(inputs={"texts": ["hi"]}, request_data=request_data, input_type="request") + + assert request_data["api_key"] == "sk-forwarded-provider-secret" + assert request_data["secret_fields"] == {"raw_headers": {}} + + +class TestAliceVerdicts: + @pytest.mark.asyncio + async def test_allow_leaves_the_inputs_untouched(self): + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock(return_value=_verdict({"verdict": "ALLOW", "categories": []})) + + result = await guardrail.apply_guardrail(inputs={"texts": ["hello"]}, request_data={}, input_type="request") + + assert result["texts"] == ["hello"] + + @pytest.mark.asyncio + async def test_block_surfaces_the_policy_message(self): + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock( + return_value=_verdict( + { + "verdict": "BLOCK", + "categories": ["self_harm"], + "correlation_id": "c1", + "message": "Blocked by your organization's policy", + } + ) + ) + + with pytest.raises(GuardrailRaisedException) as error: + await guardrail.apply_guardrail(inputs={"texts": ["bad"]}, request_data={}, input_type="request") + + assert "Blocked by your organization's policy" in str(error.value) + + @pytest.mark.asyncio + async def test_block_without_a_message_still_blocks(self): + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock(return_value=_verdict({"verdict": "BLOCK", "categories": []})) + + with pytest.raises(GuardrailRaisedException): + await guardrail.apply_guardrail(inputs={"texts": ["bad"]}, request_data={}, input_type="request") + + @pytest.mark.asyncio + async def test_mask_substitutes_by_position(self): + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock( + return_value=_verdict( + { + "verdict": "MASK", + "categories": ["pii"], + "replacements": [{"index": 1, "text": "my ssn is ***"}], + } + ) + ) + + result = await guardrail.apply_guardrail( + inputs={"texts": ["untouched", "my ssn is 123-45-6789"]}, + request_data={}, + input_type="request", + ) + + assert result["texts"] == ["untouched", "my ssn is ***"] + + @pytest.mark.asyncio + async def test_mask_that_lands_nowhere_blocks(self): + """A mask that wrote nothing would let the text through under a verdict that said not to.""" + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock( + return_value=_verdict({"verdict": "MASK", "categories": [], "replacements": [{"index": 9, "text": "***"}]}) + ) + + with pytest.raises(GuardrailRaisedException): + await guardrail.apply_guardrail(inputs={"texts": ["hello"]}, request_data={}, input_type="request") + + @pytest.mark.asyncio + async def test_mask_with_no_replacements_blocks(self): + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock(return_value=_verdict({"verdict": "MASK", "categories": []})) + + with pytest.raises(GuardrailRaisedException): + await guardrail.apply_guardrail(inputs={"texts": ["hello"]}, request_data={}, input_type="request") + + @pytest.mark.asyncio + async def test_mask_with_one_invalid_replacement_blocks_entirely(self): + """A mixed valid/invalid replacement list must not let the valid half through: that + would leave the content named by the invalid entry unmasked while looking like success.""" + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock( + return_value=_verdict( + { + "verdict": "MASK", + "categories": ["pii"], + "replacements": [{"index": 0, "text": "***"}, {"index": 9, "text": "***"}], + } + ) + ) + + with pytest.raises(GuardrailRaisedException): + await guardrail.apply_guardrail( + inputs={"texts": ["my ssn is 123-45-6789"]}, request_data={}, input_type="request" + ) + + @pytest.mark.asyncio + async def test_mask_leaves_structured_messages_identical(self): + """A new structured_messages object makes the translation layer skip the texts write-back.""" + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock( + return_value=_verdict({"verdict": "MASK", "categories": [], "replacements": [{"index": 0, "text": "***"}]}) + ) + messages = [{"role": "user", "content": "secret"}] + + result = await guardrail.apply_guardrail( + inputs={"texts": ["secret"], "structured_messages": messages}, + request_data={}, + input_type="request", + ) + + assert result["structured_messages"] is messages + + @pytest.mark.asyncio + async def test_detect_allows_and_leaves_the_text_alone(self): + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock( + return_value=_verdict({"verdict": "DETECT", "categories": ["profanity"], "correlation_id": "c1"}) + ) + + result = await guardrail.apply_guardrail(inputs={"texts": ["mild"]}, request_data={}, input_type="request") + + assert result["texts"] == ["mild"] + + +class TestAliceUnreachable: + @pytest.mark.parametrize( + "failure", + [ + pytest.param({"side_effect": httpx.ConnectError("refused")}, id="connect-error"), + pytest.param({"return_value": _verdict({"verdict": "MAYBE"})}, id="unrecognized-verdict"), + pytest.param({"return_value": _verdict({})}, id="no-verdict"), + ], + ) + @pytest.mark.asyncio + async def test_fails_closed_by_default(self, failure: dict): + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock(**failure) + + with pytest.raises(GuardrailRaisedException, match="unavailable"): + await guardrail.apply_guardrail(inputs={"texts": ["hello"]}, request_data={}, input_type="request") + + @pytest.mark.asyncio + async def test_fails_open_when_configured(self): + guardrail = _guardrail(unreachable_fallback="fail_open") + guardrail.async_handler.post = AsyncMock(side_effect=httpx.ConnectError("refused")) + + result = await guardrail.apply_guardrail(inputs={"texts": ["hello"]}, request_data={}, input_type="request") + + assert result["texts"] == ["hello"] + + +class TestAliceTransportFailures: + """Every path out of the HTTP call, since each decides whether traffic flows unscreened.""" + + @pytest.mark.asyncio + async def test_a_timeout_is_unreachable(self): + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock( + side_effect=litellm.exceptions.Timeout(message="slow", model="gpt-4o", llm_provider="openai") + ) + + with pytest.raises(GuardrailRaisedException, match="unavailable"): + await guardrail.apply_guardrail(inputs={"texts": ["hello"]}, request_data={}, input_type="request") + + @pytest.mark.parametrize("status", [500, 502, 503, 504]) + @pytest.mark.asyncio + async def test_upstream_5xx_is_unreachable(self, status: int): + guardrail = _guardrail(unreachable_fallback="fail_open") + guardrail.async_handler.post = AsyncMock( + side_effect=httpx.HTTPStatusError( + "server error", + request=Request("POST", "https://api.alice.io/v2/evaluate/litellm"), + response=_verdict({}, status_code=status), + ) + ) + + result = await guardrail.apply_guardrail(inputs={"texts": ["hello"]}, request_data={}, input_type="request") + + assert result["texts"] == ["hello"] + + @pytest.mark.asyncio + async def test_a_500_fails_closed_by_default(self): + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock( + side_effect=httpx.HTTPStatusError( + "server error", + request=Request("POST", "https://api.alice.io/v2/evaluate/litellm"), + response=_verdict({}, status_code=500), + ) + ) + + with pytest.raises(GuardrailRaisedException, match="unavailable"): + await guardrail.apply_guardrail(inputs={"texts": ["hello"]}, request_data={}, input_type="request") + + @pytest.mark.asyncio + async def test_a_4xx_is_not_treated_as_unreachable(self): + """A rejected credential is our misconfiguration, not an outage — it must not fail open.""" + guardrail = _guardrail(unreachable_fallback="fail_open") + guardrail.async_handler.post = AsyncMock( + side_effect=httpx.HTTPStatusError( + "unauthorized", + request=Request("POST", "https://api.alice.io/v2/evaluate/litellm"), + response=_verdict({}, status_code=401), + ) + ) + + with pytest.raises(httpx.HTTPStatusError): + await guardrail.apply_guardrail(inputs={"texts": ["hello"]}, request_data={}, input_type="request") + + @pytest.mark.asyncio + async def test_a_non_object_body_fails_closed_by_default(self): + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock( + return_value=Response( + status_code=200, + json=["not", "an", "object"], + request=Request("POST", "https://api.alice.io/v2/evaluate/litellm"), + ) + ) + + with pytest.raises(GuardrailRaisedException, match="unavailable"): + await guardrail.apply_guardrail(inputs={"texts": ["hello"]}, request_data={}, input_type="request") + + @pytest.mark.asyncio + async def test_a_non_object_body_fails_open_when_configured(self): + guardrail = _guardrail(unreachable_fallback="fail_open") + guardrail.async_handler.post = AsyncMock( + return_value=Response( + status_code=200, + json=["not", "an", "object"], + request=Request("POST", "https://api.alice.io/v2/evaluate/litellm"), + ) + ) + + result = await guardrail.apply_guardrail(inputs={"texts": ["hello"]}, request_data={}, input_type="request") + + assert result["texts"] == ["hello"] + + @pytest.mark.asyncio + async def test_malformed_json_fails_closed_by_default(self): + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock( + return_value=Response( + status_code=200, + content=b"not json", + request=Request("POST", "https://api.alice.io/v2/evaluate/litellm"), + ) + ) + + with pytest.raises(GuardrailRaisedException, match="unavailable"): + await guardrail.apply_guardrail(inputs={"texts": ["hello"]}, request_data={}, input_type="request") + + @pytest.mark.asyncio + async def test_malformed_json_fails_open_when_configured(self): + guardrail = _guardrail(unreachable_fallback="fail_open") + guardrail.async_handler.post = AsyncMock( + return_value=Response( + status_code=200, + content=b"not json", + request=Request("POST", "https://api.alice.io/v2/evaluate/litellm"), + ) + ) + + result = await guardrail.apply_guardrail(inputs={"texts": ["hello"]}, request_data={}, input_type="request") + + assert result["texts"] == ["hello"] + + @pytest.mark.asyncio + async def test_an_undecodable_body_fails_closed_by_default(self): + """UnicodeDecodeError is a sibling of JSONDecodeError under ValueError, not a subclass.""" + guardrail = _guardrail() + guardrail.async_handler.post = AsyncMock( + return_value=Response( + status_code=200, + content=b"\xff\xfe not utf-8", + request=Request("POST", "https://api.alice.io/v2/evaluate/litellm"), + ) + ) + + with pytest.raises(GuardrailRaisedException, match="unavailable"): + await guardrail.apply_guardrail(inputs={"texts": ["hello"]}, request_data={}, input_type="request") + + @pytest.mark.asyncio + async def test_an_undecodable_body_fails_open_when_configured(self): + guardrail = _guardrail(unreachable_fallback="fail_open") + guardrail.async_handler.post = AsyncMock( + return_value=Response( + status_code=200, + content=b"\xff\xfe not utf-8", + request=Request("POST", "https://api.alice.io/v2/evaluate/litellm"), + ) + ) + + result = await guardrail.apply_guardrail(inputs={"texts": ["hello"]}, request_data={}, input_type="request") + + assert result["texts"] == ["hello"] + + +class TestAliceSerialization: + """`request_data` carries live objects, so it cannot be posted as it stands.""" + + def test_drops_what_cannot_serialize_and_keeps_the_rest(self): + class Span: + pass + + result = _json_safe({"model": "x", "metadata": {"span": Span(), "user": "u1"}, "n": 1}) + + assert result == {"model": "x", "metadata": {"span": None, "user": "u1"}, "n": 1} + + def test_survives_a_cycle(self): + data: dict = {"a": 1} + data["self"] = data + + assert _json_safe(data) == {"a": 1, "self": None} + + def test_drops_a_model_that_will_not_dump(self): + class Stubborn: + def model_dump(self, mode: str = "python") -> dict: + raise RuntimeError("cannot serialise") + + assert _json_safe({"m": Stubborn()}) == {"m": None} + + def test_drops_a_bare_unserialisable_value(self): + class Span: + pass + + assert _json_safe(Span()) is None + + def test_dumps_pydantic_models(self): + from pydantic import BaseModel + + class Model(BaseModel): + name: str + + assert _json_safe({"m": Model(name="x")}) == {"m": {"name": "x"}} + + +def test_config_model_is_exposed_for_the_ui(): + config_model = AliceGuardrail.get_config_model() + + assert config_model is not None + assert config_model.ui_friendly_name() == "Alice" + + +def test_guardrail_name_constant(): + assert GUARDRAIL_NAME == "alice" diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_bedrock_guardrails.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_bedrock_guardrails.py index 36b356e34d0..953e3de1519 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_bedrock_guardrails.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_bedrock_guardrails.py @@ -5345,3 +5345,450 @@ def test_initialize_bedrock_forwards_aws_external_id(): assert guardrail.optional_params["aws_external_id"] == "external-id-123" finally: litellm.logging_callback_manager.remove_callback_from_list_by_object(litellm.callbacks, guardrail) + + +def _chat_chunk(content: str, finish_reason: str | None) -> litellm.ModelResponseStream: + return litellm.ModelResponseStream( + id="tid", + choices=[ + litellm.types.utils.StreamingChoices( + delta=litellm.types.utils.Delta(content=content, role="assistant"), + finish_reason=finish_reason, + index=0, + ) + ], + created=1, + model="gpt-4o-mini", + object="chat.completion.chunk", + ) + + +def _streaming_litellm_params(**extras): + from litellm.types.guardrails import LitellmParams + + return LitellmParams( + guardrail="bedrock", + mode="post_call", + guardrailIdentifier="test-id", + guardrailVersion="DRAFT", + **extras, + ) + + +def test_initialize_bedrock_wires_streaming_flags(): + from litellm.proxy.guardrails.guardrail_initializers import initialize_bedrock + + configured = initialize_bedrock( + _streaming_litellm_params( + streaming_buffer_until_moderated=False, + streaming_sampling_rate=3, + streaming_end_of_stream_only=True, + ), + {"guardrail_name": "bedrock-streaming"}, + ) + defaulted = initialize_bedrock( + _streaming_litellm_params(), + {"guardrail_name": "bedrock-defaults"}, + ) + for registered in (configured, defaulted): + litellm.logging_callback_manager.remove_callback_from_list_by_object(litellm.callbacks, registered) + + assert configured.streaming_buffer_until_moderated is False + assert configured.streaming_sampling_rate == 3 + assert configured.streaming_end_of_stream_only is True + assert defaulted.streaming_buffer_until_moderated is True + assert defaulted.streaming_sampling_rate == 5 + assert defaulted.streaming_end_of_stream_only is False + + +def test_initialize_bedrock_rejects_non_positive_sampling_rate(): + from pydantic import ValidationError + + from litellm.proxy.guardrails.guardrail_initializers import initialize_bedrock + + with pytest.raises(ValidationError): + initialize_bedrock( + _streaming_litellm_params(streaming_sampling_rate=0), + {"guardrail_name": "bedrock-bad-rate"}, + ) + + +def test_update_in_memory_litellm_params_round_trips_streaming_flags(): + guardrail = BedrockGuardrail( + guardrail_name="bedrock-update", + guardrailIdentifier="test-id", + guardrailVersion="DRAFT", + ) + + guardrail.update_in_memory_litellm_params( + _streaming_litellm_params( + streaming_buffer_until_moderated=False, + streaming_sampling_rate=7, + streaming_end_of_stream_only=True, + ) + ) + assert guardrail.streaming_buffer_until_moderated is False + assert guardrail.streaming_sampling_rate == 7 + assert guardrail.streaming_end_of_stream_only is True + + guardrail.update_in_memory_litellm_params(_streaming_litellm_params()) + assert guardrail.streaming_buffer_until_moderated is True + assert guardrail.streaming_sampling_rate == 5 + assert guardrail.streaming_end_of_stream_only is False + + +async def _run_streaming_hook_recording_order(guardrail: BedrockGuardrail) -> list: + events = [] + minimal = {"action": "NONE", "assessments": [], "outputs": []} + + async def record_scan(*args, **kwargs): + events.append("scan") + return minimal + + async def mock_stream(): + yield _chat_chunk("Hello", None) + yield _chat_chunk(" world", None) + yield _chat_chunk("", "stop") + + with patch.object(guardrail, "make_bedrock_api_request", AsyncMock(side_effect=record_scan)): + async for chunk in guardrail.async_post_call_streaming_iterator_hook( + user_api_key_dict=UserAPIKeyAuth(), + response=mock_stream(), + request_data={"model": "gpt-4o-mini", "messages": [{"role": "user", "content": "hi"}]}, + ): + content = chunk.choices[0].delta.content if chunk.choices else None + events.append(("chunk", content)) + return events + + +@pytest.mark.asyncio +async def test_unbuffered_end_of_stream_hook_yields_chunks_before_scan(): + guardrail = BedrockGuardrail( + guardrail_name="bedrock-audit-mode", + guardrailIdentifier="test-id", + guardrailVersion="DRAFT", + event_hook=GuardrailEventHooks.post_call, + default_on=True, + streaming_buffer_until_moderated=False, + streaming_end_of_stream_only=True, + ) + + events = await _run_streaming_hook_recording_order(guardrail) + + scan_index = events.index("scan") + chunk_events = [e for e in events if e != "scan"] + assert events.count("scan") == 1 + assert [e for e in events[:scan_index] if e != "scan"] == chunk_events[: scan_index] + assert ("chunk", "Hello") in events[:scan_index] + assert ("chunk", " world") in events[:scan_index] + assert len(chunk_events) == 3 + + +@pytest.mark.asyncio +async def test_buffered_default_hook_scans_before_any_chunk(): + guardrail = BedrockGuardrail( + guardrail_name="bedrock-buffered-default", + guardrailIdentifier="test-id", + guardrailVersion="DRAFT", + event_hook=GuardrailEventHooks.post_call, + default_on=True, + ) + + events = await _run_streaming_hook_recording_order(guardrail) + + assert events[0] == "scan" + assert all(e == "scan" or e[0] == "chunk" for e in events) + assert len([e for e in events if e != "scan"]) >= 1 + + +@pytest.mark.asyncio +async def test_masking_keeps_buffered_path_even_when_unbuffered_configured(): + guardrail = BedrockGuardrail( + guardrail_name="bedrock-mask-buffered", + guardrailIdentifier="test-id", + guardrailVersion="DRAFT", + event_hook=GuardrailEventHooks.post_call, + default_on=True, + mask_response_content=True, + streaming_buffer_until_moderated=False, + streaming_end_of_stream_only=True, + ) + + assert guardrail._streams_incrementally() is False + events = await _run_streaming_hook_recording_order(guardrail) + assert events[0] == "scan" + + +@pytest.mark.asyncio +async def test_streaming_end_of_stream_block_emits_error_frame_instead_of_truncating(): + """Regression for PR #38722: a topicPolicy DENY caught by the end-of-stream + scan used to raise after SSE headers were flushed, so the client saw a + silently truncated stream. The unified hook must emit the chat in-stream + error frame instead. The finish chunk is withheld while the end-of-stream + scan runs, so on a block it is dropped rather than relayed before the + frame.""" + from litellm.llms import load_guardrail_translation_mappings + from litellm.proxy.guardrails.guardrail_hooks.unified_guardrail import ( + unified_guardrail as unified_module, + ) + from litellm.proxy.guardrails.guardrail_hooks.unified_guardrail.unified_guardrail import ( + UnifiedLLMGuardrails, + ) + from litellm.types.utils import Delta, ModelResponseStream, StreamingChoices + + guardrail = BedrockGuardrail( + guardrailIdentifier="test-guardrail", + guardrailVersion="DRAFT", + streaming_end_of_stream_only=True, + streaming_buffer_until_moderated=False, + guardrail_name="bedrock-eos", + event_hook=GuardrailEventHooks.post_call, + default_on=True, + ) + blocked_response = { + "action": "GUARDRAIL_INTERVENED", + "actionReason": "Guardrail blocked.", + "outputs": [{"text": "Sorry, the model cannot answer this question."}], + "assessments": [ + {"topicPolicy": {"topics": [{"name": "Forbidden topic", "type": "DENY", "action": "BLOCKED"}]}} + ], + } + + def _chunk(content, finish_reason=None): + return ModelResponseStream( + choices=[ + StreamingChoices( + index=0, + delta={"content": content, "role": "assistant"}, + finish_reason=finish_reason, + ) + ], + ) + + async def _mock_stream(): + yield _chunk("the forbidden ") + yield _chunk("topic answer", finish_reason="stop") + + unified_module.endpoint_guardrail_translation_mappings = load_guardrail_translation_mappings() + try: + with patch.object(guardrail, "make_bedrock_api_request", new_callable=AsyncMock) as mock_api: + mock_api.side_effect = guardrail._get_http_exception_for_blocked_guardrail(blocked_response) + + out = [] + async for item in UnifiedLLMGuardrails().async_post_call_streaming_iterator_hook( + user_api_key_dict=UserAPIKeyAuth(api_key="test", request_route="/v1/chat/completions"), + response=_mock_stream(), + request_data={"guardrail_to_apply": guardrail, "model": "gpt-4"}, + ): + out.append(item) + finally: + unified_module.endpoint_guardrail_translation_mappings = None + + assert len(out) == 2 + assert isinstance(out[0], ModelResponseStream) + assert out[0].choices[0].finish_reason is None + frame = out[-1] + assert isinstance(frame, bytes) + payload = json.loads(frame.decode()[len("data: ") :]) + assert payload["error"]["message"] == "Violated guardrail policy" + assert payload["error"]["code"] == "400" + assert payload["error"]["provider_specific_fields"]["guardrailIdentifier"] == "test-guardrail" + + +def _responses_stream_events() -> list: + from litellm.types.llms.openai import ( + OutputTextDeltaEvent, + ResponseCompletedEvent, + ResponsesAPIResponse, + ResponsesAPIStreamEvents, + ) + + deltas = [ + OutputTextDeltaEvent( + type=ResponsesAPIStreamEvents.OUTPUT_TEXT_DELTA, + item_id="msg_lit6457", + output_index=0, + content_index=0, + delta=part, + ) + for part in ("Hello", " world") + ] + completed = ResponseCompletedEvent( + type=ResponsesAPIStreamEvents.RESPONSE_COMPLETED, + response=ResponsesAPIResponse( + id="resp_lit6457", + created_at=1234567890, + model="gpt-4o", + object="response", + status="completed", + output=[ + { + "type": "message", + "id": "msg_lit6457", + "status": "completed", + "role": "assistant", + "content": [{"type": "output_text", "text": "Hello world"}], + } + ], + ), + ) + return [*deltas, completed] + + +@pytest.mark.asyncio +async def test_responses_api_stream_scans_output_and_replays_buffered_events(): + """Streamed /v1/responses events must be scanned via the unified translation + layer, not fed to stream_chunk_builder (which raises APIError on them).""" + guardrail = BedrockGuardrail( + guardrail_name="bedrock-responses-stream", + guardrailIdentifier="test-id", + guardrailVersion="DRAFT", + event_hook=GuardrailEventHooks.post_call, + default_on=True, + ) + stream_events = _responses_stream_events() + order = [] + yielded = [] + + async def record_scan(*args, **kwargs): + order.append("scan") + return {"action": "NONE", "assessments": [], "outputs": []} + + async def mock_stream(): + for event in stream_events: + yield event + + with patch.object(guardrail, "make_bedrock_api_request", AsyncMock(side_effect=record_scan)): + async for chunk in guardrail.async_post_call_streaming_iterator_hook( + user_api_key_dict=UserAPIKeyAuth(api_key="test-key", request_route="/v1/responses"), + response=mock_stream(), + request_data={"model": "gpt-4o", "input": "hi"}, + ): + order.append("chunk") + yielded.append(chunk) + + assert order == ["scan", "chunk", "chunk", "chunk"] + assert len(yielded) == len(stream_events) + assert all(emitted is original for emitted, original in zip(yielded, stream_events)) + + +def _responses_failed_stream_events() -> list: + from litellm.types.llms.openai import ( + OutputTextDeltaEvent, + ResponseFailedEvent, + ResponsesAPIResponse, + ResponsesAPIStreamEvents, + ) + + deltas = [ + OutputTextDeltaEvent( + type=ResponsesAPIStreamEvents.OUTPUT_TEXT_DELTA, + item_id="msg_lit6457_failed", + output_index=0, + content_index=0, + delta=part, + ) + for part in ("Hello", " world") + ] + failed = ResponseFailedEvent( + type=ResponsesAPIStreamEvents.RESPONSE_FAILED, + response=ResponsesAPIResponse( + id="resp_lit6457_failed", + created_at=1234567890, + model="gpt-4o", + object="response", + status="failed", + output=[], + ), + ) + return [*deltas, failed] + + +@pytest.mark.asyncio +async def test_responses_api_failed_stream_scans_delta_text_before_replay(): + """A responses stream that dies mid-generation carries its text only in delta + events; the end-of-stream scan must still see that text instead of skipping + on an empty assembled string and replaying the buffer unmoderated.""" + guardrail = BedrockGuardrail( + guardrail_name="bedrock-responses-failed-stream", + guardrailIdentifier="test-id", + guardrailVersion="DRAFT", + event_hook=GuardrailEventHooks.post_call, + default_on=True, + ) + stream_events = _responses_failed_stream_events() + order = [] + scan_payloads = [] + yielded = [] + + async def record_scan(*args, **kwargs): + order.append("scan") + scan_payloads.append(str(args) + str(kwargs)) + return {"action": "NONE", "assessments": [], "outputs": []} + + async def mock_stream(): + for event in stream_events: + yield event + + with patch.object(guardrail, "make_bedrock_api_request", AsyncMock(side_effect=record_scan)): + async for chunk in guardrail.async_post_call_streaming_iterator_hook( + user_api_key_dict=UserAPIKeyAuth(api_key="test-key", request_route="/v1/responses"), + response=mock_stream(), + request_data={"model": "gpt-4o", "input": "hi"}, + ): + order.append("chunk") + yielded.append(chunk) + + assert order == ["scan", "chunk", "chunk", "chunk"] + assert "Hello world" in scan_payloads[0] + assert len(yielded) == len(stream_events) + assert all(emitted is original for emitted, original in zip(yielded, stream_events)) + + +@pytest.mark.asyncio +async def test_apply_guardrail_debug_log_masks_signed_request_headers(): + import logging + + from litellm._logging import verbose_proxy_logger + + session_token = "FakeSessionTokenValueThatMustNeverAppearInLogs1234567890" + guardrail = BedrockGuardrail( + guardrailIdentifier="test-guardrail", + guardrailVersion="DRAFT", + aws_access_key_id="ASIAFAKEACCESSKEYID1", + aws_secret_access_key="fakeSecretAccessKeyForSigning", + aws_session_token=session_token, + aws_region_name="us-east-1", + ) + + mock_response = MagicMock() + mock_response.status_code = 200 + mock_response.json.return_value = {"action": "NONE", "outputs": []} + + captured_records: list[logging.LogRecord] = [] + + class _RecordingHandler(logging.Handler): + def emit(self, record: logging.LogRecord) -> None: + captured_records.append(record) + + handler = _RecordingHandler(level=logging.DEBUG) + previous_level = verbose_proxy_logger.level + verbose_proxy_logger.addHandler(handler) + verbose_proxy_logger.setLevel(logging.DEBUG) + try: + with patch.object(guardrail.async_handler, "post", new_callable=AsyncMock) as mock_post: + mock_post.return_value = mock_response + await guardrail.make_bedrock_api_request( + source="INPUT", + messages=[{"role": "user", "content": "hello"}], + request_data={}, + ) + finally: + verbose_proxy_logger.removeHandler(handler) + verbose_proxy_logger.setLevel(previous_level) + + rendered_messages = [record.getMessage() for record in captured_records] + header_lines = [message for message in rendered_messages if "headers:" in message] + assert header_lines, "expected the signed-request debug line to be logged" + assert any("X-Amz-Security-Token" in message for message in header_lines) + assert all(session_token not in message for message in rendered_messages) diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_headroom.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_headroom.py index 7a2772ce78c..1fbc975e40a 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_headroom.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_headroom.py @@ -17,14 +17,18 @@ Tests cover: - CCR: headroom_retrieve tool injected when compressed messages contain hashes - CCR: async_should_run_agentic_loop returns True when response has headroom_retrieve tool calls - CCR: async_build_agentic_loop_plan calls retrieve endpoint and builds follow-up messages +- CCR: streaming /chat/completions is converted to a non-streaming call so the agentic + loop resolves the retrieve tool call, then fake-streamed back to the client """ import json import time +from typing import Optional from unittest.mock import AsyncMock, MagicMock, patch import httpx import pytest +import respx from fastapi import HTTPException import litellm @@ -38,7 +42,11 @@ from litellm.proxy.guardrails.guardrail_hooks.headroom.headroom import ( from litellm.proxy.spend_tracking.compression_savings import ( extract_compression_saved_tokens, ) -from litellm.types.utils import GenericGuardrailAPIInputs +from litellm.types.integrations.custom_logger import HEADROOM_CONVERTED_STREAM_KEY +from litellm.types.utils import ( + CallTypes, + GenericGuardrailAPIInputs, +) FAKE_API_BASE = "https://headroom.example.com" FAKE_API_KEY = "test-key" @@ -1893,6 +1901,199 @@ async def test_fail_open_returns_original_parts_shapes(): assert [m["content"] for m in messages] == [m["content"] for m in PARTS_MESSAGES] +CCR_HASH = "b573993006976af767214fac" + + +def _retrieve_tool_definition() -> dict: + return { + "type": "function", + "function": { + "name": HEADROOM_RETRIEVE_TOOL_NAME, + "description": "retrieve compressed content", + "parameters": {"type": "object", "properties": {"hash": {"type": "string"}}}, + }, + } + + +def _openai_completion_payload(message: dict, finish_reason: str) -> dict: + return { + "id": "chatcmpl-ccr", + "object": "chat.completion", + "created": 1700000000, + "model": "gpt-4o", + "choices": [{"index": 0, "message": message, "finish_reason": finish_reason}], + "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15}, + } + + +def _openai_tool_call_payload() -> dict: + return _openai_completion_payload( + { + "role": "assistant", + "content": None, + "tool_calls": [ + { + "id": "call_ccr", + "type": "function", + "function": { + "name": HEADROOM_RETRIEVE_TOOL_NAME, + "arguments": json.dumps({"hash": CCR_HASH}), + }, + } + ], + }, + "tool_calls", + ) + + +def _openai_text_payload(content: str) -> dict: + return _openai_completion_payload({"role": "assistant", "content": content}, "stop") + + +@pytest.mark.parametrize( + "call_type, stream, tools, expect_conversion", + [ + (CallTypes.acompletion, True, [_retrieve_tool_definition()], True), + (CallTypes.completion, True, [_retrieve_tool_definition()], True), + (CallTypes.acompletion, False, [_retrieve_tool_definition()], False), + (CallTypes.acompletion, True, [{"type": "function", "function": {"name": "get_weather"}}], False), + (CallTypes.acompletion, True, None, False), + (CallTypes.aresponses, True, [_retrieve_tool_definition()], False), + (CallTypes.anthropic_messages, True, [_retrieve_tool_definition()], False), + ], +) +@pytest.mark.asyncio +async def test_pre_call_deployment_hook_converts_stream_only_for_ccr_chat_completions( + guardrail: HeadroomGuardrail, + call_type: CallTypes, + stream: bool, + tools: Optional[list], + expect_conversion: bool, +): + kwargs = {"model": "gpt-4o", "stream": stream, "tools": tools} + + result = await guardrail.async_pre_call_deployment_hook(kwargs=kwargs, call_type=call_type) + + if not expect_conversion: + assert result is kwargs + assert HEADROOM_CONVERTED_STREAM_KEY not in kwargs + assert kwargs["stream"] is stream + return + + assert result is not None + assert result["stream"] is False + assert result[HEADROOM_CONVERTED_STREAM_KEY] is True + assert kwargs["stream"] is True + + +@pytest.mark.asyncio +async def test_pre_call_deployment_hook_still_compresses_for_deployment_level_configs( + guardrail: HeadroomGuardrail, +): + """Regression for the stream-conversion override swallowing the parent hook: + when the guardrail is attached at the deployment level and proxy pre_call never + ran, the deployment hook is the only place compression executes, so the + override must delegate to CustomGuardrail.async_pre_call_deployment_hook.""" + kwargs = { + "model": "gpt-4o", + "messages": [dict(m) for m in ORIGINAL_MESSAGES], + "stream": False, + "guardrails": ["headroom"], + "metadata": {}, + } + + with patch.object( + guardrail.async_handler, + "post", + new_callable=AsyncMock, + return_value=_make_compress_response(COMPRESSED_MESSAGES), + ): + result = await guardrail.async_pre_call_deployment_hook(kwargs=kwargs, call_type=CallTypes.acompletion) + + assert result is not None + assert result["messages"] == EXPECTED_MESSAGES + + +@pytest.mark.asyncio +async def test_pre_call_deployment_hook_converts_stream_after_deployment_level_compression( + guardrail: HeadroomGuardrail, +): + kwargs = { + "model": "gpt-4o", + "messages": [dict(m) for m in ORIGINAL_MESSAGES], + "stream": True, + "guardrails": ["headroom"], + "metadata": {}, + } + + with patch.object( + guardrail.async_handler, + "post", + new_callable=AsyncMock, + return_value=_make_compress_response(COMPRESSED_MESSAGES_WITH_HASH), + ): + result = await guardrail.async_pre_call_deployment_hook(kwargs=kwargs, call_type=CallTypes.acompletion) + + assert result is not None + assert has_headroom_retrieve_tool(result["tools"]) + assert result["stream"] is False + assert result[HEADROOM_CONVERTED_STREAM_KEY] is True + + +@pytest.mark.asyncio +async def test_streaming_chat_completion_resolves_ccr_retrieval_end_to_end( + guardrail: HeadroomGuardrail, + respx_mock: respx.MockRouter, + monkeypatch: pytest.MonkeyPatch, +): + """Regression test for streaming /chat/completions: the retrieve tool call the + model emits must be resolved by the agentic loop instead of being streamed back + to a client that never declared the tool.""" + original_content = "the full uncompressed document" + final_answer = "the document says hello" + guardrail._issued_hashes_by_call_id["ccr-call-id"] = ( + frozenset({CCR_HASH}), + time.monotonic() + 999, + ) + + monkeypatch.setenv("OPENAI_API_KEY", "sk-test") + monkeypatch.setattr(litellm, "callbacks", [guardrail]) + monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) + upstream = respx_mock.post("https://api.openai.com/v1/chat/completions").mock( + side_effect=[ + httpx.Response(200, json=_openai_tool_call_payload()), + httpx.Response(200, json=_openai_text_payload(final_answer)), + ] + ) + + with patch.object( + guardrail.async_handler, + "get", + new_callable=AsyncMock, + return_value=_make_retrieve_response(original_content), + ) as mock_get: + response = await litellm.acompletion( + model="openai/gpt-4o", + messages=[{"role": "user", "content": f"summarize hash={CCR_HASH}"}], + tools=[_retrieve_tool_definition()], + stream=True, + litellm_call_id="ccr-call-id", + ) + chunks = [chunk async for chunk in response] + + streamed_text = "".join(chunk.choices[0].delta.content or "" for chunk in chunks if chunk.choices) + assert streamed_text == final_answer + assert not any(chunk.choices and chunk.choices[0].delta.tool_calls for chunk in chunks) + mock_get.assert_called_once() + assert CCR_HASH in (mock_get.call_args.kwargs.get("url") or mock_get.call_args.args[0]) + + assert len(upstream.calls) == 2 + followup_body = json.loads(upstream.calls[1].request.content) + assert not followup_body.get("stream") + assert original_content in json.dumps(followup_body["messages"]) + assert not any(key.startswith("_headroom_interception") for key in followup_body) + + # --------------------------------------------------------------------------- # LIT-5018: the turn the model is being asked to act on is never compressed. # diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_hiddenlayer.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_hiddenlayer.py index 1b2108c837d..f5d51a601d7 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_hiddenlayer.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_hiddenlayer.py @@ -1,4 +1,6 @@ import os +import threading +import time import uuid from typing import List, cast from unittest.mock import AsyncMock, MagicMock, patch @@ -6,6 +8,7 @@ from unittest.mock import AsyncMock, MagicMock, patch import pytest from fastapi import HTTPException from httpx import Request, Response +import requests import litellm @@ -14,6 +17,7 @@ from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging from litellm.proxy.guardrails.guardrail_hooks.hiddenlayer.hiddenlayer import ( HiddenlayerGuardrail, HiddenlayerGuardrailV2, + _get_jwt, ) from litellm.proxy.guardrails.init_guardrails import init_guardrails_v2 from litellm.types.utils import ( @@ -428,7 +432,7 @@ class TestHiddenlayerGuardrail: @pytest.mark.asyncio async def test_apply_guardrail_request_with_image(self, monkeypatch: pytest.MonkeyPatch): - """Test apply_guardrail sends multimodal content (image) to HiddenLayer v1.""" + """Test apply_guardrail strips images from multimodal content before sending to HiddenLayer v1.""" monkeypatch.setenv("HIDDENLAYER_API_BASE", "https://my.hiddenlayer") guardrail = HiddenlayerGuardrail( @@ -481,12 +485,13 @@ class TestHiddenlayerGuardrail: logging_obj=logging_obj, ) - # v1 API requires string content — multimodal list is stringified + # v1 API requires string content — image_url items are stripped and the + # remaining (text-only) content is stringified before being sent. mock_post.assert_called_once() call_kwargs = mock_post.call_args.kwargs sent_content = call_kwargs["json"]["input"]["messages"][0]["content"] assert isinstance(sent_content, str) - assert sent_content == str(multimodal_content) + assert sent_content == str([{"type": "text", "text": "how much is on this receipt?"}]) # Result should be returned without error assert result is not None @@ -1088,3 +1093,47 @@ class TestHiddenlayerGuardrailV2: config_model = HiddenlayerGuardrailV2.get_config_model() assert config_model is not None assert config_model.__name__ == "HiddenlayerGuardrailConfigModel" + + +@pytest.fixture +def hanging_auth_server(): + """A server that accepts the connection and never answers, so only a timeout ends the call.""" + from http.server import BaseHTTPRequestHandler, HTTPServer + from socketserver import ThreadingMixIn + + stop: threading.Event = threading.Event() + + class SilentRequestHandler(BaseHTTPRequestHandler): + protocol_version = "HTTP/1.1" + + def do_POST(self): + stop.wait(timeout=30) + + def log_message(self, format, *args): + pass + + class ThreadedServer(ThreadingMixIn, HTTPServer): + daemon_threads = True + + server = ThreadedServer(("127.0.0.1", 0), SilentRequestHandler) + thread = threading.Thread(target=server.serve_forever, daemon=True) + thread.start() + try: + yield f"http://127.0.0.1:{server.server_port}" + finally: + stop.set() + server.shutdown() + server.server_close() + thread.join(timeout=5) + + +def test_get_jwt_gives_up_at_the_timeout_instead_of_blocking_the_event_loop(hanging_auth_server): + """ + `_get_jwt` runs synchronously inside `_call_hiddenlayer`, so an auth host that + accepts and never answers used to park the whole worker's event loop. + """ + started = time.monotonic() + with pytest.raises(requests.exceptions.Timeout): + _get_jwt(auth_url=hanging_auth_server, api_id="id", api_key="secret", timeout=1) + + assert time.monotonic() - started < 10 diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_lakera_ai_v2.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_lakera_ai_v2.py index 712cf0c2e5a..ee3f8659d51 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_lakera_ai_v2.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_lakera_ai_v2.py @@ -5,6 +5,7 @@ PR checklist requires at least one test in tests/test_litellm/. Additional tests live in tests/guardrails_tests/test_lakera_v2.py. """ +import logging from unittest.mock import AsyncMock, MagicMock, patch import pytest @@ -538,6 +539,220 @@ class TestPiiMaskingSafetyGuard: assert result["messages"][0] == SYSTEM_MSG assert "[MASKED" in result["messages"][1]["content"] + async def test_monitor_mode_masks_responses_input_when_instructions_present(self): + """ + Regression: #34940 added `instructions` to the mask-in-place safety guard, + which skips the mask branch for every Responses-API body carrying one. In + on_flagged="monitor" that dropped through to "allow", so PII in `input` + that was masked before the PR now reached the model unredacted. Monitor + means "don't block", not "don't redact" -- the input is still writable, so + it must still be masked. + """ + guardrail = LakeraAIGuardrail(api_key="test_key", on_flagged="monitor") + data = { + "instructions": "be nice", + "input": "a@b.com", + "model": "gpt-3.5-turbo", + "metadata": {}, + } + lakera_response = { + "flagged": True, + "breakdown": [{"detector_type": "pii/email", "detected": True, "message_id": 1}], + "payload": [{"detector_type": "pii/email", "start": 0, "end": 7, "message_id": 1}], + } + with patch.object(guardrail, "call_v2_guard", new_callable=AsyncMock) as mock_call: + mock_call.return_value = (lakera_response, {}) + result = await guardrail.async_pre_call_hook( + user_api_key_dict=UserAPIKeyAuth(api_key="test_key"), + cache=MagicMock(), + data=data, + call_type="responses", + ) + assert result["input"] == "[MASKED EMAIL]" + assert result["instructions"] == "be nice" + + async def test_monitor_mode_masks_pii_carried_in_responses_instructions(self): + """ + Regression: `instructions` is inspected as a synthetic leading system + message but apply_redacted_messages_back has no path to rewrite it, so + monitor mode forwarded the flagged instructions text verbatim. The + redacted instructions must be written straight back into + data["instructions"], and must not be folded into data["input"]. + """ + guardrail = LakeraAIGuardrail(api_key="test_key", on_flagged="monitor") + data = { + "instructions": "a@b.com is the contact", + "input": "hi", + "model": "gpt-3.5-turbo", + "metadata": {}, + } + lakera_response = { + "flagged": True, + "breakdown": [{"detector_type": "pii/email", "detected": True, "message_id": 0}], + "payload": [{"detector_type": "pii/email", "start": 0, "end": 7, "message_id": 0}], + } + with patch.object(guardrail, "call_v2_guard", new_callable=AsyncMock) as mock_call: + mock_call.return_value = (lakera_response, {}) + result = await guardrail.async_pre_call_hook( + user_api_key_dict=UserAPIKeyAuth(api_key="test_key"), + cache=MagicMock(), + data=data, + call_type="responses", + ) + assert result["instructions"] == "[MASKED EMAIL] is the contact" + assert "a@b.com" not in result["instructions"] + assert result["input"] == "hi" + + async def test_monitor_mode_masks_messages_when_instructions_present(self): + """ + Regression: a chat body that also carries `instructions` hit the same + guard. The messages list has a write-back path, so it must still be + masked in monitor mode, with the untouched instructions preserved. + """ + guardrail = LakeraAIGuardrail(api_key="test_key", on_flagged="monitor") + data = { + "instructions": "be nice", + "messages": [{"role": "user", "content": "a@b.com", "name": "u1"}], + "model": "gpt-3.5-turbo", + "metadata": {}, + } + lakera_response = { + "flagged": True, + "breakdown": [{"detector_type": "pii/email", "detected": True, "message_id": 1}], + "payload": [{"detector_type": "pii/email", "start": 0, "end": 7, "message_id": 1}], + } + with patch.object(guardrail, "call_v2_guard", new_callable=AsyncMock) as mock_call: + mock_call.return_value = (lakera_response, {}) + result = await guardrail.async_pre_call_hook( + user_api_key_dict=UserAPIKeyAuth(api_key="test_key"), + cache=MagicMock(), + data=data, + call_type="completion", + ) + assert result["messages"][0]["content"] == "[MASKED EMAIL]" + assert result["messages"][0]["name"] == "u1" + assert result["instructions"] == "be nice" + + async def _monitor_unmasked(self, guardrail, data, lakera_response, caplog, call_type="completion"): + """Drive the monitor path and hand back the result plus the ERROR records it logged.""" + with ( + patch.object(guardrail, "call_v2_guard", new_callable=AsyncMock) as mock_call, + caplog.at_level(logging.ERROR, logger="LiteLLM Proxy"), + ): + mock_call.return_value = (lakera_response, {}) + result = await guardrail.async_pre_call_hook( + user_api_key_dict=UserAPIKeyAuth(api_key="test_key"), + cache=MagicMock(), + data=data, + call_type=call_type, + ) + return result, [r.getMessage() for r in caplog.records if r.levelno == logging.ERROR] + + async def test_monitor_mode_leaves_combined_messages_and_input_unmasked(self, caplog): + """ + The combined messages+input shape stays unmasked in monitor mode on + purpose: build_inspection_messages flattens both into one list, so + writing the redacted result back is positionally ambiguous (Greptile P1 + on #34940, see + test_pii_only_violation_with_combined_messages_and_input_blocks_instead_of_masking). + Monitor still must not block, so the request goes through untouched and + the guardrail logs an error naming that reason. + """ + guardrail = LakeraAIGuardrail(api_key="test_key", on_flagged="monitor") + data = { + "messages": [{"role": "user", "content": ""}, {"role": "user", "content": "a@b.com"}], + "input": "responses-api content", + "model": "gpt-3.5-turbo", + "metadata": {}, + } + result, errors = await self._monitor_unmasked(guardrail, data, PII_ONLY_LAKERA_RESPONSE, caplog) + assert result["messages"][1]["content"] == "a@b.com" + assert result["input"] == "responses-api content" + assert any("messages and input are both present" in e for e in errors) + + async def test_monitor_mode_multimodal_logs_the_multimodal_reason(self, caplog): + """The multimodal shape was already unmasked before this branch existed; + it must stay that way and say which obstacle it hit, not a generic one.""" + guardrail = LakeraAIGuardrail(api_key="test_key", on_flagged="monitor") + data = { + "messages": [{"role": "user", "content": [{"type": "text", "text": "a@b.com"}]}], + "model": "gpt-3.5-turbo", + "metadata": {}, + } + result, errors = await self._monitor_unmasked(guardrail, data, PII_ONLY_LAKERA_RESPONSE, caplog) + assert result["messages"][0]["content"] == [{"type": "text", "text": "a@b.com"}] + assert any("multimodal content" in e for e in errors) + + async def test_monitor_mode_does_not_claim_masking_when_lakera_sent_no_locations(self, caplog): + """ + payload=false is a supported config for block/monitor, and it makes + Lakera report the violation without the offsets masking needs. Masking + must not silently no-op and report success -- the request goes out + unredacted, so it has to be logged as unredacted. + """ + guardrail = LakeraAIGuardrail(api_key="test_key", on_flagged="monitor", payload=False) + data = { + "instructions": "be nice", + "input": "a@b.com", + "model": "gpt-3.5-turbo", + "metadata": {}, + } + lakera_response = { + "flagged": True, + "breakdown": [{"detector_type": "pii/email", "detected": True, "message_id": 1}], + } + result, errors = await self._monitor_unmasked(guardrail, data, lakera_response, caplog, call_type="responses") + assert result["input"] == "a@b.com" + assert any("no locations to redact" in e for e in errors) + + async def test_monitor_mode_does_not_invent_a_messages_list(self, caplog): + """ + A Responses body carrying a falsy non-list `messages` key must not come + out of the guardrail with a fabricated chat messages list -- the shared + write-back helper keys off `"messages" in data`, not off it being a list. + """ + guardrail = LakeraAIGuardrail(api_key="test_key", on_flagged="monitor") + data = { + "instructions": "be nice", + "input": "a@b.com", + "messages": None, + "model": "gpt-3.5-turbo", + "metadata": {}, + } + lakera_response = { + "flagged": True, + "breakdown": [{"detector_type": "pii/email", "detected": True, "message_id": 1}], + "payload": [{"detector_type": "pii/email", "start": 0, "end": 7, "message_id": 1}], + } + result, errors = await self._monitor_unmasked(guardrail, data, lakera_response, caplog, call_type="responses") + assert result["messages"] is None + assert result["input"] == "a@b.com" + assert any("isn't a list" in e for e in errors) + + async def test_monitor_mode_mixed_violation_is_not_logged_as_an_error(self, caplog): + """ + A PII-plus-prompt-injection violation on an ordinary chat body behaves + exactly as it did before this branch existed, so it must keep logging at + warning level rather than adding error volume to every mixed detection. + """ + guardrail = LakeraAIGuardrail(api_key="test_key", on_flagged="monitor") + data = { + "messages": [{"role": "user", "content": "a@b.com"}], + "model": "gpt-3.5-turbo", + "metadata": {}, + } + lakera_response = { + "flagged": True, + "breakdown": [ + {"detector_type": "pii/email", "detected": True, "message_id": 0}, + {"detector_type": "prompt_attack", "detected": True, "message_id": 0}, + ], + "payload": [{"detector_type": "pii/email", "start": 0, "end": 7, "message_id": 0}], + } + result, errors = await self._monitor_unmasked(guardrail, data, lakera_response, caplog) + assert result["messages"][0]["content"] == "a@b.com" + assert errors == [] + async def test_pii_only_violation_with_uppercase_skipped_role_masks_without_raising(self): """ Greptile finding on BerriAI/litellm#34940: filter_messages_by_skip_flags diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/unified_guardrails/test_openai_streaming_block.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/unified_guardrails/test_openai_streaming_block.py new file mode 100644 index 00000000000..42bdf41bc88 --- /dev/null +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/unified_guardrails/test_openai_streaming_block.py @@ -0,0 +1,327 @@ +""" +Regression tests for blocking an OpenAI-format streaming response from the +unified guardrail post-call streaming iterator hook. + +When a guardrail's ``apply_guardrail`` raises ``ModifyResponseException`` +while (or at the end of) a chat completions or Responses API stream is being +relayed, the hook must emit a well-formed SSE termination sequence carrying +the block message - NOT a bare ``data: {"error": ...}`` blob that surfaces as +an HTTP 500 error frame and truncates the stream. +""" + +import json +from typing import Any, AsyncGenerator, Dict, Literal, Optional, Tuple, Union + +import pytest + +from litellm.integrations.custom_guardrail import ( + CustomGuardrail, + ModifyResponseException, +) +from litellm.proxy._types import UserAPIKeyAuth +from litellm.proxy.guardrails.guardrail_hooks.unified_guardrail.unified_guardrail import ( + UnifiedLLMGuardrails, +) +from litellm.types.utils import ( + Delta, + GenericGuardrailAPIInputs, + ModelResponseStream, + StreamingChoices, +) + +BLOCK_MESSAGE = "This response was replaced by policy." + +JsonPayload = Dict[str, object] +StreamChunk = Union[ModelResponseStream, JsonPayload, bytes] + + +class _BlockingGuardrail(CustomGuardrail): + """Mock guardrail that always blocks response scans by raising ModifyResponseException.""" + + async def apply_guardrail( + self, + inputs: GenericGuardrailAPIInputs, + request_data: dict, + input_type: Literal["request", "response"], + logging_obj: Optional[Any] = None, + ) -> GenericGuardrailAPIInputs: + raise ModifyResponseException( + message=BLOCK_MESSAGE, + model="gpt-5.4-mini", + request_data=request_data, + guardrail_name=self.guardrail_name, + ) + + +class _PassingGuardrail(CustomGuardrail): + """Mock guardrail that always lets response scans through unchanged.""" + + async def apply_guardrail( + self, + inputs: GenericGuardrailAPIInputs, + request_data: dict, + input_type: Literal["request", "response"], + logging_obj: Optional[Any] = None, + ) -> GenericGuardrailAPIInputs: + return inputs + + +def _chat_chunk(delta: Delta, finish_reason: Optional[str] = None) -> ModelResponseStream: + return ModelResponseStream( + id="chatcmpl-live", + created=1724900000, + model="gpt-5.4-mini", + choices=[StreamingChoices(index=0, delta=delta, finish_reason=finish_reason)], + ) + + +async def _chat_stream(end: bool) -> AsyncGenerator[ModelResponseStream, None]: + yield _chat_chunk(Delta(role="assistant", content="This ")) + for text in ["is ", "the ", "original ", "answer."]: + yield _chat_chunk(Delta(content=text)) + if end: + yield _chat_chunk(Delta(), finish_reason="stop") + + +async def _responses_stream(end: bool) -> AsyncGenerator[JsonPayload, None]: + original_text = "This is the original answer." + response_envelope = {"id": "resp_live", "model": "gpt-5.4-mini", "status": "in_progress", "output": []} + yield {"type": "response.created", "response": response_envelope} + yield {"type": "response.in_progress", "response": response_envelope} + yield { + "type": "response.output_item.added", + "output_index": 0, + "item": {"id": "msg_orig", "type": "message", "role": "assistant", "content": []}, + } + yield { + "type": "response.content_part.added", + "item_id": "msg_orig", + "output_index": 0, + "content_index": 0, + "part": {"type": "output_text", "text": "", "annotations": []}, + } + for delta in ["This ", "is ", "the ", "original ", "answer."]: + yield { + "type": "response.output_text.delta", + "item_id": "msg_orig", + "output_index": 0, + "content_index": 0, + "delta": delta, + } + yield { + "type": "response.output_text.done", + "item_id": "msg_orig", + "output_index": 0, + "content_index": 0, + "text": original_text, + } + if end: + yield { + "type": "response.completed", + "response": { + "id": "resp_live", + "model": "gpt-5.4-mini", + "status": "completed", + "output": [ + { + "id": "msg_orig", + "type": "message", + "role": "assistant", + "status": "completed", + "content": [{"type": "output_text", "text": original_text, "annotations": []}], + } + ], + "usage": {"input_tokens": 7, "output_tokens": 21, "total_tokens": 28}, + }, + } + + +async def _run_hook( + route: str, + stream: AsyncGenerator[Union[ModelResponseStream, JsonPayload], None], + sampling_rate: int = 1, + end_of_stream_only: bool = False, + buffer_until_moderated: bool = False, + blocks: bool = True, +) -> Tuple[StreamChunk, ...]: + guardrail = ( + _BlockingGuardrail(guardrail_name="test-blocking-guardrail", event_hook="post_call") + if blocks + else _PassingGuardrail(guardrail_name="test-passing-guardrail", event_hook="post_call") + ) + guardrail.streaming_sampling_rate = sampling_rate + guardrail.streaming_end_of_stream_only = end_of_stream_only + guardrail.streaming_buffer_until_moderated = buffer_until_moderated + + unified_guardrail = UnifiedLLMGuardrails() + user_api_key_dict = UserAPIKeyAuth(api_key="test", request_route=route) + request_data = { + "messages": [{"role": "user", "content": "hi"}], + "guardrail_to_apply": guardrail, + "metadata": {"guardrails": [guardrail.guardrail_name]}, + } + + return tuple( + [ + chunk + async for chunk in unified_guardrail.async_post_call_streaming_iterator_hook( + user_api_key_dict=user_api_key_dict, + response=stream, + request_data=request_data, + ) + ] + ) + + +def _sse_payloads(collected: Tuple[StreamChunk, ...]) -> Tuple[JsonPayload, ...]: + return tuple( + json.loads(line[len("data:") :].strip()) + for chunk in collected + if isinstance(chunk, bytes) + for block in chunk.decode().split("\n\n") + for line in block.strip().split("\n") + if line.startswith("data:") + ) + + +def _assert_no_error_frame(collected: Tuple[StreamChunk, ...]) -> None: + raw = "".join(chunk.decode() for chunk in collected if isinstance(chunk, bytes)) + assert '"error"' not in raw, f"unexpected error blob in stream: {raw!r}" + + +@pytest.mark.asyncio +async def test_chat_pre_stream_block_emits_standalone_completion(): + """Block on the first chunk: a standalone completion opens with a role delta + and ends with finish_reason content_filter.""" + collected = await _run_hook("/v1/chat/completions", _chat_stream(end=False)) + _assert_no_error_frame(collected) + payloads = _sse_payloads(collected) + assert payloads, "no block SSE chunks were emitted" + assert payloads[0]["choices"][0]["delta"] == {"role": "assistant", "content": BLOCK_MESSAGE} + assert payloads[-1]["choices"][0]["finish_reason"] == "content_filter" + + +@pytest.mark.asyncio +async def test_chat_mid_stream_block_continues_the_completion(): + """Regression for the LIT-6496 500 error frame: after chunks were already + forwarded, the block continues the same completion id and terminates with + finish_reason content_filter instead of raising into an error blob.""" + collected = await _run_hook("/v1/chat/completions", _chat_stream(end=False), sampling_rate=5) + _assert_no_error_frame(collected) + forwarded = [chunk for chunk in collected if isinstance(chunk, ModelResponseStream)] + assert forwarded, "original chunks should have streamed before the block" + payloads = _sse_payloads(collected) + assert payloads, "no block SSE chunks were emitted" + assert all(payload["id"] == "chatcmpl-live" for payload in payloads), ( + "block chunks must continue the in-progress completion, not start a new one" + ) + assert payloads[0]["choices"][0]["delta"] == {"content": BLOCK_MESSAGE} + assert payloads[-1]["choices"][0]["finish_reason"] == "content_filter" + + +@pytest.mark.asyncio +async def test_chat_end_of_stream_block_terminates_cleanly(): + """Regression for bugbot's finish-ordering finding: in end_of_stream_only + mode the original finish chunk must be withheld until moderation decides, + so a block's content_filter finish is the only stream terminator a client + ever sees - never policy text trailing after finish_reason stop.""" + collected = await _run_hook("/v1/chat/completions", _chat_stream(end=True), end_of_stream_only=True) + _assert_no_error_frame(collected) + forwarded = [chunk for chunk in collected if isinstance(chunk, ModelResponseStream)] + assert forwarded, "content chunks still stream to the client before end-of-stream moderation" + assert all(choice.finish_reason is None for chunk in forwarded for choice in chunk.choices), ( + "the original finish chunk must be withheld until moderation decides" + ) + payloads = _sse_payloads(collected) + assert BLOCK_MESSAGE in json.dumps(payloads) + assert payloads[-1]["choices"][0]["finish_reason"] == "content_filter" + + +@pytest.mark.asyncio +async def test_chat_end_of_stream_pass_releases_withheld_finish_chunk(): + """When end-of-stream moderation passes, the withheld finish chunk is + released so a clean stream still terminates normally.""" + collected = await _run_hook( + "/v1/chat/completions", _chat_stream(end=True), end_of_stream_only=True, blocks=False + ) + assert not [chunk for chunk in collected if isinstance(chunk, bytes)], ( + "a clean stream must carry no synthetic block frames" + ) + forwarded = [chunk for chunk in collected if isinstance(chunk, ModelResponseStream)] + finish_reasons = [choice.finish_reason for chunk in forwarded for choice in chunk.choices] + assert finish_reasons[-1] == "stop", "the withheld finish chunk must be released after moderation passes" + assert all(reason is None for reason in finish_reasons[:-1]) + + +@pytest.mark.asyncio +async def test_responses_buffered_block_emits_full_event_sequence(): + """Buffered moderation blocks before anything streams: a complete synthetic + Responses stream from response.created through response.completed carrying + the block message, with the original content never released.""" + collected = await _run_hook("/v1/responses", _responses_stream(end=True), buffer_until_moderated=True) + _assert_no_error_frame(collected) + assert not [chunk for chunk in collected if isinstance(chunk, dict)], ( + "buffered original chunks must never be released after a block" + ) + payloads = _sse_payloads(collected) + event_types = [payload["type"] for payload in payloads] + assert event_types[0] == "response.created" + assert "response.output_text.delta" in event_types + assert event_types[-1] == "response.completed" + completed = payloads[-1]["response"] + assert completed["status"] == "completed" + assert completed["output"][0]["content"][0]["text"] == BLOCK_MESSAGE + assert completed["usage"] == {"input_tokens": 7, "output_tokens": 21, "total_tokens": 28} + assert "original answer" not in json.dumps(payloads) + + +@pytest.mark.asyncio +async def test_responses_mid_stream_block_continues_the_response(): + """Regression for the LIT-6496 500 error frame and bugbot's unclosed-item + finding: after events were already forwarded, the block first closes the + output item still open on the wire, then appends the replacement item under + the same response id, and closes with response.completed - never a second + response.created and never a completed response with an item left open.""" + collected = await _run_hook("/v1/responses", _responses_stream(end=False)) + _assert_no_error_frame(collected) + forwarded = [chunk for chunk in collected if isinstance(chunk, dict)] + forwarded_types = [chunk["type"] for chunk in forwarded] + assert "response.created" in forwarded_types, "original events should have streamed before the block" + payloads = _sse_payloads(collected) + assert payloads, "no block SSE chunks were emitted" + block_types = [payload["type"] for payload in payloads] + assert "response.created" not in block_types, "a mid-stream block must not restart the response" + assert block_types[-1] == "response.completed" + + all_events = forwarded + list(payloads) + opened = sorted(event["output_index"] for event in all_events if event["type"] == "response.output_item.added") + closed = sorted(event["output_index"] for event in all_events if event["type"] == "response.output_item.done") + assert opened == closed, "every output item opened on the stream must be closed before response.completed" + original_done_position = block_types.index("response.output_item.done") + block_item_position = block_types.index("response.output_item.added") + assert original_done_position < block_item_position, ( + "the in-progress original item must be closed before the block item is appended" + ) + assert payloads[original_done_position]["item"]["id"] == "msg_orig" + assert payloads[block_item_position]["output_index"] == 1, ( + "the block item must continue after the original output item" + ) + completed = payloads[-1]["response"] + assert completed["id"] == "resp_live" + assert completed["output"][0]["content"][0]["text"] == BLOCK_MESSAGE + + +@pytest.mark.asyncio +async def test_responses_end_of_stream_block_reports_original_usage(): + collected = await _run_hook("/v1/responses", _responses_stream(end=True), end_of_stream_only=True) + _assert_no_error_frame(collected) + forwarded_types = [chunk["type"] for chunk in collected if isinstance(chunk, dict)] + assert "response.completed" not in forwarded_types, ( + "the original terminal event must be withheld and replaced by the block sequence" + ) + payloads = _sse_payloads(collected) + completed = payloads[-1]["response"] + assert payloads[-1]["type"] == "response.completed" + assert completed["id"] == "resp_live" + assert completed["output"][0]["content"][0]["text"] == BLOCK_MESSAGE + assert completed["usage"] == {"input_tokens": 7, "output_tokens": 21, "total_tokens": 28} diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/unified_guardrails/test_unified_guardrail.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/unified_guardrails/test_unified_guardrail.py index 8b9ecfbbeee..8cad1c634a9 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/unified_guardrails/test_unified_guardrail.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/unified_guardrails/test_unified_guardrail.py @@ -948,19 +948,24 @@ class TestStreamingTransform: assert streamed == "ABCDEFGHIJ" @pytest.mark.asyncio - async def test_incremental_diff_underflow_raises(self): + async def test_incremental_diff_underflow_emits_error_frame(self): """A transform shorter than what was already streamed cannot retract - bytes: it raises HTTPException(stream_transform_underflow).""" + bytes. Chunks have already been flushed by then, so the underflow + surfaces as the in-stream error frame, not an unraisable HTTPException.""" + import json as _json + # First sample emits "ABCDEF" (6 chars); second sample shrinks to 3. guardrail = _StreamingTextGuardrail(shrink_to="ABC", shrink_after=1) chunks = [_stream_chunk("abcdef"), _stream_chunk("ghij")] - with pytest.raises(unified_module.HTTPException) as exc_info: - await _drive_stream(UnifiedLLMGuardrails(), guardrail, chunks) + out = await _drive_stream(UnifiedLLMGuardrails(), guardrail, chunks) - assert exc_info.value.status_code == 400 - assert exc_info.value.detail["error"] == "stream_transform_underflow" + frame = out[-1] + assert isinstance(frame, bytes) + payload = _json.loads(frame.decode()[len("data: ") :]) + assert payload["error"]["message"] == "stream_transform_underflow" + assert payload["error"]["code"] == "400" @pytest.mark.asyncio async def test_incremental_diff_final_chunk_preserves_finish_reason(self): @@ -1747,3 +1752,222 @@ class TestAppliedGuardrailsReflectsExecution: async def test_ordinary_guardrail_is_auto_marked_applied(self): data = await self._run(_AutoLoggingGuardrail()) assert "auto-logging" in _applied_guardrails(data) + + +class _EosHttpBlockingGuardrail(CustomGuardrail): + """Raises the bedrock-shaped block HTTPException at end-of-stream scan time.""" + + def __init__(self): + super().__init__(guardrail_name="eos-http-block") + self.streaming_end_of_stream_only = True + + def should_run_guardrail(self, data, event_type): # type: ignore[override] + return True + + async def apply_guardrail(self, inputs, request_data, input_type, **kwargs): + raise unified_module.HTTPException( + status_code=400, + detail={ + "error": "Violated guardrail policy", + "bedrock_guardrail_response": "BLOCKED_TOPIC", + }, + ) + + +def _anthropic_sse_event(event_type, data): + import json as _json + + return f"event: {event_type}\ndata: {_json.dumps(data)}\n\n".encode() + + +def _anthropic_message_chunks(texts): + head = [ + _anthropic_sse_event( + "message_start", + { + "type": "message_start", + "message": { + "id": "msg_test", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-5", + "content": [], + "stop_reason": None, + "usage": {"input_tokens": 1, "output_tokens": 0}, + }, + }, + ), + _anthropic_sse_event( + "content_block_start", + {"type": "content_block_start", "index": 0, "content_block": {"type": "text", "text": ""}}, + ), + ] + deltas = [ + _anthropic_sse_event( + "content_block_delta", + {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": text}}, + ) + for text in texts + ] + tail = [ + _anthropic_sse_event("content_block_stop", {"type": "content_block_stop", "index": 0}), + _anthropic_sse_event( + "message_delta", + { + "type": "message_delta", + "delta": {"stop_reason": "end_turn", "stop_sequence": None}, + "usage": {"output_tokens": 5}, + }, + ), + _anthropic_sse_event("message_stop", {"type": "message_stop"}), + ] + return head + deltas + tail + + +class TestStreamingHttpErrorFrames: + """A post-flush end-of-stream guardrail block (HTTPException) must surface as + the endpoint's in-stream error frame instead of an unhandled raise that + silently truncates the SSE stream (PR #38722 defect 1).""" + + @pytest.fixture(autouse=True) + def _use_real_mappings(self): + unified_module.endpoint_guardrail_translation_mappings = load_guardrail_translation_mappings() + yield + unified_module.endpoint_guardrail_translation_mappings = None + + @pytest.mark.asyncio + async def test_chat_eos_block_emits_data_error_frame(self): + import json as _json + + guardrail = _EosHttpBlockingGuardrail() + chunks = [_stream_chunk("hello "), _stream_chunk("world", finish_reason="stop")] + + out = await _drive_stream(UnifiedLLMGuardrails(), guardrail, chunks) + + assert out[0] == chunks[0] + assert chunks[1] not in out + frame = out[-1] + assert isinstance(frame, bytes) + text = frame.decode() + assert text.startswith("data: ") + payload = _json.loads(text[len("data: ") :]) + assert payload["error"]["message"] == "Violated guardrail policy" + assert payload["error"]["code"] == "400" + + @pytest.mark.asyncio + async def test_messages_eos_block_emits_anthropic_error_event(self): + guardrail = _EosHttpBlockingGuardrail() + chunks = _anthropic_message_chunks(["hello ", "world"]) + + out = await _drive_stream( + UnifiedLLMGuardrails(), guardrail, chunks, request_route="/v1/messages" + ) + + raw = b"".join(c for c in out if isinstance(c, bytes)).decode() + assert "hello " in raw + assert "event: error" in raw + assert "Violated guardrail policy" in raw + assert "guardrail_error" in raw + + @pytest.mark.asyncio + async def test_responses_eos_block_emits_error_event_with_next_sequence(self): + guardrail = _EosHttpBlockingGuardrail() + chunks = [ + {"type": "response.created", "sequence_number": 0}, + {"type": "response.output_text.delta", "sequence_number": 1, "delta": "hello"}, + { + "type": "response.completed", + "sequence_number": 2, + "response": { + "model": "gpt-4", + "output": [{"type": "message", "content": [{"type": "output_text", "text": "hello"}]}], + }, + }, + ] + + out = await _drive_stream( + UnifiedLLMGuardrails(), guardrail, chunks, request_route="/v1/responses" + ) + + assert chunks[0] in out and chunks[1] in out + assert chunks[2] not in out + error_event = out[-1] + assert error_event.type == "error" + assert error_event.sequence_number == 2 + assert error_event.error.message == "Violated guardrail policy" + assert error_event.error.code == "400" + assert error_event.error.type == "guardrail_error" + + @pytest.mark.asyncio + async def test_pre_flush_block_still_raises_http_exception(self): + guardrail = _EosHttpBlockingGuardrail() + guardrail.streaming_buffer_until_moderated = True + chunks = [_stream_chunk("hello "), _stream_chunk("world", finish_reason="stop")] + + with pytest.raises(unified_module.HTTPException) as exc_info: + await _drive_stream(UnifiedLLMGuardrails(), guardrail, chunks) + + assert exc_info.value.status_code == 400 + assert exc_info.value.detail["error"] == "Violated guardrail policy" + + +class _AuditRecordingGuardrail(CustomGuardrail): + """Successful scan that records guardrail_information, like a flags-on audit.""" + + def __init__(self): + super().__init__(guardrail_name="audit-recorder") + self.streaming_end_of_stream_only = True + + def should_run_guardrail(self, data, event_type): # type: ignore[override] + return True + + async def apply_guardrail(self, inputs, request_data, input_type, **kwargs): + self.add_standard_logging_guardrail_information_to_request_data( + guardrail_json_response={"action": "NONE"}, + request_data=request_data, + guardrail_status="success", + ) + return inputs + + +class TestStreamingGuardrailInformationBucket: + """guardrail_information written during a chat streaming end-of-stream scan + must land in the request's ``metadata`` bucket that spend logging snapshots. + Regression for PR #38722 defect 2: the chat handler used to plant a + ``litellm_metadata`` key first, flipping the bucket so every later + guardrail_information write was diverted and /spend/logs showed null.""" + + @pytest.fixture(autouse=True) + def _use_real_mappings(self): + unified_module.endpoint_guardrail_translation_mappings = load_guardrail_translation_mappings() + yield + unified_module.endpoint_guardrail_translation_mappings = None + + @pytest.mark.asyncio + async def test_chat_eos_scan_writes_guardrail_information_to_metadata(self): + guardrail = _AuditRecordingGuardrail() + chunks = [_stream_chunk("hello "), _stream_chunk("world", finish_reason="stop")] + + async def _mock_stream(): + for chunk in chunks: + yield chunk + + user_api_key_dict = UserAPIKeyAuth( + api_key="test-key", user_id="user-1", request_route="/v1/chat/completions" + ) + request_data = {"guardrail_to_apply": guardrail, "model": "gpt-4", "metadata": {}} + + out = [] + async for item in UnifiedLLMGuardrails().async_post_call_streaming_iterator_hook( + user_api_key_dict=user_api_key_dict, + response=_mock_stream(), + request_data=request_data, + ): + out.append(item) + + assert "litellm_metadata" not in request_data + recorded = request_data["metadata"]["standard_logging_guardrail_information"] + assert len(recorded) == 1 + assert recorded[0]["guardrail_name"] == "audit-recorder" + assert recorded[0]["guardrail_status"] == "success" + assert request_data["metadata"]["user_api_key_user_id"] == "user-1" diff --git a/tests/test_litellm/proxy/guardrails/test_deferred_guardrail_logging.py b/tests/test_litellm/proxy/guardrails/test_deferred_guardrail_logging.py index e70fc61de30..8fde4cc9d5e 100644 --- a/tests/test_litellm/proxy/guardrails/test_deferred_guardrail_logging.py +++ b/tests/test_litellm/proxy/guardrails/test_deferred_guardrail_logging.py @@ -1228,3 +1228,229 @@ class TestFireDeferredStreamLogging: assert info is not None, "guardrail_information should be populated" assert len(info) == 1 assert info[0]["guardrail_name"] == "info-writer" + + +class TestResponsesIteratorDeferredLogging: + """Regression for PR #38722 defect 2 on /v1/responses streams: when the + proxy arms _on_deferred_stream_complete, the responses streaming iterator + must store the logging coroutine for ProxyLogging._fire_deferred_stream_logging + (which runs AFTER end-of-stream guardrail scans write guardrail_information) + instead of dispatching immediately with a premature metadata snapshot.""" + + def _iterator(self, logging_obj): + from litellm.responses.streaming_iterator import ( + BaseResponsesAPIStreamingIterator, + ) + + iterator = object.__new__(BaseResponsesAPIStreamingIterator) + iterator.logging_obj = logging_obj + iterator.start_time = None + iterator.completed_response = None + iterator._completed_response_logged = False + iterator._completed_response_cache_hit = None + iterator._persist_completed_response_before_logging = False + return iterator + + def _logging_obj(self): + recorded = {} + + async def dispatch_success_handlers(result=None, **kwargs): + recorded["dispatched"] = True + + logging_obj = MagicMock() + logging_obj.dispatch_success_handlers = dispatch_success_handlers + return logging_obj, recorded + + @pytest.mark.asyncio + async def test_armed_iterator_stores_deferred_coroutine(self): + logging_obj, recorded = self._logging_obj() + logging_obj._on_deferred_stream_complete = MagicMock() + iterator = self._iterator(logging_obj) + + with patch("asyncio.create_task") as mock_create_task: + iterator._log_completed_response(is_async=True) + + mock_create_task.assert_not_called() + args = logging_obj._deferred_stream_complete_args + assert isinstance(args, tuple) and len(args) == 1 + assert "dispatched" not in recorded + await args[0] + assert recorded["dispatched"] is True + + @pytest.mark.asyncio + async def test_unarmed_iterator_dispatches_immediately(self): + logging_obj, recorded = self._logging_obj() + logging_obj._on_deferred_stream_complete = None + iterator = self._iterator(logging_obj) + + created = [] + real_create_task = asyncio.create_task + + def tracking_create_task(coro): + task = real_create_task(coro) + created.append(task) + return task + + with patch("asyncio.create_task", side_effect=tracking_create_task): + iterator._log_completed_response(is_async=True) + + assert len(created) == 1 + await created[0] + assert recorded["dispatched"] is True + + +class TestArmDeferredStreamDispatch: + """Regression for PR #38722: the closure shape armed on logging_obj must + match the args the stream's logging owner stores. Bridged /v1/responses + (LiteLLMCompletionStreamingIterator) shares its inner CustomStreamWrapper's + logging_obj, which stores (assembled_response, cache_hit); arming the + single-coroutine native closure there made _fire_deferred_stream_logging + raise TypeError inside the streaming hook, leaking an in-stream 500 error + frame on every streamed /v1/responses request.""" + + def _processor(self): + return ProxyBaseLLMRequestProcessing(data={"model": "gpt-test"}) + + def _dispatch_recording_logging_obj(self): + recorded = {} + + async def dispatch_success_handlers( + result=None, start_time=None, end_time=None, cache_hit=None, prefer_async_handlers=False + ): + recorded["result"] = result + recorded["cache_hit"] = cache_hit + recorded["prefer_async_handlers"] = prefer_async_handlers + + logging_obj = MagicMock() + logging_obj.dispatch_success_handlers = dispatch_success_handlers + logging_obj._on_deferred_stream_complete = None + logging_obj._deferred_stream_complete_args = None + return logging_obj, recorded + + @pytest.mark.asyncio + async def test_bridged_responses_iterator_gets_csw_arg_shape(self): + from litellm.responses.litellm_completion_transformation.streaming_iterator import ( + LiteLLMCompletionStreamingIterator, + ) + + logging_obj, recorded = self._dispatch_recording_logging_obj() + bridged = object.__new__(LiteLLMCompletionStreamingIterator) + + self._processor()._arm_deferred_stream_dispatch( + response=bridged, + route_type="aresponses", + user_api_key_dict=MagicMock(), + logging_obj=logging_obj, + ) + + assembled = object() + logging_obj._deferred_stream_complete_args = (assembled, False) + ProxyLogging._fire_deferred_stream_logging({"litellm_logging_obj": logging_obj}) + await asyncio.sleep(0) + + assert recorded["result"] is assembled + assert recorded["cache_hit"] is False + assert recorded["prefer_async_handlers"] is True + + @pytest.mark.asyncio + async def test_router_wrapped_bridged_iterator_gets_csw_arg_shape(self): + """The router wraps iterators without _hidden_params in + HiddenParamsAsyncIteratorWrapper before the proxy arms deferral, so + every production streamed /v1/responses reaches arming wrapped; + sniffing the wrapper instead of the inner iterator armed the 1-arg + native closure against the CSW's 2-arg stored shape and leaked a + TypeError 500 frame into the stream.""" + from litellm.responses.litellm_completion_transformation.streaming_iterator import ( + LiteLLMCompletionStreamingIterator, + ) + from litellm.router_utils.add_retry_fallback_headers import ( + HiddenParamsAsyncIteratorWrapper, + ) + + logging_obj, recorded = self._dispatch_recording_logging_obj() + wrapped = HiddenParamsAsyncIteratorWrapper(object.__new__(LiteLLMCompletionStreamingIterator)) + + self._processor()._arm_deferred_stream_dispatch( + response=wrapped, + route_type="aresponses", + user_api_key_dict=MagicMock(), + logging_obj=logging_obj, + ) + + assembled = object() + logging_obj._deferred_stream_complete_args = (assembled, False) + ProxyLogging._fire_deferred_stream_logging({"litellm_logging_obj": logging_obj}) + await asyncio.sleep(0) + + assert recorded["result"] is assembled + assert recorded["cache_hit"] is False + assert recorded["prefer_async_handlers"] is True + + @pytest.mark.asyncio + async def test_native_stream_closure_enqueues_single_coroutine(self): + from litellm.litellm_core_utils.logging_worker import GLOBAL_LOGGING_WORKER + + logging_obj, _ = self._dispatch_recording_logging_obj() + + async def _agen(): + yield b"x" + + self._processor()._arm_deferred_stream_dispatch( + response=_agen(), + route_type="anthropic_messages", + user_api_key_dict=MagicMock(), + logging_obj=logging_obj, + ) + closure = logging_obj._on_deferred_stream_complete + assert closure is not None + + async def _logging_coroutine(): + return None + + coro = _logging_coroutine() + with patch.object( # test-quality-ok: GLOBAL_LOGGING_WORKER is a process-global singleton with no injection seam + GLOBAL_LOGGING_WORKER, "ensure_initialized_and_enqueue" + ) as mock_enqueue: + await closure(coro) + mock_enqueue.assert_called_once_with(async_coroutine=coro) + coro.close() + + @pytest.mark.asyncio + async def test_csw_closure_routes_through_deferred_stream_guardrails(self, monkeypatch): + from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper + + logging_obj, recorded = self._dispatch_recording_logging_obj() + csw = object.__new__(CustomStreamWrapper) + processor = self._processor() + + monkeypatch.setattr( # test-quality-ok: empty the process-global callback registry so no ambient guardrail runs + litellm, "callbacks", [] + ) + processor._arm_deferred_stream_dispatch( + response=csw, + route_type="acompletion", + user_api_key_dict=MagicMock(), + logging_obj=logging_obj, + ) + assembled = object() + await logging_obj._on_deferred_stream_complete(assembled, False) + await asyncio.sleep(0) + + assert recorded["result"] is assembled + assert recorded["cache_hit"] is False + assert recorded["prefer_async_handlers"] is True + + def test_non_native_route_generator_not_armed(self): + logging_obj, _ = self._dispatch_recording_logging_obj() + + async def _agen(): + yield b"x" + + self._processor()._arm_deferred_stream_dispatch( + response=_agen(), + route_type="acompletion", + user_api_key_dict=MagicMock(), + logging_obj=logging_obj, + ) + + assert logging_obj._on_deferred_stream_complete is None diff --git a/tests/test_litellm/proxy/guardrails/test_guardrail_endpoints.py b/tests/test_litellm/proxy/guardrails/test_guardrail_endpoints.py index 9b2117b7647..9511732fd50 100644 --- a/tests/test_litellm/proxy/guardrails/test_guardrail_endpoints.py +++ b/tests/test_litellm/proxy/guardrails/test_guardrail_endpoints.py @@ -917,7 +917,9 @@ async def test_bedrock_guardrail_make_api_request_passes_api_key(): "Content-Type": "application/json", "Authorization": "Bearer test-api-key-789", } - mock_request_instance.prepare.return_value = Mock() + mock_request_instance.prepare.return_value = Mock( + headers=mock_request_instance.headers + ) mock_aws_request.return_value = mock_request_instance await guardrail_hook.make_bedrock_api_request( diff --git a/tests/test_litellm/proxy/guardrails/test_prompt_security_guardrails.py b/tests/test_litellm/proxy/guardrails/test_prompt_security_guardrails.py index 26beaa78a46..ab4e15ff423 100644 --- a/tests/test_litellm/proxy/guardrails/test_prompt_security_guardrails.py +++ b/tests/test_litellm/proxy/guardrails/test_prompt_security_guardrails.py @@ -1,16 +1,16 @@ -from fastapi.exceptions import HTTPException -from unittest.mock import patch, AsyncMock -from httpx import Response, Request +import asyncio import base64 +from unittest.mock import AsyncMock, patch import pytest - -from litellm.proxy.guardrails.guardrail_hooks.prompt_security.prompt_security import ( - PromptSecurityGuardrailMissingSecrets, - PromptSecurityGuardrail, -) +from fastapi.exceptions import HTTPException +from httpx import ReadTimeout, Request, Response import litellm +from litellm.proxy.guardrails.guardrail_hooks.prompt_security.prompt_security import ( + PromptSecurityGuardrail, + PromptSecurityGuardrailMissingSecrets, +) from litellm.proxy.guardrails.init_guardrails import init_guardrails_v2 @@ -30,6 +30,7 @@ def test_prompt_security_guard_config(monkeypatch: pytest.MonkeyPatch): "guardrail": "prompt_security", "mode": "during_call", "default_on": True, + "file_sanitization_fail_open": False, }, } ], @@ -41,6 +42,10 @@ def test_prompt_security_guard_config(monkeypatch: pytest.MonkeyPatch): assert registered[0].guardrail_name == "prompt_security" assert registered[0].default_on is True assert registered[0].event_hook == "during_call" + assert registered[0].file_sanitization_fail_open is False + config_model = registered[0].get_config_model() + assert config_model is not None + assert config_model().file_sanitization_fail_open is True def test_prompt_security_guard_config_no_api_key(monkeypatch: pytest.MonkeyPatch): @@ -374,6 +379,86 @@ async def test_file_sanitization(monkeypatch: pytest.MonkeyPatch): assert result is not None +@pytest.mark.asyncio +@pytest.mark.parametrize( + "timeout", + ( + litellm.Timeout( + message="Prompt Security upload timed out", + model="default-model-name", + llm_provider="litellm-httpx-handler", + ), + ReadTimeout( + "Prompt Security poll timed out", + request=Request(method="GET", url="https://test.prompt.security/api/sanitizeFile"), + ), + ), + ids=("litellm", "httpx"), +) +@pytest.mark.parametrize("fail_open", (True, False), ids=("fail-open", "fail-closed")) +async def test_file_sanitization_request_timeout_policy( + monkeypatch: pytest.MonkeyPatch, timeout: Exception, fail_open: bool +): + monkeypatch.setenv("PROMPT_SECURITY_API_KEY", "test-key") + monkeypatch.setenv("PROMPT_SECURITY_API_BASE", "https://test.prompt.security") + + guardrail = PromptSecurityGuardrail( + guardrail_name="test-guard", + event_hook="pre_call", + default_on=True, + file_sanitization_fail_open=fail_open, + ) + + with patch.object(guardrail.async_handler, "post", AsyncMock(side_effect=timeout)): + if not fail_open: + with pytest.raises(HTTPException) as exc_info: + await guardrail.sanitize_file_content(b"file-content", "document.pdf") + assert exc_info.value.status_code == 408 + assert exc_info.value.detail == "File sanitization timeout" + return + + result = await guardrail.sanitize_file_content(b"file-content", "document.pdf") + + assert result == { + "action": "allow", + "content": None, + "metadata": {}, + "violations": (), + } + + +@pytest.mark.asyncio +@pytest.mark.parametrize("fail_open", (True, False), ids=("fail-open", "fail-closed")) +async def test_file_sanitization_overall_timeout_policy(monkeypatch: pytest.MonkeyPatch, fail_open: bool): + monkeypatch.setenv("PROMPT_SECURITY_API_KEY", "test-key") + monkeypatch.setenv("PROMPT_SECURITY_API_BASE", "https://test.prompt.security") + + guardrail = PromptSecurityGuardrail( + guardrail_name="test-guard", + event_hook="pre_call", + default_on=True, + file_sanitization_timeout=0.01, + file_sanitization_fail_open=fail_open, + ) + + async def hanging_post(*_args: object, **_kwargs: object) -> None: + await asyncio.sleep(60) + raise AssertionError("sanitization request should have been cancelled") + + with patch.object(guardrail.async_handler, "post", side_effect=hanging_post): + if not fail_open: + with pytest.raises(HTTPException) as exc_info: + await guardrail.sanitize_file_content(b"file-content", "document.pdf") + assert exc_info.value.status_code == 408 + assert exc_info.value.detail == "File sanitization timeout" + return + + result = await guardrail.sanitize_file_content(b"file-content", "document.pdf") + + assert result["action"] == "allow" + assert result["content"] is None + + @pytest.mark.asyncio async def test_file_sanitization_block(monkeypatch: pytest.MonkeyPatch): """Test that file sanitization blocks malicious files""" @@ -544,7 +629,7 @@ async def test_role_filtering(monkeypatch: pytest.MonkeyPatch): return mock_response with patch.object(guardrail.async_handler, "post", side_effect=mock_post): - result = await guardrail.apply_guardrail( + await guardrail.apply_guardrail( inputs=inputs, request_data=request_data, input_type="request", diff --git a/tests/test_litellm/proxy/hooks/test_proxy_track_cost_callback.py b/tests/test_litellm/proxy/hooks/test_proxy_track_cost_callback.py index ca517474a5c..8043a1aca3f 100644 --- a/tests/test_litellm/proxy/hooks/test_proxy_track_cost_callback.py +++ b/tests/test_litellm/proxy/hooks/test_proxy_track_cost_callback.py @@ -1,14 +1,14 @@ -import pytest - - from datetime import datetime from unittest.mock import AsyncMock, MagicMock, patch +import pytest + +from litellm.litellm_core_utils.internal_call_metadata import MODEL_ACCESS_GROUP_METADATA_KEY from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.hooks.proxy_track_cost_callback import ( - _ProxyDBLogger, _get_budget_reservation_from_metadata, + _ProxyDBLogger, _should_track_cost_callback, _update_database_and_spend_counters, ) @@ -570,6 +570,7 @@ async def test_update_database_and_spend_counters_updates_counters_after_db_upda proxy_logging_obj.db_spend_update_writer.update_database = AsyncMock() increment_spend_counters = AsyncMock() budget_reservation = {"reserved_cost": 0.5, "entries": []} + start_time = datetime.now() await _update_database_and_spend_counters( proxy_logging_obj=proxy_logging_obj, @@ -581,11 +582,12 @@ async def test_update_database_and_spend_counters_updates_counters_after_db_upda org_id="test_org_id", kwargs={}, completion_response=None, - start_time=datetime.now(), + start_time=start_time, end_time=datetime.now(), response_cost=0.2, budget_reservation=budget_reservation, request_tags=["tag-a"], + model_access_groups=("premium",), ) proxy_logging_obj.db_spend_update_writer.update_database.assert_awaited_once() @@ -598,6 +600,8 @@ async def test_update_database_and_spend_counters_updates_counters_after_db_upda budget_reservation=budget_reservation, end_user_id="test_end_user_id", tags=["tag-a"], + request_started_at=start_time, + model_access_groups=("premium",), ) @@ -1875,3 +1879,113 @@ async def test_track_cost_callback_logs_unauthenticated_pass_through_request( assert mock_proxy_logging.db_spend_update_writer.update_database.await_count == ( 1 if expect_spend_log else 0 ) + + +class _FakeDeploymentLookup: + """Deployment lookup returning the access groups each deployment declares.""" + + def __init__(self, deployments): + self._deployments = deployments + + def get_model_info(self, id): + if id not in self._deployments: + return None + return {"model_name": "premium-haiku", "model_info": {"id": id, "access_groups": list(self._deployments[id])}} + + +def _model_access_group_kwargs(granted, served_model_id=None): + metadata = {"user_api_key": "hashed-key", "user_api_key_user_id": "user-1"} + if granted is not None: + metadata[MODEL_ACCESS_GROUP_METADATA_KEY] = list(granted) + return { + "call_type": "acompletion", + "model": "premium-haiku", + "litellm_call_id": "test-call-id", + "litellm_params": {"metadata": metadata}, + "stream": False, + "standard_logging_object": {"response_cost": 0.25, "request_tags": None, "model_id": served_model_id}, + } + + +async def _groups_charged_by_the_callback(kwargs, deployments=None): + """The groups the callback hands the spend counters for one request. + + The callback resolves ``proxy_logging_obj`` and the router by importing them off + ``proxy_server`` inside its own body, so there is no seam to inject either through. + """ + logger = _ProxyDBLogger() + with ( + patch( # test-quality-ok: callback imports proxy_logging_obj off proxy_server in its body, no seam + "litellm.proxy.proxy_server.proxy_logging_obj" + ) as mock_proxy_logging, + patch( # test-quality-ok: the arguments to this call are the boundary under test + "litellm.proxy.hooks.proxy_track_cost_callback._update_database_and_spend_counters", + new=AsyncMock(), + ) as mock_update, + patch( # test-quality-ok: llm_router is a proxy_server global the callback reads lazily, no seam + "litellm.proxy.proxy_server.llm_router", new=_FakeDeploymentLookup(deployments or {}) + ), + ): + mock_proxy_logging.failed_tracking_alert = AsyncMock() + mock_proxy_logging.slack_alerting_instance.customer_spend_alert = AsyncMock() + mock_proxy_logging.db_spend_update_writer = MagicMock() + mock_proxy_logging.db_spend_update_writer.update_database = AsyncMock() + + await logger._PROXY_track_cost_callback( + kwargs=kwargs, + completion_response=None, + start_time=datetime.now(), + end_time=datetime.now(), + ) + + return mock_update.await_args.kwargs["model_access_groups"] + + +@pytest.mark.asyncio +async def test_track_cost_callback_charges_the_model_access_groups_auth_stamped(): + """Auth stamps the matched groups onto request metadata; the callback has to carry them through. + + Without this hop nothing writes ``spend:model_access_group:*`` on the normal path, so with + reservations disabled the budget check reads a counter no one maintains. + """ + charged = await _groups_charged_by_the_callback( + kwargs=_model_access_group_kwargs(granted=["premium", "starter"]), + ) + + assert charged == ("premium", "starter") + + +@pytest.mark.asyncio +async def test_track_cost_callback_charges_no_model_access_group_when_none_were_stamped(): + """A request no budgeted group authorized must not debit anything.""" + charged = await _groups_charged_by_the_callback( + kwargs=_model_access_group_kwargs(granted=None), + ) + + assert charged == () + + +@pytest.mark.asyncio +async def test_spend_counters_only_debit_the_group_the_served_deployment_belongs_to(): + """A caller granted two pools that both cover the model group only draws down the pool that served. + + The database writer already narrows by served deployment, so passing the unnarrowed set to the + live counters let one request block a pool the persisted spend never debited. + """ + charged = await _groups_charged_by_the_callback( + kwargs=_model_access_group_kwargs(granted=["premium", "tier0"], served_model_id="deployment-premium"), + deployments={"deployment-premium": ["premium"], "deployment-tier0": ["tier0"]}, + ) + + assert charged == ("premium",) + + +@pytest.mark.asyncio +async def test_spend_counters_keep_every_granted_group_when_the_deployment_is_unknown(): + """An unidentifiable deployment leaves the auth-time set standing, so nothing silently stops billing.""" + charged = await _groups_charged_by_the_callback( + kwargs=_model_access_group_kwargs(granted=["premium", "tier0"], served_model_id="deployment-gone"), + deployments={"deployment-premium": ["premium"]}, + ) + + assert charged == ("premium", "tier0") diff --git a/tests/test_litellm/proxy/management_endpoints/scim/test_scim_v2_endpoints.py b/tests/test_litellm/proxy/management_endpoints/scim/test_scim_v2_endpoints.py index 957f9fde645..1697b77b99a 100644 --- a/tests/test_litellm/proxy/management_endpoints/scim/test_scim_v2_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/scim/test_scim_v2_endpoints.py @@ -1,7 +1,8 @@ import logging import time -from collections.abc import Mapping +from collections.abc import Callable, Mapping, Sequence from itertools import chain +from types import MappingProxyType from typing import Final from unittest.mock import AsyncMock, MagicMock, call @@ -38,6 +39,7 @@ from litellm.proxy.management_endpoints.scim.scim_v2 import ( get_groups, get_users, get_service_provider_config, + merge_placeholder, patch_group, patch_team_membership, patch_user, @@ -52,6 +54,7 @@ from litellm.types.proxy.management_endpoints.scim_v2 import ( SCIMMember, SCIMPatchOp, SCIMPatchOperation, + SCIMPlaceholderMergeResult, SCIMServiceProviderConfig, SCIMUser, SCIMUserEmail, @@ -778,13 +781,17 @@ async def test_handle_existing_user_by_email_without_teams_preserves_memberships "litellm.proxy.management_endpoints.scim.scim_v2.ScimTransformations.transform_litellm_user_to_scim_user", AsyncMock(return_value=None), ) - mock_team_member_add = mocker.patch( # test-quality-ok: roster helpers are module-level, not injectable into the helper - "litellm.proxy.management_endpoints.scim.scim_v2.team_member_add", - AsyncMock(), + mock_team_member_add = ( + mocker.patch( # test-quality-ok: roster helpers are module-level, not injectable into the helper + "litellm.proxy.management_endpoints.scim.scim_v2.team_member_add", + AsyncMock(), + ) ) - mock_team_member_delete = mocker.patch( # test-quality-ok: roster helpers are module-level, not injectable into the helper - "litellm.proxy.management_endpoints.scim.scim_v2.team_member_delete", - AsyncMock(), + mock_team_member_delete = ( + mocker.patch( # test-quality-ok: roster helpers are module-level, not injectable into the helper + "litellm.proxy.management_endpoints.scim.scim_v2.team_member_delete", + AsyncMock(), + ) ) new_user_request = NewUserRequest( @@ -1645,6 +1652,25 @@ async def test_update_group_e2e(mocker): ScimTransformations.transform_litellm_team_to_scim_group.assert_called_once_with(updated_team) +def _rows_by_exact_id( + user_row: Callable[[Mapping[str, str]], LiteLLM_UserTable | MagicMock | None], +) -> Callable[..., tuple[LiteLLM_UserTable | MagicMock, ...]]: + """``find_many`` stand-in for the classifier's cross-field read on a table where a + member value only ever matches as an exact ``user_id``.""" + + def rows(where: Mapping[str, object], take: int | None = None) -> tuple[LiteLLM_UserTable | MagicMock, ...]: + clauses: Final = where["OR"] + assert isinstance(clauses, list) + found: Final = tuple(user_row(clause) for clause in clauses if "user_id" in clause) + return tuple(row for row in found if row is not None) + + return rows + + +def _user_row_for(where: Mapping[str, str]) -> LiteLLM_UserTable: + return LiteLLM_UserTable(user_id=where["user_id"]) + + @pytest.mark.asyncio async def test_create_group_with_nonexistent_users_rejects(mocker, monkeypatch): """ @@ -1696,9 +1722,8 @@ async def test_create_group_with_nonexistent_users_rejects(mocker, monkeypatch): return mock_user return None # new-user-1 and new-user-2 don't exist - mock_prisma_client.db.litellm_usertable.find_unique = AsyncMock(side_effect=mock_user_lookup) mock_prisma_client.db.litellm_usertable.find_first = AsyncMock(return_value=None) - mock_prisma_client.db.litellm_usertable.find_many = AsyncMock(return_value=[]) + mock_prisma_client.db.litellm_usertable.find_many = AsyncMock(side_effect=_rows_by_exact_id(mock_user_lookup)) # Mock dependencies mocker.patch( @@ -1782,9 +1807,8 @@ async def test_update_group_with_nonexistent_users_rejects(mocker, monkeypatch): return mock_user return None # new-user-3 and new-user-4 don't exist - mock_prisma_client.db.litellm_usertable.find_unique = AsyncMock(side_effect=mock_user_lookup) mock_prisma_client.db.litellm_usertable.find_first = AsyncMock(return_value=None) - mock_prisma_client.db.litellm_usertable.find_many = AsyncMock(return_value=[]) + mock_prisma_client.db.litellm_usertable.find_many = AsyncMock(side_effect=_rows_by_exact_id(mock_user_lookup)) # Mock dependencies mocker.patch( @@ -1853,9 +1877,8 @@ async def test_create_group_with_nonexistent_users_creates_when_flag_true(mocker return mock_user return None # new-user-1 and new-user-2 don't exist - mock_prisma_client.db.litellm_usertable.find_unique = AsyncMock(side_effect=mock_user_lookup) mock_prisma_client.db.litellm_usertable.find_first = AsyncMock(return_value=None) - mock_prisma_client.db.litellm_usertable.find_many = AsyncMock(return_value=[]) + mock_prisma_client.db.litellm_usertable.find_many = AsyncMock(side_effect=_rows_by_exact_id(mock_user_lookup)) # Mock user creation created_user_1 = NewUserResponse(user_id="new-user-1", key="test-key-1") @@ -1943,9 +1966,8 @@ async def test_extract_group_member_ids_with_flag_true_creates_users(mocker, mon return mock_user return None # new-user-1 doesn't exist - mock_prisma_client.db.litellm_usertable.find_unique = AsyncMock(side_effect=mock_user_lookup) mock_prisma_client.db.litellm_usertable.find_first = AsyncMock(return_value=None) - mock_prisma_client.db.litellm_usertable.find_many = AsyncMock(return_value=[]) + mock_prisma_client.db.litellm_usertable.find_many = AsyncMock(side_effect=_rows_by_exact_id(mock_user_lookup)) # Mock user creation created_user = NewUserResponse(user_id="new-user-1", key="test-key-1") @@ -2013,9 +2035,8 @@ async def test_extract_group_member_ids_with_flag_false_rejects(mocker, monkeypa return mock_user return None # new-user-1 doesn't exist - mock_prisma_client.db.litellm_usertable.find_unique = AsyncMock(side_effect=mock_user_lookup) mock_prisma_client.db.litellm_usertable.find_first = AsyncMock(return_value=None) - mock_prisma_client.db.litellm_usertable.find_many = AsyncMock(return_value=[]) + mock_prisma_client.db.litellm_usertable.find_many = AsyncMock(side_effect=_rows_by_exact_id(mock_user_lookup)) # Mock dependencies mocker.patch( @@ -3121,8 +3142,7 @@ async def test_process_group_patch_operations_add_retains_existing_members(mocke mock_prisma_client.db = mocker.MagicMock() mock_prisma_client.db.litellm_usertable = mocker.MagicMock() # new-user already exists in the DB - mock_prisma_client.db.litellm_usertable.find_unique = AsyncMock(return_value=mocker.MagicMock(user_id="new-user")) - mock_prisma_client.db.litellm_usertable.find_many = AsyncMock(return_value=()) + mock_prisma_client.db.litellm_usertable.find_many = AsyncMock(return_value=(mocker.MagicMock(user_id="new-user"),)) _, final_members, _ = await _process_group_patch_operations( patch_ops=patch_ops, @@ -3415,8 +3435,7 @@ async def test_patch_group_add_applies_delta_and_keeps_concurrent_add(mocker): ) mock_prisma_client.db.litellm_teamtable.update = AsyncMock(return_value=final_team) mock_prisma_client.db.litellm_usertable = mocker.MagicMock() - mock_prisma_client.db.litellm_usertable.find_unique = AsyncMock(return_value=mocker.MagicMock()) - mock_prisma_client.db.litellm_usertable.find_many = AsyncMock(return_value=()) + mock_prisma_client.db.litellm_usertable.find_many = AsyncMock(side_effect=_rows_by_exact_id(_user_row_for)) mocker.patch( "litellm.proxy.management_endpoints.scim.scim_v2._get_prisma_client_or_raise_exception", @@ -3509,8 +3528,7 @@ async def test_patch_group_replace_stays_absolute_against_concurrent_roster(mock ) mock_prisma_client.db.litellm_teamtable.update = AsyncMock(return_value=final_team) mock_prisma_client.db.litellm_usertable = mocker.MagicMock() - mock_prisma_client.db.litellm_usertable.find_unique = AsyncMock(return_value=mocker.MagicMock()) - mock_prisma_client.db.litellm_usertable.find_many = AsyncMock(return_value=()) + mock_prisma_client.db.litellm_usertable.find_many = AsyncMock(side_effect=_rows_by_exact_id(_user_row_for)) mocker.patch( "litellm.proxy.management_endpoints.scim.scim_v2._get_prisma_client_or_raise_exception", @@ -3640,8 +3658,7 @@ async def test_process_group_patch_add_filtered_path_without_value(mocker): prisma_client = mocker.MagicMock() prisma_client.db = mocker.MagicMock() prisma_client.db.litellm_usertable = mocker.MagicMock() - prisma_client.db.litellm_usertable.find_unique = AsyncMock(return_value=LiteLLM_UserTable(user_id="user-3")) - prisma_client.db.litellm_usertable.find_many = AsyncMock(return_value=()) + prisma_client.db.litellm_usertable.find_many = AsyncMock(return_value=(LiteLLM_UserTable(user_id="user-3"),)) _, final_members, _ = await _process_group_patch_operations( patch_ops=patch_ops, @@ -3733,12 +3750,14 @@ def _member_resolution_prisma( starts folding it, fails here instead of passing. A caller that must know which accounts match rather than merely how many - passes take=None, so an unbounded read returns every match. + passes take=None, so an unbounded read returns every match. The row keyed by + the value comes last, the order a bounded read is least prepared for, since + the database promises no order at all. """ clauses: Final = where["OR"] assert isinstance(clauses, list) fields: Final = tuple(next(iter(clause)) for clause in clauses) - assert fields == ("sso_user_id", "user_email"), fields + assert fields in (("user_id", "sso_user_id", "user_email"), ("sso_user_id", "user_email")), fields def comparison(clause: Mapping[str, object]) -> tuple[str, bool]: """The needle and whether production asked for a case-insensitive compare, @@ -3749,8 +3768,9 @@ def _member_resolution_prisma( assert isinstance(criterion, dict), criterion return criterion["equals"], criterion.get("mode") == "insensitive" - sso_needle, sso_insensitive = comparison(clauses[0]) - email_needle, email_insensitive = comparison(clauses[1]) + by_field: Final = dict(zip(fields, (comparison(clause) for clause in clauses))) + sso_needle, sso_insensitive = by_field["sso_user_id"] + email_needle, email_insensitive = by_field["user_email"] def same(stored: str, needle: str, insensitive: bool) -> bool: return stored.casefold() == needle.casefold() if insensitive else stored == needle @@ -3768,6 +3788,11 @@ def _member_resolution_prisma( if same(email, email_needle, email_insensitive) for user_id in user_ids ), + ( + user_id + for user_id in users + if "user_id" in by_field and same(user_id, by_field["user_id"][0], by_field["user_id"][1]) + ), ) ) found: Final = tuple(dict.fromkeys(matched)) @@ -4452,9 +4477,11 @@ async def test_create_group_applies_default_team_params( "litellm.proxy.management_endpoints.scim.scim_v2._get_prisma_client_or_raise_exception", AsyncMock(return_value=_member_resolution_prisma(mocker, users=set(), teams=set())), ) - new_team_mock = mocker.patch( # test-quality-ok: endpoint collaborators are module-level, not injectable into create_group - "litellm.proxy.management_endpoints.scim.scim_v2.new_team", - AsyncMock(return_value=mocker.MagicMock()), + new_team_mock = ( + mocker.patch( # test-quality-ok: endpoint collaborators are module-level, not injectable into create_group + "litellm.proxy.management_endpoints.scim.scim_v2.new_team", + AsyncMock(return_value=mocker.MagicMock()), + ) ) mocker.patch( # test-quality-ok: endpoint collaborators are module-level, not injectable into create_group "litellm.proxy.management_endpoints.scim.scim_v2.ScimTransformations.transform_litellm_team_to_scim_group", @@ -4611,9 +4638,15 @@ async def test_resolve_group_member_ids_dedupes_repeated_member(mocker, scim_ups def _identity_lookup(value: str) -> object: - """The single cross-field lookup the classifier is expected to issue.""" + """The single cross-field lookup the classifier is expected to issue per member.""" return call( - where={"OR": [{"sso_user_id": value}, {"user_email": {"equals": value, "mode": "insensitive"}}]}, + where={ + "OR": [ + {"user_id": value}, + {"sso_user_id": value}, + {"user_email": {"equals": value, "mode": "insensitive"}}, + ] + }, take=2, ) @@ -4903,9 +4936,7 @@ async def test_process_group_patch_remove_by_the_id_the_directory_added_with( @pytest.mark.asyncio -async def test_process_group_patch_remove_still_drops_a_placeholder_by_its_literal_id( - mocker, scim_upsert_user_enabled -): +async def test_process_group_patch_remove_still_drops_a_placeholder_by_its_literal_id(mocker, scim_upsert_user_enabled): """An earlier release put unmatched ids on the roster verbatim, so a remove has to keep clearing the id as written even once it also resolves.""" patch_ops = SCIMPatchOp( @@ -4916,7 +4947,10 @@ async def test_process_group_patch_remove_still_drops_a_placeholder_by_its_liter team_id="parent-group", team_alias="Parent Group", members=[], - members_with_roles=[Member(user_id="legacy@example.com", role="user"), Member(user_id="keep-user", role="user")], + members_with_roles=[ + Member(user_id="legacy@example.com", role="user"), + Member(user_id="keep-user", role="user"), + ], ) _, final_members, _ = await _process_group_patch_operations( @@ -5081,11 +5115,8 @@ async def test_process_group_patch_remove_refuses_when_two_members_share_the_id( assert "more than one member of this group" in str(exc_info.value.detail) - @pytest.mark.asyncio -async def test_resolve_group_member_ids_exact_user_id_wins_when_it_names_nobody_else( - mocker, scim_upsert_user_enabled -): +async def test_resolve_group_member_ids_exact_user_id_wins_when_it_names_nobody_else(mocker, scim_upsert_user_enabled): """The canonical user id stays authoritative, including when the same account also holds that value as its email, which is how a SCIM-provisioned account is keyed.""" prisma_client = _member_resolution_prisma( @@ -5147,10 +5178,79 @@ async def test_resolve_group_member_ids_refuses_a_user_id_that_names_another_acc assert exc_info.value.status_code == 400 assert "member-id" in str(exc_info.value.detail) create_user_mock.assert_not_called() - assert any( - record.levelno >= logging.WARNING and "someone-else" in record.getMessage() for record in caplog.records + assert any(record.levelno >= logging.WARNING and "someone-else" in record.getMessage() for record in caplog.records) + + +@pytest.mark.asyncio +async def test_resolve_group_member_ids_reads_the_exact_id_when_two_other_accounts_fill_the_lookup( + mocker, scim_upsert_user_enabled +): + """A value that is one account's id and two other accounts' identities fills the + bounded lookup with the other two. The account keyed by the value must still be + found, or the id would lose its precedence and a non-canonical type would skip + a member that names a real user.""" + prisma_client = _member_resolution_prisma( + mocker, + users={"shared"}, + teams=set(), + sso_user_id_to_user_id={"shared": "by-sso"}, + email_to_user_id={"shared": "by-email"}, + ) + create_user_mock = mocker.patch( # test-quality-ok: user creation is module-level, not injectable into the resolver + "litellm.proxy.management_endpoints.scim.scim_v2._create_user_if_not_exists", + AsyncMock(return_value=None), ) + with pytest.raises(HTTPException) as exc_info: + await _resolve_group_member_ids( + members=[SCIMMember(value="shared", type="direct")], + created_via="scim_group_membership", + prisma_client=prisma_client, + ) + + assert exc_info.value.status_code == 400 + assert "shared" in str(exc_info.value.detail) + create_user_mock.assert_not_called() + assert prisma_client.db.litellm_usertable.find_many.await_args_list == [_identity_lookup("shared")] + prisma_client.db.litellm_usertable.find_unique.assert_awaited_once_with(where={"user_id": "shared"}) + + +@pytest.mark.asyncio +async def test_resolve_group_member_ids_reads_the_user_table_once_per_member(mocker, scim_upsert_user_enabled): + """Every member costs one read of the user table, however it resolves: by its exact + id (which still outranks a non-canonical type), by identity, as a SCIM team, or not + at all. Looking the exact id up on its own before the identity read doubled the + reads of a push, and the identity read is a scan.""" + prisma_client = _member_resolution_prisma( + mocker, + users={"by-id"}, + teams={"by-team"}, + email_to_user_id={"by-email@example.com": "email-user"}, + ) + mocker.patch( # test-quality-ok: user creation is module-level, not injectable into the resolver + "litellm.proxy.management_endpoints.scim.scim_v2._create_user_if_not_exists", + AsyncMock(return_value=NewUserResponse(user_id="nobody", key="key")), + ) + + result = await _resolve_group_member_ids( + members=[ + SCIMMember(value="by-id", type="direct"), + SCIMMember(value="by-email@example.com"), + SCIMMember(value="by-team"), + SCIMMember(value="nobody"), + ], + created_via="scim_group_membership", + prisma_client=prisma_client, + ) + + assert result.all_member_ids == ["by-id", "email-user", "nobody"] + prisma_client.db.litellm_usertable.find_unique.assert_not_awaited() + assert prisma_client.db.litellm_usertable.find_many.await_args_list == [ + _identity_lookup("by-id"), + _identity_lookup("by-email@example.com"), + _identity_lookup("by-team"), + _identity_lookup("nobody"), + ] @pytest.mark.asyncio @@ -5536,10 +5636,7 @@ async def test_resolve_group_member_ids_admits_member_created_concurrently(mocke the member is still admitted: the id resolves to a real user row, so failing or dropping it would be wrong either way.""" prisma_client = _member_resolution_prisma(mocker, users=set(), teams=set()) - prisma_client.db.litellm_usertable.find_unique = AsyncMock( - side_effect=[None, LiteLLM_UserTable(user_id="raced-user")] - ) - prisma_client.db.litellm_usertable.find_many = AsyncMock(return_value=()) + prisma_client.db.litellm_usertable.find_unique = AsyncMock(return_value=LiteLLM_UserTable(user_id="raced-user")) mocker.patch( "litellm.proxy.management_endpoints.scim.scim_v2._create_user_if_not_exists", AsyncMock(return_value=None), @@ -5619,3 +5716,196 @@ async def test_patch_group_404s_when_team_deleted_mid_request(mocker): assert exc_info.value.code == "404" assert f"Group not found with ID: {group_id}" in exc_info.value.message + + +_SHADOW_MEMBER_VALUE: Final = "00u1shadow" +_SHADOWED_ACCOUNT: Final = "real-1" +_SHADOWED_GROUP: Final = "grp-eng" + + +def _shadowed_tenant_rows() -> tuple[LiteLLM_UserTable, ...]: + """A placeholder keyed by the raw member value, and the real account that value names by SSO id.""" + return ( + LiteLLM_UserTable(user_id=_SHADOW_MEMBER_VALUE, user_email=_SHADOW_MEMBER_VALUE, teams=[_SHADOWED_GROUP]), + LiteLLM_UserTable(user_id=_SHADOWED_ACCOUNT, user_email="alice@example.com", sso_user_id=_SHADOW_MEMBER_VALUE), + ) + + +def _shadow_tenant_prisma( + mocker: MockerFixture, + *, + rows: Sequence[LiteLLM_UserTable], + keys_owned_by: Mapping[str, int] = MappingProxyType({}), +) -> MagicMock: + """Prisma fake whose user rows are live: deleting one removes it from every later lookup.""" + users: Final[dict[str, LiteLLM_UserTable]] = {row.user_id: row for row in rows} + team: Final = LiteLLM_TeamTable( + team_id=_SHADOWED_GROUP, + members=[_SHADOW_MEMBER_VALUE], + members_with_roles=[Member(user_id=_SHADOW_MEMBER_VALUE, role="user")], + metadata={SCIM_MANAGED_TEAM_METADATA_KEY: True}, + ) + + async def find_unique(where: Mapping[str, str]) -> LiteLLM_UserTable | None: + return users.get(where["user_id"]) + + def clause_matches(row: LiteLLM_UserTable, clause: Mapping[str, object]) -> bool: + if "user_id" in clause: + return row.user_id == clause["user_id"] + if "sso_user_id" in clause: + return row.sso_user_id == clause["sso_user_id"] + email_filter: Final = clause["user_email"] + assert isinstance(email_filter, dict) + return (row.user_email or "").casefold() == str(email_filter["equals"]).casefold() + + async def identity_rows(where: Mapping[str, object], take: int | None = None) -> tuple[LiteLLM_UserTable, ...]: + clauses: Final = where["OR"] + assert isinstance(clauses, list) + matched: Final = tuple(row for row in users.values() if any(clause_matches(row, clause) for clause in clauses)) + return matched[:take] if take else matched + + async def delete(where: Mapping[str, str]) -> LiteLLM_UserTable | None: + return users.pop(where["user_id"], None) + + async def keys_for(where: Mapping[str, object]) -> tuple[MagicMock, ...]: + return tuple(mocker.MagicMock() for _ in range(keys_owned_by.get(str(where["user_id"]), 0))) + + async def team_lookup(where: Mapping[str, str]) -> LiteLLM_TeamTable | None: + return team if where["team_id"] == team.team_id else None + + prisma_client = mocker.MagicMock() + prisma_client.db = mocker.MagicMock() + prisma_client.db.litellm_usertable = mocker.MagicMock() + prisma_client.db.litellm_usertable.find_unique = AsyncMock(side_effect=find_unique) + prisma_client.db.litellm_usertable.find_many = AsyncMock(side_effect=identity_rows) + prisma_client.db.litellm_usertable.delete = AsyncMock(side_effect=delete) + prisma_client.db.litellm_teamtable = mocker.MagicMock() + prisma_client.db.litellm_teamtable.find_unique = AsyncMock(side_effect=team_lookup) + prisma_client.db.litellm_teamtable.update = AsyncMock(return_value=team) + prisma_client.db.litellm_verificationtoken = mocker.MagicMock() + prisma_client.db.litellm_verificationtoken.find_many = AsyncMock(side_effect=keys_for) + prisma_client.db.litellm_invitationlink = mocker.MagicMock(delete_many=AsyncMock(return_value=0)) + prisma_client.db.litellm_organizationmembership = mocker.MagicMock(delete_many=AsyncMock(return_value=0)) + prisma_client.db.litellm_teammembership = mocker.MagicMock(delete_many=AsyncMock(return_value=0)) + return prisma_client + + +@pytest.fixture +def shadowed_tenant(mocker, monkeypatch, scim_upsert_user_enabled) -> MagicMock: + from litellm.proxy import proxy_server + + prisma_client: Final = _shadow_tenant_prisma(mocker, rows=_shadowed_tenant_rows()) + monkeypatch.setattr(proxy_server, "prisma_client", prisma_client) + return prisma_client + + +async def _push_shadow_member(prisma_client: MagicMock): + return await _resolve_group_member_ids( + members=[SCIMMember(value=_SHADOW_MEMBER_VALUE)], + created_via="scim_group_membership", + prisma_client=prisma_client, + ) + + +@pytest.mark.asyncio +async def test_merge_placeholder_hands_the_group_to_the_shadowed_account(mocker, shadowed_tenant): + """Every group push of the shadowing value is refused until the placeholder is folded into + the real account; after the merge the same push resolves to that account.""" + team_member_add_mock = ( + mocker.patch( # test-quality-ok: roster helpers are module-level, not injectable into the endpoint + "litellm.proxy.management_endpoints.scim.scim_v2.team_member_add", AsyncMock() + ) + ) + team_member_delete_mock = ( + mocker.patch( # test-quality-ok: roster helpers are module-level, not injectable into the endpoint + "litellm.proxy.management_endpoints.scim.scim_v2.team_member_delete", AsyncMock() + ) + ) + + with pytest.raises(HTTPException) as before: + await _push_shadow_member(shadowed_tenant) + assert before.value.status_code == 400 + + result: Final = await merge_placeholder(user_id=_SHADOW_MEMBER_VALUE) + + assert result == SCIMPlaceholderMergeResult( + placeholder_user_id=_SHADOW_MEMBER_VALUE, + merged_into_user_id=_SHADOWED_ACCOUNT, + team_ids=(_SHADOWED_GROUP,), + ) + added: Final = team_member_add_mock.call_args.kwargs["data"] + assert (added.team_id, added.member.user_id) == (_SHADOWED_GROUP, _SHADOWED_ACCOUNT) + dropped: Final = team_member_delete_mock.call_args.kwargs["data"] + assert (dropped.team_id, dropped.user_id) == (_SHADOWED_GROUP, _SHADOW_MEMBER_VALUE) + shadowed_tenant.db.litellm_teammembership.delete_many.assert_awaited_once_with( + where={"user_id": _SHADOW_MEMBER_VALUE} + ) + shadowed_tenant.db.litellm_usertable.delete.assert_awaited_once_with(where={"user_id": _SHADOW_MEMBER_VALUE}) + + after: Final = await _push_shadow_member(shadowed_tenant) + assert after.all_member_ids == [_SHADOWED_ACCOUNT] + assert after.created_users == [] + + +@pytest.mark.asyncio +async def test_merge_placeholder_keeps_the_placeholder_when_the_roster_write_fails(mocker, shadowed_tenant): + """If the real account cannot join the team, the placeholder stays on it, or the membership is gone + from both accounts.""" + mocker.patch( # test-quality-ok: roster helpers are module-level, not injectable into the endpoint + "litellm.proxy.management_endpoints.scim.scim_v2.team_member_add", + AsyncMock(side_effect=Exception("database connection lost")), + ) + team_member_delete_mock = ( + mocker.patch( # test-quality-ok: roster helpers are module-level, not injectable into the endpoint + "litellm.proxy.management_endpoints.scim.scim_v2.team_member_delete", AsyncMock() + ) + ) + + with pytest.raises(ProxyException): + await merge_placeholder(user_id=_SHADOW_MEMBER_VALUE) + + team_member_delete_mock.assert_not_awaited() + shadowed_tenant.db.litellm_usertable.delete.assert_not_awaited() + assert await shadowed_tenant.db.litellm_usertable.find_unique(where={"user_id": _SHADOW_MEMBER_VALUE}) is not None + + +@pytest.mark.parametrize( + ("rows", "keys_owned_by", "merged", "reason"), + [ + pytest.param(_shadowed_tenant_rows(), {}, _SHADOWED_ACCOUNT, "SSO identity of its own", id="real-account"), + pytest.param( + _shadowed_tenant_rows(), {_SHADOW_MEMBER_VALUE: 2}, _SHADOW_MEMBER_VALUE, "2 virtual keys", id="owns-keys" + ), + pytest.param(_shadowed_tenant_rows()[:1], {}, _SHADOW_MEMBER_VALUE, "shadows no account", id="names-nobody"), + pytest.param( + (*_shadowed_tenant_rows(), LiteLLM_UserTable(user_id="real-2", user_email=_SHADOW_MEMBER_VALUE.upper())), + {}, + _SHADOW_MEMBER_VALUE, + "names 2 accounts (real-1, real-2)", + id="names-two-accounts", + ), + ], +) +@pytest.mark.asyncio +async def test_merge_placeholder_refuses_rows_that_are_not_a_lone_placeholder( + mocker, monkeypatch, scim_upsert_user_enabled, rows, keys_owned_by, merged, reason +): + """Only a row with no SSO identity and no keys whose id names exactly one other account is folded; + anything else could move memberships to the wrong person, so nothing is written.""" + from litellm.proxy import proxy_server + + prisma_client: Final = _shadow_tenant_prisma(mocker, rows=rows, keys_owned_by=keys_owned_by) + monkeypatch.setattr(proxy_server, "prisma_client", prisma_client) + team_member_add_mock = ( + mocker.patch( # test-quality-ok: roster helpers are module-level, not injectable into the endpoint + "litellm.proxy.management_endpoints.scim.scim_v2.team_member_add", AsyncMock() + ) + ) + + with pytest.raises(ProxyException) as exc_info: + await merge_placeholder(user_id=merged) + + assert int(exc_info.value.code) == 409 + assert reason in str(exc_info.value.message) + team_member_add_mock.assert_not_awaited() + prisma_client.db.litellm_usertable.delete.assert_not_awaited() diff --git a/tests/test_litellm/proxy/management_endpoints/test_access_group_management.py b/tests/test_litellm/proxy/management_endpoints/test_access_group_management.py index db0557cfbf0..a43f20da329 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_access_group_management.py +++ b/tests/test_litellm/proxy/management_endpoints/test_access_group_management.py @@ -2,6 +2,10 @@ Test access group management endpoints """ +from contextlib import contextmanager +from dataclasses import dataclass +from datetime import datetime +from types import SimpleNamespace from unittest.mock import AsyncMock, MagicMock, patch import pytest @@ -449,6 +453,7 @@ async def test_delete_access_group_ignores_models_that_were_already_dead(): mock_prisma = MagicMock() mock_prisma.db.litellm_proxymodeltable.find_many = AsyncMock(return_value=[deploy_broken]) mock_prisma.db.litellm_proxymodeltable.update = AsyncMock() + mock_prisma.db.litellm_modelaccessgroupbudgettable.delete = AsyncMock(return_value=None) from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth from litellm.proxy.management_endpoints.model_access_group_management_endpoints import ( @@ -468,6 +473,7 @@ async def test_delete_access_group_ignores_models_that_were_already_dead(): response = await delete_access_group( access_group="doomed-group", user_api_key_dict=UserAPIKeyAuth(user_id="admin", user_role=LitellmUserRoles.PROXY_ADMIN), + auth_cache=_FakeAuthCache(), ) assert response.models_updated == 1 @@ -568,3 +574,690 @@ async def test_create_access_group_model_missing_everywhere_still_400s(): assert exc_info.value.status_code == 400 assert model_name in str(exc_info.value.detail) + +@dataclass +class _FakeBudgetRow: + budget_id: str + max_budget: float | None = None + soft_budget: float | None = None + budget_duration: str | None = None + budget_reset_at: datetime | None = None + + +@dataclass +class _FakeAccessGroupBudgetRow: + access_group_name: str + budget_id: str | None = None + spend: float = 0.0 + litellm_budget_table: _FakeBudgetRow | None = None + + +@dataclass +class _FakeDeployment: + model_id: str + model_name: str + model_info: dict + + +class _FakeBudgetTable: + """Stands in for litellm_budgettable so a test can see whether a budget row was created, + updated in place, or left orphaned.""" + + def __init__(self, journal: list[str]) -> None: + self.journal = journal + self.rows: dict[str, _FakeBudgetRow] = {} + self.create_calls: list[dict] = [] + self.update_calls: list[tuple[str, dict]] = [] + self.deleted_ids: list[str] = [] + self._sequence = 0 + + async def create(self, data, include=None): + self._sequence += 1 + budget_id = str(data.get("budget_id") or f"budget-{self._sequence}") + row = _FakeBudgetRow( + budget_id=budget_id, + max_budget=data.get("max_budget"), + soft_budget=data.get("soft_budget"), + budget_duration=data.get("budget_duration"), + ) + self.rows[budget_id] = row + self.create_calls.append(dict(data)) + self.journal.append(f"budget_table.create:{budget_id}") + return row + + async def update(self, where, data, include=None): + budget_id = where["budget_id"] + self.update_calls.append((budget_id, dict(data))) + self.journal.append(f"budget_table.update:{budget_id}") + row = self.rows.get(budget_id) + if row is None: + return None + for field_name in ("max_budget", "soft_budget", "budget_duration"): + if data.get(field_name) is not None: + setattr(row, field_name, data[field_name]) + return row + + async def delete(self, where, include=None): + budget_id = where["budget_id"] + self.journal.append(f"budget_table.delete:{budget_id}") + self.deleted_ids.append(budget_id) + return self.rows.pop(budget_id, None) + + +class _FakeAccessGroupBudgetTable: + """Stands in for litellm_modelaccessgroupbudgettable, resolving `include` against the fake + budget table the way prisma resolves the relation.""" + + def __init__(self, journal: list[str], budget_table: _FakeBudgetTable) -> None: + self.journal = journal + self.budget_table = budget_table + self.rows: dict[str, _FakeAccessGroupBudgetRow] = {} + self.upsert_calls: list[dict] = [] + + def _resolve(self, row, include): + if row is None: + return None + row.litellm_budget_table = ( + self.budget_table.rows.get(row.budget_id) if include and row.budget_id is not None else None + ) + return row + + async def find_unique(self, where, include=None): + return self._resolve(self.rows.get(where["access_group_name"]), include) + + async def find_many(self, include=None): + self.journal.append("access_group_budget.find_many") + return [self._resolve(row, include) for row in self.rows.values()] + + async def upsert(self, where, data, include=None): + access_group_name = where["access_group_name"] + self.upsert_calls.append(dict(data)) + self.journal.append(f"access_group_budget.upsert:{access_group_name}") + existing = self.rows.get(access_group_name) + payload = data["update"] if existing is not None else data["create"] + row = existing or _FakeAccessGroupBudgetRow(access_group_name=access_group_name) + row.budget_id = payload.get("budget_id") + self.rows[access_group_name] = row + return self._resolve(row, include) + + async def delete(self, where, include=None): + access_group_name = where["access_group_name"] + self.journal.append(f"access_group_budget.delete:{access_group_name}") + return self.rows.pop(access_group_name, None) + + +class _FakeModelTable: + def __init__(self, journal: list[str], deployments) -> None: + self.journal = journal + self.deployments = list(deployments) + self.updates: list[tuple[dict, dict]] = [] + + async def find_many(self, where=None, **kwargs): + return list(self.deployments) + + async def find_unique(self, where, include=None): + return next((d for d in self.deployments if d.model_id == where["model_id"]), None) + + async def update(self, where, data, include=None): + self.journal.append(f"model_table.update:{where['model_id']}") + self.updates.append((dict(where), dict(data))) + return None + + +class _FakePrismaClient: + def __init__(self, journal: list[str], deployments=()) -> None: + self.budget_table = _FakeBudgetTable(journal) + self.access_group_budget_table = _FakeAccessGroupBudgetTable(journal, self.budget_table) + self.model_table = _FakeModelTable(journal, deployments) + self.db = SimpleNamespace( + litellm_budgettable=self.budget_table, + litellm_modelaccessgroupbudgettable=self.access_group_budget_table, + litellm_proxymodeltable=self.model_table, + ) + + def jsonify_object(self, data): + return dict(data) + + +class _FakeAuthCache: + """Spy for the auth cache the endpoints evict through. Injected into the endpoint rather than + patched over the proxy_server global, so dropping the eviction call fails a test.""" + + def __init__(self, journal: list[str] | None = None) -> None: + self.journal = journal if journal is not None else [] + self.deleted_keys: list[str] = [] + + async def async_delete_cache(self, key): + self.deleted_keys.append(key) + self.journal.append(f"auth_cache.delete:{key}") + + +def _admin(): + from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth + + return UserAPIKeyAuth(user_id="admin", user_role=LitellmUserRoles.PROXY_ADMIN) + + +def _deployment(model_id="deploy-1", model_name="gpt-4o", access_groups=("prod-models",)): + return _FakeDeployment( + model_id=model_id, + model_name=model_name, + model_info={"access_groups": list(access_groups)}, + ) + + +def _seed_budget(prisma, access_group, spend=0.0, budget_id="budget-seed", **budget_fields): + prisma.budget_table.rows[budget_id] = _FakeBudgetRow(budget_id=budget_id, **budget_fields) + prisma.access_group_budget_table.rows[access_group] = _FakeAccessGroupBudgetRow( + access_group_name=access_group, + budget_id=budget_id, + spend=spend, + ) + + +@contextmanager +def _proxy(prisma): + with patch( # test-quality-ok: the endpoints import proxy_server.prisma_client themselves; no parameter to inject + "litellm.proxy.proxy_server.prisma_client", prisma + ): + yield + + +@contextmanager +def _proxy_with_stubbed_reload(prisma): + """delete_access_group finishes by reloading the router and judging what it serves afterwards. + Both collaborators it reaches for there are module globals it imports itself, so a fake can only + get in by patching them; auth_cache and prisma are the ones with a real seam.""" + never_served_router = MagicMock() + never_served_router.get_model_ids.return_value = [] + with ( + _proxy(prisma), + patch( # test-quality-ok: live_model_ids_snapshot() reads the llm_router global; the endpoint takes no router + "litellm.proxy.proxy_server.llm_router", never_served_router + ), + patch( # test-quality-ok: the endpoint calls its module-level clear_cache import; there is no parameter for it + "litellm.proxy.management_endpoints.model_access_group_management_endpoints.clear_cache", + new=AsyncMock(return_value=ReconcileOutcome(still_desired=None, live_after=None)), + ), + ): + yield + + +def _eviction_journal(access_group): + """Both auth cache keys, in the order a write path has to evict them.""" + from litellm.proxy.common_utils.user_api_key_cache import ( + model_access_group_cache_key, + model_access_group_registry_cache_key, + ) + + return [ + f"auth_cache.delete:{model_access_group_cache_key(access_group)}", + f"auth_cache.delete:{model_access_group_registry_cache_key()}", + ] + + +def _assert_evicted_after_write(journal, access_group, write_entry): + """Exactly the two keys, in order, after the DB write. Deliberately not a tail slice: what + has to hold is that the eviction follows the write, not that nothing follows the eviction.""" + evictions = [entry for entry in journal if entry.startswith("auth_cache.delete:")] + assert evictions == _eviction_journal(access_group) + assert journal.index(write_entry) < journal.index(evictions[0]) + + +@pytest.mark.asyncio +async def test_put_access_group_budget_creates_the_row_and_its_budget(): + """First PUT has to create both halves: the budget row it links, and the access group row + that carries the link and the shared spend.""" + from litellm.proxy.management_endpoints.model_access_group_management_endpoints import ( + set_access_group_budget, + ) + from litellm.types.proxy.management_endpoints.model_management_endpoints import ( + AccessGroupBudgetRequest, + ) + + prisma = _FakePrismaClient([], deployments=[_deployment()]) + cache = _FakeAuthCache() + + with _proxy(prisma): + response = await set_access_group_budget( + access_group="prod-models", + data=AccessGroupBudgetRequest(max_budget=100.0, soft_budget=80.0, budget_duration="30d"), + user_api_key_dict=_admin(), + auth_cache=cache, + ) + + assert response.access_group == "prod-models" + assert response.spend == 0.0 + assert response.budget is not None + assert response.budget.max_budget == 100.0 + assert response.budget.soft_budget == 80.0 + assert response.budget.budget_duration == "30d" + assert len(prisma.budget_table.create_calls) == 1 + assert prisma.access_group_budget_table.rows["prod-models"].budget_id == response.budget.budget_id + + +@pytest.mark.asyncio +async def test_second_put_replaces_the_budget_instead_of_creating_another(): + """PUT is idempotent: a second call must update the budget already linked to the group, + not leave a second budget row (and a second group row) behind.""" + from litellm.proxy.management_endpoints.model_access_group_management_endpoints import ( + set_access_group_budget, + ) + from litellm.types.proxy.management_endpoints.model_management_endpoints import ( + AccessGroupBudgetRequest, + ) + + prisma = _FakePrismaClient([], deployments=[_deployment()]) + cache = _FakeAuthCache() + + with _proxy(prisma): + first = await set_access_group_budget( + access_group="prod-models", + data=AccessGroupBudgetRequest(max_budget=100.0), + user_api_key_dict=_admin(), + auth_cache=cache, + ) + second = await set_access_group_budget( + access_group="prod-models", + data=AccessGroupBudgetRequest(max_budget=250.0), + user_api_key_dict=_admin(), + auth_cache=cache, + ) + + assert first.budget is not None and second.budget is not None + assert second.budget.budget_id == first.budget.budget_id + assert second.budget.max_budget == 250.0 + assert len(prisma.budget_table.create_calls) == 1 + assert len(prisma.budget_table.rows) == 1 + assert len(prisma.access_group_budget_table.rows) == 1 + assert prisma.budget_table.update_calls[-1][0] == first.budget.budget_id + + +@pytest.mark.asyncio +async def test_put_access_group_budget_links_an_existing_budget_without_creating_one(): + from litellm.proxy.management_endpoints.model_access_group_management_endpoints import ( + set_access_group_budget, + ) + from litellm.types.proxy.management_endpoints.model_management_endpoints import ( + AccessGroupBudgetRequest, + ) + + prisma = _FakePrismaClient([], deployments=[_deployment()]) + prisma.budget_table.rows["shared-budget"] = _FakeBudgetRow(budget_id="shared-budget", max_budget=7.0) + cache = _FakeAuthCache() + + with _proxy(prisma): + response = await set_access_group_budget( + access_group="prod-models", + data=AccessGroupBudgetRequest(budget_id="shared-budget"), + user_api_key_dict=_admin(), + auth_cache=cache, + ) + + assert prisma.budget_table.create_calls == [] + assert response.budget is not None + assert response.budget.budget_id == "shared-budget" + assert response.budget.max_budget == 7.0 + assert prisma.access_group_budget_table.rows["prod-models"].budget_id == "shared-budget" + + +@pytest.mark.asyncio +async def test_put_access_group_budget_rejects_an_empty_body(): + """An empty PUT would register the group as budgeted while enforcing nothing.""" + from fastapi import HTTPException + + from litellm.proxy.management_endpoints.model_access_group_management_endpoints import ( + set_access_group_budget, + ) + from litellm.types.proxy.management_endpoints.model_management_endpoints import ( + AccessGroupBudgetRequest, + ) + + prisma = _FakePrismaClient([], deployments=[_deployment()]) + cache = _FakeAuthCache() + + with _proxy(prisma), pytest.raises(HTTPException) as exc_info: + await set_access_group_budget( + access_group="prod-models", + data=AccessGroupBudgetRequest(), + user_api_key_dict=_admin(), + auth_cache=cache, + ) + + assert exc_info.value.status_code == 400 + assert prisma.access_group_budget_table.rows == {} + assert cache.deleted_keys == [] + + +@pytest.mark.asyncio +async def test_put_access_group_budget_rejects_an_unparseable_duration(): + """An unparseable duration can only be discovered by the reset job, long after the write.""" + from fastapi import HTTPException + + from litellm.proxy.management_endpoints.model_access_group_management_endpoints import ( + set_access_group_budget, + ) + from litellm.types.proxy.management_endpoints.model_management_endpoints import ( + AccessGroupBudgetRequest, + ) + + prisma = _FakePrismaClient([], deployments=[_deployment()]) + cache = _FakeAuthCache() + + with _proxy(prisma), pytest.raises(HTTPException) as exc_info: + await set_access_group_budget( + access_group="prod-models", + data=AccessGroupBudgetRequest(max_budget=10.0, budget_duration="every other tuesday"), + user_api_key_dict=_admin(), + auth_cache=cache, + ) + + assert exc_info.value.status_code == 400 + assert prisma.budget_table.create_calls == [] + assert prisma.access_group_budget_table.rows == {} + + +def test_access_group_budget_request_rejects_rate_limit_fields(): + """tpm/rpm/max_parallel_requests are not enforced per access group, so accepting them would + promise rate limiting that never happens.""" + from pydantic import ValidationError + + from litellm.types.proxy.management_endpoints.model_management_endpoints import ( + AccessGroupBudgetRequest, + ) + + for unsupported in ({"tpm_limit": 10}, {"rpm_limit": 10}, {"max_parallel_requests": 10}): + with pytest.raises(ValidationError): + AccessGroupBudgetRequest(max_budget=1.0, **unsupported) + + +@pytest.mark.asyncio +async def test_get_access_group_budget_returns_the_budget_and_the_shared_spend(): + from litellm.proxy.management_endpoints.model_access_group_management_endpoints import ( + get_access_group_budget, + ) + + prisma = _FakePrismaClient([], deployments=[_deployment()]) + _seed_budget(prisma, "prod-models", spend=42.5, max_budget=100.0, budget_duration="30d") + + with _proxy(prisma): + response = await get_access_group_budget(access_group="prod-models") + + assert response.access_group == "prod-models" + assert response.spend == 42.5 + assert response.budget is not None + assert response.budget.max_budget == 100.0 + assert response.budget.budget_duration == "30d" + + +@pytest.mark.asyncio +async def test_get_access_group_budget_on_a_budgetless_group_is_200_not_404(): + """A real group that simply has no budget is not an error; only an unknown group is.""" + from litellm.proxy.management_endpoints.model_access_group_management_endpoints import ( + get_access_group_budget, + ) + + prisma = _FakePrismaClient([], deployments=[_deployment()]) + + with _proxy(prisma): + response = await get_access_group_budget(access_group="prod-models") + + assert response.spend == 0.0 + assert response.budget is None + + +@pytest.mark.asyncio +async def test_access_group_budget_routes_404_on_an_unknown_group(): + from fastapi import HTTPException + + from litellm.proxy.management_endpoints.model_access_group_management_endpoints import ( + delete_access_group_budget, + get_access_group_budget, + set_access_group_budget, + ) + from litellm.types.proxy.management_endpoints.model_management_endpoints import ( + AccessGroupBudgetRequest, + ) + + prisma = _FakePrismaClient([], deployments=[_deployment()]) + cache = _FakeAuthCache() + admin = _admin() + + calls = ( + lambda: get_access_group_budget(access_group="ghost-group"), + lambda: set_access_group_budget( + access_group="ghost-group", + data=AccessGroupBudgetRequest(max_budget=1.0), + user_api_key_dict=admin, + auth_cache=cache, + ), + lambda: delete_access_group_budget(access_group="ghost-group", auth_cache=cache), + ) + + with _proxy(prisma): + for make_call in calls: + with pytest.raises(HTTPException) as exc_info: + await make_call() + assert exc_info.value.status_code == 404 + + assert prisma.budget_table.create_calls == [] + assert prisma.access_group_budget_table.rows == {} + + +@pytest.mark.asyncio +async def test_delete_access_group_budget_drops_the_row_and_spares_the_shared_budget(): + """The group row goes; the LiteLLM_BudgetTable row it linked survives, as /tag/delete leaves a + tag's. That row can be shared, so deleting it would be data loss for whatever else points at it.""" + from litellm.proxy.management_endpoints.model_access_group_management_endpoints import ( + delete_access_group_budget, + ) + + prisma = _FakePrismaClient([], deployments=[_deployment()]) + _seed_budget(prisma, "prod-models", spend=12.0, max_budget=100.0) + cache = _FakeAuthCache() + + with _proxy(prisma): + response = await delete_access_group_budget(access_group="prod-models", auth_cache=cache) + + assert response.budget_deleted is True + assert prisma.access_group_budget_table.rows == {} + assert prisma.budget_table.deleted_ids == [] + assert prisma.budget_table.rows["budget-seed"].max_budget == 100.0 + + +@pytest.mark.asyncio +async def test_delete_access_group_budget_on_a_budgetless_group_still_evicts(): + """budget_deleted is False, but the group can still be sitting in the cached registry of + budgeted groups, so the eviction has to run whether or not a row was there to drop.""" + from litellm.proxy.management_endpoints.model_access_group_management_endpoints import ( + delete_access_group_budget, + ) + + journal: list[str] = [] + prisma = _FakePrismaClient(journal, deployments=[_deployment()]) + cache = _FakeAuthCache(journal) + + with _proxy(prisma): + response = await delete_access_group_budget(access_group="prod-models", auth_cache=cache) + + assert response.budget_deleted is False + assert prisma.budget_table.deleted_ids == [] + _assert_evicted_after_write(journal, "prod-models", "access_group_budget.delete:prod-models") + + +@pytest.mark.asyncio +async def test_deleting_the_access_group_strips_deployments_before_dropping_the_budget(): + """Ordering is the point: stripping first means a failure leaves an unreachable budget row, + while the reverse leaves a live group whose enforcement silently vanished. The shared + LiteLLM_BudgetTable row survives here too.""" + from litellm.proxy.management_endpoints.model_access_group_management_endpoints import ( + delete_access_group, + ) + + journal: list[str] = [] + prisma = _FakePrismaClient(journal, deployments=[_deployment()]) + _seed_budget(prisma, "prod-models", spend=3.0, max_budget=100.0) + cache = _FakeAuthCache() + + with _proxy_with_stubbed_reload(prisma): + response = await delete_access_group( + access_group="prod-models", user_api_key_dict=_admin(), auth_cache=cache + ) + + assert response.models_updated == 1 + assert prisma.access_group_budget_table.rows == {} + assert prisma.budget_table.deleted_ids == [] + assert prisma.budget_table.rows["budget-seed"].max_budget == 100.0 + assert journal.index("model_table.update:deploy-1") < journal.index("access_group_budget.delete:prod-models") + + +@pytest.mark.asyncio +async def test_access_group_info_surfaces_the_budget_and_spend(): + from litellm.proxy.management_endpoints.model_access_group_management_endpoints import ( + get_access_group_info, + ) + + prisma = _FakePrismaClient([], deployments=[_deployment()]) + _seed_budget(prisma, "prod-models", spend=9.5, max_budget=100.0, soft_budget=50.0) + + with _proxy(prisma): + info = await get_access_group_info(access_group="prod-models", user_api_key_dict=_admin()) + + assert info.model_names == ["gpt-4o"] + assert info.spend == 9.5 + assert info.budget is not None + assert info.budget.max_budget == 100.0 + assert info.budget.soft_budget == 50.0 + + +@pytest.mark.asyncio +async def test_list_access_groups_carries_each_group_budget_and_spend(): + """The dashboard renders the budget column straight off the listing, so a group's budget has to + ride along with it rather than needing a follow-up read per row.""" + from litellm.proxy.management_endpoints.model_access_group_management_endpoints import ( + list_access_groups, + ) + + journal: list[str] = [] + prisma = _FakePrismaClient( + journal, + deployments=[ + _deployment(model_id="deploy-1", model_name="gpt-4o", access_groups=("prod-models",)), + _deployment(model_id="deploy-2", model_name="gpt-4o-mini", access_groups=("free-models",)), + ], + ) + _seed_budget(prisma, "prod-models", spend=9.5, max_budget=100.0, budget_duration="30d") + + with _proxy(prisma): + listing = await list_access_groups(user_api_key_dict=_admin()) + + by_name = {group.access_group: group for group in listing.access_groups} + assert [group.access_group for group in listing.access_groups] == ["free-models", "prod-models"] + assert by_name["prod-models"].spend == 9.5 + assert by_name["prod-models"].budget is not None + assert by_name["prod-models"].budget.max_budget == 100.0 + assert by_name["prod-models"].budget.budget_duration == "30d" + assert journal.count("access_group_budget.find_many") == 1 + + +@pytest.mark.asyncio +async def test_list_access_groups_reports_a_budgetless_group_as_unbudgeted_rather_than_omitting_it(): + from litellm.proxy.management_endpoints.model_access_group_management_endpoints import ( + list_access_groups, + ) + + prisma = _FakePrismaClient([], deployments=[_deployment(access_groups=("free-models",))]) + + with _proxy(prisma): + listing = await list_access_groups(user_api_key_dict=_admin()) + + assert len(listing.access_groups) == 1 + assert listing.access_groups[0].access_group == "free-models" + assert listing.access_groups[0].budget is None + assert listing.access_groups[0].spend == 0.0 + + +@pytest.mark.asyncio +async def test_put_access_group_budget_evicts_both_auth_cache_keys(): + """Auth reads the per-group row and the registry of budgeted groups cache-first with no + freshness check, so a PUT that skips either eviction returns 200 and enforces nothing until + the TTL expires. Both keys, after the write.""" + from litellm.proxy.management_endpoints.model_access_group_management_endpoints import ( + set_access_group_budget, + ) + from litellm.types.proxy.management_endpoints.model_management_endpoints import ( + AccessGroupBudgetRequest, + ) + + journal: list[str] = [] + prisma = _FakePrismaClient(journal, deployments=[_deployment()]) + cache = _FakeAuthCache(journal) + + with _proxy(prisma): + await set_access_group_budget( + access_group="prod-models", + data=AccessGroupBudgetRequest(max_budget=100.0), + user_api_key_dict=_admin(), + auth_cache=cache, + ) + + _assert_evicted_after_write(journal, "prod-models", "access_group_budget.upsert:prod-models") + + +@pytest.mark.asyncio +async def test_delete_access_group_budget_evicts_both_auth_cache_keys(): + """Clearing a budget has the same window as setting one: until both keys are dropped, auth + keeps enforcing the budget that is already gone.""" + from litellm.proxy.management_endpoints.model_access_group_management_endpoints import ( + delete_access_group_budget, + ) + + journal: list[str] = [] + prisma = _FakePrismaClient(journal, deployments=[_deployment()]) + _seed_budget(prisma, "prod-models", spend=12.0, max_budget=100.0) + cache = _FakeAuthCache(journal) + + with _proxy(prisma): + await delete_access_group_budget(access_group="prod-models", auth_cache=cache) + + _assert_evicted_after_write(journal, "prod-models", "access_group_budget.delete:prod-models") + + +@pytest.mark.asyncio +async def test_deleting_the_access_group_evicts_both_auth_cache_keys(): + """The group-delete cascade drops the budget row too, so it owes the same two evictions.""" + from litellm.proxy.management_endpoints.model_access_group_management_endpoints import ( + delete_access_group, + ) + + journal: list[str] = [] + prisma = _FakePrismaClient(journal, deployments=[_deployment()]) + _seed_budget(prisma, "prod-models", spend=3.0, max_budget=100.0) + cache = _FakeAuthCache(journal) + + with _proxy_with_stubbed_reload(prisma): + await delete_access_group(access_group="prod-models", user_api_key_dict=_admin(), auth_cache=cache) + + _assert_evicted_after_write(journal, "prod-models", "access_group_budget.delete:prod-models") + + +@pytest.mark.asyncio +async def test_deleting_an_access_group_that_never_had_a_budget_still_evicts(): + """The cascade's delete finds no row and reports nothing dropped, but the group can still be + sitting in the cached registry of budgeted groups, so both keys have to go regardless.""" + from litellm.proxy.management_endpoints.model_access_group_management_endpoints import ( + delete_access_group, + ) + + journal: list[str] = [] + prisma = _FakePrismaClient(journal, deployments=[_deployment()]) + cache = _FakeAuthCache(journal) + + with _proxy_with_stubbed_reload(prisma): + response = await delete_access_group( + access_group="prod-models", user_api_key_dict=_admin(), auth_cache=cache + ) + + assert response.models_updated == 1 + assert prisma.access_group_budget_table.rows == {} + _assert_evicted_after_write(journal, "prod-models", "access_group_budget.delete:prod-models") diff --git a/tests/test_litellm/proxy/management_endpoints/test_auto_router_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_auto_router_endpoints.py index 726e09f3162..c525af84511 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_auto_router_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_auto_router_endpoints.py @@ -878,8 +878,10 @@ def _leg_record(**overrides: object) -> MagicMock: defaults = { "id": "leg-1", "group_id": "job-1", - "api_key_id": "key-hash", + "target_type": "key", + "target_id": "key-hash", "router_name": "my-router", + "router_names": (), "direction": "forward", "baseline_model": None, "judge_model": "anthropic/claude-sonnet-5", @@ -912,8 +914,29 @@ def _key_record( return record +def _team_record(team_id: str, team_alias: str | None) -> MagicMock: + record = MagicMock(spec=["team_id", "team_alias"]) + record.team_id = team_id + record.team_alias = team_alias + return record + + +def _user_record(user_id: str, user_email: str | None) -> MagicMock: + record = MagicMock(spec=["user_id", "user_email"]) + record.user_id = user_id + record.user_email = user_email + return record + + def _shadow_prisma( - legs=(), agg_rows=None, by_leg_rows=None, known_keys=("key-hash", "key-hash-2"), key_teams=None + legs=(), + agg_rows=None, + by_leg_rows=None, + by_router_rows=None, + known_keys=("key-hash", "key-hash-2"), + key_teams=None, + known_teams=None, + known_users=None, ) -> MagicMock: """The job-table fake honours the filters it is handed, so a read that forgets stopped_at sees rows the partial index would have released, one that forgets @@ -921,6 +944,8 @@ def _shadow_prisma( group read that matched on a leg id would come back empty.""" prisma = MagicMock() teams: Final = key_teams or {} + team_aliases: Final = known_teams or {} + user_emails: Final = known_users or {} async def find_tokens(*, where): """Honours the token filter, like the job-table fake below: the endpoint derives the @@ -931,6 +956,17 @@ def _shadow_prisma( prisma.db.litellm_verificationtoken.find_many = AsyncMock(side_effect=find_tokens) + async def find_teams(*, where): + requested = where["team_id"]["in"] + return [_team_record(t, alias) for t, alias in team_aliases.items() if t in requested] + + async def find_users(*, where): + requested = where["user_id"]["in"] + return [_user_record(u, email) for u, email in user_emails.items() if u in requested] + + prisma.db.litellm_teamtable.find_many = AsyncMock(side_effect=find_teams) + prisma.db.litellm_usertable.find_many = AsyncMock(side_effect=find_users) + async def execute_raw(sql: str, *params: object): if "SET stopped_by" in sql: group = [row for row in stored if row.group_id == params[0]] @@ -959,9 +995,19 @@ def _shadow_prisma( async def find_many_legs(where=None, **_: object): current = list(stored) w = dict(where or {}) - if "api_key_id" in w: - wanted = w["api_key_id"]["in"] if isinstance(w["api_key_id"], dict) else [w["api_key_id"]] - current = [row for row in current if row.api_key_id in wanted] + if "OR" in w: + pairs = [ + ( + branch["target_type"], + branch["target_id"]["in"] if isinstance(branch["target_id"], dict) else [branch["target_id"]], + ) + for branch in w["OR"] + ] + current = [ + row + for row in current + if any(row.target_type == target_type and row.target_id in ids for target_type, ids in pairs) + ] if "direction" in w: current = [row for row in current if row.direction == w["direction"]] if "stopped_at" in w: @@ -983,8 +1029,10 @@ def _shadow_prisma( fields = ( "id", "group_id", - "api_key_id", + "target_type", + "target_id", "router_name", + "router_names", "direction", "baseline_model", "judge_model", @@ -1009,13 +1057,19 @@ def _shadow_prisma( if "AS attempt_count" in sql: return prisma.attempt_rows if "GROUP BY group_id" in sql: - scoped = [row for row in stored if "api_key_id = $2" not in sql or row.api_key_id == params[1]] + scoped = [ + row + for row in stored + if "target_type = $2" not in sql or (row.target_type == params[1] and row.target_id == params[2]) + ] keep = set(newest_groups(scoped, params[0])) return [leg_dict(row) for row in stored if row.group_id in keep] if "FILTER (WHERE outcome != 'error')::int AS judged_count" in sql: return [{"judged_count": 10, "error_count": 2, "judge_spend": 0.031}] if "SELECT job_id AS grp" in sql: return by_leg_rows if by_leg_rows is not None else [] + if "COALESCE(a.router_name" in sql: + return by_router_rows if by_router_rows is not None else [] if 'FROM "LiteLLM_ShadowEvalFunnel"' in sql: return prisma.funnel_rows return agg_rows if agg_rows is not None else [] @@ -1058,7 +1112,7 @@ async def test_start_shadow_eval_writes_one_leg_per_key_in_one_statement(monkeyp response = await start_shadow_eval(_start_request(api_key_ids=("key-hash", "key-hash-2")), ADMIN) - sweep_sql, sweep_keys = prisma.db.execute_raw.call_args.args + sweep_sql, sweep_ids, sweep_type = prisma.db.execute_raw.call_args.args assert "stopped_at IS NULL" in sweep_sql assert "j.ends_at <= (NOW() AT TIME ZONE 'utc')" in sweep_sql assert "SET stopped_at = (NOW() AT TIME ZONE 'utc')" in sweep_sql @@ -1066,12 +1120,21 @@ async def test_start_shadow_eval_writes_one_leg_per_key_in_one_statement(monkeyp assert "j.max_budget IS NOT NULL" in sweep_sql assert ">= j.max_budget" in sweep_sql assert "SUM(a.judge_cost + a.shadow_cost + a.shadow_classifier_cost)" in sweep_sql - assert "j.api_key_id = ANY($1::text[])" in sweep_sql - assert sweep_keys == ["key-hash", "key-hash-2"] + assert "j.target_type = $2 AND j.target_id = ANY($1::text[])" in sweep_sql + assert sweep_ids == ["key-hash", "key-hash-2"] + assert sweep_type == "key" prisma.db.litellm_shadowevaljob.create_many.assert_awaited_once() rows = prisma.db.litellm_shadowevaljob.create_many.call_args.kwargs["data"] - assert [row["api_key_id"] for row in rows] == ["key-hash", "key-hash-2"] - assert len({frozenset((k, v) for k, v in row.items() if k not in ("api_key_id", "id")) for row in rows}) == 1 + assert [(row["target_type"], row["target_id"]) for row in rows] == [("key", "key-hash"), ("key", "key-hash-2")] + assert ( + len( + { + frozenset((k, tuple(v) if isinstance(v, list) else v) for k, v in row.items() if k not in ("target_id", "id")) + for row in rows + } + ) + == 1 + ) assert len({row["id"] for row in rows}) == len(rows) assert len({row["group_id"] for row in rows}) == 1 assert all(row["max_turns"] == SHADOW_EVAL_TURN_VALVE and row["created_by"] == "admin" for row in rows) @@ -1080,11 +1143,69 @@ async def test_start_shadow_eval_writes_one_leg_per_key_in_one_statement(monkeyp assert response.job_id == rows[0]["group_id"] assert response.status == "running" assert response.judged_count is None - assert [(key.api_key_id, key.max_budget, key.key_alias) for key in response.keys] == [ + assert [(target.target_id, target.max_budget, target.target_alias) for target in response.targets] == [ ("key-hash", 5.0, "prod-alpha"), ("key-hash-2", 5.0, "prod-alpha"), ] - assert all(key.max_turns == SHADOW_EVAL_TURN_VALVE for key in response.keys) + assert all(target.target_type == "key" for target in response.targets) + assert all(target.max_turns == SHADOW_EVAL_TURN_VALVE for target in response.targets) + + +@pytest.mark.asyncio +async def test_start_shadow_eval_multi_router_writes_the_set_on_every_leg(monkeypatch: pytest.MonkeyPatch): + """A multi-router job stores the full set in router_names and the first router in + router_name, so a rolling-deploy pod that predates router_names still runs a valid + single-arm eval and its unstamped attempt rows attribute to that first router.""" + import litellm.proxy.proxy_server as proxy_server + + _configure_anthropic_sdk_judge(monkeypatch) + prisma = _shadow_prisma() + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + monkeypatch.setattr(proxy_server, "llm_router", _shadow_router()) + + response = await start_shadow_eval( + _start_request(router_name=None, router_names=("my-router", "classifier-router")), ADMIN + ) + + rows = prisma.db.litellm_shadowevaljob.create_many.call_args.kwargs["data"] + assert all(row["router_name"] == "my-router" for row in rows) + assert all(row["router_names"] == ["my-router", "classifier-router"] for row in rows) + assert response.router_names == ("my-router", "classifier-router") + assert response.router_name == "my-router" + + +@pytest.mark.asyncio +async def test_start_shadow_eval_rejects_an_unconfigured_router_in_the_set(monkeypatch: pytest.MonkeyPatch): + import litellm.proxy.proxy_server as proxy_server + + _configure_anthropic_sdk_judge(monkeypatch) + prisma = _shadow_prisma() + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + monkeypatch.setattr(proxy_server, "llm_router", _shadow_router()) + + with pytest.raises(HTTPException, match="not-a-router") as exc: + await start_shadow_eval(_start_request(router_name=None, router_names=("my-router", "not-a-router")), ADMIN) + + assert exc.value.status_code == 400 + prisma.db.litellm_shadowevaljob.create_many.assert_not_called() + + +@pytest.mark.asyncio +async def test_judge_collision_is_found_on_every_router_of_the_set(monkeypatch: pytest.MonkeyPatch): + """The judge-as-candidate guard walks every candidate router: a judge that serves an + arm of the SECOND router still poisons the whole job's win rates.""" + import litellm.proxy.proxy_server as proxy_server + + _configure_anthropic_sdk_judge(monkeypatch) + prisma = _shadow_prisma() + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + monkeypatch.setattr(proxy_server, "llm_router", _shadow_router()) + + with pytest.raises(HTTPException, match="also an arm") as exc: + await start_shadow_eval(_start_request(router_name=None, router_names=("my-router", "sonnet-router")), ADMIN) + + assert exc.value.status_code == 400 + prisma.db.litellm_shadowevaljob.create_many.assert_not_called() @pytest.mark.asyncio @@ -1232,7 +1353,7 @@ async def test_start_shadow_eval_rejections( import litellm.proxy.proxy_server as proxy_server _configure_anthropic_sdk_judge(monkeypatch) - prisma = _shadow_prisma(legs=[_leg_record(id=f"leg-{key}", group_id="job-7", api_key_id=key) for key in claimed]) + prisma = _shadow_prisma(legs=[_leg_record(id=f"leg-{key}", group_id="job-7", target_id=key) for key in claimed]) monkeypatch.setattr(proxy_server, "prisma_client", prisma) monkeypatch.setattr(proxy_server, "llm_router", _shadow_router()) @@ -1315,14 +1436,14 @@ async def test_start_shadow_eval_names_the_busy_key_and_its_job(monkeypatch: pyt import litellm.proxy.proxy_server as proxy_server _configure_anthropic_sdk_judge(monkeypatch) - prisma = _shadow_prisma(legs=[_leg_record(id="leg-b", group_id="job-7", api_key_id="key-hash-2")]) + prisma = _shadow_prisma(legs=[_leg_record(id="leg-b", group_id="job-7", target_id="key-hash-2")]) monkeypatch.setattr(proxy_server, "prisma_client", prisma) monkeypatch.setattr(proxy_server, "llm_router", _shadow_router()) with pytest.raises(HTTPException) as exc: await start_shadow_eval(_start_request(api_key_ids=("key-hash", "key-hash-2")), ADMIN) assert exc.value.status_code == 409 - assert "key-hash-2 (job job-7)" in exc.value.detail + assert "key key-hash-2 (job job-7)" in exc.value.detail @pytest.mark.asyncio @@ -1441,6 +1562,180 @@ def test_start_shadow_eval_request_dedupes_and_bounds_the_key_set(): _start_request(api_key_ids=tuple(f"k{i}" for i in range(101))) +def test_start_request_bounds_the_combined_target_count_across_types(): + """The 1..100 bound counts keys, teams, and users together, so a caller cannot dodge + it by spreading targets over the three fields, and a request naming no target of any + type samples nothing and is rejected.""" + with pytest.raises(ValidationError, match="at least one target"): + _start_request(api_key_ids=(), team_ids=(), user_ids=()) + with pytest.raises(ValidationError, match="at most 100 targets"): + _start_request(api_key_ids=tuple(f"k{i}" for i in range(60)), team_ids=tuple(f"t{i}" for i in range(41))) + mixed = _start_request(api_key_ids=tuple(f"k{i}" for i in range(60)), team_ids=tuple(f"t{i}" for i in range(40))) + assert len(mixed.api_key_ids) + len(mixed.team_ids) == 100 + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "overrides,prisma_kwargs,expected_target", + [ + ( + {"api_key_ids": (), "team_ids": ("team-eng",)}, + {"known_teams": {"team-eng": "Engineering"}}, + ("team", "team-eng", "Engineering"), + ), + ( + {"api_key_ids": (), "user_ids": ("dev-alice",)}, + {"known_users": {"dev-alice": "alice@example.com"}}, + ("user", "dev-alice", "alice@example.com"), + ), + ], + ids=["team-target-labeled-by-team-alias", "user-target-labeled-by-user-email"], +) +async def test_start_shadow_eval_creates_typed_legs_for_team_and_user_targets( + monkeypatch: pytest.MonkeyPatch, overrides, prisma_kwargs, expected_target +): + import litellm.proxy.proxy_server as proxy_server + + _configure_anthropic_sdk_judge(monkeypatch) + prisma = _shadow_prisma(**prisma_kwargs) + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + monkeypatch.setattr(proxy_server, "llm_router", _shadow_router()) + + response = await start_shadow_eval(_start_request(**overrides), ADMIN) + + target_type, target_id, target_alias = expected_target + rows = prisma.db.litellm_shadowevaljob.create_many.call_args.kwargs["data"] + assert [(row["target_type"], row["target_id"]) for row in rows] == [(target_type, target_id)] + assert response.status == "running" + target = response.targets[0] + assert (target.target_type, target.target_id, target.target_alias, target.key_name) == ( + target_type, + target_id, + target_alias, + None, + ) + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "overrides,prisma_kwargs,expected_detail", + [ + ( + {"api_key_ids": (), "team_ids": ("team-eng", "team-ghost")}, + {"known_teams": {"team-eng": "Engineering"}}, + "team_ids not on this proxy: team-ghost", + ), + ( + {"api_key_ids": (), "user_ids": ("dev-alice", "dev-ghost")}, + {"known_users": {"dev-alice": "alice@example.com"}}, + "user_ids not on this proxy: dev-ghost", + ), + ], + ids=["unknown-team", "unknown-user"], +) +async def test_start_shadow_eval_rejects_teams_and_users_this_proxy_does_not_know( + monkeypatch: pytest.MonkeyPatch, overrides, prisma_kwargs, expected_detail +): + import litellm.proxy.proxy_server as proxy_server + + _configure_anthropic_sdk_judge(monkeypatch) + prisma = _shadow_prisma(**prisma_kwargs) + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + monkeypatch.setattr(proxy_server, "llm_router", _shadow_router()) + + with pytest.raises(HTTPException) as exc: + await start_shadow_eval(_start_request(**overrides), ADMIN) + assert exc.value.status_code == 400 + assert expected_detail in exc.value.detail + prisma.db.litellm_shadowevaljob.create_many.assert_not_called() + + +@pytest.mark.asyncio +async def test_start_shadow_eval_mixed_targets_create_both_legs_and_sweep_once_per_type( + monkeypatch: pytest.MonkeyPatch, +): + import litellm.proxy.proxy_server as proxy_server + + _configure_anthropic_sdk_judge(monkeypatch) + prisma = _shadow_prisma(known_teams={"team-eng": "Engineering"}) + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + monkeypatch.setattr(proxy_server, "llm_router", _shadow_router()) + + response = await start_shadow_eval(_start_request(team_ids=("team-eng",)), ADMIN) + + rows = prisma.db.litellm_shadowevaljob.create_many.call_args.kwargs["data"] + assert [(row["target_type"], row["target_id"]) for row in rows] == [("key", "key-hash"), ("team", "team-eng")] + assert len({row["group_id"] for row in rows}) == 1 + sweeps = [ + call.args + for call in prisma.db.execute_raw.await_args_list + if "SET stopped_at = (NOW() AT TIME ZONE 'utc')" in call.args[0] + ] + assert [(ids, target_type) for _, ids, target_type in sweeps] == [(["key-hash"], "key"), (["team-eng"], "team")] + assert [(t.target_type, t.target_id, t.target_alias) for t in response.targets] == [ + ("key", "key-hash", "prod-alpha"), + ("team", "team-eng", "Engineering"), + ] + + +@pytest.mark.asyncio +async def test_start_shadow_eval_names_the_busy_team_target(monkeypatch: pytest.MonkeyPatch): + import litellm.proxy.proxy_server as proxy_server + + _configure_anthropic_sdk_judge(monkeypatch) + prisma = _shadow_prisma( + legs=[_leg_record(id="leg-t", group_id="job-7", target_type="team", target_id="team-eng")], + known_teams={"team-eng": "Engineering"}, + ) + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + monkeypatch.setattr(proxy_server, "llm_router", _shadow_router()) + + with pytest.raises(HTTPException) as exc: + await start_shadow_eval(_start_request(api_key_ids=(), team_ids=("team-eng",)), ADMIN) + assert exc.value.status_code == 409 + assert "team team-eng (job job-7)" in exc.value.detail + prisma.db.litellm_shadowevaljob.create_many.assert_not_called() + + +@pytest.mark.asyncio +async def test_start_shadow_eval_claim_matches_exact_target_pairs_not_bare_ids(monkeypatch: pytest.MonkeyPatch): + """A key whose hash happens to spell a team's id must not hold the team's slot: the + claim matches (target_type, target_id) pairs, never ids across kinds.""" + import litellm.proxy.proxy_server as proxy_server + + _configure_anthropic_sdk_judge(monkeypatch) + prisma = _shadow_prisma( + legs=[_leg_record(id="leg-k", group_id="job-7", target_type="key", target_id="team-eng")], + known_teams={"team-eng": "Engineering"}, + ) + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + monkeypatch.setattr(proxy_server, "llm_router", _shadow_router()) + + response = await start_shadow_eval(_start_request(api_key_ids=(), team_ids=("team-eng",)), ADMIN) + + assert response.status == "running" + prisma.db.litellm_shadowevaljob.create_many.assert_awaited_once() + + +@pytest.mark.asyncio +async def test_list_shadow_eval_jobs_rejects_a_lone_filter_half(monkeypatch: pytest.MonkeyPatch): + """target_type and target_id only mean anything together: a bare id could name a key + or a team, and a bare type filters nothing.""" + import litellm.proxy.proxy_server as proxy_server + + prisma = _shadow_prisma(legs=[_leg_record()]) + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + + with pytest.raises(HTTPException) as id_only: + await list_shadow_eval_jobs(VIEWER, target_type=None, target_id="key-hash", limit=50) + assert id_only.value.status_code == 400 + + with pytest.raises(HTTPException) as type_only: + await list_shadow_eval_jobs(VIEWER, target_type="key", target_id=None, limit=50) + assert type_only.value.status_code == 400 + prisma.db.query_raw.assert_not_called() + + @pytest.mark.asyncio async def test_start_shadow_eval_concurrent_unique_violation_is_a_409(monkeypatch: pytest.MonkeyPatch): import litellm.proxy.proxy_server as proxy_server @@ -1530,7 +1825,7 @@ async def test_get_shadow_eval_job_pools_counts_and_slices_results_per_key(monke }, ] prisma = _shadow_prisma( - legs=[_leg_record(), _leg_record(id="leg-2", api_key_id="key-hash-2", max_turns=50)], + legs=[_leg_record(), _leg_record(id="leg-2", target_id="key-hash-2", max_turns=50)], agg_rows=tier_rows, by_leg_rows=leg_rows, ) @@ -1549,8 +1844,10 @@ async def test_get_shadow_eval_job_pools_counts_and_slices_results_per_key(monke assert response.results.by_tier[0].shadow_win_rate_pct == 50.0 assert response.results.overall_shadow_win_rate_pct == 40.0 assert response.results.overall_tie_rate_pct == 20.0 - assert [(s.group, s.turn_count) for s in response.results.by_key] == [("key-hash", 6), ("key-hash-2", 4)] - assert response.results.by_key[0].shadow_win_rate_pct == 66.7 + verdicts_by_target = {(t.target_type, t.target_id): t.verdicts for t in response.targets} + assert verdicts_by_target[("key", "key-hash")].turn_count == 6 + assert verdicts_by_target[("key", "key-hash")].shadow_win_rate_pct == 66.7 + assert verdicts_by_target[("key", "key-hash-2")].turn_count == 4 agg_sql = next(call.args[0] for call in prisma.db.query_raw.await_args_list if "real_spend" in call.args[0]) assert agg_sql.count("FILTER (WHERE real_cost IS NOT NULL AND NOT real_cache_hit)") == 2 assert response.results.by_tier[0].real_spend == 0.08 @@ -1561,13 +1858,73 @@ async def test_get_shadow_eval_job_pools_counts_and_slices_results_per_key(monke assert response.results.not_sampled_count is None assert response.results.unjudgeable_count is None assert response.results.shed_count is None - assert [(key.api_key_id, key.max_turns) for key in response.keys] == [("key-hash", 200), ("key-hash-2", 50)] + assert [(target.target_id, target.max_turns) for target in response.targets] == [ + ("key-hash", 200), + ("key-hash-2", 50), + ] totals_args = [call.args for call in prisma.db.query_raw.await_args_list if "judged_count" in call.args[0]] assert totals_args == [(totals_args[0][0], ["leg-1", "leg-2"])] error_where = prisma.db.litellm_shadowevalattempt.find_first.call_args.kwargs["where"] assert error_where == {"job_id": {"in": ["leg-1", "leg-2"]}, "outcome": "error"} +@pytest.mark.asyncio +async def test_get_shadow_eval_job_slices_results_per_router(monkeypatch: pytest.MonkeyPatch): + """A multi-router job's detail carries one slice per arm, aggregated by the arm + stamped on each attempt row, with unstamped legacy rows attributed to the job's own + router by the read (the COALESCE against the leg's router_name).""" + import litellm.proxy.proxy_server as proxy_server + + def agg(grp: str, wins: int) -> dict[str, object]: + return { + "grp": grp, + "turn_count": 4, + "real_wins": 4 - wins, + "shadow_wins": wins, + "ties": 0, + "avg_confidence": 0.8, + "real_spend": 0.08, + "shadow_spend": 0.02, + "cache_hit_turns": 0, + } + + prisma = _shadow_prisma( + legs=[_leg_record(router_names=("my-router", "alt-router"))], + agg_rows=[agg("SIMPLE", 3)], + by_router_rows=[agg("my-router", 1), agg("alt-router", 3)], + ) + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + + response = await get_shadow_eval_job("job-1", VIEWER) + + assert response.router_names == ("my-router", "alt-router") + assert response.router_name == "my-router" + assert [(s.group, s.shadow_win_rate_pct) for s in response.results.by_router] == [ + ("my-router", 25.0), + ("alt-router", 75.0), + ] + router_sql = next( + call.args[0] for call in prisma.db.query_raw.await_args_list if "COALESCE(a.router_name" in call.args[0] + ) + assert "COALESCE(a.router_name, j.router_name)" in router_sql + assert 'JOIN "LiteLLM_ShadowEvalJob" j ON j.id = a.job_id' in router_sql + assert "a.job_id = ANY($1::text[])" in router_sql + + +@pytest.mark.asyncio +async def test_job_responses_resolve_router_names_with_legacy_fallback(monkeypatch: pytest.MonkeyPatch): + """Rows from before router_names existed carry their whole set in router_name.""" + import litellm.proxy.proxy_server as proxy_server + + prisma = _shadow_prisma(legs=[_leg_record(router_names=())]) + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + + response = await get_shadow_eval_job("job-1", VIEWER) + + assert response.router_names == ("my-router",) + assert response.router_name == "my-router" + + @pytest.mark.asyncio async def test_get_shadow_eval_job_404s_and_gates_on_role(monkeypatch: pytest.MonkeyPatch): import litellm.proxy.proxy_server as proxy_server @@ -1595,7 +1952,7 @@ async def test_list_shadow_eval_jobs_collapses_legs_into_jobs_newest_first(monke _leg_record(created_at=datetime(2026, 8, 13, tzinfo=timezone.utc)), _leg_record( id="leg-2", - api_key_id="key-hash-2", + target_id="key-hash-2", stopped_at=stamp, created_at=datetime(2026, 8, 13, tzinfo=timezone.utc), ), @@ -1615,14 +1972,14 @@ async def test_list_shadow_eval_jobs_collapses_legs_into_jobs_newest_first(monke ) monkeypatch.setattr(proxy_server, "prisma_client", prisma) - jobs = await list_shadow_eval_jobs(VIEWER, api_key_id=None, limit=50) + jobs = await list_shadow_eval_jobs(VIEWER, target_type=None, target_id=None, limit=50) assert [(job.job_id, job.status) for job in jobs] == [ ("job-1", "running"), ("job-2", "stopped"), ("job-3", "completed"), ] - assert [key.api_key_id for key in jobs[0].keys] == ["key-hash", "key-hash-2"] + assert [target.target_id for target in jobs[0].targets] == ["key-hash", "key-hash-2"] assert all(job.judged_count is None and job.results is None for job in jobs) legs_sql, legs_limit = prisma.db.query_raw.await_args_list[0].args assert "GROUP BY group_id ORDER BY MAX(created_at) DESC LIMIT $1::int" in legs_sql @@ -1648,17 +2005,20 @@ async def test_list_shadow_eval_jobs_filters_to_jobs_containing_the_key(monkeypa prisma = _shadow_prisma( legs=[ _leg_record(), - _leg_record(id="leg-2", api_key_id="key-hash-2"), - _leg_record(id="leg-3", group_id="job-2", api_key_id="key-hash-2"), + _leg_record(id="leg-2", target_id="key-hash-2"), + _leg_record(id="leg-3", group_id="job-2", target_id="key-hash-2"), _leg_record(id="leg-4", group_id="job-3"), ] ) monkeypatch.setattr(proxy_server, "prisma_client", prisma) - jobs = await list_shadow_eval_jobs(VIEWER, api_key_id="key-hash-2", limit=50) + jobs = await list_shadow_eval_jobs(VIEWER, target_type="key", target_id="key-hash-2", limit=50) assert [job.job_id for job in jobs] == ["job-1", "job-2"] - assert [key.api_key_id for key in jobs[0].keys] == ["key-hash", "key-hash-2"] + assert [target.target_id for target in jobs[0].targets] == ["key-hash", "key-hash-2"] + legs_sql, *legs_params = prisma.db.query_raw.await_args_list[0].args + assert "WHERE target_type = $2 AND target_id = $3" in legs_sql + assert legs_params == [50, "key", "key-hash-2"] @pytest.mark.parametrize( @@ -1682,7 +2042,7 @@ async def test_job_status_runs_until_every_key_stops_and_completed_outranks_stop legs=[ _leg_record( id=f"leg-{index}", - api_key_id=f"key-{index}", + target_id=f"key-{index}", stopped_at=stamp if stopped else None, ends_at=datetime.now(timezone.utc) + timedelta(days=days_left), ) @@ -1691,7 +2051,7 @@ async def test_job_status_runs_until_every_key_stops_and_completed_outranks_stop ) monkeypatch.setattr(proxy_server, "prisma_client", prisma) - jobs = await list_shadow_eval_jobs(VIEWER, api_key_id=None, limit=50) + jobs = await list_shadow_eval_jobs(VIEWER, target_type=None, target_id=None, limit=50) assert [job.status for job in jobs] == [expected] @@ -1707,9 +2067,9 @@ async def test_list_reads_completed_once_every_key_spends_its_budget(monkeypatch prisma = _shadow_prisma( legs=[ _leg_record(max_turns=5), - _leg_record(id="leg-2", api_key_id="key-hash-2", max_turns=5), - _leg_record(id="leg-3", group_id="job-2", api_key_id="key-hash", max_turns=5), - _leg_record(id="leg-4", group_id="job-2", api_key_id="key-hash-2", max_turns=5), + _leg_record(id="leg-2", target_id="key-hash-2", max_turns=5), + _leg_record(id="leg-3", group_id="job-2", target_id="key-hash", max_turns=5), + _leg_record(id="leg-4", group_id="job-2", target_id="key-hash-2", max_turns=5), ] ) prisma.attempt_rows = [ @@ -1720,13 +2080,13 @@ async def test_list_reads_completed_once_every_key_spends_its_budget(monkeypatch ] monkeypatch.setattr(proxy_server, "prisma_client", prisma) - jobs = await list_shadow_eval_jobs(VIEWER, api_key_id=None, limit=50) + jobs = await list_shadow_eval_jobs(VIEWER, target_type=None, target_id=None, limit=50) by_id = {job.job_id: job for job in jobs} assert by_id["job-1"].status == "completed" - assert all(key.stopped_at is None for key in by_id["job-1"].keys) + assert all(target.stopped_at is None for target in by_id["job-1"].targets) assert by_id["job-2"].status == "running" - assert {key.api_key_id: key.attempt_count for key in by_id["job-2"].keys} == {"key-hash": 5, "key-hash-2": 3} + assert {t.target_id: t.attempt_count for t in by_id["job-2"].targets} == {"key-hash": 5, "key-hash-2": 3} @pytest.mark.asyncio @@ -1740,7 +2100,7 @@ async def test_recorded_operator_stop_outranks_budget_arithmetic(monkeypatch: py prisma.attempt_rows = [{"job_id": "leg-1", "attempt_count": 6, "spend": 0.0}] monkeypatch.setattr(proxy_server, "prisma_client", prisma) - jobs = await list_shadow_eval_jobs(VIEWER, api_key_id=None, limit=50) + jobs = await list_shadow_eval_jobs(VIEWER, target_type=None, target_id=None, limit=50) assert jobs[0].status == "stopped" assert jobs[0].stopped_by == "admin" @@ -1760,7 +2120,7 @@ async def test_backfilled_legacy_stop_never_reads_as_completion(monkeypatch: pyt prisma.attempt_rows = [{"job_id": "leg-1", "attempt_count": 6, "spend": 0.0}] monkeypatch.setattr(proxy_server, "prisma_client", prisma) - jobs = await list_shadow_eval_jobs(VIEWER, api_key_id=None, limit=50) + jobs = await list_shadow_eval_jobs(VIEWER, target_type=None, target_id=None, limit=50) assert jobs[0].status == "stopped" @@ -1808,6 +2168,56 @@ def test_max_budget_migration_is_additive_and_leaves_legacy_rows_null(): @pytest.mark.asyncio +@pytest.mark.asyncio +async def test_verdicts_keep_same_id_targets_of_different_kinds_distinct(monkeypatch): + """A team and a user can legitimately share an id; their slices must not merge.""" + from litellm.proxy import proxy_server + + leg_rows = [ + { + "grp": "leg-1", + "turn_count": 6, + "real_wins": 2, + "shadow_wins": 4, + "ties": 0, + "avg_confidence": 0.8, + "real_spend": 0.02, + "shadow_spend": 0.01, + "cache_hit_turns": 0, + }, + { + "grp": "leg-2", + "turn_count": 4, + "real_wins": 3, + "shadow_wins": 0, + "ties": 1, + "avg_confidence": 0.6, + "real_spend": 0.05, + "shadow_spend": 0.04, + "cache_hit_turns": 1, + }, + ] + prisma = _shadow_prisma( + legs=[ + _leg_record(target_type="team", target_id="dev-alice"), + _leg_record(id="leg-2", target_type="user", target_id="dev-alice"), + ], + agg_rows=leg_rows[:1], + by_leg_rows=leg_rows, + known_teams={"dev-alice": "alias"}, + known_users={"dev-alice": "alice@example.com"}, + ) + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + + response = await get_shadow_eval_job("job-1", VIEWER) + + verdicts_by_target = {(t.target_type, t.target_id): t.verdicts for t in response.targets} + assert verdicts_by_target[("team", "dev-alice")].turn_count == 6 + assert verdicts_by_target[("team", "dev-alice")].shadow_win_rate_pct == 66.7 + assert verdicts_by_target[("user", "dev-alice")].turn_count == 4 + assert verdicts_by_target[("user", "dev-alice")].shadow_win_rate_pct == 0.0 + + async def test_stop_rejects_a_job_that_already_spent_its_budget(monkeypatch: pytest.MonkeyPatch): import litellm.proxy.proxy_server as proxy_server @@ -1832,9 +2242,9 @@ async def test_list_reads_completed_once_every_key_spends_its_dollar_budget(monk prisma = _shadow_prisma( legs=[ _leg_record(max_turns=SHADOW_EVAL_TURN_VALVE, max_budget=1.0), - _leg_record(id="leg-2", api_key_id="key-hash-2", max_turns=SHADOW_EVAL_TURN_VALVE, max_budget=1.0), + _leg_record(id="leg-2", target_id="key-hash-2", max_turns=SHADOW_EVAL_TURN_VALVE, max_budget=1.0), _leg_record( - id="leg-3", group_id="job-2", api_key_id="key-hash", max_turns=SHADOW_EVAL_TURN_VALVE, max_budget=1.0 + id="leg-3", group_id="job-2", target_id="key-hash", max_turns=SHADOW_EVAL_TURN_VALVE, max_budget=1.0 ), ] ) @@ -1845,13 +2255,13 @@ async def test_list_reads_completed_once_every_key_spends_its_dollar_budget(monk ] monkeypatch.setattr(proxy_server, "prisma_client", prisma) - jobs = await list_shadow_eval_jobs(VIEWER, api_key_id=None, limit=50) + jobs = await list_shadow_eval_jobs(VIEWER, target_type=None, target_id=None, limit=50) by_id = {job.job_id: job for job in jobs} assert by_id["job-1"].status == "completed" assert by_id["job-2"].status == "running" - assert {key.api_key_id: key.spend for key in by_id["job-1"].keys} == {"key-hash": 1.0, "key-hash-2": 1.25} - assert all(key.max_budget == 1.0 for key in by_id["job-1"].keys) + assert {t.target_id: t.spend for t in by_id["job-1"].targets} == {"key-hash": 1.0, "key-hash-2": 1.25} + assert all(target.max_budget == 1.0 for target in by_id["job-1"].targets) @pytest.mark.asyncio @@ -1880,11 +2290,11 @@ async def test_legacy_jobs_without_a_dollar_budget_stay_turn_gated(monkeypatch: prisma.attempt_rows = [{"job_id": "leg-1", "attempt_count": 40, "spend": 250.0}] monkeypatch.setattr(proxy_server, "prisma_client", prisma) - jobs = await list_shadow_eval_jobs(VIEWER, api_key_id=None, limit=50) + jobs = await list_shadow_eval_jobs(VIEWER, target_type=None, target_id=None, limit=50) assert jobs[0].status == "running" - assert jobs[0].keys[0].max_budget is None - assert jobs[0].keys[0].spend == 250.0 + assert jobs[0].targets[0].max_budget is None + assert jobs[0].targets[0].spend == 250.0 @pytest.mark.asyncio @@ -1892,14 +2302,14 @@ async def test_shadow_eval_responses_name_every_shadowed_key(monkeypatch: pytest import litellm.proxy.proxy_server as proxy_server prisma = _shadow_prisma( - legs=[_leg_record(), _leg_record(id="leg-2", api_key_id="deleted-key-hash")], + legs=[_leg_record(), _leg_record(id="leg-2", target_id="deleted-key-hash")], known_keys=("key-hash", "key-hash-2"), ) monkeypatch.setattr(proxy_server, "llm_router", _shadow_router()) monkeypatch.setattr(proxy_server, "prisma_client", prisma) - jobs = await list_shadow_eval_jobs(VIEWER, api_key_id=None, limit=50) - assert [(key.key_alias, key.key_name) for key in jobs[0].keys] == [ + jobs = await list_shadow_eval_jobs(VIEWER, target_type=None, target_id=None, limit=50) + assert [(target.target_alias, target.key_name) for target in jobs[0].targets] == [ (None, None), ("prod-alpha", "sk-...lpha"), ] @@ -1907,7 +2317,7 @@ async def test_shadow_eval_responses_name_every_shadowed_key(monkeypatch: pytest assert batched_where == {"token": {"in": ["deleted-key-hash", "key-hash"]}} detail = await get_shadow_eval_job("job-1", VIEWER) - assert [key.key_alias for key in detail.keys] == [None, "prod-alpha"] + assert [target.target_alias for target in detail.targets] == [None, "prod-alpha"] @pytest.mark.asyncio @@ -1919,7 +2329,7 @@ async def test_stop_shadow_eval_stops_every_unstopped_leg_and_rejects_non_runnin import litellm.proxy.proxy_server as proxy_server earned = datetime.now(timezone.utc) - timedelta(hours=1) - prisma = _shadow_prisma(legs=[_leg_record(), _leg_record(id="leg-2", api_key_id="key-hash-2", stopped_at=earned)]) + prisma = _shadow_prisma(legs=[_leg_record(), _leg_record(id="leg-2", target_id="key-hash-2", stopped_at=earned)]) monkeypatch.setattr(proxy_server, "prisma_client", prisma) stopped = await stop_shadow_eval_job("job-1", ADMIN) @@ -1938,9 +2348,9 @@ async def test_stop_shadow_eval_stops_every_unstopped_leg_and_rejects_non_runnin assert datetime.fromisoformat(stop_stamp).tzinfo is None assert prisma.db.execute_raw.await_count == 1 prisma.db.litellm_shadowevaljob.update_many.assert_not_called() - by_key = {key.api_key_id: key.stopped_at for key in stopped.keys} - assert by_key["key-hash-2"] == earned - assert by_key["key-hash"] is not None and by_key["key-hash"] != earned + by_target = {target.target_id: target.stopped_at for target in stopped.targets} + assert by_target["key-hash-2"] == earned + assert by_target["key-hash"] is not None and by_target["key-hash"] != earned done_leg = _leg_record(ends_at=datetime.now(timezone.utc) - timedelta(days=1)) prisma_done = _shadow_prisma(legs=[done_leg]) @@ -2331,7 +2741,7 @@ async def test_get_shadow_eval_job_sums_funnel_rows_across_legs(monkeypatch: pyt }, ] prisma = _shadow_prisma( - legs=[_leg_record(), _leg_record(id="leg-2", api_key_id="key-hash-2")], + legs=[_leg_record(), _leg_record(id="leg-2", target_id="key-hash-2")], agg_rows=tier_rows, ) prisma.funnel_rows = [{"legs_with_rows": 2, "not_sampled": 30, "unjudgeable": 5, "shed": 2, "withheld": 3}] @@ -2366,7 +2776,7 @@ async def test_partially_seeded_funnel_reads_as_unknown_coverage(monkeypatch: py }, ] prisma = _shadow_prisma( - legs=[_leg_record(), _leg_record(id="leg-2", api_key_id="key-hash-2")], + legs=[_leg_record(), _leg_record(id="leg-2", target_id="key-hash-2")], agg_rows=tier_rows, ) prisma.funnel_rows = [{"legs_with_rows": 1, "not_sampled": 30, "unjudgeable": 5, "shed": 2, "withheld": 0}] diff --git a/tests/test_litellm/proxy/management_endpoints/test_config_override_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_config_override_endpoints.py index d90d589c504..03f94fbe94c 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_config_override_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_config_override_endpoints.py @@ -11,16 +11,19 @@ import litellm import litellm.proxy.proxy_server as ps from litellm.proxy._types import KeyManagementSystem, LitellmUserRoles, UserAPIKeyAuth from litellm.proxy.management_endpoints.config_override_endpoints import ( + CYBERARK_ENV_VAR_MAPPING, HASHICORP_ENV_VAR_MAPPING, _build_field_schema, _set_env_vars, ) from litellm.proxy.proxy_server import app from litellm.types.proxy.management_endpoints.config_overrides import ( + CyberArkConfig, HashicorpVaultConfig, ) VAULT_URL = "/config_overrides/hashicorp_vault" +CYBERARK_URL = "/config_overrides/cyberark" @pytest.fixture @@ -42,6 +45,7 @@ def _make_mock_proxy_config(): cfg = MagicMock() cfg.initialize_secret_manager = MagicMock() cfg._last_hashicorp_vault_config = None + cfg._cyberark_boot_env = None cfg._encrypt_env_variables = MagicMock( side_effect=lambda d: {k: f"enc_{v}" for k, v in d.items()} ) @@ -67,6 +71,8 @@ def _cleanup(): app.dependency_overrides.pop(ps.user_api_key_auth, None) for env_var in HASHICORP_ENV_VAR_MAPPING.values(): os.environ.pop(env_var, None) + for env_var in CYBERARK_ENV_VAR_MAPPING.values(): + os.environ.pop(env_var, None) def _set_admin(): @@ -275,6 +281,391 @@ async def test_hashicorp_vault_validation_errors_and_access_control( _cleanup() +@pytest.mark.asyncio +async def test_cyberark_crud_lifecycle(client, monkeypatch): + """Create → read (masked) → partial update (merge from DB) → clear field → + delete → idempotent delete → env fallback → merge from env → schema.""" + mock_prisma, mock_db = _make_mock_db() + mock_cfg = _make_mock_proxy_config() + mock_cfg._last_cyberark_config = None + monkeypatch.setattr(ps, "prisma_client", mock_prisma) + monkeypatch.setattr(ps, "proxy_config", mock_cfg) + old_client, old_kms = litellm.secret_manager_client, litellm._key_management_system + _set_admin() + + try: + # 1. POST: create with API-key auth + r = client.post( + CYBERARK_URL, + json={ + "cyberark_api_base": "https://conjur.example.com", + "cyberark_account": "myorg", + "cyberark_username": "litellm-user", + "cyberark_api_key": "my-secret-api-key", + }, + ) + assert r.status_code == 200 + assert os.environ["CYBERARK_API_BASE"] == "https://conjur.example.com" + assert os.environ["CYBERARK_API_KEY"] == "my-secret-api-key" + data = _upserted_data(mock_db) + assert data["cyberark_api_key"] == "enc_my-secret-api-key" + mock_cfg.initialize_secret_manager.assert_called_with( + key_management_system="cyberark" + ) + assert mock_cfg._last_cyberark_config is not None + + # 2. GET: sensitive fields masked + mock_db.find_unique = AsyncMock(return_value=_db_record(data)) + r = client.get(CYBERARK_URL) + assert r.status_code == 200 + vals = r.json()["values"] + assert vals["cyberark_api_base"] == "https://conjur.example.com" + assert "*" in vals["cyberark_api_key"] + assert "properties" in r.json()["field_schema"] + + # 3. POST partial: omitted fields merge from DB + r = client.post(CYBERARK_URL, json={"cyberark_api_base": "https://conjur.new.com"}) + assert r.status_code == 200 + data = _upserted_data(mock_db) + assert data["cyberark_api_base"] == "enc_https://conjur.new.com" + assert data["cyberark_api_key"] == "enc_my-secret-api-key" + assert data["cyberark_account"] == "enc_myorg" + + # 4. POST empty string: clears field, switches to cert auth + step3 = { + **data, + "client_cert": "enc_/certs/client.pem", + "client_key": "enc_/certs/client.key", + } + mock_db.find_unique = AsyncMock(return_value=_db_record(step3)) + mock_db.upsert = AsyncMock(return_value=None) + r = client.post(CYBERARK_URL, json={"cyberark_api_key": ""}) + assert r.status_code == 200 + data = _upserted_data(mock_db) + assert "cyberark_api_key" not in data + assert data["client_cert"] == "enc_/certs/client.pem" + + # 5. DELETE: clears everything + litellm.secret_manager_client = MagicMock() # test-quality-ok: endpoint hot-reloads litellm globals; test must set and restore them + litellm._key_management_system = KeyManagementSystem.CYBERARK # test-quality-ok: endpoint hot-reloads litellm globals; test must set and restore them + r = client.delete(CYBERARK_URL) + assert r.status_code == 200 + assert os.environ.get("CYBERARK_API_BASE") is None + assert litellm.secret_manager_client is None + assert mock_cfg._last_cyberark_config is None + + # 6. DELETE idempotent + mock_db.delete = AsyncMock( + side_effect=RecordNotFoundError( + data={"clientVersion": "0.0.0"}, message="Not found" + ) + ) + assert client.delete(CYBERARK_URL).status_code == 200 + + # 7. GET: env var fallback with masking + mock_db.find_unique = AsyncMock(return_value=None) + monkeypatch.setenv("CYBERARK_API_BASE", "https://conjur.env.com") + monkeypatch.setenv("CYBERARK_API_KEY", "env-api-key") + r = client.get(CYBERARK_URL) + vals = r.json()["values"] + assert vals["cyberark_api_base"] == "https://conjur.env.com" + assert "*" in vals["cyberark_api_key"] + + # 8. POST: merge from env vars + mock_cfg.initialize_secret_manager = MagicMock() + mock_db.upsert = AsyncMock(return_value=None) + r = client.post(CYBERARK_URL, json={"cyberark_api_base": "https://conjur.merged.com"}) + assert r.status_code == 200 + data = _upserted_data(mock_db) + assert data["cyberark_api_key"] == "enc_env-api-key" + + # 9. _build_field_schema + schema = _build_field_schema(CyberArkConfig) + assert "cyberark_api_base" in schema["properties"] + assert len(schema["properties"]["cyberark_api_base"]["description"]) > 0 + + finally: + litellm.secret_manager_client = old_client # test-quality-ok: endpoint hot-reloads litellm globals; test must set and restore them + litellm._key_management_system = old_kms # test-quality-ok: endpoint hot-reloads litellm globals; test must set and restore them + _cleanup() + + +@pytest.mark.asyncio +async def test_cyberark_validation_errors_and_access_control(client, monkeypatch): + """Validation (missing api base, missing auth, init failure rollback), + DELETE preserves non-CyberArk secret managers, non-admin 403.""" + mock_prisma, mock_db = _make_mock_db() + mock_cfg = MagicMock() + mock_cfg._last_cyberark_config = {"cyberark_api_base": "old"} + mock_cfg._cyberark_boot_env = None + monkeypatch.setattr(ps, "prisma_client", mock_prisma) + monkeypatch.setattr(ps, "proxy_config", mock_cfg) + old_client, old_kms = litellm.secret_manager_client, litellm._key_management_system + _set_admin() + + try: + # 1. Missing cyberark_api_base → 400 + r = client.post(CYBERARK_URL, json={"cyberark_api_key": "key"}) + assert r.status_code == 400 + assert "API Base" in r.json()["detail"] + + # 2. Missing auth → 400 (cert without key is not valid auth) + r = client.post( + CYBERARK_URL, + json={"cyberark_api_base": "https://c.com", "client_cert": "/c.pem"}, + ) + assert r.status_code == 400 + assert "authentication" in r.json()["detail"].lower() + + # 3. Init failure → 500, env vars restored, nothing persisted + mock_cfg.initialize_secret_manager = MagicMock(side_effect=Exception("fail")) + monkeypatch.setenv("CYBERARK_API_BASE", "https://conjur.old.com") + monkeypatch.setenv("CYBERARK_API_KEY", "old-key") + r = client.post( + CYBERARK_URL, + json={"cyberark_api_base": "https://bad.com", "cyberark_api_key": "bad"}, + ) + assert r.status_code == 500 + assert os.environ["CYBERARK_API_BASE"] == "https://conjur.old.com" + mock_db.upsert.assert_not_awaited() + + # 4. DELETE preserves non-CyberArk secret manager + aws = MagicMock() + litellm.secret_manager_client = aws # test-quality-ok: endpoint hot-reloads litellm globals; test must set and restore them + litellm._key_management_system = KeyManagementSystem.AWS_SECRET_MANAGER # test-quality-ok: endpoint hot-reloads litellm globals; test must set and restore them + assert client.delete(CYBERARK_URL).status_code == 200 + assert litellm.secret_manager_client is aws + + # 5. Non-admin → 403 + app.dependency_overrides[ps.user_api_key_auth] = lambda: UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, user_id="user" + ) + assert client.get(CYBERARK_URL).status_code == 403 + assert ( + client.post( + CYBERARK_URL, json={"cyberark_api_base": "https://c.com"} + ).status_code + == 403 + ) + assert client.delete(CYBERARK_URL).status_code == 403 + assert client.post(CYBERARK_URL + "/test_connection").status_code == 403 + + finally: + litellm.secret_manager_client = old_client # test-quality-ok: endpoint hot-reloads litellm globals; test must set and restore them + litellm._key_management_system = old_kms # test-quality-ok: endpoint hot-reloads litellm globals; test must set and restore them + _cleanup() + + +@pytest.mark.asyncio +async def test_cyberark_delete_restores_deployment_env_config(client, monkeypatch): + """Deleting the DB override must restore env vars the deployment started with, + and reinitialize the manager from them, instead of wiping CyberArk entirely.""" + mock_prisma, mock_db = _make_mock_db() + mock_cfg = _make_mock_proxy_config() + mock_cfg._last_cyberark_config = None + monkeypatch.setattr(ps, "prisma_client", mock_prisma) + monkeypatch.setattr(ps, "proxy_config", mock_cfg) + old_client, old_kms = litellm.secret_manager_client, litellm._key_management_system + _set_admin() + + try: + monkeypatch.setenv("CYBERARK_API_BASE", "https://conjur.boot.com") + monkeypatch.setenv("CYBERARK_API_KEY", "boot-key") + + r = client.post( + CYBERARK_URL, + json={"cyberark_api_base": "https://conjur.db.com", "cyberark_api_key": "db-key"}, + ) + assert r.status_code == 200 + assert os.environ["CYBERARK_API_BASE"] == "https://conjur.db.com" + + mock_cfg.initialize_secret_manager.reset_mock() + r = client.delete(CYBERARK_URL) + assert r.status_code == 200 + assert os.environ["CYBERARK_API_BASE"] == "https://conjur.boot.com" + assert os.environ["CYBERARK_API_KEY"] == "boot-key" + mock_cfg.initialize_secret_manager.assert_called_with(key_management_system="cyberark") + assert mock_cfg._last_cyberark_config is None + finally: + litellm.secret_manager_client = old_client # test-quality-ok: endpoint hot-reloads litellm globals; test must set and restore them + litellm._key_management_system = old_kms # test-quality-ok: endpoint hot-reloads litellm globals; test must set and restore them + _cleanup() + + +@pytest.mark.asyncio +async def test_cyberark_persist_failure_rolls_back_runtime_state(client, monkeypatch): + """If the DB upsert fails after the manager was reinitialized, the endpoint + must restore the previous env vars and reinitialize from them, so this pod + does not keep serving credentials that were never committed to the DB.""" + mock_prisma, mock_db = _make_mock_db() + mock_cfg = _make_mock_proxy_config() + mock_cfg._last_cyberark_config = None + mock_db.upsert = AsyncMock(side_effect=Exception("db write failed")) + monkeypatch.setattr(ps, "prisma_client", mock_prisma) + monkeypatch.setattr(ps, "proxy_config", mock_cfg) + old_client, old_kms = litellm.secret_manager_client, litellm._key_management_system + _set_admin() + + try: + monkeypatch.setenv("CYBERARK_API_BASE", "https://conjur.prev.com") + monkeypatch.setenv("CYBERARK_API_KEY", "prev-key") + + r = client.post( + CYBERARK_URL, + json={"cyberark_api_base": "https://conjur.new.com", "cyberark_api_key": "new-key"}, + ) + assert r.status_code == 500 + assert "persist" in r.json()["detail"].lower() + assert os.environ["CYBERARK_API_BASE"] == "https://conjur.prev.com" + assert os.environ["CYBERARK_API_KEY"] == "prev-key" + # last call must be the rollback reinit against the restored env + assert ( + mock_cfg.initialize_secret_manager.call_args_list[-1].kwargs["key_management_system"] == "cyberark" + ) + assert os.environ.get("CYBERARK_API_BASE") != "https://conjur.new.com" + finally: + litellm.secret_manager_client = old_client # test-quality-ok: endpoint hot-reloads litellm globals; test must set and restore them + litellm._key_management_system = old_kms # test-quality-ok: endpoint hot-reloads litellm globals; test must set and restore them + _cleanup() + + +@pytest.mark.asyncio +async def test_cyberark_persist_failure_restores_hashicorp_manager(client, monkeypatch): + """If CyberArk init displaced an env-configured Hashicorp manager and the DB + upsert then fails, rollback must bring the Hashicorp manager back.""" + mock_prisma, mock_db = _make_mock_db() + mock_cfg = _make_mock_proxy_config() + mock_cfg._last_cyberark_config = None + mock_db.upsert = AsyncMock(side_effect=Exception("db write failed")) + + def _fake_init(key_management_system): + litellm._key_management_system = ( # test-quality-ok: endpoint hot-reloads litellm globals; test must set and restore them + KeyManagementSystem.CYBERARK + if key_management_system == "cyberark" + else KeyManagementSystem.HASHICORP_VAULT + ) + + mock_cfg.initialize_secret_manager = MagicMock(side_effect=_fake_init) + monkeypatch.setattr(ps, "prisma_client", mock_prisma) + monkeypatch.setattr(ps, "proxy_config", mock_cfg) + old_client, old_kms = litellm.secret_manager_client, litellm._key_management_system + _set_admin() + + try: + monkeypatch.setenv("HCP_VAULT_ADDR", "https://vault.example.com") + litellm._key_management_system = KeyManagementSystem.HASHICORP_VAULT # test-quality-ok: endpoint hot-reloads litellm globals; test must set and restore them + + r = client.post( + CYBERARK_URL, + json={"cyberark_api_base": "https://conjur.new.com", "cyberark_api_key": "new-key"}, + ) + assert r.status_code == 500 + assert litellm._key_management_system == KeyManagementSystem.HASHICORP_VAULT + assert ( + mock_cfg.initialize_secret_manager.call_args_list[-1].kwargs["key_management_system"] == "hashicorp_vault" + ) + finally: + litellm.secret_manager_client = old_client # test-quality-ok: endpoint hot-reloads litellm globals; test must set and restore them + litellm._key_management_system = old_kms # test-quality-ok: endpoint hot-reloads litellm globals; test must set and restore them + os.environ.pop("HCP_VAULT_ADDR", None) + _cleanup() + + +@pytest.mark.asyncio +async def test_cyberark_audit_log_redacts_values(client, monkeypatch): + monkeypatch.setattr(litellm, "store_audit_logs", True) + mock_prisma, mock_db = _make_mock_db() + mock_cfg = _make_mock_proxy_config() + mock_cfg._last_cyberark_config = None + monkeypatch.setattr(ps, "prisma_client", mock_prisma) + monkeypatch.setattr(ps, "proxy_config", mock_cfg) + _set_admin() + + audit_calls = [] + + async def capture(request_data): + audit_calls.append(request_data) + + try: + with patch( # test-quality-ok: patching proxy-internal collaborator to isolate the endpoint + "litellm.proxy.management_helpers.audit_logs.create_audit_log_for_update", + new=capture, + ): + r = client.post( + CYBERARK_URL, + json={ + "cyberark_api_base": "https://conjur.example.com", + "cyberark_api_key": "my-very-secret-key", + }, + ) + assert r.status_code == 200 + for _ in range(3): + await asyncio.sleep(0) + + assert len(audit_calls) == 1 + log = audit_calls[0] + assert log.action == "created" + assert log.object_id == "cyberark" + assert "my-very-secret-key" not in log.updated_values + assert "conjur.example.com" not in log.updated_values + after = json.loads(log.updated_values) + assert "cyberark_api_key" in after["config"] + assert "cyberark_api_base" in after["config"] + finally: + _cleanup() + + +@pytest.mark.asyncio +async def test_cyberark_test_connection(client, monkeypatch): + """400 when not configured; success path authenticates and hits /whoami.""" + from litellm.secret_managers.cyberark_secret_manager import CyberArkSecretManager + + old_client, old_kms = litellm.secret_manager_client, litellm._key_management_system + _set_admin() + + try: + # Not configured → 400 + litellm.secret_manager_client = None # test-quality-ok: endpoint hot-reloads litellm globals; test must set and restore them + r = client.post(CYBERARK_URL + "/test_connection") + assert r.status_code == 400 + assert "not configured" in r.json()["detail"].lower() + + # Configured → authenticates and calls /whoami + mock_manager = MagicMock(spec=CyberArkSecretManager) + mock_manager.conjur_addr = "https://conjur.example.com" + mock_manager.ssl_verify = True + mock_manager._get_request_headers = MagicMock( + return_value={"Authorization": "Token abc"} + ) + litellm.secret_manager_client = mock_manager # test-quality-ok: endpoint hot-reloads litellm globals; test must set and restore them + + mock_response = MagicMock() + mock_response.raise_for_status = MagicMock() + mock_http = MagicMock() + mock_http.get = AsyncMock(return_value=mock_response) + with patch( # test-quality-ok: patching proxy-internal collaborator to isolate the endpoint + "litellm.proxy.management_endpoints.config_override_endpoints.get_async_httpx_client", + return_value=mock_http, + ): + r = client.post(CYBERARK_URL + "/test_connection") + assert r.status_code == 200 + assert "conjur.example.com" in r.json()["message"] + called_url = mock_http.get.call_args.args[0] + assert called_url == "https://conjur.example.com/whoami" + + # Auth failure → 502 + mock_manager._get_request_headers = MagicMock( + side_effect=Exception("bad credentials") + ) + r = client.post(CYBERARK_URL + "/test_connection") + assert r.status_code == 502 + assert "authentication failed" in r.json()["detail"].lower() + finally: + litellm.secret_manager_client = old_client # test-quality-ok: endpoint hot-reloads litellm globals; test must set and restore them + litellm._key_management_system = old_kms # test-quality-ok: endpoint hot-reloads litellm globals; test must set and restore them + _cleanup() + + # ── Audit-log emission for /config_overrides/hashicorp_vault ───────────────── diff --git a/tests/test_litellm/proxy/management_endpoints/test_customer_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_customer_endpoints.py index 5c163c44cb3..1225cb80224 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_customer_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_customer_endpoints.py @@ -83,6 +83,50 @@ def test_update_customer_success(mock_prisma_client, mock_user_api_key_auth): assert response.json()["alias"] == "Updated Test User" +def test_update_customer_unblock(mock_prisma_client, mock_user_api_key_auth): + mock_end_user = LiteLLM_EndUserTable(user_id="test-user-1", blocked=True) + updated_mock_end_user = LiteLLM_EndUserTable(user_id="test-user-1", blocked=False) + + mock_prisma_client.db.litellm_endusertable.find_first = AsyncMock(return_value=mock_end_user) + mock_prisma_client.db.litellm_endusertable.update = AsyncMock(return_value=updated_mock_end_user) + + response = client.post( + "/customer/update", + json={"user_id": "test-user-1", "blocked": False}, + headers={"Authorization": "Bearer test-key"}, + ) + + assert response.status_code == 200 + assert response.json()["blocked"] is False + update_mock = mock_prisma_client.db.litellm_endusertable.update + update_mock.assert_called_once() + assert update_mock.call_args.kwargs["data"]["blocked"] is False + + +def test_update_customer_keeps_blocked_when_omitted(mock_prisma_client, mock_user_api_key_auth): + """ + Regression test: updating a blocked customer without supplying `blocked` + must NOT reset it to unblocked. `blocked=False` is the model default and + should only be applied when explicitly provided by the caller. + """ + mock_end_user = LiteLLM_EndUserTable(user_id="test-user-1", blocked=True) + updated_mock_end_user = LiteLLM_EndUserTable(user_id="test-user-1", blocked=True) + + mock_prisma_client.db.litellm_endusertable.find_first = AsyncMock(return_value=mock_end_user) + mock_prisma_client.db.litellm_endusertable.update = AsyncMock(return_value=updated_mock_end_user) + + response = client.post( + "/customer/update", + json={"user_id": "test-user-1", "alias": "Updated Test User"}, + headers={"Authorization": "Bearer test-key"}, + ) + + assert response.status_code == 200 + update_mock = mock_prisma_client.db.litellm_endusertable.update + update_mock.assert_called_once() + assert "blocked" not in update_mock.call_args.kwargs["data"] + + def test_update_customer_not_found(mock_prisma_client, mock_user_api_key_auth): """ Test that update_end_user raises a 404 ProxyException when user_id does not exist. diff --git a/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py index 42e56ceabd3..7e2e680743f 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py @@ -8312,15 +8312,17 @@ async def test_key_does_not_override_explicit_budget_duration(): @patch( "litellm.proxy.management_endpoints.key_management_endpoints.rotate_mcp_server_credentials_master_key" ) -async def test_rotate_master_key_model_data_valid_for_prisma( +async def test_rotate_master_key_reencrypts_model_params_in_place( mock_rotate_mcp, ): """ - Test that _rotate_master_key produces valid data for Prisma create_many(). - - Regression test for: master key rotation fails with Prisma validation error - because created_at/updated_at are None (non-nullable DateTime) and - litellm_params/model_info are JSON strings (create_many expects dicts). + Regression test for: master key rotation wipes every non-credential column + on LiteLLM_ProxyModelTable. Rotation used to rebuild the table via + delete_many + create_many from Deployment objects, which carry no + blocked/created_at/created_by/updated_at/updated_by, so every rotation + reset blocked to False (silently unblocking blocked models) and rewrote the + audit columns. Rotation must instead update only litellm_params (the sole + encrypted column) on each existing row, keyed by model_id. """ from unittest.mock import AsyncMock, MagicMock @@ -8352,6 +8354,7 @@ async def test_rotate_master_key_model_data_valid_for_prisma( mock_tx.litellm_proxymodeltable = MagicMock() mock_tx.litellm_proxymodeltable.delete_many = AsyncMock() mock_tx.litellm_proxymodeltable.create_many = AsyncMock() + mock_tx.litellm_proxymodeltable.update_many = AsyncMock() mock_prisma_client.db.tx = MagicMock( return_value=AsyncMock( __aenter__=AsyncMock(return_value=mock_tx), @@ -8400,36 +8403,33 @@ async def test_rotate_master_key_model_data_valid_for_prisma( new_master_key="sk-new-master-key", ) - # Verify create_many was called - mock_tx.litellm_proxymodeltable.create_many.assert_called_once() + # Rotation must never rewrite whole rows: no delete + recreate + mock_tx.litellm_proxymodeltable.delete_many.assert_not_called() + mock_tx.litellm_proxymodeltable.create_many.assert_not_called() - # Get the data passed to create_many - call_args = mock_tx.litellm_proxymodeltable.create_many.call_args - created_models = call_args.kwargs.get("data") or call_args[1].get("data") + mock_tx.litellm_proxymodeltable.update_many.assert_called_once() + call_args = mock_tx.litellm_proxymodeltable.update_many.call_args - assert len(created_models) == 1 - model_data = created_models[0] + assert call_args.kwargs["where"] == { + "model_id": "model-1" + }, "the re-encrypted params must land on the same row, keyed by model_id" - # Verify timestamps are NOT present (Prisma @default(now()) should apply) - assert ( - "created_at" not in model_data - ), "created_at should be excluded so Prisma @default(now()) applies" - assert ( - "updated_at" not in model_data - ), "updated_at should be excluded so Prisma @default(now()) applies" + update_data = call_args.kwargs["data"] + assert set(update_data.keys()) == {"litellm_params"}, ( + "rotation must touch only the encrypted litellm_params column; writing any " + f"other column wipes it (blocked, audit columns), got {sorted(update_data.keys())}" + ) - # Verify litellm_params and model_info are prisma.Json wrappers, NOT JSON strings import prisma assert isinstance( - model_data["litellm_params"], prisma.Json - ), f"litellm_params should be prisma.Json for create_many(), got {type(model_data['litellm_params'])}" - assert isinstance( - model_data["model_info"], prisma.Json - ), f"model_info should be prisma.Json for create_many(), got {type(model_data['model_info'])}" - - # Verify delete_many was called inside the transaction (before create_many) - mock_tx.litellm_proxymodeltable.delete_many.assert_called_once() + update_data["litellm_params"], prisma.Json + ), f"litellm_params should be prisma.Json for update_many(), got {type(update_data['litellm_params'])}" + reencrypted_params = update_data["litellm_params"].data + assert set(reencrypted_params.keys()) >= {"model", "api_key"} + assert ( + reencrypted_params["api_key"] != "sk-decrypted-key" + ), "api_key must be stored re-encrypted under the new master key, not in plaintext" async def test_default_key_generate_params_duration(monkeypatch): @@ -11033,6 +11033,123 @@ class TestLIT1884KeyUpdateValidation: ) +class TestLIT4891SafePresetKeyTypeTransition: + def _make_existing_key(self, allowed_routes): + row = MagicMock() + row.user_id = "internal-user-123" + row.created_by = "internal-user-123" + row.token = "hashed_token" + row.team_id = None + row.max_budget = None + row.spend = 0.0 + row.organization_id = None + row.project_id = None + row.allowed_routes = allowed_routes + return row + + def _make_auth(self): + return UserAPIKeyAuth( + user_id="internal-user-123", + user_role=LitellmUserRoles.INTERNAL_USER, + ) + + async def _run_update(self, data, existing_key_row): + try: + await _validate_update_key_data( + data=data, + existing_key_row=existing_key_row, + user_api_key_dict=self._make_auth(), + llm_router=None, + premium_user=False, + prisma_client=AsyncMock(), + user_api_key_cache=MagicMock(), + ) + except HTTPException as exc: + return exc + return None + + def _assert_routes_403(self, exc): + assert exc is not None + assert exc.status_code == 403 + assert "Only proxy admins can set" in str(exc.detail) + + @pytest.mark.asyncio + async def test_non_admin_owner_can_clear_safe_preset_to_full_access(self): + assert ( + await self._run_update( + data=UpdateKeyRequest(key="sk-test", allowed_routes=[]), + existing_key_row=self._make_existing_key(allowed_routes=["llm_api_routes"]), + ) + is None + ) + + @pytest.mark.asyncio + async def test_non_admin_owner_can_switch_full_access_to_safe_preset(self): + assert ( + await self._run_update( + data=UpdateKeyRequest(key="sk-test", allowed_routes=["llm_api_routes"]), + existing_key_row=self._make_existing_key(allowed_routes=[]), + ) + is None + ) + + @pytest.mark.asyncio + async def test_non_admin_owner_can_narrow_to_read_only_preset(self): + assert ( + await self._run_update( + data=UpdateKeyRequest(key="sk-test", allowed_routes=["info_routes"]), + existing_key_row=self._make_existing_key(allowed_routes=["llm_api_routes"]), + ) + is None + ) + + @pytest.mark.asyncio + async def test_non_admin_can_resend_read_only_preset_unchanged(self): + assert ( + await self._run_update( + data=UpdateKeyRequest(key="sk-test", allowed_routes=["info_routes"]), + existing_key_row=self._make_existing_key(allowed_routes=["info_routes"]), + ) + is None + ) + + @pytest.mark.asyncio + async def test_non_admin_cannot_widen_read_only_key_to_full_access(self): + self._assert_routes_403( + await self._run_update( + data=UpdateKeyRequest(key="sk-test", allowed_routes=[]), + existing_key_row=self._make_existing_key(allowed_routes=["info_routes"]), + ) + ) + + @pytest.mark.asyncio + async def test_non_admin_cannot_widen_read_only_key_to_llm_api(self): + self._assert_routes_403( + await self._run_update( + data=UpdateKeyRequest(key="sk-test", allowed_routes=["llm_api_routes"]), + existing_key_row=self._make_existing_key(allowed_routes=["info_routes"]), + ) + ) + + @pytest.mark.asyncio + async def test_non_admin_cannot_clear_custom_route_restriction(self): + self._assert_routes_403( + await self._run_update( + data=UpdateKeyRequest(key="sk-test", allowed_routes=[]), + existing_key_row=self._make_existing_key(allowed_routes=["/chat/completions"]), + ) + ) + + @pytest.mark.asyncio + async def test_non_admin_cannot_set_non_preset_routes(self): + self._assert_routes_403( + await self._run_update( + data=UpdateKeyRequest(key="sk-test", allowed_routes=["management_routes"]), + existing_key_row=self._make_existing_key(allowed_routes=["llm_api_routes"]), + ) + ) + + class TestKeyOwnerPrivilegeEscalation: """ Policy: @@ -12007,9 +12124,10 @@ class TestAllowedRoutesCallerPermission: @pytest.mark.asyncio async def test_non_admin_update_key_explicit_empty_allowed_routes_rejected(self): - """`update_key_fn` rejects a non-admin when `allowed_routes` is - present as `[]` in the request body. The value matches the model - default but `model_fields_set` distinguishes the two.""" + """`update_key_fn` rejects a non-admin clearing a custom (non-preset) + route restriction with an explicit `[]` in the request body. The value + matches the model default but `model_fields_set` distinguishes the + two. Clearing from a safe preset is allowed (LIT-4891).""" from litellm.proxy.management_endpoints.key_management_endpoints import ( update_key_fn, ) @@ -12032,7 +12150,7 @@ class TestAllowedRoutesCallerPermission: patch( "litellm.proxy.management_endpoints.key_management_endpoints._get_and_validate_existing_key", new_callable=AsyncMock, - return_value=MagicMock(), + return_value=MagicMock(allowed_routes=["/chat/completions"]), ), ): with pytest.raises(ProxyException) as exc_info: @@ -12047,8 +12165,8 @@ class TestAllowedRoutesCallerPermission: @pytest.mark.asyncio async def test_non_admin_update_key_explicit_null_allowed_routes_rejected(self): - """`update_key_fn` rejects a non-admin when `allowed_routes` is - present as `null` in the request body.""" + """`update_key_fn` rejects a non-admin clearing a custom (non-preset) + route restriction with an explicit `null` in the request body.""" from litellm.proxy.management_endpoints.key_management_endpoints import ( update_key_fn, ) @@ -12071,7 +12189,7 @@ class TestAllowedRoutesCallerPermission: patch( "litellm.proxy.management_endpoints.key_management_endpoints._get_and_validate_existing_key", new_callable=AsyncMock, - return_value=MagicMock(), + return_value=MagicMock(allowed_routes=["/chat/completions"]), ), ): with pytest.raises(ProxyException) as exc_info: @@ -14142,6 +14260,311 @@ async def test_info_key_fn_v2_budget_table_fallback(monkeypatch): mock_prisma_client.db.query_raw.assert_not_awaited() +@pytest.mark.asyncio +async def test_info_key_fn_reports_budget_limits_usage(monkeypatch): + """ + /key/info reports current-window spend per budget window under budget_limits_usage, + keyed by budget_duration and read from the same counter enforcement uses, while + budget_limits itself comes back exactly as stored. + """ + from unittest.mock import AsyncMock, MagicMock + + from litellm.proxy._types import LiteLLM_VerificationToken + from litellm.proxy.management_endpoints.key_management_endpoints import info_key_fn + + test_key_token = "hashed_token_window_test" + budget_limits = [ + { + "reset_at": "2026-08-15T18:00:00+00:00", + "max_budget": 2.0, + "budget_duration": "1h", + } + ] + + mock_prisma_client = AsyncMock() + monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client) + mock_user_api_key_cache = AsyncMock() + monkeypatch.setattr( + "litellm.proxy.proxy_server.user_api_key_cache", mock_user_api_key_cache + ) + mock_get_current_spend = AsyncMock(return_value=0.73) + monkeypatch.setattr( + "litellm.proxy.proxy_server.get_current_spend", mock_get_current_spend + ) + + mock_key_info = MagicMock(spec=LiteLLM_VerificationToken) + mock_key_info.token = test_key_token + mock_key_info.object_permission_id = None + mock_key_info.user_id = "user-w" + mock_key_info.team_id = None + mock_key_info.litellm_budget_table = None + mock_key_info.model_dump.return_value = { + "token": test_key_token, + "budget_limits": [dict(w) for w in budget_limits], + "user_id": "user-w", + "team_id": None, + "object_permission_id": None, + "litellm_budget_table": None, + } + mock_key_info.dict.return_value = mock_key_info.model_dump.return_value + + mock_prisma_client.db.litellm_verificationtoken.find_unique = AsyncMock( + return_value=mock_key_info + ) + + user_api_key_dict = UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, + api_key="sk-test-window-key", + ) + + result = await info_key_fn( + key="sk-test-window-key", + user_api_key_dict=user_api_key_dict, + ) + + assert result["info"]["budget_limits"] == budget_limits + assert result["info"]["budget_limits_usage"] == {"1h": {"current_spend": 0.73}} + + mock_get_current_spend.assert_awaited_once() + call_kwargs = mock_get_current_spend.await_args.kwargs + assert call_kwargs["counter_key"] == f"spend:key:{test_key_token}:window:1h" + assert call_kwargs["max_budget"] == 2.0 + assert call_kwargs["window_entity_type"] == "Key" + assert call_kwargs["window_entity_id"] == test_key_token + assert call_kwargs["window_duration"] == "1h" + assert call_kwargs["window_start"] is not None + + +@pytest.mark.asyncio +async def test_info_key_fn_no_budget_limits_skips_spend_lookup(monkeypatch): + """Keys without budget windows get no budget_limits_usage field and trigger no spend lookup.""" + from unittest.mock import AsyncMock, MagicMock + + from litellm.proxy._types import LiteLLM_VerificationToken + from litellm.proxy.management_endpoints.key_management_endpoints import info_key_fn + + test_key_token = "hashed_token_no_windows" + + mock_prisma_client = AsyncMock() + monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client) + mock_user_api_key_cache = AsyncMock() + monkeypatch.setattr( + "litellm.proxy.proxy_server.user_api_key_cache", mock_user_api_key_cache + ) + mock_get_current_spend = AsyncMock(return_value=0.0) + monkeypatch.setattr( + "litellm.proxy.proxy_server.get_current_spend", mock_get_current_spend + ) + + mock_key_info = MagicMock(spec=LiteLLM_VerificationToken) + mock_key_info.token = test_key_token + mock_key_info.object_permission_id = None + mock_key_info.user_id = "user-nw" + mock_key_info.team_id = None + mock_key_info.litellm_budget_table = None + mock_key_info.model_dump.return_value = { + "token": test_key_token, + "budget_limits": None, + "user_id": "user-nw", + "team_id": None, + "object_permission_id": None, + "litellm_budget_table": None, + } + mock_key_info.dict.return_value = mock_key_info.model_dump.return_value + + mock_prisma_client.db.litellm_verificationtoken.find_unique = AsyncMock( + return_value=mock_key_info + ) + + user_api_key_dict = UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, + api_key="sk-test-no-window-key", + ) + + result = await info_key_fn( + key="sk-test-no-window-key", + user_api_key_dict=user_api_key_dict, + ) + + assert result["info"]["budget_limits"] is None + assert "budget_limits_usage" not in result["info"] + mock_get_current_spend.assert_not_awaited() + + +@pytest.mark.asyncio +async def test_info_key_fn_v2_reports_budget_limits_usage(monkeypatch): + """/v2/key/info reports budget_limits_usage per window and leaves budget_limits as stored.""" + from unittest.mock import AsyncMock, MagicMock + + from litellm.proxy._types import KeyRequest, LiteLLM_VerificationToken + from litellm.proxy.management_endpoints.key_management_endpoints import ( + info_key_fn_v2, + ) + + test_key_token = "hashed_token_v2_window_test" + budget_limits = [ + { + "reset_at": "2026-08-15T18:00:00+00:00", + "max_budget": 2.0, + "budget_duration": "1h", + }, + { + "reset_at": "2026-08-16T00:00:00+00:00", + "max_budget": 20.0, + "budget_duration": "1d", + }, + ] + + mock_prisma_client = AsyncMock() + monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client) + mock_user_api_key_cache = AsyncMock() + monkeypatch.setattr( + "litellm.proxy.proxy_server.user_api_key_cache", mock_user_api_key_cache + ) + mock_get_current_spend = AsyncMock(return_value=1.25) + monkeypatch.setattr( + "litellm.proxy.proxy_server.get_current_spend", mock_get_current_spend + ) + + mock_key = MagicMock(spec=LiteLLM_VerificationToken) + mock_key.token = test_key_token + mock_key.user_id = "user-v2-w" + mock_key.team_id = None + mock_key.model_dump.return_value = { + "token": test_key_token, + "budget_limits": [dict(w) for w in budget_limits], + "user_id": "user-v2-w", + "team_id": None, + "litellm_budget_table": None, + } + mock_key.dict.return_value = mock_key.model_dump.return_value + + mock_prisma_client.get_data = AsyncMock(return_value=[mock_key]) + + user_api_key_dict = UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, + api_key="sk-admin-v2-w", + ) + + result = await info_key_fn_v2( + data=KeyRequest(keys=[test_key_token]), + user_api_key_dict=user_api_key_dict, + ) + + assert len(result["info"]) == 1 + assert result["info"][0]["budget_limits"] == budget_limits + assert result["info"][0]["budget_limits_usage"] == { + "1h": {"current_spend": 1.25}, + "1d": {"current_spend": 1.25}, + } + assert mock_get_current_spend.await_count == 2 + counter_keys = { + call.kwargs["counter_key"] for call in mock_get_current_spend.await_args_list + } + assert counter_keys == { + f"spend:key:{test_key_token}:window:1h", + f"spend:key:{test_key_token}:window:1d", + } + assert { + call.kwargs["window_duration"] for call in mock_get_current_spend.await_args_list + } == {"1h", "1d"} + + +@pytest.mark.asyncio +async def test_build_budget_limits_usage_json_string_input(monkeypatch): + """budget_limits stored as a JSON string is parsed and reported per window.""" + import json as json_module + from unittest.mock import AsyncMock + + from litellm.proxy.management_endpoints.key_management_endpoints import ( + _build_budget_limits_usage, + ) + + mock_get_current_spend = AsyncMock(return_value=0.5) + monkeypatch.setattr( + "litellm.proxy.proxy_server.get_current_spend", mock_get_current_spend + ) + + raw = json_module.dumps( + [{"budget_duration": "1h", "max_budget": 2.0, "reset_at": None}] + ) + result = await _build_budget_limits_usage(budget_limits=raw, api_key_hash="hash-1") + + assert result == {"1h": {"current_spend": 0.5}} + mock_get_current_spend.assert_awaited_once() + + +@pytest.mark.asyncio +async def test_build_budget_limits_usage_empty_windows_returns_none(monkeypatch): + """A key with no windows (None, [], or "[]") returns None so the field is left off; no spend lookup runs.""" + from unittest.mock import AsyncMock + + from litellm.proxy.management_endpoints.key_management_endpoints import ( + _build_budget_limits_usage, + ) + + mock_get_current_spend = AsyncMock(return_value=0.0) + monkeypatch.setattr( + "litellm.proxy.proxy_server.get_current_spend", mock_get_current_spend + ) + + for stored in (None, [], "[]"): + assert await _build_budget_limits_usage(budget_limits=stored, api_key_hash="hash-1") is None + mock_get_current_spend.assert_not_awaited() + + +@pytest.mark.asyncio +async def test_build_budget_limits_usage_window_without_max_budget(monkeypatch): + """A window with only budget_duration still reports current_spend, read without a budget ceiling.""" + from unittest.mock import AsyncMock + + from litellm.proxy.management_endpoints.key_management_endpoints import ( + _build_budget_limits_usage, + ) + + mock_get_current_spend = AsyncMock(return_value=0.75) + monkeypatch.setattr( + "litellm.proxy.proxy_server.get_current_spend", mock_get_current_spend + ) + + result = await _build_budget_limits_usage( + budget_limits=[{"budget_duration": "2d"}], api_key_hash="hash-no-max" + ) + + assert result == {"2d": {"current_spend": 0.75}} + call_kwargs = mock_get_current_spend.await_args.kwargs + assert call_kwargs["counter_key"] == "spend:key:hash-no-max:window:2d" + assert call_kwargs["window_duration"] == "2d" + assert call_kwargs["max_budget"] is None + + +@pytest.mark.asyncio +async def test_build_budget_limits_usage_pydantic_windows(monkeypatch): + """BudgetLimitEntry windows (the shape UserAPIKeyAuth carries) are dumped to dicts and reported.""" + from unittest.mock import AsyncMock + + from litellm.models.team import BudgetLimitEntry + from litellm.proxy.management_endpoints.key_management_endpoints import ( + _build_budget_limits_usage, + ) + + mock_get_current_spend = AsyncMock(return_value=1.0) + monkeypatch.setattr( + "litellm.proxy.proxy_server.get_current_spend", mock_get_current_spend + ) + + result = await _build_budget_limits_usage( + budget_limits=[BudgetLimitEntry(budget_duration="7d", max_budget=10.0)], + api_key_hash="hash-2", + ) + + assert result == {"7d": {"current_spend": 1.0}} + call_kwargs = mock_get_current_spend.await_args.kwargs + assert call_kwargs["counter_key"] == "spend:key:hash-2:window:7d" + assert call_kwargs["window_duration"] == "7d" + assert call_kwargs["max_budget"] == 10.0 + + @pytest.mark.asyncio async def test_info_key_fn_reads_the_configured_budget_model_key(monkeypatch): """/key/info reads the one counter enforcement reads: the configured budget model. @@ -17066,3 +17489,49 @@ async def test_check_project_key_limits_still_rejects_real_model_outside_project assert exc_info.value.status_code == 400 assert "Model 'gpt-5.4-mini' not in project's allowed models" in exc_info.value.detail["error"] + + +def test_generate_key_request_blank_team_id_is_personal(): + """The UI Team-field clear submits team_id=""; it must count as no team (LIT-3925).""" + from litellm.proxy._types import RegenerateKeyRequest + from litellm.proxy.management_endpoints.key_management_endpoints import ( + _is_team_key, + ) + + cleared = GenerateKeyRequest(team_id="") + assert cleared.team_id is None + assert _is_team_key(data=cleared) is False + assert RegenerateKeyRequest(team_id="").team_id is None + assert GenerateKeyRequest(team_id="team-1").team_id == "team-1" + + +def test_key_generation_check_blank_team_id_uses_personal_permissions(monkeypatch): + """key_generation_check with team_id="" must take the personal-key path instead + of failing the team lookup with "Unable to find team object" (LIT-3925).""" + from litellm.proxy._types import KeyManagementRoutes + from litellm.proxy.management_endpoints.key_management_endpoints import ( + key_generation_check, + ) + + monkeypatch.setattr( + litellm, + "key_generation_settings", + { + "team_key_generation": {"allowed_team_member_roles": ["admin"]}, + "personal_key_generation": {"allowed_user_roles": ["proxy_admin", "internal_user"]}, + }, + ) + + assert ( + key_generation_check( + team_table=None, + user_api_key_dict=UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, + api_key="sk-alice", + user_id="alice", + ), + data=GenerateKeyRequest(key_alias="personal", team_id=""), + route=KeyManagementRoutes.KEY_GENERATE, + ) + is True + ) diff --git a/tests/test_litellm/proxy/management_endpoints/test_mcp_connector_import.py b/tests/test_litellm/proxy/management_endpoints/test_mcp_connector_import.py new file mode 100644 index 00000000000..9b1a0fb4f98 --- /dev/null +++ b/tests/test_litellm/proxy/management_endpoints/test_mcp_connector_import.py @@ -0,0 +1,208 @@ +import pytest + +from litellm.proxy.management_endpoints.mcp_connector_import import ( + ConnectorConversionError, + ConvertedConnector, + MCPConnectorImportRequest, + convert_connector_entries, + sanitize_connector_name, +) +from litellm.types.mcp import MCPAuth, MCPTransport + + +def _single(payload: dict) -> ConvertedConnector | ConnectorConversionError: + results = convert_connector_entries(MCPConnectorImportRequest.model_validate(payload)) + assert len(results) == 1 + return results[0] + + +class TestSanitizeConnectorName: + @pytest.mark.parametrize( + "raw,expected", + [ + ("my-server", "my_server"), + (" spaced name ", "spaced_name"), + ("already_ok", "already_ok"), + ("a.b.c", "a_b_c"), + ("---", ""), + ], + ) + def test_sanitizes_to_mcp_safe_names(self, raw, expected): + assert sanitize_connector_name(raw) == expected + + +class TestConvertMcpServersMapping: + def test_url_connector_with_authorization_token(self): + result = _single( + { + "mcpServers": { + "github-mcp": { + "url": "https://api.example.com/mcp", + "authorization_token": "secret-token", + "headers": {"X-Env": "prod"}, + "description": "GitHub connector", + } + } + } + ) + assert isinstance(result, ConvertedConnector) + assert result.request.server_name == "github_mcp" + assert result.request.alias == "github_mcp" + assert result.request.transport == MCPTransport.http + assert result.request.url == "https://api.example.com/mcp" + assert result.request.auth_type == MCPAuth.bearer_token + assert result.request.credentials == {"auth_value": "secret-token"} + assert result.request.static_headers == {"X-Env": "prod"} + assert result.request.description == "GitHub connector" + + def test_authorization_header_becomes_bearer_credentials(self): + result = _single( + { + "mcpServers": { + "srv": { + "url": "https://x.example/mcp", + "headers": {"Authorization": "Bearer header-token", "X-Env": "prod"}, + } + } + } + ) + assert isinstance(result, ConvertedConnector) + assert result.request.auth_type == MCPAuth.bearer_token + assert result.request.credentials == {"auth_value": "header-token"} + assert result.request.static_headers == {"X-Env": "prod"} + + def test_authorization_header_without_bearer_prefix_is_sent_verbatim(self): + result = _single( + {"mcpServers": {"srv": {"url": "https://x.example/mcp", "headers": {"authorization": "raw-token"}}}} + ) + assert isinstance(result, ConvertedConnector) + assert result.request.auth_type == MCPAuth.authorization + assert result.request.credentials == {"auth_value": "raw-token"} + assert result.request.static_headers is None + + def test_basic_authorization_header_is_sent_verbatim(self): + result = _single( + {"mcpServers": {"srv": {"url": "https://x.example/mcp", "headers": {"Authorization": "Basic dXNlcjpwdw=="}}}} + ) + assert isinstance(result, ConvertedConnector) + assert result.request.auth_type == MCPAuth.authorization + assert result.request.credentials == {"auth_value": "Basic dXNlcjpwdw=="} + assert result.request.static_headers is None + + def test_authorization_token_wins_over_authorization_header(self): + result = _single( + { + "mcpServers": { + "srv": { + "url": "https://x.example/mcp", + "authorization_token": "explicit-token", + "headers": {"Authorization": "Bearer header-token"}, + } + } + } + ) + assert isinstance(result, ConvertedConnector) + assert result.request.auth_type == MCPAuth.bearer_token + assert result.request.credentials == {"auth_value": "explicit-token"} + assert result.request.static_headers is None + + def test_camel_case_authorization_token_alias(self): + result = _single( + {"mcpServers": {"srv": {"url": "https://x.example/mcp", "authorizationToken": "tok"}}} + ) + assert isinstance(result, ConvertedConnector) + assert result.request.credentials == {"auth_value": "tok"} + + def test_url_connector_without_token_uses_no_auth(self): + result = _single({"mcpServers": {"open": {"url": "https://open.example/mcp"}}}) + assert isinstance(result, ConvertedConnector) + assert result.request.auth_type == MCPAuth.none + assert result.request.credentials is None + + def test_sse_type_maps_to_sse_transport(self): + result = _single({"mcpServers": {"legacy": {"type": "sse", "url": "https://sse.example/mcp"}}}) + assert isinstance(result, ConvertedConnector) + assert result.request.transport == MCPTransport.sse + + def test_stdio_connector(self): + result = _single( + { + "mcpServers": { + "local": { + "command": "npx", + "args": ["-y", "@example/mcp-server"], + "env": {"API_KEY": "value"}, + } + } + } + ) + assert isinstance(result, ConvertedConnector) + assert result.request.transport == MCPTransport.stdio + assert result.request.command == "npx" + assert result.request.args == ["-y", "@example/mcp-server"] + assert result.request.env == {"API_KEY": "value"} + + def test_disallowed_stdio_command_returns_error(self): + result = _single({"mcpServers": {"evil": {"command": "rm", "args": ["-rf", "/"]}}}) + assert isinstance(result, ConnectorConversionError) + assert "not in the allowed commands list" in result.error + + def test_unsupported_type_returns_error(self): + result = _single({"mcpServers": {"ws": {"type": "websocket", "url": "wss://x.example"}}}) + assert isinstance(result, ConnectorConversionError) + assert "Unsupported connector type" in result.error + + def test_missing_url_and_command_returns_error(self): + result = _single({"mcpServers": {"empty": {}}}) + assert isinstance(result, ConnectorConversionError) + assert "either a url or a command" in result.error + + def test_url_and_command_together_returns_error(self): + result = _single({"mcpServers": {"both": {"url": "https://x.example/mcp", "command": "npx"}}}) + assert isinstance(result, ConnectorConversionError) + assert "both a url and a command" in result.error + + def test_name_empty_after_sanitization_returns_error(self): + result = _single({"mcpServers": {"---": {"url": "https://x.example/mcp"}}}) + assert isinstance(result, ConnectorConversionError) + assert "empty after sanitization" in result.error + + +class TestConvertMcpServersList: + def test_anthropic_messages_api_list_shape(self): + result = _single( + { + "mcp_servers": [ + { + "type": "url", + "url": "https://mcp.example.com/sse", + "name": "deepwiki", + "authorization_token": "tok", + } + ] + } + ) + assert isinstance(result, ConvertedConnector) + assert result.request.server_name == "deepwiki" + assert result.request.transport == MCPTransport.http + assert result.request.credentials == {"auth_value": "tok"} + + def test_list_entry_without_name_returns_error(self): + result = _single({"mcp_servers": [{"type": "url", "url": "https://x.example/mcp"}]}) + assert isinstance(result, ConnectorConversionError) + assert "must have a name" in result.error + + def test_partial_conversion_preserves_per_entry_results(self): + results = convert_connector_entries( + MCPConnectorImportRequest.model_validate( + { + "mcpServers": { + "good": {"url": "https://good.example/mcp"}, + "bad": {"type": "websocket", "url": "wss://bad.example"}, + } + } + ) + ) + assert len(results) == 2 + assert isinstance(results[0], ConvertedConnector) + assert isinstance(results[1], ConnectorConversionError) diff --git a/tests/test_litellm/proxy/management_endpoints/test_mcp_management_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_mcp_management_endpoints.py index ceb44de5576..adab3538b58 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_mcp_management_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_mcp_management_endpoints.py @@ -1960,6 +1960,20 @@ class TestTemporaryMCPSessionEndpoints: where = find_rows.await_args.args[1] assert where == {"OR": [{"approval_status": None}, {"approval_status": {"not": "draft"}}]} + @pytest.mark.asyncio + async def test_get_all_mcp_servers_propagates_read_failures(self): + """Regression: a swallowed read failure returned [] and silently disabled the bulk-import + dedupe, so a flaky DB read turned a re-import into duplicate servers.""" + from litellm.proxy._experimental.mcp_server.db import get_all_mcp_servers + + find_rows = AsyncMock(side_effect=RuntimeError("db down")) + with patch( # test-quality-ok: the helper takes its row reader from module scope, matching the suite's pattern + "litellm.proxy._experimental.mcp_server.db._db_find_mcp_server_rows", + find_rows, + ): + with pytest.raises(RuntimeError, match="db down"): + await get_all_mcp_servers(MagicMock()) + @pytest.mark.asyncio async def test_resolve_session_server_id_refuses_an_unknown_caller_supplied_id(self): """Regression: two concurrent sessions must never land on one id. @@ -6786,3 +6800,174 @@ class TestConnectedAppViewAnnotation: assert all(server.connected_app_reachable is None for server in result) reload_mock.assert_not_awaited() + + +class TestImportMCPServers: + """Bulk connector import must be admin-only and report per-entry outcomes.""" + + @staticmethod + def _import_patches(existing_servers, create_mock, mock_manager): + return ( + patch( # test-quality-ok: endpoint takes collaborators from module scope, matching the suite's pattern + "litellm.proxy.management_endpoints.mcp_management_endpoints.get_prisma_client_or_throw", + return_value=MagicMock(), + ), + patch( # test-quality-ok: endpoint takes collaborators from module scope, matching the suite's pattern + "litellm.proxy.management_endpoints.mcp_management_endpoints.get_all_mcp_servers", + AsyncMock(return_value=existing_servers), + ), + patch( # test-quality-ok: endpoint takes collaborators from module scope, matching the suite's pattern + "litellm.proxy.management_endpoints.mcp_management_endpoints.create_mcp_server", + create_mock, + ), + patch( # test-quality-ok: endpoint takes collaborators from module scope, matching the suite's pattern + "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager", + mock_manager, + ), + ) + + @pytest.mark.asyncio + async def test_non_admin_is_rejected(self): + from litellm.proxy.management_endpoints.mcp_management_endpoints import ( + MCPConnectorImportRequest, + import_mcp_servers, + ) + + payload = MCPConnectorImportRequest.model_validate( + {"mcpServers": {"srv": {"url": "https://x.example/mcp"}}} + ) + caller = generate_mock_user_api_key_auth(user_role=LitellmUserRoles.INTERNAL_USER) + + with patch( # test-quality-ok: endpoint takes collaborators from module scope, matching the suite's pattern + "litellm.proxy.management_endpoints.mcp_management_endpoints.get_prisma_client_or_throw", + return_value=MagicMock(), + ): + with pytest.raises(HTTPException) as exc_info: + await import_mcp_servers(payload=payload, user_api_key_dict=caller) + + assert exc_info.value.status_code == 403 + + @pytest.mark.asyncio + async def test_import_reports_imported_skipped_and_errors(self): + from litellm.proxy.management_endpoints.mcp_management_endpoints import ( + MCPConnectorImportRequest, + import_mcp_servers, + ) + + payload = MCPConnectorImportRequest.model_validate( + { + "mcpServers": { + "new-server": {"url": "https://new.example/mcp", "authorization_token": "tok"}, + "existing": {"url": "https://existing.example/mcp"}, + "broken": {"type": "websocket", "url": "wss://x.example"}, + } + } + ) + admin = generate_mock_user_api_key_auth(user_role=LitellmUserRoles.PROXY_ADMIN, user_id="admin-user") + existing = generate_mock_mcp_server_db_record(server_id="existing-1", alias="existing") + created = generate_mock_mcp_server_db_record(server_id="created-1", alias="new_server") + create_mock = AsyncMock(return_value=created) + mock_manager = MagicMock() + mock_manager.reload_servers_from_database = AsyncMock() + mock_manager.add_server = AsyncMock() + + with ExitStack() as stack: + for p in self._import_patches([existing], create_mock, mock_manager): + stack.enter_context(p) + result = await import_mcp_servers(payload=payload, user_api_key_dict=admin) + + assert [entry.name for entry in result.imported] == ["new-server"] + assert result.imported[0].server_id == "created-1" + assert [entry.name for entry in result.skipped] == ["existing"] + assert "already exists" in result.skipped[0].reason + assert [entry.name for entry in result.errors] == ["broken"] + create_mock.assert_awaited_once() + sent_request = create_mock.await_args[0][1] + assert sent_request.credentials == {"auth_value": "tok"} + mock_manager.add_server.assert_awaited_once_with(created) + mock_manager.reload_servers_from_database.assert_awaited_once() + + @pytest.mark.asyncio + async def test_duplicate_names_within_payload_are_skipped(self): + from litellm.proxy.management_endpoints.mcp_management_endpoints import ( + MCPConnectorImportRequest, + import_mcp_servers, + ) + + payload = MCPConnectorImportRequest.model_validate( + { + "mcp_servers": [ + {"type": "url", "url": "https://a.example/mcp", "name": "dup srv"}, + {"type": "url", "url": "https://b.example/mcp", "name": "dup-srv"}, + ] + } + ) + admin = generate_mock_user_api_key_auth(user_role=LitellmUserRoles.PROXY_ADMIN, user_id="admin-user") + created = generate_mock_mcp_server_db_record(server_id="created-1", alias="dup_srv") + create_mock = AsyncMock(return_value=created) + mock_manager = MagicMock() + mock_manager.reload_servers_from_database = AsyncMock() + mock_manager.add_server = AsyncMock() + + with ExitStack() as stack: + for p in self._import_patches([], create_mock, mock_manager): + stack.enter_context(p) + result = await import_mcp_servers(payload=payload, user_api_key_dict=admin) + + assert len(result.imported) == 1 + assert len(result.skipped) == 1 + assert "Duplicate connector name" in result.skipped[0].reason + create_mock.assert_awaited_once() + mock_manager.add_server.assert_awaited_once_with(created) + + @pytest.mark.asyncio + async def test_no_imports_skips_registry_refresh(self): + from litellm.proxy.management_endpoints.mcp_management_endpoints import ( + MCPConnectorImportRequest, + import_mcp_servers, + ) + + payload = MCPConnectorImportRequest.model_validate( + {"mcpServers": {"existing": {"url": "https://existing.example/mcp"}}} + ) + admin = generate_mock_user_api_key_auth(user_role=LitellmUserRoles.PROXY_ADMIN, user_id="admin-user") + existing = generate_mock_mcp_server_db_record(server_id="existing-1", alias="existing") + create_mock = AsyncMock() + mock_manager = MagicMock() + mock_manager.reload_servers_from_database = AsyncMock() + mock_manager.add_server = AsyncMock() + + with ExitStack() as stack: + for p in self._import_patches([existing], create_mock, mock_manager): + stack.enter_context(p) + result = await import_mcp_servers(payload=payload, user_api_key_dict=admin) + + assert result.imported == () + create_mock.assert_not_awaited() + mock_manager.add_server.assert_not_awaited() + mock_manager.reload_servers_from_database.assert_not_awaited() + + @pytest.mark.asyncio + async def test_registration_failure_keeps_the_import_result(self): + from litellm.proxy.management_endpoints.mcp_management_endpoints import ( + MCPConnectorImportRequest, + import_mcp_servers, + ) + + payload = MCPConnectorImportRequest.model_validate( + {"mcpServers": {"new-server": {"url": "https://new.example/mcp"}}} + ) + admin = generate_mock_user_api_key_auth(user_role=LitellmUserRoles.PROXY_ADMIN, user_id="admin-user") + created = generate_mock_mcp_server_db_record(server_id="created-1", alias="new_server") + create_mock = AsyncMock(return_value=created) + mock_manager = MagicMock() + mock_manager.reload_servers_from_database = AsyncMock() + mock_manager.add_server = AsyncMock(side_effect=RuntimeError("registration boom")) + + with ExitStack() as stack: + for p in self._import_patches([], create_mock, mock_manager): + stack.enter_context(p) + result = await import_mcp_servers(payload=payload, user_api_key_dict=admin) + + assert [entry.name for entry in result.imported] == ["new-server"] + mock_manager.reload_servers_from_database.assert_awaited_once() diff --git a/tests/test_litellm/proxy/management_endpoints/test_model_management_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_model_management_endpoints.py index dc9fede1f65..4661cc17dbc 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_model_management_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_model_management_endpoints.py @@ -18,6 +18,7 @@ from litellm.proxy._types import ( ReconcileOutcome, UserAPIKeyAuth, ) +from litellm.proxy.common_utils.encrypt_decrypt_utils import encrypt_value_helper from litellm.proxy.management_endpoints.model_management_endpoints import ( ModelManagementAuthChecks, _get_team_deployments, @@ -263,6 +264,131 @@ class TestModelManagementAuthChecks: ) assert "403" in str(exc_info.value) + def test_can_user_attach_credential_admin_success(self): + result = ModelManagementAuthChecks.can_user_attach_credential( + litellm_params=LiteLLM_Params(model="test_model", litellm_credential_name="shared-credential"), + user_api_key_dict=self.admin_user, + ) + assert result is True + + def test_can_user_attach_credential_without_credential_allows_any_role(self): + result = ModelManagementAuthChecks.can_user_attach_credential( + litellm_params=LiteLLM_Params(model="test_model"), + user_api_key_dict=self.team_admin_user, + ) + assert result is True + + def test_can_user_attach_credential_team_admin_fails(self): + with pytest.raises(Exception, match="Only a proxy admin can attach a stored credential") as exc_info: + ModelManagementAuthChecks.can_user_attach_credential( + litellm_params=LiteLLM_Params(model="test_model", litellm_credential_name="shared-credential"), + user_api_key_dict=self.team_admin_user, + ) + assert exc_info.value.code == "403" + + def test_can_user_attach_credential_unchanged_existing_allows_any_role(self): + result = ModelManagementAuthChecks.can_user_attach_credential( + litellm_params=LiteLLM_Params(model="test_model", litellm_credential_name="shared-credential"), + user_api_key_dict=self.team_admin_user, + existing_litellm_params=LiteLLM_Params(model="test_model", litellm_credential_name="shared-credential"), + ) + assert result is True + + def test_can_user_attach_credential_unchanged_encrypted_existing_allows_any_role(self, monkeypatch): + monkeypatch.setenv("LITELLM_SALT_KEY", "sk-1234") + encrypted_name = encrypt_value_helper(value="shared-credential") + assert encrypted_name != "shared-credential" + result = ModelManagementAuthChecks.can_user_attach_credential( + litellm_params=LiteLLM_Params(model="test_model", litellm_credential_name="shared-credential"), + user_api_key_dict=self.team_admin_user, + existing_litellm_params=LiteLLM_Params(model="test_model", litellm_credential_name=encrypted_name), + ) + assert result is True + + @pytest.mark.asyncio + async def test_add_new_model_rejects_credential_attach_for_non_admin(self): + from litellm.proxy._types import ProxyException + from litellm.proxy.management_endpoints.model_management_endpoints import ( + add_new_model, + ) + + mock_prisma = MagicMock() + with ( + patch("litellm.proxy.proxy_server.prisma_client", mock_prisma), # test-quality-ok: endpoint reads proxy server globals with no injection seam + patch("litellm.proxy.proxy_server.store_model_in_db", True), # test-quality-ok: endpoint reads proxy server globals with no injection seam + patch("litellm.proxy.proxy_server.premium_user", True), # test-quality-ok: endpoint reads proxy server globals with no injection seam + patch( # test-quality-ok: prior auth check needs a live DB; only the credential check is under test + "litellm.proxy.management_endpoints.model_management_endpoints.ModelManagementAuthChecks.can_user_make_model_call", + new=AsyncMock(return_value=None), + ), + ): + with pytest.raises(ProxyException) as exc_info: + await add_new_model( + model_params=Deployment( + model_name="credential-model", + litellm_params=LiteLLM_Params( + model="openai/gpt-4o", litellm_credential_name="shared-credential" + ), + model_info={"id": "credential-create-test"}, + ), + user_api_key_dict=self.team_admin_user, + ) + assert exc_info.value.code == "403" + mock_prisma.db.litellm_proxymodeltable.create.assert_not_called() + + @pytest.mark.asyncio + async def test_patch_model_rejects_credential_attach_for_non_admin(self): + from litellm.proxy._types import ProxyException + from litellm.proxy.management_endpoints.model_management_endpoints import ( + patch_model, + ) + from litellm.types.router import updateLiteLLMParams + + model_id = "credential-patch-test" + db_model = Deployment( + model_name="credential-model", + litellm_params=LiteLLM_Params(model="openai/gpt-4o"), + model_info={"id": model_id}, + ) + with ( + patch("litellm.proxy.proxy_server.prisma_client", MagicMock()), # test-quality-ok: endpoint reads proxy server globals with no injection seam + patch("litellm.proxy.proxy_server.llm_router", MagicMock()), # test-quality-ok: endpoint reads proxy server globals with no injection seam + patch("litellm.proxy.proxy_server.store_model_in_db", True), # test-quality-ok: endpoint reads proxy server globals with no injection seam + patch("litellm.proxy.proxy_server.premium_user", True), # test-quality-ok: endpoint reads proxy server globals with no injection seam + patch( # test-quality-ok: stubs the DB row fetch; only the credential check is under test + "litellm.proxy.management_endpoints.model_management_endpoints.get_db_model", + new=AsyncMock(return_value=db_model), + ), + patch( # test-quality-ok: prior auth check needs a live DB; only the credential check is under test + "litellm.proxy.management_endpoints.model_management_endpoints.ModelManagementAuthChecks.can_user_make_model_call", + new=AsyncMock(return_value=None), + ), + patch( # test-quality-ok: asserts the DB write is never reached on rejection + "litellm.proxy.management_endpoints.model_management_endpoints._update_team_model_in_db", + new=AsyncMock(), + ) as mock_update, + ): + with pytest.raises(ProxyException) as exc_info: + await patch_model( + model_id=model_id, + patch_data=updateDeployment( + litellm_params=updateLiteLLMParams( + model="openai/gpt-4o", litellm_credential_name="shared-credential" + ) + ), + user_api_key_dict=self.team_admin_user, + ) + assert exc_info.value.code == "403" + mock_update.assert_not_awaited() + + def test_can_user_attach_credential_internal_user_fails(self): + with pytest.raises(Exception, match="Only a proxy admin can attach a stored credential") as exc_info: + ModelManagementAuthChecks.can_user_attach_credential( + litellm_params=LiteLLM_Params(model="test_model", litellm_credential_name="shared-credential"), + user_api_key_dict=self.normal_user, + ) + assert exc_info.value.code == "403" + class MockModelTable: def __init__(self, model_aliases: Dict[str, str], include: Optional[dict] = None): @@ -4466,3 +4592,65 @@ class TestEnforceRpmTpmOnModelAdd: _raise_if_rate_limits_required_but_missing(litellm_params=params, enforced=True) assert expected_missing in str(exc_info.value.message) assert exc_info.value.code == "400" + + +class TestBlockModelResponseSerialization: + @pytest.mark.parametrize( + ("route", "blocked"), [("/model/block", True), ("/model/unblock", False)] + ) + def test_block_routes_serialize_prisma_row_to_200(self, route, blocked): + from datetime import datetime, timezone + + from prisma import models as prisma_models + + import litellm.proxy.proxy_server as ps + from litellm.proxy.proxy_server import app + + written_at = datetime(2026, 8, 29, tzinfo=timezone.utc) + row_fields = { + "model_id": "m-block-1", + "model_name": "gpt-4o-mini", + "litellm_params": json.dumps({"model": "openai/gpt-4o-mini", "api_key": "encrypted-value"}), + "model_info": json.dumps({"id": "m-block-1"}), + "created_at": written_at, + "created_by": "admin", + "updated_at": written_at, + "updated_by": "admin", + } + existing_row = prisma_models.LiteLLM_ProxyModelTable(blocked=not blocked, **row_fields) + updated_row = prisma_models.LiteLLM_ProxyModelTable(blocked=blocked, **row_fields) + + mock_prisma = MagicMock() + mock_prisma.db.litellm_proxymodeltable.find_unique = AsyncMock(return_value=existing_row) + mock_prisma.db.litellm_proxymodeltable.update = AsyncMock(return_value=updated_row) + + admin = UserAPIKeyAuth(user_id="admin", user_role=LitellmUserRoles.PROXY_ADMIN) + app.dependency_overrides[ps.user_api_key_auth] = lambda: admin + try: + with ( + patch("litellm.proxy.proxy_server.prisma_client", mock_prisma), # test-quality-ok: proxy_server module global is the endpoint's only injection point + patch("litellm.proxy.proxy_server.store_model_in_db", True), # test-quality-ok: proxy_server module global is the endpoint's only injection point + patch( # test-quality-ok: proxy_server module global is the endpoint's only injection point + "litellm.proxy.proxy_server.llm_router", + MagicMock(**{"get_model_ids.return_value": ["m-block-1"]}), + ), + patch("litellm.proxy.proxy_server.redis_usage_cache", None), # test-quality-ok: proxy_server module global is the endpoint's only injection point + patch( # test-quality-ok: stubs the cache write so the test observes only response serialization + "litellm.proxy.management_endpoints.model_management_endpoints.clear_cache", + new=AsyncMock(return_value=ReconcileOutcome(still_desired=None, live_after=None)), + ), + patch( # test-quality-ok: audit logging is a background side effect outside this test's contract + "litellm.proxy.management_endpoints.model_management_endpoints.create_object_audit_log", + new=AsyncMock(return_value=None), + ), + ): + client = TestClient(app) + response = client.post(route, json={"model_id": "m-block-1"}) + finally: + app.dependency_overrides.pop(ps.user_api_key_auth, None) + + assert response.status_code == 200, response.text + body = response.json() + assert body["model_id"] == "m-block-1" + assert body["blocked"] is blocked + assert body["litellm_params"] == {"model": "openai/gpt-4o-mini", "api_key": "encrypted-value"} diff --git a/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py index ffa6bc601e9..30b2ab86b9a 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py @@ -3741,6 +3741,93 @@ async def test_list_team_v2_with_status_deleted(): assert len(result["teams"]) == 2 +@pytest.mark.asyncio +async def test_list_team_v2_includes_litellm_model_table(): + """ + Regression test for GH #26312: GET /v2/team/list must eagerly load the + litellm_model_table relation for active teams, same as /team/info and + /team/list, or a team's model_aliases always read back as null from this + endpoint. Deleted teams are excluded: LiteLLM_DeletedTeamTable has no such + relation in the Prisma schema, so requesting it there raises + UnknownRelationalFieldError against a real database. + + The fake find_many below only attaches litellm_model_table when its own + `include` kwarg actually asks for the relation, so the assertions below + are on what the caller gets back, not on how find_many was called. + """ + from unittest.mock import AsyncMock, Mock, patch + + from fastapi import Request + + from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth + from litellm.proxy.management_endpoints.team_endpoints import list_team_v2 + + mock_request = Mock(spec=Request) + mock_user_api_key_dict_admin = UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, + user_id="admin_user_123", + ) + + def _team_row(team_id: str, include) -> Mock: + model_table = ( + { + "id": 1, + "model_aliases": {"my-fast-model": "fake-model"}, + "created_by": "u", + "updated_by": "u", + "team": None, + } + if (include or {}).get("litellm_model_table") + else None + ) + return Mock( + team_id=team_id, + model_dump=lambda: { + "team_id": team_id, + "team_alias": "t", + "litellm_model_table": model_table, + }, + ) + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma_client: # test-quality-ok: this file's DB-mock convention + mock_db = Mock() + mock_prisma_client.db = mock_db + + mock_db.litellm_teamtable.find_many = AsyncMock( + side_effect=lambda **kw: [_team_row("team_1", kw.get("include"))] + ) + mock_db.litellm_teamtable.count = AsyncMock(return_value=1) + mock_db.litellm_verificationtoken.group_by = AsyncMock(return_value=[]) + + result = await list_team_v2( + http_request=mock_request, + user_id=None, + user_api_key_dict=mock_user_api_key_dict_admin, + page=1, + page_size=10, + status=None, + ) + + assert result["teams"][0].litellm_model_table is not None + assert result["teams"][0].litellm_model_table.model_aliases == {"my-fast-model": "fake-model"} + + mock_db.litellm_deletedteamtable.find_many = AsyncMock( + side_effect=lambda **kw: [_team_row("team_2", kw.get("include"))] + ) + mock_db.litellm_deletedteamtable.count = AsyncMock(return_value=1) + + await list_team_v2( + http_request=mock_request, + user_id=None, + user_api_key_dict=mock_user_api_key_dict_admin, + page=1, + page_size=10, + status="deleted", + ) + + assert "include" not in mock_db.litellm_deletedteamtable.find_many.call_args.kwargs + + @pytest.mark.asyncio async def test_list_team_v2_org_admin_sees_org_teams(): """ diff --git a/tests/test_litellm/proxy/management_helpers/test_object_permission_utils.py b/tests/test_litellm/proxy/management_helpers/test_object_permission_utils.py index 5ef83344c1a..f2b6b799271 100644 --- a/tests/test_litellm/proxy/management_helpers/test_object_permission_utils.py +++ b/tests/test_litellm/proxy/management_helpers/test_object_permission_utils.py @@ -1,11 +1,9 @@ import json +from unittest.mock import AsyncMock, MagicMock, patch import pytest from fastapi import HTTPException - -from unittest.mock import AsyncMock, MagicMock, patch - from litellm.proxy._types import ( LiteLLM_ObjectPermissionBase, LiteLLM_ObjectPermissionTable, @@ -13,10 +11,10 @@ from litellm.proxy._types import ( SpecialMCPServerName, ) from litellm.proxy.management_helpers.object_permission_utils import ( + _drop_stale_object_permission_mcp_servers, _extract_requested_mcp_access_groups, _extract_requested_mcp_server_ids, _resolve_team_allowed_mcp_servers, - _rewrite_object_permission_mcp_servers, _set_object_permission, enforce_all_proxy_mcp_servers_grant_is_admin_only, validate_key_mcp_servers_against_team, @@ -153,10 +151,10 @@ def test_extract_requested_mcp_server_ids_excludes_no_mcp_servers_sentinel(): assert _extract_requested_mcp_server_ids(obj_perm) == {"server-1"} -def test_rewrite_object_permission_mcp_servers_preserves_sentinel(): - obj_perm = {"mcp_servers": ["no-mcp-servers", "alias-1"]} - _rewrite_object_permission_mcp_servers(obj_perm, {"alias-1": {"server-1"}}) - assert obj_perm["mcp_servers"] == ["no-mcp-servers", "server-1"] +def test_drop_stale_object_permission_mcp_servers_preserves_sentinel_and_alias(): + obj_perm = {"mcp_servers": ["no-mcp-servers", "alias-1", "gone-id"]} + _drop_stale_object_permission_mcp_servers(obj_perm, {"alias-1": {"server-1"}, "gone-id": set()}) + assert obj_perm["mcp_servers"] == ["no-mcp-servers", "alias-1"] @pytest.mark.asyncio @@ -692,9 +690,10 @@ async def test_validate_mcp_server_alias_outside_team_scope_raises( new_callable=AsyncMock, return_value=[], ) -async def test_validate_mcp_server_alias_is_normalized_before_save( - mock_access_groups, mock_allow_all -): +async def test_validate_mcp_server_alias_persists_verbatim(mock_access_groups, mock_allow_all): + """Regression for the multi-region shared-DB setup: an alias grant must be + stored as the alias, so every instance can expand it to its own local id. + Rewriting to this instance's server_id breaks access on the other region.""" team_obj = _make_team_obj(mcp_servers=["allowed-server-id"]) object_permission = { "mcp_servers": ["allowed-alias"], @@ -706,8 +705,27 @@ async def test_validate_mcp_server_alias_is_normalized_before_save( team_obj=team_obj, ) - assert object_permission["mcp_servers"] == ["allowed-server-id"] - assert object_permission["mcp_tool_permissions"] == {"allowed-server-id": ["tool1"]} + assert object_permission["mcp_servers"] == ["allowed-alias"] + assert object_permission["mcp_tool_permissions"] == {"Allowed Server": ["tool1"]} + + +def test_alias_grant_expands_on_other_region_after_save(): + """Cross-region flow: the west instance saves an alias grant (its resolver maps + the alias to west's hash-derived id), then the central instance, whose registry + maps the same alias to a different id, expands the persisted grant. Rewriting + to west's id at save time is exactly the regression this guards against.""" + west_mgr = _make_mock_mcp_manager(servers=[_make_mock_mcp_server("west-id", alias="github-mcp")]) + central_mgr = _make_mock_mcp_manager(servers=[_make_mock_mcp_server("central-id", alias="github-mcp")]) + + object_permission = {"mcp_servers": ["github-mcp"]} + _drop_stale_object_permission_mcp_servers(object_permission, {"github-mcp": {"west-id"}}) + assert object_permission["mcp_servers"] == ["github-mcp"] + + from litellm.proxy._experimental.mcp_server.mcp_server_manager import MCPServerManager + + expand = MCPServerManager.expand_permission_list + assert expand(west_mgr, object_permission["mcp_servers"]) == ["west-id"] + assert expand(central_mgr, object_permission["mcp_servers"]) == ["central-id"] @pytest.mark.asyncio diff --git a/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py b/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py index 09bab1dc416..1d4b0264879 100644 --- a/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py +++ b/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py @@ -12,11 +12,13 @@ from unittest.mock import AsyncMock, MagicMock, Mock, patch import httpx import pytest from fastapi import HTTPException, Request, Response +from fastapi.responses import StreamingResponse from fastapi.testclient import TestClient from starlette.datastructures import FormData import litellm +from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing from litellm.constants import LITELLM_PROXY_MASTER_KEY_ALIAS from litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints import ( BaseOpenAIPassThroughHandler, @@ -30,6 +32,7 @@ from litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints import ( get_azure_ai_search_index_from_endpoint, get_vertex_base_url, is_azure_ai_search_service_level_index_create, + gigachat_proxy_route, llm_passthrough_factory_proxy_route, milvus_proxy_route, mistral_proxy_route, @@ -178,7 +181,7 @@ class TestBaseOpenAIPassThroughHandler: assert result["api-key"] == "test_api_key" assert result["test-header"] == "value" - @patch( + @patch( # test-quality-ok: patching litellm internal for unit test isolation "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.create_pass_through_route" ) async def test_base_openai_pass_through_handler(self, mock_create_pass_through): @@ -2022,15 +2025,15 @@ class TestLLMPassthroughFactoryProxyRoute: class TestVLLMProxyRoute: @pytest.mark.asyncio - @patch( + @patch( # test-quality-ok: patching litellm internal for unit test isolation "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.get_request_body", return_value={"model": "router-model", "stream": False}, ) - @patch( + @patch( # test-quality-ok: patching litellm internal for unit test isolation "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.is_passthrough_request_using_router_model", return_value=True, ) - @patch("litellm.proxy.proxy_server.llm_router") + @patch("litellm.proxy.proxy_server.llm_router") # test-quality-ok: patching litellm internal for unit test isolation async def test_vllm_proxy_route_with_router_model( self, mock_llm_router, mock_is_router, mock_get_body ): @@ -2055,15 +2058,15 @@ class TestVLLMProxyRoute: mock_llm_router.allm_passthrough_route.assert_awaited_once() @pytest.mark.asyncio - @patch( + @patch( # test-quality-ok: patching litellm internal for unit test isolation "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.get_request_body", return_value={"model": "other-model"}, ) - @patch( + @patch( # test-quality-ok: patching litellm internal for unit test isolation "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.is_passthrough_request_using_router_model", return_value=False, ) - @patch( + @patch( # test-quality-ok: patching litellm internal for unit test isolation "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.llm_passthrough_factory_proxy_route" ) async def test_vllm_proxy_route_fallback_to_factory( @@ -2085,6 +2088,312 @@ class TestVLLMProxyRoute: mock_factory_route.assert_awaited_once() +class TestGigachatProxyRoute: + @pytest.mark.asyncio + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.get_request_body", + return_value={"model": "router-model", "stream": False}, + ) + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.is_passthrough_request_using_router_model", + return_value=True, + ) + @patch("litellm.proxy.proxy_server.llm_router") # test-quality-ok: patching litellm internal for unit test isolation + async def test_gigachat_proxy_route_with_router_model( + self, mock_llm_router, mock_is_router, mock_get_body + ): + mock_request = MagicMock(spec=Request) + mock_request.method = "POST" + mock_request.headers = {"content-type": "application/json"} + mock_request.query_params = {} + mock_fastapi_response = MagicMock(spec=Response) + mock_user_api_key_dict = MagicMock() + mock_llm_router.allm_passthrough_route = AsyncMock( + return_value=httpx.Response(200, json={"response": "success"}) + ) + + result = await gigachat_proxy_route( + endpoint="/chat/completions", + request=mock_request, + fastapi_response=mock_fastapi_response, + user_api_key_dict=mock_user_api_key_dict, + ) + + mock_is_router.assert_called_once() + mock_llm_router.allm_passthrough_route.assert_awaited_once() + assert isinstance(result, Response) + + @pytest.mark.asyncio + async def test_gigachat_router_handler_keeps_cached_body_and_payload_metadata_pristine(self): + """Regression: auth-metadata injection must not leak into the cached parsed body or the upstream payload.""" + from litellm.proxy.common_utils.http_parsing_utils import get_request_body + from litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints import ( + handle_gigachat_passthrough_router_model, + ) + + body = json.dumps( + { + "model": "gigachat-router", + "messages": [{"role": "user", "content": "hi"}], + "metadata": {"client_tag": "user-supplied"}, + } + ).encode() + scope = { + "type": "http", + "method": "POST", + "headers": [(b"content-type", b"application/json")], + "query_string": b"", + "path": "/gigachat/chat/completions", + } + + async def receive(): + return {"type": "http.request", "body": body, "more_body": False} + + request = Request(scope, receive) + request_body = await get_request_body(request) + + captured: dict = {} + + class _CapturingProcessor: + def __init__(self, data: dict): + captured["data"] = data + + async def base_passthrough_process_llm_request(self, **kwargs): + return Response(content=b"{}", status_code=200) + + with patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.proxy.common_request_processing.ProxyBaseLLMRequestProcessing", + _CapturingProcessor, + ): + await handle_gigachat_passthrough_router_model( + model="gigachat-router", + endpoint="/chat/completions", + request=request, + request_body=request_body, + fastapi_response=Response(), + llm_router=MagicMock(), + user_api_key_dict=UserAPIKeyAuth(user_id="user-1", team_id="team-1"), + proxy_logging_obj=MagicMock(), + general_settings={}, + proxy_config=MagicMock(), + select_data_generator=MagicMock(), + user_model=None, + user_temperature=None, + user_request_timeout=None, + user_max_tokens=None, + user_api_base=None, + version=None, + ) + + data = captured["data"] + assert data["json"] is request_body + assert request_body["metadata"] == {"client_tag": "user-supplied"} + assert data["metadata"]["client_tag"] == "user-supplied" + assert data["metadata"]["user_api_key_user_id"] == "user-1" + assert data["metadata"]["user_api_key_team_id"] == "team-1" + cached_reread = await get_request_body(request) + assert cached_reread["metadata"] == {"client_tag": "user-supplied"} + + @pytest.mark.asyncio + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.get_request_body", + return_value={"model": "other-model"}, + ) + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.is_passthrough_request_using_router_model", + return_value=False, + ) + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.is_streaming_request_fn", + new_callable=AsyncMock, + return_value=False, + ) + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.llms.gigachat.authenticator.get_access_token", + return_value="gigachat-test-token", + ) + async def test_gigachat_proxy_route_fallback_forwards_to_gigachat_api( + self, + mock_get_token, + mock_is_streaming, + mock_is_router, + mock_get_body, + monkeypatch, + ): + monkeypatch.delenv("GIGACHAT_API_BASE", raising=False) + mock_request = MagicMock(spec=Request) + mock_fastapi_response = MagicMock(spec=Response) + mock_user_api_key_dict = MagicMock() + + captured_kwargs = {} + + async def fake_endpoint(request, fastapi_response, user_api_key_dict): + return Response(content=b'{"response": "success"}', status_code=200) + + def fake_create_pass_through_route(**kwargs): + captured_kwargs.update(kwargs) + return fake_endpoint + + with patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.create_pass_through_route", + side_effect=fake_create_pass_through_route, + ): + result = await gigachat_proxy_route( + endpoint="/chat/completions", + request=mock_request, + fastapi_response=mock_fastapi_response, + user_api_key_dict=mock_user_api_key_dict, + ) + + assert isinstance(result, Response) + assert result.status_code == 200 + assert captured_kwargs["target"] == "https://gigachat.devices.sberbank.ru/api/v1/chat/completions" + assert captured_kwargs["custom_headers"] == {"Authorization": "Bearer gigachat-test-token"} + + @pytest.mark.asyncio + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.get_request_body", + return_value={}, + ) + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.is_streaming_request_fn", + new_callable=AsyncMock, + return_value=False, + ) + @patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.llms.gigachat.authenticator.get_access_token", + return_value="gigachat-test-token", + ) + async def test_gigachat_proxy_route_models_endpoint_without_model( + self, + mock_get_token, + mock_is_streaming, + mock_get_body, + monkeypatch, + ): + monkeypatch.delenv("GIGACHAT_API_BASE", raising=False) + mock_request = MagicMock(spec=Request) + mock_fastapi_response = MagicMock(spec=Response) + mock_user_api_key_dict = MagicMock() + + captured_kwargs = {} + + async def fake_endpoint(request, fastapi_response, user_api_key_dict): + return Response(content=b'{"data": []}', status_code=200) + + def fake_create_pass_through_route(**kwargs): + captured_kwargs.update(kwargs) + return fake_endpoint + + with patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.create_pass_through_route", + side_effect=fake_create_pass_through_route, + ): + result = await gigachat_proxy_route( + endpoint="models", + request=mock_request, + fastapi_response=mock_fastapi_response, + user_api_key_dict=mock_user_api_key_dict, + ) + + assert isinstance(result, Response) + assert result.status_code == 200 + assert captured_kwargs["target"] == "https://gigachat.devices.sberbank.ru/api/v1/models" + assert captured_kwargs["custom_headers"] == {"Authorization": "Bearer gigachat-test-token"} + + @pytest.mark.asyncio + async def test_allm_passthrough_streaming_preserves_upstream_headers(self): + async def _stream() -> bytes: + yield b'data: {"id":"1"}\n\n' + + class MockPassthroughStreamingResponse: + def __init__(self): + self.status_code = 201 + self.headers = { + "content-type": "text/event-stream; charset=utf-8", + "x-request-id": "req-123", + "x-ratelimit-remaining-requests": "77", + "transfer-encoding": "chunked", + "content-encoding": "gzip", + } + self._iterator = _stream() + + def __aiter__(self): + return self + + async def __anext__(self): + return await self._iterator.__anext__() + + processor = ProxyBaseLLMRequestProcessing( + data={ + "model": "some-provider/model", + "stream": True, + "litellm_call_id": "call-123", + "litellm_logging_obj": MagicMock(litellm_call_id="call-123"), + } + ) + + mock_request = MagicMock(spec=Request) + mock_request.headers = {"content-type": "application/json"} + mock_fastapi_response = MagicMock(spec=Response) + mock_user_api_key_dict = MagicMock() + mock_user_api_key_dict.allowed_model_region = "" + mock_user_api_key_dict.spend = 0.0 + mock_proxy_logging_obj = MagicMock() + mock_proxy_logging_obj.during_call_hook = AsyncMock(return_value=None) + mock_proxy_logging_obj.update_request_status = AsyncMock(return_value=None) + mock_proxy_logging_obj.post_call_response_headers_hook = AsyncMock( + return_value={"x-test-callback-header": "callback-value"} + ) + + streaming_response = MockPassthroughStreamingResponse() + + async def _fake_route_request(*args, **kwargs): + async def _inner(): + return streaming_response + + return _inner() + + with patch.object( + processor, + "common_processing_pre_call_logic", + new=AsyncMock( + return_value=( + processor.data, + processor.data["litellm_logging_obj"], + ) + ), + ), patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.proxy.common_request_processing.route_request", + new=_fake_route_request, + ), patch( # test-quality-ok: patching litellm internal for unit test isolation + "litellm.proxy.common_request_processing.ProxyBaseLLMRequestProcessing.get_custom_headers", + return_value={"x-litellm-call-id": "call-123"}, + ): + result = await processor.base_passthrough_process_llm_request( + request=mock_request, + fastapi_response=mock_fastapi_response, + user_api_key_dict=mock_user_api_key_dict, + proxy_logging_obj=mock_proxy_logging_obj, + general_settings={}, + proxy_config=MagicMock(), + select_data_generator=MagicMock(), + llm_router=None, + model="some-provider/model", + version="test-version", + ) + + assert isinstance(result, StreamingResponse) + assert result.status_code == 201 + assert result.headers["content-type"] == "text/event-stream; charset=utf-8" + assert result.headers["x-request-id"] == "req-123" + assert result.headers["x-ratelimit-remaining-requests"] == "77" + assert result.headers["x-litellm-call-id"] == "call-123" + assert result.headers["x-test-callback-header"] == "callback-value" + assert "transfer-encoding" not in result.headers + assert "content-encoding" not in result.headers + + class TestForwardHeaders: """ Test cases for _forward_headers parameter in passthrough endpoints @@ -4627,3 +4936,76 @@ class TestPassthroughRouterModelBudgetReservation: ) self._assert_metadata_carries_attribution(captured, user_api_key_dict) + + +class TestAzureRouterModelStreamingDispatch: + """ + Regression: ``llm_router.allm_passthrough_route`` returns an awaited + ``AsyncPassthroughStreamingResponse`` for streaming calls, which is no + longer an async generator under ``inspect.isasyncgen``. The dispatch's + else branch therefore calls ``.aiter_bytes()`` / ``.status_code`` / + ``.headers`` on it. The router's ``set_response_headers`` also runs the + result through ``prepare_response_for_header_attachment``, which used to + wrap it in ``HiddenParamsAsyncIteratorWrapper`` (no ``aiter_bytes``), so + every streaming Azure router-model request 500'd with + ``AttributeError: aiter_bytes``; ``_hidden_params`` on the streaming + response keeps it unwrapped. + """ + + @pytest.mark.asyncio + async def test_azure_router_model_streaming_returns_streaming_response(self, monkeypatch): + import litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints as ep + import litellm.proxy.proxy_server as proxy_server + from litellm.passthrough.main import AsyncPassthroughStreamingResponse + + upstream_body = b"data: hello\n\n" + + async def _upstream_response() -> httpx.Response: + upstream_request = httpx.Request( + "POST", + "https://my-azure.openai.azure.com/openai/deployments/gpt-5/chat/completions", + ) + return httpx.Response( + 200, + headers={"content-type": "text/event-stream"}, + content=upstream_body, + request=upstream_request, + ) + + logging_obj = MagicMock() + logging_obj.async_flush_passthrough_collected_chunks = AsyncMock() + + from litellm.router_utils.add_retry_fallback_headers import prepare_response_for_header_attachment + + class StreamingRouter: + async def allm_passthrough_route(self, **kwargs): + streaming_response = await AsyncPassthroughStreamingResponse( + response=_upstream_response(), + litellm_logging_obj=logging_obj, + provider_config=MagicMock(), + ) + return prepare_response_for_header_attachment(streaming_response) + + async def fake_get_request_body(_request): + return {"model": "gpt-5", "stream": True} + + monkeypatch.setattr(proxy_server, "llm_router", StreamingRouter()) + monkeypatch.setattr(ep, "get_request_body", fake_get_request_body) + monkeypatch.setattr(ep, "is_passthrough_request_using_router_model", lambda *a, **k: True) + + request = MagicMock(spec=Request) + request.method = "POST" + request.headers = {"content-type": "application/json"} + request.query_params = {} + + result = await azure_proxy_route( + endpoint="openai/deployments/gpt-5/chat/completions", + request=request, + fastapi_response=MagicMock(spec=Response), + user_api_key_dict=UserAPIKeyAuth(api_key="hashed-token"), + ) + + assert isinstance(result, StreamingResponse) + assert result.status_code == 200 + body = b"".join([chunk async for chunk in result.body_iterator]) + assert body == upstream_body diff --git a/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py b/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py index a3f56adb86f..d3f17c73499 100644 --- a/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py +++ b/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py @@ -2,6 +2,7 @@ import asyncio import json import logging import os +from collections.abc import Callable from contextlib import ExitStack, contextmanager from io import BytesIO from types import SimpleNamespace @@ -29,6 +30,7 @@ from litellm.proxy.pass_through_endpoints.pass_through_endpoints import ( websocket_passthrough_request, ) from litellm.integrations.custom_logger import CustomLogger +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.proxy._types import ProxyException, UserAPIKeyAuth from litellm.types.passthrough_endpoints.pass_through_endpoints import ( LITELLM_PASS_THROUGH_RAW_BODY_STATE_KEY, @@ -5462,3 +5464,269 @@ def test_the_marker_check_distinguishes_the_two_route_kinds(): builtin = MagicMock(spec=Request) builtin.scope = {"endpoint": llm_passthrough_endpoints.anthropic_proxy_route} assert request_dispatched_to_pass_through_endpoint(builtin) is False + + +async def _drive_passthrough_request_and_capture_logging( + user_api_key_dict: UserAPIKeyAuth, + on_pre_call: Callable[[LiteLLMLoggingObj | None], None] | None = None, +) -> tuple[int, LiteLLMLoggingObj | None]: + import litellm + from litellm.llms.custom_httpx.http_handler import get_async_httpx_client + from litellm.types.llms.custom_http import httpxSpecialProvider + + def transport_handler(upstream_request: httpx.Request) -> httpx.Response: + return httpx.Response(200, json={"ok": True}) + + real_handler = get_async_httpx_client( + llm_provider=httpxSpecialProvider.PassThroughEndpoint, + params={"timeout": resolve_pass_through_request_timeout(None)}, + ) + cache_dict = litellm.in_memory_llm_clients_cache.cache_dict + cache_key = next((key for key, cached in cache_dict.items() if cached is real_handler), None) + assert cache_key is not None + cache_dict[cache_key] = SimpleNamespace(client=httpx.AsyncClient(transport=httpx.MockTransport(transport_handler))) + + mock_request = MagicMock(spec=Request) + mock_request.method = "POST" + mock_request.headers = Headers({}) + mock_request.query_params = QueryParams({}) + mock_request.body = AsyncMock(return_value=b'{"model": "gemini-2.0-flash"}') + + captured_data: dict = {} # mutable-ok: the pre-call hook records the request data into it + + async def capture_pre_call_hook(user_api_key_dict, data, call_type): + captured_data.update(data) + if on_pre_call is not None: + on_pre_call(data.get("litellm_logging_obj")) + return data + + mock_proxy_logging = MagicMock() + mock_proxy_logging.pre_call_hook = AsyncMock(side_effect=capture_pre_call_hook) + mock_proxy_logging.post_call_failure_hook = AsyncMock() + mock_proxy_logging.post_call_response_headers_hook = AsyncMock(return_value={}) + mock_proxy_logging.get_proxy_hook = MagicMock(return_value=None) + + try: + with patch( # test-quality-ok: proxy_logging_obj is a proxy_server module global read inside pass_through_request; there is no injection seam + "litellm.proxy.proxy_server.proxy_logging_obj", mock_proxy_logging + ): + response = await pass_through_request( + request=mock_request, + target="https://upstream.example.test/v1/generate", + custom_headers={}, + user_api_key_dict=user_api_key_dict, + ) + finally: + cache_dict[cache_key] = real_handler + + return response.status_code, captured_data.get("litellm_logging_obj") + + +@pytest.mark.asyncio +async def test_pass_through_request_wires_team_callbacks(): + """LIT-5152 regression: pass_through_request must resolve team-level logging + callbacks from key/team metadata and wire them into the Logging object, the + same way add_litellm_data_to_request does for normal LLM routes.""" + user_api_key_dict = UserAPIKeyAuth( + api_key="test-key", + team_id="test-team", + team_metadata={ + "logging": [ + { + "callback_name": "langfuse", + "callback_type": "success_and_failure", + "callback_vars": { + "langfuse_public_key": "pk_test", + "langfuse_secret_key": "sk_test", + "langfuse_host": "https://langfuse.example.test", + }, + } + ] + }, + ) + + status_code, logging_obj = await _drive_passthrough_request_and_capture_logging(user_api_key_dict) + + assert status_code == 200 + assert logging_obj is not None + assert logging_obj.dynamic_success_callbacks, "team success callbacks not wired into Logging" + assert logging_obj.dynamic_failure_callbacks, "team failure callbacks not wired into Logging" + assert logging_obj.standard_callback_dynamic_params.get("langfuse_public_key") == "pk_test" + assert logging_obj.standard_callback_dynamic_params.get("langfuse_secret_key") == "sk_test" + assert logging_obj.standard_callback_dynamic_params.get("langfuse_host") == "https://langfuse.example.test" + assert ("langfuse_public_key", "pk_test") in logging_obj._trusted_callback_vars + + +@pytest.mark.asyncio +async def test_pass_through_request_survives_malformed_team_logging_metadata(): + """LIT-5152 fail-open: a malformed team ``logging`` value (here a non-iterable) + raises inside callback resolution; the passthrough request must still succeed, + just without dynamic callbacks.""" + user_api_key_dict = UserAPIKeyAuth( + api_key="test-key", + team_id="test-team", + team_metadata={"logging": 5}, + ) + + status_code, logging_obj = await _drive_passthrough_request_and_capture_logging(user_api_key_dict) + + assert status_code == 200 + assert logging_obj is not None + assert not logging_obj.dynamic_success_callbacks + assert not logging_obj.dynamic_failure_callbacks + + +@pytest.mark.asyncio +async def test_pass_through_request_survives_env_reference_in_deprecated_callback_settings(): + """LIT-5152 fail-open: the deprecated ``callback_settings`` team metadata skips + AddTeamCallback validation, so an ``os.environ/`` callback var would otherwise + blow up inside ``Logging.__init__`` and fail the request; the passthrough must + instead succeed without dynamic callbacks.""" + user_api_key_dict = UserAPIKeyAuth( + api_key="test-key", + team_id="test-team", + team_metadata={ + "callback_settings": { + "success_callback": ["langfuse"], + "failure_callback": ["langfuse"], + "callback_vars": { + "langfuse_public_key": "os.environ/LANGFUSE_PUBLIC_KEY", + "langfuse_secret_key": "os.environ/LANGFUSE_SECRET_KEY", + "langfuse_host": "https://langfuse.example.test", + }, + } + }, + ) + + status_code, logging_obj = await _drive_passthrough_request_and_capture_logging(user_api_key_dict) + + assert status_code == 200 + assert logging_obj is not None + assert not logging_obj.dynamic_success_callbacks + assert not logging_obj.dynamic_failure_callbacks + assert not logging_obj.standard_callback_dynamic_params.get("langfuse_public_key") + + +@pytest.mark.asyncio +async def test_resolve_team_callback_wiring_fails_open_on_operational_error(): + """LIT-5152 fail-open: an operational error while resolving callback metadata + (e.g. team config lookup hitting a dead secret manager) must not raise; the + request proceeds without dynamic callbacks and the error is logged.""" + from litellm.proxy.pass_through_endpoints.pass_through_endpoints import ( + _resolve_team_callback_wiring, + ) + from litellm.proxy.proxy_server import ProxyConfig + + class RaisingTeamConfig(ProxyConfig): + def load_team_config(self, team_id: str) -> dict: + raise RuntimeError("secret manager unavailable") + + wiring = _resolve_team_callback_wiring( + user_api_key_dict=UserAPIKeyAuth(api_key="test-key", team_id="test-team"), + proxy_config=RaisingTeamConfig(), + route_description="pass_through_endpoint", + ) + + assert wiring.success_callbacks is None + assert wiring.failure_callbacks is None + assert wiring.logging_kwargs is None + + +@pytest.mark.asyncio +async def test_pass_through_request_leaves_guardrail_readable_metadata(): + """A pre-call guardrail reads the request headers off the passthrough logging + params without raising.""" + from litellm.proxy.guardrails.guardrail_hooks.hiddenlayer.hiddenlayer import ( + _logged_request_headers, + ) + + user_api_key_dict = UserAPIKeyAuth( + api_key="test-key", + team_id="test-team", + team_metadata={ + "logging": [ + { + "callback_name": "langfuse", + "callback_type": "success_and_failure", + "callback_vars": { + "langfuse_public_key": "pk_test", + "langfuse_secret_key": "sk_test", + }, + } + ] + }, + ) + + observed: dict[str, dict[str, str] | BaseException] = {} # mutable-ok: the pre-call hook records into it + + def read_headers_the_way_a_guardrail_does(logging_obj: LiteLLMLoggingObj | None) -> None: + assert logging_obj is not None + try: + observed["headers"] = _logged_request_headers(logging_obj) + except Exception as exc: # noqa: BLE001 - the regression is that this used to raise + observed["headers"] = exc + + status_code, logging_obj = await _drive_passthrough_request_and_capture_logging( + user_api_key_dict, on_pre_call=read_headers_the_way_a_guardrail_does + ) + + assert "headers" in observed, "the pre-call hook never ran, so nothing was observed" + assert observed["headers"] == {}, f"guardrail header read failed: {observed['headers']!r}" + assert status_code == 200 + assert logging_obj is not None + assert logging_obj.dynamic_success_callbacks, "team success callbacks must stay wired" + assert logging_obj.standard_callback_dynamic_params.get("langfuse_public_key") == "pk_test" + + +@pytest.mark.asyncio +async def test_pass_through_request_leaves_cost_router_logger_working(): + """The cost router's logger reads the deployment id off the passthrough logging + params without raising. least_busy shares the read but swallows the exception, + so this is the strategy where the break is observable.""" + from litellm._logging import verbose_logger + from litellm.caching.caching import DualCache + from litellm.router_strategy.lowest_cost import LowestCostLoggingHandler + + handler = LowestCostLoggingHandler(router_cache=DualCache()) + + user_api_key_dict = UserAPIKeyAuth( + api_key="test-key", + team_id="test-team", + team_metadata={ + "logging": [ + { + "callback_name": "langfuse", + "callback_type": "success_and_failure", + "callback_vars": { + "langfuse_public_key": "pk_test", + "langfuse_secret_key": "sk_test", + }, + } + ] + }, + ) + + status_code, logging_obj = await _drive_passthrough_request_and_capture_logging(user_api_key_dict) + assert status_code == 200 + assert logging_obj is not None + + raised: list[logging.LogRecord] = [] # mutable-ok: logging.Handler records into it + + class _RecordTracebacks(logging.Handler): + def emit(self, record: logging.LogRecord) -> None: + if record.exc_info is not None: + raised.append(record) + + recorder = _RecordTracebacks() + verbose_logger.addHandler(recorder) + try: + await handler.async_log_success_event( + kwargs=logging_obj.model_call_details, + response_obj=None, + start_time=None, + end_time=None, + ) + finally: + verbose_logger.removeHandler(recorder) + + assert not raised, f"cost router logger raised on the passthrough logging params: {raised[0].exc_info}" diff --git a/tests/test_litellm/proxy/pass_through_endpoints/test_streaming_handler_interrupt.py b/tests/test_litellm/proxy/pass_through_endpoints/test_streaming_handler_interrupt.py index 1d82a5dfc6e..56c89fed79a 100644 --- a/tests/test_litellm/proxy/pass_through_endpoints/test_streaming_handler_interrupt.py +++ b/tests/test_litellm/proxy/pass_through_endpoints/test_streaming_handler_interrupt.py @@ -28,12 +28,21 @@ def _make_streaming_response(chunks): return mock +def _unarmed_logging_obj(): + """Real Logging objects only carry _on_deferred_stream_complete when the + proxy arms deferred dispatch; a bare MagicMock's auto-attribute is truthy + and would spuriously trigger the deferral branch.""" + obj = MagicMock() + obj._on_deferred_stream_complete = None + return obj + + @pytest.mark.asyncio async def test_chunk_processor_logs_on_normal_completion(): chunks = [b"chunk-1", b"chunk-2", b"chunk-3"] response = _make_streaming_response(chunks) - mock_logging_obj = MagicMock() + mock_logging_obj = _unarmed_logging_obj() mock_passthrough_handler = MagicMock() with patch.object( @@ -66,7 +75,7 @@ async def test_chunk_processor_logs_on_client_disconnect(): chunks = [b"event-1", b"event-2", b"event-3"] response = _make_streaming_response(chunks) - mock_logging_obj = MagicMock() + mock_logging_obj = _unarmed_logging_obj() mock_passthrough_handler = MagicMock() with patch.object( @@ -104,7 +113,7 @@ async def test_chunk_processor_does_not_schedule_success_logging_for_upstream_er response = _make_streaming_response(chunks) response.status_code = 403 - mock_logging_obj = MagicMock() + mock_logging_obj = _unarmed_logging_obj() mock_passthrough_handler = MagicMock() with patch.object( @@ -134,7 +143,7 @@ async def test_chunk_processor_does_not_schedule_success_logging_for_upstream_er async def test_chunk_processor_does_not_schedule_logging_when_no_chunks(): response = _make_streaming_response([]) - mock_logging_obj = MagicMock() + mock_logging_obj = _unarmed_logging_obj() mock_passthrough_handler = MagicMock() with patch.object( @@ -189,7 +198,7 @@ async def test_chunk_processor_routes_logging_through_logging_worker(): async for chunk in PassThroughStreamingHandler.chunk_processor( response=response, request_body={"model": "claude-3-haiku"}, - litellm_logging_obj=MagicMock(), + litellm_logging_obj=_unarmed_logging_obj(), endpoint_type=EndpointType.GENERIC, start_time=datetime.now(), passthrough_success_handler_obj=MagicMock(), @@ -230,7 +239,7 @@ async def test_chunk_processor_routes_logging_through_logging_worker_on_disconne gen = PassThroughStreamingHandler.chunk_processor( response=response, request_body={"model": "claude-3-haiku"}, - litellm_logging_obj=MagicMock(), + litellm_logging_obj=_unarmed_logging_obj(), endpoint_type=EndpointType.GENERIC, start_time=datetime.now(), passthrough_success_handler_obj=MagicMock(), @@ -246,7 +255,7 @@ async def test_chunk_processor_routes_logging_through_logging_worker_on_disconne def _logging_obj_with_write_once_cst(): """Build a MagicMock that mirrors the real Logging behavior: _update_completion_start_time latches self.completion_start_time so the write-once guard actually latches.""" - obj = MagicMock() + obj = _unarmed_logging_obj() obj.completion_start_time = None def _update(*, completion_start_time): @@ -301,7 +310,7 @@ async def test_chunk_processor_does_not_reset_completion_start_time_on_later_chu response = _make_streaming_response(chunks) real_first = datetime(2020, 1, 1, 0, 0, 0) - mock_logging_obj = MagicMock() + mock_logging_obj = _unarmed_logging_obj() # Simulate first-chunk stamp having already landed (e.g. under contention or a # prior wrapper that already set it): later chunks must be no-ops. mock_logging_obj.completion_start_time = real_first @@ -387,7 +396,7 @@ async def _collect_openai_passthrough_chunks(chunks, endpoint_type): async for chunk in PassThroughStreamingHandler.chunk_processor( response=response, request_body={"model": "gpt-4o-mini", "stream": True}, - litellm_logging_obj=MagicMock(), + litellm_logging_obj=_unarmed_logging_obj(), endpoint_type=endpoint_type, start_time=datetime.now(), passthrough_success_handler_obj=MagicMock(), @@ -517,3 +526,109 @@ def test_convert_raw_bytes_survives_truncated_multibyte_sequence(): lines = PassThroughStreamingHandler._convert_raw_bytes_to_str_lines(raw_bytes) assert any('"type": "message_delta"' in line for line in lines) + + +@pytest.mark.asyncio +async def test_chunk_processor_defers_logging_until_fire_when_armed(): + """Regression for PR #38722: native /v1/messages streams route through + chunk_processor, which enqueued the spend log the moment the stream ended, + racing the guardrail end-of-stream scan and logging + guardrail_information as null. With deferred dispatch armed, the completed + stream must park the logging coroutine on logging_obj and only enqueue it + when ProxyLogging._fire_deferred_stream_logging fires after the scan.""" + from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing + from litellm.proxy.utils import ProxyLogging + + chunks = [b"event-1", b"event-2"] + response = _make_streaming_response(chunks) + + logging_obj = _unarmed_logging_obj() + logging_obj._deferred_stream_complete_args = None + + enqueued = [] + + def _capture(async_coroutine): + enqueued.append(async_coroutine) + async_coroutine.close() + + with patch.object( # test-quality-ok: GLOBAL_LOGGING_WORKER is a process-global singleton with no injection seam + GLOBAL_LOGGING_WORKER, + "ensure_initialized_and_enqueue", + side_effect=_capture, + ) as mock_enqueue: + gen = PassThroughStreamingHandler.chunk_processor( + response=response, + request_body={"model": "claude-3-haiku"}, + litellm_logging_obj=logging_obj, + endpoint_type=EndpointType.ANTHROPIC, + start_time=datetime.now(), + passthrough_success_handler_obj=MagicMock(), + url_route="/v1/messages", + route_streaming_logging=AsyncMock(), + ) + ProxyBaseLLMRequestProcessing(data={})._arm_deferred_stream_dispatch( + response=gen, + route_type="anthropic_messages", + user_api_key_dict=MagicMock(), + logging_obj=logging_obj, + ) + + received = [] + async for chunk in gen: + received.append(chunk) + await asyncio.sleep(0) + + assert received == chunks + mock_enqueue.assert_not_called() + parked = logging_obj._deferred_stream_complete_args + assert isinstance(parked, tuple) and len(parked) == 1 + assert asyncio.iscoroutine(parked[0]) + + ProxyLogging._fire_deferred_stream_logging({"litellm_logging_obj": logging_obj}) + await asyncio.sleep(0) + + mock_enqueue.assert_called_once() + + +@pytest.mark.asyncio +async def test_chunk_processor_enqueues_immediately_on_disconnect_even_when_armed(): + """Client disconnects never reach _fire_deferred_stream_logging, so parking + the coroutine there would lose the partial-usage spend log (LIT-2642); the + disconnect path must keep enqueueing immediately.""" + chunks = [b"event-1", b"event-2", b"event-3"] + response = _make_streaming_response(chunks) + + logging_obj = _unarmed_logging_obj() + + async def _armed_closure(logging_coroutine): + raise AssertionError("deferred closure must not fire on disconnect") + + logging_obj._on_deferred_stream_complete = _armed_closure + logging_obj._deferred_stream_complete_args = None + + enqueued = [] + + def _capture(async_coroutine): + enqueued.append(async_coroutine) + async_coroutine.close() + + with patch.object( # test-quality-ok: GLOBAL_LOGGING_WORKER is a process-global singleton with no injection seam + GLOBAL_LOGGING_WORKER, + "ensure_initialized_and_enqueue", + side_effect=_capture, + ) as mock_enqueue: + gen = PassThroughStreamingHandler.chunk_processor( + response=response, + request_body={"model": "claude-3-haiku"}, + litellm_logging_obj=logging_obj, + endpoint_type=EndpointType.ANTHROPIC, + start_time=datetime.now(), + passthrough_success_handler_obj=MagicMock(), + url_route="/v1/messages", + route_streaming_logging=AsyncMock(), + ) + await gen.__anext__() + await gen.aclose() + + mock_enqueue.assert_called_once() + assert logging_obj._deferred_stream_complete_args is None diff --git a/tests/test_litellm/proxy/pass_through_endpoints/test_vertex_ai_batch_passthrough.py b/tests/test_litellm/proxy/pass_through_endpoints/test_vertex_ai_batch_passthrough.py index ac79c183ca3..1d2d7d4d5c3 100644 --- a/tests/test_litellm/proxy/pass_through_endpoints/test_vertex_ai_batch_passthrough.py +++ b/tests/test_litellm/proxy/pass_through_endpoints/test_vertex_ai_batch_passthrough.py @@ -478,14 +478,14 @@ class TestVertexAIBatchPassthroughHandler: } ] - total_cost, usage = calculate_vertex_ai_batch_cost_and_usage( + result = calculate_vertex_ai_batch_cost_and_usage( vertex_ai_batch_responses, model_name="gemini-2.0-flash-001" ) - assert usage.total_tokens == 15 - assert usage.prompt_tokens == 10 - assert usage.completion_tokens == 5 - assert total_cost > 0, "batch_cost_calculator should return a non-zero cost" + assert result.usage.total_tokens == 15 + assert result.usage.prompt_tokens == 10 + assert result.usage.completion_tokens == 5 + assert result.cost > 0, "batch_cost_calculator should return a non-zero cost" def test_batch_response_transformation(self): """Test transformation of Vertex AI batch responses to OpenAI format""" @@ -664,14 +664,14 @@ class TestVertexAIBatchCostCalculation: }, ] - total_cost, usage = calculate_vertex_ai_batch_cost_and_usage( + result = calculate_vertex_ai_batch_cost_and_usage( responses, model_name="gemini-2.0-flash-001" ) - assert usage.prompt_tokens == 18 - assert usage.completion_tokens == 8 - assert usage.total_tokens == 26 - assert total_cost > 0, "batch_cost_calculator should return a non-zero cost" + assert result.usage.prompt_tokens == 18 + assert result.usage.completion_tokens == 8 + assert result.usage.total_tokens == 26 + assert result.cost > 0, "batch_cost_calculator should return a non-zero cost" def test_should_skip_responses_with_null_response_body(self): """Failed lines (response: None) are skipped without error.""" @@ -699,27 +699,29 @@ class TestVertexAIBatchCostCalculation: }, ] - total_cost, usage = calculate_vertex_ai_batch_cost_and_usage( + result = calculate_vertex_ai_batch_cost_and_usage( responses, model_name="gemini-2.0-flash-001" ) - assert usage.prompt_tokens == 18 - assert usage.completion_tokens == 8 - assert usage.total_tokens == 26 - assert total_cost > 0 + assert result.usage.prompt_tokens == 18 + assert result.usage.completion_tokens == 8 + assert result.usage.total_tokens == 26 + assert result.cost > 0 + assert result.successful_requests == 2 + assert result.failed_requests == 1 def test_should_return_zeros_for_empty_response_list(self): """Empty input → zero cost and zero usage.""" from litellm.batches.batch_utils import calculate_vertex_ai_batch_cost_and_usage - total_cost, usage = calculate_vertex_ai_batch_cost_and_usage( + result = calculate_vertex_ai_batch_cost_and_usage( [], model_name="gemini-2.0-flash-001" ) - assert total_cost == 0.0 - assert usage.total_tokens == 0 - assert usage.prompt_tokens == 0 - assert usage.completion_tokens == 0 + assert result.cost == 0.0 + assert result.usage.total_tokens == 0 + assert result.usage.prompt_tokens == 0 + assert result.usage.completion_tokens == 0 def test_should_handle_missing_usage_metadata_gracefully(self): """Response without usageMetadata → 0 tokens, 0 cost for that line.""" @@ -729,13 +731,13 @@ class TestVertexAIBatchCostCalculation: {"response": {"candidates": [{"content": {"parts": [{"text": "hi"}]}}]}}, ] - total_cost, usage = calculate_vertex_ai_batch_cost_and_usage( + result = calculate_vertex_ai_batch_cost_and_usage( responses, model_name="gemini-2.0-flash-001" ) - assert usage.prompt_tokens == 0 - assert usage.completion_tokens == 0 - assert usage.total_tokens == 0 + assert result.usage.prompt_tokens == 0 + assert result.usage.completion_tokens == 0 + assert result.usage.total_tokens == 0 @pytest.mark.asyncio async def test_openai_shaped_output_records_nonzero_cost_and_usage(self): @@ -813,7 +815,7 @@ class TestVertexAIBatchCostCalculation: try: litellm.disable_vertex_batch_output_transformation = False - cost, usage, _ = await calculate_batch_cost_and_usage( + result = await calculate_batch_cost_and_usage( file_content_dictionary=openai_shaped_responses, custom_llm_provider="vertex_ai", model_name="gemini-2.0-flash-001", @@ -822,17 +824,17 @@ class TestVertexAIBatchCostCalculation: litellm.disable_vertex_batch_output_transformation = original_flag assert ( - usage.prompt_tokens == 18 - ), f"expected 18 prompt tokens, got {usage.prompt_tokens}" + result.usage.prompt_tokens == 18 + ), f"expected 18 prompt tokens, got {result.usage.prompt_tokens}" assert ( - usage.completion_tokens == 8 - ), f"expected 8 completion tokens, got {usage.completion_tokens}" + result.usage.completion_tokens == 8 + ), f"expected 8 completion tokens, got {result.usage.completion_tokens}" assert ( - usage.total_tokens == 26 - ), f"expected 26 total tokens, got {usage.total_tokens}" + result.usage.total_tokens == 26 + ), f"expected 26 total tokens, got {result.usage.total_tokens}" assert ( - cost > 0 - ), f"expected non-zero cost for completed Vertex batch, got {cost}" + result.cost > 0 + ), f"expected non-zero cost for completed Vertex batch, got {result.cost}" @pytest.mark.asyncio async def test_raw_vertex_output_still_works_when_transformation_disabled(self): @@ -865,7 +867,7 @@ class TestVertexAIBatchCostCalculation: try: litellm.disable_vertex_batch_output_transformation = True - cost, usage, _ = await calculate_batch_cost_and_usage( + result = await calculate_batch_cost_and_usage( file_content_dictionary=raw_vertex_responses, custom_llm_provider="vertex_ai", model_name="gemini-2.0-flash-001", @@ -873,7 +875,7 @@ class TestVertexAIBatchCostCalculation: finally: litellm.disable_vertex_batch_output_transformation = original_flag - assert usage.prompt_tokens == 10 - assert usage.completion_tokens == 5 - assert usage.total_tokens == 15 - assert cost > 0, "raw Vertex shape should also produce non-zero cost" + assert result.usage.prompt_tokens == 10 + assert result.usage.completion_tokens == 5 + assert result.usage.total_tokens == 15 + assert result.cost > 0, "raw Vertex shape should also produce non-zero cost" diff --git a/tests/test_litellm/proxy/policy_engine/test_pipeline_executor.py b/tests/test_litellm/proxy/policy_engine/test_pipeline_executor.py index 054a5af4148..4fcb7d22588 100644 --- a/tests/test_litellm/proxy/policy_engine/test_pipeline_executor.py +++ b/tests/test_litellm/proxy/policy_engine/test_pipeline_executor.py @@ -749,6 +749,106 @@ async def test_single_step_pipeline_allow(monkeypatch): assert guard.calls == 1 +@pytest.mark.asyncio +async def test_allow_restores_independent_guardrails_list(monkeypatch): + """ + Request activates an independent guardrail; an unrelated pipeline runs and allows. + Expected: no modified_data escapes, so the request's guardrails list survives + and the independent guardrail still runs at later lifecycle stages (post_call). + Regression: LIT-6587 (pipeline clobbered the list with its last step's guardrail). + """ + pipeline_guard = AlwaysPassGuardrail(guardrail_name="input-scan") + + pipeline = GuardrailPipeline( + mode="pre_call", + steps=[PipelineStep(guardrail="input-scan", on_fail="block", on_pass="allow")], + ) + + monkeypatch.setattr(litellm, "callbacks", [pipeline_guard]) + + data = { + "messages": [{"role": "user", "content": "clean content"}], + "metadata": {"guardrails": ["independent-output-guard"]}, + } + result = await PipelineExecutor.execute_steps( + steps=pipeline.steps, + mode=pipeline.mode, + data=data, + user_api_key_dict=MagicMock(), + call_type="completion", + policy_name="input-pipeline-policy", + ) + + assert pipeline_guard.calls == 1 + assert result.terminal_action == "allow" + propagated = result.modified_data or data + assert propagated["metadata"]["guardrails"] == ["independent-output-guard"] + assert data["metadata"]["guardrails"] == ["independent-output-guard"] + + +@pytest.mark.asyncio +async def test_allow_does_not_leak_guardrails_into_bare_request(monkeypatch): + """A request without metadata must not gain a metadata.guardrails list from the pipeline.""" + pipeline_guard = AlwaysPassGuardrail(guardrail_name="input-scan") + + pipeline = GuardrailPipeline( + mode="pre_call", + steps=[PipelineStep(guardrail="input-scan", on_fail="block", on_pass="allow")], + ) + + monkeypatch.setattr(litellm, "callbacks", [pipeline_guard]) + + data = {"messages": [{"role": "user", "content": "clean content"}]} + result = await PipelineExecutor.execute_steps( + steps=pipeline.steps, + mode=pipeline.mode, + data=data, + user_api_key_dict=MagicMock(), + call_type="completion", + policy_name="input-pipeline-policy", + ) + + assert result.terminal_action == "allow" + propagated = result.modified_data or data + assert "guardrails" not in propagated.get("metadata", {}) + assert "metadata" not in data + + +@pytest.mark.asyncio +async def test_data_forwarding_keeps_changes_and_restores_guardrails_list(monkeypatch): + """A pass_data pipeline's modifications propagate while the request's guardrails list is restored.""" + pii_guard = PiiMaskingGuardrail(guardrail_name="pii-masker") + content_guard = ContentCheckGuardrail(guardrail_name="content-check") + + pipeline = GuardrailPipeline( + mode="pre_call", + steps=[ + PipelineStep(guardrail="pii-masker", on_fail="block", on_pass="next", pass_data=True), + PipelineStep(guardrail="content-check", on_fail="block", on_pass="allow"), + ], + ) + + monkeypatch.setattr(litellm, "callbacks", [pii_guard, content_guard]) + + data = { + "messages": [{"role": "user", "content": "Hello John Smith"}], + "metadata": {"guardrails": ["independent-output-guard"]}, + } + result = await PipelineExecutor.execute_steps( + steps=pipeline.steps, + mode=pipeline.mode, + data=data, + user_api_key_dict=MagicMock(), + call_type="completion", + policy_name="pii-then-safety", + ) + + assert result.terminal_action == "allow" + assert result.modified_data is not None + assert result.modified_data["messages"][0]["content"] == "Hello [REDACTED]" + assert result.modified_data["metadata"]["guardrails"] == ["independent-output-guard"] + + @pytest.mark.asyncio async def test_step_results_include_duration(monkeypatch): """Step results should include timing information.""" diff --git a/tests/test_litellm/proxy/proxy_server/test_routes_audio.py b/tests/test_litellm/proxy/proxy_server/test_routes_audio.py index 74542a3eaf6..de76c7257cf 100644 --- a/tests/test_litellm/proxy/proxy_server/test_routes_audio.py +++ b/tests/test_litellm/proxy/proxy_server/test_routes_audio.py @@ -12,13 +12,16 @@ from __future__ import annotations import io from unittest.mock import AsyncMock, MagicMock +import httpx import pytest from litellm.proxy import proxy_server +from litellm.types.llms.openai import HttpxBinaryResponseContent @pytest.fixture -def patched_speech(monkeypatch): +def patched_speech(monkeypatch, request): + upstream_content_type = getattr(request, "param", "audio/mpeg") monkeypatch.setattr(proxy_server, "llm_router", MagicMock()) monkeypatch.setattr( proxy_server, @@ -36,15 +39,14 @@ def patched_speech(monkeypatch): monkeypatch.setattr(proxy_server, "add_litellm_data_to_request", _add_data) - class _FakeBinaryResp: - async def aiter_bytes(self, chunk_size: int = 8192): - async def _gen(): - yield b"\x00\x01\x02" - - return _gen() - async def _llm_call(): - return _FakeBinaryResp() + return HttpxBinaryResponseContent( + httpx.Response( + status_code=200, + headers={} if upstream_content_type is None else {"content-type": upstream_content_type}, + content=b"\x00\x01\x02", + ) + ) async def _fake_route_request(*args, **kwargs): return _llm_call() @@ -79,6 +81,24 @@ def patched_speech_error(monkeypatch): yield +@pytest.fixture +def patched_speech_provider_rejection(monkeypatch, patched_speech_error): + import litellm + + async def _raise(*args, **kwargs): + raise litellm.BadRequestError( + message=( + "Gemini TTS only produces raw PCM16 audio, so response_format='mp3' is not supported." + " Supported response formats: pcm, wav." + ), + model="gemini-3.1-flash-tts-preview", + llm_provider="gemini", + ) + + monkeypatch.setattr(proxy_server, "route_request", _raise) + yield + + @pytest.fixture def patched_transcription(monkeypatch): router = MagicMock() @@ -152,6 +172,35 @@ def test_audio_speech_happy_path(client, auth_as, patched_speech, path): } +@pytest.mark.parametrize( + ("patched_speech", "response_format", "expected_content_type"), + [ + ("audio/wav", "wav", "audio/wav"), + ("audio/flac", "flac", "audio/flac"), + ("audio/pcm", "pcm", "audio/pcm"), + ("audio/wav", "mp3", "audio/wav"), + ("application/json", "flac", "audio/flac"), + (None, "wav", "audio/wav"), + (None, None, "audio/mpeg"), + ], + indirect=["patched_speech"], +) +def test_audio_speech_content_type_matches_audio_format( + client, auth_as, patched_speech, response_format, expected_content_type +): + """Regression for LIT-6482: /v1/audio/speech mislabeled wav/flac/pcm as audio/mpeg.""" + payload = { + "model": "tts-1", + "input": "Hi", + "voice": "alloy", + **({} if response_format is None else {"response_format": response_format}), + } + with auth_as(): + response = client.post("/v1/audio/speech", json=payload) + assert response.status_code == 200 + assert response.headers.get("content-type", "").split(";")[0] == expected_content_type + + @pytest.mark.parametrize("path", ["/v1/audio/speech", "/audio/speech"]) def test_audio_speech_error(client, auth_as, patched_speech_error, path): """Pins ``POST /v1/audio/speech`` and ``POST /audio/speech`` (error).""" @@ -162,6 +211,18 @@ def test_audio_speech_error(client, auth_as, patched_speech_error, path): assert len(response.content) > 0 +def test_audio_speech_bad_request_maps_to_400(client, auth_as, patched_speech_provider_rejection): + """Regression for LIT-6501: a BadRequestError from the speech path surfaced as a generic 500.""" + payload = {"model": "gemini-tts", "input": "Hi", "voice": "Kore", "response_format": "mp3"} + with auth_as(): + response = client.post("/v1/audio/speech", json=payload) + assert response.status_code == 400 + error = response.json()["error"] + assert "response_format='mp3'" in error["message"] + assert "pcm" in error["message"] + assert "wav" in error["message"] + + @pytest.mark.parametrize("path", ["/v1/audio/transcriptions", "/audio/transcriptions"]) def test_audio_transcription_happy_path(client, auth_as, patched_transcription, path): """Pins ``POST /v1/audio/transcriptions`` / ``POST /audio/transcriptions`` (happy).""" diff --git a/tests/test_litellm/proxy/proxy_server/test_routes_utils.py b/tests/test_litellm/proxy/proxy_server/test_routes_utils.py index 35b5c72f92e..1e1436fcef8 100644 --- a/tests/test_litellm/proxy/proxy_server/test_routes_utils.py +++ b/tests/test_litellm/proxy/proxy_server/test_routes_utils.py @@ -9,12 +9,13 @@ Pins (PR2): from __future__ import annotations import asyncio -from unittest.mock import AsyncMock, MagicMock +import json import pytest import litellm from litellm.proxy import proxy_server +from litellm.router_utils import pattern_match_deployments from .conftest import normalize # type: ignore[import-not-found] @@ -99,6 +100,7 @@ def test_token_counter_missing_input_returns_400( @pytest.fixture def patched_supported_params(monkeypatch): + monkeypatch.setattr(proxy_server, "llm_router", None) monkeypatch.setattr( litellm, "get_llm_provider", @@ -124,12 +126,104 @@ def test_supported_openai_params_happy_path(client, auth_as, patched_supported_p } +def test_supported_openai_params_resolves_router_alias(client, auth_as, monkeypatch): + """A router alias absent from the cost map resolves through the deployment's underlying model.""" + router = litellm.Router( + model_list=[ + { + "model_name": "claude-opus-4-6-cached", + "litellm_params": {"model": "anthropic/claude-opus-4-6", "api_key": "sk-test"}, + } + ] + ) + monkeypatch.setattr(proxy_server, "llm_router", router) + + with auth_as(): + response = client.get("/utils/supported_openai_params", params={"model": "claude-opus-4-6-cached"}) + + assert response.status_code == 200 + expected = litellm.get_supported_openai_params(model="claude-opus-4-6", custom_llm_provider="anthropic") + assert response.json() == {"supported_openai_params": expected} + assert "max_tokens" in response.json()["supported_openai_params"] + + +def test_supported_openai_params_declared_prefix_alias_resolves_through_router(client, auth_as, monkeypatch): + """Regression: an alias whose name starts with an authenticating provider's prefix skipped + router resolution and answered with that provider's params instead of the deployment's.""" + router = litellm.Router( + model_list=[ + { + "model_name": "github_copilot/gpt-4o", + "litellm_params": {"model": "anthropic/claude-opus-4-6", "api_key": "sk-test"}, + } + ] + ) + monkeypatch.setattr(proxy_server, "llm_router", router) + + with auth_as(): + response = client.get("/utils/supported_openai_params", params={"model": "github_copilot/gpt-4o"}) + + assert response.status_code == 200 + expected = litellm.get_supported_openai_params(model="claude-opus-4-6", custom_llm_provider="anthropic") + assert response.json() == {"supported_openai_params": expected} + + +def test_supported_openai_params_never_runs_oauth_for_authenticating_providers(client, auth_as, monkeypatch, tmp_path): + """Regression: github_copilot/chatgpt names answer from their declaration; resolving them + through ``get_llm_provider`` would run the provider's OAuth device flow and block the event loop.""" + monkeypatch.setenv("GITHUB_COPILOT_TOKEN_DIR", str(tmp_path)) + (tmp_path / "access-token").write_text("fake-access-token") + (tmp_path / "api-key.json").write_text( + json.dumps( + { + "token": "fake-api-key", + "expires_at": 4102444800, + "endpoints": {"api": "https://api.githubcopilot.com"}, + } + ) + ) + router = litellm.Router( + model_list=[ + { + "model_name": "copilot-alias", + "litellm_params": {"model": "github_copilot/gpt-4o"}, + }, + { + "model_name": "openai/*", + "litellm_params": {"model": "openai/*"}, + }, + ] + ) + monkeypatch.setattr(proxy_server, "llm_router", router) + + resolution_attempts: list[str] = [] + + def _oauth_tripwire(model, *args, **kwargs): + resolution_attempts.append(model) + raise AssertionError("get_llm_provider would run the OAuth device flow") + + monkeypatch.setattr(litellm, "get_llm_provider", _oauth_tripwire) + monkeypatch.setattr(pattern_match_deployments, "get_llm_provider", _oauth_tripwire) + expected = litellm.get_supported_openai_params(model="gpt-4o", custom_llm_provider="github_copilot") + + with auth_as(): + via_alias = client.get("/utils/supported_openai_params", params={"model": "copilot-alias"}) + via_direct_name = client.get("/utils/supported_openai_params", params={"model": "github_copilot/gpt-4o"}) + + assert via_alias.status_code == 200 + assert via_alias.json() == {"supported_openai_params": expected} + assert via_direct_name.status_code == 200 + assert via_direct_name.json() == {"supported_openai_params": expected} + assert resolution_attempts == [] + + def test_supported_openai_params_invalid_model(client, auth_as, monkeypatch): """Pins ``GET /utils/supported_openai_params`` (error: unknown model).""" def _raise(model): raise Exception("unknown") + monkeypatch.setattr(proxy_server, "llm_router", None) monkeypatch.setattr(litellm, "get_llm_provider", _raise) with auth_as(): response = client.get("/utils/supported_openai_params", params={"model": "??"}) diff --git a/tests/test_litellm/proxy/proxy_server/test_spend_counters.py b/tests/test_litellm/proxy/proxy_server/test_spend_counters.py index 51980342a1d..fb3de990deb 100644 --- a/tests/test_litellm/proxy/proxy_server/test_spend_counters.py +++ b/tests/test_litellm/proxy/proxy_server/test_spend_counters.py @@ -271,6 +271,81 @@ async def test_get_current_spend_floors_window_against_spend_logs(monkeypatch): ) +def _make_window_spend_prisma(row=None, spend_logs_total=0.0): + prisma = MagicMock() + prisma.db.litellm_budgetwindowspend.find_unique = AsyncMock(return_value=row) + prisma.db.litellm_spendlogs.group_by = AsyncMock( + return_value=[{"api_key": "tok", "_sum": {"spend": spend_logs_total}}] + ) + return prisma + + +@pytest.mark.asyncio +async def test_get_current_spend_floors_window_against_maintained_row(monkeypatch): + """The floor re-check runs every few seconds per pod, so the window branch + must read the maintained row and leave the unindexed spend-logs scan alone.""" + from datetime import timezone + from types import SimpleNamespace + + window_start = datetime(2026, 1, 1, tzinfo=timezone.utc) + fake_prisma = _make_window_spend_prisma( + row=SimpleNamespace(window_start=window_start, spend=15.0), + spend_logs_total=100.0, + ) + fake_cache = _make_spend_counter_cache(redis_get_value=2.0) + monkeypatch.setattr(ps, "spend_counter_cache", fake_cache) + monkeypatch.setattr(ps, "prisma_client", fake_prisma) + + counter_key = "spend:key:tok:window:7d" + result = await ps.get_current_spend( + counter_key=counter_key, + fallback_spend=0.0, + max_budget=10.0, + window_entity_type="Key", + window_entity_id="tok", + window_duration="7d", + window_start=window_start, + ) + + assert result == 15.0 + fake_prisma.db.litellm_spendlogs.group_by.assert_not_awaited() + fake_cache.redis_cache.async_set_max.assert_awaited_once_with( + key=counter_key, value=15.0 + ) + + +@pytest.mark.asyncio +async def test_get_current_spend_floors_window_against_logs_when_row_stale(monkeypatch): + """A row left behind at a crossed window boundary must not be read as the + current window's spend; the aggregate stays the fallback.""" + from datetime import timedelta, timezone + from types import SimpleNamespace + + window_start = datetime(2026, 1, 8, tzinfo=timezone.utc) + fake_prisma = _make_window_spend_prisma( + row=SimpleNamespace( + window_start=window_start - timedelta(days=7), spend=999.0 + ), + spend_logs_total=15.0, + ) + fake_cache = _make_spend_counter_cache(redis_get_value=2.0) + monkeypatch.setattr(ps, "spend_counter_cache", fake_cache) + monkeypatch.setattr(ps, "prisma_client", fake_prisma) + + result = await ps.get_current_spend( + counter_key="spend:key:tok:window:7d", + fallback_spend=0.0, + max_budget=10.0, + window_entity_type="Key", + window_entity_id="tok", + window_duration="7d", + window_start=window_start, + ) + + assert result == 15.0 + fake_prisma.db.litellm_spendlogs.group_by.assert_awaited_once() + + @pytest.mark.asyncio async def test_get_current_spend_fail_closed_rejects_when_unverifiable(monkeypatch): """With fail_closed_budget_enforcement on, an admit decision backed only by a @@ -895,6 +970,7 @@ async def test_init_and_increment_window_spend_counter_increments_when_initializ counter_key="spend:key:k:window:1d", entity_type="Key", entity_id="k", + window_duration="1d", window_start=datetime(2024, 1, 1), increment=5.0, ) @@ -922,6 +998,7 @@ async def test_init_and_increment_window_spend_counter_missing_window_start_inva counter_key="spend:key:k:window:1d", entity_type="Key", entity_id="k", + window_duration="1d", window_start=None, increment=5.0, ) @@ -1059,6 +1136,7 @@ async def test_ensure_window_spend_counter_initialized_warm_returns_true(monkeyp counter_key="spend:key:k:window:1d", entity_type="Key", entity_id="k", + window_duration="1d", window_start=datetime(2024, 1, 1), ) @@ -1091,6 +1169,7 @@ async def test_ensure_window_spend_counter_initialized_db_failure_invalid_return counter_key="spend:key:k:window:1d", entity_type="Key", entity_id="k", + window_duration="1d", window_start=datetime(2024, 1, 1), ) diff --git a/tests/test_litellm/proxy/proxy_server/test_team_model_name_translation.py b/tests/test_litellm/proxy/proxy_server/test_team_model_name_translation.py index 038d061350f..aa35fd64f18 100644 --- a/tests/test_litellm/proxy/proxy_server/test_team_model_name_translation.py +++ b/tests/test_litellm/proxy/proxy_server/test_team_model_name_translation.py @@ -1540,7 +1540,7 @@ def test_get_direct_access_models_expands_all_proxy_models_sentinel(): result = ps.get_direct_access_models(user_db_object=user, llm_router=router) - assert result == ["global-id-1", "global-id-2"] + assert result == ("global-id-1", "global-id-2") router.get_model_ids.assert_called_once_with(exclude_team_models=True) router.get_model_list.assert_not_called() @@ -1555,11 +1555,172 @@ def test_get_direct_access_models_resolves_explicit_model_names(): result = ps.get_direct_access_models(user_db_object=user, llm_router=router) - assert result == ["gpt4o-id"] + assert result == ("gpt4o-id",) router.get_model_ids.assert_not_called() router.get_model_list.assert_called_once_with(model_name="gpt-4o") +def test_get_direct_access_models_empty_models_grants_all_non_team_models(): + """An empty user.models list means unrestricted access at call time + (can_user_call_model), so the listing must resolve it like 'all-proxy-models' + instead of returning nothing. Regression for a user with models=[] and no + teams seeing an empty Models+Endpoints page.""" + router = MagicMock() + router.get_model_ids.return_value = ["global-id-1", "global-id-2"] + + user = LiteLLM_UserTable(user_id="u", models=[], teams=[]) + + result = ps.get_direct_access_models(user_db_object=user, llm_router=router) + + assert result == ("global-id-1", "global-id-2") + router.get_model_ids.assert_called_once_with(exclude_team_models=True) + router.get_model_list.assert_not_called() + + +@pytest.mark.asyncio +async def test_populate_team_access_grants_empty_models_user_direct_access(monkeypatch): + """An internal user with models=[] and no teams can call every non-team model, + so the Models+Endpoints page must list them instead of rendering empty.""" + global_row = { + "model_name": "gpt-4o", + "litellm_params": {"model": "gpt-4o"}, + "model_info": {"id": "global-id-1", "db_model": False}, + } + + router = MagicMock() + router.get_model_ids.return_value = ["global-id-1"] + + user_row = LiteLLM_UserTable( + user_id="u", + user_role=LitellmUserRoles.INTERNAL_USER.value, + models=[], + teams=[], + ) + prisma_client = MagicMock() + prisma_client.db.litellm_usertable.find_unique = AsyncMock(return_value=user_row) + + monkeypatch.setattr(ps, "get_all_team_models", AsyncMock(return_value={})) + + caller = UserAPIKeyAuth(user_id="u", user_role=LitellmUserRoles.INTERNAL_USER, team_models=[]) + + populated = await ps._populate_team_access_on_models( + user_api_key_dict=caller, + prisma_client=prisma_client, + llm_router=router, + all_models=[global_row], + ) + visible = ps._filter_models_to_user_accessible(populated) + + assert [m["model_info"]["id"] for m in visible] == ["global-id-1"] + assert visible[0]["model_info"]["direct_access"] is True + + +def test_get_direct_access_models_restricted_key_narrows_unrestricted_user(): + """A key scoped to one model cannot call the rest, so the listing must not show + every non-team model just because the user record is unrestricted.""" + router = MagicMock() + router.get_model_ids.return_value = ["gpt4o-id", "sonnet-id"] + router.get_model_access_groups.return_value = {} + router.get_model_list.side_effect = lambda model_name: ( + [{"model_info": {"id": "gpt4o-id"}}] if model_name == "gpt-4o" else [] + ) + + user = LiteLLM_UserTable(user_id="u", models=[], teams=[]) + + result = ps.get_direct_access_models(user_db_object=user, llm_router=router, key_models=("gpt-4o",)) + + assert result == ("gpt4o-id",) + + +def test_get_direct_access_models_all_proxy_models_key_keeps_team_scoped_user_grant(): + """'all-proxy-models' on the key means unrestricted, so it must leave the user's + grant alone rather than clipping it to the non-team deployment set.""" + router = MagicMock() + router.get_model_ids.return_value = ["global-id"] + router.get_model_access_groups.return_value = {} + router.get_model_list.side_effect = lambda model_name: ( + [{"model_info": {"id": "byok-id"}}] if model_name == "byok-model" else [] + ) + + user = LiteLLM_UserTable(user_id="u", models=["byok-model"], teams=[]) + + result = ps.get_direct_access_models( + user_db_object=user, + llm_router=router, + key_models=(ps.SpecialModelNames.all_proxy_models.value,), + ) + + assert result == ("byok-id",) + + +def test_get_direct_access_models_expands_access_group_grant(): + """A grant naming an access group can call the group's members at call time, so the + listing must resolve the members instead of looking up the group name as a model.""" + router = MagicMock() + router.get_model_access_groups.return_value = {"beta-models": ["gpt-4o", "sonnet"]} + router.get_model_list.side_effect = lambda model_name: { + "gpt-4o": [{"model_info": {"id": "gpt4o-id"}}], + "sonnet": [{"model_info": {"id": "sonnet-id"}}], + }.get(model_name, []) + + user = LiteLLM_UserTable(user_id="u", models=["beta-models"], teams=[]) + + result = ps.get_direct_access_models(user_db_object=user, llm_router=router) + + assert result == ("gpt4o-id", "sonnet-id") + + +@pytest.mark.asyncio +async def test_populate_team_access_hides_models_the_calling_key_cannot_call(monkeypatch): + """An unrestricted user calling with a key scoped to one model must only see that + model as direct access; the others 403 at the key check, so listing them over-promises.""" + allowed_row = { + "model_name": "gpt-4o", + "litellm_params": {"model": "gpt-4o"}, + "model_info": {"id": "gpt4o-id", "db_model": False}, + } + blocked_row = { + "model_name": "sonnet", + "litellm_params": {"model": "sonnet"}, + "model_info": {"id": "sonnet-id", "db_model": False}, + } + + router = MagicMock() + router.get_model_ids.return_value = ["gpt4o-id", "sonnet-id"] + router.get_model_access_groups.return_value = {} + router.get_model_list.side_effect = lambda model_name: ( + [{"model_info": {"id": "gpt4o-id"}}] if model_name == "gpt-4o" else [] + ) + + user_row = LiteLLM_UserTable( + user_id="u", + user_role=LitellmUserRoles.INTERNAL_USER.value, + models=[], + teams=[], + ) + prisma_client = MagicMock() + prisma_client.db.litellm_usertable.find_unique = AsyncMock(return_value=user_row) + + monkeypatch.setattr(ps, "get_all_team_models", AsyncMock(return_value={})) + + caller = UserAPIKeyAuth( + user_id="u", + user_role=LitellmUserRoles.INTERNAL_USER, + models=["gpt-4o"], + team_models=[], + ) + + populated = await ps._populate_team_access_on_models( + user_api_key_dict=caller, + prisma_client=prisma_client, + llm_router=router, + all_models=[allowed_row, blocked_row], + ) + visible = ps._filter_models_to_user_accessible(populated) + + assert [m["model_info"]["id"] for m in visible] == ["gpt4o-id"] + + @pytest.mark.asyncio async def test_populate_team_access_grants_all_proxy_models_user_direct_access( monkeypatch, diff --git a/tests/test_litellm/proxy/response_api_endpoints/test_endpoints.py b/tests/test_litellm/proxy/response_api_endpoints/test_endpoints.py index 791d64c6428..d7010de6405 100644 --- a/tests/test_litellm/proxy/response_api_endpoints/test_endpoints.py +++ b/tests/test_litellm/proxy/response_api_endpoints/test_endpoints.py @@ -3,10 +3,12 @@ Test for response_api_endpoints/endpoints.py """ import unittest +from typing import Any from unittest.mock import AsyncMock, MagicMock, patch import pytest from fastapi.testclient import TestClient +from httpx import Response import litellm from litellm.proxy.proxy_server import app @@ -82,11 +84,7 @@ class TestResponsesAPIEndpoints(unittest.TestCase): ResponseOutputMessage( type="message", role="assistant", - content=[ - ResponseOutputText( - type="output_text", text="Hello from Cursor!" - ) - ], + content=[ResponseOutputText(type="output_text", text="Hello from Cursor!")], ) ], ) @@ -121,9 +119,7 @@ class TestResponsesAPIEndpoints(unittest.TestCase): @pytest.mark.asyncio @patch("litellm.proxy.proxy_server.llm_router") @patch("litellm.proxy.proxy_server.user_api_key_auth") - async def test_responses_api_key_spend_header_includes_response_cost( - self, mock_auth, mock_router - ): + async def test_responses_api_key_spend_header_includes_response_cost(self, mock_auth, mock_router): """ Test that x-litellm-key-spend header includes the current request's response_cost for /v1/responses endpoint. @@ -159,9 +155,7 @@ class TestResponsesAPIEndpoints(unittest.TestCase): ResponseOutputMessage( type="message", role="assistant", - content=[ - ResponseOutputText(type="output_text", text="Test response") - ], + content=[ResponseOutputText(type="output_text", text="Test response")], ) ], ) @@ -356,6 +350,7 @@ class TestWSModelExtraction: from litellm.proxy.response_api_endpoints.endpoints import ( _extract_model_from_first_ws_event, ) + event = {"type": "response.create", "model": "gpt-4o", "input": "hello"} assert _extract_model_from_first_ws_event(event) == "gpt-4o" @@ -363,6 +358,7 @@ class TestWSModelExtraction: from litellm.proxy.response_api_endpoints.endpoints import ( _extract_model_from_first_ws_event, ) + event = {"type": "response.create", "response": {"model": "gpt-4o", "input": "hello"}} assert _extract_model_from_first_ws_event(event) == "gpt-4o" @@ -370,6 +366,7 @@ class TestWSModelExtraction: from litellm.proxy.response_api_endpoints.endpoints import ( _extract_model_from_first_ws_event, ) + event = { "type": "response.create", "model": "flat-model", @@ -381,6 +378,7 @@ class TestWSModelExtraction: from litellm.proxy.response_api_endpoints.endpoints import ( _extract_model_from_first_ws_event, ) + event = {"type": "response.create", "input": "hello"} assert _extract_model_from_first_ws_event(event) is None @@ -400,9 +398,7 @@ class TestResponsesWSFirstFrameValidation: ) ws = MagicMock() - ws.receive_text = AsyncMock( - return_value=json.dumps({"type": "session.update", "model": "gpt-4o"}) - ) + ws.receive_text = AsyncMock(return_value=json.dumps({"type": "session.update", "model": "gpt-4o"})) ws.send_text = AsyncMock() ws.close = AsyncMock() @@ -412,10 +408,7 @@ class TestResponsesWSFirstFrameValidation: ws.send_text.assert_awaited_once() ws.close.assert_awaited_once_with(code=1008, reason="Invalid first message") error_payload = json.loads(ws.send_text.await_args.args[0]) - assert ( - error_payload["error"]["message"] - == "First message must be a response.create JSON object." - ) + assert error_payload["error"]["message"] == "First message must be a response.create JSON object." @pytest.mark.asyncio async def test_rejects_non_object_json_first_frame(self): @@ -484,16 +477,12 @@ class TestResponsesWSFirstFrameModelAuth: ws.url = "ws://testserver/v1/responses" ws.accept = AsyncMock() ws.receive_text = AsyncMock( - return_value=json.dumps( - {"type": "response.create", "model": "gpt-4o-mini", "input": []} - ) + return_value=json.dumps({"type": "response.create", "model": "gpt-4o-mini", "input": []}) ) ws.close = AsyncMock() processor = MagicMock() - processor.common_processing_pre_call_logic = AsyncMock( - return_value=({"model": "gpt-4o-mini"}, MagicMock()) - ) + processor.common_processing_pre_call_logic = AsyncMock(return_value=({"model": "gpt-4o-mini"}, MagicMock())) async def fake_llm_call(): return None @@ -529,9 +518,7 @@ class TestResponsesWSFirstFrameModelAuth: _enforce_responses_ws_first_frame_model_auth, ) - request = Request( - {"type": "http", "method": "POST", "path": "/v1/responses", "headers": []} - ) + request = Request({"type": "http", "method": "POST", "path": "/v1/responses", "headers": []}) user_api_key_dict = MagicMock() llm_router = MagicMock() @@ -593,9 +580,7 @@ class TestReadWSModelFromFirstFrameErrors: assert result is None ws.send_text.assert_not_awaited() - ws.close.assert_awaited_once_with( - code=1008, reason="Timed out waiting for first message" - ) + ws.close.assert_awaited_once_with(code=1008, reason="Timed out waiting for first message") @pytest.mark.asyncio async def test_invalid_json_sends_error_and_closes(self): @@ -613,9 +598,7 @@ class TestReadWSModelFromFirstFrameErrors: assert result is None payload = json.loads(ws.send_text.await_args.args[0]) assert payload["error"]["message"] == "First message is not valid JSON." - ws.close.assert_awaited_once_with( - code=1008, reason="Invalid JSON in first message" - ) + ws.close.assert_awaited_once_with(code=1008, reason="Invalid JSON in first message") @pytest.mark.asyncio async def test_missing_model_sends_error_and_closes(self): @@ -624,9 +607,7 @@ class TestReadWSModelFromFirstFrameErrors: ) ws = MagicMock() - ws.receive_text = AsyncMock( - return_value=json.dumps({"type": "response.create", "input": []}) - ) + ws.receive_text = AsyncMock(return_value=json.dumps({"type": "response.create", "input": []})) ws.send_text = AsyncMock() ws.close = AsyncMock() @@ -679,10 +660,7 @@ class TestManagedResponsesSameProvider: assert self._handler("gpt-4o")._same_provider("gpt-4o-mini") is True def test_different_provider_is_not_same(self): - assert ( - self._handler("gpt-4o")._same_provider("vertex_ai/gemini-2.0-flash") - is False - ) + assert self._handler("gpt-4o")._same_provider("vertex_ai/gemini-2.0-flash") is False def test_inject_credentials_keeps_provider_for_same_provider_model(self): handler = self._handler("gpt-4o", custom_llm_provider="openai") @@ -697,18 +675,14 @@ class TestManagedResponsesSameProvider: assert "custom_llm_provider" not in call_kwargs def test_unresolvable_connection_model_falls_back_to_custom_provider(self): - handler = self._handler( - "my-custom-deployment", custom_llm_provider="openai" - ) + handler = self._handler("my-custom-deployment", custom_llm_provider="openai") assert handler._same_provider("gpt-4o-mini") is True call_kwargs: dict = {} handler._inject_credentials(call_kwargs, model="gpt-4o-mini") assert call_kwargs["custom_llm_provider"] == "openai" def test_unresolvable_connection_model_still_drops_cross_provider(self): - handler = self._handler( - "my-custom-deployment", custom_llm_provider="openai" - ) + handler = self._handler("my-custom-deployment", custom_llm_provider="openai") call_kwargs: dict = {} handler._inject_credentials(call_kwargs, model="vertex_ai/gemini-2.0-flash") assert "custom_llm_provider" not in call_kwargs @@ -840,9 +814,7 @@ def test_cursor_chat_completions_input_body_uses_responses_pipeline_and_strips_s type="message", role="assistant", status="completed", - content=[ - ResponseOutputText(type="output_text", text="agent reply", annotations=[]) - ], + content=[ResponseOutputText(type="output_text", text="agent reply", annotations=[])], ) ], ) @@ -851,9 +823,12 @@ def test_cursor_chat_completions_input_body_uses_responses_pipeline_and_strips_s app.dependency_overrides[user_api_key_auth] = _auth_override try: - with patch.object(ps, "llm_router", mock_router), patch( - "litellm.proxy.response_api_endpoints.endpoints._read_request_body", - side_effect=capturing_read_request_body, + with ( + patch.object(ps, "llm_router", mock_router), + patch( + "litellm.proxy.response_api_endpoints.endpoints._read_request_body", + side_effect=capturing_read_request_body, + ), ): client = TestClient(app) response = client.post( @@ -1488,8 +1463,8 @@ def _router_serving_only(base_model: str) -> MagicMock: mock_router.router_general_settings.pass_through_all_models = False mock_router.default_deployment = None mock_router.pattern_router.patterns = {base_model: ["anthropic/*"]} - mock_router.pattern_router.get_pattern.side_effect = ( - lambda model: [{"model_name": "anthropic/*"}] if model == base_model else None + mock_router.pattern_router.get_pattern.side_effect = lambda model: ( + [{"model_name": "anthropic/*"}] if model == base_model else None ) return mock_router @@ -1739,9 +1714,7 @@ class TestCursorGateRecognizesRoutingGroups: from litellm.proxy.response_api_endpoints.endpoints import _resolve_cursor_model_variant router = Router( - model_list=[ - {"model_name": "member-fast", "litellm_params": {"model": "openai/gpt-4o", "api_key": "fake"}} - ], + model_list=[{"model_name": "member-fast", "litellm_params": {"model": "openai/gpt-4o", "api_key": "fake"}}], routing_groups=[ {"group_name": "grouped-thinking-high", "models": ["member-fast"], "routing_strategy": "simple-shuffle"} ], @@ -1836,3 +1809,153 @@ class TestGuardrailBlockedResponsesUsage: assert usage["input_tokens"] == 0 assert usage["output_tokens"] == 0 assert usage["total_tokens"] == 0 + + +class TestResponsesInputTokens: + """Regression tests for POST /v1/responses/input_tokens. + + The docs promise OpenAI-format token counting on the proxy, but the route was + never registered, so the POST fell through to the GET/DELETE-only + /v1/responses/{response_id} route and returned 405.""" + + def _post_input_tokens( + self, + body: dict[str, Any], + path: str = "/v1/responses/input_tokens", + counter: AsyncMock | None = None, + ) -> tuple[Response, AsyncMock]: + from litellm.proxy._types import UserAPIKeyAuth + from litellm.proxy.auth.user_api_key_auth import user_api_key_auth + from litellm.proxy.response_api_endpoints.endpoints import _proxy_token_counter + from litellm.types.utils import TokenCountResponse + + token_counter_mock = ( + counter + if counter is not None + else AsyncMock( + return_value=TokenCountResponse( + total_tokens=13, + request_model=body.get("model", ""), + model_used=body.get("model", ""), + tokenizer_type="openai_api", + ) + ) + ) + app.dependency_overrides[user_api_key_auth] = lambda: UserAPIKeyAuth(api_key="sk-test", request_route=path) + app.dependency_overrides[_proxy_token_counter] = lambda: token_counter_mock + try: + client = TestClient(app) + response = client.post(path, json=body, headers={"Authorization": "Bearer sk-1234"}) + return response, token_counter_mock + finally: + app.dependency_overrides.pop(user_api_key_auth, None) + app.dependency_overrides.pop(_proxy_token_counter, None) + + def test_string_input_returns_openai_input_tokens_shape(self): + response, counter = self._post_input_tokens({"model": "gpt-4o", "input": "Hello, how are you?"}) + + assert response.status_code == 200, response.text + assert response.json() == {"object": "response.input_tokens", "input_tokens": 13} + counter.assert_awaited_once() + assert counter.call_args.kwargs["call_endpoint"] is True + token_request = counter.call_args.kwargs["request"] + assert token_request.model == "gpt-4o" + assert token_request.messages == [{"role": "user", "content": "Hello, how are you?"}] + + def test_every_route_alias_is_registered(self): + for path in ("/v1/responses/input_tokens", "/responses/input_tokens", "/openai/v1/responses/input_tokens"): + response, _ = self._post_input_tokens({"model": "gpt-4o", "input": "hi"}, path=path) + assert response.status_code == 200, f"{path}: {response.status_code} {response.text}" + + def test_input_items_instructions_and_tools_are_forwarded(self): + tools = [ + { + "type": "function", + "name": "get_weather", + "description": "Get weather for a city", + "parameters": {"type": "object", "properties": {"city": {"type": "string"}}}, + } + ] + response, counter = self._post_input_tokens( + { + "model": "gpt-4o", + "input": [{"role": "user", "content": "What is the weather in Paris?"}], + "instructions": "You are terse.", + "tools": tools, + } + ) + + assert response.status_code == 200, response.text + token_request = counter.call_args.kwargs["request"] + assert token_request.messages == [ + {"role": "system", "content": "You are terse."}, + {"role": "user", "content": "What is the weather in Paris?"}, + ] + assert token_request.tools == tools + + def test_missing_model_returns_openai_400(self): + response, counter = self._post_input_tokens({"input": "Hello"}) + + assert response.status_code == 400, response.text + assert response.json() == { + "error": { + "message": "Missing required parameter: 'model'.", + "type": "invalid_request_error", + "param": "model", + "code": "missing_required_parameter", + } + } + counter.assert_not_awaited() + + def test_missing_input_returns_openai_400(self): + response, counter = self._post_input_tokens({"model": "gpt-4o"}) + + assert response.status_code == 400, response.text + assert response.json() == { + "error": { + "message": "Missing required parameter: 'input'.", + "type": "invalid_request_error", + "param": "input", + "code": "missing_required_parameter", + } + } + counter.assert_not_awaited() + + @pytest.mark.parametrize("empty_input", ["", []]) + def test_empty_input_returns_openai_400(self, empty_input): + response, counter = self._post_input_tokens({"model": "gpt-4o", "input": empty_input}) + + assert response.status_code == 400, response.text + assert response.json() == { + "error": { + "message": """One of "input" or "previous_response_id" or 'prompt' or 'conversation' must be provided.""", + "type": "invalid_request_error", + "param": None, + "code": "missing_required_parameter", + } + } + counter.assert_not_awaited() + + def test_invalid_tools_returns_openai_400(self): + response, counter = self._post_input_tokens({"model": "gpt-4o", "input": "hi", "tools": "not-a-list"}) + + assert response.status_code == 400, response.text + error = response.json()["error"] + assert error["type"] == "invalid_request_error" + counter.assert_not_awaited() + + def test_provider_error_maps_status_code(self): + from litellm.proxy._types import ProxyException + + failing_counter = AsyncMock( + side_effect=ProxyException( + message="rate limited", + type="token_counting_error", + param="model", + code="429", + ) + ) + response, _ = self._post_input_tokens({"model": "gpt-4o", "input": "hi"}, counter=failing_counter) + + assert response.status_code == 429, response.text + assert response.json()["error"]["message"] == "rate limited" diff --git a/tests/test_litellm/proxy/spend_tracking/test_budget_reservation.py b/tests/test_litellm/proxy/spend_tracking/test_budget_reservation.py new file mode 100644 index 00000000000..f65f68812a2 --- /dev/null +++ b/tests/test_litellm/proxy/spend_tracking/test_budget_reservation.py @@ -0,0 +1,48 @@ +from typing import Final + +import pytest + +from litellm.caching import DualCache +from litellm.proxy._types import UserAPIKeyAuth +from litellm.proxy.common_utils.user_api_key_cache import UserApiKeyCache +from litellm.proxy.spend_tracking.budget_reservation import reserve_budget_for_request +from litellm.proxy.utils import ProxyLogging + +TOKEN_COUNTING_ROUTES: Final = ( + "/responses/input_tokens", + "/v1/responses/input_tokens", + "/openai/v1/responses/input_tokens", + "/utils/token_counter", +) + + +def _budgeted_token() -> UserAPIKeyAuth: + return UserAPIKeyAuth(api_key="sk-test", token="hashed-token", max_budget=100.0, spend=0.0) + + +async def _reserve(route: str) -> dict | None: + return await reserve_budget_for_request( + request_body={"model": "gpt-4o", "input": "hello"}, + route=route, + llm_router=None, + valid_token=_budgeted_token(), + team_object=None, + user_object=None, + prisma_client=None, + user_api_key_cache=UserApiKeyCache(), + proxy_logging_obj=ProxyLogging(user_api_key_cache=DualCache()), + ) + + +@pytest.mark.asyncio +@pytest.mark.parametrize("route", TOKEN_COUNTING_ROUTES) +async def test_token_counting_routes_are_exempt_from_budget_reservation(route): + assert await _reserve(route) is None + + +@pytest.mark.asyncio +async def test_non_exempt_llm_route_still_reserves_budget(): + reservation: Final = await _reserve("/v1/responses") + + assert reservation is not None + assert reservation["reserved_cost"] > 0 diff --git a/tests/test_litellm/proxy/spend_tracking/test_savings.py b/tests/test_litellm/proxy/spend_tracking/test_savings.py index e8ca569763d..7dd18587df3 100644 --- a/tests/test_litellm/proxy/spend_tracking/test_savings.py +++ b/tests/test_litellm/proxy/spend_tracking/test_savings.py @@ -6,6 +6,7 @@ import litellm from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token from litellm.proxy.spend_tracking.savings import ( _baseline_usage, + _resolve_model, compute_autorouter_savings, compute_savings_spend, marks_gateway_injection, @@ -13,6 +14,8 @@ from litellm.proxy.spend_tracking.savings import ( from litellm.router import Router from litellm.types.utils import Usage +pytestmark = pytest.mark.usefixtures("local_model_cost_map") + def _anthropic_costs(model: str) -> tuple[float, float]: info = litellm.get_model_info(model=model, custom_llm_provider="anthropic") @@ -754,24 +757,55 @@ def test_a_baseline_that_prices_caching_implicitly_still_pays_for_its_prompt(): assert reported > 0, "routing a cold first turn onto a cheaper model is a saving, not a loss" +def _priced_chat_model_without_cache_read_rate() -> tuple[str, str, str]: + """A chat model the bundled map prices per token for input and output but not for cache + reads, derived from the map itself: a hardcoded pick goes stale the moment the registry + prices that model's cache reads, which is exactly how this test's premise last broke. + Candidates go through the savings module's own resolver, so the pick is one the code + under test can actually price.""" + for key in sorted(litellm.model_cost): + entry = litellm.model_cost[key] + provider = entry.get("litellm_provider") + if not isinstance(provider, str) or not key.startswith(f"{provider}/"): + continue + if entry.get("mode") != "chat" or entry.get("cache_read_input_token_cost") is not None: + continue + if not entry.get("input_cost_per_token") or not entry.get("output_cost_per_token"): + continue + if _resolve_model(key, None) is None: + continue + priced = compute_autorouter_savings( + baseline_model=key, + selected_model="claude-haiku-4-5", + selected_provider="anthropic", + usage=_usage(fresh=1_000, cached=0, written=0, out=100), + conversation_continuing=True, + ) + if priced == 0.0: + continue + return key, key.removeprefix(f"{provider}/"), provider + raise AssertionError("the bundled map has no per-token chat model without a cache-read rate") + + def test_a_baseline_with_no_cache_read_rate_is_charged_its_input_rate(): """The same hole on the other bucket. A baseline whose entry has no `cache_read_input_token_cost` reads for 0.0, so a continuing turn priced the whole prompt at nothing and every switch away from it reported a loss. """ + baseline_key, baseline_name, baseline_provider = _priced_chat_model_without_cache_read_rate() continuing = _usage(fresh=0, cached=0, written=20_000, out=1_000) reported = compute_autorouter_savings( - baseline_model="xai/grok-4", + baseline_model=baseline_key, selected_model="claude-haiku-4-5", selected_provider="anthropic", usage=continuing, conversation_continuing=True, ) - grok = litellm.get_model_info("grok-4", "xai") - assert grok.get("cache_read_input_token_cost") is None, "pick a baseline with no cache-read rate" + baseline = litellm.get_model_info(baseline_name, baseline_provider) + assert baseline.get("cache_read_input_token_cost") is None, "pick a baseline with no cache-read rate" haiku = litellm.get_model_info("claude-haiku-4-5", "anthropic") - baseline_pays_input = 20_000 * grok["input_cost_per_token"] + 1_000 * grok["output_cost_per_token"] + baseline_pays_input = 20_000 * baseline["input_cost_per_token"] + 1_000 * baseline["output_cost_per_token"] actually_paid = 20_000 * haiku["cache_creation_input_token_cost"] + 1_000 * haiku["output_cost_per_token"] assert reported == pytest.approx(baseline_pays_input - actually_paid) @@ -1151,6 +1185,61 @@ def test_logging_payload_never_stamps_internal_calls(): assert internal is None +def test_savings_are_net_of_a_priced_classifier(): + """The classifier call is part of what routing cost, so the per-request figure + deducts it; a charge big enough to outweigh the model saving goes negative, + since the figure is signed on purpose (GH #38816).""" + from litellm.proxy.spend_tracking.savings import autorouter_savings_for_request + + gross = autorouter_savings_for_request( + model="claude-haiku-4-5", + custom_llm_provider="anthropic", + routing_decision=_routed_decision(), + usage_object=_cached_usage_object(), + ) + net = autorouter_savings_for_request( + model="claude-haiku-4-5", + custom_llm_provider="anthropic", + routing_decision={**_routed_decision(), "classifier_cost": 0.005}, + usage_object=_cached_usage_object(), + ) + assert gross is not None and net == pytest.approx(gross - 0.005) + + +@pytest.mark.parametrize("classifier_cost", [0.0, "bogus", True]) +def test_an_unpriced_classifier_deducts_nothing(classifier_cost: object): + from litellm.proxy.spend_tracking.savings import autorouter_savings_for_request + + gross = autorouter_savings_for_request( + model="claude-haiku-4-5", + custom_llm_provider="anthropic", + routing_decision=_routed_decision(), + usage_object=_cached_usage_object(), + ) + with_cost_field = autorouter_savings_for_request( + model="claude-haiku-4-5", + custom_llm_provider="anthropic", + routing_decision={**_routed_decision(), "classifier_cost": classifier_cost}, + usage_object=_cached_usage_object(), + ) + assert with_cost_field == gross + + +def test_recorded_savings_are_already_net_and_not_deducted_again(): + """The deduction lives at the figure's computation owner, so a stamped figure is + net by construction; the recorded-wins path must not subtract a second time.""" + result = compute_savings_spend( + model="claude-haiku-4-5", + custom_llm_provider="anthropic", + compression_saved_tokens=0, + gateway_injected_cache=False, + routing_decision={**_routed_decision(), "classifier_cost": 0.005}, + usage_object=_cached_usage_object(), + recorded_autorouter_savings=0.5, + ) + assert result.autorouter == 0.5 + + def test_caching_savings_require_a_gateway_injected_breakpoint(): """The same cached usage is attributed to the gateway only when it added a breakpoint. diff --git a/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py b/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py index 23eb9434585..a0dcbf802ef 100644 --- a/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py +++ b/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py @@ -2865,7 +2865,7 @@ class TestSpendLogsPayload: "model": "gpt-4o", "user": "", "team_id": "", - "metadata": '{"applied_guardrails": [], "attempted_fallbacks": null, "original_model_group": null, "batch_models": null, "mcp_tool_call_metadata": null, "vector_store_request_metadata": null, "routing_decision": null, "internal_call_origin": null, "guardrail_information": null, "compression_savings": null, "litellm_gateway_injected_cache": null, "usage_object": {"completion_tokens": 20, "prompt_tokens": 10, "total_tokens": 30, "completion_tokens_details": null, "prompt_tokens_details": null}, "model_map_information": {"model_map_key": "gpt-4o", "model_map_value": {"key": "gpt-4o", "max_tokens": 16384, "max_input_tokens": 128000, "max_output_tokens": 16384, "input_cost_per_token": 2.5e-06, "cache_creation_input_token_cost": null, "cache_read_input_token_cost": 1.25e-06, "input_cost_per_character": null, "input_cost_per_token_above_128k_tokens": null, "input_cost_per_token_above_200k_tokens": null, "input_cost_per_query": null, "input_cost_per_second": null, "input_cost_per_audio_token": null, "input_cost_per_token_batches": 1.25e-06, "output_cost_per_token_batches": 5e-06, "output_cost_per_token": 1e-05, "output_cost_per_audio_token": null, "output_cost_per_character": null, "output_cost_per_token_above_128k_tokens": null, "output_cost_per_character_above_128k_tokens": null, "output_cost_per_token_above_200k_tokens": null, "output_cost_per_second": null, "output_cost_per_reasoning_token": null, "output_cost_per_image": null, "output_vector_size": null, "litellm_provider": "openai", "mode": "chat", "supports_system_messages": true, "supports_response_schema": true, "supports_vision": true, "supports_function_calling": true, "supports_tool_choice": true, "supports_assistant_prefill": false, "supports_prompt_caching": true, "supports_audio_input": false, "supports_audio_output": false, "supports_pdf_input": false, "supports_embedding_image_input": false, "supports_native_streaming": null, "supports_web_search": true, "supports_reasoning": false, "search_context_cost_per_query": {"search_context_size_low": 0.03, "search_context_size_medium": 0.035, "search_context_size_high": 0.05}, "tpm": null, "rpm": null, "supported_openai_params": ["frequency_penalty", "logit_bias", "logprobs", "top_logprobs", "max_tokens", "max_completion_tokens", "modalities", "prediction", "n", "presence_penalty", "seed", "stop", "stream", "stream_options", "temperature", "top_p", "tools", "tool_choice", "function_call", "functions", "max_retries", "extra_headers", "parallel_tool_calls", "audio", "response_format", "user"]}}, "additional_usage_values": {"completion_tokens_details": null, "prompt_tokens_details": null}}', + "metadata": '{"applied_guardrails": [], "attempted_fallbacks": null, "original_model_group": null, "batch_models": null, "batch_successful_requests": null, "batch_failed_requests": null, "mcp_tool_call_metadata": null, "vector_store_request_metadata": null, "routing_decision": null, "internal_call_origin": null, "guardrail_information": null, "compression_savings": null, "litellm_gateway_injected_cache": null, "router_metadata": null, "usage_object": {"completion_tokens": 20, "prompt_tokens": 10, "total_tokens": 30, "completion_tokens_details": null, "prompt_tokens_details": null}, "model_map_information": {"model_map_key": "gpt-4o", "model_map_value": {"key": "gpt-4o", "max_tokens": 16384, "max_input_tokens": 128000, "max_output_tokens": 16384, "input_cost_per_token": 2.5e-06, "cache_creation_input_token_cost": null, "cache_read_input_token_cost": 1.25e-06, "input_cost_per_character": null, "input_cost_per_token_above_128k_tokens": null, "input_cost_per_token_above_200k_tokens": null, "input_cost_per_query": null, "input_cost_per_second": null, "input_cost_per_audio_token": null, "input_cost_per_token_batches": 1.25e-06, "output_cost_per_token_batches": 5e-06, "output_cost_per_token": 1e-05, "output_cost_per_audio_token": null, "output_cost_per_character": null, "output_cost_per_token_above_128k_tokens": null, "output_cost_per_character_above_128k_tokens": null, "output_cost_per_token_above_200k_tokens": null, "output_cost_per_second": null, "output_cost_per_reasoning_token": null, "output_cost_per_image": null, "output_vector_size": null, "litellm_provider": "openai", "mode": "chat", "supports_system_messages": true, "supports_response_schema": true, "supports_vision": true, "supports_function_calling": true, "supports_tool_choice": true, "supports_assistant_prefill": false, "supports_prompt_caching": true, "supports_audio_input": false, "supports_audio_output": false, "supports_pdf_input": false, "supports_embedding_image_input": false, "supports_native_streaming": null, "supports_web_search": true, "supports_reasoning": false, "search_context_cost_per_query": {"search_context_size_low": 0.03, "search_context_size_medium": 0.035, "search_context_size_high": 0.05}, "tpm": null, "rpm": null, "supported_openai_params": ["frequency_penalty", "logit_bias", "logprobs", "top_logprobs", "max_tokens", "max_completion_tokens", "modalities", "prediction", "n", "presence_penalty", "seed", "stop", "stream", "stream_options", "temperature", "top_p", "tools", "tool_choice", "function_call", "functions", "max_retries", "extra_headers", "parallel_tool_calls", "audio", "response_format", "user"]}}, "additional_usage_values": {"completion_tokens_details": null, "prompt_tokens_details": null}}', "cache_key": "Cache OFF", "spend": 0.00022500000000000002, "total_tokens": 30, @@ -2961,7 +2961,7 @@ class TestSpendLogsPayload: "model": "claude-4-sonnet-20250514", "user": "", "team_id": "", - "metadata": '{"applied_guardrails": [], "attempted_fallbacks": null, "original_model_group": null, "batch_models": null, "mcp_tool_call_metadata": null, "vector_store_request_metadata": null, "routing_decision": null, "internal_call_origin": null, "guardrail_information": null, "compression_savings": null, "litellm_gateway_injected_cache": null, "usage_object": {"completion_tokens": 503, "prompt_tokens": 2095, "total_tokens": 2598, "completion_tokens_details": null, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}, "model_map_information": {"model_map_key": "claude-4-sonnet-20250514", "model_map_value": {"key": "claude-4-sonnet-20250514", "max_tokens": 128000, "max_input_tokens": 200000, "max_output_tokens": 128000, "input_cost_per_token": 3e-06, "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "input_cost_per_character": null, "input_cost_per_token_above_128k_tokens": null, "input_cost_per_token_above_200k_tokens": null, "input_cost_per_query": null, "input_cost_per_second": null, "input_cost_per_audio_token": null, "input_cost_per_token_batches": null, "output_cost_per_token_batches": null, "output_cost_per_token": 1.5e-05, "output_cost_per_audio_token": null, "output_cost_per_character": null, "output_cost_per_token_above_128k_tokens": null, "output_cost_per_character_above_128k_tokens": null, "output_cost_per_token_above_200k_tokens": null, "output_cost_per_second": null, "output_cost_per_image": null, "output_vector_size": null, "litellm_provider": "anthropic", "mode": "chat", "supports_system_messages": null, "supports_response_schema": true, "supports_vision": true, "supports_function_calling": true, "supports_tool_choice": true, "supports_assistant_prefill": true, "supports_prompt_caching": true, "supports_audio_input": false, "supports_audio_output": false, "supports_pdf_input": true, "supports_embedding_image_input": false, "supports_native_streaming": null, "supports_web_search": false, "supports_reasoning": true, "search_context_cost_per_query": null, "tpm": null, "rpm": null, "supported_openai_params": ["stream", "stop", "temperature", "top_p", "max_tokens", "max_completion_tokens", "tools", "tool_choice", "extra_headers", "parallel_tool_calls", "response_format", "user", "reasoning_effort", "thinking"]}}, "additional_usage_values": {"completion_tokens_details": {"accepted_prediction_tokens": null, "audio_tokens": null, "reasoning_tokens": null, "rejected_prediction_tokens": null, "text_tokens": 503, "image_tokens": null}, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": 0, "text_tokens": null, "image_tokens": null}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}', + "metadata": '{"applied_guardrails": [], "attempted_fallbacks": null, "original_model_group": null, "batch_models": null, "batch_successful_requests": null, "batch_failed_requests": null, "mcp_tool_call_metadata": null, "vector_store_request_metadata": null, "routing_decision": null, "internal_call_origin": null, "guardrail_information": null, "compression_savings": null, "litellm_gateway_injected_cache": null, "router_metadata": null, "usage_object": {"completion_tokens": 503, "prompt_tokens": 2095, "total_tokens": 2598, "completion_tokens_details": null, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}, "model_map_information": {"model_map_key": "claude-4-sonnet-20250514", "model_map_value": {"key": "claude-4-sonnet-20250514", "max_tokens": 128000, "max_input_tokens": 200000, "max_output_tokens": 128000, "input_cost_per_token": 3e-06, "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "input_cost_per_character": null, "input_cost_per_token_above_128k_tokens": null, "input_cost_per_token_above_200k_tokens": null, "input_cost_per_query": null, "input_cost_per_second": null, "input_cost_per_audio_token": null, "input_cost_per_token_batches": null, "output_cost_per_token_batches": null, "output_cost_per_token": 1.5e-05, "output_cost_per_audio_token": null, "output_cost_per_character": null, "output_cost_per_token_above_128k_tokens": null, "output_cost_per_character_above_128k_tokens": null, "output_cost_per_token_above_200k_tokens": null, "output_cost_per_second": null, "output_cost_per_image": null, "output_vector_size": null, "litellm_provider": "anthropic", "mode": "chat", "supports_system_messages": null, "supports_response_schema": true, "supports_vision": true, "supports_function_calling": true, "supports_tool_choice": true, "supports_assistant_prefill": true, "supports_prompt_caching": true, "supports_audio_input": false, "supports_audio_output": false, "supports_pdf_input": true, "supports_embedding_image_input": false, "supports_native_streaming": null, "supports_web_search": false, "supports_reasoning": true, "search_context_cost_per_query": null, "tpm": null, "rpm": null, "supported_openai_params": ["stream", "stop", "temperature", "top_p", "max_tokens", "max_completion_tokens", "tools", "tool_choice", "extra_headers", "parallel_tool_calls", "response_format", "user", "reasoning_effort", "thinking"]}}, "additional_usage_values": {"completion_tokens_details": {"accepted_prediction_tokens": null, "audio_tokens": null, "reasoning_tokens": null, "rejected_prediction_tokens": null, "text_tokens": 503, "image_tokens": null}, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": 0, "text_tokens": null, "image_tokens": null}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}', "cache_key": "Cache OFF", "spend": 0.01383, "total_tokens": 2598, @@ -3055,7 +3055,7 @@ class TestSpendLogsPayload: "model": "claude-4-sonnet-20250514", "user": "", "team_id": "", - "metadata": '{"applied_guardrails": [], "attempted_fallbacks": 0, "original_model_group": "my-anthropic-model-group", "batch_models": null, "mcp_tool_call_metadata": null, "vector_store_request_metadata": null, "routing_decision": null, "internal_call_origin": null, "guardrail_information": null, "compression_savings": null, "litellm_gateway_injected_cache": null, "usage_object": {"completion_tokens": 503, "prompt_tokens": 2095, "total_tokens": 2598, "completion_tokens_details": null, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}, "model_map_information": {"model_map_key": "claude-4-sonnet-20250514", "model_map_value": {"key": "claude-4-sonnet-20250514", "max_tokens": 128000, "max_input_tokens": 200000, "max_output_tokens": 128000, "input_cost_per_token": 3e-06, "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "input_cost_per_character": null, "input_cost_per_token_above_128k_tokens": null, "input_cost_per_token_above_200k_tokens": null, "input_cost_per_query": null, "input_cost_per_second": null, "input_cost_per_audio_token": null, "input_cost_per_token_batches": null, "output_cost_per_token_batches": null, "output_cost_per_token": 1.5e-05, "output_cost_per_audio_token": null, "output_cost_per_character": null, "output_cost_per_token_above_128k_tokens": null, "output_cost_per_character_above_128k_tokens": null, "output_cost_per_token_above_200k_tokens": null, "output_cost_per_second": null, "output_cost_per_image": null, "output_vector_size": null, "litellm_provider": "anthropic", "mode": "chat", "supports_system_messages": null, "supports_response_schema": true, "supports_vision": true, "supports_function_calling": true, "supports_tool_choice": true, "supports_assistant_prefill": true, "supports_prompt_caching": true, "supports_audio_input": false, "supports_audio_output": false, "supports_pdf_input": true, "supports_embedding_image_input": false, "supports_native_streaming": null, "supports_web_search": false, "supports_reasoning": true, "search_context_cost_per_query": null, "tpm": null, "rpm": null, "supported_openai_params": ["stream", "stop", "temperature", "top_p", "max_tokens", "max_completion_tokens", "tools", "tool_choice", "extra_headers", "parallel_tool_calls", "response_format", "user", "reasoning_effort", "thinking"]}}, "additional_usage_values": {"completion_tokens_details": {"accepted_prediction_tokens": null, "audio_tokens": null, "reasoning_tokens": null, "rejected_prediction_tokens": null, "text_tokens": 503, "image_tokens": null}, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": 0, "text_tokens": null, "image_tokens": null}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}', + "metadata": '{"applied_guardrails": [], "attempted_fallbacks": 0, "original_model_group": "my-anthropic-model-group", "batch_models": null, "batch_successful_requests": null, "batch_failed_requests": null, "mcp_tool_call_metadata": null, "vector_store_request_metadata": null, "routing_decision": null, "internal_call_origin": null, "guardrail_information": null, "compression_savings": null, "litellm_gateway_injected_cache": null, "router_metadata": null, "usage_object": {"completion_tokens": 503, "prompt_tokens": 2095, "total_tokens": 2598, "completion_tokens_details": null, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}, "model_map_information": {"model_map_key": "claude-4-sonnet-20250514", "model_map_value": {"key": "claude-4-sonnet-20250514", "max_tokens": 128000, "max_input_tokens": 200000, "max_output_tokens": 128000, "input_cost_per_token": 3e-06, "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "input_cost_per_character": null, "input_cost_per_token_above_128k_tokens": null, "input_cost_per_token_above_200k_tokens": null, "input_cost_per_query": null, "input_cost_per_second": null, "input_cost_per_audio_token": null, "input_cost_per_token_batches": null, "output_cost_per_token_batches": null, "output_cost_per_token": 1.5e-05, "output_cost_per_audio_token": null, "output_cost_per_character": null, "output_cost_per_token_above_128k_tokens": null, "output_cost_per_character_above_128k_tokens": null, "output_cost_per_token_above_200k_tokens": null, "output_cost_per_second": null, "output_cost_per_image": null, "output_vector_size": null, "litellm_provider": "anthropic", "mode": "chat", "supports_system_messages": null, "supports_response_schema": true, "supports_vision": true, "supports_function_calling": true, "supports_tool_choice": true, "supports_assistant_prefill": true, "supports_prompt_caching": true, "supports_audio_input": false, "supports_audio_output": false, "supports_pdf_input": true, "supports_embedding_image_input": false, "supports_native_streaming": null, "supports_web_search": false, "supports_reasoning": true, "search_context_cost_per_query": null, "tpm": null, "rpm": null, "supported_openai_params": ["stream", "stop", "temperature", "top_p", "max_tokens", "max_completion_tokens", "tools", "tool_choice", "extra_headers", "parallel_tool_calls", "response_format", "user", "reasoning_effort", "thinking"]}}, "additional_usage_values": {"completion_tokens_details": {"accepted_prediction_tokens": null, "audio_tokens": null, "reasoning_tokens": null, "rejected_prediction_tokens": null, "text_tokens": 503, "image_tokens": null}, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": 0, "text_tokens": null, "image_tokens": null}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}', "cache_key": "Cache OFF", "spend": 0.01383, "total_tokens": 2598, diff --git a/tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py b/tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py index 5022dab32be..9e5917637a8 100644 --- a/tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py +++ b/tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py @@ -2,6 +2,7 @@ import asyncio import datetime import json from datetime import timezone +from collections.abc import Mapping from typing import Any, Final, cast from unittest.mock import AsyncMock, MagicMock, patch @@ -3956,3 +3957,71 @@ def test_passthrough_caching_carries_no_injection_marker(): ) metadata = json.loads(payload["metadata"]) assert metadata["litellm_gateway_injected_cache"] is None + + +def _routed_call_kwargs(model_info: Mapping[str, object]) -> dict[str, object]: + return { + "model": "claude-haiku-4-5", + "custom_llm_provider": "azure_ai", + "litellm_call_id": "router-corr-123", + "litellm_params": { + "metadata": { + "user_api_key": "test-key", + "model_group": "internal-router/gpt-5.4", + "deployment": "azure_ai/claude-haiku-4-5", + "model_info": model_info, + } + }, + } + + +def test_router_metadata_stamped_for_internal_router_model_deployment(): + """A deployment flagged model_info.internal_router_model gets a router_metadata + block correlating the requested model group with the selected deployment.""" + payload = get_logging_payload( + kwargs=_routed_call_kwargs({"id": "mi-1", "internal_router_model": True}), + response_obj=litellm.ModelResponse(id="chatcmpl-router-meta", choices=[], usage=litellm.Usage()), + start_time=datetime.datetime.now(timezone.utc), + end_time=datetime.datetime.now(timezone.utc), + ) + metadata = json.loads(payload["metadata"]) + assert metadata["router_metadata"] == { + "requested_model": "internal-router/gpt-5.4", + "selected_model": "azure_ai/claude-haiku-4-5", + "selected_provider": "azure_ai", + "router_correlation_id": "router-corr-123", + } + + +def test_router_metadata_absent_without_internal_router_model_flag(): + payload = get_logging_payload( + kwargs=_routed_call_kwargs({"id": "mi-1"}), + response_obj=litellm.ModelResponse(id="chatcmpl-unflagged", choices=[], usage=litellm.Usage()), + start_time=datetime.datetime.now(timezone.utc), + end_time=datetime.datetime.now(timezone.utc), + ) + metadata = json.loads(payload["metadata"]) + assert metadata["router_metadata"] is None + + +@pytest.mark.parametrize("bucket", ["metadata", "litellm_metadata"]) +def test_caller_forged_router_metadata_is_discarded(bucket): + """The raw request bucket is client-writable and _get_spend_logs_metadata projects + every SpendLogsMetadata key from it, so the server-derived value must overwrite + unconditionally or a caller could plant router provenance the router never produced.""" + payload = get_logging_payload( + kwargs={ + "model": "gpt-4o-mini", + "litellm_params": { + bucket: { + "user_api_key": "test-key", + "router_metadata": {"requested_model": "forged", "router_correlation_id": "forged-id"}, + } + }, + }, + response_obj=litellm.ModelResponse(id="chatcmpl-forged-router-meta", choices=[], usage=litellm.Usage()), + start_time=datetime.datetime.now(timezone.utc), + end_time=datetime.datetime.now(timezone.utc), + ) + metadata = json.loads(payload["metadata"]) + assert metadata["router_metadata"] is None diff --git a/tests/test_litellm/proxy/test_audio_speech_prometheus_hooks.py b/tests/test_litellm/proxy/test_audio_speech_prometheus_hooks.py index 99f6f3a9b72..959cb2b1e89 100644 --- a/tests/test_litellm/proxy/test_audio_speech_prometheus_hooks.py +++ b/tests/test_litellm/proxy/test_audio_speech_prometheus_hooks.py @@ -2,6 +2,7 @@ import asyncio import os from unittest.mock import AsyncMock, MagicMock, patch +import httpx import pytest from fastapi.testclient import TestClient @@ -29,6 +30,7 @@ def _make_mock_tts_response(): inner = MagicMock() inner.aiter_bytes = _aiter_bytes inner._hidden_params = {} + inner.response = httpx.Response(status_code=200, headers={"content-type": "audio/mpeg"}) async def _resolver(): return inner diff --git a/tests/test_litellm/proxy/test_budget_reservation.py b/tests/test_litellm/proxy/test_budget_reservation.py index 38a346e7fb7..b8fb6170d34 100644 --- a/tests/test_litellm/proxy/test_budget_reservation.py +++ b/tests/test_litellm/proxy/test_budget_reservation.py @@ -2,6 +2,7 @@ import asyncio import threading from collections.abc import Mapping from datetime import datetime, timedelta, timezone +from types import SimpleNamespace from unittest.mock import AsyncMock, MagicMock, patch import pytest @@ -10,15 +11,16 @@ from fastapi import HTTPException import litellm from litellm.caching.dual_cache import DualCache from litellm.constants import STREAM_SSE_KEEPALIVE_PING_BYTES -from litellm.llms.anthropic.experimental_pass_through.messages.streaming_iterator import ( - AnthropicMessagesStreamingResponse, -) from litellm.llms.anthropic.experimental_pass_through.messages.agentic_streaming_iterator import ( AgenticAnthropicStreamingIterator, ) +from litellm.llms.anthropic.experimental_pass_through.messages.streaming_iterator import ( + AnthropicMessagesStreamingResponse, +) from litellm.proxy._types import ( LiteLLM_BudgetTable, LiteLLM_EndUserTable, + Litellm_EntityType, LiteLLM_OrganizationTable, LiteLLM_TagTable, LiteLLM_TeamMembership, @@ -27,9 +29,16 @@ from litellm.proxy._types import ( UserAPIKeyAuth, ) from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing +from litellm.proxy.common_utils.reset_budget_job import _model_access_group_counter_key +from litellm.proxy.common_utils.user_api_key_cache import ( + UserApiKeyCache, + model_access_group_cache_key, + model_access_group_spend_counter_key, +) from litellm.proxy.spend_tracking.budget_reservation import ( TOKENIZE_OFF_EVENT_LOOP_MIN_CHARS, _approximate_input_size, + _get_model_access_group_budget_counters, estimate_request_max_cost, get_budget_window_start, invalidate_budget_reservation_counters, @@ -39,6 +48,7 @@ from litellm.proxy.spend_tracking.budget_reservation import ( ) from litellm.proxy.utils import ProxyLogging from litellm.router import Router +from litellm.types.proxy.model_access_group_budget import ModelAccessGroupBudget @pytest.fixture() @@ -809,6 +819,94 @@ async def test_should_cap_known_estimate_to_remaining_budget( ) == pytest.approx(0.9) +@pytest.mark.asyncio +async def test_fail_closed_rejects_known_estimate_exceeding_remaining_budget( + spend_counter_state, +): + """LIT-5922: with strict enforcement on, a request whose known estimate does + not fit the remaining budget must be rejected before dispatch instead of + having its reservation shrunk to the headroom and admitted, and the counter + must be restored to the pre-request spend.""" + counter_cache, key_cache = spend_counter_state + proxy_logging_obj = ProxyLogging(user_api_key_cache=key_cache) + valid_token = UserAPIKeyAuth( + token="key-budget-known-estimate-fail-closed", + spend=0.9, + max_budget=1.0, + ) + counter_cache.in_memory_cache.set_cache( + key="spend:key:key-budget-known-estimate-fail-closed", + value=0.9, + ) + + with patch( # test-quality-ok: reserve_budget_for_request takes no estimator, so pinning the estimate needs this attribute + "litellm.proxy.spend_tracking.budget_reservation.estimate_request_max_cost", + return_value=0.6, + ): + with pytest.raises(litellm.BudgetExceededError) as exc_info: + await reserve_budget_for_request( + request_body=_request_body(), + route="/chat/completions", + llm_router=None, + valid_token=valid_token, + team_object=None, + user_object=None, + prisma_client=None, + user_api_key_cache=key_cache, + proxy_logging_obj=proxy_logging_obj, + fail_closed_budget_enforcement=True, + ) + + assert exc_info.value.current_cost == pytest.approx(0.9) + assert exc_info.value.max_budget == pytest.approx(1.0) + assert "Current cost: 0.9, Estimated request cost: 0.6, Max budget: 1.0" in str(exc_info.value) + assert counter_cache.in_memory_cache.get_cache( + key="spend:key:key-budget-known-estimate-fail-closed" + ) == pytest.approx(0.9) + + +@pytest.mark.asyncio +async def test_fail_closed_tolerates_float_noise_when_estimate_exactly_fits( + spend_counter_state, +): + """0.1 + 0.2 lands a hair above 0.3 in floating point. Strict enforcement + must treat that as fitting the budget, not reject it.""" + counter_cache, key_cache = spend_counter_state + proxy_logging_obj = ProxyLogging(user_api_key_cache=key_cache) + valid_token = UserAPIKeyAuth( + token="key-budget-fail-closed-float-noise", + spend=0.1, + max_budget=0.3, + ) + counter_cache.in_memory_cache.set_cache( + key="spend:key:key-budget-fail-closed-float-noise", + value=0.1, + ) + + with patch( # test-quality-ok: reserve_budget_for_request takes no estimator, so pinning the estimate needs this attribute + "litellm.proxy.spend_tracking.budget_reservation.estimate_request_max_cost", + return_value=0.2, + ): + reservation = await reserve_budget_for_request( + request_body=_request_body(), + route="/chat/completions", + llm_router=None, + valid_token=valid_token, + team_object=None, + user_object=None, + prisma_client=None, + user_api_key_cache=key_cache, + proxy_logging_obj=proxy_logging_obj, + fail_closed_budget_enforcement=True, + ) + + assert reservation is not None + assert reservation["reserved_cost"] == pytest.approx(0.2) + assert counter_cache.in_memory_cache.get_cache( + key="spend:key:key-budget-fail-closed-float-noise" + ) == pytest.approx(0.3) + + @pytest.mark.asyncio async def test_should_clamp_reservation_to_default_when_output_cap_missing( spend_counter_state, @@ -2962,3 +3060,214 @@ async def test_small_prompt_is_tokenized_inline(spend_counter_state): assert reservation is not None assert threads == [threading.main_thread()] + + +class _ModelAccessGroupBudgetPrisma: + """Serves ``LiteLLM_ModelAccessGroupBudgetTable`` rows, recording what reached the database.""" + + def __init__(self, **max_budget_by_group) -> None: + self.rows = { + group: SimpleNamespace( + access_group_name=group, + spend=7.0, + litellm_budget_table=None if max_budget is None else SimpleNamespace(max_budget=max_budget), + ) + for group, max_budget in max_budget_by_group.items() + } + self.batches = [] + self.db = SimpleNamespace( + litellm_modelaccessgroupbudgettable=SimpleNamespace(find_many=self._find_many) + ) + + async def _find_many(self, **kwargs): + requested = list(kwargs["where"]["access_group_name"]["in"]) + self.batches.append(requested) + return [self.rows[group] for group in requested if group in self.rows] + + +async def _model_access_group_counters(matched, **max_budget_by_group): + return await _get_model_access_group_budget_counters( + valid_token=UserAPIKeyAuth(api_key="hashed", matched_model_access_groups=matched), + prisma_client=_ModelAccessGroupBudgetPrisma(**max_budget_by_group), + user_api_key_cache=UserApiKeyCache(), + ) + + +@pytest.mark.asyncio +async def test_model_access_group_with_a_budget_reserves_against_the_reset_jobs_counter_key(): + counters = await _model_access_group_counters(["premium"], premium=25.0) + + assert len(counters) == 1 + counter = counters[0] + assert counter.counter_key == _model_access_group_counter_key(SimpleNamespace(access_group_name="premium")) + assert counter.source_cache_key == model_access_group_cache_key("premium") + assert counter.max_budget == 25.0 + assert counter.fallback_spend == 7.0 + assert counter.entity_type == "Model access group" + assert counter.entity_id == "premium" + + +@pytest.mark.asyncio +async def test_model_access_group_without_a_budget_reserves_nothing(): + assert await _model_access_group_counters(["premium"], premium=None) == [] + + +@pytest.mark.asyncio +async def test_model_access_group_with_a_zero_budget_reserves_nothing(): + """Zero is how a budget is cleared, not a ceiling that blocks every request.""" + assert await _model_access_group_counters(["premium"], premium=0.0) == [] + + +@pytest.mark.asyncio +async def test_model_access_group_counters_come_from_the_auth_object(): + """Auth already resolved which granted groups serve the model; re-deriving it here would drift.""" + assert await _model_access_group_counters(None, premium=25.0) == [] + + +@pytest.mark.asyncio +async def test_repeated_model_access_group_reserves_once(): + counters = await _model_access_group_counters(["premium", "premium"], premium=25.0) + + assert [counter.entity_id for counter in counters] == ["premium"] + + +@pytest.mark.asyncio +async def test_model_access_group_counter_blocks_a_request_over_the_group_budget(spend_counter_state): + """End to end through the reservation path, which is what runs when reservations are enabled.""" + counter_cache, key_cache = spend_counter_state + prisma_client = _ModelAccessGroupBudgetPrisma(premium=1.0) + valid_token = UserAPIKeyAuth(api_key="hashed", token="tok", matched_model_access_groups=["premium"]) + + with patch( # test-quality-ok: reserve_budget_for_request takes no estimator, so pinning the estimate needs this attribute + "litellm.proxy.spend_tracking.budget_reservation.estimate_request_max_cost", + return_value=0.5, + ): + with pytest.raises(litellm.BudgetExceededError) as exc_info: + await reserve_budget_for_request( + request_body=_request_body(), + route="/chat/completions", + llm_router=None, + valid_token=valid_token, + team_object=None, + user_object=None, + prisma_client=prisma_client, + user_api_key_cache=key_cache, + proxy_logging_obj=ProxyLogging(user_api_key_cache=key_cache), + ) + + assert exc_info.value.entity_id == "premium" + assert exc_info.value.entity_type == Litellm_EntityType.MODEL_ACCESS_GROUP.value + + +async def _cache_model_access_group_budget(key_cache, group, spend, max_budget=None): + await key_cache.async_set_cache( + key=model_access_group_cache_key(group), + value=ModelAccessGroupBudget(access_group_name=group, spend=spend, max_budget=max_budget), + model_type=ModelAccessGroupBudget, + ) + + +async def _reserve_for_model_access_groups(key_cache, groups, estimate): + """Reserve against the given groups, whose rows are already cached, so nothing hits the DB.""" + with patch( # test-quality-ok: reserve_budget_for_request takes no estimator, so pinning the estimate needs this attribute + "litellm.proxy.spend_tracking.budget_reservation.estimate_request_max_cost", + return_value=estimate, + ): + return await reserve_budget_for_request( + request_body=_request_body(), + route="/chat/completions", + llm_router=None, + valid_token=UserAPIKeyAuth( + api_key="hashed", token="tok-mag-counter", matched_model_access_groups=list(groups) + ), + team_object=None, + user_object=None, + prisma_client=_ModelAccessGroupBudgetPrisma(), + user_api_key_cache=key_cache, + proxy_logging_obj=ProxyLogging(user_api_key_cache=key_cache), + ) + + +@pytest.mark.asyncio +async def test_model_access_group_counter_accumulates_across_calls_without_a_reservation(spend_counter_state): + """With reservations disabled nothing writes the counter up front, so the cost callback must. + + Otherwise the read-time budget check enforces against the DB row's spend, which the cache + holds for the full TTL, and a caller runs past the ceiling for that whole window. + """ + counter_cache, key_cache = spend_counter_state + await _cache_model_access_group_budget(key_cache, "premium", spend=1.0, max_budget=25.0) + + from litellm.proxy.proxy_server import increment_spend_counters + + counter_key = model_access_group_spend_counter_key("premium") + + await increment_spend_counters( + token=None, team_id=None, user_id=None, response_cost=0.25, model_access_groups=["premium"] + ) + assert counter_cache.in_memory_cache.get_cache(key=counter_key) == pytest.approx(1.25) + + await increment_spend_counters( + token=None, team_id=None, user_id=None, response_cost=0.75, model_access_groups=["premium", "premium", ""] + ) + assert counter_cache.in_memory_cache.get_cache(key=counter_key) == pytest.approx(2.0) + assert counter_cache.in_memory_cache.get_cache(key=model_access_group_spend_counter_key("")) is None + + +@pytest.mark.asyncio +async def test_reserved_model_access_group_is_not_charged_twice(spend_counter_state): + """The reservation already wrote this counter, so the post-call pass has to skip it.""" + counter_cache, key_cache = spend_counter_state + await _cache_model_access_group_budget(key_cache, "premium", spend=1.0, max_budget=25.0) + + reservation = await _reserve_for_model_access_groups(key_cache, ["premium"], estimate=0.6) + counter_key = model_access_group_spend_counter_key("premium") + assert counter_cache.in_memory_cache.get_cache(key=counter_key) == pytest.approx(1.6) + + from litellm.proxy.proxy_server import increment_spend_counters + + await increment_spend_counters( + token=None, + team_id=None, + user_id=None, + response_cost=0.2, + budget_reservation=reservation, + model_access_groups=["premium"], + ) + + # 1.0 recorded + the reservation reconciled down to the 0.2 actually spent. A second + # increment would land at 1.4. + assert counter_cache.in_memory_cache.get_cache(key=counter_key) == pytest.approx(1.2) + + +@pytest.mark.asyncio +async def test_unreserved_model_access_group_is_charged_alongside_a_reserved_one(spend_counter_state): + """A budgetless group reserves nothing, so only the post-call pass can charge it. + + Both groups authorized the request and both get debited, each exactly once, whether or not + the reservation path happened to hold a counter for them. + """ + counter_cache, key_cache = spend_counter_state + await _cache_model_access_group_budget(key_cache, "premium", spend=1.0, max_budget=25.0) + await _cache_model_access_group_budget(key_cache, "starter", spend=4.0) + + reservation = await _reserve_for_model_access_groups(key_cache, ["premium", "starter"], estimate=0.6) + assert [entry["entity_id"] for entry in reservation["entries"]] == ["premium"] + + from litellm.proxy.proxy_server import increment_spend_counters + + await increment_spend_counters( + token=None, + team_id=None, + user_id=None, + response_cost=0.2, + budget_reservation=reservation, + model_access_groups=["premium", "starter", "starter", "premium"], + ) + + assert counter_cache.in_memory_cache.get_cache( + key=model_access_group_spend_counter_key("premium") + ) == pytest.approx(1.2) + assert counter_cache.in_memory_cache.get_cache( + key=model_access_group_spend_counter_key("starter") + ) == pytest.approx(4.2) diff --git a/tests/test_litellm/proxy/test_common_request_processing.py b/tests/test_litellm/proxy/test_common_request_processing.py index 71d4666416d..df14224af5c 100644 --- a/tests/test_litellm/proxy/test_common_request_processing.py +++ b/tests/test_litellm/proxy/test_common_request_processing.py @@ -1659,29 +1659,57 @@ class TestCommonRequestProcessingHelpers: async def test_serialize_http_exception_detail_helper(self): """Direct unit coverage for the L1 helper across all branches.""" from litellm.proxy.common_request_processing import ( - _serialize_http_exception_detail, + serialize_http_exception_detail, ) import json as _json - assert _serialize_http_exception_detail("plain") == ("plain", None) + assert serialize_http_exception_detail("plain") == ("plain", None) - msg, fields = _serialize_http_exception_detail({"error": "Violated", "extra": "x"}) + msg, fields = serialize_http_exception_detail({"error": "Violated", "extra": "x"}) assert msg == "Violated" assert fields == {"error": "Violated", "extra": "x"} - msg, fields = _serialize_http_exception_detail({"error": {"message": "blocked", "code": "x"}}) + msg, fields = serialize_http_exception_detail({"error": {"message": "blocked", "code": "x"}}) assert msg == "blocked" assert fields == {"error": {"message": "blocked", "code": "x"}} - msg, fields = _serialize_http_exception_detail({"message": "top-level"}) + msg, fields = serialize_http_exception_detail({"message": "top-level"}) assert msg == "top-level" assert fields == {"message": "top-level"} - msg, fields = _serialize_http_exception_detail({"weird": ["a", "b"]}) + msg, fields = serialize_http_exception_detail({"weird": ["a", "b"]}) assert msg == _json.dumps({"weird": ["a", "b"]}) assert fields == {"weird": ["a", "b"]} - assert _serialize_http_exception_detail(42) == ("42", None) + assert serialize_http_exception_detail(42) == ("42", None) + + async def test_proxy_exception_from_http_exception_helper(self): + """The shared HTTPException -> ProxyException conversion keeps a clean + message, merges structured detail over existing provider_specific_fields, + and passes headers through.""" + from litellm.proxy.common_request_processing import ( + proxy_exception_from_http_exception, + ) + + exc = HTTPException( + status_code=400, + detail={"error": "Content blocked", "guardrail": "keyword-block"}, + ) + exc.provider_specific_fields = {"existing": "field", "guardrail": "stale"} + result = proxy_exception_from_http_exception(exc, {"x-litellm-call-id": "abc"}) + assert result.message == "Content blocked" + assert result.code == "400" + assert result.provider_specific_fields == { + "existing": "field", + "error": "Content blocked", + "guardrail": "keyword-block", + } + assert result.headers == {"x-litellm-call-id": "abc"} + + plain = proxy_exception_from_http_exception(HTTPException(status_code=429, detail="slow down"), {}) + assert plain.message == "slow down" + assert plain.code == "429" + assert plain.provider_specific_fields is None async def test_create_streaming_response_first_chunk_error_string_code(self): """ @@ -2521,6 +2549,152 @@ class TestStreamingOverheadHeader: assert "x-litellm-overhead-duration-ms" in headers assert headers["x-litellm-overhead-duration-ms"] == "42.5" + @staticmethod + def _timing_logging_obj(timing_metrics): + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + + logging_obj = LiteLLMLoggingObj( + model="openai/gpt-4o-mini", + messages=[{"role": "user", "content": "hi"}], + stream=True, + call_type="anthropic_messages", + start_time=None, + litellm_call_id="test-call-id", + function_id="test-function-id", + ) + logging_obj.set_response_timing_metrics(timing_metrics) + return logging_obj + + def test_get_custom_headers_reads_timing_from_logging_obj_when_response_has_no_hidden_params(self): + """ + LIT-5466: /v1/messages results and the bridge stream wrappers carry no + _hidden_params, so the timing headers come from the logging object. + """ + mock_user_api_key_dict = MagicMock(spec=UserAPIKeyAuth) + mock_user_api_key_dict.tpm_limit = None + mock_user_api_key_dict.rpm_limit = None + mock_user_api_key_dict.max_budget = None + mock_user_api_key_dict.spend = 0.0 + mock_user_api_key_dict.allowed_model_region = None + + headers = ProxyBaseLLMRequestProcessing.get_custom_headers( + user_api_key_dict=mock_user_api_key_dict, + call_id="test-call-id", + hidden_params={}, + litellm_logging_obj=self._timing_logging_obj( + {"_response_ms": 500.0, "litellm_overhead_time_ms": 42.5} + ), + ) + + assert headers["x-litellm-response-duration-ms"] == "500.0" + assert headers["x-litellm-overhead-duration-ms"] == "42.5" + + def test_get_custom_headers_skips_logging_obj_timing_on_the_failure_path(self): + """LIT-5466: a failed request reports no timing, the same as /v1/chat/completions.""" + mock_user_api_key_dict = MagicMock(spec=UserAPIKeyAuth) + mock_user_api_key_dict.tpm_limit = None + mock_user_api_key_dict.rpm_limit = None + mock_user_api_key_dict.max_budget = None + mock_user_api_key_dict.spend = 0.0 + mock_user_api_key_dict.allowed_model_region = None + + headers = ProxyBaseLLMRequestProcessing.get_custom_headers( + user_api_key_dict=mock_user_api_key_dict, + call_id="test-call-id", + hidden_params={}, + litellm_logging_obj=self._timing_logging_obj( + {"_response_ms": 500.0, "litellm_overhead_time_ms": 42.5} + ), + read_timing_from_logging_obj=False, + ) + + assert "x-litellm-response-duration-ms" not in headers + assert "x-litellm-overhead-duration-ms" not in headers + + def test_get_custom_headers_takes_both_timing_values_from_one_source(self): + """A response that timed itself but has no overhead (lazy provider streams) does not pick + up the logging object's overhead, which was measured over a different window.""" + mock_user_api_key_dict = MagicMock(spec=UserAPIKeyAuth) + mock_user_api_key_dict.tpm_limit = None + mock_user_api_key_dict.rpm_limit = None + mock_user_api_key_dict.max_budget = None + mock_user_api_key_dict.spend = 0.0 + mock_user_api_key_dict.allowed_model_region = None + + headers = ProxyBaseLLMRequestProcessing.get_custom_headers( + user_api_key_dict=mock_user_api_key_dict, + call_id="test-call-id", + hidden_params={"_response_ms": 300.0}, + litellm_logging_obj=self._timing_logging_obj( + {"_response_ms": 500.0, "litellm_overhead_time_ms": 42.5} + ), + ) + + assert headers["x-litellm-response-duration-ms"] == "300.0" + assert "x-litellm-overhead-duration-ms" not in headers + + def test_get_custom_headers_survives_a_logging_object_without_timing_metrics(self): + """Duck-typed logging objects (older custom code, test doubles) must not break headers.""" + mock_user_api_key_dict = MagicMock(spec=UserAPIKeyAuth) + mock_user_api_key_dict.tpm_limit = None + mock_user_api_key_dict.rpm_limit = None + mock_user_api_key_dict.max_budget = None + mock_user_api_key_dict.spend = 0.0 + mock_user_api_key_dict.allowed_model_region = None + + class _NoTimingLoggingObj: + litellm_call_id = "test-call-id" + litellm_params = {} + + headers = ProxyBaseLLMRequestProcessing.get_custom_headers( + user_api_key_dict=mock_user_api_key_dict, + call_id="test-call-id", + hidden_params={}, + litellm_logging_obj=_NoTimingLoggingObj(), + ) + + assert "x-litellm-overhead-duration-ms" not in headers + + def test_get_custom_headers_prefers_response_hidden_params_over_logging_obj_timing(self): + """A response that carries its own timing (chat completions) is not overridden.""" + mock_user_api_key_dict = MagicMock(spec=UserAPIKeyAuth) + mock_user_api_key_dict.tpm_limit = None + mock_user_api_key_dict.rpm_limit = None + mock_user_api_key_dict.max_budget = None + mock_user_api_key_dict.spend = 0.0 + mock_user_api_key_dict.allowed_model_region = None + + headers = ProxyBaseLLMRequestProcessing.get_custom_headers( + user_api_key_dict=mock_user_api_key_dict, + call_id="test-call-id", + hidden_params={"_response_ms": 300.0, "litellm_overhead_time_ms": 7.5}, + litellm_logging_obj=self._timing_logging_obj( + {"_response_ms": 500.0, "litellm_overhead_time_ms": 42.5} + ), + ) + + assert headers["x-litellm-response-duration-ms"] == "300.0" + assert headers["x-litellm-overhead-duration-ms"] == "7.5" + + def test_get_custom_headers_omits_timing_when_no_source_has_it(self): + """No timing on the response and none on the logging object leaves both headers out.""" + mock_user_api_key_dict = MagicMock(spec=UserAPIKeyAuth) + mock_user_api_key_dict.tpm_limit = None + mock_user_api_key_dict.rpm_limit = None + mock_user_api_key_dict.max_budget = None + mock_user_api_key_dict.spend = 0.0 + mock_user_api_key_dict.allowed_model_region = None + + headers = ProxyBaseLLMRequestProcessing.get_custom_headers( + user_api_key_dict=mock_user_api_key_dict, + call_id="test-call-id", + hidden_params={}, + litellm_logging_obj=self._timing_logging_obj({}), + ) + + assert "x-litellm-response-duration-ms" not in headers + assert "x-litellm-overhead-duration-ms" not in headers + def test_get_custom_headers_omits_overhead_when_none(self): """ get_custom_headers() omits x-litellm-overhead-duration-ms @@ -4520,7 +4694,7 @@ class TestAllmPassthroughStreamingProviderGate: } return ProxyBaseLLMRequestProcessing(data=data) - async def _run(self, processing_obj, monkeypatch, chunks): + async def _run(self, processing_obj, monkeypatch, chunks, stream=None): import litellm.proxy.common_request_processing as crp from litellm.proxy._types import UserAPIKeyAuth as RealUserAPIKeyAuth @@ -4528,9 +4702,11 @@ class TestAllmPassthroughStreamingProviderGate: for chunk in chunks: yield chunk + upstream_stream = stream if stream is not None else streaming_response() + async def fake_route_request(**kwargs): async def _llm_call(): - return streaming_response() + return upstream_stream return _llm_call() @@ -4555,6 +4731,40 @@ class TestAllmPassthroughStreamingProviderGate: skip_pre_call_logic=True, ) + @pytest.mark.asyncio + async def test_client_disconnect_closes_unbuffered_passthrough_stream(self, monkeypatch): + """Starlette abandons the body iterator when the client disconnects, so the + unbuffered passthrough branch must return _UpstreamClosingStreamingResponse, + whose shielded cleanup closes the upstream stream; that close is what flushes + buffered passthrough usage into spend logs.""" + processing_obj = self._build_processing_obj("gigachat") + monkeypatch.setattr(litellm, "callbacks", []) + upstream_closed = asyncio.Event() + + async def hanging_stream(): + try: + yield b"chunk-1" + await asyncio.Event().wait() + finally: + upstream_closed.set() + + result = await self._run(processing_obj, monkeypatch, [], stream=hanging_stream()) + + assert isinstance(result, _UpstreamClosingStreamingResponse) + + first_chunk_sent = asyncio.Event() + + async def receive(): + await first_chunk_sent.wait() + return {"type": "http.disconnect"} + + async def send(message): + if message["type"] == "http.response.body" and message.get("body"): + first_chunk_sent.set() + + await result({"type": "http"}, receive, send) + await asyncio.wait_for(upstream_closed.wait(), timeout=5) + @pytest.mark.asyncio async def test_non_bedrock_stream_is_not_buffered(self, monkeypatch): processing_obj = self._build_processing_obj("anthropic") diff --git a/tests/test_litellm/proxy/test_litellm_pre_call_utils.py b/tests/test_litellm/proxy/test_litellm_pre_call_utils.py index 9fb31d2a6db..ee0e2014951 100644 --- a/tests/test_litellm/proxy/test_litellm_pre_call_utils.py +++ b/tests/test_litellm/proxy/test_litellm_pre_call_utils.py @@ -33,6 +33,8 @@ from litellm.proxy.litellm_pre_call_utils import ( check_if_token_is_service_account, clean_headers, ) +from litellm.litellm_core_utils.core_helpers import get_litellm_metadata_from_kwargs +from litellm.litellm_core_utils.internal_call_metadata import MODEL_ACCESS_GROUP_METADATA_KEY from litellm.litellm_core_utils.get_provider_specific_headers import ( ProviderSpecificHeaderUtils, ) @@ -1073,6 +1075,7 @@ async def test_key_metadata_enable_prompt_caching_promoted_to_request_root(key_v "_code_interpreter_interception_active", "_code_interpreter_interception_converted_stream", "_code_interpreter_interception_sandbox_key", + "_headroom_interception_converted_stream", "max_agentic_loops", ], ) @@ -1107,6 +1110,7 @@ async def test_add_litellm_data_to_request_strips_callback_control_fields( "_code_interpreter_interception_active": True, "_code_interpreter_interception_converted_stream": True, "_code_interpreter_interception_sandbox_key": "forged-key", + "_headroom_interception_converted_stream": True, "max_agentic_loops": 9999, } sample_value = sample_values[control_field] @@ -7681,3 +7685,37 @@ async def test_add_litellm_data_to_request_keeps_litellm_metadata_on_litellm_met ) assert updated["litellm_metadata"]["trace_id"] == "abc" + + +def _stamp_model_access_groups(matched_model_access_groups, metadata_variable_name="metadata"): + user_api_key_dict = UserAPIKeyAuth(api_key="hashed-key") + user_api_key_dict.matched_model_access_groups = matched_model_access_groups + return LiteLLMProxyRequestSetup.add_user_api_key_auth_to_request_metadata( + data={metadata_variable_name: {}}, + user_api_key_dict=user_api_key_dict, + _metadata_variable_name=metadata_variable_name, + )[metadata_variable_name] + + +def test_matched_model_access_groups_are_stamped_into_request_metadata(): + """The post-call spend writer reads the groups off request metadata, not off UserAPIKeyAuth.""" + stamped = _stamp_model_access_groups(["tier-a", "tier-b"]) + + assert stamped[MODEL_ACCESS_GROUP_METADATA_KEY] == ["tier-a", "tier-b"] + assert MODEL_ACCESS_GROUP_METADATA_KEY not in _stamp_model_access_groups(None) + + +def test_stamped_model_access_groups_survive_the_litellm_metadata_merge(): + """ + The key must keep its ``user_api_key`` prefix: when a request carries both metadata dicts, + get_litellm_metadata_from_kwargs returns litellm_metadata and copies a key over from metadata + only when that substring is in its name, so an unprefixed key is silently dropped. + """ + kwargs = { + "litellm_params": { + "metadata": _stamp_model_access_groups(["tier-a"]), + "litellm_metadata": {"trace_id": "abc"}, + } + } + + assert get_litellm_metadata_from_kwargs(kwargs)[MODEL_ACCESS_GROUP_METADATA_KEY] == ["tier-a"] diff --git a/tests/test_litellm/proxy/test_proxy_cli.py b/tests/test_litellm/proxy/test_proxy_cli.py index 6ea6f208bb5..3e70dee23b7 100644 --- a/tests/test_litellm/proxy/test_proxy_cli.py +++ b/tests/test_litellm/proxy/test_proxy_cli.py @@ -2452,6 +2452,96 @@ class TestReadReplicaConnectionParams: assert "DATABASE_URL_READ_REPLICA" not in captured +class TestMaxIdleConnectionLifetimeDefault: + """The proxy defaults `max_idle_connection_lifetime` below common infra idle + timeouts so stale pooled connections are recycled instead of failing requests.""" + + def _config(self, tmp_path, general_settings): + config_path = tmp_path / "config.yaml" + config_path.write_text(yaml.dump({"model_list": [], "general_settings": general_settings})) + return str(config_path) + + def test_default_applied_to_database_and_direct_url(self, tmp_path): + captured = _run_server_and_capture_urls( + self._config(tmp_path, {}), + direct_url="postgresql://t:t@localhost:5432/t", + ) + + for env_var in ("DATABASE_URL", "DIRECT_URL"): + query = urlparse.parse_qs(urlparse.urlparse(captured[env_var]).query) + assert query["max_idle_connection_lifetime"] == ["60"], env_var + + def test_url_pinned_value_wins_over_default(self, tmp_path): + captured = _run_server_and_capture_urls( + self._config(tmp_path, {}), + database_url="postgresql://t:t@localhost:5432/t?max_idle_connection_lifetime=300", + ) + + query = urlparse.parse_qs(urlparse.urlparse(captured["DATABASE_URL"]).query) + assert query["max_idle_connection_lifetime"] == ["300"] + + def test_url_pinned_value_wins_over_config_key(self, tmp_path): + captured = _run_server_and_capture_urls( + self._config(tmp_path, {"database_max_idle_connection_lifetime": 45}), + database_url="postgresql://t:t@localhost:5432/t?max_idle_connection_lifetime=300", + ) + + query = urlparse.parse_qs(urlparse.urlparse(captured["DATABASE_URL"]).query) + assert query["max_idle_connection_lifetime"] == ["300"] + + def test_config_key_overrides_default(self, tmp_path): + captured = _run_server_and_capture_urls( + self._config(tmp_path, {"database_max_idle_connection_lifetime": 45}), + ) + + query = urlparse.parse_qs(urlparse.urlparse(captured["DATABASE_URL"]).query) + assert query["max_idle_connection_lifetime"] == ["45"] + + def test_extra_connection_params_override_default(self, tmp_path): + captured = _run_server_and_capture_urls( + self._config( + tmp_path, + {"database_extra_connection_params": {"max_idle_connection_lifetime": 120}}, + ), + ) + + query = urlparse.parse_qs(urlparse.urlparse(captured["DATABASE_URL"]).query) + assert query["max_idle_connection_lifetime"] == ["120"] + + def test_read_replica_gets_the_default(self, tmp_path): + captured = _run_server_and_capture_urls( + self._config(tmp_path, {}), + read_replica_url="postgresql://t:t@reader:5432/t", + ) + + query = urlparse.parse_qs(urlparse.urlparse(captured["DATABASE_URL_READ_REPLICA"]).query) + assert query["max_idle_connection_lifetime"] == ["60"] + + def test_replica_pinned_value_wins(self, tmp_path): + captured = _run_server_and_capture_urls( + self._config(tmp_path, {"database_max_idle_connection_lifetime": 45}), + read_replica_url="postgresql://t:t@reader:5432/t?max_idle_connection_lifetime=200", + ) + + query = urlparse.parse_qs(urlparse.urlparse(captured["DATABASE_URL_READ_REPLICA"]).query) + assert query["max_idle_connection_lifetime"] == ["200"] + + def test_config_key_reaches_the_read_replica(self, tmp_path): + captured = _run_server_and_capture_urls( + self._config(tmp_path, {"database_max_idle_connection_lifetime": 45}), + read_replica_url="postgresql://t:t@reader:5432/t", + ) + + query = urlparse.parse_qs(urlparse.urlparse(captured["DATABASE_URL_READ_REPLICA"]).query) + assert query["max_idle_connection_lifetime"] == ["45"] + + def test_idle_lifetime_params_prefers_configured_value(self): + from litellm.proxy.db.db_url_settings import idle_lifetime_params + + assert dict(idle_lifetime_params(45)) == {"max_idle_connection_lifetime": 45} + assert dict(idle_lifetime_params(None)) == {"max_idle_connection_lifetime": 60} + + class TestTokenAuthCliFlags: """`--azure_postgresql_auth` has to reach the URL assembly the same way the env var does.""" diff --git a/tests/test_litellm/proxy/test_proxy_logging_hook_detection.py b/tests/test_litellm/proxy/test_proxy_logging_hook_detection.py index 542572e1e56..9f1321aec2c 100644 --- a/tests/test_litellm/proxy/test_proxy_logging_hook_detection.py +++ b/tests/test_litellm/proxy/test_proxy_logging_hook_detection.py @@ -346,14 +346,14 @@ async def test_post_call_stream_guardrail_keeps_own_iterator_on_chat_completions @pytest.mark.asyncio -async def test_unified_guardrail_iterator_accepts_explicit_guardrail(monkeypatch): +async def test_unified_guardrail_iterator_accepts_explicit_guardrail(): """ The dispatch passes each guardrail explicitly instead of through a shared request_data key, so chaining two unified-routed guardrails cannot drop - all but the last one. + all but the last one. The block fires after the deltas were already + flushed to the client, so it surfaces as a trailing in-stream error frame + rather than a raised HTTPException. """ - from fastapi import HTTPException - from litellm.proxy.utils import unified_guardrail guardrail = _content_filter_guardrail("BLOCK") @@ -367,14 +367,19 @@ async def test_unified_guardrail_iterator_accepts_explicit_guardrail(monkeypatch for chunk in _anthropic_stream_chunks(["the", " zebra runs"]): yield chunk - with pytest.raises(HTTPException): - async for _ in unified_guardrail.async_post_call_streaming_iterator_hook( - user_api_key_dict=UserAPIKeyAuth(api_key="sk-1234", request_route="/v1/messages"), - response=fake_stream(), - request_data=request_data, - guardrail_to_apply=guardrail, - ): - pass + delivered = [] + async for item in unified_guardrail.async_post_call_streaming_iterator_hook( + user_api_key_dict=UserAPIKeyAuth(api_key="sk-1234", request_route="/v1/messages"), + response=fake_stream(), + request_data=request_data, + guardrail_to_apply=guardrail, + ): + delivered.append(item) + + raw = b"".join(c for c in delivered if isinstance(c, bytes)).decode() + assert "event: error" in raw + assert "guardrail_error" in raw + assert raw.index("guardrail_error") > raw.index(" zebra runs") @pytest.mark.asyncio diff --git a/tests/test_litellm/proxy/test_proxy_server.py b/tests/test_litellm/proxy/test_proxy_server.py index 949088ea3ba..91fca8f1e27 100644 --- a/tests/test_litellm/proxy/test_proxy_server.py +++ b/tests/test_litellm/proxy/test_proxy_server.py @@ -1,4 +1,5 @@ import asyncio +import contextlib import importlib import json import os @@ -7816,6 +7817,7 @@ async def test_window_spend_counter_reseeds_from_spend_logs_on_counter_miss(): counter_cache = DualCache() window_start = datetime.now(timezone.utc) - timedelta(hours=1) fake_prisma = MagicMock() + fake_prisma.db.litellm_budgetwindowspend.find_unique = AsyncMock(return_value=None) fake_prisma.db.litellm_spendlogs.group_by = AsyncMock( return_value=[{"api_key": "key-window", "_sum": {"spend": 2.25}}] ) @@ -7830,6 +7832,7 @@ async def test_window_spend_counter_reseeds_from_spend_logs_on_counter_miss(): counter_key="spend:key:key-window:window:1h", entity_type="Key", entity_id="key-window", + window_duration="1h", window_start=window_start, increment=0.5, ) @@ -7933,6 +7936,7 @@ async def test_window_spend_counter_redis_clean_miss_skips_stale_in_memory(): counter_cache.redis_cache = fake_redis fake_prisma = MagicMock() + fake_prisma.db.litellm_budgetwindowspend.find_unique = AsyncMock(return_value=None) fake_prisma.db.litellm_spendlogs.group_by = AsyncMock( return_value=[{"api_key": "key-window-stale-local", "_sum": {"spend": 2.25}}] ) @@ -7947,6 +7951,7 @@ async def test_window_spend_counter_redis_clean_miss_skips_stale_in_memory(): counter_key=counter_key, entity_type="Key", entity_id="key-window-stale-local", + window_duration="1h", window_start=window_start, increment=0.5, ) @@ -7995,6 +8000,7 @@ async def test_window_spend_counter_redis_concurrent_seed_does_not_double_seed() counter_cache.redis_cache = fake_redis fake_prisma = MagicMock() + fake_prisma.db.litellm_budgetwindowspend.find_unique = AsyncMock(return_value=None) fake_prisma.db.litellm_spendlogs.group_by = AsyncMock( return_value=[{"api_key": "key-window-concurrent-seed", "_sum": {"spend": 2.25}}] ) @@ -8009,6 +8015,7 @@ async def test_window_spend_counter_redis_concurrent_seed_does_not_double_seed() counter_key=counter_key, entity_type="Key", entity_id="key-window-concurrent-seed", + window_duration="1h", window_start=window_start, increment=0.5, ) @@ -8041,6 +8048,7 @@ async def test_window_spend_counter_skips_invalid_window_start(): counter_key="spend:key:key-invalid-window:window:not-a-duration", entity_type="Key", entity_id="key-invalid-window", + window_duration="not-a-duration", window_start=None, increment=0.5, ) @@ -8068,6 +8076,7 @@ async def test_window_spend_counter_does_not_seed_zero_when_db_unavailable(): counter_key=counter_key, entity_type="Key", entity_id="key-window-db-unavailable", + window_duration="1h", window_start=datetime.now(timezone.utc) - timedelta(hours=1), ) @@ -9565,6 +9574,76 @@ class TestDeleteDeploymentSync: assert result is None, f"Expected None on DB failure to signal fetch error, got {result!r}" + @pytest.mark.asyncio + async def test_get_models_from_db_reads_from_writer_not_replica(self): + """ + Regression for #38556: with DATABASE_URL_READ_REPLICA configured, the model + reconcile after /model/new used to read via the replica, so a lagging replica + made the reload miss the just-committed row and fail the request with a 500. + The reconcile read must be pinned to the writer. + """ + from unittest.mock import AsyncMock, MagicMock + + from litellm.proxy.db.prisma_client import PrismaWrapper + from litellm.proxy.db.routing_prisma_wrapper import RoutingPrismaWrapper + from litellm.proxy.proxy_server import ProxyConfig + + writer_inner = MagicMock(name="writer_prisma") + reader_inner = MagicMock(name="reader_prisma") + committed_row = MagicMock(name="just_committed_model_row") + writer_inner.litellm_proxymodeltable.find_many = AsyncMock(return_value=[committed_row]) + reader_inner.litellm_proxymodeltable.find_many = AsyncMock(return_value=[]) + + mock_prisma = MagicMock() + mock_prisma.db = RoutingPrismaWrapper( + writer=PrismaWrapper(original_prisma=writer_inner, iam_token_db_auth=False), + reader=PrismaWrapper(original_prisma=reader_inner, iam_token_db_auth=False), + ) + + result = await ProxyConfig()._get_models_from_db(prisma_client=mock_prisma) + + assert result == [committed_row], f"Expected the writer's just-committed row, got {result!r}" + reader_inner.litellm_proxymodeltable.find_many.assert_not_awaited() + + @pytest.mark.asyncio + async def test_get_models_from_db_falls_back_to_replica_when_writer_down(self): + """ + The writer pin must not break reader-only degraded mode: a proxy that + starts during a primary outage (writer connect failed, replica healthy) + must still load DB-backed models through the replica instead of sending + the reconcile read to the unavailable writer. + """ + from types import SimpleNamespace + from unittest.mock import AsyncMock, MagicMock + + from litellm.proxy.db.prisma_client import PrismaWrapper + from litellm.proxy.db.routing_prisma_wrapper import RoutingPrismaWrapper + from litellm.proxy.proxy_server import ProxyConfig + + writer_inner = MagicMock(name="writer_prisma") + reader_inner = MagicMock(name="reader_prisma") + replica_row = MagicMock(name="replica_model_row") + writer_inner.litellm_proxymodeltable = SimpleNamespace( + find_many=AsyncMock(side_effect=RuntimeError("writer unreachable")), + create=MagicMock(name="writer_create"), + ) + reader_inner.litellm_proxymodeltable = SimpleNamespace( + find_many=AsyncMock(return_value=[replica_row]), + create=MagicMock(name="reader_create"), + ) + + mock_prisma = MagicMock() + mock_prisma.db = RoutingPrismaWrapper( + writer=PrismaWrapper(original_prisma=writer_inner, iam_token_db_auth=False), + reader=PrismaWrapper(original_prisma=reader_inner, iam_token_db_auth=False), + ) + mock_prisma.db._writer_unavailable = True + + result = await ProxyConfig()._get_models_from_db(prisma_client=mock_prisma) + + assert result == [replica_row], f"Expected the replica's rows in degraded mode, got {result!r}" + writer_inner.litellm_proxymodeltable.find_many.assert_not_awaited() + def test_get_config_list_includes_cancel_on_disconnect(monkeypatch): """Follow-up to #30223: the flag must be discoverable via /config/list, @@ -10366,6 +10445,75 @@ async def test_update_config_general_settings_emits_audit_log(monkeypatch): assert before["some_api_key"] != "sk-stored-secret" +@pytest.mark.asyncio +async def test_update_config_field_rejects_out_of_range_alerting_args(monkeypatch): + """Out-of-range alerting_args must be rejected at save time. If they land in the + DB, SlackAlertingArgs raises during the config reload and alerting breaks.""" + from unittest.mock import MagicMock + + from fastapi import HTTPException + + import litellm.proxy.proxy_server as proxy_server_module + from litellm.proxy._types import ConfigFieldUpdate + from litellm.proxy.proxy_server import update_config_general_settings + + monkeypatch.setattr(proxy_server_module, "prisma_client", MagicMock()) + + admin = UserAPIKeyAuth( + api_key="hashed-admin", + user_id="admin-1", + user_role=LitellmUserRoles.PROXY_ADMIN, + ) + with pytest.raises(HTTPException) as exc_info: + await update_config_general_settings( + data=ConfigFieldUpdate( + field_name="alerting_args", + field_value={ + "daily_spend_per_user_threshold": -5.0, + "user_spend_check_interval": 20, + }, + config_type="general_settings", + ), + user_api_key_dict=admin, + ) + + assert exc_info.value.status_code == 400 + error_msg = exc_info.value.detail["error"] + assert "daily_spend_per_user_threshold" in error_msg + assert "user_spend_check_interval" in error_msg + + +@pytest.mark.asyncio +async def test_update_config_field_accepts_valid_alerting_args(monkeypatch): + import litellm.proxy.proxy_server as proxy_server_module + from litellm.proxy._types import ConfigFieldUpdate + from litellm.proxy.proxy_server import update_config_general_settings + + fake = _fake_prisma_with_config({}) + monkeypatch.setattr(proxy_server_module, "prisma_client", fake) + monkeypatch.setattr(litellm, "store_audit_logs", False) + + admin = UserAPIKeyAuth( + api_key="hashed-admin", + user_id="admin-1", + user_role=LitellmUserRoles.PROXY_ADMIN, + ) + await update_config_general_settings( + data=ConfigFieldUpdate( + field_name="alerting_args", + field_value={ + "daily_spend_per_user_threshold": 5.0, + "user_spend_check_interval": 60, + }, + config_type="general_settings", + ), + user_api_key_dict=admin, + ) + + written = json.loads(fake.db.litellm_config.upsert.call_args.kwargs["data"]["update"]["param_value"]) + assert written["alerting_args"]["daily_spend_per_user_threshold"] == 5.0 + + @pytest.mark.asyncio async def test_update_config_general_settings_applies_ssrf_globals(monkeypatch): import litellm.proxy.proxy_server as proxy_server_module @@ -11130,6 +11278,244 @@ def test_startup_is_silent_when_mock_testing_params_disabled(caplog): assert MOCK_TESTING_CONFIG_KEY not in caplog.text +# --------------------------------------------------------------------------- +# Budget window spend row enqueue (LiteLLM_BudgetWindowSpend writer) +# --------------------------------------------------------------------------- + + +@contextlib.contextmanager +def _window_spend_enqueue_env(cached_objects: dict): + """Point increment_spend_counters at throwaway caches and a real + WindowSpendUpdateQueue, and hand back the queue to inspect.""" + from litellm.caching.dual_cache import DualCache + from litellm.proxy.db.db_transaction_queue.window_spend_update_queue import ( + WindowSpendUpdateQueue, + ) + import litellm.proxy.proxy_server as ps + + user_api_key_cache = MagicMock() + user_api_key_cache.async_get_cache = AsyncMock(side_effect=lambda key, **_: cached_objects.get(key)) + + queue = WindowSpendUpdateQueue() + proxy_logging_obj = MagicMock() + proxy_logging_obj.db_spend_update_writer.window_spend_update_queue = queue + + originals = ( + ps.user_api_key_cache, + ps.spend_counter_cache, + ps.prisma_client, + ps.proxy_logging_obj, + ) + ps.user_api_key_cache = user_api_key_cache + ps.spend_counter_cache = DualCache() + ps.prisma_client = None + ps.proxy_logging_obj = proxy_logging_obj + try: + yield queue + finally: + ( + ps.user_api_key_cache, + ps.spend_counter_cache, + ps.prisma_client, + ps.proxy_logging_obj, + ) = originals + + +async def _drain(queue): + return list(await queue.flush_and_get_aggregated_window_spend_transactions()) + + +@pytest.mark.asyncio +async def test_key_window_spend_row_is_enqueued_with_the_actual_cost(): + from litellm.proxy.proxy_server import increment_spend_counters + + reset_at = datetime.now(timezone.utc) + timedelta(days=10) + key_obj = MagicMock() + key_obj.budget_limits = [ + {"budget_duration": "30d", "max_budget": 100.0, "reset_at": reset_at.isoformat()} + ] + + with _window_spend_enqueue_env({"hashed-token": key_obj}) as queue: + await increment_spend_counters( + token="hashed-token", team_id=None, user_id=None, response_cost=0.25 + ) + enqueued = await _drain(queue) + + assert len(enqueued) == 1 + assert enqueued[0]["entity_type"] == "key" + assert enqueued[0]["entity_id"] == "hashed-token" + assert enqueued[0]["window_duration"] == "30d" + assert enqueued[0]["spend"] == pytest.approx(0.25) + assert enqueued[0]["window_start"] == (reset_at - timedelta(days=30)).astimezone(timezone.utc).replace( + tzinfo=None + ).isoformat(timespec="microseconds") + + +@pytest.mark.asyncio +async def test_team_window_spend_row_is_enqueued(): + from litellm.proxy.proxy_server import increment_spend_counters + + reset_at = datetime.now(timezone.utc) + timedelta(days=3) + team_obj = MagicMock() + team_obj.budget_limits = [ + {"budget_duration": "7d", "max_budget": 50.0, "reset_at": reset_at.isoformat()} + ] + + with _window_spend_enqueue_env({"team_id:team-1": team_obj}) as queue: + await increment_spend_counters( + token=None, team_id="team-1", user_id=None, response_cost=1.5 + ) + enqueued = await _drain(queue) + + assert len(enqueued) == 1 + assert enqueued[0]["entity_type"] == "team" + assert enqueued[0]["entity_id"] == "team-1" + assert enqueued[0]["window_duration"] == "7d" + assert enqueued[0]["spend"] == pytest.approx(1.5) + + +@pytest.mark.asyncio +async def test_window_spend_row_is_enqueued_even_when_the_counter_was_reserved(): + """A reservation only pre-charged the cache counter with an estimate; the + row still owes the actual cost, so the enqueue must not be skipped.""" + from litellm.proxy.proxy_server import increment_spend_counters + import litellm.proxy.spend_tracking.budget_reservation as br + + reset_at = datetime.now(timezone.utc) + timedelta(days=10) + key_obj = MagicMock() + key_obj.budget_limits = [ + {"budget_duration": "30d", "max_budget": 100.0, "reset_at": reset_at.isoformat()} + ] + reservation = { + "entries": [ + {"counter_key": "spend:key:hashed-token", "reserved": 1.0}, + {"counter_key": "spend:key:hashed-token:window:30d", "reserved": 1.0}, + ] + } + + original_reconcile = br.reconcile_budget_reservation + br.reconcile_budget_reservation = AsyncMock(return_value=None) + try: + with _window_spend_enqueue_env({"hashed-token": key_obj}) as queue: + await increment_spend_counters( + token="hashed-token", + team_id=None, + user_id=None, + response_cost=0.25, + budget_reservation=reservation, + ) + enqueued = await _drain(queue) + finally: + br.reconcile_budget_reservation = original_reconcile + + assert len(enqueued) == 1 + assert enqueued[0]["spend"] == pytest.approx(0.25) + + +@pytest.mark.asyncio +async def test_sliding_window_without_reset_at_is_not_enqueued(): + """Windows with no reset_at slide with wall clock, so window_start moves on + every request and no single row can represent them; the read path keeps + using its LiteLLM_SpendLogs fallback instead.""" + from litellm.proxy.proxy_server import increment_spend_counters + + key_obj = MagicMock() + key_obj.budget_limits = [{"budget_duration": "30d", "max_budget": 100.0}] + + with _window_spend_enqueue_env({"hashed-token": key_obj}) as queue: + await increment_spend_counters( + token="hashed-token", team_id=None, user_id=None, response_cost=0.25 + ) + enqueued = await _drain(queue) + + assert enqueued == [] + + +@pytest.mark.asyncio +async def test_each_configured_window_gets_its_own_row_enqueue(): + from litellm.proxy.proxy_server import increment_spend_counters + + now = datetime.now(timezone.utc) + key_obj = MagicMock() + key_obj.budget_limits = [ + {"budget_duration": "1d", "max_budget": 5.0, "reset_at": (now + timedelta(hours=5)).isoformat()}, + {"budget_duration": "30d", "max_budget": 100.0, "reset_at": (now + timedelta(days=10)).isoformat()}, + ] + + with _window_spend_enqueue_env({"hashed-token": key_obj}) as queue: + await increment_spend_counters( + token="hashed-token", team_id=None, user_id=None, response_cost=0.25 + ) + enqueued = await _drain(queue) + + assert sorted(item["window_duration"] for item in enqueued) == ["1d", "30d"] + assert all(item["spend"] == pytest.approx(0.25) for item in enqueued) + + +@pytest.mark.asyncio +async def test_no_window_spend_row_enqueued_without_budget_limits(): + from litellm.proxy.proxy_server import increment_spend_counters + + key_obj = MagicMock() + key_obj.budget_limits = None + + with _window_spend_enqueue_env({"hashed-token": key_obj}) as queue: + await increment_spend_counters( + token="hashed-token", team_id=None, user_id=None, response_cost=0.25 + ) + enqueued = await _drain(queue) + + assert enqueued == [] + + +@pytest.mark.asyncio +async def test_window_spend_row_carries_the_request_start_time(): + """The seed sums LiteLLM_SpendLogs only up to this point, so it must be the + same start the spend log row was written with.""" + from litellm.proxy.proxy_server import increment_spend_counters + + reset_at = datetime.now(timezone.utc) + timedelta(days=10) + key_obj = MagicMock() + key_obj.budget_limits = [ + {"budget_duration": "30d", "max_budget": 100.0, "reset_at": reset_at.isoformat()} + ] + + with _window_spend_enqueue_env({"hashed-token": key_obj}) as queue: + await increment_spend_counters( + token="hashed-token", + team_id=None, + user_id=None, + response_cost=0.25, + request_started_at=datetime(2026, 8, 10, 12, 0, 0, 500_000, tzinfo=timezone.utc), + ) + enqueued = await _drain(queue) + + assert enqueued[0]["started_at"] == "2026-08-10T12:00:00.500000" + + +@pytest.mark.asyncio +async def test_team_window_spend_row_carries_the_request_start_time(): + from litellm.proxy.proxy_server import increment_spend_counters + + reset_at = datetime.now(timezone.utc) + timedelta(days=3) + team_obj = MagicMock() + team_obj.budget_limits = [ + {"budget_duration": "7d", "max_budget": 50.0, "reset_at": reset_at.isoformat()} + ] + + with _window_spend_enqueue_env({"team_id:team-1": team_obj}) as queue: + await increment_spend_counters( + token=None, + team_id="team-1", + user_id=None, + response_cost=1.5, + request_started_at=datetime(2026, 8, 10, 12, 0, 0, 500_000, tzinfo=timezone.utc), + ) + enqueued = await _drain(queue) + + assert enqueued[0]["started_at"] == "2026-08-10T12:00:00.500000" + + def _mock_startup_prisma_client(health_check_error=None, connect_error=None): client = MagicMock() client.connect = AsyncMock(side_effect=connect_error) diff --git a/tests/test_litellm/proxy/test_proxy_utils.py b/tests/test_litellm/proxy/test_proxy_utils.py index 6920cc0dae3..dcaad968663 100644 --- a/tests/test_litellm/proxy/test_proxy_utils.py +++ b/tests/test_litellm/proxy/test_proxy_utils.py @@ -1266,13 +1266,14 @@ class TestCreateSmtpConnection: patch("smtplib.SMTP_SSL") as mock_smtp_ssl, patch("smtplib.SMTP") as mock_smtp, ): - result = _create_smtp_connection(smtp_host="mail.example.com", smtp_port=465) + result = _create_smtp_connection(smtp_host="mail.example.com", smtp_port=465, timeout=30.0) mock_smtp.assert_not_called() assert result is mock_smtp_ssl.return_value _, kwargs = mock_smtp_ssl.call_args assert kwargs["host"] == "mail.example.com" assert kwargs["port"] == 465 + assert kwargs["timeout"] == 30.0 context = kwargs["context"] assert isinstance(context, ssl.SSLContext) assert context.verify_mode == ssl.CERT_REQUIRED @@ -1286,11 +1287,11 @@ class TestCreateSmtpConnection: patch("smtplib.SMTP_SSL") as mock_smtp_ssl, patch("smtplib.SMTP") as mock_smtp, ): - result = _create_smtp_connection(smtp_host="mail.example.com", smtp_port=587) + result = _create_smtp_connection(smtp_host="mail.example.com", smtp_port=587, timeout=30.0) mock_smtp_ssl.assert_not_called() assert result is mock_smtp.return_value - mock_smtp.assert_called_once_with(host="mail.example.com", port=587) + mock_smtp.assert_called_once_with(host="mail.example.com", port=587, timeout=30.0) class TestSendEmailStartTls: diff --git a/tests/test_litellm/proxy/utils/prisma_and_spend/conftest.py b/tests/test_litellm/proxy/utils/prisma_and_spend/conftest.py index 19abcb5d66d..fce51c9296c 100644 --- a/tests/test_litellm/proxy/utils/prisma_and_spend/conftest.py +++ b/tests/test_litellm/proxy/utils/prisma_and_spend/conftest.py @@ -14,6 +14,7 @@ from __future__ import annotations import asyncio import sys +import threading from dataclasses import dataclass, field from email.message import EmailMessage from pathlib import Path @@ -320,6 +321,7 @@ class _SentMessage: body: Optional[str] starttls_called: bool login_args: Optional[tuple] + thread_ident: int @dataclass @@ -328,6 +330,7 @@ class InMemorySMTP: sent: List[_SentMessage] = field(default_factory=list) raise_on_send: Optional[Exception] = None + connection_kwargs: List[Dict[str, Any]] = field(default_factory=list) def server_factory(self) -> Callable[..., Any]: outer = self @@ -370,10 +373,12 @@ class InMemorySMTP: body=body, starttls_called=self._starttls_called, login_args=self._login_args, + thread_ident=threading.get_ident(), ) ) def _factory(*args: Any, **kwargs: Any) -> _Conn: + outer.connection_kwargs.append(dict(kwargs)) return _Conn() return _factory diff --git a/tests/test_litellm/proxy/utils/prisma_and_spend/test_send_email.py b/tests/test_litellm/proxy/utils/prisma_and_spend/test_send_email.py index 739e942de52..0e8aba0a03b 100644 --- a/tests/test_litellm/proxy/utils/prisma_and_spend/test_send_email.py +++ b/tests/test_litellm/proxy/utils/prisma_and_spend/test_send_email.py @@ -6,6 +6,7 @@ Symbols pinned here: from __future__ import annotations +import threading from typing import Any import pytest @@ -51,9 +52,7 @@ async def test_send_email_dispatches_via_smtp(in_memory_smtp: Any) -> None: @pytest.mark.asyncio -async def test_send_email_starttls_uses_ssl( - in_memory_smtp: Any, monkeypatch: pytest.MonkeyPatch -) -> None: +async def test_send_email_starttls_uses_ssl(in_memory_smtp: Any, monkeypatch: pytest.MonkeyPatch) -> None: monkeypatch.setenv("SMTP_USE_SSL", "True") await send_email( receiver_email="to@invalid", @@ -82,9 +81,7 @@ async def test_send_email_error_missing_sender_email( ) -> None: monkeypatch.delenv("SMTP_SENDER_EMAIL", raising=False) with pytest.raises(ValueError, match="SMTP_SENDER_EMAIL"): - await send_email( - receiver_email="x@y", subject="s", html="

h

" - ) + await send_email(receiver_email="x@y", subject="s", html="

h

") @pytest.mark.asyncio @@ -105,6 +102,49 @@ async def test_send_email_error_missing_html() -> None: await send_email(receiver_email="x@y", subject="s", html=None) +@pytest.mark.asyncio +async def test_send_email_sets_connection_timeout(in_memory_smtp: Any) -> None: + await send_email( + receiver_email="to@invalid", + subject="Hi", + html="

x

", + ) + assert in_memory_smtp.connection_kwargs[0].get("timeout") == 30.0 + + +@pytest.mark.asyncio +async def test_send_email_timeout_env_override(in_memory_smtp: Any, monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setenv("SMTP_TIMEOUT", "5") + monkeypatch.setenv("SMTP_USE_SSL", "True") + await send_email( + receiver_email="to@invalid", + subject="Hi", + html="

x

", + ) + assert in_memory_smtp.connection_kwargs[0].get("timeout") == 5.0 + + +@pytest.mark.asyncio +async def test_send_email_malformed_timeout_is_swallowed(in_memory_smtp: Any, monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setenv("SMTP_TIMEOUT", "30s") + await send_email( + receiver_email="to@invalid", + subject="Hi", + html="

x

", + ) + assert in_memory_smtp.sent == [] + + +@pytest.mark.asyncio +async def test_send_email_runs_off_event_loop_thread(in_memory_smtp: Any) -> None: + await send_email( + receiver_email="to@invalid", + subject="Hi", + html="

x

", + ) + assert in_memory_smtp.sent[0].thread_ident != threading.get_ident() + + @pytest.mark.asyncio async def test_send_email_smtp_failure_is_swallowed( in_memory_smtp: Any, @@ -113,7 +153,5 @@ async def test_send_email_smtp_failure_is_swallowed( does not raise so a failing email never blocks the proxy. """ in_memory_smtp.raise_on_send = RuntimeError("smtp boom") - await send_email( - receiver_email="to@invalid", subject="Hi", html="

x

" - ) + await send_email(receiver_email="to@invalid", subject="Hi", html="

x

") assert in_memory_smtp.sent == [] diff --git a/tests/test_litellm/proxy/utils/proxy_logging/test_alerting.py b/tests/test_litellm/proxy/utils/proxy_logging/test_alerting.py index cede859cb38..77c0f71dbf9 100644 --- a/tests/test_litellm/proxy/utils/proxy_logging/test_alerting.py +++ b/tests/test_litellm/proxy/utils/proxy_logging/test_alerting.py @@ -115,6 +115,35 @@ async def test_budget_alerts_slack_when_slack_alerting(proxy_logging): assert snapshot == {"type": "user_budget", "user_info_is_callinfo": True, "user_id": "u1"} +@pytest.mark.asyncio +async def test_budget_alerts_webhook_only_forwards_to_slack_alerting_instance(proxy_logging): + proxy_logging.alerting = ["webhook"] + captured: Dict[str, Any] = {} + + async def fake_alert(**kwargs): + captured.update(kwargs) + + proxy_logging.slack_alerting_instance = MagicMock(budget_alerts=fake_alert) + proxy_logging.email_logging_instance = None + await proxy_logging.budget_alerts(type="user_budget", user_info=_user_info()) + snapshot = { + "type": captured["type"], + "user_info_is_callinfo": isinstance(captured["user_info"], CallInfo), + "user_id": captured["user_info"].user_id, + } + assert snapshot == {"type": "user_budget", "user_info_is_callinfo": True, "user_id": "u1"} + + +@pytest.mark.asyncio +async def test_budget_alerts_email_only_skips_slack_alerting_instance(proxy_logging): + proxy_logging.alerting = ["email"] + proxy_logging.slack_alerting_instance = MagicMock(budget_alerts=AsyncMock()) + proxy_logging.email_logging_instance = MagicMock(budget_alerts=AsyncMock()) + await proxy_logging.budget_alerts(type="user_budget", user_info=_user_info()) + proxy_logging.slack_alerting_instance.budget_alerts.assert_not_called() + proxy_logging.email_logging_instance.budget_alerts.assert_called_once() + + @pytest.mark.asyncio async def test_budget_alerts_soft_budget_with_alert_emails_bypasses_global(proxy_logging): proxy_logging.alerting = None diff --git a/tests/test_litellm/proxy/utils/proxy_logging/test_pre_call_hook.py b/tests/test_litellm/proxy/utils/proxy_logging/test_pre_call_hook.py index 0971ce09d79..9d2a27ce9d3 100644 --- a/tests/test_litellm/proxy/utils/proxy_logging/test_pre_call_hook.py +++ b/tests/test_litellm/proxy/utils/proxy_logging/test_pre_call_hook.py @@ -9,11 +9,14 @@ import pytest from fastapi import HTTPException import litellm +from litellm.caching.caching import DualCache from litellm.exceptions import RejectedRequestError from litellm.integrations.custom_guardrail import CustomGuardrail from litellm.integrations.custom_logger import CustomLogger +from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.utils import ProxyLogging from litellm.types.guardrails import GuardrailEventHooks +from litellm.types.utils import CallTypesLiteral def _load(module: str, name: str): @@ -473,7 +476,13 @@ class _RedactingGuardrail(CustomGuardrail): kwargs.setdefault("event_hook", GuardrailEventHooks.pre_call) super().__init__(guardrail_name="redactor", **kwargs) - async def async_pre_call_hook(self, user_api_key_dict, cache, data, call_type): # type: ignore[override] + async def async_pre_call_hook( + self, + user_api_key_dict: UserAPIKeyAuth, + cache: DualCache, + data: dict, + call_type: CallTypesLiteral, + ) -> dict | None: for msg in data.get("messages", []): if "SECRET" in msg.get("content", ""): msg["content"] = msg["content"].replace("SECRET", "[REDACTED]") @@ -488,7 +497,13 @@ class _BlockOnSecretGuardrail(CustomGuardrail): kwargs.setdefault("event_hook", GuardrailEventHooks.pre_call) super().__init__(guardrail_name="blocker", **kwargs) - async def async_pre_call_hook(self, user_api_key_dict, cache, data, call_type): # type: ignore[override] + async def async_pre_call_hook( + self, + user_api_key_dict: UserAPIKeyAuth, + cache: DualCache, + data: dict, + call_type: CallTypesLiteral, + ) -> dict | None: if any("SECRET" in msg.get("content", "") for msg in data.get("messages", [])): raise HTTPException(status_code=400, detail="blocked: SECRET detected") return None @@ -560,7 +575,13 @@ async def test_scan_raw_request_guardrail_does_not_undo_later_masking( separate marker (PII_TOKEN) that only the redactor reacts to.""" class _PiiRedactor(_RedactingGuardrail): - async def async_pre_call_hook(self, user_api_key_dict, cache, data, call_type): # type: ignore[override] + async def async_pre_call_hook( + self, + user_api_key_dict: UserAPIKeyAuth, + cache: DualCache, + data: dict, + call_type: CallTypesLiteral, + ) -> dict | None: for msg in data.get("messages", []): if "PII_TOKEN" in msg.get("content", ""): msg["content"] = msg["content"].replace("PII_TOKEN", "[REDACTED]") @@ -692,7 +713,13 @@ async def test_scan_raw_request_warns_when_guardrail_mutation_discarded( super().__init__(**kwargs) self.scan_raw_request = True - async def async_pre_call_hook(self, user_api_key_dict, cache, data, call_type): # type: ignore[override] + async def async_pre_call_hook( + self, + user_api_key_dict: UserAPIKeyAuth, + cache: DualCache, + data: dict, + call_type: CallTypesLiteral, + ) -> dict | None: for msg in data.get("messages", []): msg["content"] = msg["content"].replace("SECRET", "[REDACTED]") return data diff --git a/tests/test_litellm/repositories/test_unit_of_work.py b/tests/test_litellm/repositories/test_unit_of_work.py index 1ebfd917e36..1a76b537e95 100644 --- a/tests/test_litellm/repositories/test_unit_of_work.py +++ b/tests/test_litellm/repositories/test_unit_of_work.py @@ -1,9 +1,11 @@ +from dataclasses import fields from datetime import datetime, timezone from typing import Any, Dict, List, Mapping, Tuple import pytest from litellm.repositories.unit_of_work import ( + LinkedSpendResetWrites, budget_cascade_unit_of_work, spend_reset_unit_of_work, ) @@ -32,6 +34,7 @@ class FakeBatch: self.litellm_teammembership = FakeBatchTable("litellm_teammembership", self.calls) self.litellm_organizationtable = FakeBatchTable("litellm_organizationtable", self.calls) self.litellm_tagtable = FakeBatchTable("litellm_tagtable", self.calls) + self.litellm_modelaccessgroupbudgettable = FakeBatchTable("litellm_modelaccessgroupbudgettable", self.calls) self.litellm_endusertable = FakeBatchTable("litellm_endusertable", self.calls) async def commit(self) -> None: @@ -90,6 +93,7 @@ async def test_budget_cascade_dependents_and_window_advance_share_one_batch(): uow.keys.queue_spend_zero(where=linked) uow.organizations.queue_spend_zero(where=linked) uow.tags.queue_spend_zero(where=linked) + uow.model_access_groups.queue_spend_zero(where=linked) uow.endusers.queue_spend_zero(where={"user_id": {"in": ["enduser-1"]}}) uow.budgets.queue_window_advance(budget_id="budget-1", budget_reset_at=reset_at) assert batch.commit_count == 0 @@ -100,6 +104,7 @@ async def test_budget_cascade_dependents_and_window_advance_share_one_batch(): ("litellm_verificationtoken.update_many", linked, {"spend": 0}), ("litellm_organizationtable.update_many", linked, {"spend": 0}), ("litellm_tagtable.update_many", linked, {"spend": 0}), + ("litellm_modelaccessgroupbudgettable.update_many", linked, {"spend": 0}), ("litellm_endusertable.update_many", {"user_id": {"in": ["enduser-1"]}}, {"spend": 0}), ("litellm_budgettable.update_many", {"budget_id": "budget-1"}, {"budget_reset_at": reset_at}), ] @@ -117,6 +122,39 @@ async def test_budget_window_advance_tolerates_a_tier_deleted_mid_chunk(): assert [call[0] for call in batch.calls] == ["litellm_budgettable.update_many"] +async def test_every_cascade_dependent_writes_to_its_own_table_on_the_one_batch(): + """Walks the dataclass instead of naming tables, so a dependent added to + BudgetCascadeUnitOfWork later cannot go uncovered. + + The named test above only proves the tables it lists, and an unbound + dependent surfaces as an AttributeError from whichever tests happen to + open a cascade. This pins the real contract: every field writes, each to a + distinct table, all on the same batch. + """ + reset_at = datetime(2026, 8, 3, 12, 0, tzinfo=timezone.utc) + batches: List[FakeBatch] = [] + + def _new_batch() -> FakeBatch: + # Fresh per call like db.batch_(), unlike the `lambda: batch` above: a + # second transaction would otherwise alias onto the first and hide. + batches.append(FakeBatch()) + return batches[-1] + + async with budget_cascade_unit_of_work(_new_batch) as uow: + writes = [getattr(uow, field.name) for field in fields(uow)] + for write in writes: + if isinstance(write, LinkedSpendResetWrites): + write.queue_spend_zero(where={"budget_id": "budget-1"}) + else: + write.queue_window_advance(budget_id="budget-1", budget_reset_at=reset_at) + + assert len(batches) == 1, "the cascade must open exactly one transaction" + batch = batches[0] + assert len(batch.calls) == len(writes), "a dependent bound to a batch of its own would not land here" + assert len({call[0] for call in batch.calls}) == len(writes), "two dependents share one table" + assert batch.commit_count == 1 + + async def test_budget_cascade_raising_inside_block_skips_commit(): """A failure part-way through must leave budget_reset_at where it was, so the tier is still due on the next tick.""" diff --git a/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py b/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py index b96d2eb5322..b2b8eb5da80 100644 --- a/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py +++ b/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py @@ -124,6 +124,25 @@ class TestLiteLLMCompletionResponsesConfig: assert "extra_field" not in result["file"] assert "another_field" not in result["file"] + def test_transform_input_file_item_to_file_item_keeps_filename(self): + """OpenAI rejects file_data with no filename beside it, so dropping it 400s the request""" + result = ( + LiteLLMCompletionResponsesConfig._transform_input_file_item_to_file_item( + { + "type": "input_file", + "filename": "report.pdf", + "file_data": "data:application/pdf;base64,JVBERi0=", + } + ) + ) + assert result == { + "type": "file", + "file": { + "file_data": "data:application/pdf;base64,JVBERi0=", + "filename": "report.pdf", + }, + } + def test_transform_input_file_item_to_file_item_with_file_url(self): """file_url should be mapped to file_id for downstream URL handling""" result = ( @@ -629,6 +648,72 @@ class TestLiteLLMCompletionResponsesConfig: assert responses_api_response.status == "incomplete" + def test_tool_call_only_response_emits_no_null_text_message_item(self): + """A tool-calls-only turn (message content None, e.g. from Anthropic) + must not emit a message output item whose output_text has text null. + OpenAI rejects such an item on replay with + "Invalid type for 'input[..].content[..].text': expected a string, but + got null instead." Native OpenAI tool-only turns carry no message item.""" + chat_completion_response = ModelResponse( + id="test-response-id", + created=1234567890, + model="claude-sonnet-4-5", + object="chat.completion", + choices=[ + Choices( + finish_reason="tool_calls", + index=0, + message=Message( + content=None, + role="assistant", + tool_calls=[ + ChatCompletionMessageToolCall( + id="toolu_01OnlyToolCall", + type="function", + function=Function(name="get_weather", arguments='{"city": "SF"}'), + ) + ], + ), + ) + ], + ) + + responses_api_response = LiteLLMCompletionResponsesConfig.transform_chat_completion_response_to_responses_api_response( + request_input="what's the weather in SF?", + responses_api_request={}, + chat_completion_response=chat_completion_response, + ) + + output_types = [item.type for item in responses_api_response.output] + assert "message" not in output_types + assert "function_call" in output_types + + def test_content_bearing_response_still_emits_message_item(self): + """Turns with real text content must keep their message output item.""" + chat_completion_response = ModelResponse( + id="test-response-id", + created=1234567890, + model="claude-sonnet-4-5", + object="chat.completion", + choices=[ + Choices( + finish_reason="stop", + index=0, + message=Message(content="It is sunny.", role="assistant"), + ) + ], + ) + + responses_api_response = LiteLLMCompletionResponsesConfig.transform_chat_completion_response_to_responses_api_response( + request_input="what's the weather in SF?", + responses_api_request={}, + chat_completion_response=chat_completion_response, + ) + + message_items = [item for item in responses_api_response.output if item.type == "message"] + assert len(message_items) == 1 + assert message_items[0].content[0].text == "It is sunny." + def test_transform_chat_completion_response_preserves_hidden_params(self): """Test that _hidden_params from chat completion response are preserved in responses API response""" # Setup @@ -966,6 +1051,55 @@ class TestFunctionCallTransformation: assert result[0]["tool_calls"][0]["function"]["arguments"] == "{}" + def test_function_call_transformation_json_encodes_object_arguments(self): + """A decoded arguments object must be JSON-encoded, not str()'d. + + Clients and providers sometimes send `arguments` as an object rather + than a JSON string; `str()` on a dict produces a Python repr with + single quotes, which downstream JSON parsers reject with errors like + "Expecting ',' delimiter". + """ + function_call_item = { + "type": "function_call", + "name": "shell", + "arguments": {"command": "ls", "timeout": 30, "flags": ["-l", "-a"]}, + "call_id": "call_123", + "id": "call_123", + "status": "completed", + } + + result = LiteLLMCompletionResponsesConfig._transform_responses_api_function_call_to_chat_completion_message( + function_call=function_call_item + ) + + arguments = result[0].get("tool_calls", [])[0].get("function", {}).get("arguments") + assert json.loads(arguments) == {"command": "ls", "timeout": 30, "flags": ["-l", "-a"]} + assert "'" not in arguments + + def test_create_tool_call_chunk_json_encodes_object_arguments(self): + """Cached tool_call definitions with object arguments stay valid JSON.""" + chunk = LiteLLMCompletionResponsesConfig._create_tool_call_chunk( + tool_use_definition={ + "id": "call_456", + "type": "function", + "function": {"name": "shell", "arguments": {"command": "ls"}}, + }, + tool_call_id="call_456", + index=0, + ) + + assert json.loads(chunk["function"]["arguments"]) == {"command": "ls"} + + def test_create_tool_call_chunk_keeps_empty_arguments_default(self): + """Missing arguments still fall back to an empty JSON object.""" + chunk = LiteLLMCompletionResponsesConfig._create_tool_call_chunk( + tool_use_definition={"id": "call_789", "type": "function", "function": {"name": "shell"}}, + tool_call_id="call_789", + index=0, + ) + + assert chunk["function"]["arguments"] == "{}" + def test_complete_input_transformation_with_function_calls(self): """Test the complete transformation with the exact input from the issue""" test_input = [ @@ -3275,6 +3409,7 @@ class TestEnsureOutputItemContentPartAdded: iterator._pending_tool_events = [] iterator._tool_output_index_by_call_id = {} iterator._tool_args_by_call_id = {} + iterator._tool_item_id_by_call_id = {} iterator._tool_call_id_by_index = {} iterator._ambiguous_tool_call_indexes = set() iterator._next_tool_output_index = 1 diff --git a/tests/test_litellm/responses/litellm_completion_transformation/test_streaming_iterator_transformation.py b/tests/test_litellm/responses/litellm_completion_transformation/test_streaming_iterator_transformation.py index 823f656ddc5..4a03913f55a 100644 --- a/tests/test_litellm/responses/litellm_completion_transformation/test_streaming_iterator_transformation.py +++ b/tests/test_litellm/responses/litellm_completion_transformation/test_streaming_iterator_transformation.py @@ -10,6 +10,7 @@ before response.completed, and that every event of a bridged stream carries the spend tracking stores, so a follow-up previous_response_id still finds the conversation. """ +import json from unittest.mock import AsyncMock, MagicMock import pytest @@ -131,7 +132,7 @@ def test_tool_call_delta_is_emitted_as_responses_events(): evt2 = iterator._transform_chat_completion_chunk_to_response_api_chunk(chunk) assert evt2 is not None assert evt2.type == ResponsesAPIStreamEvents.FUNCTION_CALL_ARGUMENTS_DELTA - assert evt2.item_id == "call_1" + assert evt2.item_id == "fc_call_1" assert evt2.output_index == 1 # The delta will be a chunk of the arguments, not the full arguments assert len(evt2.delta) <= 10 # Chunks are max 10 characters @@ -196,7 +197,7 @@ def test_tool_calls_present_only_in_final_response_are_emitted_before_completed( # The last event should be FUNCTION_CALL_ARGUMENTS_DONE assert evt.type == ResponsesAPIStreamEvents.FUNCTION_CALL_ARGUMENTS_DONE - assert evt.item_id == "call_2" + assert evt.item_id == "fc_call_2" assert evt.output_index == 1 assert evt.arguments == '{"y":2}' @@ -290,7 +291,7 @@ def test_tool_call_arguments_are_chunked_to_match_openai_behavior(): # Verify each delta is at most 10 characters for evt in delta_events: assert len(evt.delta) <= 10 - assert evt.item_id == "call_test" + assert evt.item_id == "fc_call_test" assert evt.output_index == 1 assert hasattr(evt, "__dict__") and "sequence_number" in evt.__dict__ @@ -348,7 +349,8 @@ def test_tool_call_delta_without_id_uses_index_mapping(): if evt.type == ResponsesAPIStreamEvents.OUTPUT_ITEM_ADDED ] assert len(output_item_added_events) == 1 - assert output_item_added_events[0].item.id == "call_abc123" + assert output_item_added_events[0].item.id == "fc_call_abc123" + assert output_item_added_events[0].item.call_id == "call_abc123" def test_parallel_tool_calls_without_ids_use_index_mapping(): @@ -403,8 +405,8 @@ def test_parallel_tool_calls_without_ids_use_index_mapping(): arguments_by_call_id.setdefault(evt.item_id, "") arguments_by_call_id[evt.item_id] += evt.delta - assert arguments_by_call_id["call_a"] == '{"x":1}' - assert arguments_by_call_id["call_b"] == '{"y":2}' + assert arguments_by_call_id["fc_call_a"] == '{"x":1}' + assert arguments_by_call_id["fc_call_b"] == '{"y":2}' def test_reused_index_with_new_call_id_marks_fallback_ambiguous(): @@ -460,10 +462,10 @@ def test_reused_index_with_new_call_id_marks_fallback_ambiguous(): arguments_by_call_id.setdefault(evt.item_id, "") arguments_by_call_id[evt.item_id] += evt.delta - assert arguments_by_call_id["call_a"] == '{"a":' - assert arguments_by_call_id["call_b"] == '{"b":' - assert arguments_by_call_id["call_a"] != '{"a":1}' - assert arguments_by_call_id["call_b"] != '{"b":1}' + assert arguments_by_call_id["fc_call_a"] == '{"a":' + assert arguments_by_call_id["fc_call_b"] == '{"b":' + assert arguments_by_call_id["fc_call_a"] != '{"a":1}' + assert arguments_by_call_id["fc_call_b"] != '{"b":1}' @pytest.mark.asyncio @@ -523,3 +525,91 @@ async def test_streaming_response_id_falls_back_when_upstream_yields_nothing(): assert response_ids assert len(set(response_ids)) == 1 assert response_ids[0].startswith("resp_") + + +def test_object_tool_call_arguments_stream_as_valid_json(): + """A provider that sends decoded object arguments must still stream valid JSON. + + `str()` on a dict yields a Python repr with single quotes, which clients + parsing function_call_arguments reject with errors like + "Expecting ',' delimiter". + """ + iterator = LiteLLMCompletionStreamingIterator( + model="test-model", + litellm_custom_stream_wrapper=AsyncMock(), + request_input="Test input", + responses_api_request={}, + ) + iterator._queue_tool_call_delta_events( + [ + { + "index": 0, + "id": "call_obj", + "type": "function", + "function": {"name": "shell", "arguments": {"command": "ls", "flags": ["-l"]}}, + } + ] + ) + + streamed_arguments = "".join( + evt.delta + for evt in iterator._pending_tool_events + if evt.type == ResponsesAPIStreamEvents.FUNCTION_CALL_ARGUMENTS_DELTA + ) + + assert json.loads(streamed_arguments) == {"command": "ls", "flags": ["-l"]} + + +def test_streamed_anthropic_tool_call_events_correlate_on_normalized_item_id(): + iterator = LiteLLMCompletionStreamingIterator( + model="test-model", + litellm_custom_stream_wrapper=AsyncMock(), + request_input="Test input", + responses_api_request={}, + ) + + response = ModelResponse( + id="resp-anthropic", + created=123, + model="test-model", + object="chat.completion", + choices=[ + { + "index": 0, + "finish_reason": "tool_calls", + "message": { + "role": "assistant", + "content": None, + "tool_calls": [ + { + "id": "toolu_01AbCdEf", + "type": "function", + "function": {"name": "get_weather", "arguments": '{"city":"Paris"}'}, + "index": 0, + } + ], + }, + } + ], + ) + iterator.litellm_model_response = response + + events = [] + while True: + evt = iterator.common_done_event_logic(sync_mode=True) + events.append(evt) + if evt.type == ResponsesAPIStreamEvents.OUTPUT_ITEM_DONE: + break + + added = [e for e in events if e.type == ResponsesAPIStreamEvents.OUTPUT_ITEM_ADDED] + deltas = [e for e in events if e.type == ResponsesAPIStreamEvents.FUNCTION_CALL_ARGUMENTS_DELTA] + dones = [e for e in events if e.type == ResponsesAPIStreamEvents.FUNCTION_CALL_ARGUMENTS_DONE] + item_dones = [e for e in events if e.type == ResponsesAPIStreamEvents.OUTPUT_ITEM_DONE] + + assert len(added) == 1 and len(dones) == 1 and len(item_dones) == 1 and deltas + assert added[0].item.id == "fc_toolu_01AbCdEf" + assert added[0].item.call_id == "toolu_01AbCdEf" + assert item_dones[0].item.id == "fc_toolu_01AbCdEf" + assert item_dones[0].item.call_id == "toolu_01AbCdEf" + for evt in deltas + dones: + assert evt.item_id == added[0].item.id diff --git a/tests/test_litellm/responses/test_custom_tool_call.py b/tests/test_litellm/responses/test_custom_tool_call.py index c605ef24934..5122c1c1d67 100644 --- a/tests/test_litellm/responses/test_custom_tool_call.py +++ b/tests/test_litellm/responses/test_custom_tool_call.py @@ -20,6 +20,7 @@ from litellm.responses.litellm_completion_transformation.transformation import ( from litellm.responses.litellm_completion_transformation.custom_tools import ( extract_custom_tool_names, is_custom_tool_call, + openai_shaped_tool_call_item_id, unwrap_custom_tool_arguments, build_tool_call_item_kwargs, convert_custom_tool_to_function_tool, @@ -129,6 +130,41 @@ class TestCustomToolUtilities: assert kwargs["arguments"] == raw assert "input" not in kwargs + def test_openai_shaped_tool_call_item_id_prefixes_foreign_ids(self): + """Anthropic-style tool ids must be normalized to OpenAI's item id + shapes (fc/ctc prefixes) so replaying the item to OpenAI does not 400 + with "Expected an ID that begins with 'fc'".""" + assert openai_shaped_tool_call_item_id("function_call", "toolu_01Abc") == "fc_toolu_01Abc" + assert openai_shaped_tool_call_item_id("function_call", "srvtoolu_01Xyz") == "fc_srvtoolu_01Xyz" + assert openai_shaped_tool_call_item_id("custom_tool_call", "toolu_01Abc") == "ctc_toolu_01Abc" + assert openai_shaped_tool_call_item_id("function_call", "fc_already") == "fc_already" + assert openai_shaped_tool_call_item_id("custom_tool_call", "ctc_already") == "ctc_already" + assert openai_shaped_tool_call_item_id("function_call", "") == "" + assert openai_shaped_tool_call_item_id("message", "toolu_01Abc") == "toolu_01Abc" + + def test_build_tool_call_item_kwargs_normalizes_item_id_keeps_call_id(self): + """The streaming item id gets the OpenAI shape while call_id stays raw + so tool_result pairing (which keys off call_id) keeps working.""" + function_kwargs = build_tool_call_item_kwargs( + call_id="toolu_01Abc", + name="get_weather", + arguments_or_input="{}", + status="completed", + custom_tool_names=set(), + ) + assert function_kwargs["id"] == "fc_toolu_01Abc" + assert function_kwargs["call_id"] == "toolu_01Abc" + + custom_kwargs = build_tool_call_item_kwargs( + call_id="toolu_01Def", + name="apply_patch", + arguments_or_input=json.dumps({"content": "patch"}), + status="completed", + custom_tool_names={"apply_patch"}, + ) + assert custom_kwargs["id"] == "ctc_toolu_01Def" + assert custom_kwargs["call_id"] == "toolu_01Def" + def test_unwrap_custom_tool_arguments_oversized_returns_raw(self): """Arguments larger than the safety cap are returned unchanged to avoid OOM on JSON parsing a pathologically large string.""" @@ -293,6 +329,52 @@ class TestTransformationCustomTools: assert item.name == "regular_tool" assert item.arguments == json.dumps({"param": "value"}) + def test_transform_anthropic_tool_call_ids_get_openai_item_id_shape(self): + """Anthropic tool ids (toolu_/srvtoolu_) surfacing through the bridge + must be emitted with fc/ctc-prefixed item ids so a Responses client can + replay them to OpenAI verbatim, while call_id stays raw for pairing.""" + from litellm.types.utils import ModelResponse, Choices, Message, ChatCompletionMessageToolCall, Function + + client_call = ChatCompletionMessageToolCall( + id="toolu_01ClientCall", + type="function", + function=Function(name="get_weather", arguments=json.dumps({"city": "SF"})), + ) + server_call = ChatCompletionMessageToolCall( + id="srvtoolu_01ServerCall", + type="function", + function=Function(name="web_search", arguments=json.dumps({"query": "zig"})), + ) + custom_call = ChatCompletionMessageToolCall( + id="toolu_01CustomCall", + type="function", + function=Function(name="apply_patch", arguments=json.dumps({"content": "patch content"})), + ) + + message = Message(role="assistant", content=None, tool_calls=[client_call, server_call, custom_call]) + choices = [Choices(index=0, message=message, finish_reason="tool_calls")] + response = ModelResponse( + id="test_response", choices=choices, created=1234567890, model="claude-sonnet-4-5", object="chat.completion" + ) + responses_api_request = { + "tools": [{"type": "custom", "name": "apply_patch"}, {"type": "function", "name": "get_weather"}] + } + + result = LiteLLMCompletionResponsesConfig.transform_chat_completion_tools_to_responses_tools( + response, responses_api_request=responses_api_request + ) + + assert [item.id for item in result] == [ + "fc_toolu_01ClientCall", + "fc_srvtoolu_01ServerCall", + "ctc_toolu_01CustomCall", + ] + assert [item.call_id for item in result] == [ + "toolu_01ClientCall", + "srvtoolu_01ServerCall", + "toolu_01CustomCall", + ] + def test_transform_mixed_tool_calls(self): """Test transformation with both custom and regular tool calls.""" from litellm.types.utils import ModelResponse, Choices, Message, ChatCompletionMessageToolCall, Function diff --git a/tests/test_litellm/responses/test_streaming_iterator.py b/tests/test_litellm/responses/test_streaming_iterator.py index 38407c94fe7..677faf7f655 100644 --- a/tests/test_litellm/responses/test_streaming_iterator.py +++ b/tests/test_litellm/responses/test_streaming_iterator.py @@ -305,3 +305,24 @@ def test_stream_cache_write_completes_when_asyncio_run_closes_the_loop(monkeypat asyncio.run(_short_lived_script()) assert len(writes) == 1 + + +def test_run_post_success_hooks_does_not_report_generation_time_as_overhead(): + """LIT-5466: the provider call is timed to first byte, so at stream completion the total minus + that duration is token generation, not LiteLLM overhead.""" + logging_obj = _logging_obj_stub() + logging_obj.model_call_details = {"litellm_params": {}, "llm_api_duration_ms": 200.0} + logging_obj.caching_details = None + + class _CompletedEvent: + def __init__(self) -> None: + self._hidden_params: dict = {} + + iterator = _make_iterator(sse_events=[], logging_obj=logging_obj) + iterator.completed_response = _CompletedEvent() + iterator.start_time = datetime(2025, 1, 1, 0, 0, 0) + + iterator._run_post_success_hooks(datetime(2025, 1, 1, 0, 0, 10)) + + assert iterator.completed_response._hidden_params["_response_ms"] == 10000.0 + assert "litellm_overhead_time_ms" not in iterator.completed_response._hidden_params diff --git a/tests/test_litellm/router_strategy/test_complexity_router.py b/tests/test_litellm/router_strategy/test_complexity_router.py index eee4e9aa185..1ec8be88c9b 100644 --- a/tests/test_litellm/router_strategy/test_complexity_router.py +++ b/tests/test_litellm/router_strategy/test_complexity_router.py @@ -4425,6 +4425,265 @@ class _DummyPlugin: return context +class TestClassificationMode: + """Test classification_mode='user_turn': classify only requests whose newest turn is a new + human ask; tool-loop continuation turns replay the session's held routing decision.""" + + REASONING_ASK = { + "role": "user", + "content": "Let's think step by step and reason through this problem carefully.", + } + SIMPLE_ASK = {"role": "user", "content": "Hello!"} + ASSISTANT_ANSWER = {"role": "assistant", "content": "the answer"} + TOOL_CALL_1 = { + "role": "assistant", + "content": None, + "tool_calls": [{"id": "call_1", "type": "function", "function": {"name": "read_file", "arguments": "{}"}}], + } + TOOL_RESULT_1 = {"role": "tool", "tool_call_id": "call_1", "content": "file contents"} + TOOL_CALL_2 = { + "role": "assistant", + "content": None, + "tool_calls": [{"id": "call_2", "type": "function", "function": {"name": "run_tests", "arguments": "{}"}}], + } + TOOL_RESULT_2 = {"role": "tool", "tool_call_id": "call_2", "content": "3 passed"} + + @pytest.fixture + def user_turn_config(self, basic_config) -> dict: + return {**basic_config, "classification_mode": "user_turn"} + + @staticmethod + def _request_kwargs(session_id: str) -> dict: + return {"metadata": {"session_id": session_id}} + + def _router(self, mock_router_instance, config: dict) -> ComplexityRouter: + mock_router_instance.cache = DualCache() + return ComplexityRouter( + model_name="test-router", + litellm_router_instance=mock_router_instance, + complexity_router_config=config, + ) + + def _tool_loop_turns(self) -> list[list[dict]]: + return [ + [self.REASONING_ASK], + [self.REASONING_ASK, self.TOOL_CALL_1, self.TOOL_RESULT_1], + [self.REASONING_ASK, self.TOOL_CALL_1, self.TOOL_RESULT_1, self.TOOL_CALL_2, self.TOOL_RESULT_2], + ] + + def test_default_mode_is_every_request(self, complexity_router): + assert complexity_router.config.classification_mode == "every_request" + + def test_invalid_classification_mode_rejected(self, mock_router_instance, basic_config): + with pytest.raises(ValidationError): + ComplexityRouter( + model_name="test-router", + litellm_router_instance=mock_router_instance, + complexity_router_config={**basic_config, "classification_mode": "sometimes"}, + ) + + @pytest.mark.asyncio + async def test_user_turn_mode_classifies_tool_loop_once(self, mock_router_instance, user_turn_config): + """The mutation check: a 3-request tool loop drives exactly one classification, and both + continuation turns hold the classified model under the user_turn_continuation cause.""" + router = self._router(mock_router_instance, user_turn_config) + with patch.object(router, "_classify_and_route", wraps=router._classify_and_route) as spy: + responses = [ + await router.async_pre_routing_hook( + model="test-model", request_kwargs=self._request_kwargs("loop-1"), messages=turn + ) + for turn in self._tool_loop_turns() + ] + assert spy.call_count == 1 + assert [r.model for r in responses] == ["o1-preview", "o1-preview", "o1-preview"] + assert [r.routing_decision["cause"] for r in responses[1:]] == [ + "user_turn_continuation", + "user_turn_continuation", + ] + + @pytest.mark.asyncio + async def test_every_request_default_classifies_every_tool_loop_turn(self, mock_router_instance, basic_config): + """Pins today's default: every request classifies, including tool-loop continuations.""" + router = self._router(mock_router_instance, basic_config) + with patch.object(router, "_classify_and_route", wraps=router._classify_and_route) as spy: + responses = [ + await router.async_pre_routing_hook( + model="test-model", request_kwargs=self._request_kwargs("loop-2"), messages=turn + ) + for turn in self._tool_loop_turns() + ] + assert spy.call_count == 3 + assert [r.model for r in responses] == ["o1-preview", "o1-preview", "o1-preview"] + assert all(r.routing_decision["cause"] != "user_turn_continuation" for r in responses) + + @pytest.mark.asyncio + async def test_continuation_without_session_id_still_classifies(self, mock_router_instance, user_turn_config): + """No resolvable session id means no held decision to replay, so every request classifies.""" + router = self._router(mock_router_instance, user_turn_config) + with patch.object(router, "_classify_and_route", wraps=router._classify_and_route) as spy: + responses = [ + await router.async_pre_routing_hook(model="test-model", request_kwargs={}, messages=turn) + for turn in self._tool_loop_turns() + ] + assert spy.call_count == 3 + assert [r.model for r in responses] == ["o1-preview", "o1-preview", "o1-preview"] + assert all(r.routing_decision["cause"] != "user_turn_continuation" for r in responses) + + @pytest.mark.asyncio + async def test_plugins_suppress_user_turn_gate(self, mock_router_instance, basic_config): + """A replayed decision would bypass the plugin pipeline, so plugins force every request + through _classify_and_route, exactly as they do for session_affinity.""" + router = self._router( + mock_router_instance, + {**basic_config, "classification_mode": "user_turn", "plugins": [_DummyPlugin()]}, + ) + with patch.object(router, "_classify_and_route", wraps=router._classify_and_route) as spy: + responses = [ + await router.async_pre_routing_hook( + model="test-model", request_kwargs=self._request_kwargs("loop-3"), messages=turn + ) + for turn in self._tool_loop_turns() + ] + assert spy.call_count == 3 + assert [r.model for r in responses] == ["o1-preview", "o1-preview", "o1-preview"] + assert all(r.routing_decision["cause"] != "user_turn_continuation" for r in responses) + + @pytest.mark.asyncio + async def test_new_human_ask_reclassifies_and_repins(self, mock_router_instance, user_turn_config): + """Unlike session_affinity, a new human ask never short-circuits on the pin: the session + re-classifies, moves tier, and the moved decision becomes the next held decision.""" + router = self._router(mock_router_instance, user_turn_config) + first = await router.async_pre_routing_hook( + model="test-model", request_kwargs=self._request_kwargs("s-repin"), messages=[self.REASONING_ASK] + ) + second = await router.async_pre_routing_hook( + model="test-model", + request_kwargs=self._request_kwargs("s-repin"), + messages=[self.REASONING_ASK, self.ASSISTANT_ANSWER, self.SIMPLE_ASK], + ) + third = await router.async_pre_routing_hook( + model="test-model", + request_kwargs=self._request_kwargs("s-repin"), + messages=[self.REASONING_ASK, self.ASSISTANT_ANSWER, self.SIMPLE_ASK, self.TOOL_CALL_1, self.TOOL_RESULT_1], + ) + assert first.model == "o1-preview" + assert second.model == "gpt-4o-mini" + assert third.model == "gpt-4o-mini" + assert third.routing_decision["cause"] == "user_turn_continuation" + + @pytest.mark.asyncio + async def test_new_ask_with_trailing_system_reminder_reclassifies(self, mock_router_instance, user_turn_config): + """Claude Code appends a system-role reminder after the human turn; that trailing plumbing + must not turn a new ask into a continuation, and a continuation turn carrying the same + trailing reminder stays a continuation.""" + router = self._router(mock_router_instance, user_turn_config) + reminder = {"role": "system", "content": "100 tokens left"} + first = await router.async_pre_routing_hook( + model="test-model", request_kwargs=self._request_kwargs("s-reminder"), messages=[self.REASONING_ASK] + ) + second = await router.async_pre_routing_hook( + model="test-model", + request_kwargs=self._request_kwargs("s-reminder"), + messages=[self.REASONING_ASK, self.ASSISTANT_ANSWER, self.SIMPLE_ASK, reminder], + ) + third = await router.async_pre_routing_hook( + model="test-model", + request_kwargs=self._request_kwargs("s-reminder"), + messages=[ + self.REASONING_ASK, + self.ASSISTANT_ANSWER, + self.SIMPLE_ASK, + reminder, + self.TOOL_CALL_1, + self.TOOL_RESULT_1, + reminder, + ], + ) + assert first.model == "o1-preview" + assert second.model == "gpt-4o-mini" + assert second.routing_decision["cause"] != "user_turn_continuation" + assert third.model == "gpt-4o-mini" + assert third.routing_decision["cause"] == "user_turn_continuation" + + @pytest.mark.asyncio + async def test_escalation_keyword_turn_is_a_new_ask(self, mock_router_instance, user_turn_config): + """An escalation keyword arrives as human text, so the turn classifies and escalates + instead of replaying the held decision.""" + router = self._router(mock_router_instance, user_turn_config) + first = await router.async_pre_routing_hook( + model="test-model", request_kwargs=self._request_kwargs("s-esc"), messages=[self.SIMPLE_ASK] + ) + second = await router.async_pre_routing_hook( + model="test-model", + request_kwargs=self._request_kwargs("s-esc"), + messages=[self.SIMPLE_ASK, self.ASSISTANT_ANSWER, {"role": "user", "content": "LITELLM ESCALATE"}], + ) + assert first.model == "gpt-4o-mini" + assert second.model == "gpt-4o" + assert second.routing_decision["escalated"] is True + + @pytest.mark.asyncio + async def test_messages_surface_tool_result_shapes(self, mock_router_instance, user_turn_config): + """Messages-surface shapes: a tool_result-only user turn is a continuation, while an ask + riding alongside a tool_result in the same turn is a new ask.""" + router = self._router(mock_router_instance, user_turn_config) + tool_use = {"role": "assistant", "content": [{"type": "tool_use", "id": "x", "name": "t", "input": {}}]} + tool_result = {"type": "tool_result", "tool_use_id": "x", "content": "ok"} + first = await router.async_pre_routing_hook( + model="test-model", request_kwargs=self._request_kwargs("s-msgs"), messages=[self.REASONING_ASK] + ) + pure = await router.async_pre_routing_hook( + model="test-model", + request_kwargs=self._request_kwargs("s-msgs"), + messages=[self.REASONING_ASK, tool_use, {"role": "user", "content": [tool_result]}], + ) + hybrid = await router.async_pre_routing_hook( + model="test-model", + request_kwargs=self._request_kwargs("s-msgs"), + messages=[ + self.REASONING_ASK, + tool_use, + {"role": "user", "content": [tool_result, {"type": "text", "text": "Hello!"}]}, + ], + ) + assert first.model == "o1-preview" + assert pure.model == "o1-preview" + assert pure.routing_decision["cause"] == "user_turn_continuation" + assert hybrid.model == "gpt-4o-mini" + + @pytest.mark.asyncio + async def test_session_affinity_wins_when_both_knobs_are_on(self, mock_router_instance, user_turn_config): + """With session_affinity also on, the pin short-circuits new asks too and keeps its own + cause, so the session stays on turn 1's model.""" + router = self._router(mock_router_instance, {**user_turn_config, "session_affinity": True}) + first = await router.async_pre_routing_hook( + model="test-model", request_kwargs=self._request_kwargs("s-both"), messages=[self.REASONING_ASK] + ) + second = await router.async_pre_routing_hook( + model="test-model", + request_kwargs=self._request_kwargs("s-both"), + messages=[self.REASONING_ASK, self.ASSISTANT_ANSWER, self.SIMPLE_ASK], + ) + assert first.model == "o1-preview" + assert second.model == "o1-preview" + assert second.routing_decision["cause"] == "session_affinity_pin" + + def test_user_turn_mode_enables_tier_and_deployment_pins(self, mock_router_instance, basic_config): + """user_turn implies the tier pin machinery (the pin write is what gives a continuation + a held decision) and the tier pin implies the deployment pin; plugins suppress both.""" + default = self._router(mock_router_instance, basic_config) + enabled = self._router(mock_router_instance, {**basic_config, "classification_mode": "user_turn"}) + suppressed = self._router( + mock_router_instance, + {**basic_config, "classification_mode": "user_turn", "plugins": [_DummyPlugin()]}, + ) + assert default._uses_tier_pin is False + assert enabled._uses_tier_pin is True + assert enabled._uses_deployment_pin is True + assert suppressed._uses_tier_pin is False + assert suppressed._uses_deployment_pin is False + + class TestRoutingPlugins: """Test the `complexity_router_config.plugins` field: narrows the classified tier's candidate pool before a model is picked. Discussion: @@ -9762,3 +10021,719 @@ class TestHeuristicFirst: ) outcome = await router.aclassify(NO_SIGNAL_PROMPT) assert outcome.cause == "default_model_fallback" + + +def _windowed_router(*deployments: tuple) -> Router: + """Real Router; each deployment is (group, provider_model, declared window or None). + None means no declared override on a model the cost map does not know: unresolvable.""" + return Router( + model_list=[ + { + "model_name": group, + "litellm_params": {"model": provider_model, "mock_response": "ok"}, + **({"model_info": {"max_input_tokens": window}} if window is not None else {}), + } + for group, provider_model, window in deployments + ] + ) + + +_SMALL = ("small-model", "openai/gpt-3.5-turbo", 16385) +_BIG = ("big-model", "openai/gpt-4o-mini", 200000) + +# A long agentic session whose newest ask is trivial: low-density filler the heuristic scores +# SIMPLE, sized well past a 16,385-token window so the fit check must move it. +_CONTEXT_FILLER = "The meeting notes were saved to the shared folder for later review this week. " * 2000 +_OVERSIZED_TURNS = [ + {"role": "user", "content": "Here is everything discussed so far. " + _CONTEXT_FILLER}, + {"role": "assistant", "content": "Noted, I have read all of it."}, + {"role": "user", "content": "ok continue"}, +] +# ~40k CJK chars: chars/4 says ~10k tokens, the real tokenizer says several times that. A +# character-based shortcut would skip counting and dispatch this to a 16k window. +_CJK_TURNS = [ + {"role": "user", "content": "会议记录已经保存到共享文件夹里,供大家本周晚些时候查阅和讨论使用。" * 1300}, + {"role": "user", "content": "ok continue"}, +] + + +def _tier_config(**overrides) -> Dict: + return {"tiers": {"SIMPLE": "small-model", "COMPLEX": "big-model"}, **overrides} + + +class TestContextWindowEscalation: + """A tier decided on complexity alone must still hold the prompt, or the provider 400s. + + The classifier never weighs prompt size (token count is a 0.10-weight scoring dimension, + below every tier boundary), so a long session ending in a trivial ask lands on the + smallest tier and dies upstream with no retry. The gate checks fit pre-dispatch, against + windows resolved through the real Router deployment chain. + """ + + @pytest.mark.asyncio + async def test_an_oversized_simple_prompt_escalates_to_the_lowest_tier_that_fits(self): + """The LIT-6503 regression: SIMPLE verdict, 17k-token prompt, 16,385-token tier model. + + Unfixed, this dispatched to the small model and the provider rejected it with a + context-window 400 that neither the retry layer nor tier-keyed fallbacks catch. + """ + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=_windowed_router(_SMALL, _BIG), + complexity_router_config=_tier_config(), + ) + + result = await router.async_pre_routing_hook(model="test-router", request_kwargs={}, messages=_OVERSIZED_TURNS) + + assert result is not None + assert result.model == "big-model" + assert result.routing_decision["context_escalated"] is True + assert result.routing_decision["context_escalation_original_tier"] == "SIMPLE" + assert result.routing_decision["tier"] == "COMPLEX" + assert "context_escalation" in result.routing_decision["signals"] + + @pytest.mark.asyncio + async def test_a_prompt_that_fits_routes_exactly_as_before(self): + """The gate must be invisible for normal traffic: same model, no escalation facts.""" + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=_windowed_router(_SMALL, _BIG), + complexity_router_config=_tier_config(), + ) + + result = await router.async_pre_routing_hook( + model="test-router", request_kwargs={}, messages=[{"role": "user", "content": "ok continue"}] + ) + + assert result is not None + assert result.model == "small-model" + assert "context_escalated" not in result.routing_decision + assert "context_escalation_original_tier" not in result.routing_decision + + @pytest.mark.asyncio + async def test_the_pick_prefers_a_fitting_group_inside_the_decided_tier(self): + """A tier holding both a small and a large group keeps the request and picks the one + that fits, which is cheaper than escalating and preserves the classifier's decision.""" + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=_windowed_router(_SMALL, ("mid-model", "openai/gpt-4o-mini", 200000), _BIG), + complexity_router_config={"tiers": {"SIMPLE": ["small-model", "mid-model"], "COMPLEX": "big-model"}}, + ) + + result = await router.async_pre_routing_hook(model="test-router", request_kwargs={}, messages=_OVERSIZED_TURNS) + + assert result is not None + assert result.model == "mid-model" + assert result.routing_decision["tier"] == "SIMPLE" + assert "context_escalated" not in result.routing_decision + + @pytest.mark.asyncio + async def test_a_group_is_only_as_safe_as_its_smallest_deployment(self): + """One group name can front deployments with different windows, and the core router + picks among them with no fit check, so retaining the group on its largest member + turns the pick into a coin flip against a 400. The gate judges the group by its + smallest resolvable window and escalates past it.""" + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=Router( + model_list=[ + { + "model_name": "mixed-pool", + "litellm_params": {"model": "openai/gpt-3.5-turbo", "mock_response": "ok"}, + "model_info": {"max_input_tokens": 16385}, + }, + { + "model_name": "mixed-pool", + "litellm_params": {"model": "openai/gpt-4o-mini", "mock_response": "ok"}, + "model_info": {"max_input_tokens": 200000}, + }, + { + "model_name": "big-model", + "litellm_params": {"model": "openai/gpt-4o-mini", "mock_response": "ok"}, + "model_info": {"max_input_tokens": 200000}, + }, + ] + ), + complexity_router_config={"tiers": {"SIMPLE": "mixed-pool", "COMPLEX": "big-model"}}, + ) + + result = await router.async_pre_routing_hook(model="test-router", request_kwargs={}, messages=_OVERSIZED_TURNS) + + assert result is not None + assert result.model == "big-model" + assert result.routing_decision["context_escalated"] is True + + @pytest.mark.asyncio + async def test_token_dense_text_cannot_slip_past_the_counting_shortcut(self): + """CJK text runs several tokens per four characters, so a chars/4 shortcut would skip + the real count and dispatch an oversized prompt. The skip is gated on the UTF-8 byte + length, which the token count can never exceed.""" + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=_windowed_router(_SMALL, _BIG), + complexity_router_config=_tier_config(), + ) + + result = await router.async_pre_routing_hook(model="test-router", request_kwargs={}, messages=_CJK_TURNS) + + assert result is not None + assert result.model == "big-model" + assert result.routing_decision["context_escalated"] is True + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "deployments,tiers,expected_model", + [ + ( + (("small-model", "openai/unmapped-model-under-test", None), _BIG), + {"SIMPLE": "small-model", "COMPLEX": "big-model"}, + "small-model", + ), + ( + (_SMALL, ("mid-model", "openai/another-unmapped-model", None), _BIG), + {"SIMPLE": "small-model", "MEDIUM": "mid-model", "COMPLEX": "big-model"}, + "big-model", + ), + ((_SMALL,), {"SIMPLE": "small-model"}, "small-model"), + ], + ids=["unknown-window-stays", "unproven-target-skipped", "nothing-fits-stays"], + ) + async def test_unknown_windows_are_never_acted_on(self, deployments, tiers, expected_model): + """No faith in either direction: a model with no resolvable window is never escalated + away from (its misfit is unprovable) and never escalated onto (its fit is unprovable); + when nothing provably fits, the classified tier stands and the client owns overflow.""" + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=_windowed_router(*deployments), + complexity_router_config={"tiers": tiers}, + ) + + result = await router.async_pre_routing_hook(model="test-router", request_kwargs={}, messages=_OVERSIZED_TURNS) + + assert result is not None + assert result.model == expected_model + + @pytest.mark.asyncio + async def test_the_disabled_gate_dispatches_on_complexity_alone(self): + """The escape hatch: enable_context_window_escalation false restores today's behavior.""" + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=_windowed_router(_SMALL, _BIG), + complexity_router_config=_tier_config(enable_context_window_escalation=False), + ) + + result = await router.async_pre_routing_hook(model="test-router", request_kwargs={}, messages=_OVERSIZED_TURNS) + + assert result is not None + assert result.model == "small-model" + assert "context_escalated" not in result.routing_decision + + @pytest.mark.asyncio + async def test_out_of_band_system_and_tools_count_against_the_window(self): + """The Claude Code shape that live-testing caught: a tiny ask riding a top-level + `system` block and tool definitions that together dwarf the message list. None of + that reaches resolved messages on /v1/messages, so a gate reading only messages + dispatches a provably oversized request and the provider 400s anyway.""" + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=_windowed_router(_SMALL, _BIG), + complexity_router_config=_tier_config(), + ) + + result = await router.async_pre_routing_hook( + model="test-router", + request_kwargs={ + "proxy_server_request": { + "body": { + "system": _CONTEXT_FILLER, + "tools": [{"name": f"tool_{i}", "description": _CONTEXT_FILLER[:500]} for i in range(20)], + } + } + }, + messages=[{"role": "user", "content": "reply with exactly: rig check ok"}], + ) + + assert result is not None + assert result.model == "big-model" + assert result.routing_decision["context_escalated"] is True + + @pytest.mark.asyncio + async def test_an_escalated_first_turn_never_becomes_the_session_pin(self): + """Escalation describes the prompt's size, not the session: once the client compacts, + the next turn fits again, so pinning the big-window tier would hold the whole session + on it for the TTL. The escalated turn routes big, and the next fitting turn classifies + fresh instead of inheriting a pin.""" + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=_windowed_router(_SMALL, _BIG), + complexity_router_config=_tier_config(session_affinity=True), + ) + session_kwargs = lambda: {"metadata": {"session_id": "s-1", "user_api_key_hash": "k-1"}} # noqa: E731 + + first = await router.async_pre_routing_hook( + model="test-router", request_kwargs=session_kwargs(), messages=_OVERSIZED_TURNS + ) + second = await router.async_pre_routing_hook( + model="test-router", request_kwargs=session_kwargs(), messages=[{"role": "user", "content": "ok continue"}] + ) + + assert first is not None and first.model == "big-model" + assert second is not None and second.model == "small-model" + assert second.routing_decision["cause"] != "session_affinity_pin" + + @pytest.mark.asyncio + async def test_a_pinned_session_escalates_per_request_and_keeps_its_pin(self): + """The pin fast path skips classification, not physics: an oversized turn on a session + pinned to the small tier is served by the fitting tier, while the stored pin keeps the + session's own model so the first turn that fits again routes exactly as pinned.""" + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=_windowed_router(_SMALL, _BIG), + complexity_router_config=_tier_config(session_affinity=True), + ) + session_kwargs = lambda: {"metadata": {"session_id": "s-2", "user_api_key_hash": "k-2"}} # noqa: E731 + + pinned = await router.async_pre_routing_hook( + model="test-router", request_kwargs=session_kwargs(), messages=[{"role": "user", "content": "ok continue"}] + ) + oversized = await router.async_pre_routing_hook( + model="test-router", request_kwargs=session_kwargs(), messages=_OVERSIZED_TURNS + ) + back_to_small = await router.async_pre_routing_hook( + model="test-router", request_kwargs=session_kwargs(), messages=[{"role": "user", "content": "ok continue"}] + ) + + assert pinned is not None and pinned.model == "small-model" + assert oversized is not None and oversized.model == "big-model" + assert oversized.routing_decision["cause"] == "session_affinity_pin" + assert oversized.routing_decision["context_escalated"] is True + assert oversized.routing_decision["context_escalation_original_tier"] == "SIMPLE" + assert back_to_small is not None and back_to_small.model == "small-model" + assert back_to_small.routing_decision["cause"] == "session_affinity_pin" + + @pytest.mark.asyncio + async def test_the_adaptive_cold_start_never_samples_a_model_that_cannot_hold_the_prompt(self): + """The bandit's exploration is still bounded by physics: with the whole classified tier + unobserved, cold start samples only among models whose window holds the prompt.""" + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=Router( + model_list=[ + { + "model_name": "small-model", + "litellm_params": {"model": "openai/gpt-3.5-turbo", "mock_response": "ok"}, + "model_info": {"max_input_tokens": 16385}, + }, + { + "model_name": "mid-model", + "litellm_params": {"model": "openai/gpt-4o-mini", "mock_response": "ok"}, + "model_info": {"max_input_tokens": 200000}, + }, + ] + ), + complexity_router_config={"adaptive": True, "tiers": {"SIMPLE": ["small-model", "mid-model"]}}, + ) + + result = await router.async_pre_routing_hook(model="test-router", request_kwargs={}, messages=_OVERSIZED_TURNS) + + assert result is not None + assert result.model == "mid-model" + + @pytest.mark.asyncio + async def test_the_gate_never_resolves_an_authenticating_provider(self, monkeypatch, tmp_path): + """Resolving github_copilot runs its OAuth device flow, so a window question must adopt + the declaration instead of resolving: the copilot group reads as unknown-window and the + request stays put, with zero copilot resolutions recorded.""" + import json + import time + + monkeypatch.setenv("GITHUB_COPILOT_TOKEN_DIR", str(tmp_path)) + (tmp_path / "api-key.json").write_text(json.dumps({"token": "tid=test", "expires_at": int(time.time()) + 3600})) + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=Router( + model_list=[ + {"model_name": "cop-pool", "litellm_params": {"model": "github_copilot/gpt-4o"}}, + { + "model_name": "big-model", + "litellm_params": {"model": "openai/gpt-4o-mini", "mock_response": "ok"}, + "model_info": {"max_input_tokens": 200000}, + }, + ] + ), + complexity_router_config={"tiers": {"SIMPLE": "cop-pool", "COMPLEX": "big-model"}}, + ) + real_get_llm_provider = litellm.get_llm_provider + copilot_resolutions: List = [] + + def _guarded(*args, **kwargs): + target = str(kwargs.get("model") or (args[0] if args else "")) + str(kwargs.get("custom_llm_provider") or "") + if "github_copilot" in target: + copilot_resolutions.append(target) + raise RuntimeError("the gate must not resolve an authenticating provider") + return real_get_llm_provider(*args, **kwargs) + + monkeypatch.setattr(litellm, "get_llm_provider", _guarded) + + result = await router.async_pre_routing_hook(model="test-router", request_kwargs={}, messages=_OVERSIZED_TURNS) + + assert result is not None + assert result.model == "cop-pool" + assert copilot_resolutions == [] + + @pytest.mark.asyncio + async def test_the_full_routing_path_serves_the_escalated_deployment(self): + """End to end through Router.async_get_available_deployment: the auto-router alias with + an oversized prompt resolves to the big tier's deployment, and a small prompt to the + small tier's, with no mocking anywhere in the resolution chain.""" + router = Router( + model_list=[ + { + "model_name": "smart-router", + "litellm_params": { + "model": "auto_router/complexity_router", + "complexity_router_config": {"tiers": {"SIMPLE": "small-model", "COMPLEX": "big-model"}}, + }, + }, + { + "model_name": "small-model", + "litellm_params": {"model": "openai/gpt-3.5-turbo", "mock_response": "ok"}, + "model_info": {"max_input_tokens": 16385}, + }, + { + "model_name": "big-model", + "litellm_params": {"model": "openai/gpt-4o-mini", "mock_response": "ok"}, + "model_info": {"max_input_tokens": 200000}, + }, + ] + ) + + oversized = await router.async_get_available_deployment( + model="smart-router", request_kwargs={}, messages=_OVERSIZED_TURNS + ) + small = await router.async_get_available_deployment( + model="smart-router", request_kwargs={}, messages=[{"role": "user", "content": "ok continue"}] + ) + + assert oversized["model_name"] == "big-model" + assert small["model_name"] == "small-model" + + +IMG_PART = {"type": "image_url", "image_url": {"url": "data:image/png;base64,aGk="}} +PLAN_BODY = { + "messages": [{"role": "system", "content": [{"type": "text", "text": "Plan mode is active. Do not execute."}]}] +} + + +class TestModalityRouting: + """modality_routing: the response gate replaces a routed model that cannot take images.""" + + IMAGE_MESSAGE = [{"role": "user", "content": [{"type": "text", "text": "What color is this?"}, IMG_PART]}] + BASE_TIERS = {"SIMPLE": "text-cheap", "MEDIUM": "vision-mid", "COMPLEX": "vision-big"} + BASE_VISION = {"text-cheap": False, "vision-mid": True, "vision-big": True, "vision-default": True} + + @staticmethod + def _router(mock_router_instance, config, vision_by_model): + """vision_by_model: model name -> True/False (deployment model_info) or None (undeclared).""" + + def get_model_list(model_name=None): + if model_name not in vision_by_model: + return [] + declared = vision_by_model[model_name] + return [ + { + "model_name": model_name, + "litellm_params": {"model": f"openai/unmapped-{model_name}"}, + "model_info": {} if declared is None else {"supports_vision": declared}, + } + ] + + mock_router_instance.get_model_list = get_model_list + return ComplexityRouter( + model_name="modality-test-router", + litellm_router_instance=mock_router_instance, + complexity_router_config=config, + ) + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "config_extra, vision, send_image, expected_model, expect_marker", + [ + ({}, {"text-cheap": False}, True, "text-cheap", False), + ({"modality_routing": True}, {"text-cheap": False}, False, "text-cheap", False), + ({"modality_routing": True}, {"text-cheap": None}, True, "text-cheap", False), + ], + ids=["flag_off", "no_image", "undeclared_model_stays_routable"], + ) + async def test_gate_leaves_ungated_requests_untouched( + self, mock_router_instance, config_extra, vision, send_image, expected_model, expect_marker + ): + router = self._router(mock_router_instance, {"tiers": dict(self.BASE_TIERS), **config_extra}, vision) + request = self.IMAGE_MESSAGE if send_image else [{"role": "user", "content": "What color is the sky?"}] + result = await router.async_pre_routing_hook(model="m", request_kwargs={}, messages=request) + assert result.model == expected_model + assert result.routing_decision["cause"] == "heuristic_scorer" + assert ("modality:image" in (result.routing_decision.get("signals") or ())) is expect_marker + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "part", + [ + IMG_PART, + {"type": "input_image", "image_url": "data:image/png;base64,aGk="}, + {"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": "aGk="}}, + {"type": "tool_result", "tool_use_id": "tu_1", "content": [dict(IMG_PART, type="image")]}, + ], + ids=["image_url", "input_image", "anthropic_image", "tool_result_nested"], + ) + async def test_every_image_dialect_escalates(self, mock_router_instance, part): + router = self._router( + mock_router_instance, {"tiers": dict(self.BASE_TIERS), "modality_routing": True}, dict(self.BASE_VISION) + ) + message = [{"role": "user", "content": [{"type": "text", "text": "What color is this?"}, part]}] + result = await router.async_pre_routing_hook(model="m", request_kwargs={}, messages=message) + assert result.model == "vision-mid" + assert result.routing_decision["cause"] == "modality_escalation" + assert "modality_escalated_from:SIMPLE" in result.routing_decision["signals"] + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "path, expected_model, expected_cause", + [ + ("classifier_escalates", "vision-mid", "modality_escalation"), + ("same_tier_repick_keeps_cause", "vision-cheap", "heuristic_scorer"), + ("keyword_tier_escalates", "vision-mid", "modality_escalation"), + ("no_ask_capable_default_kept", "vision-default", "default_fallback"), + ("no_ask_text_default_displaced", "vision-mid", "modality_escalation"), + ("custom_tiers_walk", "premium-model", "modality_escalation"), + ("pin_kept_bypasses", "text-cheap", "session_affinity_pin"), + ("pin_replacement_gated", "vision-big", "modality_escalation"), + ("adaptive_pick_rewritten", "vision-mid", "modality_escalation"), + ], + ) + async def test_placements_across_decision_paths(self, mock_router_instance, path, expected_model, expected_cause): + config = {"tiers": dict(self.BASE_TIERS), "modality_routing": True} + vision = dict(self.BASE_VISION) + request_kwargs = {} + messages = self.IMAGE_MESSAGE + if path == "same_tier_repick_keeps_cause": + config["tiers"]["SIMPLE"] = ["text-cheap", "vision-cheap"] + vision["vision-cheap"] = True + with patch( # test-quality-ok: the mixed-pool repick is unreachable deterministically without pinning the first random pick + "litellm.router_strategy.complexity_router.complexity_router.random.choice", + side_effect=lambda pool: sorted(pool)[0], + ): + router = self._router(mock_router_instance, config, vision) + result = await router.async_pre_routing_hook(model="m", request_kwargs={}, messages=messages) + assert result.model == expected_model + assert result.routing_decision["cause"] == expected_cause + assert result.routing_decision["signals"][-1] == "modality:image" + return + if path == "keyword_tier_escalates": + config["keyword_tier_rules"] = [{"keywords": ["quick lookup"], "tier": "SIMPLE"}] + messages = [ + {"role": "user", "content": [{"type": "text", "text": "quick lookup: what is this?"}, IMG_PART]} + ] + elif path == "no_ask_capable_default_kept": + config["default_model"] = "vision-default" + messages = [{"role": "user", "content": [IMG_PART]}] + elif path == "no_ask_text_default_displaced": + config["default_model"] = "text-default" + vision["text-default"] = False + messages = [{"role": "user", "content": [IMG_PART]}] + elif path == "custom_tiers_walk": + config = { + "classifier_type": "llm", + "classifier_llm_config": {"model": "gpt-4o-mini"}, + "fallback_tier": "cheap", + "tier_definitions": [ + {"name": "cheap", "description": "trivial asks"}, + {"name": "premium", "description": "hard asks"}, + ], + "tiers": {"cheap": "cheap-model", "premium": "premium-model"}, + "keyword_tier_rules": [{"keywords": ["quick lookup"], "tier": "cheap"}], + "modality_routing": True, + } + vision = {"cheap-model": False, "premium-model": True} + messages = [ + {"role": "user", "content": [{"type": "text", "text": "quick lookup: what is this?"}, IMG_PART]} + ] + elif path in ("pin_kept_bypasses", "pin_replacement_gated"): + cache = AsyncMock() + cache.async_get_cache = AsyncMock(return_value={"model": "text-cheap", "tier": "SIMPLE"}) + mock_router_instance.cache = cache + config["session_affinity"] = True + request_kwargs = {"metadata": {"session_id": "s1"}} + if path == "pin_replacement_gated": + config["tiers"]["MEDIUM"] = "text-mid" + vision["text-mid"] = False + messages = [ + {"role": "user", "content": [{"type": "text", "text": "LITELLM ESCALATE describe this"}, IMG_PART]} + ] + elif path == "adaptive_pick_rewritten": + config["adaptive"] = True + mock_router_instance.model_list = [] + mock_router_instance.model_name_to_deployment_indices = {} + router = self._router(mock_router_instance, config, vision) + result = await router.async_pre_routing_hook(model="m", request_kwargs=request_kwargs, messages=messages) + assert result.model == expected_model + assert result.routing_decision["cause"] == expected_cause + if path == "adaptive_pick_rewritten": + assert request_kwargs["metadata"]["adaptive_router_chosen_model"] == expected_model + + @pytest.mark.asyncio + async def test_plan_floored_decision_never_falls_to_default_model(self, mock_router_instance): + """An upward-only walk cannot undercut the floor; default_model must not either.""" + config = { + "tiers": {"SIMPLE": "vision-cheap", "MEDIUM": "text-mid"}, + "default_model": "vision-default", + "plan_mode_min_tier": "MEDIUM", + "modality_routing": True, + } + vision = {"vision-cheap": True, "text-mid": False, "vision-default": True} + router = self._router(mock_router_instance, config, vision) + with pytest.raises(litellm.BadRequestError, match="no model"): + await router.async_pre_routing_hook( + model="m", + request_kwargs={"proxy_server_request": {"body": PLAN_BODY}}, + messages=[{"role": "user", "content": [{"type": "text", "text": "plan this"}, IMG_PART]}], + ) + + @pytest.mark.asyncio + async def test_at_floor_plan_turn_never_falls_to_default_model(self, mock_router_instance): + """A sentinel turn whose classified tier already satisfies the floor keeps its ordinary + cause, so the record carries no floor marker; the default arm must still refuse it.""" + config = { + "tiers": {"SIMPLE": "text-a", "MEDIUM": "text-b"}, + "default_model": "vision-default", + "plan_mode_min_tier": "SIMPLE", + "modality_routing": True, + } + vision = {"text-a": False, "text-b": False, "vision-default": True} + router = self._router(mock_router_instance, config, vision) + with pytest.raises(litellm.BadRequestError, match="no model"): + await router.async_pre_routing_hook( + model="m", + request_kwargs={"proxy_server_request": {"body": PLAN_BODY}}, + messages=[{"role": "user", "content": [{"type": "text", "text": "plan this"}, IMG_PART]}], + ) + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "default_model, default_vision, expect_error", + [(None, None, True), ("text-default", False, True), ("vision-default", True, False)], + ids=["no_default", "text_only_default", "vision_default_serves"], + ) + async def test_no_capable_tier_above_uses_default_or_rejects( + self, mock_router_instance, default_model, default_vision, expect_error + ): + config = {"tiers": {"SIMPLE": "text-cheap", "COMPLEX": "text-big"}, "modality_routing": True} + vision = {"text-cheap": False, "text-big": False} + if default_model is not None: + config["default_model"] = default_model + vision[default_model] = default_vision + router = self._router(mock_router_instance, config, vision) + if expect_error: + with pytest.raises(litellm.BadRequestError, match="no model"): + await router.async_pre_routing_hook(model="m", request_kwargs={}, messages=self.IMAGE_MESSAGE) + return + result = await router.async_pre_routing_hook(model="m", request_kwargs={}, messages=self.IMAGE_MESSAGE) + assert result.model == "vision-default" + assert result.routing_decision["cause"] == "modality_escalation" + assert "modality_escalated_from:SIMPLE" in result.routing_decision["signals"] + + @pytest.mark.asyncio + async def test_mixed_deployment_group_is_treated_text_only(self, mock_router_instance): + def get_model_list(model_name=None): + declared = {"mixed-group": [True, False], "vision-big": [True]}.get(model_name) + if declared is None: + return [] + return [ + { + "model_name": model_name, + "litellm_params": {"model": f"openai/unmapped-{model_name}-{i}"}, + "model_info": {"supports_vision": accepts}, + } + for i, accepts in enumerate(declared) + ] + + mock_router_instance.get_model_list = get_model_list + router = ComplexityRouter( + model_name="modality-test-router", + litellm_router_instance=mock_router_instance, + complexity_router_config={ + "tiers": {"SIMPLE": "mixed-group", "COMPLEX": "vision-big"}, + "modality_routing": True, + }, + ) + result = await router.async_pre_routing_hook(model="m", request_kwargs={}, messages=self.IMAGE_MESSAGE) + assert result.model == "vision-big" + assert result.routing_decision["cause"] == "modality_escalation" + + @pytest.mark.asyncio + async def test_continuation_turn_screenshot_escalates_past_the_held_model(self, mock_router_instance): + """classification_mode user_turn replays the held model on continuation turns; a + continuation carrying a screenshot must still be re-placed when that model is text-only.""" + mock_router_instance.cache = DualCache() + config = { + "tiers": dict(self.BASE_TIERS), + "classification_mode": "user_turn", + "modality_routing": True, + } + router = self._router(mock_router_instance, config, dict(self.BASE_VISION)) + first = await router.async_pre_routing_hook( + model="m", + request_kwargs={"metadata": {"session_id": "cont-1"}}, + messages=[{"role": "user", "content": "hi there"}], + ) + assert first.model == "text-cheap" + continuation = [ + {"role": "user", "content": "hi there"}, + {"role": "assistant", "content": [{"type": "tool_use", "id": "tu_1", "name": "screenshot", "input": {}}]}, + { + "role": "user", + "content": [ + { + "type": "tool_result", + "tool_use_id": "tu_1", + "content": [{"type": "image", "source": {"type": "base64", "data": "aGk="}}], + } + ], + }, + ] + second = await router.async_pre_routing_hook( + model="m", request_kwargs={"metadata": {"session_id": "cont-1"}}, messages=continuation + ) + assert second.model == "vision-mid" + assert second.routing_decision["cause"] == "modality_escalation" + assert "modality_escalated_from:SIMPLE" in second.routing_decision["signals"] + + @pytest.mark.asyncio + async def test_rewrite_carries_the_context_escalation_record(self, mock_router_instance): + """A context-window escalation and a modality re-place are separate facts on one + record; rewriting for the image must not drop the sibling gate's fields.""" + from litellm.types.router import PreRoutingHookResponse + + router = self._router( + mock_router_instance, + {"tiers": dict(self.BASE_TIERS), "modality_routing": True}, + dict(self.BASE_VISION), + ) + decision = router._build_routing_decision( + routed_model="text-cheap", + cause="heuristic_scorer", + tier=ComplexityTier.SIMPLE, + context_escalation_original_tier=ComplexityTier.SIMPLE, + ) + response = PreRoutingHookResponse(model="text-cheap", messages=None, routing_decision=decision) + rewritten = await router._gate_response_modality(response, None, self.IMAGE_MESSAGE, {}) + assert rewritten.model == "vision-mid" + assert rewritten.routing_decision["cause"] == "modality_escalation" + assert rewritten.routing_decision["context_escalated"] is True + assert rewritten.routing_decision["context_escalation_original_tier"] == "SIMPLE" + + def test_modality_escalation_is_never_pinnable(self): + from litellm.router_strategy.complexity_router.complexity_router import _decision_is_pinnable + + assert _decision_is_pinnable({"cause": "modality_escalation"}) is False + assert _decision_is_pinnable({"cause": "heuristic_scorer"}) is True diff --git a/tests/test_litellm/router_utils/pre_call_checks/test_deployment_affinity_check.py b/tests/test_litellm/router_utils/pre_call_checks/test_deployment_affinity_check.py index b3a2bdda53c..60433921de6 100644 --- a/tests/test_litellm/router_utils/pre_call_checks/test_deployment_affinity_check.py +++ b/tests/test_litellm/router_utils/pre_call_checks/test_deployment_affinity_check.py @@ -598,6 +598,45 @@ async def test_async_filter_deployments_falls_back_when_cached_deployment_is_unh assert filtered == healthy_deployments +@pytest.mark.asyncio +async def test_async_filter_deployments_does_not_pin_when_target_order_is_set(): + user_key = "user-key-order-fallback" + stable_model_map_key = "claude-sonnet-4-5@20250929" + cache = AsyncMock() + cache.async_get_cache = AsyncMock(return_value={"model_id": "deployment-1"}) + callback = DeploymentAffinityCheck( + cache=cache, + ttl_seconds=123, + enable_user_key_affinity=True, + enable_responses_api_affinity=False, + ) + healthy_deployments = [ + { + "model_name": stable_model_map_key, + "litellm_params": {"model": f"vertex_ai/{stable_model_map_key}"}, + "model_info": {"id": "deployment-1"}, + }, + { + "model_name": stable_model_map_key, + "litellm_params": { + "model": f"bedrock/global.anthropic.{stable_model_map_key}-v1:0" + }, + "model_info": {"id": "deployment-2"}, + }, + ] + + filtered = await callback.async_filter_deployments( + model="some-router-model-group", + healthy_deployments=healthy_deployments, + messages=None, + request_kwargs={"_target_order": 2, "metadata": {"user_api_key_hash": user_key}}, + parent_otel_span=None, + ) + + assert filtered == healthy_deployments + cache.async_get_cache.assert_not_called() + + @pytest.mark.asyncio async def test_async_user_key_affinity_ttl_expiry_allows_reroute(): """ diff --git a/tests/test_litellm/router_utils/pre_call_checks/test_prompt_caching_deployment_check.py b/tests/test_litellm/router_utils/pre_call_checks/test_prompt_caching_deployment_check.py index f54a1cfa284..79ae00e155c 100644 --- a/tests/test_litellm/router_utils/pre_call_checks/test_prompt_caching_deployment_check.py +++ b/tests/test_litellm/router_utils/pre_call_checks/test_prompt_caching_deployment_check.py @@ -150,6 +150,25 @@ async def test_async_filter_deployments_narrows_prompt_above_model_minimum(): assert filtered == [deployments[1]] +@pytest.mark.asyncio +async def test_async_filter_deployments_does_not_pin_when_target_order_is_set(): + cache = DualCache() + check = PromptCachingDeploymentCheck(cache=cache) + deployments = _deployments("anthropic/claude-opus-4-6", "anthropic/claude-opus-4-6") + messages = _messages(word_count=5000) + + await PromptCachingCache(cache=cache).async_add_model_id(model_id="dep-2", messages=messages, tools=None) + + filtered = await check.async_filter_deployments( + model=MODEL_GROUP_ALIAS, + healthy_deployments=deployments, + messages=messages, + request_kwargs={"_target_order": 2}, + ) + + assert filtered == deployments + + @pytest.mark.asyncio async def test_async_filter_deployments_narrows_for_group_whose_model_minimum_is_lower(): """ diff --git a/tests/test_litellm/router_utils/test_fallback_event_handlers.py b/tests/test_litellm/router_utils/test_fallback_event_handlers.py index 8336926c050..894b2d9e74f 100644 --- a/tests/test_litellm/router_utils/test_fallback_event_handlers.py +++ b/tests/test_litellm/router_utils/test_fallback_event_handlers.py @@ -11,7 +11,10 @@ from litellm.router_utils.fallback_event_handlers import ( AttemptedFallbackTargets, _trigger_cooldown_for_failed_deployment, fallback_attempt_key, + clear_pre_routing_selection, get_fallback_model_group, + get_pre_routing_selection, + record_pre_routing_selection, run_async_fallback, ) @@ -1090,3 +1093,119 @@ async def test_run_async_fallback_preserves_original_model_group_on_nested_fallb metadata = router.received_kwargs["metadata"] assert metadata["attempted_fallbacks"] == 2 assert metadata["original_model_group"] == "primary-model" + + +class TestPreRoutingSelectionCarriesToFallbacks: + """#38832: a complexity/auto router picks a tier behind the router name, but fallback + lookup kept using the router name, so the tier's configured chain never ran.""" + + def test_selection_is_recorded_in_the_metadata_bucket(self): + kwargs = {"model": "smart-router", "metadata": {}} + record_pre_routing_selection(kwargs, "tier1") + assert kwargs["metadata"]["pre_routing_selected_model"] == "tier1" + assert get_pre_routing_selection(kwargs) == "tier1" + + def test_selection_is_recorded_in_the_litellm_metadata_bucket(self): + kwargs = {"model": "smart-router", "litellm_metadata": {}} + record_pre_routing_selection(kwargs, "tier2") + assert get_pre_routing_selection(kwargs) == "tier2" + + def test_a_bucket_survives_the_kwargs_copy_that_fallbacks_run_on(self): + """The bucket is shared by reference, which is the whole reason this works.""" + outer = {"model": "smart-router", "metadata": {}} + inner = {**outer} + record_pre_routing_selection(inner, "tier1") + assert get_pre_routing_selection(outer) == "tier1" + + def test_no_selection_reads_as_none(self): + assert get_pre_routing_selection({"model": "smart-router", "metadata": {}}) is None + assert get_pre_routing_selection({"model": "smart-router"}) is None + + def test_missing_kwargs_is_a_no_op(self): + """A caller with no kwargs must not raise, and must not leak the selection anywhere.""" + record_pre_routing_selection(None, "tier1") + + assert get_pre_routing_selection({}) is None + + def test_a_non_dict_bucket_is_ignored(self): + kwargs = {"model": "smart-router", "metadata": "not-a-dict"} + record_pre_routing_selection(kwargs, "tier1") + assert get_pre_routing_selection(kwargs) is None + + def test_fallbacks_resolve_against_the_selected_tier(self): + """The lookup the router performs, keyed on the tier rather than the router name.""" + fallbacks = [{"tier1": ["backup-a", "backup-b"]}, {"tier2": ["backup-c"]}] + assert get_fallback_model_group(fallbacks=fallbacks, model_group="tier1")[0] == ["backup-a", "backup-b"] + assert get_fallback_model_group(fallbacks=fallbacks, model_group="smart-router")[0] is None + + +class TestPreRoutingSelectionIsPerHop: + """#38832 review: the buckets also carry whatever the caller sent, and a fallback hop + inherits the previous hop's tier, so a hop must start without a selection.""" + + def test_a_caller_supplied_selection_is_dropped(self): + kwargs = {"model": "plain", "metadata": {"pre_routing_selected_model": "tier1"}} + + clear_pre_routing_selection(kwargs) + + assert get_pre_routing_selection(kwargs) is None + assert "pre_routing_selected_model" not in kwargs["metadata"] + + def test_both_buckets_are_cleared(self): + kwargs = { + "metadata": {"pre_routing_selected_model": "tier1"}, + "litellm_metadata": {"pre_routing_selected_model": "tier2"}, + } + + clear_pre_routing_selection(kwargs) + + assert get_pre_routing_selection(kwargs) is None + + def test_the_rest_of_the_bucket_is_left_alone(self): + kwargs = {"metadata": {"pre_routing_selected_model": "tier1", "tags": ["a"]}} + + clear_pre_routing_selection(kwargs) + + assert kwargs["metadata"] == {"tags": ["a"]} + + def test_clearing_is_a_no_op_without_a_usable_bucket(self): + kwargs = {"model": "plain", "metadata": "not-a-dict"} + + clear_pre_routing_selection(None) + clear_pre_routing_selection(kwargs) + + assert kwargs == {"model": "plain", "metadata": "not-a-dict"} + + def test_a_selection_recorded_after_clearing_is_kept(self): + """Clearing runs before routing, so the hook's own write must survive it.""" + kwargs = {"model": "smart-router", "metadata": {"pre_routing_selected_model": "stale"}} + + clear_pre_routing_selection(kwargs) + record_pre_routing_selection(kwargs, "tier1") + + assert get_pre_routing_selection(kwargs) == "tier1" + + +class TestOrderedFallbackLookupGroups: + def test_tier_first_then_requested_group_deduped(self): + from litellm.router_utils.fallback_event_handlers import ( + PRE_ROUTING_SELECTED_MODEL_KEY, + fallback_lookup_groups, + ) + + kwargs = {"litellm_metadata": {PRE_ROUTING_SELECTED_MODEL_KEY: "tier1"}} + assert fallback_lookup_groups(kwargs, "smart-router") == ("tier1", "smart-router") + assert fallback_lookup_groups(kwargs, "tier1") == ("tier1",) + assert fallback_lookup_groups({}, "smart-router") == ("smart-router",) + assert fallback_lookup_groups({}, None) == () + + def test_first_resolving_group_wins_and_generic_idx_survives_a_miss(self): + from litellm.router_utils.fallback_event_handlers import ( + get_fallback_model_group_for_lookup_groups, + ) + + fallbacks = [{"tier1": ["backup-a"]}, {"smart-router": ["backup-b"]}, {"*": ["backup-c"]}] + assert get_fallback_model_group_for_lookup_groups(fallbacks, ("tier1", "smart-router")) == (["backup-a"], None) + assert get_fallback_model_group_for_lookup_groups(fallbacks, ("tier9", "smart-router")) == (["backup-b"], None) + assert get_fallback_model_group_for_lookup_groups(fallbacks, ("tier9", "no-such")) == (["backup-c"], 2) + assert get_fallback_model_group_for_lookup_groups([{"tier1": ["backup-a"]}], ("no", "nope")) == (None, None) diff --git a/tests/test_litellm/router_utils/test_pattern_match_deployments.py b/tests/test_litellm/router_utils/test_pattern_match_deployments.py new file mode 100644 index 00000000000..795d448ef5f --- /dev/null +++ b/tests/test_litellm/router_utils/test_pattern_match_deployments.py @@ -0,0 +1,78 @@ +"""Behavior pins for ``litellm/router_utils/pattern_match_deployments.py``.""" + +from __future__ import annotations + +from litellm.router_utils import pattern_match_deployments +from litellm.router_utils.pattern_match_deployments import PatternMatchRouter + + +def _wildcard_deployment(model_name: str) -> dict: + return {"model_name": model_name, "litellm_params": {"model": model_name}} + + +def _matched_models(matches: list[dict] | None) -> list[str]: + return [deployment["litellm_params"]["model"] for deployment in matches or []] + + +def test_get_pattern_never_resolves_declared_authenticating_providers(monkeypatch): + """Regression: resolving a github_copilot/chatgpt name through ``get_llm_provider`` runs the + provider's OAuth device flow; the auth layer walks every wildcard router on every request, so + a single metadata lookup for an unserved name would block the proxy's event loop.""" + resolution_attempts: list[str] = [] + + def _oauth_tripwire(model, *args, **kwargs): + resolution_attempts.append(model) + raise AssertionError("get_llm_provider would run the OAuth device flow") + + monkeypatch.setattr(pattern_match_deployments, "get_llm_provider", _oauth_tripwire) + + unmatched_router = PatternMatchRouter() + unmatched_router.add_pattern("anthropic/*", _wildcard_deployment("anthropic/*")) + assert unmatched_router.get_pattern("github_copilot/gpt-4o") is None + + matched_router = PatternMatchRouter() + matched_router.add_pattern("github_copilot/*", _wildcard_deployment("github_copilot/*")) + assert _matched_models(matched_router.get_pattern("github_copilot/gpt-4o")) == ["github_copilot/gpt-4o"] + assert _matched_models(matched_router.get_pattern("gpt-4o", custom_llm_provider="github_copilot")) == [ + "github_copilot/gpt-4o" + ] + + assert resolution_attempts == [] + + +def test_get_pattern_bare_provider_name_never_matches_that_providers_wildcard(monkeypatch): + """Regression: a bare ``github_copilot`` adopted itself as its provider and retried as + ``github_copilot/github_copilot``, false-matching the wildcard for a name no deployment serves.""" + + def _unknown_provider(model, *args, **kwargs): + raise ValueError(f"unknown provider for {model}") + + monkeypatch.setattr(pattern_match_deployments, "get_llm_provider", _unknown_provider) + router = PatternMatchRouter() + router.add_pattern("github_copilot/*", _wildcard_deployment("github_copilot/*")) + assert router.get_pattern("github_copilot") is None + + +def test_get_pattern_missing_model_returns_none(monkeypatch): + """Regression: a request without a model reaches the auth layer's pattern walk as ``None``; the + declared-provider guard raised ``TypeError`` where the old inline resolve swallowed every + resolver error, so the proxy's missing-model 400 became a crash.""" + + def _unknown_provider(model, *args, **kwargs): + raise ValueError(f"unknown provider for {model}") + + monkeypatch.setattr(pattern_match_deployments, "get_llm_provider", _unknown_provider) + router = PatternMatchRouter() + router.add_pattern("openai/*", _wildcard_deployment("openai/*")) + assert router.get_pattern(None) is None + + +def test_get_pattern_still_resolves_unqualified_names(monkeypatch): + monkeypatch.setattr( + pattern_match_deployments, + "get_llm_provider", + lambda model, **kwargs: (model, "openai", None, None), + ) + router = PatternMatchRouter() + router.add_pattern("openai/*", _wildcard_deployment("openai/*")) + assert _matched_models(router.get_pattern("gpt-4o")) == ["openai/gpt-4o"] diff --git a/tests/test_litellm/test_claude_fable_5_config.py b/tests/test_litellm/test_claude_fable_5_config.py index 99c59ffa58e..3ecf94602d9 100644 --- a/tests/test_litellm/test_claude_fable_5_config.py +++ b/tests/test_litellm/test_claude_fable_5_config.py @@ -1,5 +1,5 @@ """ -Validate Claude Fable 5 model configuration entries. +Validate Claude Fable 5 and Claude Fable 5.1 model configuration entries. Fable 5 is a new tier above Opus ($10/$50 per MTok) with the same adaptive-only API surface as Opus 4.7/4.8. The cost-map entries below are what make the model @@ -210,6 +210,156 @@ def test_adaptive_thinking_detected_for_fable_5(local_model_cost_map, model): assert AnthropicModelInfo._is_adaptive_thinking_model(model, "anthropic") is True +FABLE_5_1_VARIANTS = ( + "claude-fable-5-1", + "anthropic.claude-fable-5-1", + "global.anthropic.claude-fable-5-1", + "us.anthropic.claude-fable-5-1", + "eu.anthropic.claude-fable-5-1", + "vertex_ai/claude-fable-5-1", + "vertex_ai/claude-fable-5-1@default", + "azure_ai/claude-fable-5-1", +) + + +def test_fable_5_1_model_pricing_and_capabilities(): + model_data = _load_root_cost_map() + + expected_models = [ + ("claude-fable-5-1", "anthropic"), + ("anthropic.claude-fable-5-1", "bedrock_converse"), + ("vertex_ai/claude-fable-5-1", "vertex_ai-anthropic_models"), + ("azure_ai/claude-fable-5-1", "azure_ai"), + ] + + for model_name, provider in expected_models: + assert model_name in model_data, f"Missing model entry: {model_name}" + info = model_data[model_name] + + assert info["litellm_provider"] == provider + assert info["mode"] == "chat" + assert info["max_input_tokens"] == 1000000 + assert info["max_output_tokens"] == 128000 + assert info["max_tokens"] == 128000 + + assert info["input_cost_per_token"] == 1e-05 + assert info["output_cost_per_token"] == 5e-05 + assert info["cache_creation_input_token_cost"] == 1.25e-05 + assert info["cache_creation_input_token_cost_above_1hr"] == 2e-05 + + assert "input_cost_per_token_above_200k_tokens" not in info + assert "output_cost_per_token_above_200k_tokens" not in info + + assert info["supports_assistant_prefill"] is False + assert info["supports_forced_tool_use"] is False + assert info["supports_function_calling"] is True + assert info["supports_prompt_caching"] is True + assert info["supports_reasoning"] is True + assert info["supports_tool_choice"] is True + assert info["supports_vision"] is True + assert info["supports_xhigh_reasoning_effort"] is True + assert info["supports_max_reasoning_effort"] is True + assert info["prompt_cache_min_tokens"] == 512 + + +@pytest.mark.parametrize( + "cost_map", + [_load_root_cost_map(), GetModelCostMap.load_local_model_cost_map()], + ids=["root", "bundled_backup"], +) +def test_fable_5_1_cache_reads_cost_a_quarter_of_fable_5(cost_map): + """Fable 5.1 prices cache hits at 0.025x base input instead of the usual + 0.1x, so copying Fable 5's cache-read price overcharges every cache hit 4x.""" + for model_name in FABLE_5_1_VARIANTS: + info = cost_map[model_name] + geo_premium = model_name.startswith(("us.", "eu.")) + expected = 2.75e-07 if geo_premium else 2.5e-07 + assert info["cache_read_input_token_cost"] == expected, model_name + assert info["cache_read_input_token_cost"] == pytest.approx( + info["input_cost_per_token"] * 0.025 + ), model_name + + +def test_fable_5_1_bedrock_regional_model_pricing(): + model_data = _load_root_cost_map() + + expected_models = { + "global.anthropic.claude-fable-5-1": { + "input_cost_per_token": 1e-05, + "output_cost_per_token": 5e-05, + "cache_creation_input_token_cost": 1.25e-05, + "cache_read_input_token_cost": 2.5e-07, + }, + "us.anthropic.claude-fable-5-1": { + "input_cost_per_token": 1.1e-05, + "output_cost_per_token": 5.5e-05, + "cache_creation_input_token_cost": 1.375e-05, + "cache_read_input_token_cost": 2.75e-07, + }, + "eu.anthropic.claude-fable-5-1": { + "input_cost_per_token": 1.1e-05, + "output_cost_per_token": 5.5e-05, + "cache_creation_input_token_cost": 1.375e-05, + "cache_read_input_token_cost": 2.75e-07, + }, + } + + for model_name, expected in expected_models.items(): + assert model_name in model_data, f"Missing model entry: {model_name}" + info = model_data[model_name] + assert info["litellm_provider"] == "bedrock_converse" + assert info["max_input_tokens"] == 1000000 + assert info["max_output_tokens"] == 128000 + assert info["bedrock_output_config_effort_ceiling"] == "xhigh" + for key, value in expected.items(): + assert info[key] == value + + +def test_fable_5_1_geo_multiplier_without_fast_mode(): + """Fable 5.1 has no fast mode, so a ``fast`` key here would misprice + ``speed='fast'`` requests.""" + model_data = _load_root_cost_map() + assert model_data["claude-fable-5-1"]["provider_specific_entry"] == {"us": 1.1} + + +def test_fable_5_1_present_in_bundled_backup(): + backup = GetModelCostMap.load_local_model_cost_map() + root = _load_root_cost_map() + for model_name in FABLE_5_1_VARIANTS: + assert model_name in backup, f"Missing from backup cost map: {model_name}" + assert backup[model_name] == root[model_name], model_name + + +def test_fable_5_1_registered_for_bedrock_converse(): + assert "anthropic.claude-fable-5-1" in BEDROCK_CONVERSE_MODELS + + +def test_fable_5_1_provider_resolves_via_model_info(local_model_cost_map): + info = litellm.get_model_info(model="claude-fable-5-1") + assert info["litellm_provider"] == "anthropic" + assert info["max_input_tokens"] == 1000000 + assert info["max_output_tokens"] == 128000 + + +@pytest.mark.parametrize( + "model", + [ + "claude-fable-5-1", + "anthropic/claude-fable-5-1", + "anthropic.claude-fable-5-1", + "bedrock/us.anthropic.claude-fable-5-1", + "bedrock/invoke/eu.anthropic.claude-fable-5-1", + "bedrock/global.anthropic.claude-fable-5-1", + "vertex_ai/claude-fable-5-1", + "azure_ai/claude-fable-5-1", + ], +) +def test_adaptive_thinking_detected_for_fable_5_1(local_model_cost_map, model): + from litellm.llms.anthropic.common_utils import AnthropicModelInfo + + assert AnthropicModelInfo._is_adaptive_thinking_model(model, "anthropic") is True + + @pytest.mark.parametrize( "cost_map", [_load_root_cost_map(), GetModelCostMap.load_local_model_cost_map()], diff --git a/tests/test_litellm/test_dockerfile_apk_repository.py b/tests/test_litellm/test_dockerfile_apk_repository.py new file mode 100644 index 00000000000..cbd772defbf --- /dev/null +++ b/tests/test_litellm/test_dockerfile_apk_repository.py @@ -0,0 +1,52 @@ +""" +Static checks on the root Dockerfile's apk repository configuration. + +The base image (cgr.dev/chainguard/wolfi-base) only configures the +authenticated Chainguard apk repo (https://apk.cgr.dev/chainguard) in +/etc/apk/repositories, which requires a Chainguard enterprise subscription. +Anyone pulling the published litellm image and running `apk add` inside it +hits SSL/auth failures with no fallback repo configured, so nothing can be +installed. See https://github.com/BerriAI/litellm/issues/33518 +""" + +import os +import re + +import pytest + +DOCKERFILE_PATH = os.path.join( + os.path.dirname(__file__), + "..", + "..", + "Dockerfile", +) + + +def _runtime_stage(dockerfile_text: str) -> str: + """Return the contents of the final `FROM ... AS runtime` build stage.""" + match = re.search(r"^FROM .*\bAS runtime\b(.*)\Z", dockerfile_text, re.MULTILINE | re.DOTALL) + assert match, "Dockerfile has no `FROM ... AS runtime` stage" + return match.group(1) + + +@pytest.mark.skipif( + not os.path.exists(DOCKERFILE_PATH), + reason="Dockerfile not present in this checkout", +) +def test_runtime_stage_adds_public_wolfi_repo(): + """The runtime stage must add the public Wolfi apk repo so `apk add` + works for users without a Chainguard enterprise subscription.""" + with open(DOCKERFILE_PATH, "r", encoding="utf-8") as f: + contents = f.read() + + runtime_stage = _runtime_stage(contents) + + assert re.search( + r"echo\s+[\"']?https://packages\.wolfi\.dev/os[\"']?\s*>>\s*/etc/apk/repositories", + runtime_stage, + ), ( + "Runtime stage must append the public Wolfi apk repo " + '(RUN echo "https://packages.wolfi.dev/os" >> /etc/apk/repositories) ' + "so `apk add` works without Chainguard enterprise credentials. " + "See https://github.com/BerriAI/litellm/issues/33518" + ) diff --git a/tests/test_litellm/test_dockerfile_bedrock_realtime_extra.py b/tests/test_litellm/test_dockerfile_bedrock_realtime_extra.py new file mode 100644 index 00000000000..44572aed08e --- /dev/null +++ b/tests/test_litellm/test_dockerfile_bedrock_realtime_extra.py @@ -0,0 +1,56 @@ +""" +Static checks that every proxy Docker image installs the `bedrock-realtime` extra. + +Bedrock Nova Sonic speech-to-speech (`/v1/realtime`) needs `aws-sdk-bedrock-runtime`, +which only ships in the `bedrock-realtime` extra. An image whose `uv sync` stages +omit the extra fails every Nova Sonic realtime session with +"Missing aws_sdk_bedrock_runtime. Install with: pip install aws-sdk-bedrock-runtime". +""" + +import os +import re +from typing import Final + +import pytest + +REPO_ROOT: Final = os.path.join(os.path.dirname(__file__), "..", "..") + +PROXY_DOCKERFILES: Final = ( + "Dockerfile", + os.path.join("docker", "Dockerfile.non_root"), + os.path.join("docker", "Dockerfile.database"), + os.path.join("gateway", "Dockerfile"), +) + +CONTINUED_LINE_RE: Final = re.compile(r"(?:\\\n|[^\n])+") +UV_SYNC_BOUNDARY_RE: Final = re.compile(r"(?=uv sync)") + + +def _uv_sync_invocations(dockerfile_text: str) -> tuple[str, ...]: + """Return each `uv sync ...` command, split apart when one RUN holds several (if/else branches).""" + return tuple( + part + for line in CONTINUED_LINE_RE.finditer(dockerfile_text) + for part in UV_SYNC_BOUNDARY_RE.split(line.group(0)) + if part.startswith("uv sync") + ) + + +@pytest.mark.parametrize("relative_path", PROXY_DOCKERFILES) +def test_every_uv_sync_installs_bedrock_realtime_extra(relative_path: str): + dockerfile_path: Final = os.path.join(REPO_ROOT, relative_path) + if not os.path.exists(dockerfile_path): + pytest.skip(f"{relative_path} not present in this checkout") + + with open(dockerfile_path, "r", encoding="utf-8") as f: + contents: Final = f.read() + + invocations: Final = _uv_sync_invocations(contents) + assert invocations, f"{relative_path} has no `uv sync` invocation" + + missing: Final = tuple(invocation for invocation in invocations if "--extra bedrock-realtime" not in invocation) + assert not missing, ( + f"{relative_path}: {len(missing)} of {len(invocations)} `uv sync` invocations omit " + "`--extra bedrock-realtime`, so aws-sdk-bedrock-runtime is absent and Bedrock Nova Sonic " + "/v1/realtime sessions fail with 'Missing aws_sdk_bedrock_runtime'" + ) diff --git a/tests/test_litellm/test_fireworks_serverless_model_costs.py b/tests/test_litellm/test_fireworks_serverless_model_costs.py index 0458af0da0e..a7a9e0fc37d 100644 --- a/tests/test_litellm/test_fireworks_serverless_model_costs.py +++ b/tests/test_litellm/test_fireworks_serverless_model_costs.py @@ -84,3 +84,41 @@ def test_bare_fireworks_ids_resolve_through_prefixed_entries(): assert info["output_cost_per_token"] == pytest.approx(expected["output_cost_per_token"]) assert info["max_input_tokens"] == expected["max_input_tokens"] assert info["max_output_tokens"] == expected["max_output_tokens"] + + +TWIN_PINNED_PRICES = { + "deepseek-v4-flash-0731": { + "input_cost_per_token": 2.2e-07, + "cache_read_input_token_cost": 7e-09, + "output_cost_per_token": 6.6e-07, + }, +} + + +def test_deepseek_v4_flash_0731_twins_pin_published_pricing(model_data): + """Both 0731 entries carry the price published at docs.fireworks.ai/serverless/pricing.""" + for bare_suffix, expected in TWIN_PINNED_PRICES.items(): + for key in ( + f"fireworks_ai/{bare_suffix}", + f"fireworks_ai/accounts/fireworks/models/{bare_suffix}", + ): + entry = model_data[key] + for field, value in expected.items(): + assert entry[field] == pytest.approx(value), f"{key}.{field}" + + +def test_fireworks_account_prefixed_twins_agree_on_price(model_data): + """Every accounts/fireworks/models/X entry prices identically to its bare fireworks_ai/X twin.""" + prefix = "fireworks_ai/accounts/fireworks/models/" + pairs_checked = 0 + for key, entry in model_data.items(): + if not key.startswith(prefix): + continue + bare_key = f"fireworks_ai/{key[len(prefix):]}" + bare_entry = model_data.get(bare_key) + if bare_entry is None: + continue + pairs_checked += 1 + for field in sorted({f for f in (*entry, *bare_entry) if "cost" in f}): + assert entry.get(field) == bare_entry.get(field), f"{key} vs {bare_key}: {field}" + assert pairs_checked >= 20 diff --git a/tests/test_litellm/test_friendli_glm_5_3_flash_model_metadata.py b/tests/test_litellm/test_friendli_glm_5_3_flash_model_metadata.py new file mode 100644 index 00000000000..7e94205fb09 --- /dev/null +++ b/tests/test_litellm/test_friendli_glm_5_3_flash_model_metadata.py @@ -0,0 +1,35 @@ +import json +from pathlib import Path + +from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider + + +def test_friendli_glm_5_3_flash_model_info(): + model = "friendliai/zai-org/GLM-5.3-Flash" + json_path = Path(__file__).parents[2] / "model_prices_and_context_window.json" + with open(json_path) as f: + model_cost = json.load(f) + + info = model_cost.get(model) + assert ( + info is not None + ), f"{model} not found in model_prices_and_context_window.json" + assert info["litellm_provider"] == "friendliai" + assert info["mode"] == "chat" + assert info["input_cost_per_token"] == 1.5e-07 + assert info["output_cost_per_token"] == 5e-07 + assert info["cache_read_input_token_cost"] == 3e-08 + assert info["max_input_tokens"] == 1048576 + assert info["max_output_tokens"] == 1048576 + assert info["supports_function_calling"] is True + assert info["supports_reasoning"] is True + assert info["reasoning_effort_levels"] == ["low", "high", "max"] + assert info["supports_tool_choice"] is True + assert info["supports_prompt_caching"] is True + assert info["supports_vision"] is True + assert info["supports_image_input"] is True + assert info["supports_video_input"] is True + + routed_model, provider, _, _ = get_llm_provider(model=model) + assert routed_model == "zai-org/GLM-5.3-Flash" + assert provider == "friendliai" diff --git a/tests/test_litellm/test_friendli_glm_5_3_model_metadata.py b/tests/test_litellm/test_friendli_glm_5_3_model_metadata.py new file mode 100644 index 00000000000..5282b0f589e --- /dev/null +++ b/tests/test_litellm/test_friendli_glm_5_3_model_metadata.py @@ -0,0 +1,34 @@ +import json +from pathlib import Path + +from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider + + +def test_friendli_glm_5_3_model_info(): + model = "friendliai/zai-org/GLM-5.3" + json_path = Path(__file__).parents[2] / "model_prices_and_context_window.json" + with open(json_path) as f: + model_cost = json.load(f) + + info = model_cost.get(model) + assert ( + info is not None + ), f"{model} not found in model_prices_and_context_window.json" + assert info["litellm_provider"] == "friendliai" + assert info["mode"] == "chat" + assert info["input_cost_per_token"] == 1.26e-06 + assert info["output_cost_per_token"] == 3.96e-06 + assert info["cache_read_input_token_cost"] == 2.34e-07 + assert info["max_input_tokens"] == 1048576 + assert info["max_output_tokens"] == 1048576 + assert info["supports_function_calling"] is True + assert info["supports_reasoning"] is True + assert info["reasoning_effort_levels"] == ["low", "high", "max"] + assert info["supports_tool_choice"] is True + assert info["supports_prompt_caching"] is True + assert info["supports_vision"] is False + assert info["supports_image_input"] is False + + routed_model, provider, _, _ = get_llm_provider(model=model) + assert routed_model == "zai-org/GLM-5.3" + assert provider == "friendliai" diff --git a/tests/test_litellm/test_openai_embedding_encoding_format_default.py b/tests/test_litellm/test_openai_embedding_encoding_format_default.py index 94e4e3c81e5..7a42eaf0f0a 100644 --- a/tests/test_litellm/test_openai_embedding_encoding_format_default.py +++ b/tests/test_litellm/test_openai_embedding_encoding_format_default.py @@ -1,124 +1,121 @@ -from unittest.mock import MagicMock, patch +import json +from typing import Final +import httpx import pytest +import respx -from litellm import embedding +import litellm -@pytest.mark.parametrize( - "set_env, env_value, expected", - [ - (False, None, "float"), - (True, "base64", "base64"), - ], -) -def test_openai_embedding_encoding_format_default( - monkeypatch, set_env, env_value, expected -): - monkeypatch.delenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", raising=False) - if set_env: - monkeypatch.setenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", env_value) - - mock_response = MagicMock() - mock_response.parse.return_value = MagicMock( - model_dump=lambda: { - "data": [{"embedding": [0.1, 0.2, 0.3], "index": 0}], - "model": "text-embedding-ada-002", - "object": "list", - "usage": {"prompt_tokens": 1, "total_tokens": 1}, - } +def _mock_openai_embedding_route(respx_mock: respx.MockRouter) -> respx.Route: + return respx_mock.post("https://api.openai.com/v1/embeddings").mock( + return_value=httpx.Response( + 200, + json={ + "object": "list", + "data": [{"object": "embedding", "index": 0, "embedding": [0.1, 0.2, 0.3]}], + "model": "text-embedding-3-small", + "usage": {"prompt_tokens": 2, "total_tokens": 2}, + }, + ) ) - mock_response.headers = {} - with patch( - "litellm.llms.openai.openai.OpenAIChatCompletion._get_openai_client" - ) as mock_get_client: - mock_client_instance = MagicMock() - mock_get_client.return_value = mock_client_instance - mock_client_instance.embeddings.with_raw_response.create.return_value = ( - mock_response - ) - embedding( - model="text-embedding-ada-002", - input="Hello world", - ) +@pytest.fixture(autouse=True) +def clear_default_encoding_format_env(monkeypatch: pytest.MonkeyPatch): + monkeypatch.delenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", raising=False) - call_kwargs = ( - mock_client_instance.embeddings.with_raw_response.create.call_args[1] - ) - assert call_kwargs["encoding_format"] == expected + +def test_embedding_openai_omits_encoding_format_when_client_omits_it(respx_mock: respx.MockRouter) -> None: + mock_route: Final = _mock_openai_embedding_route(respx_mock) + + response: Final = litellm.embedding(model="openai/text-embedding-3-small", input=["hello"], api_key="sk-test") + + request_body: Final = json.loads(mock_route.calls.last.request.read()) + assert "encoding_format" not in request_body + assert response.data[0]["embedding"] == [0.1, 0.2, 0.3] + + +def test_embedding_openai_forwards_explicit_encoding_format(respx_mock: respx.MockRouter) -> None: + mock_route: Final = _mock_openai_embedding_route(respx_mock) + + litellm.embedding( + model="openai/text-embedding-3-small", input=["hello"], api_key="sk-test", encoding_format="base64" + ) + + request_body: Final = json.loads(mock_route.calls.last.request.read()) + assert request_body["encoding_format"] == "base64" + + +def test_embedding_openai_explicit_encoding_format_wins_over_env_var( + respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch +) -> None: + monkeypatch.setenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", "float") + mock_route: Final = _mock_openai_embedding_route(respx_mock) + + litellm.embedding( + model="openai/text-embedding-3-small", input=["hello"], api_key="sk-test", encoding_format="base64" + ) + + request_body: Final = json.loads(mock_route.calls.last.request.read()) + assert request_body["encoding_format"] == "base64" + + +@pytest.mark.parametrize("env_value", ["float", "base64"]) +def test_embedding_openai_env_var_sets_default_encoding_format( + respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch, env_value: str +) -> None: + monkeypatch.setenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", env_value) + mock_route: Final = _mock_openai_embedding_route(respx_mock) + + litellm.embedding(model="openai/text-embedding-3-small", input=["hello"], api_key="sk-test") + + request_body: Final = json.loads(mock_route.calls.last.request.read()) + assert request_body["encoding_format"] == env_value @pytest.mark.parametrize("env_none", ["none", "NONE", " none "]) -def test_openai_embedding_encoding_format_env_none_omits_param( - monkeypatch, env_none -): - """LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT=none omits encoding_format (provider default).""" +def test_embedding_openai_env_none_omits_encoding_format( + respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch, env_none: str +) -> None: monkeypatch.setenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", env_none) + mock_route: Final = _mock_openai_embedding_route(respx_mock) - mock_response = MagicMock() - mock_response.parse.return_value = MagicMock( - model_dump=lambda: { - "data": [{"embedding": [0.1, 0.2, 0.3], "index": 0}], - "model": "text-embedding-ada-002", - "object": "list", - "usage": {"prompt_tokens": 1, "total_tokens": 1}, - } + litellm.embedding(model="openai/text-embedding-3-small", input=["hello"], api_key="sk-test") + + request_body: Final = json.loads(mock_route.calls.last.request.read()) + assert "encoding_format" not in request_body + + +@pytest.mark.asyncio +async def test_aembedding_openai_omits_encoding_format_when_client_omits_it( + respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch +) -> None: + monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) + mock_route: Final = _mock_openai_embedding_route(respx_mock) + + response: Final = await litellm.aembedding(model="openai/text-embedding-3-small", input=["hello"], api_key="sk-test") + + request_body: Final = json.loads(mock_route.calls.last.request.read()) + assert "encoding_format" not in request_body + assert response.data[0]["embedding"] == [0.1, 0.2, 0.3] + + +def test_embedding_openai_omitted_encoding_format_maps_provider_errors( + respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch +) -> None: + respx_mock.post("https://api.openai.com/v1/embeddings").mock( + return_value=httpx.Response( + 429, + headers={"retry-after": "42", "x-should-retry": "false"}, + json={"error": {"message": "rate limited", "type": "rate_limit_error"}}, + ) ) - mock_response.headers = {} - with patch( - "litellm.llms.openai.openai.OpenAIChatCompletion._get_openai_client" - ) as mock_get_client: - mock_client_instance = MagicMock() - mock_get_client.return_value = mock_client_instance - mock_client_instance.embeddings.with_raw_response.create.return_value = ( - mock_response + with pytest.raises(litellm.RateLimitError) as exc_info: + litellm.embedding( + model="openai/text-embedding-3-small", input=["hello"], api_key="sk-test", max_retries=0 ) - embedding( - model="text-embedding-ada-002", - input="Hello world", - ) - - call_kwargs = ( - mock_client_instance.embeddings.with_raw_response.create.call_args[1] - ) - assert "encoding_format" not in call_kwargs - - -def test_openai_embedding_encoding_format_explicit_overrides_env(monkeypatch): - """Request `encoding_format` wins over LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT.""" - monkeypatch.setenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", "float") - - mock_response = MagicMock() - mock_response.parse.return_value = MagicMock( - model_dump=lambda: { - "data": [{"embedding": [0.1, 0.2, 0.3], "index": 0}], - "model": "text-embedding-ada-002", - "object": "list", - "usage": {"prompt_tokens": 1, "total_tokens": 1}, - } - ) - mock_response.headers = {} - - with patch( - "litellm.llms.openai.openai.OpenAIChatCompletion._get_openai_client" - ) as mock_get_client: - mock_client_instance = MagicMock() - mock_get_client.return_value = mock_client_instance - mock_client_instance.embeddings.with_raw_response.create.return_value = ( - mock_response - ) - - embedding( - model="text-embedding-ada-002", - input="Hello world", - encoding_format="base64", - ) - - call_kwargs = ( - mock_client_instance.embeddings.with_raw_response.create.call_args[1] - ) - assert call_kwargs["encoding_format"] == "base64" + assert int(exc_info.value.litellm_response_headers["retry-after"]) == 42 diff --git a/tests/test_litellm/test_redis.py b/tests/test_litellm/test_redis.py index 826beb74a27..a96e8541e06 100644 --- a/tests/test_litellm/test_redis.py +++ b/tests/test_litellm/test_redis.py @@ -1,3 +1,4 @@ +import inspect import json from types import SimpleNamespace from unittest.mock import AsyncMock, MagicMock, patch @@ -600,6 +601,72 @@ def test_reconnect_kwargs_in_cluster_kwargs(): assert "socket_keepalive" in kwargs +def test_retry_attempts_in_cluster_kwargs(): + """cluster_error_retry_attempts must survive the cluster kwarg allow-list so + operators can bound worst-case retry latency on a Redis Cluster: it was being + silently dropped because the allow-list was built from redis.RedisCluster's + decorated __init__ without unwrapping it, so getfullargspec saw an empty + (self, *args, **kwargs) wrapper signature.""" + kwargs = _get_redis_cluster_kwargs() + assert "cluster_error_retry_attempts" in kwargs + + +def test_async_only_kwargs_in_cluster_kwargs_when_async_client_requested(): + """decode_responses is on the async cluster client's constructor and not the sync + one, on every redis-py the matrix covers. Introspecting the sync class regardless + of which client is actually built silently drops it for every async cluster caller.""" + sync_kwargs = _get_redis_cluster_kwargs() + async_kwargs = _get_redis_cluster_kwargs(async_redis.RedisCluster) + + assert "decode_responses" not in sync_kwargs + assert "decode_responses" in async_kwargs + + +@patch( # test-quality-ok: redis-py >= 6 keeps no cluster_error_retry_attempts attribute on the built client, so the constructor call is the only place the value is observable + "litellm.caching.redis_cluster_node_isolation.get_litellm_async_redis_cluster_class" +) +def test_async_cluster_forwards_retry_attempts(mock_get_cluster_class): + """Regression: cluster_error_retry_attempts must reach the constructed async + cluster client. Silently dropping it removes an operator's only lever for + bounding a stuck node's worst-case retry latency, and the client falls back + to redis-py's own default (3 retries) instead.""" + mock_cluster_cls = mock_get_cluster_class.return_value + get_redis_async_client( + startup_nodes=[{"host": "cluster-node", "port": 6379}], + cluster_error_retry_attempts=2, + ) + + call_kwargs = mock_cluster_cls.call_args[1] + assert call_kwargs["cluster_error_retry_attempts"] == 2 + + +def test_async_cluster_passes_async_only_kwargs(): + """Regression: decode_responses is an async-cluster-only constructor arg. When + the allow-list came from the sync class it was filtered out and values came + back as bytes instead of str.""" + client = get_redis_async_client( + startup_nodes=[{"host": "cluster-node", "port": 6379}], + decode_responses=True, + ) + + assert client.connection_kwargs["decode_responses"] is True + + +@pytest.mark.parametrize("cluster_client", [redis.RedisCluster, async_redis.RedisCluster], ids=["sync", "async"]) +def test_cluster_kwargs_exclude_variadic_parameters(cluster_client): + """*args / **kwargs are signature placeholders, not connection settings, and + must never land in the allow-list regardless of which cluster client is + introspected.""" + variadic = { + name + for name, param in inspect.signature(cluster_client).parameters.items() + if param.kind in (param.VAR_POSITIONAL, param.VAR_KEYWORD) + } + + leaked = variadic & set(_get_redis_cluster_kwargs(cluster_client)) + assert not leaked, f"variadic params leaked into the allow-list: {leaked}" + + @patch("litellm.caching.redis_cluster_node_isolation.get_litellm_async_redis_cluster_class") def test_async_cluster_sets_reconnect_defaults(mock_get_cluster_class): """ diff --git a/tests/test_litellm/test_register_model_custom_pricing.py b/tests/test_litellm/test_register_model_custom_pricing.py index 39f498b4e58..452a15334ef 100644 --- a/tests/test_litellm/test_register_model_custom_pricing.py +++ b/tests/test_litellm/test_register_model_custom_pricing.py @@ -793,3 +793,203 @@ def test_embedding_direct_sdk_custom_pricing_still_registers_shared_key(): finally: litellm.model_cost.pop(model_key, None) _invalidate_model_cost_lowercase_map() + + +def test_update_dictionary_merges_nested_dicts_without_aliasing(): + """A nested dict must be merged copy-on-write: the pre-existing nested dict + object stays untouched, and the caller's incoming nested dict is never + inserted by reference into the merged result. + """ + from litellm.utils import _update_dictionary + + existing_nested = {"hours_utc": "01:00-02:00"} + existing = {"off_peak_pricing": existing_nested} + incoming_nested = {"windows": [{"hours_utc": "16:00-19:00", "weekdays": [2]}]} + incoming = {"off_peak_pricing": incoming_nested} + + merged = _update_dictionary(existing, incoming) + + assert merged["off_peak_pricing"] == { + "hours_utc": "01:00-02:00", + "windows": [{"hours_utc": "16:00-19:00", "weekdays": [2]}], + } + assert existing_nested == {"hours_utc": "01:00-02:00"} + assert merged["off_peak_pricing"] is not incoming_nested + + fresh = _update_dictionary({}, incoming) + assert fresh["off_peak_pricing"] == incoming_nested + assert fresh["off_peak_pricing"] is not incoming_nested + + +def test_router_deployments_sharing_backend_keep_their_own_off_peak_pricing(): + """Two deployments of the same backend model with different + ``off_peak_pricing`` blocks must each keep their own schedule under their + unique model id, and neither block may leak onto the shared backend keys. + + Before the fix, ``register_model`` inserted the first deployment's block by + reference into the built-in ``gpt-4o-mini`` entry, and the second + deployment's registration merged its keys into that same object, corrupting + the first deployment's schedule and polluting the built-in entry. + """ + from litellm import Router + + active_block = { + "windows": [{"hours_utc": "16:00-19:00", "weekdays": [2]}], + "input_cost_per_token": 5e-07, + "output_cost_per_token": 1e-06, + } + inactive_block = { + "hours_utc": "05:00-06:00", + "input_cost_per_token": 5e-07, + "output_cost_per_token": 1e-06, + } + shared_keys = ["gpt-4o-mini", "openai/gpt-4o-mini"] + deployment_ids = ["offpeak-alias-dep-1", "offpeak-alias-dep-2"] + original_entries = _snapshot_model_cost_entries(shared_keys) + + router = Router( + model_list=[ + { + "model_name": "offpeak-active-weekday", + "litellm_params": { + "model": "openai/gpt-4o-mini", + "api_key": "fake-key-for-registration", + }, + "model_info": { + "id": deployment_ids[0], + "input_cost_per_token": 1e-06, + "output_cost_per_token": 2e-06, + "off_peak_pricing": dict(active_block), + }, + }, + { + "model_name": "offpeak-inactive-hours", + "litellm_params": { + "model": "openai/gpt-4o-mini", + "api_key": "fake-key-for-registration", + }, + "model_info": { + "id": deployment_ids[1], + "input_cost_per_token": 1e-06, + "output_cost_per_token": 2e-06, + "off_peak_pricing": dict(inactive_block), + }, + }, + ] + ) + + try: + registered_first = litellm.model_cost[deployment_ids[0]]["off_peak_pricing"] + registered_second = litellm.model_cost[deployment_ids[1]]["off_peak_pricing"] + assert registered_first == active_block + assert registered_second == inactive_block + for shared_key in shared_keys: + shared_entry = litellm.model_cost.get(shared_key) or {} + assert not shared_entry.get("off_peak_pricing") + finally: + for deployment_id in deployment_ids: + litellm.model_cost.pop(deployment_id, None) + _restore_model_cost_entries(original_entries) + del router + + +def test_router_off_peak_only_deployment_inherits_builtin_base_rates(): + """A deployment that sets only ``off_peak_pricing`` on its model_info must + still be costed from its deployment-scoped entry: the base token rates are + inherited from the backend model's built-in cost map entry, since the + shared backend key deliberately never carries the off-peak block. + """ + from litellm import Router + + block = { + "hours_utc": "00:00-00:00", + "input_cost_per_token": 5e-05, + "output_cost_per_token": 1e-04, + } + shared_keys = ["gpt-4o-mini", "openai/gpt-4o-mini"] + deployment_id = "offpeak-only-dep-1" + original_entries = _snapshot_model_cost_entries(shared_keys + [deployment_id]) + builtin_info = litellm.get_model_info(model="openai/gpt-4o-mini") + + router = Router( + model_list=[ + { + "model_name": "offpeak-only", + "litellm_params": { + "model": "openai/gpt-4o-mini", + "api_key": "fake-key-for-registration", + }, + "model_info": {"id": deployment_id, "off_peak_pricing": dict(block)}, + } + ] + ) + + try: + entry = litellm.model_cost[deployment_id] + assert entry["off_peak_pricing"] == block + assert entry["input_cost_per_token"] is not None + assert entry["input_cost_per_token"] == builtin_info["input_cost_per_token"] + assert entry["output_cost_per_token"] == builtin_info["output_cost_per_token"] + for shared_key in shared_keys: + shared_entry = litellm.model_cost.get(shared_key) or {} + assert not shared_entry.get("off_peak_pricing") + finally: + _restore_model_cost_entries(original_entries) + del router + + +def test_use_custom_pricing_for_model_sees_off_peak_only_model_info(): + from litellm.litellm_core_utils.litellm_logging import use_custom_pricing_for_model + + block = {"hours_utc": "00:00-00:00", "input_cost_per_token": 5e-05} + assert use_custom_pricing_for_model({"metadata": {"model_info": {"off_peak_pricing": block}}}) is True + assert use_custom_pricing_for_model({"metadata": {"model_info": {"off_peak_pricing": None}}}) is False + assert use_custom_pricing_for_model({"metadata": {"model_info": {"id": "some-id"}}}) is False + + +def test_completion_cost_applies_off_peak_only_deployment_pricing(): + """End to end through the cost calculator: with ``custom_pricing`` set and + a ``router_model_id`` whose entry carries only an always-on off-peak block, + the request bills at the block's rates rather than the shared backend rate. + """ + from litellm import Router + from litellm.types.utils import ModelResponse, Usage + + block = { + "hours_utc": "00:00-00:00", + "input_cost_per_token": 5e-05, + "output_cost_per_token": 1e-04, + } + shared_keys = ["gpt-4o-mini", "openai/gpt-4o-mini"] + deployment_id = "offpeak-only-dep-2" + original_entries = _snapshot_model_cost_entries(shared_keys + [deployment_id]) + + router = Router( + model_list=[ + { + "model_name": "offpeak-only", + "litellm_params": { + "model": "openai/gpt-4o-mini", + "api_key": "fake-key-for-registration", + }, + "model_info": {"id": deployment_id, "off_peak_pricing": dict(block)}, + } + ] + ) + + try: + response = ModelResponse( + model="gpt-4o-mini", + usage=Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150), + ) + cost = litellm.completion_cost( + completion_response=response, + model="openai/gpt-4o-mini", + custom_llm_provider="openai", + custom_pricing=True, + router_model_id=deployment_id, + ) + assert cost == pytest.approx(100 * 5e-05 + 50 * 1e-04) + finally: + _restore_model_cost_entries(original_entries) + del router diff --git a/tests/test_litellm/test_router.py b/tests/test_litellm/test_router.py index 97286017ffe..84f6344be35 100644 --- a/tests/test_litellm/test_router.py +++ b/tests/test_litellm/test_router.py @@ -8155,6 +8155,71 @@ class TestUpsertDeploymentRollback: assert len(router.model_list) == 1 +class TestUpsertDeploymentRename: + """ + Issue #38360: renaming a model wrote the new `model_name` to the db, but the reload's + `upsert_deployment` compared only `litellm_params` and `model_info`. A rename with no + other edit therefore compared equal and the router kept the old name until a restart, + so `/model/info` and `/v1/models` served the stale name and the new one was unroutable. + """ + + @staticmethod + def _router() -> "litellm.Router": + return litellm.Router( + model_list=[ + { + "model_name": "old-name", + "litellm_params": {"model": "openai/gpt-4o", "api_key": "sk-test"}, + "model_info": {"id": "rename-1", "db_model": True}, + } + ] + ) + + @staticmethod + def _deployment(model_name: str, tpm: int | None = None): + from litellm.types.router import Deployment, LiteLLM_Params, ModelInfo + + return Deployment( + model_name=model_name, + litellm_params=LiteLLM_Params(model="openai/gpt-4o", api_key="sk-test", tpm=tpm), + model_info=ModelInfo(id="rename-1", db_model=True), + ) + + def test_rename_only_updates_the_router(self): + router = self._router() + + assert router.upsert_deployment(deployment=self._deployment("new-name")) is not None + + assert [model["model_name"] for model in router.model_list] == ["new-name"] + renamed = router.get_deployment(model_id="rename-1") + assert renamed is not None + assert renamed.model_name == "new-name" + + def test_rename_only_makes_the_new_name_routable(self): + router = self._router() + + router.upsert_deployment(deployment=self._deployment("new-name")) + + assert router.get_model_ids(model_name="new-name") == ["rename-1"] + assert router.get_model_ids(model_name="old-name") == [] + + def test_rename_alongside_another_edit_still_updates(self): + router = self._router() + + router.upsert_deployment(deployment=self._deployment("new-name", tpm=1234)) + + assert router.get_model_ids(model_name="new-name") == ["rename-1"] + renamed = router.get_deployment(model_id="rename-1") + assert renamed is not None + assert renamed.litellm_params.tpm == 1234 + + def test_unchanged_deployment_is_still_a_no_op(self): + router = self._router() + + assert router.upsert_deployment(deployment=self._deployment("old-name")) is None + assert [model["model_name"] for model in router.model_list] == ["old-name"] + + class TestConsumedRequestTagsStamp: """Issue #36621: when a request's tags select a tagged pre-routing strategy, those tags are consumed by the selection; the hook must stamp the rewritten model group so @@ -11530,3 +11595,138 @@ class TestTierParamsTheTargetAccepts: accepted = router._tier_params_the_target_accepts("no-such-group", {"reasoning_effort": "max"}, {}) assert accepted == {"reasoning_effort": "max"} + + +class TestPreRoutingTierDrivesFallbacks: + """#38832: a complexity/auto router picks a tier behind the router name, but fallback + lookup stayed on the router name, so the tier's configured chain never ran and a + provider failure on the tier's first hop was returned to the client.""" + + class _TierRouter(litellm.Router): + async def async_pre_routing_hook( + self, model, request_kwargs, messages=None, input=None, specific_deployment=False + ): + from litellm.types.router import PreRoutingHookResponse + + if model == "smart-router": + return PreRoutingHookResponse(model="tier1", messages=messages) + return None + + @classmethod + def _router(cls, fallbacks) -> "litellm.Router": + return cls._TierRouter( + model_list=[ + { + "model_name": "smart-router", + "litellm_params": {"model": "openai/gpt-4o-mini", "api_key": "sk-x"}, + }, + { + "model_name": "tier1", + "litellm_params": { + "model": "openai/gpt-4o-mini", + "api_key": "sk-x", + "mock_response": "litellm.RateLimitError", + }, + }, + { + "model_name": "backup-a", + "litellm_params": { + "model": "openai/gpt-4o-mini", + "api_key": "sk-x", + "mock_response": "from backup-a", + }, + }, + { + "model_name": "backup-b", + "litellm_params": { + "model": "openai/gpt-4o-mini", + "api_key": "sk-x", + "mock_response": "from backup-b", + }, + }, + { + "model_name": "failing-backup", + "litellm_params": { + "model": "openai/gpt-4o-mini", + "api_key": "sk-x", + "mock_response": "litellm.RateLimitError", + }, + }, + { + "model_name": "plain", + "litellm_params": { + "model": "openai/gpt-4o-mini", + "api_key": "sk-x", + "mock_response": "litellm.RateLimitError", + }, + }, + ], + fallbacks=fallbacks, + num_retries=0, + ) + + @pytest.mark.asyncio + async def test_the_selected_tier_fallback_chain_runs(self): + router = self._router([{"tier1": ["backup-a"]}]) + + response = await router.acompletion( + model="smart-router", messages=[{"role": "user", "content": "hi"}] + ) + + assert response.choices[0].message.content == "from backup-a" + + @pytest.mark.asyncio + async def test_a_chain_keyed_on_the_router_name_is_not_used(self): + """The router name has no chain of its own, so nothing should rescue this call.""" + router = self._router([{"tier2": ["backup-a"]}]) + + with pytest.raises(litellm.RateLimitError): + await router.acompletion(model="smart-router", messages=[{"role": "user", "content": "hi"}]) + + @pytest.mark.asyncio + async def test_a_chain_keyed_on_the_router_name_rescues_when_no_tier_chain_exists(self): + """The documented contract: configs keyed on the requested name keep working behind auto-routers.""" + router = self._router([{"smart-router": ["backup-a"]}]) + + response = await router.acompletion(model="smart-router", messages=[{"role": "user", "content": "hi"}]) + + assert response.choices[0].message.content == "from backup-a" + + @pytest.mark.asyncio + async def test_the_tier_chain_wins_over_the_router_name_chain(self): + router = self._router([{"tier1": ["backup-a"]}, {"smart-router": ["backup-b"]}]) + + response = await router.acompletion(model="smart-router", messages=[{"role": "user", "content": "hi"}]) + + assert response.choices[0].message.content == "from backup-a" + + @pytest.mark.asyncio + async def test_a_request_without_a_pre_routing_hook_still_uses_its_own_group(self): + router = self._router([{"tier1": ["backup-a"]}]) + + response = await router.acompletion( + model="tier1", messages=[{"role": "user", "content": "hi"}] + ) + + assert response.choices[0].message.content == "from backup-a" + + @pytest.mark.asyncio + async def test_a_caller_cannot_pick_the_chain_by_sending_the_selection(self): + """The metadata bucket carries caller-supplied keys, so only the hook may set the tier.""" + router = self._router([{"tier1": ["backup-a"]}]) + + with pytest.raises(litellm.RateLimitError): + await router.acompletion( + model="plain", + messages=[{"role": "user", "content": "hi"}], + metadata={"pre_routing_selected_model": "tier1"}, + ) + + @pytest.mark.asyncio + async def test_each_fallback_hop_resolves_its_own_chain(self): + """The second hop must key off the group it is running, not the tier that failed.""" + router = self._router([{"tier1": ["failing-backup"]}, {"failing-backup": ["backup-b"]}]) + + response = await router.acompletion(model="smart-router", messages=[{"role": "user", "content": "hi"}]) + + assert response.choices[0].message.content == "from backup-b" diff --git a/tests/test_litellm/test_router_model_cost_isolation.py b/tests/test_litellm/test_router_model_cost_isolation.py index b580b03574e..30b265905f3 100644 --- a/tests/test_litellm/test_router_model_cost_isolation.py +++ b/tests/test_litellm/test_router_model_cost_isolation.py @@ -538,6 +538,157 @@ def test_inherit_builtin_cache_pricing_noop_for_unknown_backend(): assert model_info == {"input_cost_per_token": 0.000003} +def test_inherit_builtin_base_rates_for_off_peak_fills_missing_rates(): + """Direct unit test of the helper: an entry carrying only an + off_peak_pricing block inherits the backend model's built-in base token + rates, so cost lookup via the deployment id can bill standard rates + outside the windows. + """ + backend_model = "gpt-4o-mini" + builtin_info = litellm.get_model_info(model=backend_model, custom_llm_provider="openai") + off_peak_block = { + "hours_utc": "00:00-00:00", + "input_cost_per_token": 5e-07, + "output_cost_per_token": 1e-06, + } + model_info = {"off_peak_pricing": off_peak_block} + + Router._inherit_builtin_base_rates_for_off_peak( + model_info=model_info, + backend_model=backend_model, + custom_llm_provider="openai", + ) + + assert model_info["input_cost_per_token"] == builtin_info["input_cost_per_token"] + assert model_info["output_cost_per_token"] == builtin_info["output_cost_per_token"] + assert model_info["off_peak_pricing"] == off_peak_block + + +def test_inherit_builtin_base_rates_for_off_peak_carries_threshold_rates(): + """A backend with above-threshold pricing hands the whole rate structure to + the deployment entry, so peak-hour billing of large prompts through that + entry matches the shared backend entry instead of flattening to the base + rate. + """ + backend_model = "gemini/gemini-2.5-pro" + builtin_info = litellm.get_model_info(model=backend_model) + assert builtin_info["input_cost_per_token_above_200k_tokens"] is not None + + model_info = { + "off_peak_pricing": {"hours_utc": "00:00-00:00", "input_cost_per_token": 5e-07}, + } + + Router._inherit_builtin_base_rates_for_off_peak( + model_info=model_info, + backend_model=backend_model, + custom_llm_provider="gemini", + ) + + assert model_info["input_cost_per_token"] == builtin_info["input_cost_per_token"] + assert ( + model_info["input_cost_per_token_above_200k_tokens"] + == builtin_info["input_cost_per_token_above_200k_tokens"] + ) + assert ( + model_info["output_cost_per_token_above_200k_tokens"] + == builtin_info["output_cost_per_token_above_200k_tokens"] + ) + + +def test_inherit_builtin_base_rates_for_off_peak_carries_companion_billing_fields(): + """Billing rules that are not literal cost rates, like the web search + billing unit, must ride along, or grounding and regional uplifts would + bill differently through the deployment entry than through the shared + backend entry. + """ + backend_model = "gemini-3-pro-image" + raw_entry = litellm.model_cost[backend_model] + assert raw_entry.get("web_search_billing_unit") is not None + + model_info = { + "off_peak_pricing": {"hours_utc": "00:00-00:00", "input_cost_per_token": 5e-07}, + } + + Router._inherit_builtin_base_rates_for_off_peak( + model_info=model_info, + backend_model=backend_model, + custom_llm_provider=None, + ) + + assert model_info["web_search_billing_unit"] == raw_entry["web_search_billing_unit"] + assert model_info["input_cost_per_token"] == raw_entry["input_cost_per_token"] + + +def test_inherit_builtin_base_rates_for_off_peak_tiered_only_backend_stores_no_zero(): + """A tiered-only backend has no flat token rates; get_model_info synthesizes + zeros for them, and storing those would mark the deployment explicitly + priced free. The tier table itself must carry over as an isolated copy so + mutating the deployment entry never touches the shared cost map. + """ + backend_model = "dashscope/qwen-flash" + raw_tiers = litellm.model_cost[backend_model]["tiered_pricing"] + + model_info = { + "off_peak_pricing": {"hours_utc": "00:00-00:00", "input_cost_per_token": 5e-07}, + } + + Router._inherit_builtin_base_rates_for_off_peak( + model_info=model_info, + backend_model=backend_model, + custom_llm_provider="dashscope", + ) + + assert model_info.get("input_cost_per_token") != 0 + assert model_info.get("output_cost_per_token") != 0 + assert model_info["tiered_pricing"] == raw_tiers + assert model_info["tiered_pricing"] is not raw_tiers + assert model_info["tiered_pricing"][0] is not raw_tiers[0] + + original_first_tier = copy.deepcopy(raw_tiers[0]) + model_info["tiered_pricing"][0]["input_cost_per_token"] = 123.0 + assert raw_tiers[0] == original_first_tier + + +def test_inherit_builtin_base_rates_for_off_peak_leaves_explicit_rates_alone(): + """An entry that sets its own base rate beside the block already counts as + a full custom pricing entry; the helper must not mix builtin rates into it. + """ + model_info = { + "off_peak_pricing": {"hours_utc": "00:00-00:00", "input_cost_per_token": 5e-07}, + "input_cost_per_token": 3e-06, + } + + Router._inherit_builtin_base_rates_for_off_peak( + model_info=model_info, + backend_model="gpt-4o-mini", + custom_llm_provider="openai", + ) + + assert model_info["input_cost_per_token"] == 3e-06 + assert "output_cost_per_token" not in model_info + + +def test_inherit_builtin_base_rates_for_off_peak_noop_without_block_or_backend(): + """Nothing happens without an off_peak_pricing block, and an unmapped + backend model leaves the entry unchanged rather than raising. + """ + plain_info = {"id": "dep-1"} + Router._inherit_builtin_base_rates_for_off_peak( + model_info=plain_info, + backend_model="gpt-4o-mini", + custom_llm_provider="openai", + ) + assert plain_info == {"id": "dep-1"} + + off_peak_info = {"off_peak_pricing": {"hours_utc": "00:00-00:00", "input_cost_per_token": 5e-07}} + Router._inherit_builtin_base_rates_for_off_peak( + model_info=off_peak_info, + backend_model="this-backend-model-does-not-exist-x9y8z7", + custom_llm_provider=None, + ) + assert "input_cost_per_token" not in off_peak_info + + def test_custom_pricing_field_denylist_covers_all_builtin_pricing_fields(): """The shared-backend-key stripping in Router relies on CustomPricingLiteLLMParams enumerating every per-deployment pricing field. @@ -2130,3 +2281,83 @@ def test_every_declaring_deployment_is_named(caplog): assert "azure-ptu-east" in warnings[0] assert "azure-ptu-west" in warnings[0] assert "plain-gpt-4o" not in warnings[0] + + +def _simulate_price_data_reload_with_provider_sets(monkeypatch, fetched_catalog): + """Like `_simulate_price_data_reload`, plus the provider model-set refresh the proxy's + `_swap_in_model_cost_map` does before replaying, so bare names in the new catalog resolve.""" + monkeypatch.setattr(litellm, "model_cost", fetched_catalog) + _invalidate_model_cost_lowercase_map() + litellm.add_known_models(model_cost_map=fetched_catalog) + reapply_runtime_model_cost_registrations() + + +def test_a_config_deployment_dropped_by_a_stale_cost_map_comes_back_on_reload(monkeypatch): + """ + Booting on the bundled backup, a bare model that only the remote catalog knows + cannot be provider-resolved, so the proxy router (ignore_invalid_deployments) drops + it. Once a reload brings in a catalog that knows the model, the deployment must be + served again with its access groups, and exactly once however many reloads follow. + """ + backend = "lit-5766-only-in-remote-catalog" + try: + router = Router( + model_list=[ + { + "model_name": "new-model", + "litellm_params": {"model": backend, "api_key": "k"}, + "model_info": {"id": "new-id", "access_groups": ["team-models"]}, + }, + { + "model_name": "control-model", + "litellm_params": {"model": "hosted_vllm/control-backend", "api_key": "k"}, + "model_info": {"id": "control-id", "access_groups": ["team-models"]}, + }, + ], + ignore_invalid_deployments=True, + ) + assert router.get_model_names() == ["control-model"] + assert router.get_model_access_groups(model_name="new-model") == {} + + fresh_catalog = {**litellm.model_cost, backend: {"litellm_provider": "openai", "mode": "chat"}} + _simulate_price_data_reload_with_provider_sets(monkeypatch, fresh_catalog) + _simulate_price_data_reload_with_provider_sets(monkeypatch, fresh_catalog) + + assert sorted(router.get_model_names()) == ["control-model", "new-model"] + assert router.get_model_access_groups(model_name="new-model") == {"team-models": ["new-model"]} + assert [d["model_info"]["id"] for d in router.model_list] == ["control-id", "new-id"] + assert "new-id" in litellm.model_cost + finally: + litellm.open_ai_chat_completion_models.discard(backend) + litellm.models_by_provider["openai"].discard(backend) + + +def test_a_config_deployment_dropped_for_a_permanent_reason_is_not_retried_on_reload(monkeypatch): + """ + Only provider-resolution drops can be healed by a fresh catalog. A deployment that + fails after its provider resolved (here a pass-through vertex entry with no project) + has already touched router state, so replaying it on every reload would leak into + `deployment_names` each time. + """ + router = Router( + model_list=[ + { + "model_name": "vertex-passthrough", + "litellm_params": {"model": "vertex_ai/gemini-2.5-flash", "use_in_pass_through": True}, + "model_info": {"id": "vertex-id"}, + }, + { + "model_name": "control-model", + "litellm_params": {"model": "hosted_vllm/control-backend", "api_key": "k"}, + "model_info": {"id": "control-id"}, + }, + ], + ignore_invalid_deployments=True, + ) + assert router.get_model_names() == ["control-model"] + names_after_boot = list(router.deployment_names) + + _simulate_price_data_reload_with_provider_sets(monkeypatch, dict(litellm.model_cost)) + + assert router.get_model_names() == ["control-model"] + assert router.deployment_names == names_after_boot diff --git a/tests/test_litellm/test_router_order_fallback.py b/tests/test_litellm/test_router_order_fallback.py index 7743cb005d0..fde870e5abe 100644 --- a/tests/test_litellm/test_router_order_fallback.py +++ b/tests/test_litellm/test_router_order_fallback.py @@ -6,12 +6,19 @@ should be tried first, and higher order deployments should be used as fallbacks when lower order deployments fail. """ -from typing import Optional +import json +from typing import Final, Optional +import httpx import pytest +from openai import AsyncOpenAI +import litellm from litellm import Router -from litellm.utils import _get_order_filtered_deployments +from litellm.integrations.custom_logger import CustomLogger +from litellm.router_utils.prompt_caching_cache import PromptCachingCache +from litellm.types.router import RouterRateLimitError +from litellm.utils import _get_deployment_order, _get_order_filtered_deployments # --------------------------------------------------------------------------- # Unit tests for _get_order_filtered_deployments @@ -49,13 +56,22 @@ class TestGetOrderFilteredDeployments: assert len(result) == 1 assert result[0]["model_info"]["id"] == "b" - def test_target_order_no_match_returns_all(self): + def test_target_order_no_match_returns_empty(self): deps = [ self._make_deployment(1, "a"), self._make_deployment(2, "b"), ] result = _get_order_filtered_deployments(deps, target_order=99) - assert len(result) == 2 + assert result == [] + + def test_target_order_no_match_does_not_reselect_lower_order(self): + deps = [ + self._make_deployment(1, "a"), + self._make_deployment(2, "b"), + ] + remaining_after_pre_call = [deps[0]] + result = _get_order_filtered_deployments(remaining_after_pre_call, target_order=2) + assert result == [] def test_no_order_set_returns_all(self): deps = [ @@ -406,35 +422,239 @@ async def test_router_order_fallback_with_hidden_model_group_alias(): assert response._hidden_params["model_id"] == "2" +@pytest.mark.asyncio +async def test_router_order_fallback_does_not_reselect_order_1_when_order_2_is_filtered_out(): + class _DropOrder2(CustomLogger): + async def async_filter_deployments( + self, model, healthy_deployments, messages, request_kwargs=None, parent_otel_span=None + ): + return [d for d in healthy_deployments if _get_deployment_order(d) != 2] + + drop_order_2: Final = _DropOrder2() + router = Router( + model_list=[ + { + "model_name": "test-model", + "litellm_params": { + "model": "gpt-4o", + "api_key": "key", + "mock_response": "litellm.RateLimitError", + "order": 1, + }, + "model_info": {"id": "1"}, + }, + { + "model_name": "test-model", + "litellm_params": { + "model": "gpt-4o", + "api_key": "key", + "mock_response": "success from order 2", + "order": 2, + }, + "model_info": {"id": "2"}, + }, + ], + num_retries=0, + ) + litellm.callbacks.append(drop_order_2) + try: + with pytest.raises(RouterRateLimitError, match="No deployments available") as exc_info: + await router.acompletion( + model="test-model", + messages=[{"role": "user", "content": "hi"}], + ) + assert "success from order 2" not in str(exc_info.value) + finally: + litellm.callbacks.remove(drop_order_2) + + +@pytest.mark.asyncio +async def test_router_order_fallback_ignores_prompt_cache_pin_on_target_order(): + messages = [{"role": "user", "content": "word " * 5000}] + router = Router( + model_list=[ + { + "model_name": "test-model", + "litellm_params": { + "model": "gpt-4o", + "api_key": "bad", + "mock_response": Exception("azure peak load"), + "order": 1, + }, + "model_info": {"id": "1"}, + }, + { + "model_name": "test-model", + "litellm_params": { + "model": "gpt-4o", + "api_key": "good", + "mock_response": "success from order 2", + "order": 2, + }, + "model_info": {"id": "2"}, + }, + ], + num_retries=0, + optional_pre_call_checks=["prompt_caching"], + ) + await PromptCachingCache(cache=router.cache).async_add_model_id( + model_id="1", + messages=messages, + tools=None, + ) + response = await router.acompletion(model="test-model", messages=messages) + assert response._hidden_params["model_id"] == "2" + + +@pytest.mark.asyncio +async def test_router_order_fallback_retries_keep_target_order(): + seen_target_orders: Final = [] + + class _RecordTargetOrder(CustomLogger): + async def async_filter_deployments( + self, model, healthy_deployments, messages, request_kwargs=None, parent_otel_span=None + ): + seen_target_orders.append((request_kwargs or {}).get("_target_order")) + return healthy_deployments + + recorder: Final = _RecordTargetOrder() + router = Router( + model_list=[ + { + "model_name": "test-model", + "litellm_params": { + "model": "gpt-4o", + "api_key": "bad", + "mock_response": Exception("fail order 1"), + "order": 1, + }, + "model_info": {"id": "1"}, + }, + { + "model_name": "test-model", + "litellm_params": { + "model": "gpt-4o", + "api_key": "bad", + "mock_response": Exception("fail order 2"), + "order": 2, + }, + "model_info": {"id": "2"}, + }, + ], + num_retries=1, + ) + litellm.callbacks.append(recorder) + try: + with pytest.raises(Exception, match="fail order 2"): + await router.acompletion( + model="test-model", + messages=[{"role": "user", "content": "hi"}], + ) + finally: + litellm.callbacks.remove(recorder) + assert seen_target_orders.count(2) >= 2 + + +@pytest.mark.asyncio +async def test_generic_api_call_strips_target_order_from_provider_kwargs(): + captured: Final = {} + + async def _fake_provider(**provider_kwargs): + captured.update(provider_kwargs) + return "ok" + + router = Router( + model_list=[ + { + "model_name": "test-model", + "litellm_params": {"model": "gpt-4o", "api_key": "key", "order": 2}, + "model_info": {"id": "2"}, + }, + ], + ) + response = await router._ageneric_api_call_with_fallbacks_helper( + model="test-model", + original_generic_function=_fake_provider, + _target_order=2, + messages=[{"role": "user", "content": "hi"}], + ) + assert response == "ok" + assert captured["model"] == "gpt-4o" + assert "_target_order" not in captured + + +@pytest.mark.asyncio +async def test_text_completion_order_fallback_hop_does_not_send_target_order_upstream(): + upstream_bodies: Final[list[dict]] = [] + + def _upstream(request: httpx.Request) -> httpx.Response: + upstream_bodies.append(json.loads(request.content)) + return httpx.Response( + 200, + json={ + "id": "cmpl-1", + "object": "text_completion", + "created": 0, + "model": "gpt-3.5-turbo-instruct", + "choices": [{"text": "ok from order 2", "index": 0, "logprobs": None, "finish_reason": "stop"}], + "usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2}, + }, + ) + + upstream_client: Final = AsyncOpenAI( + api_key="key", + base_url="http://upstream.test", + http_client=httpx.AsyncClient(transport=httpx.MockTransport(_upstream)), + ) + router = Router( + model_list=[ + { + "model_name": "test-model", + "litellm_params": { + "model": "text-completion-openai/gpt-3.5-turbo-instruct", + "api_key": "key", + "mock_response": Exception("fail order 1"), + "order": 1, + }, + "model_info": {"id": "1"}, + }, + { + "model_name": "test-model", + "litellm_params": { + "model": "text-completion-openai/gpt-3.5-turbo-instruct", + "api_key": "key", + "api_base": "http://upstream.test", + "order": 2, + }, + "model_info": {"id": "2"}, + }, + ], + num_retries=0, + ) + try: + response = await router.atext_completion(model="test-model", prompt="hi", client=upstream_client) + finally: + await upstream_client.close() + + assert response._hidden_params["model_id"] == "2" + assert upstream_bodies + assert all("_target_order" not in body for body in upstream_bodies) + + def test_check_non_standard_fallback_format(): from litellm.router_utils.fallback_event_handlers import ( _check_non_standard_fallback_format, ) # Standard formats - assert ( - _check_non_standard_fallback_format([{"gpt-3.5-turbo": ["claude-3-haiku"]}]) - == False - ) + assert _check_non_standard_fallback_format([{"gpt-3.5-turbo": ["claude-3-haiku"]}]) == False assert _check_non_standard_fallback_format([{"model": ["qwen-backup"]}]) == False - assert ( - _check_non_standard_fallback_format( - [{"model": ["qwen-backup"], "region": ["us-east-1"]}] - ) - == False - ) + assert _check_non_standard_fallback_format([{"model": ["qwen-backup"], "region": ["us-east-1"]}]) == False # Non-standard formats assert _check_non_standard_fallback_format([{"model": "qwen-backup"}]) == True assert ( - _check_non_standard_fallback_format( - [{"model": "qwen-backup", "messages": [{"role": "user", "content": "hi"}]}] - ) - == True - ) - assert ( - _check_non_standard_fallback_format( - [{"model": ["qwen-backup"], "api_key": "some-key"}] - ) + _check_non_standard_fallback_format([{"model": "qwen-backup", "messages": [{"role": "user", "content": "hi"}]}]) == True ) + assert _check_non_standard_fallback_format([{"model": ["qwen-backup"], "api_key": "some-key"}]) == True diff --git a/tests/test_litellm/test_sync_together_ai_models.py b/tests/test_litellm/test_sync_together_ai_models.py index 7c1287e94b8..b8a85bcfbdc 100644 --- a/tests/test_litellm/test_sync_together_ai_models.py +++ b/tests/test_litellm/test_sync_together_ai_models.py @@ -105,7 +105,6 @@ def test_added_chat_model_matches_reviewed_registry_shape() -> None: "input_cost_per_token": 3e-06, "litellm_provider": "together_ai", "max_input_tokens": 1048576, - "max_output_tokens": 1048576, "max_tokens": 1048576, "mode": "chat", "output_cost_per_token": 1.5e-05, @@ -138,7 +137,35 @@ def test_moderation_type_maps_to_chat_mode() -> None: outcome = sync.compute_sync({}, RECORDED_CATALOG, RECORDED_DOC) guard = outcome.cost_map["together_ai/meta-llama/Llama-Guard-4-12B"] assert guard["mode"] == "chat" - assert guard["max_output_tokens"] == 1048576 + assert "max_output_tokens" not in guard + + +def test_output_ceiling_comes_from_the_rule_never_from_context_length() -> None: + glm = next(model for model in RECORDED_CATALOG if model.id == "zai-org/GLM-5.2") + fresh = sync.compute_sync({}, [_chat_model("acme/unreviewed", ctx=1048576), glm], _doc({"x": "2026-01-01"})) + unreviewed = fresh.cost_map["together_ai/acme/unreviewed"] + assert "max_output_tokens" not in unreviewed + assert (unreviewed["max_input_tokens"], unreviewed["max_tokens"]) == (1048576, 1048576) + reviewed = fresh.cost_map["together_ai/zai-org/GLM-5.2"] + assert (reviewed["max_input_tokens"], reviewed["max_output_tokens"], reviewed["max_tokens"]) == ( + 1048575, + 128000, + 128000, + ) + inflated = { + "together_ai/zai-org/GLM-5.2": { + "input_cost_per_token": 1.4e-06, + "litellm_provider": "together_ai", + "max_input_tokens": 1048575, + "max_output_tokens": 1048575, + "max_tokens": 1048575, + "mode": "chat", + "output_cost_per_token": 4.4e-06, + } + } + corrected = sync.compute_sync(inflated, [glm], _doc({"x": "2026-01-01"})) + assert corrected.cost_map["together_ai/zai-org/GLM-5.2"]["max_output_tokens"] == 128000 + assert any("max_output_tokens: 1048575 -> 128000" in line for line in corrected.updated) def test_docs_removed_but_live_model_stays_live_with_warning() -> None: diff --git a/tests/test_litellm/test_together_ai_model_metadata.py b/tests/test_litellm/test_together_ai_model_metadata.py index 45f0370386b..c9e2863d240 100644 --- a/tests/test_litellm/test_together_ai_model_metadata.py +++ b/tests/test_litellm/test_together_ai_model_metadata.py @@ -15,6 +15,7 @@ COST_MAP_ADAPTER: Final = TypeAdapter(CostMap) SERVERLESS_CHAT_MODELS: Final = ( "together_ai/moonshotai/Kimi-K3", "together_ai/zai-org/GLM-5.2", + "together_ai/zai-org/GLM-5.3", "together_ai/zai-org/GLM-5.3-Flash", "together_ai/deepseek-ai/DeepSeek-V4-Pro-0813", "together_ai/deepseek-ai/DeepSeek-V4-Flash-0731", @@ -107,6 +108,8 @@ def test_together_glm_52_pricing(cost_map: CostMap): info = cost_map["together_ai/zai-org/GLM-5.2"] assert info["input_cost_per_token"] == 1.4e-06 assert info["output_cost_per_token"] == 4.4e-06 + assert info["max_input_tokens"] == 1048575 + assert info["max_output_tokens"] == 128000 assert info["supports_function_calling"] is True assert info["supports_reasoning"] is True @@ -117,7 +120,7 @@ def test_together_glm_53_flash_pricing_and_capabilities(cost_map: CostMap): assert info["output_cost_per_token"] == 5e-07 assert info["cache_read_input_token_cost"] == 3e-08 assert info["max_input_tokens"] == 1048575 - assert info["max_output_tokens"] == 1048575 + assert info["max_output_tokens"] == 128000 assert info["supports_function_calling"] is True assert info["supports_parallel_function_calling"] is True assert info["supports_prompt_caching"] is True @@ -127,6 +130,18 @@ def test_together_glm_53_flash_pricing_and_capabilities(cost_map: CostMap): assert info["supports_reasoning"] is True +def test_together_chat_entries_never_carry_context_length_as_output_ceiling(cost_map: CostMap): + inflated = sorted( + model + for model, info in cost_map.items() + if info.get("litellm_provider") == "together_ai" + and info.get("mode") == "chat" + and "max_output_tokens" in info + and info["max_output_tokens"] == info.get("max_input_tokens") + ) + assert inflated == [] + + def test_together_multilingual_e5_embedding_entry(cost_map: CostMap): info = cost_map["together_ai/intfloat/multilingual-e5-large-instruct"] assert info["mode"] == "embedding" diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py index 1ff50bd0116..521e91daded 100644 --- a/tests/test_litellm/test_utils.py +++ b/tests/test_litellm/test_utils.py @@ -6,6 +6,7 @@ from typing import Final from unittest.mock import AsyncMock, MagicMock, patch import pytest +import respx from jsonschema import validate @@ -1004,6 +1005,7 @@ def test_aaamodel_prices_and_context_window_json_is_valid(): "gemini_native_audio": {"type": "boolean"}, "gemini_audio_only_live": {"type": "boolean"}, "supports_embedding_image_input": {"type": "boolean"}, + "supports_forced_tool_use": {"type": "boolean"}, "supports_function_calling": {"type": "boolean"}, "supports_image_input": {"type": "boolean"}, "supports_nova_canvas_image_edit": {"type": "boolean"}, @@ -1415,6 +1417,26 @@ def test_get_provider_rerank_config(): assert isinstance(config, HostedVLLMRerankConfig) +def test_get_provider_text_to_speech_config_vertex_gemini_skips_cloud_tts(): + """Regression for LIT-6501: mapping vertex Gemini TTS params through Google Cloud TTS + dropped response_format before the speech_to_completion bridge could honor it.""" + from litellm.llms.vertex_ai.text_to_speech.transformation import VertexAITextToSpeechConfig + from litellm.utils import LlmProviders + + assert ( + ProviderConfigManager.get_provider_text_to_speech_config( + model="gemini-2.5-flash-preview-tts", provider=LlmProviders.VERTEX_AI + ) + is None + ) + assert isinstance( + ProviderConfigManager.get_provider_text_to_speech_config( + model="en-US-Studio-O", provider=LlmProviders.VERTEX_AI + ), + VertexAITextToSpeechConfig, + ) + + # Models that should be skipped during testing OLD_PROVIDERS = ["aleph_alpha", "palm"] SKIP_MODELS = [ @@ -4432,6 +4454,53 @@ class TestVertexEmbeddingEncodingFormat: assert optional_params.get("outputDimensionality") == 256 +class TestBedrockCohereEmbeddingDispatch: + """All bedrock cohere.embed models must route to BedrockCohereEmbeddingConfig, + not just multilingual-v3/v4: english-v3 was falling into the unmapped + else-branch and rejecting encoding_format. Issue #38659.""" + + @pytest.mark.parametrize( + "model", + [ + "cohere.embed-english-v3", + "cohere.embed-multilingual-v3", + "cohere.embed-v4:0", + ], + ) + def test_cohere_embed_models_accept_encoding_format(self, model): + optional_params = litellm.utils.get_optional_params_embeddings( + model=model, + encoding_format="float", + custom_llm_provider="bedrock", + ) + assert optional_params.get("embedding_types") == ["float"] + + @pytest.mark.parametrize( + "model", + [ + "cohere.embed-english-v3", + "cohere.embed-multilingual-v3", + "cohere.embed-v4:0", + ], + ) + def test_cohere_embed_models_map_base64_to_float(self, model): + optional_params = litellm.utils.get_optional_params_embeddings( + model=model, + encoding_format="base64", + custom_llm_provider="bedrock", + ) + assert optional_params.get("embedding_types") == ["float"] + + def test_cohere_embed_english_v3_maps_dimensions(self): + optional_params = litellm.utils.get_optional_params_embeddings( + model="cohere.embed-english-v3", + encoding_format="float", + dimensions=512, + custom_llm_provider="bedrock", + ) + assert optional_params.get("output_dimension") == 512 + + @pytest.mark.parametrize( "model", [ @@ -5689,3 +5758,61 @@ class TestDefaultReasoningEffortHydration: model_info = dict(_get_model_info_helper(model="gpt-5.6-terra", custom_llm_provider="openai")) assert model_info.get("default_reasoning_effort") is None + + +class TestHuggingFaceConfigFetch: + """The Hugging Face config.json fetch runs on background logging threads during cost + calculation, so an unbounded request can hang a whole test job; the timeout is the fix.""" + + @pytest.fixture + def hf_config_route(self): + with respx.mock(assert_all_called=True) as respx_mock: + yield respx_mock.get(url__regex=r"https://huggingface\.co/.*/config\.json").respond( + json={"max_position_embeddings": 512} + ) + + def test_get_max_tokens_reads_hf_config_with_a_bounded_timeout(self, hf_config_route): + from litellm.constants import HF_CONFIG_FETCH_TIMEOUT_SECONDS + from litellm.utils import get_max_tokens + + assert get_max_tokens("huggingface/some-org/some-model") == 512 + request_timeout = hf_config_route.calls.last.request.extensions["timeout"] + assert request_timeout["read"] == HF_CONFIG_FETCH_TIMEOUT_SECONDS + + def test_get_max_position_embeddings_reads_hf_config_with_a_bounded_timeout(self, hf_config_route): + from litellm.constants import HF_CONFIG_FETCH_TIMEOUT_SECONDS + from litellm.utils import _get_max_position_embeddings + + assert _get_max_position_embeddings("some-org/some-model") == 512 + request_timeout = hf_config_route.calls.last.request.extensions["timeout"] + assert request_timeout["read"] == HF_CONFIG_FETCH_TIMEOUT_SECONDS + + +class TestIsVisionExplicitlyDisabled: + """github_copilot and chatgpt run an OAuth device flow inside get_llm_provider; the + explicit-disable lookup must adopt the declared prefix instead of resolving it, exactly + as _supports_factory does, or a capability check on a copilot deployment blocks routing + on a device-code prompt.""" + + @pytest.mark.parametrize("model", ["github_copilot/gpt-4o", "chatgpt/gpt-5"]) + def test_never_resolves_an_authenticating_prefix(self, model, monkeypatch): + from litellm.utils import is_vision_explicitly_disabled + + lookups: list = [] + + def _record(*args, **kwargs): + lookups.append((args, kwargs)) + raise RuntimeError("provider resolution must not run for an authenticating provider") + + monkeypatch.setattr(litellm, "get_llm_provider", _record) + + assert is_vision_explicitly_disabled(model) is False + assert lookups == [] + + def test_explicit_false_detected_and_absent_reads_enabled(self): + from litellm.utils import is_vision_explicitly_disabled + + assert ( + is_vision_explicitly_disabled("fireworks_ai/accounts/fireworks/models/deepseek-v4-flash-0731") is True + ) + assert is_vision_explicitly_disabled("anthropic/claude-sonnet-4-5") is False diff --git a/tests/test_litellm/test_video_generation.py b/tests/test_litellm/test_video_generation.py index b166e902d6e..2a60ff9c4b5 100644 --- a/tests/test_litellm/test_video_generation.py +++ b/tests/test_litellm/test_video_generation.py @@ -532,6 +532,41 @@ class TestVideoGeneration: assert abs(cost_for("runwayml/seedance2_5", "480p", 8.0) - 1.6) < 0.001 assert abs(cost_for("runwayml/gen4.5", None, 8.0) - 0.96) < 0.001 + def test_completion_cost_veo_31_tiers_pin_published_rates(self, monkeypatch): + """The gemini and vertex_ai veo 3.1 entries bill Google's published per-second tier rates.""" + from litellm.cost_calculator import completion_cost + + local_map_path = os.path.join( + os.path.dirname(__file__), "..", "..", "model_prices_and_context_window.json" + ) + with open(local_map_path, "r") as f: + monkeypatch.setattr(litellm, "model_cost", json.load(f)) + + def cost_for(model: str, provider: str, resolution: str | None, duration: float) -> float: + mock_response = MagicMock() + mock_response.usage = { + "duration_seconds": duration, + **({"video_resolution": resolution} if resolution else {}), + } + type(mock_response)._hidden_params = {} + return completion_cost( + completion_response=mock_response, + model=model, + call_type="create_video", + custom_llm_provider=provider, + ) + + for provider in ("gemini", "vertex_ai"): + for suffix in ("generate-preview", "generate-001"): + standard = f"{provider}/veo-3.1-{suffix}" + fast = f"{provider}/veo-3.1-fast-{suffix}" + assert abs(cost_for(standard, provider, None, 8.0) - 3.2) < 1e-6 + assert abs(cost_for(standard, provider, "1080p", 8.0) - 3.2) < 1e-6 + assert abs(cost_for(standard, provider, "4k", 8.0) - 4.8) < 1e-6 + assert abs(cost_for(fast, provider, "720p", 8.0) - 0.8) < 1e-6 + assert abs(cost_for(fast, provider, "1080p", 8.0) - 0.96) < 1e-6 + assert abs(cost_for(fast, provider, "4k", 8.0) - 2.4) < 1e-6 + def test_video_generation_with_files(self): """Test video generation with file uploads.""" config = OpenAIVideoConfig() diff --git a/type-discipline-budget.json b/type-discipline-budget.json index f3f1a7defe7..52cb9628252 100644 --- a/type-discipline-budget.json +++ b/type-discipline-budget.json @@ -1,21 +1,21 @@ { "LIT001": { - "limit": 22705 + "limit": 22364 }, "LIT002": { - "limit": 26854 + "limit": 26777 }, "LIT003": { "limit": 269 }, "LIT004": { - "limit": 43 + "limit": 40 }, "LIT005": { "limit": 0 }, "LIT006": { - "limit": 1063 + "limit": 1039 }, "LIT007": { "limit": 0 @@ -27,12 +27,12 @@ "limit": 0 }, "LIT010": { - "limit": 16564 + "limit": 16507 }, "LIT011": { - "limit": 5577 + "limit": 5535 }, "LIT012": { - "limit": 4508 + "limit": 4495 } } diff --git a/ui/litellm-dashboard/eslint-budgets.json b/ui/litellm-dashboard/eslint-budgets.json index bbf69c4a77a..e8207d179bd 100644 --- a/ui/litellm-dashboard/eslint-budgets.json +++ b/ui/litellm-dashboard/eslint-budgets.json @@ -4,5 +4,8 @@ "complexity": { "max": 140, "target": 80 }, "max-depth": { "max": 70, "target": 30 }, "local/no-large-inline-object-arg": { "max": 559, "target": 300 }, - "local/no-long-condition-chain": { "max": 265, "target": 120 } + "local/no-long-condition-chain": { "max": 265, "target": 120 }, + "testing-library/no-container": { "max": 133, "target": 50 }, + "testing-library/no-node-access": { "max": 716, "target": 500 }, + "testing-library/prefer-screen-queries": { "max": 18, "target": 18 } } diff --git a/ui/litellm-dashboard/eslint.config.mjs b/ui/litellm-dashboard/eslint.config.mjs index 23cc5096bb1..f5e3b23b3ec 100644 --- a/ui/litellm-dashboard/eslint.config.mjs +++ b/ui/litellm-dashboard/eslint.config.mjs @@ -104,10 +104,13 @@ const eslintConfig = [ plugins: { "testing-library": testingLibrary, "jest-dom": jestDom }, rules: { "testing-library/await-async-queries": "error", + "testing-library/no-container": "warn", + "testing-library/no-node-access": "warn", "testing-library/no-wait-for-multiple-assertions": "error", "testing-library/no-wait-for-side-effects": "error", "testing-library/prefer-find-by": "error", "testing-library/prefer-presence-queries": "error", + "testing-library/prefer-screen-queries": "warn", "jest-dom/prefer-checked": "error", "jest-dom/prefer-empty": "error", "jest-dom/prefer-enabled-disabled": "error", diff --git a/ui/litellm-dashboard/package-lock.json b/ui/litellm-dashboard/package-lock.json index d2a64b93384..4e5b0c1dda6 100644 --- a/ui/litellm-dashboard/package-lock.json +++ b/ui/litellm-dashboard/package-lock.json @@ -4891,9 +4891,9 @@ } }, "node_modules/baseline-browser-mapping": { - "version": "2.10.27", - "resolved": "https://registry.npmjs.org/baseline-browser-mapping/-/baseline-browser-mapping-2.10.27.tgz", - "integrity": "sha512-zEs/ufmZoUd7WftKpKyXaT6RFxpQ5Qm9xytKRHvJfxFV9DFJkZph9RvJ1LcOUi0Z1ZVijMte65JbILeV+8QQEA==", + "version": "2.11.20", + "resolved": "https://registry.npmjs.org/baseline-browser-mapping/-/baseline-browser-mapping-2.11.20.tgz", + "integrity": "sha512-H0ulySigv6icDJ1F7SjtdCD6PrhTpdYCmP0CactWy1+ekh0AFd0o1Wn5T8b+hnTmdBx19u9yhL6wvCylXMY7zw==", "license": "Apache-2.0", "bin": { "baseline-browser-mapping": "dist/cli.cjs" @@ -4939,9 +4939,9 @@ } }, "node_modules/browserslist": { - "version": "4.28.2", - "resolved": "https://registry.npmjs.org/browserslist/-/browserslist-4.28.2.tgz", - "integrity": "sha512-48xSriZYYg+8qXna9kwqjIVzuQxi+KYWp2+5nCYnYKPTr0LvD89Jqk2Or5ogxz0NUMfIjhh2lIUX/LyX9B4oIg==", + "version": "4.28.8", + "resolved": "https://registry.npmjs.org/browserslist/-/browserslist-4.28.8.tgz", + "integrity": "sha512-V2NpofLblG64mfOtSgDhOJESZEGogzDMBv/q+W6oc4LXWP/q75eOXoOaaOu1EOadB9U4Bwx/e0yzbvwKH8zalA==", "dev": true, "funding": [ { @@ -4959,11 +4959,11 @@ ], "license": "MIT", "dependencies": { - "baseline-browser-mapping": "^2.10.12", - "caniuse-lite": "^1.0.30001782", - "electron-to-chromium": "^1.5.328", - "node-releases": "^2.0.36", - "update-browserslist-db": "^1.2.3" + "baseline-browser-mapping": "^2.11.12", + "caniuse-lite": "^1.0.30001809", + "electron-to-chromium": "^1.5.402", + "node-releases": "^2.0.53", + "update-browserslist-db": "^1.3.0" }, "bin": { "browserslist": "cli.js" @@ -5042,9 +5042,9 @@ } }, "node_modules/caniuse-lite": { - "version": "1.0.30001791", - "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001791.tgz", - "integrity": "sha512-yk0l/YSrOnFZk3UROpDLQD9+kC1l4meK/wed583AXrzoarMGJcbRi2Q4RaUYbKxYAsZ8sWmaSa/DsLmdBeI1vQ==", + "version": "1.0.30001810", + "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001810.tgz", + "integrity": "sha512-TITQPUkaz+aVk5GL6NhOdwk1aEaNTSDPsGFWrTuhKGtjTF70jL/Oht2W4c6rXUe5fu7Ie19VIahAXHIIiWWNeg==", "funding": [ { "type": "opencollective", @@ -5706,9 +5706,9 @@ } }, "node_modules/electron-to-chromium": { - "version": "1.5.349", - "resolved": "https://registry.npmjs.org/electron-to-chromium/-/electron-to-chromium-1.5.349.tgz", - "integrity": "sha512-QsWVGyRuY07Aqb234QytTfwd5d9AJlfNIQ5wIOl1L+PZDzI9d9+Fn0FRale/QYlFxt/bUnB0/nLd1jFPGxGK1A==", + "version": "1.5.416", + "resolved": "https://registry.npmjs.org/electron-to-chromium/-/electron-to-chromium-1.5.416.tgz", + "integrity": "sha512-K6bvB2BjnNrugtIih6ewlbBI9DXa976jIdiIlRLHhBoEI9a4JaQjjHyF+A1IQI543aQYR4LnmOrT/K5fZj0aPA==", "dev": true, "license": "ISC" }, @@ -9901,11 +9901,14 @@ } }, "node_modules/node-releases": { - "version": "2.0.38", - "resolved": "https://registry.npmjs.org/node-releases/-/node-releases-2.0.38.tgz", - "integrity": "sha512-3qT/88Y3FbH/Kx4szpQQ4HzUbVrHPKTLVpVocKiLfoYvw9XSGOX2FmD2d6DrXbVYyAQTF2HeF6My8jmzx7/CRw==", + "version": "2.0.54", + "resolved": "https://registry.npmjs.org/node-releases/-/node-releases-2.0.54.tgz", + "integrity": "sha512-YHs7BmmcsdAI5Ozuf8JZo6PT0mv2GIWC9vMfvUC3dp65M8hn7Ux8CPL+2oBI7juNuj9d0ndhTcznq2ODBps9cQ==", "dev": true, - "license": "MIT" + "license": "MIT", + "engines": { + "node": ">=18" + } }, "node_modules/nuqs": { "version": "2.9.4", @@ -12350,9 +12353,9 @@ } }, "node_modules/update-browserslist-db": { - "version": "1.2.3", - "resolved": "https://registry.npmjs.org/update-browserslist-db/-/update-browserslist-db-1.2.3.tgz", - "integrity": "sha512-Js0m9cx+qOgDxo0eMiFGEueWztz+d4+M3rGlmKPT+T4IS/jP4ylw3Nwpu6cpTTP8R1MAC1kF4VbdLt3ARf209w==", + "version": "1.3.2", + "resolved": "https://registry.npmjs.org/update-browserslist-db/-/update-browserslist-db-1.3.2.tgz", + "integrity": "sha512-UQ+MSxlhRm1bzjhU+DcuXfjFO1FzNtqhK5+9Yvlp90ItDLk5vT932A0rFu619nf7RVS+Y/VeaUW1jaRDqZ8VJw==", "dev": true, "funding": [ { diff --git a/ui/litellm-dashboard/public/assets/logos/alice.svg b/ui/litellm-dashboard/public/assets/logos/alice.svg new file mode 100644 index 00000000000..f18f887b98c --- /dev/null +++ b/ui/litellm-dashboard/public/assets/logos/alice.svg @@ -0,0 +1,4 @@ + + + + diff --git a/ui/litellm-dashboard/public/assets/logos/gigachat.svg b/ui/litellm-dashboard/public/assets/logos/gigachat.svg new file mode 100644 index 00000000000..e7abe47b221 --- /dev/null +++ b/ui/litellm-dashboard/public/assets/logos/gigachat.svg @@ -0,0 +1,27 @@ + + + + + diff --git a/ui/litellm-dashboard/src/app/(dashboard)/admin-panel/_components/AdminPanel.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/admin-panel/_components/AdminPanel.test.tsx index 8c3ca7bd9ff..ee55c568bac 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/admin-panel/_components/AdminPanel.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/admin-panel/_components/AdminPanel.test.tsx @@ -9,6 +9,8 @@ const mockAddAllowedIP = vi.fn(); const mockDeleteAllowedIP = vi.fn(); vi.mock("@/components/networking", () => ({ + getProxyBaseUrl: () => "http://localhost:4000", + getGlobalLitellmHeaderName: () => "Authorization", getSSOSettings: (...args: unknown[]) => mockGetSSOSettings(...args), getAllowedIPs: (...args: unknown[]) => mockGetAllowedIPs(...args), addAllowedIP: (...args: unknown[]) => mockAddAllowedIP(...args), diff --git a/ui/litellm-dashboard/src/app/(dashboard)/admin-panel/_components/AdminPanel.tsx b/ui/litellm-dashboard/src/app/(dashboard)/admin-panel/_components/AdminPanel.tsx index a98eb50ce77..1f35f46dcd4 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/admin-panel/_components/AdminPanel.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/admin-panel/_components/AdminPanel.tsx @@ -18,6 +18,7 @@ import LoggingSettings from "@/components/Settings/AdminSettings/LoggingSettings import SSOSettings from "@/components/Settings/AdminSettings/SSOSettings/SSOSettings"; import UISettings from "@/components/Settings/AdminSettings/UISettings/UISettings"; import UserBannerSettings from "@/components/Settings/AdminSettings/UserBannerSettings/UserBannerSettings"; +import CyberArk from "@/components/Settings/AdminSettings/CyberArk/CyberArk"; import HashicorpVault from "@/components/Settings/AdminSettings/HashicorpVault/HashicorpVault"; import PluginSettings from "@/components/Settings/AdminSettings/PluginSettings/PluginSettings"; import SSOModals from "@/components/SSOModals"; @@ -395,6 +396,11 @@ const AdminPanel: React.FC = ({ proxySettings }) => { label: "Hashicorp Vault", children: , }, + { + key: "cyberark", + label: "CyberArk Conjur", + children: , + }, { key: "plugins", label: "Plugins", diff --git a/ui/litellm-dashboard/src/app/(dashboard)/agents/_components/AgentsTable.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/agents/_components/AgentsTable.test.tsx index 06099a9fc22..4d18ec2ef5f 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/agents/_components/AgentsTable.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/agents/_components/AgentsTable.test.tsx @@ -74,6 +74,42 @@ describe("AgentsTable", () => { expect(onDeleteClick).toHaveBeenCalledWith("agent-9", "Doomed Agent"); }); + it("filters agents by name or by agent card description", async () => { + const user = userEvent.setup(); + render( + , + ); + + const search = screen.getByPlaceholderText("Search agent names or descriptions..."); + await user.type(search, "billing"); + expect(screen.getByText("Billing Router")).toBeInTheDocument(); + expect(screen.queryByText("Second Agent")).not.toBeInTheDocument(); + + await user.clear(search); + await user.type(search, "support tickets"); + expect(screen.getByText("Second Agent")).toBeInTheDocument(); + expect(screen.queryByText("Billing Router")).not.toBeInTheDocument(); + }); + + it("shows the no-match empty state when the search matches nothing", async () => { + const user = userEvent.setup(); + render(); + + await user.type(screen.getByPlaceholderText("Search agent names or descriptions..."), "zzzz"); + expect(screen.queryByText("Test Agent")).not.toBeInTheDocument(); + expect(screen.getByText("No matching agents")).toBeInTheDocument(); + }); + it("hides the actions column entirely for non-admins", () => { const agent = makeAgent({ agent_id: "agent-2" }); render(); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/agents/_components/AgentsTable.tsx b/ui/litellm-dashboard/src/app/(dashboard)/agents/_components/AgentsTable.tsx index 67c7ed74180..35ed6b66425 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/agents/_components/AgentsTable.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/agents/_components/AgentsTable.tsx @@ -1,13 +1,15 @@ "use client"; import { SortingState } from "@tanstack/react-table"; -import { Bot, CircleCheck } from "lucide-react"; +import { Bot, CircleCheck, Search as SearchIcon, X } from "lucide-react"; import React, { useMemo, useState } from "react"; import { Agent } from "@/components/agents/types"; import { DataTable } from "@/components/shared/DataTable"; +import { InputGroup, InputGroupAddon, InputGroupButton, InputGroupInput } from "@/components/ui/input-group"; import { Switch } from "@/components/ui/switch"; import { Tooltip, TooltipContent, TooltipProvider, TooltipTrigger } from "@/components/ui/tooltip"; +import { filterBySearchTerm } from "@/utils/searchUtils"; import { getAgentsTableColumns } from "./AgentsTableColumns"; @@ -24,14 +26,18 @@ interface AgentsTableProps { const DEFAULT_SORTING: SortingState = [{ id: "created_at", desc: true }]; -function EmptyState() { +function EmptyState({ isFiltered }: { isFiltered: boolean }) { return (
-
No agents yet
-
Add an agent to make it available in your organization.
+
{isFiltered ? "No matching agents" : "No agents yet"}
+
+ {isFiltered + ? "Adjust the search to see more agents." + : "Add an agent to make it available in your organization."} +
); } @@ -47,6 +53,11 @@ const AgentsTable: React.FC = ({ onDeleteClick, }) => { const [sorting, setSorting] = useState(DEFAULT_SORTING); + const [searchTerm, setSearchTerm] = useState(""); + const filteredAgents = useMemo( + () => filterBySearchTerm(agents, searchTerm, (agent) => [agent.agent_name, agent.agent_card_params?.description]), + [agents, searchTerm], + ); const columns = useMemo( () => getAgentsTableColumns({ isAdmin, onAgentClick, onDeleteClick }), @@ -55,7 +66,7 @@ const AgentsTable: React.FC = ({ return ( agent.agent_id || String(index)} sortingMode="client" @@ -63,10 +74,27 @@ const AgentsTable: React.FC = ({ onSortingChange={setSorting} isLoading={isLoading} loadingMessage="Loading agents…" - noDataMessage={} + noDataMessage={ 0} />} size="compact" toolbar={() => ( -
+
+ + + + + setSearchTerm(e.target.value)} + /> + {searchTerm && ( + + setSearchTerm("")}> + + + + )} + ({ createAgentCall: vi.fn(), @@ -309,8 +310,7 @@ describe("AddAgentForm submit payload", () => { await user.type(await screen.findByLabelText("Allowed Models"), "gpt-4o,"); await user.keyboard("{Escape}"); - await user.click(screen.getByLabelText("Allowed Agents (Sub-Agents)")); - await user.click(await screen.findByTitle("Sub Agent One")); + await chooseSelectOption(user, screen.getByLabelText("Allowed Agents (Sub-Agents)"), "Sub Agent One"); await user.keyboard("{Escape}"); await user.click(screen.getByText(/Configure which models, agents, and MCP tools/)); await user.click(screen.getByRole("button", { name: /^Next/ })); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/api-reference/_components/APIReferenceView.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/api-reference/_components/APIReferenceView.test.tsx index b2e0a42eecb..dae19032e20 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/api-reference/_components/APIReferenceView.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/api-reference/_components/APIReferenceView.test.tsx @@ -13,17 +13,17 @@ describe("APIReferenceView", () => { it("uses the API doc base url when provided", () => { const apiDocUrl = "https://docs.litellm.test"; - const { getAllByTestId } = render(); + render(); - const codeBlocks = getAllByTestId(codeBlockTestId); + const codeBlocks = screen.getAllByTestId(codeBlockTestId); expect(codeBlocks[0]).toHaveTextContent(new RegExp(apiDocUrl)); }); it("falls back to the proxy base url when the docs url is missing", () => { const proxyUrl = "https://proxy.litellm.test"; - const { getAllByTestId } = render(); + render(); - const codeBlocks = getAllByTestId(codeBlockTestId); + const codeBlocks = screen.getAllByTestId(codeBlockTestId); expect(codeBlocks[0]).toHaveTextContent(new RegExp(proxyUrl)); }); @@ -31,7 +31,7 @@ describe("APIReferenceView", () => { const apiDocUrl = "https://docs-preferred.litellm.test"; const proxyUrl = "https://proxy-backup.litellm.test"; - const { getAllByTestId } = render( + render( { />, ); - const codeBlocks = getAllByTestId(codeBlockTestId); + const codeBlocks = screen.getAllByTestId(codeBlockTestId); const renderedCode = codeBlocks[0].textContent ?? ""; expect(renderedCode).toContain(apiDocUrl); expect(renderedCode).not.toContain(proxyUrl); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/budget_modal.integration.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/budget_modal.integration.test.tsx index bc920e55abd..17a297d203d 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/budget_modal.integration.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/budget_modal.integration.test.tsx @@ -4,6 +4,7 @@ import React from "react"; import { beforeEach, describe, expect, it, vi } from "vitest"; import BudgetModal from "./budget_modal"; +import { chooseSelectOption } from "../../../../../tests/test-utils"; const { createMock } = vi.hoisted(() => ({ createMock: vi.fn() })); @@ -63,8 +64,7 @@ describe("BudgetModal", () => { await openOptionalSettings(user); fireEvent.change(screen.getByLabelText("Max Budget (USD)"), { target: { value: "42.567" } }); - await user.click(screen.getByRole("combobox")); - await user.click(await screen.findByText("monthly")); + await chooseSelectOption(user, screen.getByRole("combobox"), "monthly"); await create(user); @@ -80,8 +80,7 @@ describe("BudgetModal", () => { await openOptionalSettings(user); fireEvent.change(screen.getByLabelText("Max Budget (USD)"), { target: { value: "42.567" } }); - await user.click(screen.getByRole("combobox")); - await user.click(await screen.findByText("monthly")); + await chooseSelectOption(user, screen.getByRole("combobox"), "monthly"); await user.click(screen.getByText("Optional Settings")); await waitFor(() => expect(screen.queryByLabelText("Max Budget (USD)")).not.toBeInTheDocument()); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/edit_budget_modal.integration.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/edit_budget_modal.integration.test.tsx index 3fa96b54f1d..fe0ecfc10dd 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/edit_budget_modal.integration.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/edit_budget_modal.integration.test.tsx @@ -6,6 +6,7 @@ import { beforeEach, describe, expect, it, vi } from "vitest"; import type { components } from "@/lib/http/schema"; import EditBudgetModal from "./edit_budget_modal"; +import { chooseSelectOption } from "../../../../../tests/test-utils"; const { updateMock } = vi.hoisted(() => ({ updateMock: vi.fn() })); @@ -73,8 +74,7 @@ describe("EditBudgetModal", () => { await user.clear(screen.getByLabelText("Max Budget (USD)")); fireEvent.change(screen.getByLabelText("Max Budget (USD)"), { target: { value: "42.567" } }); - await user.click(screen.getByRole("combobox")); - await user.click(await screen.findByText("monthly")); + await chooseSelectOption(user, screen.getByRole("combobox"), "monthly"); await save(user); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/caching/_components/cache_settings/RedisTypeSelector.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/caching/_components/cache_settings/RedisTypeSelector.test.tsx index 9d4d5a6d425..372f27e2be1 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/caching/_components/cache_settings/RedisTypeSelector.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/caching/_components/cache_settings/RedisTypeSelector.test.tsx @@ -1,12 +1,10 @@ import { describe, expect, it } from "vitest"; import RedisTypeSelector from "./RedisTypeSelector"; -import { render } from "@testing-library/react"; +import { render, screen } from "@testing-library/react"; describe("RedisTypeSelector", () => { it("should render the component", () => { - const { getAllByText } = render( - {}} />, - ); - expect(getAllByText(/Redis/i).length).toBeGreaterThan(0); + render( {}} />); + expect(screen.getAllByText(/Redis/i).length).toBeGreaterThan(0); }); }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/caching/_components/coordination_redis_settings/CoordinationRedisTypeSelector.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/caching/_components/coordination_redis_settings/CoordinationRedisTypeSelector.test.tsx index 76287e6e724..563c684237a 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/caching/_components/coordination_redis_settings/CoordinationRedisTypeSelector.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/caching/_components/coordination_redis_settings/CoordinationRedisTypeSelector.test.tsx @@ -5,6 +5,7 @@ import userEvent from "@testing-library/user-event"; import { renderWithProviders } from "@/../tests/test-utils"; import CoordinationRedisTypeSelector from "./CoordinationRedisTypeSelector"; import { COORDINATION_REDIS_TYPE_DESCRIPTIONS } from "./coordinationRedisFields"; +import { chooseSelectOption } from "../../../../../../tests/test-utils"; describe("CoordinationRedisTypeSelector", () => { it("labels the control and shows the current selection", () => { @@ -36,8 +37,7 @@ describe("CoordinationRedisTypeSelector", () => { const user = userEvent.setup(); renderWithProviders(); - await user.click(screen.getByRole("combobox")); - await user.click(await screen.findByText("Cluster")); + await chooseSelectOption(user, screen.getByRole("combobox"), "Cluster"); expect(onTypeChange).toHaveBeenCalledTimes(1); expect(onTypeChange.mock.calls[0][0]).toBe("cluster"); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CacheLeakageCard.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CacheLeakageCard.test.tsx index 8c36b934789..f320d8e0f97 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CacheLeakageCard.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CacheLeakageCard.test.tsx @@ -1,4 +1,4 @@ -import { fireEvent, render } from "@testing-library/react"; +import { fireEvent, render, screen } from "@testing-library/react"; import { describe, expect, it, vi } from "vitest"; import type { DailyData, KeyMetricWithMetadata, SpendMetrics } from "@/components/UsagePage/types"; @@ -79,25 +79,25 @@ const renderWith = (results: DailyData[], overrides: Partial describe("CacheLeakageCard", () => { it("ranks leaking keys by uncached prompt tokens and shows cache hit ratio", () => { - const { getByText, getByLabelText } = renderWith([ + renderWith([ dayWithKeys("2026-07-12", { "hash-caching": key("caching-key", { prompt_tokens: 1000, cache_read_input_tokens: 900 }), "hash-leaky": key("leaky-key", { prompt_tokens: 10000, cache_read_input_tokens: 0 }), }), ]); - expect(getByText("leaky-key")).toBeInTheDocument(); - expect(getByText("0.0%")).toBeInTheDocument(); - expect(getByText("90.0%")).toBeInTheDocument(); + expect(screen.getByText("leaky-key")).toBeInTheDocument(); + expect(screen.getByText("0.0%")).toBeInTheDocument(); + expect(screen.getByText("90.0%")).toBeInTheDocument(); [ "Input tokens you sent in this range that weren't served from or written to the cache", "Share of your input tokens that were served from the cache", "About how much you'd save if this uncached input used prompt caching. Estimated as uncached input tokens times what your cached traffic already nets per cached token (realized cache savings, after write premiums, ÷ cache read and write tokens). Blank when caching is not currently saving anything overall.", - ].forEach((info) => expect(getByLabelText(info)).toBeInTheDocument()); + ].forEach((info) => expect(screen.getByLabelText(info)).toBeInTheDocument()); }); it("sorts by the clicked column, worst cache hit rate first", () => { - const { getAllByRole, getByText } = renderWith([ + renderWith([ dayWithKeys("2026-07-12", { "hash-a": key("alpha", { prompt_tokens: 10000, @@ -111,48 +111,48 @@ describe("CacheLeakageCard", () => { }), }), ]); - const firstDataRow = () => getAllByRole("row")[1]; + const firstDataRow = () => screen.getAllByRole("row")[1]; expect(firstDataRow()).toHaveTextContent("alpha"); - fireEvent.click(getByText("Cache hit rate")); + fireEvent.click(screen.getByText("Cache hit rate")); expect(firstDataRow()).toHaveTextContent("bravo"); - fireEvent.click(getByText("Cache hit rate")); + fireEvent.click(screen.getByText("Cache hit rate")); expect(firstDataRow()).toHaveTextContent("alpha"); }); it("switches to the model view and lists only Anthropic models", () => { - const { getByText, queryByText } = renderWith([ + renderWith([ dayWithModels("2026-07-12", { "claude-sonnet-5": { prompt_tokens: 5000, cache_read_input_tokens: 0 }, "gpt-4o": { prompt_tokens: 8000, cache_read_input_tokens: 0 }, }), ]); - fireEvent.click(getByText("By model")); + fireEvent.click(screen.getByText("By model")); - expect(getByText("Cache leakage by model")).toBeInTheDocument(); - expect(getByText("claude-sonnet-5")).toBeInTheDocument(); - expect(queryByText("gpt-4o")).not.toBeInTheDocument(); + expect(screen.getByText("Cache leakage by model")).toBeInTheDocument(); + expect(screen.getByText("claude-sonnet-5")).toBeInTheDocument(); + expect(screen.queryByText("gpt-4o")).not.toBeInTheDocument(); }); it("shows an empty state when no key used tokens in the range", () => { - const { getByText, queryByRole } = renderWith([dayWithKeys("2026-07-12", {})]); + renderWith([dayWithKeys("2026-07-12", {})]); - expect(getByText("No key usage in this range.")).toBeInTheDocument(); - expect(queryByRole("table")).not.toBeInTheDocument(); + expect(screen.getByText("No key usage in this range.")).toBeInTheDocument(); + expect(screen.queryByRole("table")).not.toBeInTheDocument(); }); it("tells the user the table is still filling in while fallback pages stream", () => { const day = dayWithKeys("2026-07-12", { "hash-leaky": key("leaky-key", { prompt_tokens: 10000, cache_read_input_tokens: 0 }), }); - const { getByText, getByRole } = renderWith([day], { isFetchingMore: true }); + renderWith([day], { isFetchingMore: true }); - expect(getByRole("table")).toBeInTheDocument(); + expect(screen.getByRole("table")).toBeInTheDocument(); expect( - getByText("Data is still loading; rows and totals will update as the rest of the range arrives."), + screen.getByText("Data is still loading; rows and totals will update as the rest of the range arrives."), ).toBeInTheDocument(); }); @@ -160,10 +160,10 @@ describe("CacheLeakageCard", () => { const day = dayWithKeys("2026-07-12", { "hash-leaky": key("leaky-key", { prompt_tokens: 10000, cache_read_input_tokens: 0 }), }); - const { queryByText } = renderWith([day], { loading: true }); + renderWith([day], { loading: true }); expect( - queryByText("Data is still loading; rows and totals will update as the rest of the range arrives."), + screen.queryByText("Data is still loading; rows and totals will update as the rest of the range arrives."), ).not.toBeInTheDocument(); }); @@ -171,10 +171,10 @@ describe("CacheLeakageCard", () => { const day = dayWithKeys("2026-07-12", { "hash-leaky": key("leaky-key", { prompt_tokens: 10000, cache_read_input_tokens: 0 }), }); - const { queryByText } = renderWith([day]); + renderWith([day]); expect( - queryByText("Data is still loading; rows and totals will update as the rest of the range arrives."), + screen.queryByText("Data is still loading; rows and totals will update as the rest of the range arrives."), ).not.toBeInTheDocument(); }); }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CostOptimizationView.activity.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CostOptimizationView.activity.test.tsx index 1cc7bec13d1..03250e3e53b 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CostOptimizationView.activity.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CostOptimizationView.activity.test.tsx @@ -1,5 +1,5 @@ import React from "react"; -import { fireEvent, render, waitFor } from "@testing-library/react"; +import { fireEvent, render, waitFor, screen } from "@testing-library/react"; import { describe, expect, it, vi } from "vitest"; import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; @@ -56,7 +56,7 @@ describe("CostOptimizationView daily activity", () => { useAuthorizedMock.mockReturnValue({ accessToken: "test-token", userId: "u1", userRole: "proxy_admin" }); const queryClient = new QueryClient({ defaultOptions: { queries: { retry: false } } }); - const { getByRole, getByTestId, findByTestId, queryByText } = render( + render( , @@ -64,12 +64,12 @@ describe("CostOptimizationView daily activity", () => { await waitFor(() => expect(mockUserDailyActivityAggregatedCall).toHaveBeenCalledTimes(1)); - fireEvent.click(getByRole("tab", { name: "Prompt Caching" })); - await findByTestId("caching-settings"); + fireEvent.click(screen.getByRole("tab", { name: "Prompt Caching" })); + await screen.findByTestId("caching-settings"); expect(mockUserDailyActivityAggregatedCall).toHaveBeenCalledTimes(1); expect(mockUserDailyActivityCall).not.toHaveBeenCalled(); - expect(queryByText(/Currently fetching spend data/)).not.toBeInTheDocument(); + expect(screen.queryByText(/Currently fetching spend data/)).not.toBeInTheDocument(); }); it("shows the fetch-progress banner while the paginated fallback streams pages in", async () => { @@ -84,13 +84,13 @@ describe("CostOptimizationView daily activity", () => { useAuthorizedMock.mockReturnValue({ accessToken: "test-token", userId: "u1", userRole: "proxy_admin" }); const queryClient = new QueryClient({ defaultOptions: { queries: { retry: false } } }); - const { findByText, getByRole } = render( + render( , ); - expect(await findByText(/Currently fetching spend data: fetched 1 \/ 3 pages/)).toBeInTheDocument(); - expect(getByRole("button", { name: "Stop" })).toBeInTheDocument(); + expect(await screen.findByText(/Currently fetching spend data: fetched 1 \/ 3 pages/)).toBeInTheDocument(); + expect(screen.getByRole("button", { name: "Stop" })).toBeInTheDocument(); }); }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CostOptimizationView.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CostOptimizationView.test.tsx index d5df5aa75da..028367555a1 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CostOptimizationView.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CostOptimizationView.test.tsx @@ -1,5 +1,5 @@ import React from "react"; -import { fireEvent, render } from "@testing-library/react"; +import { fireEvent, render, screen } from "@testing-library/react"; import { beforeEach, describe, expect, it, vi } from "vitest"; import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; @@ -45,32 +45,32 @@ describe("CostOptimizationView", () => { }); it("renders the standard page header with the sidebar's Cost Optimization icon", () => { - const { container, getByRole, getByText } = renderView(); + const { container } = renderView(); - expect(getByRole("heading", { level: 1, name: "Cost Optimization" })).toBeInTheDocument(); - expect(getByText(/Track and configure the mechanisms that save you money/)).toBeInTheDocument(); + expect(screen.getByRole("heading", { level: 1, name: "Cost Optimization" })).toBeInTheDocument(); + expect(screen.getByText(/Track and configure the mechanisms that save you money/)).toBeInTheDocument(); expect(container.querySelector(".lucide-piggy-bank")).not.toBeNull(); }); it("renders the four cost-optimization tabs", () => { - const { getByText } = renderView(); + renderView(); - expect(getByText("Overall")).toBeInTheDocument(); - expect(getByText("Prompt Compression")).toBeInTheDocument(); - expect(getByText("Prompt Caching")).toBeInTheDocument(); - expect(getByText("Auto-Router")).toBeInTheDocument(); + expect(screen.getByText("Overall")).toBeInTheDocument(); + expect(screen.getByText("Prompt Compression")).toBeInTheDocument(); + expect(screen.getByText("Prompt Caching")).toBeInTheDocument(); + expect(screen.getByText("Auto-Router")).toBeInTheDocument(); }); it("defaults to the Overall tab and switches the active tab on click", () => { - const { getByRole } = renderView(); + renderView(); - expect(getByRole("tab", { name: "Overall" })).toHaveAttribute("aria-selected", "true"); - expect(getByRole("tab", { name: "Prompt Compression" })).toHaveAttribute("aria-selected", "false"); + expect(screen.getByRole("tab", { name: "Overall" })).toHaveAttribute("aria-selected", "true"); + expect(screen.getByRole("tab", { name: "Prompt Compression" })).toHaveAttribute("aria-selected", "false"); - fireEvent.click(getByRole("tab", { name: "Prompt Compression" })); + fireEvent.click(screen.getByRole("tab", { name: "Prompt Compression" })); - expect(getByRole("tab", { name: "Overall" })).toHaveAttribute("aria-selected", "false"); - expect(getByRole("tab", { name: "Prompt Compression" })).toHaveAttribute("aria-selected", "true"); + expect(screen.getByRole("tab", { name: "Overall" })).toHaveAttribute("aria-selected", "false"); + expect(screen.getByRole("tab", { name: "Prompt Compression" })).toHaveAttribute("aria-selected", "true"); }); // Unlike the other three pages in this cleanup, Cost Optimization keeps its @@ -80,21 +80,21 @@ describe("CostOptimizationView", () => { // are proxy-admin-only, so those are what disappear. describe("proxy-admin-only tabs", () => { it.each(["Internal User", "Internal Viewer", "Org Admin"])("shows %s the Overall tab only", (userRole) => { - const { getByRole, queryByRole } = renderView(userRole); + renderView(userRole); - expect(getByRole("tab", { name: "Overall" })).toBeInTheDocument(); - expect(queryByRole("tab", { name: "Prompt Compression" })).not.toBeInTheDocument(); - expect(queryByRole("tab", { name: "Prompt Caching" })).not.toBeInTheDocument(); - expect(queryByRole("tab", { name: "Auto-Router" })).not.toBeInTheDocument(); + expect(screen.getByRole("tab", { name: "Overall" })).toBeInTheDocument(); + expect(screen.queryByRole("tab", { name: "Prompt Compression" })).not.toBeInTheDocument(); + expect(screen.queryByRole("tab", { name: "Prompt Caching" })).not.toBeInTheDocument(); + expect(screen.queryByRole("tab", { name: "Auto-Router" })).not.toBeInTheDocument(); }); it("never mounts the panels behind the admin-only endpoints for an internal user", () => { - const { getByTestId, queryByTestId } = renderView("Internal User"); + renderView("Internal User"); - expect(getByTestId("usage-tab")).toBeInTheDocument(); - expect(queryByTestId("compression-tab")).not.toBeInTheDocument(); - expect(queryByTestId("caching-tab")).not.toBeInTheDocument(); - expect(queryByTestId("autorouter-benchmarks-tab")).not.toBeInTheDocument(); + expect(screen.getByTestId("usage-tab")).toBeInTheDocument(); + expect(screen.queryByTestId("compression-tab")).not.toBeInTheDocument(); + expect(screen.queryByTestId("caching-tab")).not.toBeInTheDocument(); + expect(screen.queryByTestId("autorouter-benchmarks-tab")).not.toBeInTheDocument(); }); }); }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/PromptCachingTab.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/PromptCachingTab.test.tsx index 38517dab0ab..2c602033171 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/PromptCachingTab.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/PromptCachingTab.test.tsx @@ -1,4 +1,4 @@ -import { render, waitFor } from "@testing-library/react"; +import { render, waitFor, screen } from "@testing-library/react"; import { describe, expect, it, vi } from "vitest"; const mockGetGeneralSettingsCall = vi.fn(); @@ -37,10 +37,10 @@ describe("PromptCachingTab", () => { cancelled: false, cancel: vi.fn(), }; - const { getByTestId } = render(); + render(); - expect(getByTestId("caching-settings")).toBeInTheDocument(); - expect(getByTestId("cache-leakage-card")).toBeInTheDocument(); + expect(screen.getByTestId("caching-settings")).toBeInTheDocument(); + expect(screen.getByTestId("cache-leakage-card")).toBeInTheDocument(); await waitFor(() => expect(mockCacheLeakageCard).toHaveBeenCalledWith(expect.objectContaining({ activity }))); }); }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/ShadowEvalSection.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/ShadowEvalSection.test.tsx index ef6e224761d..a1de608d0bb 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/ShadowEvalSection.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/ShadowEvalSection.test.tsx @@ -39,6 +39,35 @@ vi.mock("@/app/(dashboard)/hooks/keys/useKeys", () => ({ })), })); +vi.mock("@/app/(dashboard)/hooks/teams/useTeams", () => ({ + useInfiniteTeams: vi.fn(() => ({ + data: { pages: [{ teams: [{ team_id: "team-eng", team_alias: "engineering" }], page: 1, total_pages: 1 }] }, + isLoading: false, + fetchNextPage: vi.fn(), + hasNextPage: false, + isFetchingNextPage: false, + })), +})); + +vi.mock("@/app/(dashboard)/hooks/users/useUsers", () => ({ + useInfiniteUsers: vi.fn(() => ({ + data: { + pages: [ + { + users: [{ user_id: "dev-alice", user_alias: null, user_email: "alice@example.com" }], + page: 1, + total_pages: 1, + }, + ], + }, + isPending: false, + isError: false, + fetchNextPage: vi.fn(), + hasNextPage: false, + isFetchingNextPage: false, + })), +})); + vi.mock("@/app/(dashboard)/hooks/models/useModels", () => ({ useAutoRouters: vi.fn(() => ({ data: [ @@ -60,7 +89,7 @@ vi.mock("@/app/(dashboard)/hooks/models/useModelCostMap", () => ({ })), })); -import ShadowEvalSection, { shadowedKeyLabel } from "./ShadowEvalSection"; +import ShadowEvalSection, { shadowedTargetLabel } from "./ShadowEvalSection"; import { useShadowEvalJob, useShadowEvalJobs, @@ -68,23 +97,26 @@ import { useStopShadowEval, type ShadowEvalJob, } from "./useShadowEval"; +import { chooseSelectOption } from "../../../../../tests/test-utils"; const job = (overrides: Partial = {}): ShadowEvalJob => ({ job_id: "job-1", status: "running", router_name: "claude-auto", + router_names: ["claude-auto"], direction: "forward", baseline_model: null, judge_model: "anthropic/claude-sonnet-5", shadow_percentage: 10, - keys: [ + targets: [ { - api_key_id: "hashed-key-abc", + target_type: "key", + target_id: "hashed-key-abc", max_turns: 10000, max_budget: 10, spend: 3.21, stopped_at: null, - key_alias: "prod-alpha", + target_alias: "prod-alpha", key_name: "sk-...alpha", }, ], @@ -129,7 +161,6 @@ const job = (overrides: Partial = {}): ShadowEvalJob => ({ cache_hit_turns: 2, }, ], - by_key: [], overall_shadow_win_rate_pct: 48.0, overall_tie_rate_pct: 22.0, sampled_real_spend: 0.6, @@ -144,17 +175,18 @@ const job = (overrides: Partial = {}): ShadowEvalJob => ({ ...overrides, }); -const keyEntry = ( - api_key_id: string, - overrides: Partial = {}, -): ShadowEvalJob["keys"][number] => ({ - api_key_id, +const targetEntry = ( + target_id: string, + overrides: Partial = {}, +): ShadowEvalJob["targets"][number] => ({ + target_type: "key", + target_id, max_turns: 10000, max_budget: 10, spend: 0, stopped_at: null, attempt_count: null, - key_alias: null, + target_alias: null, key_name: null, ...overrides, }); @@ -235,8 +267,8 @@ describe("ShadowEvalSection", () => { it("gives every active job its own card with a stop button, with the form still offered", () => { mockHooks({ jobs: [ - job({ job_id: "job-a", status: "running", keys: [keyEntry("key-a")] }), - job({ job_id: "job-b", status: "running", keys: [keyEntry("key-b")] }), + job({ job_id: "job-a", status: "running", targets: [targetEntry("key-a")] }), + job({ job_id: "job-b", status: "running", targets: [targetEntry("key-b")] }), ], }); render(); @@ -347,7 +379,7 @@ describe("ShadowEvalSection", () => { }); it("shows spend without a budget cap for a job from before spend budgets existed", () => { - const j = job({ keys: [keyEntry("hashed-key-abc", { max_budget: null, spend: 3.21 })] }); + const j = job({ targets: [targetEntry("hashed-key-abc", { max_budget: null, spend: 3.21 })] }); mockHooks({ jobs: [j], detailsById: { "job-1": j } }); render(); expect(screen.getByText(/\$3\.21 eval spend/)).toBeInTheDocument(); @@ -406,8 +438,7 @@ describe("ShadowEvalSection", () => { await user.click(within(keyList).getByText("prod-alpha")); await user.click(keyInput); await user.click(within(keyList).getByText("staging-beta")); - await user.click(screen.getByPlaceholderText("Select an auto-router")); - await user.click(await screen.findByText("gpt-auto")); + await chooseSelectOption(user, screen.getByPlaceholderText("Select up to 4 auto-routers"), "gpt-auto"); expect(screen.getByText("Start shadow eval")).toBeDisabled(); @@ -417,7 +448,38 @@ describe("ShadowEvalSection", () => { const expectedBody = { api_key_ids: ["hash-alpha", "hash-beta"], - router_name: "gpt-auto", + team_ids: [], + user_ids: [], + router_names: ["gpt-auto"], + direction: "forward", + shadow_percentage: 10, + duration_days: 7, + max_budget: 10, + judge_model: "anthropic/claude-sonnet-5", + }; + expect(start.mutate).toHaveBeenCalledWith(expectedBody); + }); + + it("submits a team-only job with team_ids and no keys", async () => { + const user = userEvent.setup(); + const { start } = mockHooks({}); + render(); + + expect(screen.getByText("Start shadow eval")).toBeDisabled(); + + await user.click(screen.getByPlaceholderText("Search teams by alias")); + const teamList = await screen.findByTestId("paginated-multi-select-list"); + await user.click(within(teamList).getByText("engineering")); + await chooseSelectOption(user, screen.getByPlaceholderText("Select up to 4 auto-routers"), "gpt-auto"); + await user.click(screen.getByPlaceholderText("Select a judge model")); + await user.click(await screen.findByRole("option", { name: /anthropic\/claude-sonnet-5/ })); + await user.click(screen.getByText("Start shadow eval")); + + const expectedBody = { + api_key_ids: [], + team_ids: ["team-eng"], + user_ids: [], + router_names: ["gpt-auto"], direction: "forward", shadow_percentage: 10, duration_days: 7, @@ -439,8 +501,7 @@ describe("ShadowEvalSection", () => { await user.click(screen.getByPlaceholderText("Search keys by alias")); const keyList = await screen.findByTestId("paginated-multi-select-list"); await user.click(within(keyList).getByText("prod-alpha")); - await user.click(screen.getByPlaceholderText("Select an auto-router")); - await user.click(await screen.findByText("gpt-auto")); + await chooseSelectOption(user, screen.getByPlaceholderText("Select up to 4 auto-routers"), "gpt-auto"); await user.click(screen.getByPlaceholderText("Select a judge model")); await user.click(await screen.findByRole("option", { name: /anthropic\/claude-sonnet-5/ })); @@ -453,7 +514,9 @@ describe("ShadowEvalSection", () => { const expectedBody = { api_key_ids: ["hash-alpha"], - router_name: "gpt-auto", + team_ids: [], + user_ids: [], + router_names: ["gpt-auto"], direction: "reverse", baseline_model: "prod-claude", shadow_percentage: 10, @@ -464,6 +527,119 @@ describe("ShadowEvalSection", () => { expect(start.mutate).toHaveBeenCalledWith(expectedBody); }); + it("submits every picked auto-router so one job compares them on the same traffic", async () => { + const user = userEvent.setup(); + const { start } = mockHooks({}); + render(); + + await user.click(screen.getByPlaceholderText("Search keys by alias")); + const keyList = await screen.findByTestId("paginated-multi-select-list"); + await user.click(within(keyList).getByText("prod-alpha")); + const routerInput = screen.getByPlaceholderText("Select up to 4 auto-routers"); + await user.click(routerInput); + await user.click(await screen.findByText("gpt-auto")); + await user.click(routerInput); + await user.click(await screen.findByText("claude-auto")); + expect( + screen.getByText("Every router sees the same sampled requests, judged against the same live responses"), + ).toBeInTheDocument(); + await user.click(screen.getByPlaceholderText("Select a judge model")); + await user.click(await screen.findByRole("option", { name: /anthropic\/claude-sonnet-5/ })); + await user.click(screen.getByText("Start shadow eval")); + + const expectedBody = { + api_key_ids: ["hash-alpha"], + team_ids: [], + user_ids: [], + router_names: ["gpt-auto", "claude-auto"], + direction: "forward", + shadow_percentage: 10, + duration_days: 7, + max_budget: 10, + judge_model: "anthropic/claude-sonnet-5", + }; + expect(start.mutate).toHaveBeenCalledWith(expectedBody); + }); + + it("blocks starting a reverse job with more than one router and says why", async () => { + const user = userEvent.setup(); + mockHooks({}); + render(); + + await user.click(screen.getByPlaceholderText("Search keys by alias")); + const keyList = await screen.findByTestId("paginated-multi-select-list"); + await user.click(within(keyList).getByText("prod-alpha")); + const routerInput = screen.getByPlaceholderText("Select up to 4 auto-routers"); + await user.click(routerInput); + await user.click(await screen.findByText("gpt-auto")); + await user.click(routerInput); + await user.click(await screen.findByText("claude-auto")); + await user.click(screen.getByText("Adoption check: key's traffic vs the router")); + await user.click(await screen.findByText("Regression check: router's picks vs a baseline")); + await user.click(screen.getByPlaceholderText("Select a judge model")); + await user.click(await screen.findByRole("option", { name: /anthropic\/claude-sonnet-5/ })); + await user.click(screen.getByPlaceholderText("Select a baseline model")); + await user.click(screen.getByRole("option", { name: /prod-claude/ })); + + expect(screen.getByText("A regression check compares one router to its baseline")).toBeInTheDocument(); + expect(screen.getByText("Start shadow eval")).toBeDisabled(); + }); + + it("renders a per-router comparison table only when the job ran several routers", () => { + const routerSlice = (group: string, wins: number) => ({ + group, + turn_count: 20, + real_win_rate_pct: 100 - wins - 10, + shadow_win_rate_pct: wins, + tie_rate_pct: 10, + avg_judge_confidence: 0.8, + real_spend: 0.4, + shadow_spend: 0.2, + cache_hit_turns: 0, + }); + const base = job(); + const multi = job({ + router_names: ["claude-auto", "gpt-auto"], + results: { ...base.results!, by_router: [routerSlice("claude-auto", 40), routerSlice("gpt-auto", 70)] }, + }); + mockHooks({ jobs: [multi], detailsById: { "job-1": multi } }); + render(); + + expect(screen.getByText("Router")).toBeInTheDocument(); + const rows = screen.getAllByRole("row").map((row) => row.textContent ?? ""); + expect(rows.some((text) => text.includes("claude-auto") && text.includes("40.0%"))).toBe(true); + expect(rows.some((text) => text.includes("gpt-auto") && text.includes("70.0%"))).toBe(true); + expect( + screen.getByText( + (_, element) => + element?.textContent === "Shadowing 10% of prod-alpha traffic via claude-auto, gpt-auto" && + element.tagName === "P", + ), + ).toBeInTheDocument(); + }); + + it("renders a job from an older proxy that predates router_names", () => { + const legacy = { ...job(), router_names: undefined } as unknown as ShadowEvalJob; + mockHooks({ jobs: [legacy], detailsById: { "job-1": legacy } }); + render(); + + expect( + screen.getByText( + (_, element) => + element?.textContent === "Shadowing 10% of prod-alpha traffic via claude-auto" && element.tagName === "P", + ), + ).toBeInTheDocument(); + }); + + it("keeps the per-router table hidden for a single-router job", () => { + const base = job(); + const single = job({ results: { ...base.results!, by_router: [] } }); + mockHooks({ jobs: [single], detailsById: { "job-1": single } }); + render(); + + expect(screen.queryByText("Router")).not.toBeInTheDocument(); + }); + it("flips the arm labels and headline for a reverse job's results", () => { const j = job({ direction: "reverse", baseline_model: "openai/gpt-4o" }); mockHooks({ jobs: [j], detailsById: { "job-1": j } }); @@ -485,9 +661,13 @@ describe("ShadowEvalSection", () => { }); it("labels the shadowed key by alias, then masked name, then truncated hash", () => { - expect(shadowedKeyLabel(job().keys[0])).toBe("prod-alpha"); - expect(shadowedKeyLabel(keyEntry("hashed-key-abc", { key_name: "sk-...alpha" }))).toBe("sk-...alpha"); - expect(shadowedKeyLabel(keyEntry("hashed-key-abc"))).toBe("hashed-key…"); + expect(shadowedTargetLabel(job().targets[0])).toBe("prod-alpha"); + expect(shadowedTargetLabel(targetEntry("hashed-key-abc", { key_name: "sk-...alpha" }))).toBe("sk-...alpha"); + expect(shadowedTargetLabel(targetEntry("hashed-key-abc"))).toBe("hashed-key…"); + expect(shadowedTargetLabel(targetEntry("team-eng", { target_type: "team" }))).toBe("team-eng"); + expect(shadowedTargetLabel(targetEntry("team-eng", { target_type: "team", target_alias: "engineering" }))).toBe( + "engineering", + ); }); it("breaks results down per key, so one key exhausting its own budget is visible while a sibling runs on", () => { @@ -495,15 +675,12 @@ describe("ShadowEvalSection", () => { jobs: [ job({ judged_count: 205, - keys: [ - keyEntry("hash-spent", { max_budget: 2, spend: 1.5, stopped_at: "2026-08-08T00:00:00Z" }), - keyEntry("hash-hungry", { max_budget: 5, spend: 0.2 }), - ], - results: { - by_tier: [], - by_current_model: [], - by_key: [ - { + targets: [ + targetEntry("hash-spent", { + max_budget: 2, + spend: 1.5, + stopped_at: "2026-08-08T00:00:00Z", + verdicts: { group: "hash-spent", turn_count: 200, real_win_rate_pct: 20.0, @@ -514,7 +691,12 @@ describe("ShadowEvalSection", () => { shadow_spend: 0.5, cache_hit_turns: 0, }, - ], + }), + targetEntry("hash-hungry", { max_budget: 5, spend: 0.2 }), + ], + results: { + by_tier: [], + by_current_model: [], overall_shadow_win_rate_pct: 60.0, overall_tie_rate_pct: 20.0, sampled_real_spend: 0.9, @@ -539,7 +721,7 @@ describe("ShadowEvalSection", () => { expect(screen.getByText(/205 turns judged/)).toBeInTheDocument(); expect(screen.getByText(/Shadowing 10% of/)).toBeInTheDocument(); - expect(screen.getByText("2 keys")).toBeInTheDocument(); + expect(screen.getByText("2 targets")).toBeInTheDocument(); }); it("reads a key that spent its budget as completed even before the sweep stamps it", () => { @@ -547,9 +729,9 @@ describe("ShadowEvalSection", () => { mockHooks({ jobs: [ job({ - keys: [ - keyEntry("hash-spent", { max_budget: 2, spend: 2, attempt_count: 40 }), - keyEntry("hash-hungry", legacyTurnBudgetLeg), + targets: [ + targetEntry("hash-spent", { max_budget: 2, spend: 2, attempt_count: 40 }), + targetEntry("hash-hungry", legacyTurnBudgetLeg), ], }), ], @@ -571,9 +753,9 @@ describe("ShadowEvalSection", () => { job({ judged_count: 0, results: null, - keys: [ - keyEntry("hash-spent", { max_budget: 0.5, spend: 0.5, attempt_count: 2 }), - keyEntry("hash-hungry", { max_budget: 5, spend: 0.01, attempt_count: 1 }), + targets: [ + targetEntry("hash-spent", { max_budget: 0.5, spend: 0.5, attempt_count: 2 }), + targetEntry("hash-hungry", { max_budget: 5, spend: 0.01, attempt_count: 1 }), ], }), ], @@ -594,9 +776,9 @@ describe("ShadowEvalSection", () => { jobs: [ job({ status: "completed", - keys: [ - keyEntry("hash-spent", { max_turns: 200, stopped_at: "2026-08-08T00:00:00Z" }), - keyEntry("hash-hungry", { max_turns: 500 }), + targets: [ + targetEntry("hash-spent", { max_turns: 200, stopped_at: "2026-08-08T00:00:00Z" }), + targetEntry("hash-hungry", { max_turns: 500 }), ], }), ], diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/ShadowEvalSection.tsx b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/ShadowEvalSection.tsx index 39dee28390a..c66d74074c2 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/ShadowEvalSection.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/ShadowEvalSection.tsx @@ -2,32 +2,24 @@ import React, { useMemo, useState } from "react"; -import { useInfiniteKeys } from "@/app/(dashboard)/hooks/keys/useKeys"; import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized"; -import { useModelCostMap } from "@/app/(dashboard)/hooks/models/useModelCostMap"; -import { useAutoRouters, usePlainModelGroups } from "@/app/(dashboard)/hooks/models/useModels"; -import { PaginatedMultiSelect } from "@/components/shared/PaginatedMultiSelect"; -import { SearchSelect, type SearchSelectOption } from "@/components/shared/SearchSelect"; import { Badge } from "@/components/ui/badge"; import { Button } from "@/components/ui/button"; import { CircleHelp } from "lucide-react"; -import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card"; +import { Card } from "@/components/ui/card"; import { Tooltip, TooltipContent, TooltipProvider, TooltipTrigger } from "@/components/ui/tooltip"; -import { Input } from "@/components/ui/input"; -import { Label } from "@/components/ui/label"; -import { Select, SelectContent, SelectItem, SelectTrigger, SelectValue } from "@/components/ui/select"; import { Table, TableBody, TableCell, TableHead, TableHeader, TableRow } from "@/components/ui/table"; import { ApiError } from "@/lib/http/client"; import { usd } from "./costOptimizationUtils"; +import { StartForm } from "./ShadowEvalStartForm"; import { useShadowEvalJob, useShadowEvalJobs, - useStartShadowEval, useStopShadowEval, type ShadowEvalJob, - type ShadowEvalJobKey, + type ShadowEvalJobTarget, type ShadowEvalSlice, } from "./useShadowEval"; @@ -66,42 +58,46 @@ const routerMatchedOrBeatPct = ( ? 100 - results.overall_shadow_win_rate_pct : results.overall_shadow_win_rate_pct + results.overall_tie_rate_pct; -export const shadowedKeyLabel = (key: ShadowEvalJobKey): string => - key.key_alias || key.key_name || `${key.api_key_id.slice(0, 10)}…`; +export const shadowedTargetLabel = (target: ShadowEvalJobTarget): string => + target.target_alias || + target.key_name || + (target.target_type === "key" ? `${target.target_id.slice(0, 10)}…` : target.target_id); -const shadowedKeysLabel = (job: ShadowEvalJob): string => - job.keys.length === 1 ? shadowedKeyLabel(job.keys[0]) : `${job.keys.length} keys`; +const shadowedTargetsLabel = (job: ShadowEvalJob): string => + job.targets.length === 1 ? shadowedTargetLabel(job.targets[0]) : `${job.targets.length} targets`; const totalBudget = (job: ShadowEvalJob): number | null => - job.keys.reduce( - (sum, key) => (sum === null || key.max_budget == null ? null : sum + key.max_budget), + job.targets.reduce( + (sum, target) => (sum === null || target.max_budget == null ? null : sum + target.max_budget), 0, ); -const totalSpend = (job: ShadowEvalJob): number => job.keys.reduce((sum, key) => sum + (key.spend ?? 0), 0); +const totalSpend = (job: ShadowEvalJob): number => job.targets.reduce((sum, target) => sum + (target.spend ?? 0), 0); -const keySpent = (key: ShadowEvalJobKey): boolean => { - const spendBudgetReached = key.max_budget != null && key.spend != null && key.spend >= key.max_budget; - const turnValveReached = key.attempt_count != null && key.attempt_count >= key.max_turns; +const targetSpent = (target: ShadowEvalJobTarget): boolean => { + const spendBudgetReached = target.max_budget != null && target.spend != null && target.spend >= target.max_budget; + const turnValveReached = target.attempt_count != null && target.attempt_count >= target.max_turns; return spendBudgetReached || turnValveReached; }; -const keyStatus = (job: ShadowEvalJob, key: ShadowEvalJobKey): string => { - if (job.status === "completed" || (key.stopped_at == null && keySpent(key))) return "completed"; - return key.stopped_at != null ? "stopped" : "running"; +const targetStatus = (job: ShadowEvalJob, target: ShadowEvalJobTarget): string => { + if (job.status === "completed" || (target.stopped_at == null && targetSpent(target))) return "completed"; + return target.stopped_at != null ? "stopped" : "running"; }; +const jobRouters = (job: ShadowEvalJob): string => (job.router_names ?? [job.router_name]).join(", "); + const jobHeadline = (job: ShadowEvalJob): React.ReactNode => job.direction === "reverse" ? ( <> - Comparing {job.router_name} to{" "} + Comparing {jobRouters(job)} to{" "} {job.baseline_model} on {job.shadow_percentage}% of{" "} - {shadowedKeysLabel(job)} traffic + {shadowedTargetsLabel(job)} traffic ) : ( <> - Shadowing {job.shadow_percentage}% of {shadowedKeysLabel(job)} traffic - via {job.router_name} + Shadowing {job.shadow_percentage}% of {shadowedTargetsLabel(job)}{" "} + traffic via {jobRouters(job)} ); @@ -255,13 +251,12 @@ const VerdictBar: React.FC<{ direction: ShadowEvalDirection; results: NonNullabl ); }; -const KeyTable: React.FC<{ job: ShadowEvalJob }> = ({ job }) => { - const slices = new Map((job.results?.by_key ?? []).map((slice) => [slice.group, slice])); +const TargetTable: React.FC<{ job: ShadowEvalJob }> = ({ job }) => { return ( - Key + Target Status {["Budget used", "Router wins", `${otherArmLabel(job.direction)} wins`].map((label) => ( @@ -271,18 +266,23 @@ const KeyTable: React.FC<{ job: ShadowEvalJob }> = ({ job }) => { - {job.keys.map((key) => { - const slice = slices.get(key.api_key_id); + {job.targets.map((target) => { + const slice = target.verdicts; return ( - - {shadowedKeyLabel(key)} + + + {shadowedTargetLabel(target)} + {target.target_type !== "key" && ( + {target.target_type} + )} + - + - {key.max_budget != null - ? `${usd(key.spend ?? 0)} / ${usd(key.max_budget)}` - : `${(key.attempt_count ?? slice?.turn_count ?? 0).toLocaleString()} / ${key.max_turns.toLocaleString()} turns`} + {target.max_budget != null + ? `${usd(target.spend ?? 0)} / ${usd(target.max_budget)}` + : `${(target.attempt_count ?? slice?.turn_count ?? 0).toLocaleString()} / ${target.max_turns.toLocaleString()} turns`} {slice ? ( <> @@ -318,9 +318,9 @@ const ResultsBody: React.FC<{ job: ShadowEvalJob; resultsError?: boolean }> = ({ const hasVerdicts = results != null && (results.by_tier.length > 0 || results.by_current_model.length > 0); return ( <> - {job.keys.length > 1 && ( + {job.targets.length > 1 && (
- +
)} {/* results == null re-stated for TS narrowing; hasVerdicts alone cannot narrow it */} @@ -343,6 +343,11 @@ const ResultsBody: React.FC<{ job: ShadowEvalJob; resultsError?: boolean }> = ({ + {(results.by_router ?? []).length > 1 && ( +
+ +
+ )} {results.by_current_model.length > 0 && ( { - const { data: costMap } = useModelCostMap(); - return useMemo(() => { - if (!costMap) return []; - const chatModels = Object.entries(costMap as Record) - .filter(([, value]) => value?.mode === "chat" && value?.litellm_provider) - .map(([key, value]) => (key.startsWith(`${value.litellm_provider}/`) ? key : `${value.litellm_provider}/${key}`)); - return [...new Set(chatModels)].toSorted((a, b) => a.localeCompare(b)); - }, [costMap]); -}; - -const useJudgeModelOptions = (): SearchSelectOption[] => { - const chatModels = useChatModelNames(); - return useMemo(() => { - const pinned: SearchSelectOption[] = RECOMMENDED_JUDGE_MODELS.map((model) => ({ - label: model, - value: model, - sublabel: "Recommended", - })); - const pinnedNames = new Set(RECOMMENDED_JUDGE_MODELS); - const rest = chatModels.filter((model) => !pinnedNames.has(model)).map((model) => ({ label: model, value: model })); - return [...pinned, ...rest]; - }, [chatModels]); -}; - -const useBaselineModelOptions = (): SearchSelectOption[] => { - const configuredGroups = usePlainModelGroups(); - const chatModels = useChatModelNames(); - return useMemo(() => { - const configured = [...configuredGroups] - .toSorted((a, b) => a.localeCompare(b)) - .map((model) => ({ label: model, value: model, sublabel: "Configured on this gateway" })); - const rest = chatModels - .filter((model) => !configuredGroups.has(model)) - .map((model) => ({ label: model, value: model })); - return [...configured, ...rest]; - }, [configuredGroups, chatModels]); -}; - -const DIRECTION_OPTIONS: readonly { value: ShadowEvalDirection; label: string }[] = [ - { value: "forward", label: "Adoption check: key's traffic vs the router" }, - { value: "reverse", label: "Regression check: router's picks vs a baseline" }, -] as const; - -const START_FORM_DESCRIPTION: Record = { - forward: - "Duplicates a sampled slice of the selected keys' traffic through the auto-router and has an LLM judge compare both answers blind. Each key gets its own spend budget. The router's answers are never served to users; judge calls bill to the shadowed key.", - reverse: - "Duplicates a sampled slice of the traffic the auto-router already serves against a fixed baseline model and has an LLM judge compare both answers blind. Each key gets its own spend budget. The baseline's answers are never served to users; judge calls bill to the shadowed key.", -}; - -const DURATION_OPTIONS = [ - { value: "1", label: "1 day" }, - { value: "3", label: "3 days" }, - { value: "7", label: "7 days" }, - { value: "14", label: "14 days" }, - { value: "30", label: "30 days" }, -] as const; - -const Field: React.FC<{ label: string; htmlFor?: string; className?: string; children: React.ReactNode }> = ({ - label, - htmlFor, - className, - children, -}) => ( -
- - {children} -
-); - -const KeySelect: React.FC<{ value: string[]; onChange: (tokens: string[]) => void }> = ({ value, onChange }) => { - const [search, setSearch] = useState(""); - const { data, isPending, isError, fetchNextPage, hasNextPage, isFetchingNextPage } = useInfiniteKeys(50, { - selectedKeyAlias: search || null, - }); - const options = useMemo( - () => - (data?.pages ?? []) - .flatMap((page) => page.keys) - .map((key) => ({ - label: key.key_alias || key.key_name || key.token, - value: key.token, - sublabel: key.token, - })), - [data], - ); - return ( - void fetchNextPage()} - hasNextPage={hasNextPage} - isFetchingNextPage={isFetchingNextPage} - isLoading={isPending} - placeholder="Search keys by alias" - emptyText="No matching keys" - errorText={isError ? "Keys could not be loaded. Refresh the page to retry." : undefined} - /> - ); -}; - -const StartForm: React.FC = () => { - const { accessToken } = useAuthorized(); - const [apiKeyIds, setApiKeyIds] = useState([]); - const [routerName, setRouterName] = useState(""); - const [direction, setDirection] = useState("forward"); - const [baselineModel, setBaselineModel] = useState(""); - const [percentage, setPercentage] = useState("10"); - const [durationDays, setDurationDays] = useState("7"); - const [judgeModel, setJudgeModel] = useState(""); - const [maxBudget, setMaxBudget] = useState("10"); - const { data: autoRouters } = useAutoRouters(); - const judgeModelOptions = useJudgeModelOptions(); - const baselineModelOptions = useBaselineModelOptions(); - const start = useStartShadowEval(); - - const routerOptions = useMemo(() => { - const names = new Set( - (autoRouters ?? []).map((deployment) => deployment.model_name).filter((name): name is string => Boolean(name)), - ); - return [...names].toSorted().map((name) => ({ label: name, value: name })); - }, [autoRouters]); - - const parsedPct = Number.parseFloat(percentage); - const percentageValid = parsedPct >= 0.1 && parsedPct <= 100; - const parsedMaxBudget = Number.parseFloat(maxBudget); - const maxBudgetValid = parsedMaxBudget >= 0.01 && parsedMaxBudget <= 10000; - const baselinePicked = direction === "forward" || baselineModel !== ""; - const filled = apiKeyIds.length > 0 && [routerName, judgeModel].every((field) => field !== "") && baselinePicked; - const boundsValid = percentageValid && maxBudgetValid; - const valid = Boolean(accessToken) && filled && boundsValid; - const handleStart = () => { - const startBody = { - api_key_ids: apiKeyIds, - router_name: routerName, - direction, - ...(direction === "reverse" ? { baseline_model: baselineModel } : {}), - shadow_percentage: parsedPct, - duration_days: Number.parseInt(durationDays, 10), - max_budget: parsedMaxBudget, - judge_model: judgeModel, - }; - start.mutate(startBody); - }; - - return ( - - - Start a shadow eval -

{START_FORM_DESCRIPTION[direction]}

-
- -
- - - - - - - - - - -
- setPercentage(e.target.value)} - /> - % of traffic -
-
- {percentage.trim() !== "" && !percentageValid && ( -

Enter a value from 0.1 to 100

- )} -
-
- - - - -
- $ - setMaxBudget(e.target.value)} - /> - max shadow + judge spend, per key -
- {maxBudget.trim() !== "" && !maxBudgetValid && ( -

Enter a value from 0.01 to 10000

- )} -
- {direction === "reverse" && ( - - - - )} - - - -
- -
-
- ); -}; - const previousSummary = (job: ShadowEvalJob): string => { const results = job.results; if (results) return pct(routerMatchedOrBeatPct(job.direction, results)); @@ -774,8 +503,9 @@ const ShadowEvalSection: React.FC = () => {

Shadow eval

- Blind-judge the auto-router on your real traffic: against the models a key uses today before switching, or - against a fixed baseline after it has switched. + Blind-judge the auto-router on the real traffic of a key, team, or user (teams and users cover + JWT-authenticated traffic): against the models they use today before switching, or against a fixed baseline + after they have switched.

diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/ShadowEvalStartForm.tsx b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/ShadowEvalStartForm.tsx new file mode 100644 index 00000000000..f96910a4ad6 --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/ShadowEvalStartForm.tsx @@ -0,0 +1,432 @@ +"use client"; + +import React, { useMemo, useState } from "react"; + +import { useInfiniteKeys } from "@/app/(dashboard)/hooks/keys/useKeys"; +import { useInfiniteUsers } from "@/app/(dashboard)/hooks/users/useUsers"; +import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized"; +import { useModelCostMap } from "@/app/(dashboard)/hooks/models/useModelCostMap"; +import { useAutoRouters, usePlainModelGroups } from "@/app/(dashboard)/hooks/models/useModels"; +import { MultiSelect } from "@/components/shared/MultiSelect"; +import { PaginatedMultiSelect } from "@/components/shared/PaginatedMultiSelect"; +import TeamMultiSelect from "@/components/common_components/team_multi_select"; +import { userOptionLabel } from "@/components/common_components/UserDropdown"; +import { SearchSelect, type SearchSelectOption } from "@/components/shared/SearchSelect"; +import { Button } from "@/components/ui/button"; +import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card"; +import { Input } from "@/components/ui/input"; +import { Label } from "@/components/ui/label"; +import { Select, SelectContent, SelectItem, SelectTrigger, SelectValue } from "@/components/ui/select"; + +import { useStartShadowEval, type ShadowEvalJob } from "./useShadowEval"; + +type ShadowEvalDirection = ShadowEvalJob["direction"]; + +const MAX_ROUTERS = 4; + +const RECOMMENDED_JUDGE_MODELS = ["anthropic/claude-sonnet-5", "openai/gpt-4o", "gemini/gemini-2.5-pro"] as const; + +interface CostMapEntry { + litellm_provider?: string; + mode?: string; +} + +const useChatModelNames = (): string[] => { + const { data: costMap } = useModelCostMap(); + return useMemo(() => { + if (!costMap) return []; + const chatModels = Object.entries(costMap as Record) + .filter(([, value]) => value?.mode === "chat" && value?.litellm_provider) + .map(([key, value]) => (key.startsWith(`${value.litellm_provider}/`) ? key : `${value.litellm_provider}/${key}`)); + return [...new Set(chatModels)].toSorted((a, b) => a.localeCompare(b)); + }, [costMap]); +}; + +const useJudgeModelOptions = (): SearchSelectOption[] => { + const chatModels = useChatModelNames(); + return useMemo(() => { + const pinned: SearchSelectOption[] = RECOMMENDED_JUDGE_MODELS.map((model) => ({ + label: model, + value: model, + sublabel: "Recommended", + })); + const pinnedNames = new Set(RECOMMENDED_JUDGE_MODELS); + const rest = chatModels.filter((model) => !pinnedNames.has(model)).map((model) => ({ label: model, value: model })); + return [...pinned, ...rest]; + }, [chatModels]); +}; + +const useBaselineModelOptions = (): SearchSelectOption[] => { + const configuredGroups = usePlainModelGroups(); + const chatModels = useChatModelNames(); + return useMemo(() => { + const configured = [...configuredGroups] + .toSorted((a, b) => a.localeCompare(b)) + .map((model) => ({ label: model, value: model, sublabel: "Configured on this gateway" })); + const rest = chatModels + .filter((model) => !configuredGroups.has(model)) + .map((model) => ({ label: model, value: model })); + return [...configured, ...rest]; + }, [configuredGroups, chatModels]); +}; + +const DIRECTION_OPTIONS: readonly { value: ShadowEvalDirection; label: string }[] = [ + { value: "forward", label: "Adoption check: key's traffic vs the router" }, + { value: "reverse", label: "Regression check: router's picks vs a baseline" }, +] as const; + +const START_FORM_DESCRIPTION: Record = { + forward: + "Duplicates a sampled slice of the selected targets' traffic (keys, teams, or users) through the auto-router and has an LLM judge compare both answers blind. Each target gets its own spend budget. The router's answers are never served to users; judge calls bill to the sampled traffic's own identity.", + reverse: + "Duplicates a sampled slice of the traffic the auto-router already serves against a fixed baseline model and has an LLM judge compare both answers blind. Each target gets its own spend budget. The baseline's answers are never served to users; judge calls bill to the sampled traffic's own identity.", +}; + +const DURATION_OPTIONS = [ + { value: "1", label: "1 day" }, + { value: "3", label: "3 days" }, + { value: "7", label: "7 days" }, + { value: "14", label: "14 days" }, + { value: "30", label: "30 days" }, +] as const; + +const Field: React.FC<{ label: string; htmlFor?: string; className?: string; children: React.ReactNode }> = ({ + label, + htmlFor, + className, + children, +}) => ( +
+ + {children} +
+); + +const KeySelect: React.FC<{ value: string[]; onChange: (tokens: string[]) => void }> = ({ value, onChange }) => { + const [search, setSearch] = useState(""); + const { data, isPending, isError, fetchNextPage, hasNextPage, isFetchingNextPage } = useInfiniteKeys(50, { + selectedKeyAlias: search || null, + }); + const options = useMemo( + () => + (data?.pages ?? []) + .flatMap((page) => page.keys) + .map((key) => ({ + label: key.key_alias || key.key_name || key.token, + value: key.token, + sublabel: key.token, + })), + [data], + ); + return ( + void fetchNextPage()} + hasNextPage={hasNextPage} + isFetchingNextPage={isFetchingNextPage} + isLoading={isPending} + placeholder="Search keys by alias" + emptyText="No matching keys" + errorText={isError ? "Keys could not be loaded. Refresh the page to retry." : undefined} + /> + ); +}; + +const UserSelect: React.FC<{ value: string[]; onChange: (ids: string[]) => void }> = ({ value, onChange }) => { + const [search, setSearch] = useState(""); + const { data, isPending, isError, fetchNextPage, hasNextPage, isFetchingNextPage } = useInfiniteUsers( + 50, + search || undefined, + ); + const options = useMemo( + () => + Array.from( + new Map( + (data?.pages ?? []) + .flatMap((page) => page.users) + .map((user) => [user.user_id, { label: userOptionLabel(user), value: user.user_id }] as const), + ).values(), + ), + [data], + ); + return ( + void fetchNextPage()} + hasNextPage={hasNextPage} + isFetchingNextPage={isFetchingNextPage} + isLoading={isPending} + placeholder="Search users by email" + emptyText="No matching users" + errorText={isError ? "Users could not be loaded. Refresh the page to retry." : undefined} + /> + ); +}; + +const RouterField: React.FC<{ + options: SearchSelectOption[]; + routerNames: string[]; + onChange: (names: string[]) => void; + direction: ShadowEvalDirection; +}> = ({ options, routerNames, onChange, direction }) => ( + + + {routerNames.length > MAX_ROUTERS && ( +

Pick at most {MAX_ROUTERS} auto-routers

+ )} + {direction === "reverse" && routerNames.length > 1 && ( +

A regression check compares one router to its baseline

+ )} + {direction === "forward" && routerNames.length > 1 && ( +

+ Every router sees the same sampled requests, judged against the same live responses +

+ )} +
+); + +interface StartFormValidityInputs { + accessToken: string | null | undefined; + apiKeyIds: string[]; + teamIds: string[]; + userIds: string[]; + routerNames: string[]; + direction: ShadowEvalDirection; + baselineModel: string; + judgeModel: string; + percentage: string; + maxBudget: string; +} + +const startFormValidity = (inputs: StartFormValidityInputs) => { + const parsedPct = Number.parseFloat(inputs.percentage); + const percentageValid = parsedPct >= 0.1 && parsedPct <= 100; + const parsedMaxBudget = Number.parseFloat(inputs.maxBudget); + const maxBudgetValid = parsedMaxBudget >= 0.01 && parsedMaxBudget <= 10000; + const baselinePicked = inputs.direction === "forward" || inputs.baselineModel !== ""; + const targetsPicked = inputs.apiKeyIds.length + inputs.teamIds.length + inputs.userIds.length > 0; + const routerCountValid = inputs.routerNames.length >= 1 && inputs.routerNames.length <= MAX_ROUTERS; + const routersMatchDirection = inputs.direction === "forward" || inputs.routerNames.length === 1; + const routersValid = routerCountValid && routersMatchDirection; + const modelsPicked = routersValid && inputs.judgeModel !== "" && baselinePicked; + const filled = targetsPicked && modelsPicked; + const boundsValid = percentageValid && maxBudgetValid; + const valid = Boolean(inputs.accessToken) && filled && boundsValid; + return { parsedPct, parsedMaxBudget, percentageValid, maxBudgetValid, valid }; +}; + +interface StartBodyInputs { + apiKeyIds: string[]; + teamIds: string[]; + userIds: string[]; + routerNames: string[]; + direction: ShadowEvalDirection; + baselineModel: string; + shadowPercentage: number; + durationDays: number; + maxBudget: number; + judgeModel: string; +} + +const buildStartBody = (inputs: StartBodyInputs) => ({ + api_key_ids: inputs.apiKeyIds, + team_ids: inputs.teamIds, + user_ids: inputs.userIds, + router_names: inputs.routerNames, + direction: inputs.direction, + ...(inputs.direction === "reverse" ? { baseline_model: inputs.baselineModel } : {}), + shadow_percentage: inputs.shadowPercentage, + duration_days: inputs.durationDays, + max_budget: inputs.maxBudget, + judge_model: inputs.judgeModel, +}); + +export const StartForm: React.FC = () => { + const { accessToken } = useAuthorized(); + const [apiKeyIds, setApiKeyIds] = useState([]); + const [teamIds, setTeamIds] = useState([]); + const [userIds, setUserIds] = useState([]); + const [routerNames, setRouterNames] = useState([]); + const [direction, setDirection] = useState("forward"); + const [baselineModel, setBaselineModel] = useState(""); + const [percentage, setPercentage] = useState("10"); + const [durationDays, setDurationDays] = useState("7"); + const [judgeModel, setJudgeModel] = useState(""); + const [maxBudget, setMaxBudget] = useState("10"); + const { data: autoRouters } = useAutoRouters(); + const judgeModelOptions = useJudgeModelOptions(); + const baselineModelOptions = useBaselineModelOptions(); + const start = useStartShadowEval(); + + const routerOptions = useMemo(() => { + const names = new Set( + (autoRouters ?? []).map((deployment) => deployment.model_name).filter((name): name is string => Boolean(name)), + ); + return [...names].toSorted().map((name) => ({ label: name, value: name })); + }, [autoRouters]); + + const validityInputs: StartFormValidityInputs = { + accessToken, + apiKeyIds, + teamIds, + userIds, + routerNames, + direction, + baselineModel, + judgeModel, + percentage, + maxBudget, + }; + const { parsedPct, parsedMaxBudget, percentageValid, maxBudgetValid, valid } = startFormValidity(validityInputs); + const handleStart = () => { + const bodyInputs: StartBodyInputs = { + apiKeyIds, + teamIds, + userIds, + routerNames, + direction, + baselineModel, + shadowPercentage: parsedPct, + durationDays: Number.parseInt(durationDays, 10), + maxBudget: parsedMaxBudget, + judgeModel, + }; + start.mutate(buildStartBody(bodyInputs)); + }; + + return ( + + + Start a shadow eval +

{START_FORM_DESCRIPTION[direction]}

+
+ +
+ + + + + + + + + + + + + + +
+ setPercentage(e.target.value)} + /> + % of traffic +
+
+ {percentage.trim() !== "" && !percentageValid && ( +

Enter a value from 0.1 to 100

+ )} +
+
+ + + + +
+ $ + setMaxBudget(e.target.value)} + /> + max shadow + judge spend, per target +
+ {maxBudget.trim() !== "" && !maxBudgetValid && ( +

Enter a value from 0.01 to 10000

+ )} +
+ {direction === "reverse" && ( + + + + )} + + + +
+ +
+
+ ); +}; diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/UsageTab.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/UsageTab.test.tsx index df23e5509bf..f85a667a074 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/UsageTab.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/UsageTab.test.tsx @@ -1,4 +1,4 @@ -import { render } from "@testing-library/react"; +import { render, screen } from "@testing-library/react"; import userEvent from "@testing-library/user-event"; import { beforeEach, describe, expect, it, vi } from "vitest"; import type { ToolSpendResponse } from "@/components/networking"; @@ -152,13 +152,13 @@ describe("UsageTab", () => { gateway_injected_caching_savings_spend: 0.006, compression_saved_tokens: 100000, }; - const { getByText } = renderWith([day("2026-07-12", firstDay), day("2026-07-13", secondDay)]); + renderWith([day("2026-07-12", firstDay), day("2026-07-13", secondDay)]); - expect(getByText("$0.1500")).toBeInTheDocument(); - expect(getByText("$0.1400")).toBeInTheDocument(); - expect(getByText("$0.0100")).toBeInTheDocument(); - expect(getByText("$0.0160")).toBeInTheDocument(); - expect(getByText("140,000 tokens compressed")).toBeInTheDocument(); + expect(screen.getByText("$0.1500")).toBeInTheDocument(); + expect(screen.getByText("$0.1400")).toBeInTheDocument(); + expect(screen.getByText("$0.0100")).toBeInTheDocument(); + expect(screen.getByText("$0.0160")).toBeInTheDocument(); + expect(screen.getByText("140,000 tokens compressed")).toBeInTheDocument(); }); const twoDays = () => [ @@ -167,11 +167,11 @@ describe("UsageTab", () => { ]; it("opens on a running total anchored at $0 at the start of the range", () => { - const { getByTestId } = renderWith(twoDays()); + renderWith(twoDays()); // Cumulative prepends a synthetic $0 point at the range start (Jul 1) so the // line rises from zero rather than floating; the daily running totals follow. - const series = readSeries(getByTestId("area-chart")); + const series = readSeries(screen.getByTestId("area-chart")); expect(series).toHaveLength(3); expect(series[0]).toMatchObject({ date: "Jul 1", Compression: 0, "Prompt caching": 0 }); expect(series[1]).toMatchObject({ Compression: 0.04, "Prompt caching": 0.006 }); @@ -183,12 +183,12 @@ describe("UsageTab", () => { // The original complaint: a one-day range plotted a single floating dot. The // synthetic start anchor gives the line a zero origin to climb from. const oneDay = new Date(2026, 6, 24); - const { getByTestId } = renderWith( - [day("2026-07-24", { compression_savings_spend: 0.2, gateway_injected_caching_savings_spend: 0.05 })], - { from: oneDay, to: oneDay }, - ); + renderWith([day("2026-07-24", { compression_savings_spend: 0.2, gateway_injected_caching_savings_spend: 0.05 })], { + from: oneDay, + to: oneDay, + }); - const series = readSeries(getByTestId("area-chart")); + const series = readSeries(screen.getByTestId("area-chart")); expect(series).toHaveLength(2); expect(series[0]).toMatchObject({ date: "Jul 24", Compression: 0, "Prompt caching": 0 }); expect(series[1]).toMatchObject({ date: "Jul 24", Compression: 0.2, "Prompt caching": 0.05 }); @@ -202,49 +202,49 @@ describe("UsageTab", () => { day("2026-07-13", { gateway_injected_caching_savings_spend: 0.1 }), day("2026-07-12", { gateway_injected_caching_savings_spend: 0.04 }), ]; - const { getByTestId, getByRole } = renderWith(newestFirst); + renderWith(newestFirst); // The $0 anchor leads, then the days climb oldest to newest. - const cumulative = readSeries(getByTestId("area-chart")); + const cumulative = readSeries(screen.getByTestId("area-chart")); expect(cumulative.map((p: { date: string }) => p.date)).toEqual(["Jul 1", "Jul 12", "Jul 13"]); expect(cumulative[1]["Prompt caching"]).toBeCloseTo(0.04, 5); expect(cumulative[2]["Prompt caching"]).toBeCloseTo(0.14, 5); expect(cumulative[2]["Prompt caching"]).toBeGreaterThan(cumulative[1]["Prompt caching"]); - await userEvent.click(getByRole("tab", { name: "Per day" })); - const perDay = readSeries(getByTestId("bar-chart")); + await userEvent.click(screen.getByRole("tab", { name: "Per day" })); + const perDay = readSeries(screen.getByTestId("bar-chart")); expect(perDay.map((p: { date: string }) => p.date)).toEqual(["Jul 12", "Jul 13"]); }); it("draws bars of the raw per-interval readings on the other tab", async () => { - const { getByRole, getByTestId, queryByTestId } = renderWith(twoDays()); + renderWith(twoDays()); // Cumulative opens on the area line. - expect(getByTestId("area-chart")).toBeInTheDocument(); + expect(screen.getByTestId("area-chart")).toBeInTheDocument(); - await userEvent.click(getByRole("tab", { name: "Per day" })); + await userEvent.click(screen.getByRole("tab", { name: "Per day" })); // Per day switches to a bar chart of the unaccumulated daily savings, with no // synthetic anchor prepended. - expect(queryByTestId("area-chart")).not.toBeInTheDocument(); - const series = readSeries(getByTestId("bar-chart")); + expect(screen.queryByTestId("area-chart")).not.toBeInTheDocument(); + const series = readSeries(screen.getByTestId("bar-chart")); expect(series).toHaveLength(2); expect(series[0]).toMatchObject({ Compression: 0.04, "Prompt caching": 0.006 }); expect(series[1]).toMatchObject({ Compression: 0.1, "Prompt caching": 0.01 }); }); it("says what the line means and over what range", async () => { - const { getByText, getByRole } = renderWith(twoDays()); + renderWith(twoDays()); - expect(getByText("Running total saved · Jul 1 – Jul 14 (UTC)")).toBeInTheDocument(); - await userEvent.click(getByRole("tab", { name: "Per day" })); - expect(getByText("Saved per day · Jul 1 – Jul 14 (UTC)")).toBeInTheDocument(); + expect(screen.getByText("Running total saved · Jul 1 – Jul 14 (UTC)")).toBeInTheDocument(); + await userEvent.click(screen.getByRole("tab", { name: "Per day" })); + expect(screen.getByText("Saved per day · Jul 1 – Jul 14 (UTC)")).toBeInTheDocument(); }); it("builds the per-driver donut from the range totals, not the running total", () => { - const { getByTestId } = renderWith(twoDays()); + renderWith(twoDays()); - const slices = JSON.parse(getByTestId("donut-chart").getAttribute("data-slices") ?? "[]"); + const slices = JSON.parse(screen.getByTestId("donut-chart").getAttribute("data-slices") ?? "[]"); expect(slices).toEqual([ { driver: "Compression", color: "emerald", usd: expect.closeTo(0.14, 5) }, { driver: "Prompt caching", color: "blue", usd: expect.closeTo(0.016, 5) }, @@ -252,9 +252,9 @@ describe("UsageTab", () => { }); it("omits a driver slice when that driver has no savings", () => { - const { getByTestId } = renderWith([day("2026-07-12", { compression_savings_spend: 0.04 })]); + renderWith([day("2026-07-12", { compression_savings_spend: 0.04 })]); - const slices = JSON.parse(getByTestId("donut-chart").getAttribute("data-slices") ?? "[]"); + const slices = JSON.parse(screen.getByTestId("donut-chart").getAttribute("data-slices") ?? "[]"); expect(slices).toEqual([{ driver: "Compression", color: "emerald", usd: expect.closeTo(0.04, 5) }]); }); @@ -262,7 +262,7 @@ describe("UsageTab", () => { // Stacking sums the series into one bar. Auto-router savings go negative when a // model switch pays for a cold cache, and that segment would be drawn below the // axis while the rest of the bar still read as the day's total. - const { getByRole, getByTestId } = renderWith([ + renderWith([ day("2026-07-12", { compression_savings_spend: 0.1, gateway_injected_caching_savings_spend: 0.02, @@ -270,8 +270,8 @@ describe("UsageTab", () => { }), ]); - await userEvent.click(getByRole("tab", { name: "Per day" })); - const bars = getByTestId("bar-chart"); + await userEvent.click(screen.getByRole("tab", { name: "Per day" })); + const bars = screen.getByTestId("bar-chart"); expect(bars).toHaveAttribute("data-stack", "false"); expect(readSeries(bars)[0]).toMatchObject({ "Auto-router": -0.05 }); }); @@ -281,10 +281,10 @@ describe("UsageTab", () => { // per day"). Hand-rolled rows made it compete with the legend and the toggle for // width, so the header grew a line on one tab and the chart moved with it. CardHeader // sizes the action column to its content and gives the rest to the title column. - const { getByRole, getByTestId, container } = renderWith(twoDays()); + const { container } = renderWith(twoDays()); const header = () => { - const legend = getByTestId("chart-legend"); + const legend = screen.getByTestId("chart-legend"); const action = legend.closest('[data-slot="card-action"]') as HTMLElement; const cardHeader = action.parentElement as HTMLElement; const description = cardHeader.querySelector('[data-slot="card-description"]') as HTMLElement; @@ -295,12 +295,12 @@ describe("UsageTab", () => { expect(before.action).toBeTruthy(); expect(before.description).toBeTruthy(); // the toggle rides in the same action slot as the legend, so neither moves alone - expect(before.action.contains(getByRole("tablist"))).toBe(true); + expect(before.action.contains(screen.getByRole("tablist"))).toBe(true); // the subtitle lives outside that slot, so its length cannot reposition the controls expect(before.action.contains(before.description)).toBe(false); expect(before.description).toHaveTextContent(/Running total saved/); - await userEvent.click(getByRole("tab", { name: "Per day" })); + await userEvent.click(screen.getByRole("tab", { name: "Per day" })); const after = header(); expect(after.action).toBe(before.action); @@ -314,7 +314,7 @@ describe("UsageTab", () => { // Switching models leaves the new one with a cold cache, so a route can cost more // than the baseline would have. A negative slice is meaningless in a donut, but the // total has to keep the loss or the page can only ever report good news. - const { getByText, getByTestId } = renderWith([ + renderWith([ day("2026-07-12", { compression_savings_spend: 0.1, gateway_injected_caching_savings_spend: 0.02, @@ -322,16 +322,16 @@ describe("UsageTab", () => { }), ]); - expect(getByText("$0.0700")).toBeInTheDocument(); - expect(getByText("-$0.0500")).toBeInTheDocument(); + expect(screen.getByText("$0.0700")).toBeInTheDocument(); + expect(screen.getByText("-$0.0500")).toBeInTheDocument(); - const slices = JSON.parse(getByTestId("donut-chart").getAttribute("data-slices") ?? "[]"); + const slices = JSON.parse(screen.getByTestId("donut-chart").getAttribute("data-slices") ?? "[]"); expect(slices.map((d: { driver: string }) => d.driver)).toEqual(["Compression", "Prompt caching"]); - expect(getByTestId("donut-chart")).toHaveAttribute("data-label", "$0.1200"); + expect(screen.getByTestId("donut-chart")).toHaveAttribute("data-label", "$0.1200"); }); it("carries auto-router savings into the summary card, donut slice, and cumulative series", () => { - const { getByText, getByTestId } = renderWith([ + renderWith([ day("2026-07-12", { compression_savings_spend: 0.04, gateway_injected_caching_savings_spend: 0.006, @@ -345,11 +345,11 @@ describe("UsageTab", () => { ]); // Total saved now sums three drivers, and the auto-router card carries its own total. - expect(getByText("$0.2260")).toBeInTheDocument(); - expect(getByText("$0.0700")).toBeInTheDocument(); + expect(screen.getByText("$0.2260")).toBeInTheDocument(); + expect(screen.getByText("$0.0700")).toBeInTheDocument(); // The driver donut gains a third slice priced from the range totals. - const slices = JSON.parse(getByTestId("donut-chart").getAttribute("data-slices") ?? "[]"); + const slices = JSON.parse(screen.getByTestId("donut-chart").getAttribute("data-slices") ?? "[]"); expect(slices).toEqual([ { driver: "Compression", color: "emerald", usd: expect.closeTo(0.14, 5) }, { driver: "Prompt caching", color: "blue", usd: expect.closeTo(0.016, 5) }, @@ -357,7 +357,7 @@ describe("UsageTab", () => { ]); // And the cumulative line accumulates the auto-router series alongside the others. - const series = readSeries(getByTestId("area-chart")); + const series = readSeries(screen.getByTestId("area-chart")); expect(series[2]["Auto-router"]).toBeCloseTo(0.07, 5); }); @@ -371,9 +371,9 @@ describe("UsageTab", () => { start_date: "2026-07-12", end_date: "2026-07-12", }; - const { findAllByTestId } = renderWith([day("2026-07-12", {})], { toolSpend }); + renderWith([day("2026-07-12", {})], { toolSpend }); - const bars = await findAllByTestId("bar-chart"); + const bars = await screen.findAllByTestId("bar-chart"); const series = JSON.parse(bars[0].getAttribute("data-series") ?? "[]"); expect(series[0]).toMatchObject({ tool_name: "search", spend: 4.0 }); // The 64px bar cap is this card's opt-in; the shared BarChart must not cap @@ -391,14 +391,16 @@ describe("UsageTab", () => { start_date: "2026-07-12", end_date: "2026-07-12", }; - const { findAllByTestId, getAllByTestId } = renderWith([day("2026-07-12", {})], { toolSpend }); + renderWith([day("2026-07-12", {})], { toolSpend }); - const bars = await findAllByTestId("bar-chart"); + const bars = await screen.findAllByTestId("bar-chart"); const [totalByTool, dailyByTool] = bars.slice(-2); expect(dailyByTool).toHaveAttribute("data-show-legend", "false"); expect(totalByTool).toHaveAttribute("data-colors", dailyByTool.getAttribute("data-colors")); - const toolLegends = getAllByTestId("chart-legend").filter((legend) => legend.textContent === "search,read_file"); + const toolLegends = screen + .getAllByTestId("chart-legend") + .filter((legend) => legend.textContent === "search,read_file"); expect(toolLegends).toHaveLength(1); }); @@ -415,23 +417,23 @@ describe("UsageTab", () => { it.each(["Internal User", "Internal Viewer", "Org Admin"])( "hides the card and never calls the endpoint for %s", async (userRole) => { - const { queryByText, getByTestId } = renderWith([day("2026-07-12", { compression_savings_spend: 0.04 })], { + renderWith([day("2026-07-12", { compression_savings_spend: 0.04 })], { toolSpend, userRole, }); // Liveness gate: the daily-activity charts still render for this role, // so the absence below is the gate, not an empty tab. - expect(getByTestId("donut-chart")).toBeInTheDocument(); - expect(queryByText("Spend by tool")).not.toBeInTheDocument(); + expect(screen.getByTestId("donut-chart")).toBeInTheDocument(); + expect(screen.queryByText("Spend by tool")).not.toBeInTheDocument(); await vi.waitFor(() => expect(mockGetToolSpend).not.toHaveBeenCalled()); }, ); it("keeps the card and the endpoint call for an admin", async () => { - const { findByText } = renderWith([day("2026-07-12", { compression_savings_spend: 0.04 })], { toolSpend }); + renderWith([day("2026-07-12", { compression_savings_spend: 0.04 })], { toolSpend }); - expect(await findByText("Spend by tool")).toBeInTheDocument(); + expect(await screen.findByText("Spend by tool")).toBeInTheDocument(); expect(mockGetToolSpend).toHaveBeenCalled(); }); }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/useShadowEval.ts b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/useShadowEval.ts index eef98320e67..107df0f594a 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/useShadowEval.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/useShadowEval.ts @@ -7,7 +7,7 @@ import { $api, fetchClient } from "@/lib/http/api"; import type { components } from "@/lib/http/schema"; export type ShadowEvalJob = components["schemas"]["ShadowEvalJobResponse"]; -export type ShadowEvalJobKey = components["schemas"]["ShadowEvalJobKeyResponse"]; +export type ShadowEvalJobTarget = components["schemas"]["ShadowEvalJobTargetResponse"]; export type ShadowEvalSlice = components["schemas"]["ShadowEvalSlice"]; export type StartShadowEvalRequest = components["schemas"]["StartShadowEvalRequest"]; diff --git a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_configs.ts b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_configs.ts index 03cfeed42ff..7785a8e44ab 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_configs.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_configs.ts @@ -312,4 +312,10 @@ export const GUARDRAIL_PRESETS: Record = { mode: "pre_call", defaultOn: false, }, + alice: { + provider: "Alice", + guardrailNameSuggestion: "Alice", + mode: "pre_call", + defaultOn: false, + }, }; diff --git a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_data.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_data.test.ts index 13909e48185..1e486639840 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_data.test.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_data.test.ts @@ -27,6 +27,7 @@ const EXPECTED_PARTNER_LOGO_FILES: Record = { deepkeep: "deepkeep.svg", repelloai: "repelloai.png", straiker: "straiker.svg", + alice: "alice.svg", }; describe("guardrail_garden_data logos", () => { diff --git a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_data.ts b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_data.ts index 744af89a357..931b3a111d8 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_data.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_data.ts @@ -464,6 +464,16 @@ export const PARTNER_GUARDRAIL_CARDS: GuardrailCardInfo[] = [ tags: ["Agentic", "Prompt Injection", "Tool Misuse", "MCP", "Skills"], providerKey: "Straiker", }, + { + id: "alice", + name: "Alice", + description: + "Policy-based guardrails for prompts and model responses, evaluated per application so one proxy can enforce a different policy set per team or product.", + category: "partner", + logo: guardrailLogoMap["Alice"], + tags: ["Content Moderation", "Prompt Injection", "PII", "Policy"], + providerKey: "Alice", + }, ]; export const ALL_CARDS = [...LITELLM_CONTENT_FILTER_CARDS, ...PARTNER_GUARDRAIL_CARDS]; diff --git a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_detail.tsx b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_detail.tsx index 0a0f70b6bc2..dda56a06ab1 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_detail.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_detail.tsx @@ -1,6 +1,7 @@ import React, { useState } from "react"; import { ArrowLeft } from "lucide-react"; import { Button } from "@/components/ui/button"; +import { cn } from "@/lib/cva.config"; import AddGuardrailForm from "./add_guardrail_form"; import { Logo } from "@/components/molecules/logo/Logo"; import { GUARDRAIL_PRESETS } from "./guardrail_garden_configs"; @@ -40,7 +41,7 @@ const GuardrailDetailView: React.FC = ({ card, onBack, const tabs = [{ key: "overview", label: "Overview" }, ...(card.eval ? [{ key: "eval", label: "Eval Results" }] : [])]; return ( -
+
{/* Back link */}
= ({ card, onBack,
{/* ── Header block (Vertex-style) ── */} -
+
-

{card.name}

+

{card.name}

-

{card.description}

+

{card.description}

{/* Action buttons — outlined style like Vertex */}
@@ -66,21 +67,18 @@ const GuardrailDetailView: React.FC = ({ card, onBack,
{/* ── Tab bar ──────────────────────────────────── */} -
-
+
+
{tabs.map((tab) => (
setActiveTab(tab.key)} - style={{ - padding: "12px 20px", - fontSize: 14, - color: activeTab === tab.key ? "#1a73e8" : "#5f6368", - borderBottom: activeTab === tab.key ? "3px solid #1a73e8" : "3px solid transparent", - cursor: "pointer", - fontWeight: activeTab === tab.key ? 500 : 400, - marginBottom: -1, - }} + className={cn( + "-mb-px cursor-pointer border-b-[3px] px-5 py-3 text-sm", + activeTab === tab.key + ? "border-info font-medium text-info" + : "border-transparent font-normal text-muted-foreground", + )} > {tab.label}
@@ -90,31 +88,27 @@ const GuardrailDetailView: React.FC = ({ card, onBack, {/* ── Tab content ──────────────────────────────── */} {activeTab === "overview" && ( -
+
{/* Left column — overview + details table */} -
-

Overview

-

{card.description}

+
+

Overview

+

{card.description}

-

Guardrail Details

-

Details are as follows

+

Guardrail Details

+

Details are as follows

-
+
- - - + + + {detailRows.map((row, i) => ( - - - + + + ))} @@ -122,37 +116,30 @@ const GuardrailDetailView: React.FC = ({ card, onBack, {/* Right column — metadata sidebar like Vertex */} -
+
{/* Guardrail ID */} -
-
Guardrail ID
-
litellm/{card.id}
+
+
Guardrail ID
+
litellm/{card.id}
{/* Type */} -
-
Type
-
+
+
Type
+
{card.category === "litellm" ? "Content Filter" : "Partner"}
{/* Tags — pill style like Vertex */} {card.tags.length > 0 && ( -
-
Tags
-
+
+
Tags
+
{card.tags.map((tag) => ( {tag} @@ -166,19 +153,19 @@ const GuardrailDetailView: React.FC = ({ card, onBack, {activeTab === "eval" && (
-

Eval Results

-
- Property - - {card.name} -
Property{card.name}
{row.property}{row.value}
{row.property}{row.value}
+

Eval Results

+
- - - + + + {evalRows.map((row, i) => ( - - - + + + ))} diff --git a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_info.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_info.test.tsx index 2b90a1d8cbc..fcffc2122e7 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_info.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_info.test.tsx @@ -1,5 +1,5 @@ import * as networking from "@/components/networking"; -import { fireEvent, render, waitFor, within } from "@testing-library/react"; +import { fireEvent, render, waitFor, within, screen } from "@testing-library/react"; import userEvent from "@testing-library/user-event"; import { afterEach, describe, expect, it, vi } from "vitest"; import GuardrailInfoView from "./guardrail_info"; @@ -65,21 +65,19 @@ describe("Guardrail Info", () => { vi.mocked(networking.getGuardrailProviderSpecificParams).mockResolvedValue({}); - const { getAllByText, getByText } = render( - {}} accessToken="123" isAdmin={true} />, - ); + render( {}} accessToken="123" isAdmin={true} />); // Wait for the loading to complete and data to be rendered await waitFor(() => { // The guardrail name appears in multiple places (title and settings tab) - const elements = getAllByText("Test Guardrail"); + const elements = screen.getAllByText("Test Guardrail"); expect(elements.length).toBeGreaterThan(0); }); // Verify other key elements are present - expect(getByText("Back to Guardrails")).toBeInTheDocument(); - expect(getByText("Overview")).toBeInTheDocument(); - expect(getByText("Settings")).toBeInTheDocument(); + expect(screen.getByText("Back to Guardrails")).toBeInTheDocument(); + expect(screen.getByText("Overview")).toBeInTheDocument(); + expect(screen.getByText("Settings")).toBeInTheDocument(); }); it("should render a tag-based mode object rather than crashing the detail view", async () => { @@ -105,11 +103,9 @@ describe("Guardrail Info", () => { vi.mocked(networking.getGuardrailProviderSpecificParams).mockResolvedValue({}); - const { findAllByText } = render( - {}} accessToken="123" isAdmin={true} />, - ); + render( {}} accessToken="123" isAdmin={true} />); - expect(await findAllByText("pre_call, post_call (tag-based)")).not.toHaveLength(0); + expect(await screen.findAllByText("pre_call, post_call (tag-based)")).not.toHaveLength(0); }); it("should render the provider logo from the bundled guardrail logo map", async () => { @@ -135,11 +131,9 @@ describe("Guardrail Info", () => { vi.mocked(networking.getGuardrailProviderSpecificParams).mockResolvedValue({}); - const { findByAltText } = render( - {}} accessToken="123" isAdmin={true} />, - ); + render( {}} accessToken="123" isAdmin={true} />); - const logo = await findByAltText("Presidio PII logo"); + const logo = await screen.findByAltText("Presidio PII logo"); expect(logo).toHaveAttribute("src", expect.stringContaining("microsoft_azure.svg")); }); @@ -167,25 +161,27 @@ describe("Guardrail Info", () => { vi.mocked(networking.getGuardrailProviderSpecificParams).mockResolvedValue({}); - const { getByText, findByText, container } = render( + const { container } = render( {}} accessToken="123" isAdmin={true} />, ); await waitFor(() => { - expect(getByText("Settings")).toBeInTheDocument(); + expect(screen.getByText("Settings")).toBeInTheDocument(); }); // Click the Settings tab - fireEvent.click(getByText("Settings")); + fireEvent.click(screen.getByText("Settings")); // Wait for the Settings panel to render await waitFor(() => { - expect(getByText("Guardrail Settings")).toBeInTheDocument(); + expect(screen.getByText("Guardrail Settings")).toBeInTheDocument(); }); await userEvent.hover(within(container).getByRole("img", { name: "Config guardrail details" })); - expect(await findByText("Guardrail is defined in the config file and cannot be edited.")).toBeInTheDocument(); + expect( + await screen.findByText("Guardrail is defined in the config file and cannot be edited."), + ).toBeInTheDocument(); }); it("should render the guardrail info", async () => { @@ -216,12 +212,10 @@ describe("Guardrail Info", () => { vi.mocked(networking.getGuardrailProviderSpecificParams).mockResolvedValue({}); - const { getByText } = render( - {}} accessToken="123" isAdmin={true} />, - ); + render( {}} accessToken="123" isAdmin={true} />); await waitFor(() => { - expect(getByText("PII Entity Configuration")).toBeInTheDocument(); + expect(screen.getByText("PII Entity Configuration")).toBeInTheDocument(); }); }); it("should handle content filter updates correctly", async () => { @@ -251,30 +245,28 @@ describe("Guardrail Info", () => { vi.mocked(networking.getGuardrailProviderSpecificParams).mockResolvedValue({}); vi.mocked(networking.updateGuardrailCall).mockResolvedValue({ status: "success" }); - const { getByText, getByLabelText } = render( - {}} accessToken="123" isAdmin={true} />, - ); + render( {}} accessToken="123" isAdmin={true} />); await waitFor(() => { - expect(getByText("Settings")).toBeInTheDocument(); + expect(screen.getByText("Settings")).toBeInTheDocument(); }); // Go to Settings tab - fireEvent.click(getByText("Settings")); + fireEvent.click(screen.getByText("Settings")); await waitFor(() => { - expect(getByText("Guardrail Settings")).toBeInTheDocument(); + expect(screen.getByText("Guardrail Settings")).toBeInTheDocument(); }); // Enter Edit Mode - fireEvent.click(getByText("Edit Settings")); + fireEvent.click(screen.getByText("Edit Settings")); // Modify Guardrail Name to force an update - const nameInput = getByLabelText("Guardrail Name"); + const nameInput = screen.getByLabelText("Guardrail Name"); fireEvent.change(nameInput, { target: { value: "Updated Name" } }); // Save with only name change - const saveButton = getByText("Save Changes"); + const saveButton = screen.getByText("Save Changes"); fireEvent.click(saveButton); await waitFor(() => { @@ -300,16 +292,16 @@ describe("Guardrail Info", () => { // Enter Edit Mode again to make changes await waitFor(() => { - expect(getByText("Edit Settings")).toBeInTheDocument(); + expect(screen.getByText("Edit Settings")).toBeInTheDocument(); }); - fireEvent.click(getByText("Edit Settings")); + fireEvent.click(screen.getByText("Edit Settings")); // Now modify the values using the mock button - const simulateChangeButton = getByText("Simulate Change"); + const simulateChangeButton = screen.getByText("Simulate Change"); fireEvent.click(simulateChangeButton); // Save again - fireEvent.click(getByText("Save Changes")); + fireEvent.click(screen.getByText("Save Changes")); await waitFor(() => { expect(networking.updateGuardrailCall).toHaveBeenCalled(); @@ -339,12 +331,10 @@ describe("Guardrail Info", () => { }); vi.mocked(networking.getGuardrailProviderSpecificParams).mockResolvedValue({}); - const { findByRole, getByRole, getByText } = render( - {}} accessToken="123" isAdmin={true} />, - ); + render( {}} accessToken="123" isAdmin={true} />); - expect(await findByRole("tab", { name: "Overview" })).toHaveAttribute("aria-selected", "true"); - expect(getByRole("tab", { name: "Settings" })).toHaveAttribute("aria-selected", "false"); - expect(getByText("Guardrail Settings")).toBeInTheDocument(); + expect(await screen.findByRole("tab", { name: "Overview" })).toHaveAttribute("aria-selected", "true"); + expect(screen.getByRole("tab", { name: "Settings" })).toHaveAttribute("aria-selected", "false"); + expect(screen.getByText("Guardrail Settings")).toBeInTheDocument(); }); }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_info_helpers.tsx b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_info_helpers.tsx index 83038b8e0e7..c1f2ddcf51c 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_info_helpers.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_info_helpers.tsx @@ -1,5 +1,6 @@ import aimSecurityLogo from "../../../../../public/assets/logos/aim_security.jpeg"; import aktoLogo from "../../../../../public/assets/logos/akto.svg"; +import aliceLogo from "../../../../../public/assets/logos/alice.svg"; import aporiaLogo from "../../../../../public/assets/logos/aporia.png"; import bedrockLogo from "../../../../../public/assets/logos/bedrock.svg"; import catoNetworksLogo from "../../../../../public/assets/logos/cato_networks.svg"; @@ -83,6 +84,7 @@ export const guardrail_provider_map: Record = { Deepkeep: "deepkeep", QostodianNexus: "qostodian_nexus", Repelloai: "repelloai", + Alice: "alice", }; // Function to populate provider map from API response - updates the original map @@ -204,6 +206,7 @@ export const guardrailLogoMap = { "Qostodian Nexus": qohashLogo.src, "RepelloAI Argus": repelloAiLogo.src, Straiker: straikerLogo.src, + Alice: aliceLogo.src, } satisfies Record; export const getGuardrailLogo = (displayName: string): string | undefined => diff --git a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/pii_components.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/pii_components.test.tsx index 2f839487111..30ff2cf7c5f 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/pii_components.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/pii_components.test.tsx @@ -1,4 +1,4 @@ -import { render } from "@testing-library/react"; +import { render, screen } from "@testing-library/react"; import { describe, it, expect } from "vitest"; import { CategoryFilter, QuickActions, PiiEntityList } from "./pii_components"; import type { PiiEntityCategory } from "@/components/guardrails/types"; @@ -6,25 +6,21 @@ import type { PiiEntityCategory } from "@/components/guardrails/types"; describe("CategoryFilter", () => { it("should render", () => { const emptyCategories: PiiEntityCategory[] = []; - const { getByText } = render( - {}} />, - ); - expect(getByText("Filter by category")).toBeInTheDocument(); + render( {}} />); + expect(screen.getByText("Filter by category")).toBeInTheDocument(); }); }); describe("QuickActions", () => { it("should render", () => { - const { getByText } = render( - {}} onUnselectAll={() => {}} hasSelectedEntities={false} />, - ); - expect(getByText("Quick Actions")).toBeInTheDocument(); + render( {}} onUnselectAll={() => {}} hasSelectedEntities={false} />); + expect(screen.getByText("Quick Actions")).toBeInTheDocument(); }); }); describe("PiiEntityList", () => { it("should render", () => { - const { getByText } = render( + render( { entityToCategoryMap={new Map()} />, ); - expect(getByText("No PII types match your filter criteria")).toBeInTheDocument(); + expect(screen.getByText("No PII types match your filter criteria")).toBeInTheDocument(); }); }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/pii_configuration.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/pii_configuration.test.tsx index 00c568ef35b..4f822578fe8 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/pii_configuration.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/pii_configuration.test.tsx @@ -1,10 +1,10 @@ -import { render } from "@testing-library/react"; +import { render, screen } from "@testing-library/react"; import { describe, it, expect } from "vitest"; import PiiConfiguration from "./pii_configuration"; describe("PiiConfiguration", () => { it("should render", () => { - const { getByText } = render( + render( { entityCategories={[]} />, ); - expect(getByText("Configure PII Protection")).toBeInTheDocument(); + expect(screen.getByText("Configure PII Protection")).toBeInTheDocument(); }); }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/configOverrides/cyberArkApi.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/configOverrides/cyberArkApi.ts new file mode 100644 index 00000000000..910fe2b17e5 --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/configOverrides/cyberArkApi.ts @@ -0,0 +1,38 @@ +import { getProxyBaseUrl, getGlobalLitellmHeaderName } from "@/components/networking"; +import { createApiClient } from "@/lib/http/client"; + +export interface CyberArkFieldSchema { + description?: string; + properties: Record; +} + +export interface CyberArkConfigResponse { + config_type: string; + values: Record; + field_schema: CyberArkFieldSchema; +} + +export interface CyberArkStatusResponse { + status: string; + message: string; +} + +const apiClient = createApiClient({ + getBaseUrl: getProxyBaseUrl, + getAuthHeaderName: getGlobalLitellmHeaderName, +}); + +export const getCyberArkConfig = async (accessToken: string): Promise => + apiClient.get("/config_overrides/cyberark", { accessToken }); + +export const updateCyberArkConfig = async ( + accessToken: string, + config: Record, +): Promise => + apiClient.post("/config_overrides/cyberark", { accessToken, body: config }); + +export const deleteCyberArkConfig = async (accessToken: string): Promise => + apiClient.delete("/config_overrides/cyberark", { accessToken }); + +export const testCyberArkConnection = async (accessToken: string): Promise => + apiClient.post("/config_overrides/cyberark/test_connection", { accessToken }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/configOverrides/useCyberArkConfig.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/configOverrides/useCyberArkConfig.ts new file mode 100644 index 00000000000..cfc3acdfe26 --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/configOverrides/useCyberArkConfig.ts @@ -0,0 +1,24 @@ +import { getCyberArkConfig, type CyberArkConfigResponse } from "./cyberArkApi"; +import { useQuery } from "@tanstack/react-query"; +import useAuthorized from "../useAuthorized"; +import { createQueryKeys } from "../common/queryKeysFactory"; + +export const cyberArkKeys = createQueryKeys("cyberArkConfig"); + +export const useCyberArkConfig = () => { + const { accessToken } = useAuthorized(); + + const queryOptions = { + queryKey: cyberArkKeys.list({}), + queryFn: async () => { + if (!accessToken) { + throw new Error("Access token is required"); + } + return getCyberArkConfig(accessToken); + }, + enabled: !!accessToken, + staleTime: 60 * 60 * 1000, + gcTime: 60 * 60 * 1000, + }; + return useQuery(queryOptions); +}; diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/configOverrides/useDeleteCyberArkConfig.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/configOverrides/useDeleteCyberArkConfig.ts new file mode 100644 index 00000000000..cebba3a202d --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/configOverrides/useDeleteCyberArkConfig.ts @@ -0,0 +1,19 @@ +import { deleteCyberArkConfig } from "./cyberArkApi"; +import { useMutation, useQueryClient } from "@tanstack/react-query"; +import { cyberArkKeys } from "./useCyberArkConfig"; + +export const useDeleteCyberArkConfig = (accessToken: string | null) => { + const queryClient = useQueryClient(); + + return useMutation({ + mutationFn: async () => { + if (!accessToken) { + throw new Error("Access token is required"); + } + return deleteCyberArkConfig(accessToken); + }, + onSuccess: () => { + queryClient.invalidateQueries({ queryKey: cyberArkKeys.all }); + }, + }); +}; diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/configOverrides/useUpdateCyberArkConfig.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/configOverrides/useUpdateCyberArkConfig.ts new file mode 100644 index 00000000000..f5c88e833f7 --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/configOverrides/useUpdateCyberArkConfig.ts @@ -0,0 +1,19 @@ +import { updateCyberArkConfig } from "./cyberArkApi"; +import { useMutation, useQueryClient } from "@tanstack/react-query"; +import { cyberArkKeys } from "./useCyberArkConfig"; + +export const useUpdateCyberArkConfig = (accessToken: string | null) => { + const queryClient = useQueryClient(); + + return useMutation({ + mutationFn: async (config: Record) => { + if (!accessToken) { + throw new Error("Access token is required"); + } + return updateCyberArkConfig(accessToken, config); + }, + onSuccess: () => { + queryClient.invalidateQueries({ queryKey: cyberArkKeys.all }); + }, + }); +}; diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/modelAccessGroups/useDeleteModelAccessGroupBudget.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/modelAccessGroups/useDeleteModelAccessGroupBudget.ts new file mode 100644 index 00000000000..3ed8af15cde --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/modelAccessGroups/useDeleteModelAccessGroupBudget.ts @@ -0,0 +1,30 @@ +import { useMutation, useQueryClient } from "@tanstack/react-query"; +import { fetchClient } from "@/lib/http/api"; +import type { components } from "@/lib/http/schema"; +import { modelAccessGroupKeys } from "./useModelAccessGroups"; + +type DeleteModelAccessGroupBudgetResponse = components["schemas"]["DeleteAccessGroupBudgetResponse"]; + +const deleteModelAccessGroupBudget = async ( + accessGroup: string, +): Promise => { + const { data } = await fetchClient.DELETE("/access_group/{access_group}/budget", { + params: { path: { access_group: accessGroup } }, + }); + return data; +}; + +/** + * Clear a model access group's shared budget. The group and its deployments are untouched, + * and the recorded spend goes with the budget row. + */ +export const useDeleteModelAccessGroupBudget = () => { + const queryClient = useQueryClient(); + + return useMutation({ + mutationFn: deleteModelAccessGroupBudget, + onSuccess: () => { + queryClient.invalidateQueries({ queryKey: modelAccessGroupKeys.all }); + }, + }); +}; diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/modelAccessGroups/useModelAccessGroups.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/modelAccessGroups/useModelAccessGroups.ts new file mode 100644 index 00000000000..703c51b75b1 --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/modelAccessGroups/useModelAccessGroups.ts @@ -0,0 +1,31 @@ +import { useQuery } from "@tanstack/react-query"; +import { createQueryKeys } from "../common/queryKeysFactory"; +import { all_admin_roles } from "@/utils/roles"; +import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized"; +import { fetchClient } from "@/lib/http/api"; +import type { components } from "@/lib/http/schema"; + +export type ModelAccessGroupBudget = components["schemas"]["AccessGroupBudget"]; +export type ModelAccessGroup = components["schemas"]["AccessGroupInfo"]; + +export const modelAccessGroupKeys = createQueryKeys("modelAccessGroups"); + +const fetchModelAccessGroups = async (): Promise => { + const { data } = await fetchClient.GET("/access_group/list"); + return data?.access_groups ?? []; +}; + +/** + * Model access groups: the free-text labels on a deployment's `model_info.access_groups`, + * with the shared budget each one carries. Unrelated to the `/v1/access_group` table that + * the Access Groups page drives. + */ +export const useModelAccessGroups = () => { + const { accessToken, userRole } = useAuthorized(); + + return useQuery({ + queryKey: modelAccessGroupKeys.list({}), + queryFn: fetchModelAccessGroups, + enabled: Boolean(accessToken) && all_admin_roles.includes(userRole || ""), + }); +}; diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/modelAccessGroups/useSetModelAccessGroupBudget.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/modelAccessGroups/useSetModelAccessGroupBudget.ts new file mode 100644 index 00000000000..cdea15bedb4 --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/modelAccessGroups/useSetModelAccessGroupBudget.ts @@ -0,0 +1,35 @@ +import { useMutation, useQueryClient } from "@tanstack/react-query"; +import { fetchClient } from "@/lib/http/api"; +import type { components } from "@/lib/http/schema"; +import { modelAccessGroupKeys } from "./useModelAccessGroups"; + +export type SetModelAccessGroupBudgetParams = components["schemas"]["AccessGroupBudgetRequest"]; +type SetModelAccessGroupBudgetResponse = components["schemas"]["AccessGroupBudgetResponse"]; + +export interface SetModelAccessGroupBudgetVariables { + accessGroup: string; + params: SetModelAccessGroupBudgetParams; +} + +const setModelAccessGroupBudget = async ({ + accessGroup, + params, +}: SetModelAccessGroupBudgetVariables): Promise => { + const { data } = await fetchClient.PUT("/access_group/{access_group}/budget", { + params: { path: { access_group: accessGroup } }, + body: params, + }); + return data; +}; + +/** Set or replace a model access group's shared budget. The write is idempotent. */ +export const useSetModelAccessGroupBudget = () => { + const queryClient = useQueryClient(); + + return useMutation({ + mutationFn: setModelAccessGroupBudget, + onSuccess: () => { + queryClient.invalidateQueries({ queryKey: modelAccessGroupKeys.all }); + }, + }); +}; diff --git a/ui/litellm-dashboard/src/app/(dashboard)/mcp-servers/_components/ImportMCPServers.tsx b/ui/litellm-dashboard/src/app/(dashboard)/mcp-servers/_components/ImportMCPServers.tsx new file mode 100644 index 00000000000..d04160bca3e --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/mcp-servers/_components/ImportMCPServers.tsx @@ -0,0 +1,134 @@ +import React, { useState } from "react"; +import { Button } from "@/components/ui/button"; +import { Dialog, DialogContent, DialogHeader, DialogTitle } from "@/components/ui/dialog"; +import { Textarea } from "@/components/ui/textarea"; +import { Alert, AlertTitle } from "@/components/shared/Alert"; +import { importMCPServers } from "@/components/networking"; +import { toast } from "@/lib/toast"; +import { MCPConnectorImportResponse, parseConnectorConfig } from "./importConnectorConfig"; + +interface ImportMCPServersProps { + accessToken: string; + open: boolean; + onClose: () => void; + onImported: () => void; +} + +const PLACEHOLDER = `{ + "mcpServers": { + "my_server": { + "url": "https://example.com/mcp", + "authorization_token": "..." + } + } +}`; + +const ImportMCPServers: React.FC = ({ accessToken, open, onClose, onImported }) => { + const [configText, setConfigText] = useState(""); + const [parseError, setParseError] = useState(null); + const [isImporting, setIsImporting] = useState(false); + const [result, setResult] = useState(null); + + const handleClose = () => { + setConfigText(""); + setParseError(null); + setResult(null); + onClose(); + }; + + const handleImport = async () => { + const parsed = parseConnectorConfig(configText); + if (!parsed.ok) { + setParseError(parsed.error); + return; + } + setParseError(null); + setIsImporting(true); + try { + const response = (await importMCPServers(accessToken, parsed.payload)) as MCPConnectorImportResponse; + setResult(response); + if (response.imported.length > 0) { + toast.success(`Imported ${response.imported.length} MCP server${response.imported.length === 1 ? "" : "s"}`); + onImported(); + } + } catch (error) { + console.error("Failed to import MCP servers:", error); + setParseError("Import request failed. Check the proxy logs for details."); + } finally { + setIsImporting(false); + } + }; + + return ( + !isOpen && handleClose()}> + + + Import MCP Connectors + +
+

+ Paste an Anthropic connector configuration: the mcpServers mapping from a Claude Desktop / + Claude Code config file, or the mcp_servers array from the Anthropic Messages API. +

+
MetricValue
MetricValue
{row.metric}{row.value}
{row.metric}{row.value}