diff --git a/.circleci/config.yml b/.circleci/config.yml index dbeb412506f..abcdbf45187 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -133,6 +133,26 @@ commands: done echo "record/replay proxy did not become ready" >&2 exit 1 + start_fake_openai_endpoint: + description: "Start the canned OpenAI mock (tests/_fake_openai_endpoint_server.py) on host port 8190 and wait until healthy. Models whose api_base points here (via FAKE_OPENAI_API_BASE) get well-formed chat/text/embedding responses with realistic usage, so the E2E run neither pays for nor depends on the live provider. A request whose model is '429' returns HTTP 429 for rate-limit/cooldown tests. Run after uv deps are synced." + steps: + - run: + name: Start fake OpenAI endpoint + background: true + command: | + uv run --no-sync python tests/_fake_openai_endpoint_server.py --host 0.0.0.0 --port 8190 + - run: + name: Wait for fake OpenAI endpoint + command: | + for i in $(seq 1 30); do + if curl -sf http://localhost:8190/health >/dev/null 2>&1; then + echo "fake OpenAI endpoint is up" + exit 0 + fi + sleep 1 + done + echo "fake OpenAI endpoint did not become ready" >&2 + exit 1 setup_litellm_enterprise_pip: steps: - run: @@ -168,6 +188,8 @@ jobs: name: win/default shell: powershell.exe working_directory: ~/project + environment: + UV_PYTHON: "3.11" steps: - checkout - run: @@ -200,7 +222,7 @@ jobs: if (-not (Select-String -Path $PROFILE -SimpleMatch $uvBin -Quiet)) { Add-Content -Path $PROFILE -Value "`$env:Path = `"$uvBin;`$env:Path`"" } - uv sync --frozen --group dev --python (Get-Command python).Source + uv sync --frozen --group dev --python 3.11 - run: name: Run Windows-specific test command: | @@ -594,6 +616,8 @@ jobs: working_directory: ~/project resource_class: large parallelism: 4 + environment: + FAKE_OPENAI_API_BASE: http://127.0.0.1:8190 steps: - checkout - setup_google_dns @@ -609,6 +633,7 @@ jobs: paths: - ~/.cache/uv key: v1-uv-cache-{{ checksum "uv.lock" }} + - start_fake_openai_endpoint # Run pytest and generate JUnit XML report - setup_litellm_enterprise_pip - run: @@ -1549,6 +1574,7 @@ jobs: name: Install Dependencies command: | uv sync --frozen --all-groups --all-extras --python 3.12 + - start_fake_openai_endpoint - start_postgres: db_name: litellm_test - attach_workspace: @@ -1586,6 +1612,7 @@ jobs: -e DATABASE_URL="postgresql://postgres:postgres@host.docker.internal:5432/litellm_test" \ -e DEFAULT_NUM_WORKERS_LITELLM_PROXY=1 \ -e DISABLE_SCHEMA_UPDATE="True" \ + -e FAKE_OPENAI_API_BASE=http://host.docker.internal:8190 \ --name my-app \ --add-host=host.docker.internal:host-gateway \ -v $(pwd)/litellm/proxy/example_config_yaml/bad_schema.prisma:/app/schema.prisma \ @@ -1648,6 +1675,7 @@ jobs: zstd -d litellm-docker-database.tar.zst --stdout | docker load docker tag litellm-docker-database:ci my-app:latest - start_openai_record_replay_proxy + - start_fake_openai_endpoint - run: name: Run Docker container command: | @@ -1655,6 +1683,7 @@ jobs: -p 4000:4000 \ -e DATABASE_URL=postgresql://postgres:postgres@host.docker.internal:5432/circle_test \ -e USE_PRISMA_MIGRATE=True \ + -e FAKE_OPENAI_API_BASE=http://host.docker.internal:8190 \ -e AZURE_API_KEY=$AZURE_API_KEY \ -e REDIS_HOST=$REDIS_HOST \ -e REDIS_PASSWORD=$REDIS_PASSWORD \ @@ -1817,6 +1846,7 @@ jobs: zstd -d litellm-docker-database.tar.zst --stdout | docker load docker images | grep litellm-docker-database - start_openai_record_replay_proxy + - start_fake_openai_endpoint - run: name: Run Docker container # intentionally give bad redis credentials here @@ -1830,6 +1860,7 @@ jobs: -e REDIS_PORT=$REDIS_PORT \ -e LITELLM_MASTER_KEY="sk-1234" \ -e OPENAI_API_KEY=$OPENAI_API_KEY \ + -e FAKE_OPENAI_API_BASE=http://host.docker.internal:8190 \ -e LITELLM_LICENSE=$LITELLM_LICENSE \ -e OTEL_EXPORTER="in_memory" \ -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \ @@ -1889,6 +1920,7 @@ jobs: -e REDIS_PORT=$REDIS_PORT \ -e LITELLM_MASTER_KEY="sk-1234" \ -e OPENAI_API_KEY=$OPENAI_API_KEY \ + -e FAKE_OPENAI_API_BASE=http://host.docker.internal:8190 \ -e LITELLM_LICENSE="bad-license" \ --add-host host.docker.internal:host-gateway \ --name my-app-3 \ @@ -1938,6 +1970,7 @@ jobs: uv sync --frozen --all-groups --all-extras --python 3.12 - start_postgres - start_redis + - start_fake_openai_endpoint - attach_workspace: at: ~/project - run: @@ -1961,6 +1994,7 @@ jobs: -e REDIS_PORT=6379 \ -e LITELLM_MASTER_KEY="sk-1234" \ -e OPENAI_API_KEY=$OPENAI_API_KEY \ + -e FAKE_OPENAI_API_BASE=http://host.docker.internal:8190 \ -e LITELLM_LICENSE=$LITELLM_LICENSE \ -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \ -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \ @@ -2020,6 +2054,7 @@ jobs: command: | uv sync --frozen --all-groups --all-extras --python 3.12 - start_postgres + - start_fake_openai_endpoint - attach_workspace: at: ~/project - run: @@ -2039,6 +2074,7 @@ jobs: -e REDIS_PASSWORD=$REDIS_PASSWORD \ -e REDIS_PORT=$REDIS_PORT \ -e LITELLM_MASTER_KEY="sk-1234" \ + -e FAKE_OPENAI_API_BASE=http://host.docker.internal:8190 \ -e LITELLM_LICENSE=$LITELLM_LICENSE \ -e USE_DDTRACE=True \ -e DD_API_KEY=$DD_API_KEY \ @@ -2060,6 +2096,7 @@ jobs: -e REDIS_PASSWORD=$REDIS_PASSWORD \ -e REDIS_PORT=$REDIS_PORT \ -e LITELLM_MASTER_KEY="sk-1234" \ + -e FAKE_OPENAI_API_BASE=http://host.docker.internal:8190 \ -e LITELLM_LICENSE=$LITELLM_LICENSE \ -e USE_DDTRACE=True \ -e DD_API_KEY=$DD_API_KEY \ @@ -2112,6 +2149,7 @@ jobs: command: | uv sync --frozen --all-groups --all-extras --python 3.12 - start_postgres + - start_fake_openai_endpoint - attach_workspace: at: ~/project - run: @@ -2129,6 +2167,7 @@ jobs: -e DATABASE_URL=postgresql://postgres:postgres@host.docker.internal:5432/circle_test \ -e STORE_MODEL_IN_DB="True" \ -e LITELLM_MASTER_KEY="sk-1234" \ + -e FAKE_OPENAI_API_BASE=http://host.docker.internal:8190 \ -e LITELLM_LICENSE=$LITELLM_LICENSE \ --add-host host.docker.internal:host-gateway \ --name my-app \ @@ -2187,6 +2226,7 @@ jobs: command: | docker build -t my-app:latest -f docker/build_from_pip/Dockerfile.build_from_pip . - start_postgres + - start_fake_openai_endpoint - run: name: Run Docker container # intentionally give bad redis credentials here @@ -2200,6 +2240,7 @@ jobs: -e REDIS_PORT=$REDIS_PORT \ -e LITELLM_MASTER_KEY="sk-1234" \ -e OPENAI_API_KEY=$OPENAI_API_KEY \ + -e FAKE_OPENAI_API_BASE=http://host.docker.internal:8190 \ -e LITELLM_LICENSE=$LITELLM_LICENSE \ -e OTEL_EXPORTER="in_memory" \ -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \ diff --git a/.githooks/commit-msg b/.githooks/commit-msg new file mode 100755 index 00000000000..b64e38a2286 --- /dev/null +++ b/.githooks/commit-msg @@ -0,0 +1,75 @@ +#!/usr/bin/env bash +# +# commit-msg — enforce Conventional Commits 1.0.0 +# https://www.conventionalcommits.org/en/v1.0.0/ +# +# Subject format: ()!: +# - must be one of the angular types (feat, fix, ...) +# - () is optional +# - ! is optional and marks a breaking change +# - is mandatory and must be non-empty +# +# Bypass: commit with --no-verify. +# Merge, revert, fixup!, squash!, and amend! messages are passed through. + +set -eu + +COMMIT_MSG_FILE="${1:-}" +if [ -z "$COMMIT_MSG_FILE" ] || [ ! -f "$COMMIT_MSG_FILE" ]; then + echo "commit-msg: missing commit message file" >&2 + exit 1 +fi + +# First non-comment, non-empty line is the subject. +subject="" +while IFS= read -r line || [ -n "$line" ]; do + case "$line" in + ''|'#'*) continue ;; + esac + subject="$line" + break +done < "$COMMIT_MSG_FILE" + +if [ -z "$subject" ]; then + echo "commit-msg: empty commit message" >&2 + exit 1 +fi + +# Pass-through commits generated by git itself. +case "$subject" in + "Merge "*|"Revert \""*|"fixup! "*|"squash! "*|"amend! "*) + exit 0 + ;; +esac + +ALLOWED_TYPES="feat|fix|docs|style|refactor|perf|test|build|ci|chore|revert" +# Description must not start with an uppercase letter — kept in sync with the +# subjectPattern in .github/workflows/conventional-commits.yml so the local +# hook is the strictly tighter of the two gates. (Without this guard, a commit +# like "feat: Add thing" passes locally but fails the PR-title CI check.) +PATTERN="^(${ALLOWED_TYPES})(\([^)]+\))?!?: [^A-Z].*" + +if printf '%s' "$subject" | grep -Eq "$PATTERN"; then + exit 0 +fi + +cat >&2 <()!: + (description must start with a lowercase letter) + + Allowed types: feat, fix, docs, style, refactor, perf, test, build, ci, chore, revert + Examples: + feat(router): add weighted round-robin strategy + fix(bedrock): decouple STS region from aws_region_name + chore(deps): bump black to 26.3.1 + refactor!: drop Python 3.8 support + +See https://www.conventionalcommits.org/en/v1.0.0/ + +To bypass (use sparingly): git commit --no-verify +EOF +exit 1 diff --git a/.githooks/pre-push b/.githooks/pre-push new file mode 100755 index 00000000000..c2267c8501c --- /dev/null +++ b/.githooks/pre-push @@ -0,0 +1,92 @@ +#!/usr/bin/env bash +# +# pre-push — enforce Conventional Branches +# https://conventional-branch.github.io/ +# +# Branch format: / +# must be one of: feature, bugfix, hotfix, release, chore +# +# Protected branches (always allowed): +# - main +# - litellm_internal_staging +# - dependabot/* +# - gh-readonly-queue/* +# +# Tag pushes and branch deletions are skipped. +# Bypass: git push --no-verify. + +set -eu + +ZERO_OID="0000000000000000000000000000000000000000" +ZERO_OID_SHA256="0000000000000000000000000000000000000000000000000000000000000000" +ALLOWED_TYPES="feature|bugfix|hotfix|release|chore" +BRANCH_PATTERN="^(${ALLOWED_TYPES})/.+" + +PROTECTED_NAMES="main litellm_internal_staging" +PROTECTED_PREFIXES="dependabot/ gh-readonly-queue/" + +is_protected() { + branch="$1" + for name in $PROTECTED_NAMES; do + if [ "$branch" = "$name" ]; then + return 0 + fi + done + for prefix in $PROTECTED_PREFIXES; do + case "$branch" in "$prefix"*) return 0 ;; esac + done + return 1 +} + +invalid="" + +while read -r local_ref local_oid remote_ref remote_oid; do + # Branch deletion (no local commit being pushed). + if [ "$local_oid" = "$ZERO_OID" ] || [ "$local_oid" = "$ZERO_OID_SHA256" ]; then + continue + fi + + # Only validate branch pushes; ignore tags and other ref namespaces. + case "$remote_ref" in + refs/heads/*) ;; + *) continue ;; + esac + + branch="${remote_ref#refs/heads/}" + + if is_protected "$branch"; then + continue + fi + + if ! printf '%s' "$branch" | grep -Eq "$BRANCH_PATTERN"; then + invalid="$invalid $branch" + fi +done + +if [ -n "$invalid" ]; then + cat >&2 </ + + Allowed types: feature, bugfix, hotfix, release, chore + Examples: + feature/weighted-round-robin + bugfix/streaming-empty-chunks + chore/bump-deps + hotfix/auth-bypass + + Protected (always allowed): main, litellm_internal_staging, + dependabot/*, gh-readonly-queue/*. + +See https://conventional-branch.github.io/ + +Rename with: git branch -m +To bypass (use sparingly): git push --no-verify +EOF + exit 1 +fi + +exit 0 diff --git a/.github/deploy-on-aws.png b/.github/deploy-on-aws.png new file mode 100644 index 00000000000..06d41f2a5e0 Binary files /dev/null and b/.github/deploy-on-aws.png differ diff --git a/.github/deploy-on-gcp.png b/.github/deploy-on-gcp.png new file mode 100644 index 00000000000..e831a8c2e4e Binary files /dev/null and b/.github/deploy-on-gcp.png differ diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index 99f79c0b272..9658baeb89a 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -4,7 +4,7 @@ ## Linear ticket - + ## Pre-Submission checklist diff --git a/.github/scripts/_agent_shin_actions.py b/.github/scripts/_agent_shin_actions.py new file mode 100644 index 00000000000..b3d1ff055b3 --- /dev/null +++ b/.github/scripts/_agent_shin_actions.py @@ -0,0 +1,50 @@ +"""Dry-run wrapper(s) around Agent Shin GitHub mutations. + +The rollout scripts currently need only one mutation wrapped, so this module +exposes a single ``maybe_post_comment`` helper. It takes a ``dry_run: bool`` +keyword argument and the body is intentionally trivial: + + if dry_run: + print(...) # log what we would do, return + return + real_mutation(...) # otherwise, actually do it + +That shape means a dry-run preview differs from the real run in exactly one +line per side effect: the call site. So when you `python3 script.py` locally +without ``--close``, you can be confident the actions printed are the ones the +GitHub Action would have performed (modulo ordering on retry/error paths, +which are deliberately simple). Any further mutation a rollout script needs +should get the same ``maybe_*`` treatment instead of calling the raw +``triage_with_llm`` mutation directly. + +Importing from this module pulls in the real mutation from ``triage_with_llm`` +— call sites in the rollout scripts should NEVER import ``post_comment`` +directly; that would skip the dry-run gate and is the bug class this module +exists to prevent. +""" + +from __future__ import annotations + +import sys +import textwrap + +# Import the module itself rather than the bare names so monkeypatching +# `triage_with_llm.post_comment` (or any of the other mutations) in tests is +# reflected here — `from triage_with_llm import post_comment` would bind the +# original function to a local name and bypass the patch, defeating the whole +# point of these wrappers. +import triage_with_llm + + +def _log(line: str) -> None: + """Print a single dry-run line to stdout (one log statement per side effect).""" + print(line, file=sys.stdout, flush=True) + + +def maybe_post_comment(repo: str, number: int, body: str, *, dry_run: bool) -> None: + """Post a comment on ``repo#number`` — or, in dry-run, log what we would post.""" + if dry_run: + _log(f"[DRY RUN] comment {repo}#{number}:") + _log(textwrap.indent(body, " ")) + return + triage_with_llm.post_comment(repo, number, body) diff --git a/.github/scripts/agent_shin_shared.py b/.github/scripts/agent_shin_shared.py new file mode 100644 index 00000000000..8f3dc3c2322 --- /dev/null +++ b/.github/scripts/agent_shin_shared.py @@ -0,0 +1,211 @@ +"""Constants and helpers shared by Agent Shin's triage scripts. + +Both `triage_with_llm.py` (the LLM-judge entrypoint) and +`close_low_quality_prs.py` (the daily Greptile-score sweep) need to +agree on the same notions of: + + * What counts as a Greptile-authored review comment + (``GREPTILE_BOT_LOGINS``) and how to extract a confidence score from + its body (``SCORE_PATTERN`` / :func:`extract_greptile_score`). + * How long the 2-hour grace window is (``GRACE_PERIOD_SECONDS``) and + the HTML marker stamped into a grace-warning comment so the *other* + script can see "Agent Shin already warned" and behave accordingly + (``GRACE_COMMENT_MARKER``). + * Who Agent Shin is on GitHub (``AGENT_SHIN_DEFAULT_BOT_LOGIN``). + * How GitHub-style ISO-8601 timestamps round-trip into timezone-aware + :class:`datetime.datetime` (:func:`parse_iso8601`). + +Keeping these in one module means a future change (new Greptile output +format, a longer grace window, a new allowlisted account) is a single edit +instead of two — the original split version had to call out in comments +that the two copies "must stay in sync" precisely because nothing +enforced it. +""" + +from __future__ import annotations + +import datetime as dt +import json +import os +import re +import subprocess +from typing import Iterable + +GREPTILE_BOT_LOGINS = frozenset({"greptile-apps", "greptile-apps[bot]"}) + +SCORE_PATTERN = re.compile( + r"confidence\s*score\s*[:\-]?\s*(\d+)\s*/\s*5", + re.IGNORECASE, +) + +GRACE_COMMENT_MARKER = "" + +# Hidden HTML marker stamped on every Agent Shin auto-close comment (the LLM +# judge's grace/review-gate close and the daily Greptile sweep's close). +# `was_closed_by_agent_shin` requires this marker — not just the closing actor — +# before `@agent-shin reconsider` may reopen, because the `github-actions[bot]` +# identity is shared with every other workflow in the repo and is not unique to +# Agent Shin. Both close paths must stamp it or the reconsider path silently +# rejects the contributor. +AGENT_SHIN_CLOSE_MARKER = "" + +# 2 hours between the grace warning and the auto-close. Short enough to +# dogfood the "fix it before it closes" loop in one sitting; bump back up +# (e.g. 86400 for a day) for the public rollout. +GRACE_PERIOD_SECONDS = 7200 + +AGENT_SHIN_DEFAULT_BOT_LOGIN = "github-actions[bot]" + + +def _logins(*names: str) -> frozenset[str]: + """Build a login set normalized for case-insensitive membership checks. + + Callers compare via ``login.lower() in ``, so the stored values + must be lowercase. Normalizing here lets the literals keep each + account's canonical GitHub casing (e.g. ``SwiftWinds``) for + readability without breaking the lookup. + """ + return frozenset(name.lower() for name in names) + + +# Dogfood rollout gate. While this set is non-empty, Agent Shin acts ONLY on +# PRs/issues authored by these logins and skips everyone else. For an +# allowlisted author the usual internal/external classification is bypassed, so +# an internal account (e.g. a maintainer's own work login) still gets triaged +# while the bot is being tested on a small set of accounts. Empty the set to +# lift the restriction and restore full triage for the public rollout. Logins +# are compared case-insensitively. +ALLOWLIST_LOGINS = _logins("mateo-berri", "SwiftWinds") + +# `gh {pr,issue} list` has no "fetch everything" flag — `--limit` is the only +# control and it defaults to 30. Pass a ceiling far above any realistic open +# backlog (low thousands today) so gh paginates the API until the queue is +# exhausted rather than silently truncating. The bulk sweeps MUST see the whole +# backlog: gh lists newest-first, so a low cap drops the *oldest* PRs/issues — +# exactly the stale ones a low-quality sweep is meant to catch. +GH_LIST_ALL_LIMIT = 100_000 + + +def extract_greptile_score(comments: Iterable[dict]) -> tuple[int, dict] | None: + """Return (score, comment) for the most recent Greptile-authored comment + that contains a "Confidence Score: X/5". Returns None if no such comment. + + "Most recent" is determined by the comment's `updated_at` (falling back to + `created_at`), so re-reviews override earlier passes. + """ + candidates: list[tuple[str, int, dict]] = [] + for comment in comments: + user = (comment.get("user") or {}).get("login", "") + if user not in GREPTILE_BOT_LOGINS: + continue + body = comment.get("body") or "" + match = SCORE_PATTERN.search(body) + if not match: + continue + score = int(match.group(1)) + timestamp = comment.get("updated_at") or comment.get("created_at") or "" + candidates.append((timestamp, score, comment)) + + if not candidates: + return None + + candidates.sort(key=lambda triple: triple[0]) + _, score, comment = candidates[-1] + return score, comment + + +def parse_iso8601(value: str) -> dt.datetime: + """Parse a GitHub ISO-8601 timestamp into a timezone-aware datetime.""" + return dt.datetime.fromisoformat(value.replace("Z", "+00:00")) + + +def gh(*args: str) -> str: + """Run a `gh` CLI command and return stdout. Raises on non-zero exit. + + Shared by both Agent Shin entrypoints so a future change here + (timeout handling, logging, retry on transient failures) only needs + to be made once. + """ + result = subprocess.run( + ["gh", *args], + capture_output=True, + text=True, + check=True, + ) + return result.stdout + + +def list_open_items(kind: str, *, repo: str | None, fields: str) -> list[dict]: + """Return EVERY open PR (``kind="pr"``) or issue (``kind="issue"``) in ``repo``. + + Wraps ``gh {pr,issue} list`` with ``--limit GH_LIST_ALL_LIMIT`` so the full + backlog is fetched instead of the default 30 (or any other arbitrary cap). + Both bulk sweeps — the daily Greptile closer and the one-shot rollout + heads-up — rely on this seeing the whole queue, including the oldest items. + + ``fields`` is the comma-separated ``--json`` field list the caller needs + (e.g. ``"number"`` for the rollout, the full set for the closer). + """ + if kind not in ("pr", "issue"): + raise ValueError(f"kind must be 'pr' or 'issue', got {kind!r}") + repo_args = ["--repo", repo] if repo else [] + raw = gh( + kind, + "list", + "--state", + "open", + "--limit", + str(GH_LIST_ALL_LIMIT), + "--json", + fields, + *repo_args, + ) + return json.loads(raw) + + +def seconds_since_latest_marker_comment( + comments: Iterable[dict], + *, + marker: str, + bot_login: str | None = None, + now: dt.datetime | None = None, +) -> float | None: + """Return seconds since the bot's most recent comment containing ``marker``. + + Filters comments by author so a contributor who quotes the HTML + marker (e.g. via GitHub's "Quote reply" feature, which preserves + HTML comments in the raw markdown of the quoted text) is not + mistaken for a bot warning — that would silently reset cooldown + timers and suppress legitimate notifications. + + ``bot_login`` defaults to the `AGENT_SHIN_BOT_LOGIN` env override or + ``AGENT_SHIN_DEFAULT_BOT_LOGIN`` so callers normally don't need to + pass it. ``now`` is injectable for tests / callers (like the daily + sweep) that want every age calculation pinned to one snapshot. + """ + expected_login = ( + bot_login + or os.environ.get("AGENT_SHIN_BOT_LOGIN") + or AGENT_SHIN_DEFAULT_BOT_LOGIN + ).lower() + latest: dt.datetime | None = None + for comment in comments: + author = ((comment.get("user") or {}).get("login") or "").lower() + if author != expected_login: + continue + body = comment.get("body") or "" + if marker not in body: + continue + created = comment.get("created_at") + if not created: + continue + try: + ts = parse_iso8601(created) + except ValueError: + continue + if latest is None or ts > latest: + latest = ts + if latest is None: + return None + reference = now if now is not None else dt.datetime.now(dt.timezone.utc) + return (reference - latest).total_seconds() diff --git a/.github/scripts/close_low_quality_prs.py b/.github/scripts/close_low_quality_prs.py new file mode 100644 index 00000000000..7b9bbb579e3 --- /dev/null +++ b/.github/scripts/close_low_quality_prs.py @@ -0,0 +1,573 @@ +#!/usr/bin/env python3 +""" +Auto-close low-quality pull requests. + +Closes open PRs (including drafts, regardless of age) that satisfy ALL of: + 1. Have a Greptile (`greptile-apps`) review comment whose latest + "Confidence Score: X/5" is below the configured threshold (default: 4). + 2. Are authored by an external OSS contributor (internal BerriAI + contributors are exempt). + 3. Do not carry an opt-out label (default: "do not close"). + +`--min-age-days` is retained as an opt-in safety net for one-off backfill +runs (default: 0). The team's intent is that the count of open PRs equals +the count of PRs internal collaborators need to action on, so neither age +nor draft status acts as a free pass. + +For each match, the script posts an explanatory comment and closes the PR. +Because OSS contributors *cannot* reopen a PR closed by the bot/maintainer +(GitHub limitation), the close-comment instructs them to push their fixes +and **open a fresh PR**, or to comment `@agent-shin reconsider` on the +closed PR to have the LLM judge re-evaluate (and reopen on pass). + +Requires the `gh` CLI to be authenticated. + +Usage examples: + # Dry run (default) - prints what would be closed + python3 close_low_quality_prs.py + + # Actually close matching PRs + python3 close_low_quality_prs.py --close + + # Restrict to PRs at least N days old (one-off backfill safety net) + python3 close_low_quality_prs.py --min-age-days 7 --min-score 4 --close +""" + +from __future__ import annotations + +import argparse +import datetime as dt +import json +import os +import subprocess +import sys +from typing import Iterable + +# Add this script's directory to `sys.path` so the sibling +# `agent_shin_shared` module is importable when the script is invoked +# directly (e.g. `python3 .github/scripts/close_low_quality_prs.py ...`). +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +from agent_shin_shared import ( # noqa: E402 -- sys.path adjusted above + AGENT_SHIN_CLOSE_MARKER, + ALLOWLIST_LOGINS, + GRACE_COMMENT_MARKER, + GRACE_PERIOD_SECONDS, + GREPTILE_BOT_LOGINS, + SCORE_PATTERN, + extract_greptile_score, + gh, + list_open_items, + parse_iso8601, + seconds_since_latest_marker_comment, +) + +# `GREPTILE_BOT_LOGINS` and `SCORE_PATTERN` (Greptile's GitHub App login +# variants and the "Confidence Score: X/5" regex) are imported from +# `agent_shin_shared` so the LLM judge in `triage_with_llm.py` and this +# daily Greptile sweep read the score through the same set of logins +# and the same regex. + +# `author_association` values for internal BerriAI contributors who should be +# exempt from auto-triage. +INTERNAL_AUTHOR_ASSOCIATIONS = frozenset({"OWNER", "MEMBER", "COLLABORATOR"}) + +# Default labels that exempt a PR from auto-close. Defined at module scope (not +# as a mutable argparse default) so that `--optout-label foo` REPLACES the +# defaults instead of appending to them — the argparse `action="append"` + +# `default=[...]` combination silently mutates the shared default list. +DEFAULT_OPTOUT_LABELS = ("do not close", "keep open", "wip") + +# `GRACE_COMMENT_MARKER` (HTML marker appended to grace-period warning +# comments — used by either script to recognize that a warning was +# already posted) and `GRACE_PERIOD_SECONDS` (length of the grace +# period between the warning and the actual auto-close, 2 hours) are +# imported from `agent_shin_shared` so the Agent Shin LLM judge and +# this daily Greptile sweep agree on the same marker and duration. + + +def fetch_open_prs(repo: str | None) -> list[dict]: + """Fetch all open PRs (number, createdAt, isDraft, labels, author). + + Includes drafts: `gh pr list --state open` returns both ready-for-review + and draft PRs by default. This is the desired behavior — drafts are not + a free pass; the internal-collaborator open-PR queue should reflect every + PR that needs human attention regardless of draft status. + """ + fields = "number,title,createdAt,isDraft,labels,author,url" + return list_open_items("pr", repo=repo, fields=fields) + + +def fetch_pr_author_association(pr_number: int, repo: str | None) -> str: + """Return the GitHub `author_association` for a PR, uppercase. + + Values: OWNER, MEMBER, COLLABORATOR, CONTRIBUTOR, FIRST_TIME_CONTRIBUTOR, + FIRST_TIMER, MANNEQUIN, NONE. Returns "" on lookup failure. + """ + endpoint = ( + f"repos/{repo}/pulls/{pr_number}" + if repo + else f"repos/{{owner}}/{{repo}}/pulls/{pr_number}" + ) + try: + data = json.loads(gh("api", endpoint)) + except subprocess.CalledProcessError: + return "" + return (data.get("author_association") or "").upper() + + +def is_external_pr_author(pr: dict, repo: str | None) -> bool: + """Return True if the PR author is an external OSS contributor. + + Internal = `OWNER` / `MEMBER` / `COLLABORATOR` association, or a bot login. + """ + login = ((pr.get("author") or {}).get("login") or "").lower() + if login.endswith("[bot]") or login in {"dependabot", "github-actions"}: + return False + association = fetch_pr_author_association(pr["number"], repo) + # Fail-safe: if the API lookup failed (empty string), treat the author as + # internal so we don't auto-close their PR. Auto-close is destructive, so + # an unknown association should never make a PR eligible for closing. + if not association or association in INTERNAL_AUTHOR_ASSOCIATIONS: + return False + return True + + +def fetch_pr_comments(pr_number: int, repo: str | None) -> list[dict]: + """Fetch issue-level comments on a PR (where Greptile posts its summary).""" + endpoint = ( + f"repos/{repo}/issues/{pr_number}/comments?per_page=100" + if repo + else f"repos/{{owner}}/{{repo}}/issues/{pr_number}/comments?per_page=100" + ) + raw = gh("api", "--paginate", endpoint) + comments: list[dict] = [] + for line in raw.strip().splitlines(): + line = line.strip() + if not line: + continue + try: + parsed = json.loads(line) + except json.JSONDecodeError: + # A malformed line should not blow up the whole sweep. Skip and + # carry on so the remaining PRs in this run still get evaluated. + continue + if isinstance(parsed, list): + comments.extend(parsed) + else: + comments.append(parsed) + return comments + + +def has_optout_label(pr: dict, optout_labels: set[str]) -> bool: + labels = {label.get("name", "").lower() for label in pr.get("labels", [])} + return bool(labels & {lbl.lower() for lbl in optout_labels}) + + +def seconds_since_last_grace_warning( + comments: Iterable[dict], + *, + bot_login: str | None = None, + now: dt.datetime | None = None, +) -> float | None: + """Return seconds since the bot's most recent grace-period warning, or + None if no such warning has ever been posted on this PR. + + Thin wrapper over + `agent_shin_shared.seconds_since_latest_marker_comment` — the + centralized helper handles the bot-author filter, marker match, + timestamp parsing, and `now` injection. Keeping this wrapper + preserves the closer's "already-fetched comments + injectable now" + interface so callers (and tests) don't need to change. + """ + return seconds_since_latest_marker_comment( + comments, + marker=GRACE_COMMENT_MARKER, + bot_login=bot_login, + now=now, + ) + + +def format_grace_warning_comment(score: int, threshold: int) -> str: + """Comment posted on the FIRST low-Greptile-score detection — gives + the contributor a 2-hour grace window before the auto-close fires on + the next daily cron run. + + Mirrors `format_grace_warning_pr_comment` in + `triage_with_llm.py` in spirit (2-hour grace + escape hatches), but + framed around Greptile's confidence score instead of the LLM judge's + rubric since the close trigger here is the Greptile signal. + """ + return ( + "🚅 Hi, thanks for the PR! I'm **Agent Shin**, the automated triage bot for this " + "repository.\n" + "\n" + "Heads up: Greptile's most recent review scored this PR " + f"**{score}/5**, below our merge bar of **{threshold}/5**.\n" + "\n" + "If the score isn't lifted in the next **2 hours**, I'll auto-close this PR. That's " + "**not** us saying the change isn't worthwhile. We want the open-PR list to mirror " + "what a maintainer can act on *right now*, so contributors like you don't get lost in " + "a backlog. Take your time; everything below still works after the close.\n" + "\n" + "**During the grace period:** push fixes that address Greptile's feedback, then comment " + "`@greptileai` to request a fresh review. If " + f"the new score is **{threshold}/5 or higher**, the PR stays open and no further " + "action is needed on your side.\n" + "\n" + "**If the PR does get auto-closed in 2 hours, you still have an easy recovery path:**\n" + "\n" + "- Comment `@greptileai` to request a fresh review. **This still works even after " + f"the PR is closed**, and a score of {threshold}/5 or higher is one of the signals " + "that lifts the PR back into the review queue. A low Greptile score isn't a blocker.\n" + "- Comment `@agent-shin reconsider` after pushing fixes; I'll re-run the rubric and " + "reopen the PR if both gates (description rubric + Greptile score) now pass.\n" + "\n" + f"{GRACE_COMMENT_MARKER}" + ) + + +def post_grace_warning( + pr: dict, + score: int, + threshold: int, + repo: str | None, + dry_run: bool, +) -> None: + """Post the 2-hour grace-period warning comment on `pr`. + + The warning carries `GRACE_COMMENT_MARKER` so subsequent runs can + detect that the contributor has already been told about the + pending close. Does NOT close the PR — the close happens on the + next eligible run after `GRACE_PERIOD_SECONDS` elapses (handled + by `close_pr`). + """ + pr_number = pr["number"] + repo_args = ["--repo", repo] if repo else [] + + if dry_run: + print( + f" [DRY RUN] Would post grace warning to PR #{pr_number} " + f"(greptile={score}/5): {pr['title']}" + ) + return + + comment_body = format_grace_warning_comment(score, threshold) + gh("pr", "comment", str(pr_number), "--body", comment_body, *repo_args) + print(f" Posted grace warning on PR #{pr_number} (greptile={score}/5)") + + +def format_close_comment(score: int, threshold: int) -> str: + """Comment posted when a low-Greptile-score PR is auto-closed. + + Carries `AGENT_SHIN_CLOSE_MARKER` so the `@agent-shin reconsider` path + (guarded by `was_closed_by_agent_shin`) recognizes this as an Agent Shin + close and is allowed to reopen the PR once it passes again; without the + marker that recovery path the comment advertises silently rejects the + contributor. + """ + score_sentence = ( + f"Greptile's most recent review scored this PR **{score}/5**, below " + f"our merge bar of **{threshold}/5**, and the 2-hour grace period since " + "the warning has elapsed.\n\n" + ) + return ( + f"Closing as part of automated PR triage.\n\n" + f"{score_sentence}" + "We close low-confidence PRs aggressively to keep the review queue " + "manageable for maintainers and contributors alike. **This is not a " + "rejection of the idea.** To bring this back:\n\n" + "1. Push the fixes that address Greptile's feedback (continue using " + "your existing branch is fine).\n" + "2. **Open a new PR** with the updated branch. Greptile will review " + "it again, and if it scores " + f"**{threshold}/5 or higher** a maintainer will take another look.\n\n" + "_Why open a new PR instead of reopening this one?_ GitHub does not " + "let external contributors reopen a PR that was closed by a bot or " + "maintainer, so a fresh PR is the most reliable path forward. If you " + "would prefer this exact PR re-evaluated, comment " + "`@agent-shin reconsider` once you've pushed the fixes; Agent Shin " + "will re-run triage and reopen this PR if it now meets the bar. " + "You can also comment `@greptileai` to request a fresh Greptile " + "review; that works **even after the PR is closed**.\n\n" + "Thanks for contributing to LiteLLM. We know auto-closures can sting; " + "the goal is to keep the project healthy, not to dismiss your work." + f"\n\n{AGENT_SHIN_CLOSE_MARKER}" + ) + + +def close_pr( + pr: dict, + score: int, + threshold: int, + age_days: int, + repo: str | None, + dry_run: bool, + label: str | None, +) -> None: + """Post the explanatory comment and close the PR.""" + pr_number = pr["number"] + repo_args = ["--repo", repo] if repo else [] + + if dry_run: + print( + f" [DRY RUN] Would close PR #{pr_number} " + f"(age={age_days}d, greptile={score}/5): {pr['title']}" + ) + return + + comment_body = format_close_comment(score, threshold) + gh("pr", "comment", str(pr_number), "--body", comment_body, *repo_args) + + if label: + try: + gh("pr", "edit", str(pr_number), "--add-label", label, *repo_args) + except subprocess.CalledProcessError as exc: + stderr = (exc.stderr or "").strip() + print(f" warn: failed to add label '{label}' to #{pr_number}: {stderr}") + + gh("pr", "close", str(pr_number), *repo_args) + print(f" Closed PR #{pr_number} (greptile={score}/5, age={age_days}d)") + + +def evaluate_pr( + pr: dict, + now: dt.datetime, + min_age_days: int, + min_score: int, + repo: str | None, + optout_labels: set[str], + allowlist: frozenset[str] = ALLOWLIST_LOGINS, +) -> tuple[str, int | None, int | None]: + """Decide what to do with `pr` on this triage run. + + Returns (action, score_or_none, age_days_or_none) where action is one of: + "skip-too-young", "skip-optout-label", "skip-not-allowlisted", + "skip-internal", "skip-no-greptile-score", "skip-score-ok", + "warn-grace", "skip-in-grace-period", or "close". + + Drafts are NOT skipped — the goal is "open PR count == PRs internal + collaborators need to action on", and a draft that Greptile scored <4/5 + is still in that queue. Authors can opt out via the `wip` label (see + `DEFAULT_OPTOUT_LABELS`) if they need to keep a long-lived draft open. + + Grace-period semantics: the first time a PR fails the rubric, the + action is `warn-grace` — the caller should post a warning comment but + NOT close the PR. On a subsequent run, if the warning is still less + than `GRACE_PERIOD_SECONDS` old AND the PR still fails, the action is + `skip-in-grace-period`. Once the warning ages out and the rubric is + still failing, the action is `close`. + """ + if has_optout_label(pr, optout_labels): + return ("skip-optout-label", None, None) + + created = parse_iso8601(pr["createdAt"]) + age_days = (now - created).days + # `min_age_days` defaults to 0 (close as soon as Greptile scores low). + # Set a positive value via --min-age-days for one-off backfill runs that + # want to skip very-young PRs. + if min_age_days > 0 and age_days < min_age_days: + return ("skip-too-young", None, age_days) + + # While the allowlist is active it is the sole author gate: only those + # logins are acted on and the external-only restriction is bypassed for + # them. Otherwise auto-close only external OSS contributors — internal + # contributors (BerriAI org members) handle their own backlog. + login = ((pr.get("author") or {}).get("login") or "").lower() + if allowlist: + if login not in allowlist: + return ("skip-not-allowlisted", None, age_days) + elif not is_external_pr_author(pr, repo): + return ("skip-internal", None, age_days) + + comments = fetch_pr_comments(pr["number"], repo) + extraction = extract_greptile_score(comments) + if extraction is None: + return ("skip-no-greptile-score", None, age_days) + + score, _ = extraction + if score >= min_score: + return ("skip-score-ok", score, age_days) + + grace_age = seconds_since_last_grace_warning(comments, now=now) + if grace_age is None: + return ("warn-grace", score, age_days) + if grace_age < GRACE_PERIOD_SECONDS: + return ("skip-in-grace-period", score, age_days) + + return ("close", score, age_days) + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--repo", + type=str, + default=None, + help="Repository (owner/repo). Auto-detected if omitted.", + ) + parser.add_argument( + "--min-age-days", + type=int, + default=0, + help=( + "Minimum age (in days) before a PR is eligible. Default 0 = " + "close as soon as Greptile flags it. Set a positive value for " + "one-off backfill runs that want to spare very-young PRs." + ), + ) + parser.add_argument( + "--min-score", + type=int, + default=4, + choices=range(1, 6), + help="Greptile score below which a PR is closed (default: 4 -> closes <4/5).", + ) + parser.add_argument( + "--optout-label", + action="append", + default=None, + help=( + "Label(s) that exempt a PR from auto-close. Repeat to add more. " + "Case-insensitive. When omitted, defaults to " + f"{list(DEFAULT_OPTOUT_LABELS)!r}; passing this flag REPLACES the " + "defaults (argparse `append` with a mutable default would append " + "instead, which we explicitly avoid)." + ), + ) + parser.add_argument( + "--close-label", + type=str, + default=None, + help=( + "Optional label to add to PRs that get auto-closed " + "(e.g. 'auto-closed-low-quality'). Must already exist on the repo." + ), + ) + parser.add_argument( + "--close", + action="store_true", + help="Actually close matching PRs (default is dry-run).", + ) + parser.add_argument( + "--limit", + type=int, + default=None, + help="Maximum number of PRs to close in one run (safety net).", + ) + args = parser.parse_args() + + dry_run = not args.close + if dry_run: + print("=== DRY RUN MODE (pass --close to actually close PRs) ===\n") + + print("Fetching open PRs...") + prs = fetch_open_prs(args.repo) + print(f"Found {len(prs)} open PRs.\n") + + now = dt.datetime.now(dt.timezone.utc) + optout_labels = set(args.optout_label or DEFAULT_OPTOUT_LABELS) + + closed = 0 + summary = { + "close": 0, + "warn-grace": 0, + "skip-in-grace-period": 0, + "skip-too-young": 0, + "skip-optout-label": 0, + "skip-not-allowlisted": 0, + "skip-internal": 0, + "skip-no-greptile-score": 0, + "skip-score-ok": 0, + } + + # `warned` tracks grace-warning comments posted in this run so the + # `--limit` safety net bounds *all* destructive write actions, not + # just closures. Without this cap, a backlog of PRs failing the + # threshold simultaneously could flood contributors with comments. + warned = 0 + for pr in sorted(prs, key=lambda p: p["createdAt"]): + try: + action, score, age_days = evaluate_pr( + pr, + now, + args.min_age_days, + args.min_score, + args.repo, + optout_labels, + ) + summary[action] = summary.get(action, 0) + 1 + + if action == "warn-grace": + assert score is not None + print( + f"#{pr['number']}: \"{pr['title']}\" " + f"(age={age_days}d, greptile={score}/5) -> warn-grace" + ) + post_grace_warning( + pr, + score=score, + threshold=args.min_score, + repo=args.repo, + dry_run=dry_run, + ) + if not dry_run: + warned += 1 + if args.limit is not None and (warned + closed) >= args.limit: + print( + f"\nReached --limit={args.limit} " + f"(closed={closed}, warned={warned}); stopping." + ) + break + continue + + if action != "close": + continue + + assert score is not None and age_days is not None + print( + f"#{pr['number']}: \"{pr['title']}\" " + f"(age={age_days}d, greptile={score}/5) -> close" + ) + close_pr( + pr, + score=score, + threshold=args.min_score, + age_days=age_days, + repo=args.repo, + dry_run=dry_run, + label=args.close_label, + ) + + if not dry_run: + closed += 1 + if args.limit is not None and (warned + closed) >= args.limit: + print( + f"\nReached --limit={args.limit} " + f"(closed={closed}, warned={warned}); stopping." + ) + break + except Exception as exc: # noqa: BLE001 - per-PR errors don't abort the sweep + summary["error"] = summary.get("error", 0) + 1 + print( + f"!! PR #{pr.get('number')}: {exc}", + file=sys.stderr, + ) + continue + + print("\n=== Summary ===") + for key, value in summary.items(): + print(f" {key:28s} {value}") + if dry_run: + print(f"\nTotal would close: {summary['close']}") + else: + print(f"\nTotal closed: {closed}") + print( + f"Total {'would warn (grace)' if dry_run else 'warned (grace)'}: " + f"{summary['warn-grace']}" + ) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/.github/scripts/triage-requirements.txt b/.github/scripts/triage-requirements.txt new file mode 100644 index 00000000000..a18f05fbb95 --- /dev/null +++ b/.github/scripts/triage-requirements.txt @@ -0,0 +1,282 @@ +# Hash-pinned dependency set for the Agent Shin triage scripts. +# Installed in privileged triage workflows, so every package is pinned to an +# exact version with SHA-256 hashes and installed with pip --require-hashes. +# +# Regenerate after bumping openai: +# echo 'openai==' \ +# | uv pip compile - --generate-hashes --python-version 3.12 \ +# --no-annotate --no-header -o .github/scripts/triage-requirements.txt + +annotated-types==0.7.0 \ + --hash=sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53 \ + --hash=sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89 +anyio==4.14.0 \ + --hash=sha256:b47c1f9ccf73e67021df785332508f99379c68fa7d0684e8e3492cb1d4b23f89 \ + --hash=sha256:dd9b7a2a9799ed6552fde617b2c5df02b7fdd7d88392fc48101e51bae46164d9 +certifi==2026.6.17 \ + --hash=sha256:024c88eeec92ca068db80f02b8b07c9cef7b9fe261d1d535abfd5abd6f6af432 \ + --hash=sha256:2227dcbaafe0d2f59279d1762ddddc37783ed4354594f194ffc31d20f41fc3db +distro==1.9.0 \ + --hash=sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed \ + --hash=sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2 +h11==0.16.0 \ + --hash=sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1 \ + 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--git a/.github/scripts/triage_rollout_heads_up.py b/.github/scripts/triage_rollout_heads_up.py new file mode 100644 index 00000000000..a5dedb1c9e7 --- /dev/null +++ b/.github/scripts/triage_rollout_heads_up.py @@ -0,0 +1,557 @@ +#!/usr/bin/env python3 +"""One-shot 7-day heads-up sweep for the Agent Shin rollout. + +Posts a friendly "the OSS triage bot kicks in next Monday" comment on every +open external PR/issue that currently *would* fail the new rubric — i.e., +every PR/issue Agent Shin would close once the rollout completes. The point +is to give contributors a full week to fix their description before the bot +ever takes a destructive action, so nobody is surprised by an auto-close. + +The script is designed to run **exactly once** at rollout, fired by a manual +``workflow_dispatch`` (``dry_run=false``) on the heads-up workflow. Re-runs +are safe: every comment is stamped with the hidden ``HEADS_UP_MARKER`` and +PRs/issues that already carry the marker are skipped. + +Dry-run vs. real run +-------------------- +Defaults to dry-run. Passing ``--close`` flips into real mode. Every GitHub +mutation goes through ``_agent_shin_actions``, which has a one-line +``if dry_run: log else: do_it`` per call, so the only difference between a +dry-run preview and the real run is the call site that actually hits the +GitHub API. + +Local preview:: + + python3 .github/scripts/triage_rollout_heads_up.py --repo BerriAI/litellm + +Real run (the manual rollout dispatch uses this):: + + python3 .github/scripts/triage_rollout_heads_up.py --repo BerriAI/litellm --close +""" + +from __future__ import annotations + +import argparse +import datetime as dt +import json +import os +import sys +from pathlib import Path +from typing import Any + +# Make the sibling triage_with_llm + _agent_shin_actions importable when this +# script is invoked directly (the GitHub workflow does `python3 .github/scripts/...`). +_SCRIPTS_DIR = Path(__file__).resolve().parent +if str(_SCRIPTS_DIR) not in sys.path: + sys.path.insert(0, str(_SCRIPTS_DIR)) + +from _agent_shin_actions import maybe_post_comment # noqa: E402 +from agent_shin_shared import ( # noqa: E402 + AGENT_SHIN_DEFAULT_BOT_LOGIN, + ALLOWLIST_LOGINS, + list_open_items, +) +from triage_with_llm import ( # noqa: E402 + DEFAULT_MODEL, + call_llm_judge, + fetch_issue, + fetch_pr, + gh, + is_internal_contributor, + review_gate, + triage, +) + +# Hidden marker so re-runs skip PRs/issues we've already notified. Distinct from +# the within-grace / ready / regressed markers so it can't be confused with the +# steady-state lifecycle comments. +HEADS_UP_MARKER = "" + +# Placeholder until the litellm-docs PR ships. The rollout blog post explains +# the new rubric, the 7-day grace, and how to recover after an auto-close. +# TODO(docs): replace with the canonical URL once the litellm-docs PR merges. +ROLLOUT_BLOG_URL = "https://docs.litellm.ai/docs/agent_shin_triage_rollout" + +# Default cutoff is one week from "now". Computed at runtime so the wording +# stays correct even if the rollout is merged later than planned. The user can +# override with --close-on YYYY-MM-DD when running the script manually. +DEFAULT_GRACE_DAYS = 7 + +# The daily auto-close sweeps (close_low_quality_prs.yml at 09:00 UTC and +# review_gate.yml at 09:30 UTC) are what actually close a still-failing item, +# so the deadline we promise contributors has to name that wall-clock moment. +ACTIVATION_TIME_UTC = "09:00 UTC" + + +def _format_cutoff(cutoff: dt.date) -> str: + """Human-readable, timezone-explicit cutoff, e.g. ``Monday, June 1, 2026 + (09:00 UTC)`` — the moment a still-failing PR/issue gets closed.""" + return ( + f"{cutoff.strftime('%A, %B')} {cutoff.day}, {cutoff.year} " + f"({ACTIVATION_TIME_UTC})" + ) + + +def _rubric_section_pr() -> str: + return ( + "**Going forward, every external PR needs ONE of:**\n" + "\n" + "- A linked GitHub issue using a closing keyword: " + "`Fixes #1234`, `Closes #1234`, or `Resolves #1234`, OR\n" + "- All three of: a clear **problem description**, **expected vs. " + "actual behavior**, and **end-to-end QA proof** (at least one of a " + "short screen recording / video, before/after screenshots, or the " + "exact commands you ran with their real output; mocked or stubbed " + "runs don't count).\n" + "\n" + "PRs also need a **Greptile confidence score of 4/5 or higher** before " + "the bot will tag them `ready for review`. You can `@greptileai` to " + "request a fresh review at any time, including after the PR is closed." + ) + + +def _rubric_section_issue() -> str: + return ( + "**Going forward, every external issue needs:**\n" + "\n" + "- For **bug reports**: end-to-end evidence of the bug (at least one " + "of a screen recording / video, a screenshot, or the exact commands " + "you ran with their real output / traceback) plus expected vs. actual " + "behavior. Written steps with no run output don't count, and mocked " + "or stubbed runs don't count.\n" + "- For **feature requests**: a clear description of the proposed " + "feature plus a use case + concrete example (config, API call, UI " + "flow, or scenario showing what's blocked today)." + ) + + +def _description_only_note(kind: str) -> str: + noun = "PR" if kind == "pr" else "issue" + return ( + f"⚠️ **The requirements must live in the {noun} *description*, not in " + "comments.** Some PRs/issues collect 100+ comments from humans and " + "bots; reading the entire thread on every triage run would balloon " + "GitHub API usage (we'd start getting 429'd) and blow out the LLM " + "judge's context. The bot only reads the description, so anything " + "you add as a comment will be invisible to it." + ) + + +def _missing_section(verdict: dict, greptile_score: int | None) -> str: + """Bullet list of what's currently missing on this PR/issue. + + Combines the LLM judge's `missing` list (rubric items) with a Greptile + shortfall (for PRs) so the contributor sees one list of things to fix. + """ + missing = list(verdict.get("missing") or []) + if greptile_score is not None and greptile_score < 4: + missing.insert( + 0, + f"Greptile's most recent review scored this PR {greptile_score}/5 " + "(below the 4/5 bar Agent Shin will require).", + ) + if not missing: + return ( + "_The bot couldn't articulate a specific missing piece; see the " + "rubric link above and double-check the description includes all " + "of it before the rollout._" + ) + bullets = "\n".join(f"- {m}" for m in missing) + return f"**What this one is currently missing:**\n\n{bullets}" + + +def _recovery_section(kind: str) -> str: + if kind == "pr": + return ( + "**If the bot closes this PR after the rollout:** update the " + "description with the missing pieces, then either open a fresh " + "PR or comment `@agent-shin reconsider` on the closed PR. If " + "Greptile re-scores you at 4/5 or higher I'll reopen and tag " + "the PR `ready for review`. (`@greptileai` works on closed PRs " + "too; a fresh review is one of the signals that lifts you back " + "into the queue.) This is **not** us losing interest in your " + "change; far from it. We just need open PRs to be a list of " + "things a maintainer can act on, so we can get to yours faster." + ) + return ( + "**If the bot closes this issue after the rollout:** edit the issue " + "description to add the missing pieces, then comment `@agent-shin " + "reconsider` on the closed issue. I'll re-evaluate and, if the rubric " + "is met, reopen it. (GitHub doesn't let external authors reopen an " + "issue a maintainer or bot closed, so the comment is the reliable " + "path.) This is **not** us saying the bug isn't real or the request " + "isn't useful; it's so the remaining open issues are a list of things " + "a maintainer can act on." + ) + + +def format_heads_up_comment( + *, kind: str, verdict: dict, greptile_score: int | None, cutoff: dt.date +) -> str: + """Compose the friendly 7-day heads-up comment posted on a failing PR/issue.""" + noun = "PR" if kind == "pr" else "issue" + rubric = _rubric_section_pr() if kind == "pr" else _rubric_section_issue() + cutoff_str = _format_cutoff(cutoff) + explanation = (verdict.get("explanation") or "").strip() + explanation_block = ( + f"> _(The judge's note for this one: {explanation})_\n\n" if explanation else "" + ) + + return ( + "🚅 **Heads-up: we're turning on the OSS triage bot in " + f"{DEFAULT_GRACE_DAYS} days, on {cutoff_str}.**\n" + "\n" + "We're rolling out **Agent Shin**, an LLM-as-judge triage bot for " + f"external {noun}s. Once it's live, the bot reads each open " + f"{noun}'s description, scores it against a small rubric, and " + f"auto-closes any {noun} that's missing the basics, with a single " + f"comment explaining what's missing and how to recover. Full " + f"context: [Agent Shin rollout blog post]({ROLLOUT_BLOG_URL}).\n" + "\n" + f"{rubric}\n" + "\n" + f"{_description_only_note(kind)}\n" + "\n" + f"{_missing_section(verdict, greptile_score)}\n" + "\n" + f"{explanation_block}" + "**Timeline (you have a week):**\n" + "\n" + f"- We turn the bot on in {DEFAULT_GRACE_DAYS} days, on " + f"**{cutoff_str}**. You have until then to update this {noun}'s " + "description with the missing pieces above.\n" + f"- If this {noun} still fails the rubric at **{cutoff_str}**, " + "we'll close it.\n" + f"- From then on the bot runs daily, and every {noun} that fails " + "the rubric gets a **2-hour lifetime**: one warning comment, then " + "auto-close 2 hours later.\n" + "\n" + f"{_recovery_section(kind)}\n" + "\n" + f"{HEADS_UP_MARKER}" + ) + + +def _list_open_numbers(repo: str, kind: str) -> list[int]: + """Return every open PR or issue number in ``repo``. + + Delegates to ``list_open_items`` so the full backlog is fetched (no cap) + and the `gh {pr,issue} list` invocation stays in one shared place. ``gh + issue list`` would include PRs, but ``list_open_items`` uses the dedicated + command per kind, so the two never mix. + """ + return [ + item["number"] for item in list_open_items(kind, repo=repo, fields="number") + ] + + +def _has_heads_up_marker(item: dict) -> bool: + """Cheap fast-path: check the PR/issue body itself for the marker. + + The marker is appended to the *comment* we post, not the body, so this + will only fire if the body literally contains the marker text. We still + do the comment-marker check separately below; this body check just lets + us short-circuit for PRs/issues that quote the marker for any reason. + """ + body = item.get("body") or "" + return HEADS_UP_MARKER in body + + +def _comments_have_marker(repo: str, number: int) -> bool: + """True if the bot already posted a comment carrying the marker. + + Used for idempotency: a re-run skips items the previous run notified. + Filters by author (matching the sibling marker-checks in + ``triage_with_llm._has_marker`` and + ``agent_shin_shared.seconds_since_latest_marker_comment``) so a + contributor who quotes the heads-up via GitHub's "Quote reply" — which + preserves HTML comments in the raw markdown — can't trick the + idempotency check into silently skipping a real heads-up. + + Comments live on the unified issues endpoint regardless of whether the + item is a PR or an issue, so no ``kind`` argument is required here. + """ + expected_login = ( + os.environ.get("AGENT_SHIN_BOT_LOGIN") or AGENT_SHIN_DEFAULT_BOT_LOGIN + ).lower() + raw = gh( + "api", + "--paginate", + f"repos/{repo}/issues/{number}/comments?per_page=100", + ) + for line in raw.splitlines(): + line = line.strip() + if not line: + continue + try: + payload = json.loads(line) + except json.JSONDecodeError: + continue + comments = payload if isinstance(payload, list) else [payload] + for comment in comments: + author = ((comment.get("user") or {}).get("login") or "").lower() + if author != expected_login: + continue + if HEADS_UP_MARKER in (comment.get("body") or ""): + return True + return False + + +def _evaluate_pr(*, repo: str, number: int, model: str, judge: Any = None) -> dict: + """Run the future PR rubric (review_gate) in dry-run and return the result.""" + return review_gate( + repo=repo, + number=number, + close=False, # we only want the verdict, never act here + model=model, + judge=judge, + ) + + +def _evaluate_issue(*, repo: str, number: int, model: str, judge: Any = None) -> dict: + """Run the future issue rubric (triage kind='issue') in dry-run.""" + return triage( + repo=repo, + kind="issue", + number=number, + close=False, + model=model, + judge=judge, + ) + + +def _would_be_closed(kind: str, result: dict) -> bool: + """True if the future triage would auto-close this PR/issue based on the + rubric (regardless of grace-period gating). + + For PRs we trust ``review_gate``'s ``passing`` field — it combines the LLM + verdict and the Greptile score. For issues we read the LLM verdict + directly. Both fields are ``None``/missing on skip paths + (skip-internal-author, skip-llm-error, etc.) where the future bot would + NOT close the item — those return False. + """ + if kind == "pr": + passing = result.get("passing") + if passing is None: + return False # skipped — nothing for the heads-up to warn about + return passing is False + verdict = result.get("verdict") or {} + return (verdict.get("verdict") or "").lower() == "fail" + + +def _process_one( + *, + repo: str, + kind: str, + number: int, + model: str, + cutoff: dt.date, + dry_run: bool, + judge: Any = None, + skip_marker_check: bool = False, + allowlist: frozenset[str] = ALLOWLIST_LOGINS, +) -> dict: + """Evaluate one PR/issue and post a heads-up if it would be auto-closed. + + Returns a per-item dict for the summary table. + """ + base = {"kind": kind, "number": number} + fetcher = fetch_pr if kind == "pr" else fetch_issue + item = fetcher(repo, number) + + if (item.get("state") or "") != "open": + return {**base, "action": "skip-not-open"} + if allowlist: + login = (item.get("user") or {}).get("login") or "" + if login.lower() not in allowlist: + return {**base, "action": "skip-not-allowlisted"} + elif is_internal_contributor(item): + return {**base, "action": "skip-internal-author"} + if not skip_marker_check and _has_heads_up_marker(item): + return {**base, "action": "skip-already-marked-in-body"} + if not skip_marker_check and _comments_have_marker(repo, number): + return {**base, "action": "skip-already-notified"} + + if kind == "pr": + result = _evaluate_pr(repo=repo, number=number, model=model, judge=judge) + else: + result = _evaluate_issue(repo=repo, number=number, model=model, judge=judge) + + if not _would_be_closed(kind, result): + return {**base, "action": "skip-passing", "evaluator": result.get("action")} + + verdict = result.get("verdict") or {} + greptile_score = result.get("greptile_score") if kind == "pr" else None + comment = format_heads_up_comment( + kind=kind, verdict=verdict, greptile_score=greptile_score, cutoff=cutoff + ) + maybe_post_comment(repo, number, comment, dry_run=dry_run) + return { + **base, + "action": "heads-up-posted" if not dry_run else "would-post-heads-up", + "verdict": (verdict.get("verdict") or "").lower(), + "greptile_score": greptile_score, + } + + +def _print_summary(results: list[dict]) -> None: + """Tally per-action counts so a dry-run preview tells you at a glance how + many comments the real run would post.""" + counts: dict[str, int] = {} + for r in results: + counts[r["action"]] = counts.get(r["action"], 0) + 1 + print("\n=== rollout heads-up summary ===") + for action in sorted(counts): + print(f" {action:35s} {counts[action]}") + print(f" total {len(results)}") + + +def run( + *, + repo: str, + close: bool, + cutoff: dt.date, + model: str, + kinds: tuple[str, ...] = ("pr", "issue"), + judge: Any = None, + only_numbers: dict[str, list[int]] | None = None, + skip_marker_check: bool = False, +) -> list[dict]: + """Sweep ``repo`` and post heads-up comments. Returns the per-item results.""" + dry_run = not close + if dry_run: + print( + f"[DRY RUN] sweeping {repo}; --close not passed, no comments will be posted." + ) + else: + print(f"[REAL RUN] sweeping {repo}; comments WILL be posted.") + print(f"Cutoff date in comment body: {cutoff.isoformat()}") + + results: list[dict] = [] + for kind in kinds: + if only_numbers and kind in only_numbers: + numbers = list(only_numbers[kind]) + else: + numbers = _list_open_numbers(repo, kind) + print(f"\n--- {kind}s: {len(numbers)} open ---") + for n in numbers: + try: + result = _process_one( + repo=repo, + kind=kind, + number=n, + model=model, + cutoff=cutoff, + dry_run=dry_run, + judge=judge, + skip_marker_check=skip_marker_check, + ) + except ( + Exception + ) as exc: # noqa: BLE001 - per-item errors don't abort the sweep + result = { + "kind": kind, + "number": n, + "action": "error", + "error": str(exc), + } + print(f"!! {kind}#{n}: {exc}", file=sys.stderr) + print(f" {kind}#{n}: {result['action']}") + results.append(result) + _print_summary(results) + return results + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--repo", required=True, help="owner/repo") + parser.add_argument( + "--close", + action="store_true", + help=( + "Actually post comments. Without this flag the script is in " + "dry-run mode and only logs what it would do." + ), + ) + parser.add_argument( + "--close-on", + type=dt.date.fromisoformat, + default=None, + help=( + "Cutoff date shown in the heads-up comment as the rollout date " + f"(default: today + {DEFAULT_GRACE_DAYS} days)." + ), + ) + parser.add_argument( + "--model", + default=os.environ.get("TRIAGE_MODEL") or DEFAULT_MODEL, + help=f"Model for the rubric LLM judge (default: {DEFAULT_MODEL}).", + ) + parser.add_argument( + "--kind", + choices=("pr", "issue", "both"), + default="both", + help="Restrict the sweep to PRs or issues only (default: both).", + ) + parser.add_argument( + "--only-pr", + type=int, + action="append", + default=[], + help="Limit the PR sweep to these PR numbers (repeat for several).", + ) + parser.add_argument( + "--only-issue", + type=int, + action="append", + default=[], + help="Limit the issue sweep to these issue numbers (repeat for several).", + ) + parser.add_argument( + "--ignore-existing-marker", + action="store_true", + help=( + "Re-post on PRs/issues that already carry the heads-up marker. " + "Useful for testing the comment wording on a known PR." + ), + ) + args = parser.parse_args() + + cutoff = args.close_on or ( + dt.datetime.now(dt.timezone.utc).date() + dt.timedelta(days=DEFAULT_GRACE_DAYS) + ) + + kinds: tuple[str, ...] + if args.kind == "pr": + kinds = ("pr",) + elif args.kind == "issue": + kinds = ("issue",) + else: + kinds = ("pr", "issue") + + only: dict[str, list[int]] = {} + if args.only_pr: + only["pr"] = args.only_pr + if args.only_issue: + only["issue"] = args.only_issue + + # The script must NOT hit the LLM in dry-run if no key is set — we still + # want a useful preview that says "skip-no-llm-key" for items that would + # have been judged. Production runs require OPENAI_API_KEY. + if args.close and not os.environ.get("OPENAI_API_KEY"): + parser.error("OPENAI_API_KEY must be set for --close (real-run) mode.") + + run( + repo=args.repo, + close=args.close, + cutoff=cutoff, + model=args.model, + kinds=kinds, + only_numbers=only or None, + skip_marker_check=args.ignore_existing_marker, + ) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/.github/scripts/triage_with_llm.py b/.github/scripts/triage_with_llm.py new file mode 100644 index 00000000000..d2536058e01 --- /dev/null +++ b/.github/scripts/triage_with_llm.py @@ -0,0 +1,1778 @@ +#!/usr/bin/env python3 +""" +Agent Shin — LLM-as-judge triage for external OSS pull requests and issues. + +Evaluates a single PR or issue against the contribution rubric and, when the +LLM judge marks it as failing, posts an explanatory comment + closes the +PR/issue. Re-triggers on `reopened` so contributors can iterate back in by +filling in the missing pieces and reopening. + +Internal BerriAI contributors (`author_association` in {OWNER, MEMBER, +COLLABORATOR}) and bot accounts are skipped entirely. + +Usage: + triage_with_llm.py --repo owner/repo --pr 1234 + triage_with_llm.py --repo owner/repo --issue 5678 + triage_with_llm.py --repo owner/repo --pr 1234 --close # actually close + triage_with_llm.py --repo owner/repo --pr 1234 --print-prompt # show prompt + +Defaults are SAFE: without `--close` the script writes a verdict to stdout (and, +when running in GitHub Actions, to $GITHUB_STEP_SUMMARY) but takes no GitHub +write actions. + +Environment: + GH_TOKEN / GITHUB_TOKEN - for `gh` CLI auth (auto-set in Actions) + OPENAI_API_KEY - required when --close is passed + OPENAI_BASE_URL - optional (route to any OpenAI-compatible API) + TRIAGE_MODEL - optional model override (default: gpt-5.4-mini) +""" + +from __future__ import annotations + +import argparse +import datetime as dt +import json +import os +import re +import subprocess +import sys +import textwrap +import urllib.parse +from typing import Any, Iterable + +# Add this script's directory to `sys.path` so the sibling +# `agent_shin_shared` module is importable when the script is invoked +# directly (e.g. `python3 .github/scripts/triage_with_llm.py ...`) and +# also when the tests load this script via +# `importlib.util.spec_from_file_location`. +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +from agent_shin_shared import ( # noqa: E402 -- sys.path adjusted above + AGENT_SHIN_CLOSE_MARKER, + AGENT_SHIN_DEFAULT_BOT_LOGIN, + ALLOWLIST_LOGINS, + GRACE_COMMENT_MARKER, + GRACE_PERIOD_SECONDS, + GREPTILE_BOT_LOGINS, + SCORE_PATTERN, + extract_greptile_score, + gh, + parse_iso8601, + seconds_since_latest_marker_comment, +) + +DEFAULT_MODEL = "gpt-5.4-mini" + +INTERNAL_ASSOCIATIONS = frozenset({"OWNER", "MEMBER", "COLLABORATOR"}) + +# `AGENT_SHIN_DEFAULT_BOT_LOGIN` is imported from `agent_shin_shared`. +# When the workflow uses the default `secrets.GITHUB_TOKEN`, the +# closure / reopen event's `actor.login` is `github-actions[bot]`. The +# env override `AGENT_SHIN_BOT_LOGIN` exists for local debugging and for +# repos that wire Agent Shin to a PAT. + +# HTML marker appended to every reconsider verdict comment. We grep for this +# on subsequent reconsider triggers to enforce a short cooldown so that +# repeated `@agent-shin reconsider` comments don't burn CI/LLM budget. +# Using a unique HTML comment keeps the marker invisible to humans while +# being trivially greppable from a comments-list API response. +RECONSIDER_COMMENT_MARKER = "" + +# Minimum gap between two reconsider verdicts on the same PR/issue. Set to +# 10 minutes — long enough that a contributor can't trivially spam the +# trigger, short enough that a genuine "I just pushed a fix and reupdated +# the body" iteration loop isn't punished. +RECONSIDER_RATE_LIMIT_SECONDS = 600 + +# `GRACE_COMMENT_MARKER` (HTML marker on the grace-period warning comment +# posted on the first low-quality detection — used on subsequent triage +# runs to detect that a warning was already posted and measure how long +# ago it was posted) and `GRACE_PERIOD_SECONDS` (length of the grace +# period between the warning and the actual auto-close, 2 hours) are +# imported from `agent_shin_shared` so the daily Greptile sweep and the +# LLM judge agree on the same marker and duration. + +# --- Review-gate ("ready for review" label lifecycle) configuration ---------- +# The review gate keeps a single label in sync with whether a PR currently +# clears BOTH quality bars: the LLM rubric (clear problem + expected/actual + +# QA proof, or a linked issue) AND Greptile's most recent confidence score. +READY_FOR_REVIEW_LABEL = "ready for review" +DEFAULT_GRACE_DAYS = 1 # 24h before an un-passing, un-tagged PR is auto-closed +DEFAULT_MIN_GREPTILE_SCORE = 4 # Greptile < 4/5 counts as "not passing" + +# Hidden HTML-comment markers stamped into review-gate comments. They never +# render in the GitHub UI but let the gate detect its own prior actions so it +# (a) posts the within-grace "what's missing" notice at most once and (b) can +# tell a first-time pass ("ready for review") from a recovery after a +# regression ("all clear again"). +READY_MARKER = "" +REGRESSED_MARKER = "" +WITHIN_GRACE_MARKER = "" + +# `GREPTILE_BOT_LOGINS` (Greptile's GitHub App login variants — +# `greptile-apps[bot]` in REST API comments, `greptile-apps` in +# `gh pr view --json` output) and `SCORE_PATTERN` (regex matching lines +# like `Confidence Score: 3/5`) are imported from `agent_shin_shared` +# so the daily sweep and the review gate read the score through the +# same set of logins / patterns. + +# `AGENT_SHIN_CLOSE_MARKER` is imported from `agent_shin_shared` so this LLM +# judge and the daily Greptile sweep stamp the same marker on their close +# comments — `was_closed_by_agent_shin` keys the reconsider reopen path off it. + +# Model families that require `reasoning_effort` to be set, and that reject +# `temperature != 1` unless `reasoning_effort` is "none". For these models we +# pass `reasoning_effort="none"` so a `temperature=0` deterministic judgment +# is still accepted. See litellm/llms/openai/chat/gpt_5_transformation.py for +# the full set of constraints LiteLLM applies to these models. +GPT5_FAMILY_PREFIX = "gpt-5" + +# Regexes for picking off "obvious passes" without burning LLM tokens. +# +# Keep this list to GitHub's documented PR-closing keywords only +# (https://docs.github.com/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue). +# Casual mentions like "see #1234" or "ref #1234" are intentionally NOT +# auto-passed — they should fall through to the LLM judge, which has the +# stricter rubric "a bare issue number without a closing keyword counts only +# if it's clearly the related issue (not a passing mention)". +LINKED_ISSUE_PATTERN = re.compile( + r"\b(?:fixes|fix|fixed|closes|close|closed|resolves|resolve|resolved)\s+" + r"(?:#\d+|https?://github\.com/[\w.-]+/[\w.-]+/issues/\d+)", + re.IGNORECASE, +) +HTML_COMMENT_PATTERN = re.compile(r"", re.DOTALL) + + +# --------------------------------------------------------------------------- +# gh helpers +# +# `gh` is imported from `agent_shin_shared` so a future change (timeout, +# logging, retry) only needs to be made once. + + +def fetch_pr(repo: str, number: int) -> dict: + """Return the full GitHub REST representation of a PR.""" + return json.loads(gh("api", f"repos/{repo}/pulls/{number}")) + + +def fetch_issue(repo: str, number: int) -> dict: + """Return the full GitHub REST representation of an issue.""" + return json.loads(gh("api", f"repos/{repo}/issues/{number}")) + + +def post_comment(repo: str, number: int, body: str) -> None: + """Post an issue-style comment (works for both issues and PRs).""" + gh( + "api", + f"repos/{repo}/issues/{number}/comments", + "-X", + "POST", + "-f", + f"body={body}", + ) + + +def close_pr(repo: str, number: int) -> None: + """Close a pull request (state=closed).""" + gh( + "api", + f"repos/{repo}/pulls/{number}", + "-X", + "PATCH", + "-f", + "state=closed", + ) + + +def reopen_pr(repo: str, number: int) -> None: + """Reopen a previously-closed pull request (state=open). + + Used by the `@agent-shin reconsider` comment-trigger flow: the bot has + write access via GH_TOKEN, so it can reopen on the contributor's behalf + even though GitHub doesn't let the OSS author do it themselves. + """ + gh( + "api", + f"repos/{repo}/pulls/{number}", + "-X", + "PATCH", + "-f", + "state=open", + ) + + +def close_issue(repo: str, number: int, *, not_planned: bool = True) -> None: + """Close an issue, marking state_reason=not_planned by default.""" + args = [ + "api", + f"repos/{repo}/issues/{number}", + "-X", + "PATCH", + "-f", + "state=closed", + ] + if not_planned: + args.extend(["-f", "state_reason=not_planned"]) + gh(*args) + + +def reopen_issue(repo: str, number: int) -> None: + """Reopen a previously-closed issue (state=open, state_reason=reopened).""" + gh( + "api", + f"repos/{repo}/issues/{number}", + "-X", + "PATCH", + "-f", + "state=open", + "-f", + "state_reason=reopened", + ) + + +def add_label(repo: str, number: int, label: str) -> None: + """Add a label to a PR/issue (GitHub creates the label if it's missing).""" + gh( + "api", + f"repos/{repo}/issues/{number}/labels", + "-X", + "POST", + "-f", + f"labels[]={label}", + ) + + +def remove_label(repo: str, number: int, label: str) -> None: + """Remove a label from a PR/issue. A missing label (404) is not an error.""" + encoded = urllib.parse.quote(label, safe="") + try: + gh( + "api", + f"repos/{repo}/issues/{number}/labels/{encoded}", + "-X", + "DELETE", + ) + except subprocess.CalledProcessError as exc: + stderr = (exc.stderr or "").lower() + if "404" in stderr or "not found" in stderr: + return + raise + + +def _iter_paginated_json(*api_args: str) -> Any: + """Yield JSON objects from `gh api --paginate ... -q '.[]'`. + + `gh api --paginate` on a JSON-array endpoint concatenates pages into + one stream; `-q '.[]'` flattens that stream into newline-delimited + objects (jq-style). This keeps memory bounded for chatty endpoints + like issue events/comments on long-lived PRs. + """ + raw = gh("api", "--paginate", *api_args, "-q", ".[]") + for line in raw.splitlines(): + line = line.strip() + if not line: + continue + try: + yield json.loads(line) + except json.JSONDecodeError: + # A malformed line should not blow up the whole guard. Skip and + # carry on — at worst the guard fail-closes (returns False / + # None) and the caller treats it as "unknown". + continue + + +def fetch_last_close_event( + repo: str, number: int +) -> tuple[str | None, dt.datetime | None]: + """Return the actor login and timestamp of the most recent `closed` event. + + Either field may be None: actor when the events API returns nothing + (unusual for a closed item, but possible on transient errors), and + timestamp when the event lacks `created_at` or the value can't be + parsed. `was_closed_by_agent_shin` fail-closes on either. + """ + actor: str | None = None + closed_at: dt.datetime | None = None + for event in _iter_paginated_json(f"repos/{repo}/issues/{number}/events"): + if event.get("event") != "closed": + continue + actor = (event.get("actor") or {}).get("login") + created = event.get("created_at") + if not created: + closed_at = None + continue + try: + closed_at = parse_iso8601(created) + except ValueError: + closed_at = None + return actor, closed_at + + +# How much older than the latest `closed` event the Agent Shin marker +# comment is allowed to be while still counting as "this close was Agent +# Shin's". Agent Shin posts the close comment immediately before closing, +# so the marker timestamp is normally at most a few seconds before the +# close event; the buffer just absorbs clock skew between the comments +# API and the events API. +AGENT_SHIN_CLOSE_MARKER_SKEW_SECONDS = 300 + + +def was_closed_by_agent_shin( + repo: str, number: int, *, bot_login: str | None = None +) -> bool: + """Return True iff Agent Shin itself most-recently closed this PR/issue. + + This is the guard that stops `@agent-shin reconsider` from reopening an + item Agent Shin did not close — a maintainer closing for non-rubric + reasons (security, duplicate, design rejection), or a different workflow + (stale/duplicate sweeps) closing under the shared `github-actions[bot]` + identity. Three independent signals must all hold, because that identity + is not unique to Agent Shin and a marker comment from a prior + closed/reopened cycle would otherwise vouch for an unrelated close: + + 1. The most recent `closed` event's actor is the bot identity. + 2. Agent Shin left one of its auto-close comments, detected via + `AGENT_SHIN_CLOSE_MARKER`. The actor check alone can't tell an + Agent Shin close from any other `github-actions[bot]` close. + 3. That marker comment was posted at (or just before) the latest + close event, not on a previous close in an + Agent-Shin-close -> reconsider-reopen -> other-bot-reclose cycle. + + The check is intentionally fail-closed: any uncertainty about who closed + the item is treated as "not Agent Shin" so the destructive reopen path + stays gated. + """ + expected = ( + bot_login + or os.environ.get("AGENT_SHIN_BOT_LOGIN") + or AGENT_SHIN_DEFAULT_BOT_LOGIN + ).lower() + actor, closed_at = fetch_last_close_event(repo, number) + if not actor or actor.lower() != expected or closed_at is None: + return False + marker_seconds = seconds_since_last_agent_shin_close( + repo, number, bot_login=bot_login + ) + if marker_seconds is None: + return False + close_age_seconds = (dt.datetime.now(dt.timezone.utc) - closed_at).total_seconds() + return marker_seconds <= close_age_seconds + AGENT_SHIN_CLOSE_MARKER_SKEW_SECONDS + + +def _seconds_since_latest_marker_comment( + repo: str, + number: int, + *, + marker: str, + bot_login: str | None = None, +) -> float | None: + """Return seconds since the bot's most recent comment with ``marker``. + + Fetches comments via `_iter_paginated_json` and delegates the + iteration / author-filter / timestamp logic to + `agent_shin_shared.seconds_since_latest_marker_comment` so the daily + Greptile sweep and the LLM judge use one source of truth for the + "bot already posted X" detection. The wall-clock `now` is resolved + against this module's `dt` so tests that freeze time via + `monkeypatch.setattr(triage_module, "dt", ...)` still apply. + """ + return seconds_since_latest_marker_comment( + _iter_paginated_json(f"repos/{repo}/issues/{number}/comments"), + marker=marker, + bot_login=bot_login, + now=dt.datetime.now(dt.timezone.utc), + ) + + +def seconds_since_last_reconsider_verdict( + repo: str, number: int, *, bot_login: str | None = None +) -> float | None: + """Return seconds since the bot's most recent reconsider verdict comment. + + Detects comments by matching the HTML marker `RECONSIDER_COMMENT_MARKER` + appended by `format_reopen_comment` and + `format_reconsider_still_failing_comment`. Returns None when the bot + has never posted a reconsider verdict on this PR/issue (or when the + only matching comments are missing a `created_at` timestamp, which + shouldn't happen on a real GitHub response). + """ + return _seconds_since_latest_marker_comment( + repo, number, marker=RECONSIDER_COMMENT_MARKER, bot_login=bot_login + ) + + +def seconds_since_last_grace_warning( + repo: str, number: int, *, bot_login: str | None = None +) -> float | None: + """Return seconds since the bot's most recent grace-period warning. + + Detects warning comments by matching the HTML marker + `GRACE_COMMENT_MARKER` appended by `format_grace_warning_pr_comment` + and `format_grace_warning_issue_comment`. Returns None when no + grace warning has ever been posted on this PR/issue — that's the + "first low-quality detection" signal that drives the warning path. + """ + return _seconds_since_latest_marker_comment( + repo, number, marker=GRACE_COMMENT_MARKER, bot_login=bot_login + ) + + +def seconds_since_last_agent_shin_close( + repo: str, number: int, *, bot_login: str | None = None +) -> float | None: + """Return seconds since Agent Shin's most recent auto-close comment. + + Detects close comments by matching `AGENT_SHIN_CLOSE_MARKER` (stamped by + `format_pr_close_comment` / `format_issue_close_comment`). Returns None + when Agent Shin has never closed this PR/issue — the signal + `was_closed_by_agent_shin` uses to keep the reconsider reopen path gated + against closures performed by other workflows sharing the bot identity. + """ + return _seconds_since_latest_marker_comment( + repo, number, marker=AGENT_SHIN_CLOSE_MARKER, bot_login=bot_login + ) + + +# --------------------------------------------------------------------------- +# Author classification + + +def is_internal_contributor(item: dict) -> bool: + """Return True if the PR/issue author should be exempted from triage. + + Fail-safe: if `author_association` is missing or empty (which should never + happen on a successful GitHub REST response but is possible on schema + changes or partial responses), treat the author as INTERNAL so the + destructive close path never fires on an unknown contributor. This matches + the sibling `is_external_pr_author` in `close_low_quality_prs.py`. + """ + login = ((item.get("user") or {}).get("login") or "").lower() + if login.endswith("[bot]") or login in {"dependabot", "github-actions"}: + return True + association = (item.get("author_association") or "").upper() + if not association or association in INTERNAL_ASSOCIATIONS: + return True + return False + + +# --------------------------------------------------------------------------- +# Greptile score + age helpers (`extract_greptile_score`, `parse_iso8601`) +# live in `agent_shin_shared` — they're imported at the top of this module +# so both `triage_with_llm.py` and `close_low_quality_prs.py` share a +# single source of truth for the Confidence-Score regex and ISO-8601 +# parsing. + + +# --------------------------------------------------------------------------- +# Prompt construction + + +def strip_html_comments(text: str) -> str: + """Remove HTML comments — template placeholder text shouldn't fool the judge.""" + return HTML_COMMENT_PATTERN.sub("", text or "") + + +def has_linked_issue(text: str) -> bool: + """Heuristic: does this body link to an open issue (Fixes #123 etc.)?""" + return bool(LINKED_ISSUE_PATTERN.search(strip_html_comments(text or ""))) + + +def build_pr_prompt(*, title: str, body: str) -> str: + cleaned_body = strip_html_comments(body or "").strip() or "(empty)" + # Dedent the static template *before* interpolating dynamic fields so that + # multi-line bodies (whose 2nd+ lines start at column 0) don't defeat the + # common-indent computation in textwrap.dedent. + template = textwrap.dedent(""" + You are "Agent Shin", the OSS triage bot for the LiteLLM open-source + repository (BerriAI/litellm). Decide whether this external pull request + meets the project's contribution standards. + + A PR PASSES triage only if BOTH (1) AND (2) are satisfied. A linked + issue alone is NOT enough — it covers context, not proof. + + (1) CONTEXT — the PR provides AT LEAST ONE of: + (a) A link to a related GitHub issue. Acceptable forms: + "Fixes #1234", "Closes #1234", "Resolves #1234", + "Refs https://github.com/BerriAI/litellm/issues/1234". A + bare "#1234" without a closing keyword counts only if it + is clearly the related issue (not a passing mention). + (b) A clear problem description in the body (what bug or + missing feature this addresses, beyond the title) AND + expected vs. actual behavior (or, for features, "what's + possible now vs. with this PR"). + + (2) END-TO-END QA PROOF: the PR body contains AT LEAST ONE of: + (a) A screen recording / video showing the behavior before + and after the change (the bug reproducing, then the fix + working). For a brand-new feature with no meaningful + "before", a recording of it working end-to-end is fine. + (b) A screenshot (or before/after screenshots) showing the + fix or feature working. + (c) Specific commands that were actually run (curl, python, + a CLI invocation, etc.) PAIRED WITH their real + output, demonstrating the change works end-to-end against + the real system. Commands whose external dependencies + (LLM provider, DB, network) are mocked or stubbed do NOT + satisfy (2c); they are not end-to-end. + + `has_qa_proof` must be set to `true` only when (2a), (2b), + or a non-mocked (2c) is actually present in the body. If the + only "proof" is mocked tests, `has_qa_proof` is `false` and + the verdict is "fail". + + The following do NOT count as QA proof: + - Generic claims like "I tested it", "works locally", "all + tests pass", or a checked "I added tests" checkbox with no + output shown. + - A description of what tests exist or were added, without + their actual output in the PR body. + - `pytest` (or any test runner) executed against the + repository's own unit tests. Those mock the LLM provider, + DB, and network, so they are NOT end-to-end and never + satisfy (2), no matter how much passing output is pasted. + - A linked issue. The linked issue is context (1a), never + proof (2). + + FAIL the PR if EITHER (1) or (2) is missing. Do not bias toward PASS: + if QA proof is absent, the verdict is "fail" even when the rest of + the PR is well-written. + + Respond with a single JSON object, no prose: + + {{ + "verdict": "pass" | "fail", + "linked_issue": boolean, + "has_problem_description": boolean, + "has_expected_vs_actual": boolean, + "has_qa_proof": boolean, + "qa_proof_type": "video" | "screenshot" | "commands_with_output" | "none", + "missing": ["plain-english strings naming what is missing"], + "explanation": "1-2 sentence reasoning for the team to skim" + }} + + --- + PR title: {title} + + PR body: + --- + {cleaned_body} + --- + """).strip() + return template.format(title=title, cleaned_body=cleaned_body) + + +def build_issue_prompt(*, title: str, body: str) -> str: + cleaned_body = strip_html_comments(body or "").strip() or "(empty)" + # Dedent the static template *before* interpolating dynamic fields so that + # multi-line bodies (whose 2nd+ lines start at column 0) don't defeat the + # common-indent computation in textwrap.dedent. + template = textwrap.dedent(""" + You are "Agent Shin", the OSS triage bot for the LiteLLM open-source + repository (BerriAI/litellm). Decide whether this GitHub issue meets + the project's reporting standards. + + For a BUG REPORT the issue PASSES triage only when it contains BOTH: + (1) END-TO-END EVIDENCE OF THE BUG (the "before"; set + `has_repro=true` only when this is present): AT LEAST ONE of: + (a) A screen recording / video of the bug happening. + (b) A screenshot of the bug. + (c) The exact command(s) actually run (curl, python, a CLI + invocation, etc.) PAIRED WITH their real output, traceback, + or logs showing the failure against the real system. + Commands whose external dependencies (LLM provider, DB, + network) are mocked or stubbed do NOT count. + Prose-only "steps to reproduce" with no run output, video, or + screenshot do NOT satisfy (1). + (2) Expected vs. actual behavior (`has_expected_vs_actual`). + + FAIL the bug report if either (1) or (2) is missing. Do not bias + toward PASS: if the bug isn't demonstrated end-to-end, the verdict is + "fail" even when the report is well-written. + + For a FEATURE REQUEST the issue PASSES triage only when it contains + ALL of: + - A clear description of the proposed feature (what should LiteLLM do + that it does not today). + - Motivation / use case with a concrete example (config, API call, + UI flow, or scenario showing what's blocked today). + + For an issue that is neither a bug report nor a feature request (a + question, support request, or discussion), PASS as long as it has a + clear, specific ask and is not empty or template placeholder text. + + Respond with a single JSON object, no prose: + + {{ + "verdict": "pass" | "fail", + "kind": "bug" | "feature" | "other", + "has_repro": boolean, + "has_expected_vs_actual": boolean, + "has_motivation_example": boolean, + "missing": ["plain-english strings naming what is missing"], + "explanation": "1-2 sentence reasoning for the team to skim" + }} + + --- + Issue title: {title} + + Issue body: + --- + {cleaned_body} + --- + """).strip() + return template.format(title=title, cleaned_body=cleaned_body) + + +# --------------------------------------------------------------------------- +# LLM call + verdict parsing + + +def call_llm_judge( + prompt: str, *, model: str, api_key: str, base_url: str | None +) -> str: + """Call an OpenAI-compatible chat completions endpoint. Returns raw text.""" + # Import inside the function so unit tests that monkey-patch this never + # need the openai package installed. + from openai import OpenAI + + client = ( + OpenAI(api_key=api_key, base_url=base_url) + if base_url + else OpenAI(api_key=api_key) + ) + kwargs: dict[str, Any] = { + "model": model, + "messages": [{"role": "user", "content": prompt}], + "temperature": 0, + "response_format": {"type": "json_object"}, + } + # gpt-5.x reasoning models reject `temperature != 1` unless + # `reasoning_effort` is explicitly "none". Set it via `extra_body` so this + # works across openai SDK versions regardless of whether the SDK natively + # types `reasoning_effort` as a top-level chat-completions param yet. + if model.lower().startswith(GPT5_FAMILY_PREFIX): + kwargs["extra_body"] = {"reasoning_effort": "none"} + response = client.chat.completions.create(**kwargs) + return response.choices[0].message.content or "" + + +def parse_verdict(raw: str) -> dict: + """Parse the LLM's JSON response. Tolerates ```json fences and stray text.""" + if not raw: + raise ValueError("empty LLM response") + text = raw.strip() + if text.startswith("```"): + text = re.sub(r"^```(?:json)?\s*", "", text) + text = re.sub(r"\s*```$", "", text) + try: + return json.loads(text) + except json.JSONDecodeError: + match = re.search(r"\{.*\}", text, re.DOTALL) + if not match: + raise ValueError(f"could not extract JSON from LLM response: {raw[:200]}") + return json.loads(match.group(0)) + + +# --------------------------------------------------------------------------- +# Comment composition + + +def _format_missing(missing: list[str]) -> str: + if not missing: + return "- (see explanation below)" + return "\n".join(f"- {m}" for m in missing) + + +# Rubric items the judge can mark present. The first element of each tuple is +# the verdict-JSON boolean field, the second is the human-readable label we +# render in the "what you got right" section of close / grace-warning comments. +_PR_PRESENT_LABELS: tuple[tuple[str, str], ...] = ( + ("linked_issue", "Linked a related GitHub issue"), + ("has_problem_description", "Clear problem description"), + ("has_expected_vs_actual", "Expected vs. actual behavior"), + ("has_qa_proof", "End-to-end QA proof"), +) + +# Issue rubric labels grouped by `kind`. The judge sets `kind` to one of +# {"bug", "feature", "other"}; when "other" we render both groups so we don't +# silently drop a present-flag the judge actually set to True. +_ISSUE_BUG_LABELS: tuple[tuple[str, str], ...] = ( + ( + "has_repro", + "End-to-end evidence of the bug (video, screenshot, or command + real output)", + ), + ("has_expected_vs_actual", "Expected vs. actual behavior"), +) +_ISSUE_FEATURE_LABELS: tuple[tuple[str, str], ...] = ( + ("has_motivation_example", "Motivation and concrete example"), +) + + +def _format_present_for_pr(verdict: dict) -> list[str]: + """Human-readable rubric items the judge confirmed are present on a PR. + + Drives the "what you got right" section in close / grace-warning comments. + The user gave explicit feedback: contributors should see what they nailed + *before* the list of gaps, so the comment doesn't read as pure rejection. + """ + return [label for field, label in _PR_PRESENT_LABELS if verdict.get(field)] + + +def _format_present_for_issue(verdict: dict) -> list[str]: + """Human-readable rubric items the judge confirmed are present on an issue. + + Branches on the judge's `kind` field. For `"other"` (or missing kind) we + render the union so a present-flag isn't dropped just because the judge + couldn't classify the issue cleanly. + """ + kind = (verdict.get("kind") or "").lower() + groups: list[tuple[tuple[str, str], ...]] = [] + if kind in ("bug", "other", ""): + groups.append(_ISSUE_BUG_LABELS) + if kind in ("feature", "other", ""): + groups.append(_ISSUE_FEATURE_LABELS) + out: list[str] = [] + for group in groups: + for field, label in group: + if verdict.get(field) and label not in out: + out.append(label) + return out + + +def _format_present_block(items: list[str]) -> str: + """Render the optional "what you got right" block. Empty string when the + judge didn't confirm anything as present — better to omit the section + entirely than to show "What you got right: (nothing)". + """ + if not items: + return "" + bullets = "\n".join(f"- ✅ {item}" for item in items) + return f"**What you got right:**\n\n{bullets}\n\n" + + +def format_pr_close_comment(verdict: dict) -> str: + missing_lines = _format_missing(verdict.get("missing") or []) + present_block = _format_present_block(_format_present_for_pr(verdict)) + explanation = verdict.get("explanation") or "" + return ( + "🚅 Hi, thanks for the PR! I'm **Agent Shin**, the automated triage bot for this " + "repository. " + "[What's this and why am I getting it?](https://docs.litellm.ai/blog/agent-shin-triage)\n" + "\n" + "I read the description against our " + "[contribution rubric](https://github.com/BerriAI/litellm/blob/main/.github/pull_request_template.md). " + "Here's how it lined up:\n" + "\n" + f"{present_block}" + "**What's still missing:**\n" + "\n" + f"{missing_lines}\n" + "\n" + f"> {explanation}\n" + "\n" + "**Closing this PR isn't a rejection of the change.** We want the open-PR list to " + "mirror what a maintainer can act on *right now*, so contributors don't get lost in a " + 'backlog. A closed PR is a soft "park this for later"; your work is still here, ' + "the diff is still here, and getting it reopened is one comment away. Take your time.\n" + "\n" + "**To bring this PR back:**\n" + "\n" + "- Update the description with the missing pieces, then comment `@agent-shin reconsider` " + "on this PR. I'll re-evaluate and reopen if it now passes.\n" + "- Or **Open a new PR** with the same fix and the updated description. GitHub doesn't " + "always let external contributors reopen a bot-closed PR, so a fresh PR is the most " + "reliable path back into the review queue.\n" + "- If Greptile's most recent score on this PR was below 4/5, comment `@greptileai` to " + "request a fresh review; that **still works even after the PR is closed**, and a " + "stronger score is one of the signals that lifts the PR back into the queue. A low " + "Greptile score isn't a blocker.\n" + "\n" + '**What "end-to-end QA proof" means**, since it\'s the most common gap: at least one ' + "of a short before/after screen recording / video (the bug reproducing, then the fix " + "working; for a brand-new feature, a recording of it working end-to-end), a screenshot " + "(or before/after screenshots) of it working, or the exact commands you ran paired " + "with their **real output** against the real system. Running `pytest` on the repo's " + "unit tests doesn't count; those mock the LLM provider, DB, and network, so they " + "aren't end-to-end. Output from a real, no-mocks integration run is what we look " + "for. A linked issue alone isn't enough either: it covers context, not proof. See " + "[the full rubric](https://docs.litellm.ai/blog/agent-shin-triage#the-rubric-for-pull-requests).\n" + "\n" + "Internal BerriAI contributors: this rubric doesn't apply to you; ping a maintainer.\n" + "\n" + "_(I'm an LLM, so I'm not infallible. If you think I got this wrong, comment " + "`@agent-shin reconsider` or ping a maintainer; they'll override me.)_" + f"\n\n{AGENT_SHIN_CLOSE_MARKER}" + ) + + +def format_issue_close_comment(verdict: dict) -> str: + missing_lines = _format_missing(verdict.get("missing") or []) + present_block = _format_present_block(_format_present_for_issue(verdict)) + explanation = verdict.get("explanation") or "" + return ( + "🚅 Hi, thanks for filing this! I'm **Agent Shin**, the automated triage bot for this " + "repository. " + "[What's this and why am I getting it?](https://docs.litellm.ai/blog/agent-shin-triage)\n" + "\n" + "I read the issue against our reporting checklist. Here's how it lined up:\n" + "\n" + f"{present_block}" + "**What's still missing:**\n" + "\n" + f"{missing_lines}\n" + "\n" + f"> {explanation}\n" + "\n" + "**Closing this isn't us saying the bug isn't real or the request isn't useful.** We " + "want the open-issue list to mirror what a maintainer can act on *right now*, so " + "reports like yours don't get buried in a backlog. A closed issue is a soft \"park " + 'this for later"; your report is still here, and getting it reopened is one comment ' + "away. Take your time.\n" + "\n" + "**To bring this issue back:**\n" + "\n" + "1. Edit the issue description to add the missing pieces:\n" + " - For **bug reports**: end-to-end evidence of the bug (a screen recording / " + "video, a screenshot, or the exact commands you ran with their real output / " + "traceback) plus expected vs. actual behavior. Written steps with no run output, " + "video, or screenshot don't count, and mocked or stubbed runs don't count.\n" + " - For **feature requests**: a concrete description of what should change, plus a " + "use case and example (config / API call / UI flow).\n" + "2. Comment `@agent-shin reconsider`. I'll re-run triage and reopen the issue if it " + "now meets the bar. (GitHub doesn't let external authors reopen an issue a maintainer " + "or bot closed, so the comment-based reconsider is the reliable path.)\n" + "\n" + "Internal BerriAI contributors: this rubric doesn't apply to you; ping a maintainer.\n" + "\n" + "_(I'm an LLM, so I'm not infallible. If you think I got this wrong, comment " + "`@agent-shin reconsider` or ping a maintainer; they'll override me.)_" + f"\n\n{AGENT_SHIN_CLOSE_MARKER}" + ) + + +def format_grace_warning_pr_comment(verdict: dict) -> str: + """Comment posted on the FIRST low-quality detection — gives the + contributor a 2-hour grace window to fix the PR before the next + triage run actually closes it. + + This is the "before-close" warning. On the second triage run, if the + grace marker is older than `GRACE_PERIOD_SECONDS` AND the PR still + fails the rubric, the close path runs (which posts + `format_pr_close_comment` and closes the PR). + """ + missing_lines = _format_missing(verdict.get("missing") or []) + present_block = _format_present_block(_format_present_for_pr(verdict)) + explanation = verdict.get("explanation") or "" + return ( + "🚅 Hi, thanks for the PR! I'm **Agent Shin**, the automated triage bot for this " + "repository. " + "[What's this and why am I getting it?](https://docs.litellm.ai/blog/agent-shin-triage)\n" + "\n" + "I read the description against our " + "[contribution rubric](https://github.com/BerriAI/litellm/blob/main/.github/pull_request_template.md). " + "Here's how it lined up:\n" + "\n" + f"{present_block}" + "**What's still missing:**\n" + "\n" + f"{missing_lines}\n" + "\n" + f"> {explanation}\n" + "\n" + "If the description isn't updated in the next **2 hours**, I'll auto-close this PR. " + "That's **not** us saying we don't care about the change; we want the open-PR list to " + "mirror what a maintainer can act on *right now*, so contributors don't get lost in a " + 'backlog. A closed PR is a soft "park this for later," not a rejection. Take your ' + "time; everything below still works after the close.\n" + "\n" + "**During the grace period:** just update the PR description with the missing pieces. " + "No need to ping me; I'll re-check on the next sweep and skip the auto-close if it " + "now passes. See " + "[what counts as QA proof](https://docs.litellm.ai/blog/agent-shin-triage#the-rubric-for-pull-requests) " + "for the full rubric (a linked issue alone isn't enough; it covers context, not proof).\n" + "\n" + "**If the PR does get auto-closed in 2 hours, you still have easy recovery paths:**\n" + "\n" + "- Comment `@agent-shin reconsider` after updating the description. I'll re-evaluate " + "and reopen the PR if it now passes.\n" + "- Comment `@greptileai` to request a fresh Greptile review; that **still works even " + "after the PR is closed**, and a stronger score is one of the signals that lifts the " + "PR back into the queue. So a low Greptile score isn't a blocker either.\n" + "\n" + "Internal BerriAI contributors: this rubric doesn't apply to you; ping a maintainer.\n" + "\n" + "_(I'm an LLM, so I'm not infallible. If you think I got this wrong, ping a " + "maintainer; they'll override me.)_\n" + "\n" + f"{GRACE_COMMENT_MARKER}" + ) + + +def format_grace_warning_issue_comment(verdict: dict) -> str: + """Issue analogue of `format_grace_warning_pr_comment`.""" + missing_lines = _format_missing(verdict.get("missing") or []) + present_block = _format_present_block(_format_present_for_issue(verdict)) + explanation = verdict.get("explanation") or "" + return ( + "🚅 Hi, thanks for filing this! I'm **Agent Shin**, the automated triage bot for this " + "repository. " + "[What's this and why am I getting it?](https://docs.litellm.ai/blog/agent-shin-triage)\n" + "\n" + "I read the issue against our reporting checklist. Here's how it lined up:\n" + "\n" + f"{present_block}" + "**What's still missing:**\n" + "\n" + f"{missing_lines}\n" + "\n" + f"> {explanation}\n" + "\n" + "If the issue isn't updated in the next **2 hours**, I'll auto-close it. That's **not** us " + "saying the bug isn't real or the request isn't useful; we want the open-issue list " + "to mirror what a maintainer can act on *right now*, so reports like yours don't get " + 'buried in a backlog. A closed issue is a soft "park this for later," not a ' + "rejection. Take your time; reopening is one comment away.\n" + "\n" + "**During the grace period:** just edit the issue description with the missing " + "pieces. No need to ping me; I'll re-check on the next sweep and skip the auto-close " + "if it now passes.\n" + "\n" + "Missing pieces, depending on what this is:\n" + "\n" + "- For **bug reports**: end-to-end evidence of the bug (a screen recording / video, a " + "screenshot, or the exact commands you ran with their real output / traceback) plus " + "expected vs. actual behavior. Written steps with no run output don't count, and " + "mocked or stubbed runs don't count.\n" + "- For **feature requests**: a concrete description of what should change, plus a use " + "case and example (config / API call / UI flow).\n" + "\n" + "**If the issue does get auto-closed in 2 hours**, comment `@agent-shin reconsider` " + "and I'll re-evaluate. If it now meets the bar, I'll reopen the issue.\n" + "\n" + "Internal BerriAI contributors: this rubric doesn't apply to you; ping a maintainer.\n" + "\n" + "_(I'm an LLM, so I'm not infallible. If you think I got this wrong, ping a " + "maintainer; they'll override me.)_\n" + "\n" + f"{GRACE_COMMENT_MARKER}" + ) + + +# --------------------------------------------------------------------------- +# Step-summary helpers + + +def write_step_summary(content: str) -> None: + """When running inside GitHub Actions, append to the step summary file.""" + path = os.environ.get("GITHUB_STEP_SUMMARY") + if not path: + return + try: + with open(path, "a", encoding="utf-8") as handle: + handle.write(content) + if not content.endswith("\n"): + handle.write("\n") + except OSError as exc: + print(f"warn: failed to write step summary: {exc}", file=sys.stderr) + + +# --------------------------------------------------------------------------- +# Core orchestration + + +def format_reopen_comment(kind: str) -> str: + """Comment posted when Agent Shin reopens after a successful reconsider.""" + noun = "PR" if kind == "pr" else "issue" + # The trailing HTML marker is used by `seconds_since_last_reconsider_verdict` + # to enforce a cooldown between repeated `@agent-shin reconsider` triggers. + # Keep the marker on its own line so it doesn't disturb the rendered text. + return ( + f"♻️ **Re-evaluated and reopened.** Thanks for updating the {noun}!\n" + "\n" + "Agent Shin re-ran triage on the latest description and it now meets " + "the bar. A maintainer will take another look soon; please don't " + f"close this {noun} again unless asked to.\n" + "\n" + "_(If a maintainer ends up closing this for non-rubric reasons, that " + "decision stands; comment `@agent-shin reconsider` again only if you " + "have substantively new information.)_\n" + "\n" + f"{RECONSIDER_COMMENT_MARKER}" + ) + + +def format_reconsider_still_failing_comment(kind: str, verdict: dict) -> str: + """Comment posted when reconsider re-runs triage but the verdict is still fail.""" + missing_lines = _format_missing(verdict.get("missing") or []) + explanation = verdict.get("explanation") or "" + noun = "PR" if kind == "pr" else "issue" + # The trailing HTML marker is used by `seconds_since_last_reconsider_verdict` + # to enforce a cooldown between repeated `@agent-shin reconsider` triggers. + return ( + f"⏸️ **Re-evaluated; this {noun} still doesn't meet the rubric.**\n" + "\n" + "Agent Shin re-ran triage on the current description but is still " + "missing:\n" + "\n" + f"{missing_lines}\n" + "\n" + f"> {explanation}\n" + "\n" + "Update the description with the missing pieces and comment " + "`@agent-shin reconsider` again, or ping a maintainer if you think " + "I got this wrong.\n" + "\n" + "_(I'm an LLM and I'm not infallible.)_\n" + "\n" + f"{RECONSIDER_COMMENT_MARKER}" + ) + + +# --------------------------------------------------------------------------- +# Review gate — "ready for review" label lifecycle + +_UNSET = object() + + +def _combine_missing( + verdict: dict, greptile_score: int | None, min_score: int +) -> list[str]: + """Merge the LLM rubric's `missing` list with a Greptile-score shortfall.""" + missing = list(verdict.get("missing") or []) + if greptile_score is not None and greptile_score < min_score: + missing.insert( + 0, + f"Greptile's most recent review scored this PR {greptile_score}/5 " + f"(below the {min_score}/5 bar)", + ) + return missing or ["(see explanation below)"] + + +def _has_marker( + comments: Iterable[dict], marker: str, *, bot_login: str | None = None +) -> bool: + """Return True iff the bot itself posted a comment containing ``marker``. + + Filters by author so a contributor who quotes the marker (e.g. via + GitHub's "Quote reply" feature, which preserves HTML comments in + raw markdown) is not mistaken for a bot action — that would + silently suppress notifications or change which "recovered" wording + is selected. Matches the author-filter pattern used by the sibling + `_seconds_since_latest_marker_comment` helper. + """ + expected_login = ( + bot_login + or os.environ.get("AGENT_SHIN_BOT_LOGIN") + or AGENT_SHIN_DEFAULT_BOT_LOGIN + ).lower() + for comment in comments: + author = ((comment.get("user") or {}).get("login") or "").lower() + if author != expected_login: + continue + if marker in (comment.get("body") or ""): + return True + return False + + +def format_ready_for_review_comment( + verdict: dict, + greptile_score: int | None, + min_greptile_score: int = DEFAULT_MIN_GREPTILE_SCORE, +) -> str: + """Posted the first time a PR clears the bar (label added).""" + score_line = ( + f" Greptile scored it **{greptile_score}/5**." + if greptile_score is not None + else "" + ) + explanation = verdict.get("explanation") or "" + return ( + "✅ **Triage passed, tagging `ready for review`.**\n" + "\n" + "Agent Shin checked this PR against the " + "[contribution rubric](https://github.com/BerriAI/litellm/blob/main/.github/pull_request_template.md) " + "and it clears the bar (a linked issue, or a clear problem description " + f"+ expected vs. actual + QA proof).{score_line}\n" + "\n" + f"> {explanation}\n" + "\n" + "A maintainer will take it from here. If a later re-check finds the PR " + f"has regressed (Greptile drops below {min_greptile_score}/5, " + "the QA proof is removed, etc.) I'll pull the tag and comment with " + "what's missing; fix it and the tag comes back automatically.\n" + f"{READY_MARKER}" + ) + + +def format_all_clear_comment(verdict: dict, greptile_score: int | None) -> str: + """Posted when a PR recovers after a regression (label re-added).""" + score_line = ( + f" Greptile is back to **{greptile_score}/5**." + if greptile_score is not None + else "" + ) + explanation = verdict.get("explanation") or "" + return ( + "✅ **All clear again, re-adding `ready for review`.**\n" + "\n" + "Thanks for addressing the earlier feedback. On re-check this PR meets " + f"the contribution bar once more.{score_line}\n" + "\n" + f"> {explanation}\n" + "\n" + "A maintainer will take another look.\n" + f"{READY_MARKER}" + ) + + +def format_regression_comment( + missing: list[str], explanation: str, grace_days: int +) -> str: + """Posted when a previously-tagged PR regresses (label removed, PR stays open). + + Discloses the same ``grace_days`` deadline the state machine enforces: + once that window elapses with the PR still failing, the close path fires. + Hiding the deadline behind a bare "stays open" would surprise contributors + with an auto-close they were never warned about. + """ + window = "24 hours" if grace_days == 1 else f"{grace_days} days" + return ( + "⚠️ **Removing the `ready for review` tag.**\n" + "\n" + "On a re-check this PR no longer meets the contribution bar. What's " + "missing now:\n" + "\n" + f"{_format_missing(missing)}\n" + "\n" + f"> {explanation}\n" + "\n" + f"The PR stays open for ~{window}; address the points above and Agent " + 'Shin will post an "all clear" comment and re-add the tag ' + "automatically. If the points still aren't addressed after that " + "window, the PR is auto-closed; that's not a rejection, and you can " + "comment `@agent-shin reconsider` to have it re-evaluated and reopened " + "once it passes.\n" + f"{REGRESSED_MARKER}" + ) + + +def format_within_grace_comment( + missing: list[str], explanation: str, grace_days: int +) -> str: + """Posted once while a failing PR is still inside its grace window.""" + window = "24 hours" if grace_days == 1 else f"{grace_days} days" + return ( + "🚅 Hi, thanks for the PR! This is **Agent Shin**, the automated triage " + "bot. This PR doesn't quite meet the contribution bar yet:\n" + "\n" + f"{_format_missing(missing)}\n" + "\n" + f"> {explanation}\n" + "\n" + f"You have ~{window} from when this PR was opened to add the missing " + "pieces; just update the description and I'll re-check on the next " + "sweep. Once it passes I'll tag it `ready for review`. If it does get " + "auto-closed, that's not a rejection; comment `@agent-shin reconsider` " + "and I'll re-evaluate and reopen if it now passes.\n" + f"{WITHIN_GRACE_MARKER}" + ) + + +def review_gate( + *, + repo: str, + number: int, + close: bool, + model: str, + judge: Any = None, + greptile_score: Any = _UNSET, + comments: Any = _UNSET, + now: dt.datetime | None = None, + grace_days: int = DEFAULT_GRACE_DAYS, + min_greptile_score: int = DEFAULT_MIN_GREPTILE_SCORE, + label: str = READY_FOR_REVIEW_LABEL, + allowlist: frozenset[str] = ALLOWLIST_LOGINS, +) -> dict: + """Reconcile the `ready for review` label with a PR's current quality. + + A PR is *passing* when it clears BOTH gates: the LLM rubric (linked issue, + or problem description + expected/actual + QA proof) AND Greptile's most + recent confidence score (>= ``min_greptile_score``; absence of a score is + not held against the PR). The gate then drives a small state machine, using + the label itself as the persisted state so comments fire only on + transitions (never on every scheduled run): + + passing, untagged -> add label + "ready for review" / "all clear" + passing, tagged -> noop-passing + not passing, tagged -> remove label + regression comment (stays open) + not passing, untagged, old -> close + comment (past the grace window) + not passing, untagged, new -> one-time "what's missing" notice (within grace) + + ``close`` gates every destructive side effect: with ``close=False`` the + function returns a ``would-*`` preview and touches nothing, mirroring the + dry-run contract of :func:`triage`. ``judge``/``greptile_score``/ + ``comments``/``now`` are injectable for tests; in production they are + resolved from the OpenAI judge, the PR's Greptile comment, the live comment + list, and the wall clock respectively. + """ + item = fetch_pr(repo, number) + + title = item.get("title") or "" + body = item.get("body") or "" + login = (item.get("user") or {}).get("login") or "" + association = item.get("author_association") or "" + state = item.get("state") or "" + # GitHub label names are case-insensitive; compare lowercased so a repo + # that already has e.g. "Ready for Review" is recognized as the same + # label as our READY_FOR_REVIEW_LABEL constant ("ready for review"). + labels_now = {(lbl.get("name") or "").lower() for lbl in (item.get("labels") or [])} + label_key = label.lower() + created_raw = item.get("created_at") or "" + + base_result = { + "kind": "pr", + "number": number, + "title": title, + "author": login, + "author_association": association, + "state": state, + "labeled": label_key in labels_now, + "review_gate": True, + } + + if state != "open": + return {**base_result, "action": "skip-not-open"} + + if allowlist: + if login.lower() not in allowlist: + return {**base_result, "action": "skip-not-allowlisted"} + elif is_internal_contributor(item): + return {**base_result, "action": "skip-internal-author"} + + # Resolve the comment list once — used for both the Greptile score and the + # marker-based dedup below. + if comments is _UNSET: + comments = list(_iter_paginated_json(f"repos/{repo}/issues/{number}/comments")) + + # --- rubric verdict: linked-issue short-circuit, else the LLM judge ------- + if has_linked_issue(body): + verdict = { + "verdict": "pass", + "linked_issue": True, + "missing": [], + "explanation": "Linked-issue regex matched; LLM was not called.", + } + rubric_pass = True + else: + prompt = build_pr_prompt(title=title, body=body) + if judge is None: + api_key = os.environ.get("OPENAI_API_KEY") + if not api_key: + return {**base_result, "action": "skip-no-llm-key"} + base_url = os.environ.get("OPENAI_BASE_URL") or None + + def judge(p: str) -> str: + return call_llm_judge( + p, model=model, api_key=api_key, base_url=base_url + ) + + try: + verdict = parse_verdict(judge(prompt)) + except Exception as exc: # noqa: BLE001 - judge errors must never act + return {**base_result, "action": "skip-llm-error", "error": str(exc)} + rubric_pass = (verdict.get("verdict") or "").lower() == "pass" + + # --- Greptile score ------------------------------------------------------- + if greptile_score is _UNSET: + extraction = extract_greptile_score(comments) + greptile_score = extraction[0] if extraction else None + greptile_ok = greptile_score is None or greptile_score >= min_greptile_score + passing = rubric_pass and greptile_ok + + # --- age ------------------------------------------------------------------ + age_days = None + if created_raw: + reference = now or dt.datetime.now(dt.timezone.utc) + age_days = (reference - parse_iso8601(created_raw)).days + + label_present = label_key in labels_now + explanation = verdict.get("explanation") or "" + # When the rubric short-circuited to pass (linked-issue regex) but + # Greptile dragged the PR below the bar, the synthetic verdict's + # explanation ("LLM was not called") would mislead a contributor reading + # the regression / close comment. Surface the real reason instead. + if rubric_pass and not greptile_ok: + explanation = ( + f"Greptile's most recent review scored this PR " + f"{greptile_score}/5 (below the {min_greptile_score}/5 bar)." + ) + verdict = {**verdict, "explanation": explanation} + base_result = { + **base_result, + "verdict": verdict, + "greptile_score": greptile_score, + "passing": passing, + "age_days": age_days, + } + + if passing: + if label_present: + return {**base_result, "action": "noop-passing"} + recovered = _has_marker(comments, REGRESSED_MARKER) + comment = ( + format_all_clear_comment(verdict, greptile_score) + if recovered + else format_ready_for_review_comment( + verdict, greptile_score, min_greptile_score + ) + ) + if not close: + return {**base_result, "action": "would-label-ready", "comment": comment} + post_comment(repo, number, comment) + add_label(repo, number, label) + return {**base_result, "action": "labeled-ready", "comment": comment} + + missing = _combine_missing(verdict, greptile_score, min_greptile_score) + + if label_present: + comment = format_regression_comment(missing, explanation, grace_days) + if not close: + return {**base_result, "action": "would-remove-label", "comment": comment} + remove_label(repo, number, label) + post_comment(repo, number, comment) + return {**base_result, "action": "label-removed-regressed", "comment": comment} + + # Not passing and not tagged. If the PR was previously tagged and then + # regressed (we removed the label and posted REGRESSED_MARKER), honor the + # "PR stays open — fix it and the tag comes back" promise from + # `format_regression_comment` and skip the close path. Without this guard, + # any PR older than `grace_days` would be closed on the next evaluation, + # giving the contributor no realistic window to address the regression. + # + # The promise has a deliberate expiration: once `grace_days` have elapsed + # since the regression notice, fall through to the close path so a PR that + # was abandoned post-regression doesn't sit open forever. + if _has_marker(comments, REGRESSED_MARKER): + reference = now or dt.datetime.now(dt.timezone.utc) + seconds_since_regression = seconds_since_latest_marker_comment( + comments, marker=REGRESSED_MARKER, now=reference + ) + grace_seconds = grace_days * 86400 + if seconds_since_regression is None or seconds_since_regression < grace_seconds: + return {**base_result, "action": "regressed-already-notified"} + + # Not passing and not tagged: close if past the grace window, else notify once. + if age_days is not None and age_days >= grace_days: + comment = format_pr_close_comment({**verdict, "missing": missing}) + if not close: + return {**base_result, "action": "would-close", "comment": comment} + post_comment(repo, number, comment) + close_pr(repo, number) + return {**base_result, "action": "closed", "comment": comment} + + if _has_marker(comments, WITHIN_GRACE_MARKER): + return {**base_result, "action": "within-grace-already-notified"} + comment = format_within_grace_comment(missing, explanation, grace_days) + if not close: + return { + **base_result, + "action": "would-notify-within-grace", + "comment": comment, + } + post_comment(repo, number, comment) + return {**base_result, "action": "within-grace-notified", "comment": comment} + + +def triage( + *, + repo: str, + kind: str, + number: int, + close: bool, + model: str, + judge: Any = None, + print_prompt: bool = False, + reconsider: bool = False, + allowlist: frozenset[str] = ALLOWLIST_LOGINS, +) -> dict: + """Triage a single PR or issue. Returns a result dict for logging/tests. + + `judge` is an optional callable `(prompt) -> str` for tests / dry-run with + a stub. In production, leave it None and the script uses `call_llm_judge`. + + When `reconsider=True`, the closed-state guard is skipped and a + fail-but-no-comment is replaced with a "still failing" comment + leave + closed; a pass triggers `reopen_pr`/`reopen_issue` plus a reopen comment. + Reconsider mode is intended for the `@agent-shin reconsider` comment + trigger. Like regular triage, `close=False` keeps reconsider in dry-run + (returns `would-reopen` / `would-reconsider-still-failing` so a local + operator can preview without write side effects); the workflow only + passes `--close` when `AGENT_SHIN_ENABLED=true`. + + Reconsider mode adds two extra safety guards on top of the regular + triage skip-internal-author check: + + 1. **Bot-closed guard.** Only reopens if the most recent close was + performed by the bot identity (default `github-actions[bot]`). + This stops a contributor from using `@agent-shin reconsider` to + override a maintainer's close for non-rubric reasons. + 2. **Rate-limit guard.** If the bot has already posted a reconsider + verdict on this PR/issue within `RECONSIDER_RATE_LIMIT_SECONDS`, + skip — repeated triggers from the same contributor shouldn't burn + CI minutes or LLM budget. + """ + fetcher = {"pr": fetch_pr, "issue": fetch_issue}[kind] + item = fetcher(repo, number) + + title = item.get("title") or "" + body = item.get("body") or "" + login = (item.get("user") or {}).get("login") or "" + association = item.get("author_association") or "" + state = item.get("state") or "" + + base_result = { + "kind": kind, + "number": number, + "title": title, + "author": login, + "author_association": association, + "state": state, + "reconsider": reconsider, + } + + # Reconsider only makes sense on a closed PR/issue. A "reconsider on an + # open PR" is a no-op (the regular triage flow already evaluates open + # PRs); return a clear skip so the workflow can short-circuit. + if reconsider: + if state != "closed": + return {**base_result, "action": "skip-not-closed"} + else: + if state != "open": + return {**base_result, "action": "skip-not-open"} + + if allowlist: + if login.lower() not in allowlist: + return {**base_result, "action": "skip-not-allowlisted"} + elif is_internal_contributor(item): + return {**base_result, "action": "skip-internal-author"} + + # Reconsider-only guards — these run BEFORE the LLM call so a + # maintainer-closed PR / rate-limited trigger never spends LLM budget. + if reconsider: + if not was_closed_by_agent_shin(repo, number): + return {**base_result, "action": "skip-not-bot-closed"} + age = seconds_since_last_reconsider_verdict(repo, number) + if age is not None and age < RECONSIDER_RATE_LIMIT_SECONDS: + return { + **base_result, + "action": "skip-rate-limited", + "rate_limit_age_seconds": age, + "rate_limit_window_seconds": RECONSIDER_RATE_LIMIT_SECONDS, + } + + if kind == "pr": + # Short-circuit: if body very clearly links a related issue, just pass. + if has_linked_issue(body): + base = { + **base_result, + "action": "pass-linked-issue", + "verdict": { + "verdict": "pass", + "linked_issue": True, + "explanation": "Linked-issue regex matched; LLM was not called.", + }, + } + if reconsider: + # Pass-on-reconsider -> reopen the PR with a friendly comment. + reopen_body = format_reopen_comment(kind) + if not close: + return { + **base, + "action": "would-reopen", + "comment": reopen_body, + } + post_comment(repo, number, reopen_body) + reopen_pr(repo, number) + return { + **base, + "action": "reopened", + "comment": reopen_body, + } + return base + prompt = build_pr_prompt(title=title, body=body) + else: + prompt = build_issue_prompt(title=title, body=body) + + if print_prompt: + return {**base_result, "action": "print-prompt", "prompt": prompt} + + if judge is None: + api_key = os.environ.get("OPENAI_API_KEY") + if not api_key: + # No key configured — never take a destructive action. Report skip. + return { + **base_result, + "action": "skip-no-llm-key", + "prompt_preview": prompt[:200], + } + base_url = os.environ.get("OPENAI_BASE_URL") or None + + def judge(p: str) -> str: + return call_llm_judge(p, model=model, api_key=api_key, base_url=base_url) + + try: + raw = judge(prompt) + verdict = parse_verdict(raw) + except Exception as exc: # noqa: BLE001 - judge errors must never close PRs + return {**base_result, "action": "skip-llm-error", "error": str(exc)} + + decision = (verdict.get("verdict") or "").lower() + + if reconsider: + # Reconsider: an explicit `pass` -> reopen + post reopen comment; + # anything else (fail, missing/malformed verdict, typo) -> leave + # closed + post a "still failing" comment so the contributor can + # iterate again. Reopen is destructive, so a flaky/empty verdict + # must not satisfy the gate. + # In dry-run (`close=False`) we return `would-*` actions instead + # of touching GitHub state, mirroring the regular triage flow's + # `would-close`. This lets a local operator preview the outcome + # of `python triage_with_llm.py --reconsider --pr N` without + # risking accidental comments or reopens. + if decision == "pass": + reopen_body = format_reopen_comment(kind) + if not close: + return { + **base_result, + "action": "would-reopen", + "verdict": verdict, + "comment": reopen_body, + } + post_comment(repo, number, reopen_body) + if kind == "pr": + reopen_pr(repo, number) + else: + reopen_issue(repo, number) + return { + **base_result, + "action": "reopened", + "verdict": verdict, + "comment": reopen_body, + } + still_failing = format_reconsider_still_failing_comment(kind, verdict) + if not close: + return { + **base_result, + "action": "would-reconsider-still-failing", + "verdict": verdict, + "comment": still_failing, + } + post_comment(repo, number, still_failing) + return { + **base_result, + "action": "reconsider-still-failing", + "verdict": verdict, + "comment": still_failing, + } + + if decision != "fail": + return {**base_result, "action": "pass-llm", "verdict": verdict} + + # Grace-period flow: on the first low-quality detection, post a warning + # comment instead of closing immediately. On a subsequent triage run + # (manual re-trigger, or the daily `close_low_quality_prs.py` cron + # finding the same PR in its own pass), if `GRACE_PERIOD_SECONDS` has + # elapsed since the warning AND the PR still fails the rubric, close. + grace_age = seconds_since_last_grace_warning(repo, number) + if grace_age is None: + warning_body = ( + format_grace_warning_pr_comment(verdict) + if kind == "pr" + else format_grace_warning_issue_comment(verdict) + ) + if not close: + return { + **base_result, + "action": "would-warn-grace", + "verdict": verdict, + "comment": warning_body, + } + post_comment(repo, number, warning_body) + return { + **base_result, + "action": "warned-grace", + "verdict": verdict, + "comment": warning_body, + } + if grace_age < GRACE_PERIOD_SECONDS: + return { + **base_result, + "action": "skip-in-grace-period", + "verdict": verdict, + "grace_age_seconds": grace_age, + "grace_period_seconds": GRACE_PERIOD_SECONDS, + } + + # The grace window has elapsed. `--close` still gates the destructive + # write so a dry-run preview never posts or closes — the workflow only + # passes `--close` when `AGENT_SHIN_ENABLED=true`, which keeps the bot + # inert by default. + if not close: + return {**base_result, "action": "would-close", "verdict": verdict} + + comment_body = ( + format_pr_close_comment(verdict) + if kind == "pr" + else format_issue_close_comment(verdict) + ) + post_comment(repo, number, comment_body) + if kind == "pr": + close_pr(repo, number) + else: + close_issue(repo, number) + + return { + **base_result, + "action": "closed", + "verdict": verdict, + "comment": comment_body, + } + + +# --------------------------------------------------------------------------- +# CLI + + +def render_summary(result: dict) -> str: + """Render a human-readable summary block (used for stdout + step summary).""" + lines = ["## Agent Shin verdict", ""] + lines.append( + f"- **{result['kind'].upper()} #{result['number']}**: {result.get('title', '')}" + ) + lines.append( + f"- **Author**: `{result.get('author', '')}` ({result.get('author_association', '')})" + ) + lines.append(f"- **State**: {result.get('state', '')}") + lines.append(f"- **Action**: `{result['action']}`") + verdict = result.get("verdict") + if verdict: + lines.append("") + lines.append("```json") + lines.append(json.dumps(verdict, indent=2)) + lines.append("```") + error = result.get("error") + if error: + lines.append("") + lines.append(f"_LLM error: {error}_") + comment = result.get("comment") + if comment: + lines.append("") + lines.append("### Posted comment:") + lines.append("") + lines.append("> " + comment.replace("\n", "\n> ")) + return "\n".join(lines) + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--repo", required=True, help="Repository (owner/repo).") + target = parser.add_mutually_exclusive_group(required=True) + target.add_argument("--pr", type=int, help="Pull request number to triage.") + target.add_argument("--issue", type=int, help="Issue number to triage.") + parser.add_argument( + "--close", + action="store_true", + help="Actually post comment + close on fail (default: dry run).", + ) + parser.add_argument( + "--model", + # `os.environ.get("TRIAGE_MODEL", DEFAULT_MODEL)` would return "" when + # GitHub Actions exposes an unset repo variable as an empty-string env + # var, silently bypassing DEFAULT_MODEL and causing every call to fail + # as `skip-llm-error`. The `or` guard collapses empty -> default. + default=os.environ.get("TRIAGE_MODEL") or DEFAULT_MODEL, + help=f"OpenAI-compatible model name (default: {DEFAULT_MODEL}).", + ) + parser.add_argument( + "--print-prompt", + action="store_true", + help="Print the prompt that would be sent to the judge and exit.", + ) + parser.add_argument( + "--reconsider", + action="store_true", + help=( + "Re-run triage on a CLOSED PR/issue and reopen it on pass. " + "Used by the `@agent-shin reconsider` comment-trigger workflow. " + "Only invoke this from a workflow that has already gated on " + "AGENT_SHIN_ENABLED=true and verified the commenter is the " + "PR/issue author or an internal collaborator." + ), + ) + parser.add_argument( + "--review-gate", + action="store_true", + help=( + "Reconcile the `ready for review` label for an OPEN PR: tag on " + "pass, remove the tag + comment on regression, close after the " + "grace window if it never passed. PR-only." + ), + ) + parser.add_argument( + "--grace-days", + type=int, + default=DEFAULT_GRACE_DAYS, + help=( + "Review-gate only: hours/24 a failing, un-tagged PR may stay open " + f"before auto-close (default: {DEFAULT_GRACE_DAYS} = 24h)." + ), + ) + parser.add_argument( + "--min-greptile-score", + type=int, + default=DEFAULT_MIN_GREPTILE_SCORE, + choices=range(1, 6), + help=( + "Review-gate only: Greptile score below which a PR counts as not " + f"passing (default: {DEFAULT_MIN_GREPTILE_SCORE} -> <4/5 regresses)." + ), + ) + args = parser.parse_args() + + kind = "pr" if args.pr is not None else "issue" + number = args.pr if args.pr is not None else args.issue + + if args.review_gate: + if kind != "pr": + parser.error("--review-gate applies to pull requests only (use --pr).") + result = review_gate( + repo=args.repo, + number=number, + close=args.close, + model=args.model, + grace_days=args.grace_days, + min_greptile_score=args.min_greptile_score, + ) + else: + result = triage( + repo=args.repo, + kind=kind, + number=number, + close=args.close, + model=args.model, + print_prompt=args.print_prompt, + reconsider=args.reconsider, + ) + + if result.get("action") == "print-prompt": + print(result["prompt"]) + return 0 + + summary = render_summary(result) + print(summary) + write_step_summary(summary + "\n") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/.github/workflows/check-ui-api-types.yml b/.github/workflows/check-ui-api-types.yml index eeb5545b15e..d8053c15683 100644 --- a/.github/workflows/check-ui-api-types.yml +++ b/.github/workflows/check-ui-api-types.yml @@ -54,7 +54,7 @@ jobs: run: uv run --no-sync prisma generate --schema litellm/proxy/schema.prisma - name: Set up Node.js - uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0 + uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0.0 with: node-version: "20" cache: "npm" diff --git a/.github/workflows/close_low_quality_prs.yml b/.github/workflows/close_low_quality_prs.yml new file mode 100644 index 00000000000..2401be84000 --- /dev/null +++ b/.github/workflows/close_low_quality_prs.yml @@ -0,0 +1,92 @@ +name: Close Low-Quality PRs + +# Auto-close any open PR (including drafts, regardless of age) authored by an +# external OSS contributor that Greptile reviewed with a confidence score +# below 4/5. Closures are explained in a comment that tells the contributor +# to push fixes and open a fresh PR (since OSS authors cannot reopen a PR +# closed by a bot/maintainer) or comment `@agent-shin reconsider` to have +# Agent Shin re-evaluate. +# +# Manual one-off run: +# gh workflow run "Close Low-Quality PRs" -f close=true +# +# Dry-run preview (no PRs are touched): +# gh workflow run "Close Low-Quality PRs" -f close=false + +on: + schedule: + # Daily at 09:00 UTC. Pairs well with the stale-issue workflow at midnight. + - cron: "0 9 * * *" + workflow_dispatch: + inputs: + close: + description: "Actually close matching PRs (false = dry run)." + required: false + default: "false" + type: choice + options: + - "true" + - "false" + min_age_days: + description: "Minimum PR age in days (default 0 = no age filter)." + required: false + default: "0" + min_score: + description: "Greptile score below which a PR is closed (1-5)." + required: false + default: "4" + limit: + description: "Maximum number of PRs to close in a single run." + required: false + default: "25" + +permissions: + contents: read + pull-requests: write + issues: write + +jobs: + close-low-quality-prs: + if: github.repository == 'BerriAI/litellm' + runs-on: ubuntu-latest + steps: + - name: Checkout triage script + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + sparse-checkout: .github/scripts + persist-credentials: false + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" + + - name: Run low-quality PR closer + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + # Scheduled runs are ALWAYS dry-run, even when AGENT_SHIN_ENABLED is + # "true", so the team can QA the closer's verdicts in step summaries + # before any contributor sees a PR closed. Real closures only happen + # on manual workflow_dispatch with close=true (and the variable set). + CLOSE_FLAG: ${{ github.event.inputs.close || 'false' }} + AGENT_SHIN_ENABLED: ${{ vars.AGENT_SHIN_ENABLED }} + MIN_AGE_DAYS: ${{ github.event.inputs.min_age_days || '0' }} + MIN_SCORE: ${{ github.event.inputs.min_score || '4' }} + LIMIT: ${{ github.event.inputs.limit || '25' }} + run: | + set -euo pipefail + ARGS=( + --repo "${{ github.repository }}" + --min-age-days "${MIN_AGE_DAYS}" + --min-score "${MIN_SCORE}" + --limit "${LIMIT}" + ) + if [ "${AGENT_SHIN_ENABLED:-false}" != "true" ]; then + echo "::notice::AGENT_SHIN_ENABLED is not 'true' -> forcing dry-run regardless of close input." + elif [ "${GITHUB_EVENT_NAME:-}" = "workflow_dispatch" ] && [ "${CLOSE_FLAG}" = "true" ]; then + ARGS+=(--close) + echo "::notice::Running in close-on-fail mode." + else + echo "::notice::AGENT_SHIN_ENABLED is true but this trigger is dry-run (scheduled event or close=false)." + fi + python3 .github/scripts/close_low_quality_prs.py "${ARGS[@]}" diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml index babe3b62933..d3a165a11da 100644 --- a/.github/workflows/codeql.yml +++ b/.github/workflows/codeql.yml @@ -43,14 +43,14 @@ jobs: persist-credentials: false - name: Initialize CodeQL - uses: github/codeql-action/init@ebcb5b36ded6beda4ceefea6a8bc4cc885255bb3 # v3 + uses: github/codeql-action/init@ebcb5b36ded6beda4ceefea6a8bc4cc885255bb3 # v3.34.1 with: languages: ${{ matrix.language }} build-mode: ${{ matrix.build-mode }} config-file: ./.github/codeql/codeql-config.yml - name: Perform CodeQL Analysis - uses: github/codeql-action/analyze@ebcb5b36ded6beda4ceefea6a8bc4cc885255bb3 # v3 + uses: github/codeql-action/analyze@ebcb5b36ded6beda4ceefea6a8bc4cc885255bb3 # v3.34.1 with: category: "/language:${{ matrix.language }}" output: sarif-results @@ -77,7 +77,7 @@ jobs: output: sarif-results/python.sarif - name: Upload SARIF - uses: github/codeql-action/upload-sarif@ebcb5b36ded6beda4ceefea6a8bc4cc885255bb3 # v3 + uses: github/codeql-action/upload-sarif@ebcb5b36ded6beda4ceefea6a8bc4cc885255bb3 # v3.34.1 with: sarif_file: sarif-results category: "/language:${{ matrix.language }}" diff --git a/.github/workflows/conventional-commits.yml b/.github/workflows/conventional-commits.yml new file mode 100644 index 00000000000..69ade24d028 --- /dev/null +++ b/.github/workflows/conventional-commits.yml @@ -0,0 +1,46 @@ +name: Conventional PR Title + +# Squash-merge replaces the merge commit subject with the PR title, so +# enforcing Conventional Commits at the PR-title level is what actually gates +# the commits that land on the default branch. The local commit-msg hook +# (.githooks/commit-msg) is a best-effort assist; this workflow is the gate. +# +# See https://www.conventionalcommits.org/en/v1.0.0/ + +on: + pull_request: + types: [opened, edited, reopened, synchronize, labeled, unlabeled] + +permissions: + pull-requests: read + +jobs: + lint-pr-title: + name: Validate PR title + runs-on: ubuntu-latest + steps: + - name: Check title against Conventional Commits + uses: amannn/action-semantic-pull-request@48f256284bd46cdaab1048c3721360e808335d50 # v6.1.1 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + # Must mirror the type list in .githooks/commit-msg. + types: | + feat + fix + docs + style + refactor + perf + test + build + ci + chore + revert + requireScope: false + subjectPattern: ^(?![A-Z]).+$ + subjectPatternError: | + The subject "{subject}" must start with a lowercase character. + # Allow merges/reverts that GitHub generates automatically. + ignoreLabels: | + ignore-semantic-pull-request diff --git a/.github/workflows/create-release.yml b/.github/workflows/create-release.yml index a726a921a2b..4834775e329 100644 --- a/.github/workflows/create-release.yml +++ b/.github/workflows/create-release.yml @@ -52,6 +52,22 @@ jobs: // are stable maintenance releases, not pre-releases. const isPrerelease = /(?:rc|nightly|alpha|beta|[-.]dev)/i.test(tag); + // A stable release should only claim the repo "latest" badge when its + // version is >= the current latest. Otherwise a backport (e.g. 1.84.6) + // would steal "latest" from a newer line (e.g. 1.88.1). + const versionKey = (rawTag) => { + const m = String(rawTag).match(/^v?(\d+)\.(\d+)\.(\d+)/); + if (!m) return null; + const maintenance = String(rawTag).match(/(?:\.post|\.patch\.)(\d+)/i); + return [Number(m[1]), Number(m[2]), Number(m[3]), maintenance ? Number(maintenance[1]) : 0]; + }; + const isAtLeast = (a, b) => { + for (let i = 0; i < a.length; i++) { + if (a[i] !== b[i]) return a[i] > b[i]; + } + return true; + }; + const cosignSection = [ `## Verify Docker Image Signature`, ``, @@ -90,6 +106,22 @@ jobs: ].join('\n'); try { + let makeLatest = "false"; + const newVersion = versionKey(tag); + if (!isPrerelease && newVersion) { + let latestVersion = null; + try { + const latest = await github.rest.repos.getLatestRelease({ + owner: context.repo.owner, + repo: context.repo.repo, + }); + latestVersion = versionKey(latest.data.tag_name); + } catch (error) { + if (error.status !== 404) throw error; + } + makeLatest = (!latestVersion || isAtLeast(newVersion, latestVersion)) ? "true" : "false"; + } + const response = await github.rest.repos.createRelease({ draft: true, generate_release_notes: true, @@ -108,6 +140,7 @@ jobs: release_id: response.data.id, body: updatedBody, draft: false, + make_latest: makeLatest, }); } catch (error) { diff --git a/.github/workflows/osv-scan.yml b/.github/workflows/osv-scan.yml new file mode 100644 index 00000000000..0cd94fdd9e2 --- /dev/null +++ b/.github/workflows/osv-scan.yml @@ -0,0 +1,44 @@ +name: OSV Scan + +on: + pull_request: + branches: + - main + - litellm_internal_staging + - litellm_oss_branch + - "litellm_**" + schedule: + - cron: "23 6 * * *" + workflow_dispatch: + +permissions: {} + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + osv-scan: + name: osv-scan + runs-on: ubuntu-latest + timeout-minutes: 10 + permissions: + contents: read + steps: + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false + + - name: Download osv-scanner v2.3.8 + run: | + curl -fsSL --retry 3 -o "$RUNNER_TEMP/osv-scanner" \ + https://github.com/google/osv-scanner/releases/download/v2.3.8/osv-scanner_linux_amd64 + echo "bc98e15319ed0d515e3f9235287ba53cdc5535d576d24fd573978ecfe9ab92dc $RUNNER_TEMP/osv-scanner" | sha256sum -c - + chmod +x "$RUNNER_TEMP/osv-scanner" + + - name: Scan lockfiles + run: | + "$RUNNER_TEMP/osv-scanner" scan source \ + --config osv-scanner.toml \ + -L uv.lock \ + -L ui/litellm-dashboard/package-lock.json diff --git a/.github/workflows/test-linting.yml b/.github/workflows/test-linting.yml index b5e45a38cf9..950d6ca31a6 100644 --- a/.github/workflows/test-linting.yml +++ b/.github/workflows/test-linting.yml @@ -14,11 +14,15 @@ permissions: jobs: lint: runs-on: ubuntu-latest - timeout-minutes: 5 + timeout-minutes: 15 steps: - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + # Check out the PR head, not the default refs/pull/N/merge: the merge ref + # folds in newer base commits, which the diff-based gates (ruff delta, + # Any-discipline) would otherwise blame on this branch. with: + ref: ${{ github.event.pull_request.head.sha }} fetch-depth: 0 clean: true persist-credentials: false @@ -67,15 +71,27 @@ jobs: uv run --no-sync ruff check . cd .. + - name: Check strict-rule budget (delta vs base) + env: + BASE_SHA: ${{ github.event.pull_request.base.sha }} + run: | + uv run --no-sync python scripts/ruff_strict_gate.py --base "$BASE_SHA" + + - name: Check type-discipline budget (mutable collections / casts / type guards / kwargs / unexplained suppressions, delta vs base) + env: + BASE_SHA: ${{ github.event.pull_request.base.sha }} + run: | + uv run --no-sync python scripts/type_discipline_gate.py --base "$BASE_SHA" + - name: Print OpenAI version run: | uv run --no-sync python -c "import openai; print(f'OpenAI version: {openai.__version__}')" - - name: Run MyPy type checking + - name: Check basedpyright budget (delta vs base) + env: + BASE_SHA: ${{ github.event.pull_request.base.sha }} run: | - cd litellm - uv run --no-sync mypy . - cd .. + (uv run --no-sync basedpyright --outputjson || true) | uv run --no-sync python scripts/type_check_gate.py --base "$BASE_SHA" - name: Check for circular imports run: | @@ -87,6 +103,33 @@ jobs: run: | uv run --no-sync python -c "from litellm import *" || (echo '🚨 import failed, this means you introduced unprotected imports! 🚨'; exit 1) + # Intentionally NON-GATING. This job turns red when a *-budget.json ceiling is + # raised (or a rule/budget is dropped) so a loosening is obvious in review, but it + # must be kept OUT of the branch-protection required-checks list so a justified + # bump can still be merged by a human who has seen and accepted the red. + budget-ratchet: + runs-on: ubuntu-latest + timeout-minutes: 5 + permissions: + contents: read + + steps: + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + fetch-depth: 0 + persist-credentials: false + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" + + - name: Ratchet check (budgets may only decrease; non-gating) + env: + BASE_SHA: ${{ github.event.pull_request.base.sha }} + run: | + python scripts/budget_ratchet_check.py --base "$BASE_SHA" + secret-scan: runs-on: ubuntu-latest timeout-minutes: 5 diff --git a/.github/workflows/test-litellm-ui-build.yml b/.github/workflows/test-litellm-ui-build.yml index 68497b10dbb..0fd8093949d 100644 --- a/.github/workflows/test-litellm-ui-build.yml +++ b/.github/workflows/test-litellm-ui-build.yml @@ -25,7 +25,7 @@ jobs: persist-credentials: false - name: Setup Node.js - uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0 + uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0.0 with: node-version: "20" cache: "npm" @@ -77,7 +77,7 @@ jobs: - name: Setup Node.js if: steps.changed.outputs.has_files == 'true' - uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0 + uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0.0 with: node-version: "20" cache: "npm" @@ -111,4 +111,4 @@ jobs: if: ${{ !cancelled() && steps.changed.outputs.has_files == 'true' }} run: | npx eslint . -f json -o "$RUNNER_TEMP/lint-report.json" || true - node scripts/check-lint-budgets.mjs "$RUNNER_TEMP/lint-report.json" eslint-budgets.json + node scripts/check-lint-budgets.mjs "$RUNNER_TEMP/lint-report.json" eslint-budgets.json --check eslint-metrics.json diff --git a/.github/workflows/test-rust.yml b/.github/workflows/test-rust.yml new file mode 100644 index 00000000000..3d0a159cdc7 --- /dev/null +++ b/.github/workflows/test-rust.yml @@ -0,0 +1,65 @@ +name: LiteLLM Rust + +on: + push: + paths: + - "litellm-rust/**" + - ".github/workflows/test-rust.yml" + pull_request: + branches: + - main + - litellm_internal_staging + - litellm_oss_branch + - "litellm_**" + paths: + - "litellm-rust/**" + - ".github/workflows/test-rust.yml" + +permissions: + contents: read + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + rust-checks: + name: rustfmt, clippy, test + runs-on: ubuntu-latest + timeout-minutes: 10 + defaults: + run: + working-directory: litellm-rust + env: + CARGO_TERM_COLOR: always + + steps: + - name: Checkout repository + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false + + - name: Set up Rust + run: | + rustup toolchain install stable --profile minimal --component clippy,rustfmt + rustup default stable + + - name: Cache Cargo registry and target + uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0 + with: + path: | + ~/.cargo/registry + ~/.cargo/git + litellm-rust/target + key: ${{ runner.os }}-cargo-${{ hashFiles('litellm-rust/Cargo.lock') }} + restore-keys: | + ${{ runner.os }}-cargo- + + - name: Check Rust formatting + run: cargo fmt --check + + - name: Run Clippy + run: cargo clippy --workspace --all-targets --locked -- -D warnings + + - name: Run Rust tests + run: cargo test --workspace --locked diff --git a/.github/workflows/test-unit-misc.yml b/.github/workflows/test-unit-misc.yml index a7363ac3b43..2226d519331 100644 --- a/.github/workflows/test-unit-misc.yml +++ b/.github/workflows/test-unit-misc.yml @@ -32,7 +32,9 @@ jobs: tests/test_litellm/repositories tests/test_litellm/images tests/test_litellm/interactions + tests/test_litellm/ocr tests/test_litellm/passthrough + tests/test_litellm/sandbox tests/test_litellm/vector_stores tests/test_litellm/test_*.py workers: 2 diff --git a/.github/workflows/test-unit-proxy-endpoints.yml b/.github/workflows/test-unit-proxy-endpoints.yml index 0a9513ec024..d9b6a348b60 100644 --- a/.github/workflows/test-unit-proxy-endpoints.yml +++ b/.github/workflows/test-unit-proxy-endpoints.yml @@ -11,8 +11,6 @@ on: permissions: contents: read - id-token: write - pull-requests: write concurrency: group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} @@ -20,6 +18,10 @@ concurrency: jobs: proxy-endpoints: + permissions: + contents: read + id-token: write + pull-requests: write uses: ./.github/workflows/_test-unit-base.yml with: test-path: >- @@ -52,6 +54,10 @@ jobs: # is independent and its coverage artifact is uploaded separately. # See: https://www.notion.so/36c43b8acdab81ee845fd5365128a2fc proxy-server: + permissions: + contents: read + id-token: write + pull-requests: write uses: ./.github/workflows/_test-unit-base.yml with: test-path: tests/test_litellm/proxy/proxy_server diff --git a/.github/workflows/test_server_root_path.yml b/.github/workflows/test_server_root_path.yml index 57ff746c9c8..985653796c2 100644 --- a/.github/workflows/test_server_root_path.yml +++ b/.github/workflows/test_server_root_path.yml @@ -32,17 +32,16 @@ jobs: df -h / - name: Set up Docker Buildx - uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f # v3.12 + uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f # v3.12.0 - name: Build Docker image - uses: docker/build-push-action@0adf9959216b96bec444f325f1e493d4aa344497 #v6.14 + uses: docker/build-push-action@0adf9959216b96bec444f325f1e493d4aa344497 # v6.14.0 with: context: . file: ./docker/Dockerfile.non_root tags: litellm-test:${{ github.sha }} load: true - cache-from: type=gha - cache-to: type=gha,mode=max + push: false - name: Start LiteLLM container with SERVER_ROOT_PATH run: | diff --git a/.github/workflows/triage_issue_with_llm.yml b/.github/workflows/triage_issue_with_llm.yml new file mode 100644 index 00000000000..765453cf2c6 --- /dev/null +++ b/.github/workflows/triage_issue_with_llm.yml @@ -0,0 +1,96 @@ +name: Agent Shin — Issue triage + +# LLM-as-judge triage for external GitHub issues. +# +# DRY-RUN BY DEFAULT. See .github/workflows/triage_pr_with_llm.yml for the +# enablement procedure — same repo variable (`AGENT_SHIN_ENABLED=true`) +# unlocks the PR and issue triage flows together. + +on: + issues: + types: [opened, reopened] + workflow_dispatch: + inputs: + issue_number: + description: "Issue number to triage manually." + required: true + close: + description: "If true and AGENT_SHIN_ENABLED=true, actually close on fail." + required: false + default: "false" + type: choice + options: + - "true" + - "false" + +permissions: + contents: read + issues: write + +jobs: + triage: + if: github.repository == 'BerriAI/litellm' + runs-on: ubuntu-latest + steps: + - name: Checkout triage script + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + sparse-checkout: .github/scripts + persist-credentials: false + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" + + - name: Install LLM client + run: pip install --no-cache-dir --require-hashes -r .github/scripts/triage-requirements.txt + + - name: Run Agent Shin + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + # Only expose the LLM key when the bot is enabled or a collaborator + # triggers it manually, so an external user can't force paid LLM + # calls by churning issues while the bot is still in dry-run. + # The Python script calls the LLM whenever this var is set + # (regardless of `--close`); stripping `--close` doesn't suppress + # the API call, only the destructive side effects. + OPENAI_API_KEY: ${{ (vars.AGENT_SHIN_ENABLED == 'true' || github.event_name == 'workflow_dispatch') && secrets.OPENAI_API_KEY || '' }} + OPENAI_BASE_URL: ${{ vars.OPENAI_BASE_URL }} + TRIAGE_MODEL: ${{ vars.TRIAGE_MODEL }} + AGENT_SHIN_ENABLED: ${{ vars.AGENT_SHIN_ENABLED }} + DISPATCH_CLOSE: ${{ github.event.inputs.close }} + ISSUE_NUMBER: ${{ github.event.issue.number || github.event.inputs.issue_number }} + run: | + set -euo pipefail + ARGS=(--repo "${{ github.repository }}" --issue "${ISSUE_NUMBER}") + # Fail-safe gating: only the EXACT string "true" enables the + # destructive --close path. The workflow_dispatch input is a + # `choice` dropdown of "true"/"false" so the UI is constrained, + # but the API (`gh workflow run -f close=...`) accepts any + # string, and a `!= "false"` check would treat "True", "yes", + # "1", "TRUE", typos, and accidental whitespace as enabling + # closure. Mirror the Greptile closer's `= "true"` pattern. + if [ "${AGENT_SHIN_ENABLED:-false}" = "true" ] && [ "${DISPATCH_CLOSE:-false}" = "true" ]; then + ARGS+=(--close) + echo "::notice::Agent Shin is ENABLED and running in close-on-fail mode." + elif [ "${AGENT_SHIN_ENABLED:-false}" = "true" ]; then + echo "::notice::Agent Shin is ENABLED but this trigger is dry-run (workflow_dispatch close != 'true')." + else + echo "::notice::Agent Shin is in DRY-RUN mode (AGENT_SHIN_ENABLED is not 'true'). No comments will be posted; no issues will be closed." + fi + # Automatic `issues` events stay dry-run regardless until the team + # explicitly invokes workflow_dispatch with close=true. + if [ "${GITHUB_EVENT_NAME:-}" = "issues" ]; then + # filter out --close rather than substituting to "" (which would + # leave an empty positional arg that argparse rejects) + FILTERED=() + for arg in "${ARGS[@]}"; do + if [ "${arg}" != "--close" ]; then + FILTERED+=("${arg}") + fi + done + ARGS=("${FILTERED[@]}") + echo "::notice::issues trigger -> forcing dry-run." + fi + python3 .github/scripts/triage_with_llm.py "${ARGS[@]}" diff --git a/.github/workflows/triage_reconsider.yml b/.github/workflows/triage_reconsider.yml new file mode 100644 index 00000000000..f35f681d09a --- /dev/null +++ b/.github/workflows/triage_reconsider.yml @@ -0,0 +1,172 @@ +name: Agent Shin — reconsider + +# Comment-trigger workflow: when the PR/issue author (or an internal +# collaborator) comments `@agent-shin reconsider` on a CLOSED PR/issue, +# Agent Shin re-runs LLM-judge triage on the current title+body and: +# +# - on PASS: posts a "re-evaluated and reopened" comment + reopens. +# - on FAIL: posts a "still missing X" comment and leaves it closed, +# so the contributor can iterate again. +# +# This exists because GitHub does NOT let an external (non-write-access) +# OSS contributor reopen a PR/issue closed by a bot or maintainer. Without +# this comment trigger, a contributor whose PR Agent Shin auto-closed +# would have no path back into the review queue except opening a fresh PR +# (which loses the original PR's history). The bot, on the other hand, +# has write access via GH_TOKEN and can reopen on their behalf. +# +# DRY-RUN BY DEFAULT — gated on `vars.AGENT_SHIN_ENABLED == 'true'` just +# like the other Agent Shin workflows. The workflow also gates on the +# commenter being either the PR/issue author or an internal collaborator +# (OWNER/MEMBER/COLLABORATOR) so random commenters cannot DOS the LLM +# judge or force a reopen. + +on: + issue_comment: + types: [created] + +permissions: + contents: read + issues: write + pull-requests: write + +jobs: + reconsider: + if: | + github.repository == 'BerriAI/litellm' + && contains(github.event.comment.body, '@agent-shin reconsider') + runs-on: ubuntu-latest + steps: + - name: Authorize commenter + # Only the PR/issue author OR an internal collaborator may trigger + # a reconsider. Outside random commenters could otherwise spam the + # phrase to burn LLM budget or, if a fail-open bug were ever + # introduced, force a reopen on someone else's behalf. + # + # We expose the authorization decision as a step output and gate + # every subsequent (potentially destructive) step on it. A `run:` + # step with `exit 0` would NOT stop the job — only `if:` gating + # on a known-true output is safe here. + id: auth + env: + COMMENTER: ${{ github.event.comment.user.login }} + AUTHOR: ${{ github.event.issue.user.login }} + ASSOCIATION: ${{ github.event.comment.author_association }} + run: | + set -euo pipefail + if [ "${COMMENTER}" = "${AUTHOR}" ]; then + echo "::notice::Authorized: commenter is the PR/issue author." + echo "authorized=true" >> "$GITHUB_OUTPUT" + exit 0 + fi + case "${ASSOCIATION}" in + OWNER|MEMBER|COLLABORATOR) + echo "::notice::Authorized: commenter is an internal collaborator (${ASSOCIATION})." + echo "authorized=true" >> "$GITHUB_OUTPUT" + ;; + *) + echo "::notice::Commenter '${COMMENTER}' (${ASSOCIATION}) is not authorized to trigger reconsider; skipping subsequent steps." + echo "authorized=false" >> "$GITHUB_OUTPUT" + ;; + esac + + - name: React 👀 to acknowledge the reconsider + # Add an eyes reaction to the triggering comment the moment we accept + # it, so the contributor gets instant feedback that the bot saw their + # `@agent-shin reconsider` before the slower triage steps run. Gated on + # AGENT_SHIN_ENABLED so dry-run leaves no visible trace. Best-effort: + # a reactions API hiccup must never fail the actual reconsider. + if: steps.auth.outputs.authorized == 'true' && vars.AGENT_SHIN_ENABLED == 'true' + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + COMMENT_ID: ${{ github.event.comment.id }} + run: | + set -euo pipefail + gh api --method POST \ + -H "Accept: application/vnd.github+json" \ + "repos/${{ github.repository }}/issues/comments/${COMMENT_ID}/reactions" \ + -f content=eyes \ + || echo "::warning::failed to add 👀 reaction (non-fatal)" + + - name: Checkout triage script + if: steps.auth.outputs.authorized == 'true' + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + sparse-checkout: .github/scripts + persist-credentials: false + + - name: Set up Python + if: steps.auth.outputs.authorized == 'true' + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" + + - name: Install LLM client + if: steps.auth.outputs.authorized == 'true' + run: pip install --no-cache-dir --require-hashes -r .github/scripts/triage-requirements.txt + + - name: Run Agent Shin reconsider + if: steps.auth.outputs.authorized == 'true' + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + # Only expose the LLM key when the bot is enabled, so a PR/issue + # author can't force paid LLM calls by spamming `@agent-shin + # reconsider` while the bot is still in dry-run. The Python script + # calls the LLM whenever this var is set (regardless of `--close`); + # stripping `--close` doesn't suppress the API call, only the + # destructive side effects. Mirror the gating used by every other + # Agent Shin workflow (triage_pr_with_llm.yml, review_gate.yml, ...). + OPENAI_API_KEY: ${{ vars.AGENT_SHIN_ENABLED == 'true' && secrets.OPENAI_API_KEY || '' }} + OPENAI_BASE_URL: ${{ vars.OPENAI_BASE_URL }} + TRIAGE_MODEL: ${{ vars.TRIAGE_MODEL }} + AGENT_SHIN_ENABLED: ${{ vars.AGENT_SHIN_ENABLED }} + # `issue_comment` events fire for both issues and PR comments. + # `issue.pull_request` is set iff this is a PR comment, so we use + # its presence to decide whether to invoke `--pr N` or `--issue N`. + IS_PR: ${{ github.event.issue.pull_request != null }} + NUMBER: ${{ github.event.issue.number }} + run: | + set -euo pipefail + if [ "${IS_PR}" = "true" ]; then + ARGS=(--repo "${{ github.repository }}" --pr "${NUMBER}" --reconsider) + else + ARGS=(--repo "${{ github.repository }}" --issue "${NUMBER}" --reconsider) + fi + # Reconsider's destructive actions (post comment + reopen) are + # gated on `--close`, mirroring the regular triage workflows. + # When AGENT_SHIN_ENABLED is not the EXACT string "true", we + # still run the script so its verdict + would-X action lands in + # the step summary for QA — but without `--close`, the script + # returns `would-reopen` / `would-reconsider-still-failing` + # instead of touching GitHub state. + # + # Use the positive `= "true"` gate (not `!= "true" -> exit`) so + # the workflow guardrails in + # tests/test_litellm/test_github_triage_workflows.py see the + # canonical fail-safe enable pattern. Unknown values like + # "True", "yes", "1", or typos fall through to the dry-run + # branch, which is the safe default. + if [ "${AGENT_SHIN_ENABLED:-false}" = "true" ]; then + ARGS+=(--close) + echo "::notice::Agent Shin reconsider ENABLED — running real triage (close=true)." + else + echo "::notice::AGENT_SHIN_ENABLED is not 'true' -> reconsider stays in dry-run (no comment, no reopen)." + fi + python3 .github/scripts/triage_with_llm.py "${ARGS[@]}" + + - name: React 👍 when the reconsider finishes + # Once the reconsider run has completed successfully, add a thumbs-up so + # the contributor sees the bot is done (the 👀 stays, signalling + # seen -> handled). `success()` keeps this from firing if the run + # errored, and the AGENT_SHIN_ENABLED gate keeps dry-run inert. + if: success() && steps.auth.outputs.authorized == 'true' && vars.AGENT_SHIN_ENABLED == 'true' + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + COMMENT_ID: ${{ github.event.comment.id }} + run: | + set -euo pipefail + gh api --method POST \ + -H "Accept: application/vnd.github+json" \ + "repos/${{ github.repository }}/issues/comments/${COMMENT_ID}/reactions" \ + -f content=+1 \ + || echo "::warning::failed to add 👍 reaction (non-fatal)" diff --git a/.github/workflows/triage_rollout_heads_up.yml b/.github/workflows/triage_rollout_heads_up.yml new file mode 100644 index 00000000000..903960151e2 --- /dev/null +++ b/.github/workflows/triage_rollout_heads_up.yml @@ -0,0 +1,92 @@ +name: Agent Shin — rollout heads-up (one-shot) + +# Fires the 7-day heads-up comment on every open external PR/issue that the +# new triage bot would auto-close. The real sweep is a deliberate one-shot: +# trigger it at rollout via a manual `workflow_dispatch` with `dry_run=false`. +# The script is idempotent (skips items that already carry the +# `` marker), so a re-run is harmless. +# +# The automatic push trigger runs DRY-RUN only, so merging the script to +# `litellm_internal_staging` never posts a comment; it just confirms the +# workflow is wired up. Posting real comments requires the manual dispatch, +# which is also the only trigger that exposes `OPENAI_API_KEY`. The heads-up +# is intentionally NOT gated on `AGENT_SHIN_ENABLED`: it has to warn +# contributors while that flag is still off, ahead of the flip that turns on +# auto-closing. +# +# The workflow is a thin shell over `.github/scripts/triage_rollout_heads_up.py`. +# Dry-run vs. real run differ in EXACTLY one CLI flag (`--close`), added only +# on a manual dispatch with `dry_run=false`. + +on: + push: + branches: + - litellm_internal_staging + paths: + # The presence of this script on staging IS the rollout merge marker. + # Editing the file later would re-fire the workflow; that's safe because + # the script skips PRs/issues that already have the heads-up marker. + - ".github/scripts/triage_rollout_heads_up.py" + workflow_dispatch: + inputs: + dry_run: + description: "Dry run (true = preview only, false = actually post comments)." + required: false + default: "true" + type: choice + options: + - "true" + - "false" + +permissions: + contents: read + issues: write + pull-requests: write + +jobs: + heads-up: + if: github.repository == 'BerriAI/litellm' + runs-on: ubuntu-latest + steps: + - name: Checkout triage scripts + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + sparse-checkout: .github/scripts + persist-credentials: false + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" + + - name: Install LLM client + run: pip install --no-cache-dir --require-hashes -r .github/scripts/triage-requirements.txt + + - name: Run heads-up sweep + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + # Only the manual dispatch (the real-run trigger) needs the LLM key. + # The automatic push trigger runs dry-run and never posts, so it gets + # no key. Mirrors the sibling triage workflows, which expose the key + # only on an enabled/dispatched run rather than unconditionally. + OPENAI_API_KEY: ${{ github.event_name == 'workflow_dispatch' && secrets.OPENAI_API_KEY || '' }} + OPENAI_BASE_URL: ${{ vars.OPENAI_BASE_URL }} + TRIAGE_MODEL: ${{ vars.TRIAGE_MODEL }} + # The real run is a deliberate manual dispatch with dry_run=false. + # Use the EXACT "false" comparison so any unexpected input value + # fail-closes to dry-run (mirrors the AGENT_SHIN_ENABLED pattern in + # the sibling workflows). The automatic push trigger always stays + # dry-run, so merging the script never posts. + DRY_RUN_INPUT: ${{ github.event.inputs.dry_run }} + run: | + set -euo pipefail + ARGS=(--repo "${{ github.repository }}") + if [ "${GITHUB_EVENT_NAME:-}" = "workflow_dispatch" ] && [ "${DRY_RUN_INPUT:-true}" = "false" ]; then + ARGS+=(--close) + echo "::notice::Manual rollout dispatch with dry_run=false -> heads-up comments WILL be posted." + elif [ "${GITHUB_EVENT_NAME:-}" = "workflow_dispatch" ]; then + echo "::notice::Manual dispatch in dry-run mode -> previewing only, no comments will be posted." + else + echo "::notice::Automatic push trigger -> dry-run preview only. Fire the real rollout sweep with a manual workflow_dispatch (dry_run=false)." + fi + python3 .github/scripts/triage_rollout_heads_up.py "${ARGS[@]}" diff --git a/.github/workflows/zizmor.yml b/.github/workflows/zizmor.yml index 9a1e899fed5..db79fe43038 100644 --- a/.github/workflows/zizmor.yml +++ b/.github/workflows/zizmor.yml @@ -2,9 +2,9 @@ name: GitHub Actions Security Analysis on: push: - branches: [main] + branches: [main, litellm_internal_staging] pull_request: - branches: [main] + branches: [main, litellm_internal_staging] concurrency: group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} @@ -18,9 +18,7 @@ jobs: runs-on: ubuntu-latest timeout-minutes: 5 permissions: - security-events: write contents: read - actions: read steps: - name: Checkout repository uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 @@ -28,4 +26,9 @@ jobs: persist-credentials: false - name: Run zizmor - uses: zizmorcore/zizmor-action@71321a20a9ded102f6e9ce5718a2fcec2c4f70d8 # v0.5.2 + uses: zizmorcore/zizmor-action@5f14fd08f7cf1cb1609c1e344975f152c7ee938d # v0.5.6 + with: + version: "1.24.1" + min-severity: medium + advanced-security: false + annotations: true diff --git a/.gitignore b/.gitignore index 572830d35f6..fda3311fe02 100644 --- a/.gitignore +++ b/.gitignore @@ -74,7 +74,6 @@ tests/local_testing/log.txt .codegpt litellm/proxy/_new_new_secret_config.yaml litellm/proxy/custom_guardrail.py -**/.mypy_cache/ litellm/proxy/application.log tests/llm_translation/vertex_test_account.json tests/llm_translation/test_vertex_key.json diff --git a/CLAUDE.md b/CLAUDE.md index 02a9630b486..b721064aaa7 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -29,13 +29,19 @@ If you ever make public-facing PR descriptions, comments, issues, commit message - don't use "—". Instead, reach for ";", ".", etc. - don't use the pattern "It's not X, it's Y", "You're not X, you're Y", etc. - don't use bulleted or numbered lists unless it would be nonsensical not to. Instead, prefer prose -- don't add a trailing "." at the end of paragraphs (just like this file) +- don't add a trailing "." at the end of paragraphs (just like this file). That means every paragraph, not just the last one (of the markdown file, PR description, GitHub comment, etc.). Rule of thumb: unless there's a sentence immediately after, don't add a "." - don't use →. Instead, prefer not to use arrows, and if need be, use -> instead Don't hesitate to use values in .env to get needed API keys and other secrets, as long as you never add them to conversation history, commit them, or include them in GitHub issues / PRs Run tests, format your code, and lint your code before each commit +When you fix violations gated by `ruff-strict-budget.json` or `basedpyright-code-budget.json`, run `make lint-budget-update` and commit the lowered baselines so the ceilings ratchet down instead of leaving stale headroom + +If you're trying to create a new function that relies on untyped stuff, instead of adding more Any's and pushing `reportAny` / `reportExplicitAny` closer to their basedpyright ceilings, just validate it in the caller with Pydantic (a model or `TypeAdapter` that returns the typed thing or raises will do) and then pass the now typed variable in + +If you get an LIT001 or LIT002 fail, refactor the code to follow functional programming best practices rather than introducing mutable data structures. For example, build values in one shot with comprehensions or generators wrapped in `tuple()` / `frozenset()` instead of seeding an empty `list`/`dict`/`set` and mutating it over time. Ideally `# mutable-ok` is never used; reach for it only as a genuine last resort when an immutable rewrite is truly impossible, and always pair it with a real reason + Ask to commit and push your work when you're done (or if you're confident that your code is good and works, just do it) When you must use real LLM models to, for example, write e2e tests, write a QA runbook, etc., make sure to use the latest models (doesn't have to be smartest, can also be a modern small, fast one. No strong preference for smart vs fast here, just use something modern) as of the year and month of the current date. Do a web search as necessary to figure that out @@ -52,6 +58,21 @@ Do not put names of customers or customer company names in code, PRs, and issues CI supply-chain safety: Never pipe a remote script into a shell (`curl ... | bash`, `wget ... | sh`); download the artifact to a file, verify its SHA-256 checksum, then install. Pin every external tool to a specific version with a full URL (not `latest` or `stable`). Verify checksums for all downloaded binaries, using the provider's official `.sha256` / `.sha256sum` sidecar when available. These rules apply to every download in CI +Follow these coding conventions for new/updated code (a three-line fix in a legacy file shouldn't trigger huge drive-by refactors): + +- Composition over inheritance +- Never-nester: early returns over deep nesting +- Don't throw; model failures as values (One function (e.g., raise_public) maps error union to existing public exception contracts via exhaustive match + assert_never) +- No mutation; don't reassign variables, global or local. Instead of mutable lists and dicts, prefer tuples, frozen dataclasses (with slots=True), etc. +- Use dependency injection +- Fully typed; no `Any` or coarse types like `dict[str, Any]` or just `dict`. Every function parameter must be strongly typed +- Use tagged unions + match +- No monster files or god objects +- No file sprawl: deliberate file and folder structure +- Standard over hand-rolled: use the official SDK or a library where one exists; where none does, follow industry standards instead of inventing local conventions + +Follow conventional commits for commit names and PR titles + ## Think Before Coding **Don't assume. Don't hide confusion. Surface tradeoffs** diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 8ac83341f64..1080579d0fa 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -38,18 +38,25 @@ Before contributing code to LiteLLM, you must sign our [Contributor License Agre git clone https://github.com/YOUR_USERNAME/litellm.git cd litellm -# Create a new branch for your feature -git checkout -b your-feature-branch +# Create a new branch for your feature (see "Commit and Branch Conventions" below) +git checkout -b feature/your-feature # Install development dependencies make install-dev +# Install git hooks that enforce commit + branch conventions (one-time, opt-in) +make install-hooks + # Verify your setup works make help ``` That's it! Your local development environment is ready. +## Commit and Branch Conventions + +Commits follow [Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0/) and branches follow [Conventional Branches](https://conventional-branch.github.io/). Run `make install-hooks` once per clone to enable the local git hooks that enforce these — see the [contributor docs](https://docs.litellm.ai/docs/extras/contributing_code#commit-and-branch-conventions) for the full type list, examples, the protected-branch bypass list, and how to opt out. + ### 2. Development Workflow Here's the recommended workflow for making changes: @@ -67,12 +74,12 @@ make lint # Run unit tests to ensure nothing is broken make test-unit -# Commit your changes +# Commit your changes (must follow Conventional Commits — see above) git add . -git commit -m "Your descriptive commit message" +git commit -m "feat(scope): your descriptive commit message" -# Push and create a PR -git push origin your-feature-branch +# Push and create a PR (branch must follow Conventional Branches — see above) +git push origin feature/your-feature ``` ## Adding Testing @@ -147,7 +154,7 @@ Individual linting commands: ```bash make format-check # Check Black formatting make lint-ruff # Run Ruff linting -make lint-mypy # Run MyPy type checking +make lint-basedpyright # Run basedpyright type checking make check-circular-imports # Check for circular imports make check-import-safety # Check import safety ``` @@ -209,7 +216,7 @@ LiteLLM follows the [Google Python Style Guide](https://google.github.io/stylegu Our automated quality checks include: - **Black** for consistent code formatting - **Ruff** for linting and code quality -- **MyPy** for static type checking +- **basedpyright** for static type checking - **Circular import detection** - **Import safety validation** @@ -223,7 +230,7 @@ If `make lint` fails: 1. **Formatting issues**: Run `make format` to auto-fix 2. **Ruff issues**: Check the output and fix manually -3. **MyPy issues**: Add proper type hints +3. **basedpyright issues**: Add proper type hints 4. **Circular imports**: Refactor import dependencies 5. **Import safety**: Fix any unprotected imports @@ -238,7 +245,7 @@ If `make test-unit` fails: ### 3. Common Development Tips -- **Use type hints**: MyPy requires proper type annotations +- **Use type hints**: basedpyright requires proper type annotations - **Write descriptive commit messages**: Help reviewers understand your changes - **Keep PRs focused**: One feature/fix per PR - **Test edge cases**: Don't just test the happy path diff --git a/Dockerfile b/Dockerfile index 9ad9ab31b65..af49dc8d8cf 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,8 +1,8 @@ # Base image for building -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:31da6565f35af6401031c1d7aa91dc84ac76c5c48edd17fb90f0ed9e3173c7a9 +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:c61ac6919b811ea53c4782d69f1fe05218ba3c25d53f01b6ab7892e621bd4370 # Runtime image -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:31da6565f35af6401031c1d7aa91dc84ac76c5c48edd17fb90f0ed9e3173c7a9 +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:c61ac6919b811ea53c4782d69f1fe05218ba3c25d53f01b6ab7892e621bd4370 ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a FROM $UV_IMAGE AS uvbin @@ -68,22 +68,24 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime USER root -RUN apk add --no-cache bash openssl tzdata nodejs npm python3 libsndfile && \ - npm install -g npm@11.14.0 tar@7.5.11 glob@13.0.6 @isaacs/brace-expansion@5.0.1 brace-expansion@5.0.5 minimatch@10.2.4 diff@8.0.3 picomatch@4.0.4 && \ - GLOBAL="$(npm root -g)" && \ - for pkg in tar glob @isaacs/brace-expansion brace-expansion minimatch diff picomatch; do \ - name="${pkg##*/}"; \ - find "$GLOBAL/npm" -type d -name "$name" -path "*/node_modules/$pkg" | while read d; do \ - rm -rf "$d" && cp -rL "$GLOBAL/$pkg" "$d"; \ - done; \ - done && \ - npm cache clean --force && \ - { apk del --no-cache npm 2>/dev/null || true; } +# node (without npm) is required by the prisma CLI at runtime +RUN apk add --no-cache bash openssl tzdata nodejs python3 libsndfile WORKDIR /app ENV PATH="/app/.venv/bin:${PATH}" -COPY --from=builder /app /app +# Copy only what runtime needs. The application is installed inside the venv; +# the rest of the builder's /app is source and build metadata that must not +# ship (manifest-scanning tools attribute everything in it to this image). +# entrypoint.sh invokes litellm/proxy/prisma_migration.py by source path. +COPY --from=builder /app/.venv /app/.venv +COPY --from=builder /app/docker /app/docker +COPY --from=builder /app/schema.prisma /app/schema.prisma +COPY --from=builder /app/litellm/proxy/prisma_migration.py /app/litellm/proxy/prisma_migration.py +# enterprise/ is imported by source path at runtime (proxy_cli puts the +# working directory on sys.path; litellm/proxy/hooks resolves +# enterprise.enterprise_hooks from it) +COPY --from=builder /app/enterprise /app/enterprise # Prisma binaries live in $HOME/.cache (default prisma-python location), # which is /root/.cache here. Copy only the Prisma subdirs — copying the # whole /root/.cache drags in the uv build cache (~660 MB, includes a diff --git a/Makefile b/Makefile index a00a90da601..076eac0f4a7 100644 --- a/Makefile +++ b/Makefile @@ -5,7 +5,9 @@ test-unit-integrations test-unit-core-utils test-unit-other test-unit-root \ test-proxy-unit-a test-proxy-unit-b test-integration test-unit-helm \ info lint lint-dev format \ - install-dev install-proxy-dev install-test-deps \ + lint-basedpyright lint-basedpyright-budget-update \ + lint-ruff-budget lint-ruff-budget-update lint-budget-update lint-gate \ + install-dev install-proxy-dev install-test-deps install-hooks \ install-helm-unittest check-circular-imports check-import-safety # Default target @@ -17,12 +19,18 @@ help: @echo " make install-proxy-dev-ci - Install proxy dev dependencies (CI-compatible)" @echo " make install-test-deps - Install the full local test environment" @echo " make install-helm-unittest - Install helm unittest plugin" + @echo " make install-hooks - Install git hooks (Conventional Commits + Branches)" @echo " make format - Apply Black code formatting" @echo " make format-check - Check Black code formatting (matches CI)" - @echo " make lint - Run all linting (Ruff, MyPy, Black check, circular imports, import safety)" + @echo " make lint - Run all linting (Ruff, basedpyright, Black check, circular imports, import safety)" @echo " make lint-ruff - Run Ruff linting only" - @echo " make lint-mypy - Run MyPy type checking only" + @echo " make lint-basedpyright - Run basedpyright strict, gated by per-rule error counts" + @echo " make lint-basedpyright-budget-update - Re-capture the basedpyright per-rule budget (ratchet)" @echo " make lint-black - Check Black formatting (matches CI)" + @echo " make lint-ruff-budget - Gate the codebase total of each strict ruff rule against its ceiling" + @echo " make lint-gate - Strict ruff gate in CI-parity mode (fetches staging, simulates the merge)" + @echo " make lint-ruff-budget-update - Re-capture per-rule baselines in ruff-strict-budget.json (ratchet)" + @echo " make lint-budget-update - Re-capture all ratchet budgets (ruff + basedpyright)" @echo " make check-circular-imports - Check for circular imports" @echo " make check-import-safety - Check import safety" @echo " make test - Run all tests" @@ -68,6 +76,11 @@ install-test-deps: install-proxy-dev install-helm-unittest: helm plugin install https://github.com/helm-unittest/helm-unittest --version v0.4.4 || echo "ignore error if plugin exists" +# Install git hooks that enforce Conventional Commits and Conventional Branches. +# Opt-in: not chained into install-dev. +install-hooks: + ./scripts/install_git_hooks.sh + # Formatting format: install-dev cd litellm && $(UV_RUN) black . && cd .. @@ -111,11 +124,30 @@ lint-ruff-FULL-dev: install-dev if [ -n "$$files" ]; then echo "$$files" | xargs $(UV_RUN) ruff check; \ else echo "No changed .py files to check."; fi -lint-mypy: install-dev - cd litellm && $(UV_RUN) mypy . --ignore-missing-imports && cd .. +lint-basedpyright: install-dev + git fetch origin litellm_internal_staging + ($(UV_RUN) basedpyright --outputjson || true) | $(UV_RUN) python scripts/type_check_gate.py --base origin/litellm_internal_staging + +lint-basedpyright-budget-update: install-dev + ($(UV_RUN) basedpyright --outputjson || true) | $(UV_RUN) python scripts/type_check_gate.py --update lint-black: format-check +lint-ruff-budget: install-dev + $(UV_RUN) python scripts/ruff_strict_gate.py + +# Strict gate, invoked the same way CI does in test-linting.yml so a local pass +# means the CI check will pass too. +lint-gate: install-dev + git fetch origin litellm_internal_staging + $(UV_RUN) python scripts/ruff_strict_gate.py --base origin/litellm_internal_staging + +lint-ruff-budget-update: install-dev + $(UV_RUN) python scripts/ruff_strict_gate.py --update + +# Ratchet all budgets in one shot (ruff strict + basedpyright) +lint-budget-update: lint-ruff-budget-update lint-basedpyright-budget-update + check-circular-imports: install-dev cd litellm && $(UV_RUN) python ../tests/documentation_tests/test_circular_imports.py && cd .. @@ -123,10 +155,10 @@ check-import-safety: install-dev @$(UV_RUN) python -c "from litellm import *; print('[from litellm import *] OK! no issues!');" || (echo '🚨 import failed, this means you introduced unprotected imports! 🚨'; exit 1) # Combined linting (matches test-linting.yml workflow) -lint: format-check lint-ruff lint-mypy check-circular-imports check-import-safety +lint: format-check lint-ruff lint-basedpyright check-circular-imports check-import-safety lint-ruff-budget # Faster linting for local development (only checks changed code) -lint-dev: lint-format-changed lint-mypy check-circular-imports check-import-safety +lint-dev: lint-format-changed check-circular-imports check-import-safety # Testing targets test: install-test-deps diff --git a/README.md b/README.md index d600f3952c6..3d0f7282d7c 100644 --- a/README.md +++ b/README.md @@ -6,10 +6,10 @@

Open Source AI Gateway for 100+ LLMs. Self-hosted. Enterprise-ready. Call any LLM in OpenAI format.

- Deploy to Render - - Deploy on Railway - + Deploy to Render + Deploy on Railway + Deploy on AWS + Deploy on GCP

LiteLLM Proxy Server (AI Gateway) | Hosted Proxy | Enterprise Tier | Website

@@ -327,6 +327,7 @@ curl -X POST 'http://0.0.0.0:4000/v1/chat/completions' \ | [Maritalk (`maritalk`)](https://docs.litellm.ai/docs/providers/maritalk) | ✅ | ✅ | ✅ | | | | | | | | | [Meta - Llama API (`meta_llama`)](https://docs.litellm.ai/docs/providers/meta_llama) | ✅ | ✅ | ✅ | | | | | | | | | [Mistral AI API (`mistral`)](https://docs.litellm.ai/docs/providers/mistral) | ✅ | ✅ | ✅ | ✅ | | | | | | | +| [ModelScope (`modelscope`)](https://docs.litellm.ai/docs/providers/modelscope) | ✅ | ✅ | ✅ | | ✅ | | | | | | | [Moonshot (`moonshot`)](https://docs.litellm.ai/docs/providers/moonshot) | ✅ | ✅ | ✅ | | | | | | | | | [Morph (`morph`)](https://docs.litellm.ai/docs/providers/morph) | ✅ | ✅ | ✅ | | | | | | | | | [Nebius AI Studio (`nebius`)](https://docs.litellm.ai/docs/providers/nebius) | ✅ | ✅ | ✅ | ✅ | | | | | | | @@ -344,6 +345,7 @@ curl -X POST 'http://0.0.0.0:4000/v1/chat/completions' \ | [OVHCloud AI Endpoints (`ovhcloud`)](https://docs.litellm.ai/docs/providers/ovhcloud) | ✅ | ✅ | ✅ | | | | | | | | | [Perplexity AI (`perplexity`)](https://docs.litellm.ai/docs/providers/perplexity) | ✅ | ✅ | ✅ | | | | | | | | | [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) | ✅ | ✅ | ✅ | | | | | | | | | [Recraft (`recraft`)](https://docs.litellm.ai/docs/providers/recraft) | | | | | ✅ | | | | | | | [Replicate (`replicate`)](https://docs.litellm.ai/docs/providers/replicate) | ✅ | ✅ | ✅ | | | | | | | | @@ -404,6 +406,140 @@ You can use LiteLLM through either the Proxy Server or Python SDK. Both give you Support for more providers. Missing a provider or LLM Platform, raise a [feature request](https://github.com/BerriAI/litellm/issues/new?assignees=&labels=enhancement&projects=&template=feature_request.yml&title=%5BFeature%5D%3A+). +### Deploy on AWS or GCP with Terraform + +Run the LiteLLM proxy as a production-ready componentized stack (gateway, backend, UI on separate services; managed Postgres + Redis + object store) using the published Terraform modules. Both modules are on the [public Terraform Registry](https://registry.terraform.io/namespaces/BerriAI) — no auth needed. + +#### AWS — ECS Fargate + Aurora + ElastiCache + ALB + +[![Launch in AWS CloudShell](https://img.shields.io/badge/Launch-AWS_CloudShell-FF9900?logo=amazon-aws&logoColor=white)](https://console.aws.amazon.com/cloudshell/home) — opens an in-browser shell, already authenticated to your AWS account. Once inside, run: + +```bash +git clone https://github.com/BerriAI/litellm.git +cd litellm/terraform/litellm/aws/examples/default +cp terraform.tfvars.example terraform.tfvars # edit region/tenant/env +terraform init && terraform apply +``` + +[Module page →](https://registry.terraform.io/modules/BerriAI/litellm/aws/latest) + +Or call the module from your own root config: + +```hcl +# main.tf +terraform { + required_version = ">= 1.6.0" + required_providers { + aws = { source = "hashicorp/aws", version = "~> 5.60" } + } +} + +provider "aws" { + region = "us-west-2" +} + +module "litellm" { + source = "BerriAI/litellm/aws" + version = "~> 1.89" + + region = "us-west-2" + azs = ["us-west-2a", "us-west-2b"] + tenant = "acme" + env = "prod" + + # Production: provide an ACM cert. Without one, set allow_plaintext_alb = true + # (dev/trial only). + # acm_certificate_arn = "arn:aws:acm:us-west-2:111122223333:certificate/..." + allow_plaintext_alb = true +} + +output "litellm_url" { + value = module.litellm.alb_dns_name +} +``` + +```bash +terraform init +terraform apply +``` + +Provider API keys live in AWS Secrets Manager; reference ARNs via `gateway_extra_secrets`. Full input list and architecture diagram on the [registry page](https://registry.terraform.io/modules/BerriAI/litellm/aws/latest?tab=inputs). + +#### GCP — Cloud Run + Cloud SQL + Memorystore + HTTPS LB + +[![Open in Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.png)](https://ssh.cloud.google.com/cloudshell/editor?cloudshell_git_repo=https%3A%2F%2Fgithub.com%2FBerriAI%2Flitellm&cloudshell_workspace=terraform%2Flitellm%2Fgcp%2Fexamples%2Fdefault&cloudshell_tutorial=TUTORIAL.md&cloudshell_image=gcr.io/ds-artifacts-cloudshell/deploystack_custom_image&shellonly=true) + +Real 1-click. Opens Cloud Shell, clones this repo, and walks you through `terraform apply` via a built-in [DeployStack tutorial](./terraform/litellm/gcp/examples/default/TUTORIAL.md) — pick the project, the tutorial sets up the Artifact Registry remote repo, writes `terraform.tfvars` from your answers, and runs apply. + +[Module page →](https://registry.terraform.io/modules/BerriAI/litellm/google/latest) + +To call the module from your own config instead, Cloud Run can't pull from `ghcr.io` directly, so first set up a one-time Artifact Registry remote repo backed by GHCR: + +```bash +gcloud artifacts repositories create litellm \ + --location=us-central1 \ + --repository-format=docker \ + --mode=remote-repository \ + --remote-docker-repo=https://ghcr.io \ + --project=my-gcp-project +``` + +Then: + +```hcl +# main.tf +terraform { + required_version = ">= 1.6.0" + required_providers { + google = { source = "hashicorp/google", version = "~> 6.10" } + google-beta = { source = "hashicorp/google-beta", version = "~> 6.10" } + } +} + +provider "google" { project = "my-gcp-project"; region = "us-central1" } +provider "google-beta" { project = "my-gcp-project"; region = "us-central1" } + +module "litellm" { + source = "BerriAI/litellm/google" + version = "~> 1.89" + + project_id = "my-gcp-project" + region = "us-central1" + tenant = "acme" + env = "prod" + + # Replace my-gcp-project with your GCP project ID (same value as project_id above). + image_registry = "us-central1-docker.pkg.dev/my-gcp-project/litellm/berriai" + + # Production: provide DNS already pointing at the LB IP for Google-managed certs. + # Without one, set allow_plaintext_lb = true (dev/trial only). + # lb_domains = ["proxy.example.com"] + allow_plaintext_lb = true +} + +output "litellm_url" { + value = module.litellm.load_balancer_url +} +``` + +```bash +terraform init +terraform apply +``` + +Provider API keys live in Secret Manager; reference resource IDs (e.g. `projects/my-gcp-project/secrets/openai-api-key`) via `gateway_extra_secrets`. Full input list and architecture diagram on the [registry page](https://registry.terraform.io/modules/BerriAI/litellm/google/latest?tab=inputs). + +#### Both stacks include + +- The full componentized split (gateway / backend / UI as independent services) +- Managed Postgres (writer + reader) and Redis +- Versioned object store for proxy state + file uploads +- An auto-generated `LITELLM_MASTER_KEY` in your cloud's secret manager +- A one-off migration job that runs `prisma migrate deploy` before the proxy starts +- The same `proxy_config` surface as the [Helm chart](./helm/litellm/) — pass YAML as a typed map + +The Terraform modules live at [`terraform/litellm/aws/`](./terraform/litellm/aws/) and [`terraform/litellm/gcp/`](./terraform/litellm/gcp/) in this repo; the registry entries are read-only mirrors updated on each release. + ### Run in Developer Mode #### Services 1. Setup .env file in root diff --git a/backend/Dockerfile b/backend/Dockerfile index 2cfdde8a517..667bdb073eb 100644 --- a/backend/Dockerfile +++ b/backend/Dockerfile @@ -1,5 +1,5 @@ -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:31da6565f35af6401031c1d7aa91dc84ac76c5c48edd17fb90f0ed9e3173c7a9 -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:31da6565f35af6401031c1d7aa91dc84ac76c5c48edd17fb90f0ed9e3173c7a9 +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:c61ac6919b811ea53c4782d69f1fe05218ba3c25d53f01b6ab7892e621bd4370 +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:c61ac6919b811ea53c4782d69f1fe05218ba3c25d53f01b6ab7892e621bd4370 ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a FROM $UV_IMAGE AS uvbin diff --git a/backend/main.py b/backend/main.py index 4092cd63f69..292ece48e7d 100644 --- a/backend/main.py +++ b/backend/main.py @@ -20,7 +20,11 @@ DatabaseURLSettings.from_env().apply_to_env() from litellm.proxy.proxy_server import app -from backend.routes.allowlist import BACKEND_EXACT_PATHS, BACKEND_PATH_PREFIXES +from backend.routes.allowlist import ( + BACKEND_EXACT_PATHS, + BACKEND_MOUNT_PATHS, + BACKEND_PATH_PREFIXES, +) def _is_backend_route(route) -> bool: @@ -29,8 +33,9 @@ def _is_backend_route(route) -> bool: if path is None: return False if isinstance(route, Mount): - # Static UI mounts are served by the dedicated UI container, not here. - return False + # The dashboard UI static mounts are served by the dedicated UI container. + # Only Mounts in the backend allowlist (e.g. swagger docs) remain on backend. + return path in BACKEND_MOUNT_PATHS if path in BACKEND_EXACT_PATHS: return True return any(path.startswith(prefix) for prefix in BACKEND_PATH_PREFIXES) diff --git a/backend/routes/allowlist.py b/backend/routes/allowlist.py index 610ba3dbd69..2f65f99c292 100644 --- a/backend/routes/allowlist.py +++ b/backend/routes/allowlist.py @@ -120,6 +120,9 @@ BACKEND_PATH_PREFIXES: tuple[str, ...] = ( "/robots.txt", # Health (k8s probes) "/health", + # Plugin system + "/api/plugins", + "/plugin-proxy/", ) BACKEND_EXACT_PATHS: frozenset[str] = frozenset( @@ -133,3 +136,9 @@ BACKEND_EXACT_PATHS: frozenset[str] = frozenset( "/fallback/login", } ) + +BACKEND_MOUNT_PATHS: frozenset[str] = frozenset( + { + "/swagger", # API documentation static assets belong to the backend + } +) diff --git a/basedpyright-code-budget.json b/basedpyright-code-budget.json new file mode 100644 index 00000000000..f5b0a9aaf81 --- /dev/null +++ b/basedpyright-code-budget.json @@ -0,0 +1,194 @@ +{ + "reportAny": { + "baseline": 24989, + "slack": 2500 + }, + "reportArgumentType": { + "baseline": 1934, + "slack": 180 + }, + "reportAssignmentType": { + "baseline": 220, + "slack": 22 + }, + "reportAttributeAccessIssue": { + "baseline": 346, + "slack": 35 + }, + "reportCallIssue": { + "baseline": 87, + "slack": 10 + }, + "reportConstantRedefinition": { + "baseline": 39, + "slack": 4 + }, + "reportDeprecated": { + "baseline": 217, + "slack": 22 + }, + "reportDuplicateImport": { + "baseline": 28, + "slack": 3 + }, + "reportExplicitAny": { + "baseline": 6931, + "slack": 700 + }, + "reportFunctionMemberAccess": { + "baseline": 7, + "slack": 3 + }, + "reportGeneralTypeIssues": { + "baseline": 151, + "slack": 15 + }, + "reportIncompatibleMethodOverride": { + "baseline": 52, + "slack": 5 + }, + "reportIncompatibleVariableOverride": { + "baseline": 8, + "slack": 3 + }, + "reportInconsistentOverload": { + "baseline": 12, + "slack": 3 + }, + "reportIndexIssue": { + "baseline": 26, + "slack": 3 + }, + "reportInvalidTypeForm": { + "baseline": 23, + "slack": 3 + }, + "reportInvalidTypeVarUse": { + "baseline": 2, + "slack": 3 + }, + "reportMatchNotExhaustive": { + "baseline": 1, + "slack": 3 + }, + "reportMissingParameterType": { + "baseline": 3933, + "slack": 390 + }, + "reportMissingTypeArgument": { + "baseline": 10612, + "slack": 1000 + }, + "reportMissingTypeStubs": { + "baseline": 27, + "slack": 10 + }, + "reportOperatorIssue": { + "baseline": 6, + "slack": 3 + }, + "reportOptionalCall": { + "baseline": 4, + "slack": 3 + }, + "reportOptionalIterable": { + "baseline": 3, + "slack": 3 + }, + "reportOptionalMemberAccess": { + "baseline": 724, + "slack": 72 + }, + "reportOptionalOperand": { + "baseline": 3, + "slack": 3 + }, + "reportOptionalSubscript": { + "baseline": 11, + "slack": 3 + }, + "reportPossiblyUnboundVariable": { + "baseline": 52, + "slack": 10 + }, + "reportPrivateUsage": { + "baseline": 1625, + "slack": 160 + }, + "reportRedeclaration": { + "baseline": 8, + "slack": 3 + }, + "reportReturnType": { + "baseline": 126, + "slack": 100 + }, + "reportTypedDictNotRequiredAccess": { + "baseline": 20, + "slack": 3 + }, + "reportUndefinedVariable": { + "baseline": 2, + "slack": 3 + }, + "reportUnknownArgumentType": { + "baseline": 30603, + "slack": 3000 + }, + "reportUnknownLambdaType": { + "baseline": 75, + "slack": 10 + }, + "reportUnknownMemberType": { + "baseline": 27037, + "slack": 2500 + }, + "reportUnknownParameterType": { + "baseline": 13612, + "slack": 1000 + }, + "reportUnknownVariableType": { + "baseline": 21445, + "slack": 2000 + }, + "reportUnnecessaryCast": { + "baseline": 118, + "slack": 10 + }, + "reportUnnecessaryComparison": { + "baseline": 683, + "slack": 100 + }, + "reportUnnecessaryContains": { + "baseline": 4, + "slack": 3 + }, + "reportUnnecessaryIsInstance": { + "baseline": 808, + "slack": 80 + }, + "reportUntypedBaseClass": { + "baseline": 110, + "slack": 11 + }, + "reportUntypedFunctionDecorator": { + "baseline": 22, + "slack": 3 + }, + "reportUnusedClass": { + "baseline": 22, + "slack": 3 + }, + "reportUnusedFunction": { + "baseline": 137, + "slack": 10 + }, + "reportUnusedImport": { + "baseline": 670, + "slack": 50 + }, + "reportUnusedVariable": { + "baseline": 865, + "slack": 50 + } +} diff --git a/codecov.yaml b/codecov.yaml index 58681b884d0..3baea13e2d3 100644 --- a/codecov.yaml +++ b/codecov.yaml @@ -35,6 +35,22 @@ component_management: - component_id: "Enterprise" paths: - "enterprise/**" + - component_id: "Batches" + paths: + - "*/proxy/batches_endpoints/**" + - "litellm/batches/**" + - "*/llms/*/batches/**" + - component_id: "Videos" + paths: + - "litellm/videos/**" + - "*/proxy/video_endpoints/**" + - "*/llms/*/videos/**" + - component_id: "Realtime" + paths: + - "litellm/realtime_api/**" + - "*/proxy/realtime_endpoints/**" + - "*/llms/*/realtime/**" + - "litellm/litellm_core_utils/realtime_streaming.py" comment: layout: "header, diff, flags, components" # show component info in the PR comment diff --git a/db_scripts/create_views.py b/db_scripts/create_views.py index 3027b38958d..2b34664452d 100644 --- a/db_scripts/create_views.py +++ b/db_scripts/create_views.py @@ -15,7 +15,7 @@ db = Prisma( ) -async def check_view_exists(): # noqa: PLR0915 +async def check_view_exists(): """ Checks if the LiteLLM_VerificationTokenView and MonthlyGlobalSpend exists in the user's db. @@ -34,8 +34,7 @@ async def check_view_exists(): # noqa: PLR0915 print("LiteLLM_VerificationTokenView Exists!") # noqa except Exception: # If an error occurs, the view does not exist, so create it - await db.execute_raw( - """ + await db.execute_raw(""" CREATE VIEW "LiteLLM_VerificationTokenView" AS SELECT v.*, @@ -45,8 +44,7 @@ async def check_view_exists(): # noqa: PLR0915 t.rpm_limit AS team_rpm_limit FROM "LiteLLM_VerificationToken" v LEFT JOIN "LiteLLM_TeamTable" t ON v.team_id = t.team_id; - """ - ) + """) print("LiteLLM_VerificationTokenView Created!") # noqa diff --git a/db_scripts/partition_spend_logs.sql b/db_scripts/partition_spend_logs.sql new file mode 100644 index 00000000000..08fcbddb6f8 --- /dev/null +++ b/db_scripts/partition_spend_logs.sql @@ -0,0 +1,99 @@ +-- Converts an existing LiteLLM_SpendLogs table into a native Postgres +-- range-partitioned table keyed on "startTime". +-- +-- Why: at high request volume, retention via DELETE leaves dead tuples that +-- autovacuum cannot reclaim quickly enough, so the table keeps growing on disk +-- (seen at 450GB+ after ~1 month). With partitioning, retention drops whole +-- partitions, which is instant and returns disk to the OS immediately. +-- +-- This is an opt-in, manual operation. The default LiteLLM schema is NOT +-- partitioned, so existing installs are unaffected until you run this. +-- +-- IMPORTANT +-- * Test on a staging copy first and take a backup. +-- * Postgres cannot convert a populated table to partitioned in place, so this +-- renames the old table aside and creates a fresh partitioned table. +-- * The partition key ("startTime") must be part of the primary key, so the +-- PK becomes composite ("request_id", "startTime"). LiteLLM's write path uses +-- INSERT ... ON CONFLICT DO NOTHING, which is compatible with this. +-- * Choose a partition granularity ("day" is the recommended default for +-- high-volume tables) and keep it consistent with SPEND_LOG_PARTITION_INTERVAL. +-- +-- After running this, enable the feature and set a retention period in +-- proxy_config.yaml: +-- general_settings: +-- use_spend_logs_partitioning: true +-- maximum_spend_logs_retention_period: "30d" +-- The spend-log cleanup job then verifies the table is partitioned and reclaims +-- disk by dropping expired partitions instead of deleting rows. It also +-- pre-creates upcoming partitions on each run. To roll back, see +-- db_scripts/unpartition_spend_logs.sql. + +BEGIN; + +ALTER TABLE "LiteLLM_SpendLogs" RENAME TO "LiteLLM_SpendLogs_legacy"; + +-- Renaming a table does NOT rename its indexes, and index names are unique per +-- schema. Move the legacy table's indexes aside so the CREATE INDEX statements +-- below actually create indexes on the new partitioned table instead of being +-- silently skipped by IF NOT EXISTS, and so the new PK keeps the canonical +-- name instead of getting a "_pkey1" suffix. +ALTER INDEX IF EXISTS "LiteLLM_SpendLogs_pkey" + RENAME TO "LiteLLM_SpendLogs_legacy_pkey"; +ALTER INDEX IF EXISTS "LiteLLM_SpendLogs_startTime_idx" + RENAME TO "LiteLLM_SpendLogs_legacy_startTime_idx"; +ALTER INDEX IF EXISTS "LiteLLM_SpendLogs_startTime_request_id_idx" + RENAME TO "LiteLLM_SpendLogs_legacy_startTime_request_id_idx"; +ALTER INDEX IF EXISTS "LiteLLM_SpendLogs_end_user_idx" + RENAME TO "LiteLLM_SpendLogs_legacy_end_user_idx"; +ALTER INDEX IF EXISTS "LiteLLM_SpendLogs_session_id_idx" + RENAME TO "LiteLLM_SpendLogs_legacy_session_id_idx"; + +CREATE TABLE "LiteLLM_SpendLogs" ( + LIKE "LiteLLM_SpendLogs_legacy" INCLUDING DEFAULTS INCLUDING GENERATED +) PARTITION BY RANGE ("startTime"); + +ALTER TABLE "LiteLLM_SpendLogs" + ADD PRIMARY KEY ("request_id", "startTime"); + +-- Recreate every index Prisma defines on the table. LIKE ... INCLUDING DEFAULTS +-- INCLUDING GENERATED copies columns and defaults but NOT indexes, so without +-- these the admin-UI cost-reporting queries that filter by end_user/session_id +-- fall back to sequential scans. On a partitioned parent these propagate to +-- every current and future partition automatically. +CREATE INDEX IF NOT EXISTS "LiteLLM_SpendLogs_startTime_idx" + ON "LiteLLM_SpendLogs" ("startTime"); + +CREATE INDEX IF NOT EXISTS "LiteLLM_SpendLogs_startTime_request_id_idx" + ON "LiteLLM_SpendLogs" ("startTime", "request_id"); + +CREATE INDEX IF NOT EXISTS "LiteLLM_SpendLogs_end_user_idx" + ON "LiteLLM_SpendLogs" ("end_user"); + +CREATE INDEX IF NOT EXISTS "LiteLLM_SpendLogs_session_id_idx" + ON "LiteLLM_SpendLogs" ("session_id"); + +-- Safety net: any row whose startTime has no explicit partition lands here so +-- writes never fail. The cleanup job never drops the DEFAULT partition. +CREATE TABLE IF NOT EXISTS "LiteLLM_SpendLogs_pdefault" + PARTITION OF "LiteLLM_SpendLogs" DEFAULT; + +COMMIT; + +-- Backfill (optional). Rows route to the correct partition automatically. +-- For large legacy tables, copy in time-bounded batches during a low-traffic +-- window instead of one statement, or simply keep "LiteLLM_SpendLogs_legacy" +-- read-only until its data ages past your retention, then DROP it. +-- +-- Backfilled rows land in the DEFAULT partition until explicit partitions +-- cover their dates. Postgres refuses to create a partition whose range +-- overlaps rows already in DEFAULT, so the cleanup job may log a warning when +-- pre-creating today's partition right after a backfill; it recovers on its +-- own once those dates age out, and future partitions are unaffected because +-- they are always created ahead of writes. +-- +-- INSERT INTO "LiteLLM_SpendLogs" +-- SELECT * FROM "LiteLLM_SpendLogs_legacy" +-- WHERE "startTime" >= now() - interval '30 days'; +-- +-- DROP TABLE "LiteLLM_SpendLogs_legacy"; diff --git a/db_scripts/unpartition_spend_logs.sql b/db_scripts/unpartition_spend_logs.sql new file mode 100644 index 00000000000..0bd82513e4a --- /dev/null +++ b/db_scripts/unpartition_spend_logs.sql @@ -0,0 +1,69 @@ +-- Rolls back db_scripts/partition_spend_logs.sql: converts the native +-- range-partitioned "LiteLLM_SpendLogs" table back into a plain, +-- non-partitioned table matching the default LiteLLM schema. +-- +-- When/why: run this if you want to stop using partition-based retention and +-- return to DELETE-based cleanup, or to restore the original single-column +-- primary key ("request_id") that the partitioned layout had to widen to a +-- composite ("request_id", "startTime"). +-- +-- IMPORTANT +-- * Test on a staging copy first and take a backup. +-- * Postgres cannot convert a partitioned table back in place, so this +-- renames the partitioned table aside and creates a fresh plain table. +-- * The composite PK could in principle hold the same "request_id" in more +-- than one partition, so rows are copied with ON CONFLICT DO NOTHING to +-- restore the single-column PK without failing on such duplicates. +-- * For large tables the INSERT ... SELECT copies every surviving row and may +-- run long; do it during a low-traffic window. +-- * Also remove use_spend_logs_partitioning from proxy_config.yaml (or set it +-- to false) so the cleanup job returns to DELETE-based retention. + +BEGIN; + +ALTER TABLE "LiteLLM_SpendLogs" RENAME TO "LiteLLM_SpendLogs_partitioned"; + +-- Renaming a table does NOT rename its indexes, and index names are unique per +-- schema. Move the partitioned table's indexes aside so the CREATE INDEX +-- statements below actually create indexes on the new plain table instead of +-- being silently skipped by IF NOT EXISTS, and so the new PK keeps the +-- canonical name. +ALTER INDEX IF EXISTS "LiteLLM_SpendLogs_pkey" + RENAME TO "LiteLLM_SpendLogs_partitioned_pkey"; +ALTER INDEX IF EXISTS "LiteLLM_SpendLogs_pkey1" + RENAME TO "LiteLLM_SpendLogs_partitioned_pkey1"; +ALTER INDEX IF EXISTS "LiteLLM_SpendLogs_startTime_idx" + RENAME TO "LiteLLM_SpendLogs_partitioned_startTime_idx"; +ALTER INDEX IF EXISTS "LiteLLM_SpendLogs_startTime_request_id_idx" + RENAME TO "LiteLLM_SpendLogs_partitioned_startTime_request_id_idx"; +ALTER INDEX IF EXISTS "LiteLLM_SpendLogs_end_user_idx" + RENAME TO "LiteLLM_SpendLogs_partitioned_end_user_idx"; +ALTER INDEX IF EXISTS "LiteLLM_SpendLogs_session_id_idx" + RENAME TO "LiteLLM_SpendLogs_partitioned_session_id_idx"; + +CREATE TABLE "LiteLLM_SpendLogs" ( + LIKE "LiteLLM_SpendLogs_partitioned" INCLUDING DEFAULTS INCLUDING GENERATED +); + +ALTER TABLE "LiteLLM_SpendLogs" + ADD PRIMARY KEY ("request_id"); + +CREATE INDEX IF NOT EXISTS "LiteLLM_SpendLogs_startTime_idx" + ON "LiteLLM_SpendLogs" ("startTime"); + +CREATE INDEX IF NOT EXISTS "LiteLLM_SpendLogs_startTime_request_id_idx" + ON "LiteLLM_SpendLogs" ("startTime", "request_id"); + +CREATE INDEX IF NOT EXISTS "LiteLLM_SpendLogs_end_user_idx" + ON "LiteLLM_SpendLogs" ("end_user"); + +CREATE INDEX IF NOT EXISTS "LiteLLM_SpendLogs_session_id_idx" + ON "LiteLLM_SpendLogs" ("session_id"); + +INSERT INTO "LiteLLM_SpendLogs" +SELECT * FROM "LiteLLM_SpendLogs_partitioned" +ON CONFLICT ("request_id") DO NOTHING; + +DROP TABLE "LiteLLM_SpendLogs_partitioned"; + +COMMIT; diff --git a/docker/Dockerfile.database b/docker/Dockerfile.database index c84003a065f..50ef55e3261 100644 --- a/docker/Dockerfile.database +++ b/docker/Dockerfile.database @@ -1,8 +1,8 @@ # Base image for building -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:3258be472764337fd13095bcbb3182da170243b5819fd67ad4c0754590588b31 +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:c61ac6919b811ea53c4782d69f1fe05218ba3c25d53f01b6ab7892e621bd4370 # Runtime image -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:3258be472764337fd13095bcbb3182da170243b5819fd67ad4c0754590588b31 +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:c61ac6919b811ea53c4782d69f1fe05218ba3c25d53f01b6ab7892e621bd4370 ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a FROM $UV_IMAGE AS uvbin @@ -66,36 +66,31 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime USER root -RUN apk add --no-cache bash openssl tzdata nodejs npm python3 libsndfile && \ - npm install -g npm@11.12.1 tar@7.5.11 glob@11.1.0 @isaacs/brace-expansion@5.0.1 minimatch@10.2.4 diff@8.0.3 && \ - GLOBAL="$(npm root -g)" && \ - find "$GLOBAL/npm" -type d -name "tar" -path "*/node_modules/tar" | while read d; do \ - rm -rf "$d" && cp -rL "$GLOBAL/tar" "$d"; \ - done && \ - find "$GLOBAL/npm" -type d -name "glob" -path "*/node_modules/glob" | while read d; do \ - rm -rf "$d" && cp -rL "$GLOBAL/glob" "$d"; \ - done && \ - find "$GLOBAL/npm" -type d -name "brace-expansion" -path "*/node_modules/@isaacs/brace-expansion" | while read d; do \ - rm -rf "$d" && cp -rL "$GLOBAL/@isaacs/brace-expansion" "$d"; \ - done && \ - find "$GLOBAL/npm" -type d -name "minimatch" -path "*/node_modules/minimatch" | while read d; do \ - rm -rf "$d" && cp -rL "$GLOBAL/minimatch" "$d"; \ - done && \ - find "$GLOBAL/npm" -type d -name "diff" -path "*/node_modules/diff" | while read d; do \ - rm -rf "$d" && cp -rL "$GLOBAL/diff" "$d"; \ - done && \ - npm cache clean --force && \ - { apk del --no-cache npm 2>/dev/null || true; } +# node (without npm) is required by the prisma CLI at runtime +RUN apk add --no-cache bash openssl tzdata nodejs python3 libsndfile WORKDIR /app ENV PATH="/app/.venv/bin:${PATH}" -COPY --from=builder /app /app +# Copy only what runtime needs. The application is installed inside the venv; +# the rest of the builder's /app is source and build metadata that must not +# ship (manifest-scanning tools attribute everything in it to this image). +# entrypoint.sh invokes litellm/proxy/prisma_migration.py by source path. +COPY --from=builder /app/.venv /app/.venv +COPY --from=builder /app/docker /app/docker +COPY --from=builder /app/schema.prisma /app/schema.prisma +COPY --from=builder /app/litellm/proxy/prisma_migration.py /app/litellm/proxy/prisma_migration.py +# enterprise/ is imported by source path at runtime (proxy_cli puts the +# working directory on sys.path; litellm/proxy/hooks resolves +# enterprise.enterprise_hooks from it) +COPY --from=builder /app/enterprise /app/enterprise # Prisma binaries live in $HOME/.cache (default prisma-python location), # which is /root/.cache here. Copy them from the builder so they survive # deployments that volume-mount /app/.cache (e.g. readOnlyRootFilesystem # + emptyDir) — otherwise the mount would shadow the baked-in query engine. -COPY --from=builder /root/.cache /root/.cache +# Only the Prisma subdirs: the whole /root/.cache drags in the uv build cache. +COPY --from=builder /root/.cache/prisma /root/.cache/prisma +COPY --from=builder /root/.cache/prisma-python /root/.cache/prisma-python RUN find /app/.venv -type f -path "*/tornado/test/*" -delete && \ find /app/.venv -type d -path "*/tornado/test" -delete diff --git a/docker/Dockerfile.non_root b/docker/Dockerfile.non_root index 8717e5b3fcd..ab02b43d0f9 100644 --- a/docker/Dockerfile.non_root +++ b/docker/Dockerfile.non_root @@ -1,6 +1,6 @@ # Base images -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:3258be472764337fd13095bcbb3182da170243b5819fd67ad4c0754590588b31 -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:3258be472764337fd13095bcbb3182da170243b5819fd67ad4c0754590588b31 +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:c61ac6919b811ea53c4782d69f1fe05218ba3c25d53f01b6ab7892e621bd4370 +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:c61ac6919b811ea53c4782d69f1fe05218ba3c25d53f01b6ab7892e621bd4370 ARG PROXY_EXTRAS_SOURCE=published ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a @@ -95,7 +95,21 @@ RUN for i in 1 2 3; do \ apk add --no-cache python3 bash openssl tzdata libsndfile nodejs && break || sleep 5; \ done -COPY --from=builder /app /app +# Copy only what runtime needs. The application is installed inside the venv; +# the rest of the builder's /app is source and build metadata that must not +# ship (manifest-scanning tools attribute everything in it to this image). +# entrypoint.sh invokes litellm/proxy/prisma_migration.py by source path. +# Prisma caches live under /app/.cache here (XDG_CACHE_HOME / +# PRISMA_BINARY_CACHE_DIR) so the runtime prisma generate finds them. +COPY --from=builder /app/.venv /app/.venv +COPY --from=builder /app/docker /app/docker +COPY --from=builder /app/schema.prisma /app/schema.prisma +COPY --from=builder /app/litellm/proxy/prisma_migration.py /app/litellm/proxy/prisma_migration.py +# enterprise/ is imported by source path at runtime (proxy_cli puts the +# working directory on sys.path; litellm/proxy/hooks resolves +# enterprise.enterprise_hooks from it) +COPY --from=builder /app/enterprise /app/enterprise +COPY --from=builder /app/.cache /app/.cache COPY --from=builder /var/lib/litellm/ui /var/lib/litellm/ui COPY --from=builder /var/lib/litellm/assets /var/lib/litellm/assets diff --git a/docker/build_from_pip/litellm_config.yaml b/docker/build_from_pip/litellm_config.yaml index 51223026170..f54647853ef 100644 --- a/docker/build_from_pip/litellm_config.yaml +++ b/docker/build_from_pip/litellm_config.yaml @@ -3,7 +3,7 @@ model_list: litellm_params: model: openai/fake api_key: fake-key - api_base: https://exampleopenaiendpoint-production.up.railway.app/ + api_base: os.environ/FAKE_OPENAI_API_BASE general_settings: alerting: ["slack"] \ No newline at end of file diff --git a/docs/plugin_architecture.md b/docs/plugin_architecture.md new file mode 100644 index 00000000000..8801761531d --- /dev/null +++ b/docs/plugin_architecture.md @@ -0,0 +1,141 @@ +# LiteLLM Plugin Architecture + +Plugins let external services appear as selectable modes in the litellm UI sidebar alongside the AI Gateway. + +--- + +## Quick start + +### 1. Configure the plugin + +Add a `plugins` block to your litellm `config.yaml`: + +```yaml +general_settings: + master_key: sk-... + plugins: + - name: my-plugin # unique identifier (no spaces) + display_name: My Plugin # shown in the UI dropdown + url: "https://my-plugin.example.com" + plugin_key: "sk-..." # plugin's own auth credential +``` + +`plugin_key` is injected as `Authorization: Bearer ` on every +request proxied through `/plugin-proxy/my-plugin/*`. The caller's litellm +credential is stripped before forwarding so the plugin never receives a live +litellm API key. + +### 2. Implement two endpoints on your service + +| Endpoint | Method | Purpose | +|---|---|---| +| `GET /api/plugin-manifest` | public | Returns plugin metadata for the UI | +| `POST /api/plugin-auth` | public | Decrypts the identity claim for seamless sign-in | + +#### `GET /api/plugin-manifest` + +```json +{ + "name": "my-plugin", + "display_name": "My Plugin", + "version": "1.0.0", + "nav_items": [ + { "key": "home", "label": "Home", "icon": "HomeOutlined", "path": "/" }, + { "key": "reports", "label": "Reports", "icon": "BarChartOutlined", "path": "/reports" } + ], + "capabilities": ["reports", "data"] +} +``` + +#### `POST /api/plugin-auth` + +Receives `{ "session_claim": "" }`. + +The proxy never shares `LITELLM_SALT_KEY` with your plugin. Each plugin is +provisioned with its own dedicated key, derived as +`HMAC-SHA256(LITELLM_SALT_KEY, plugin_name)`. Compute it once on the proxy +host and hand the result to your plugin as a secret (e.g. `PLUGIN_AUTH_KEY`): + +```bash +python -c 'import base64,hmac,hashlib,os; \ +print(base64.urlsafe_b64encode(hmac.new(os.environ["LITELLM_SALT_KEY"].encode(), b"my-plugin", hashlib.sha256).digest()).decode())' +``` + +A compromised plugin holding only this scoped key cannot recover +`LITELLM_SALT_KEY` or decrypt any other litellm secret. + +Decrypt and validate the claim with that key: + +```python +import json, os, time +from cryptography.fernet import Fernet + +_CLAIM_TTL_SECONDS = 30 + +def plugin_auth(session_claim: str) -> dict: + cipher = Fernet(os.environ["PLUGIN_AUTH_KEY"].encode()) + claim = json.loads(cipher.decrypt(session_claim.encode(), ttl=_CLAIM_TTL_SECONDS)) + if claim.get("plugin") != "my-plugin": + raise ValueError("claim audience mismatch") + if int(claim.get("exp", 0)) < int(time.time()): + raise ValueError("claim expired") + return claim +``` + +The claim is `{ "plugin", "user_id", "user_role", "exp" }`; it carries no +litellm bearer token. Establish the plugin's own session from `user_id` / +`user_role` and authenticate API calls back to litellm through the +`/plugin-proxy/my-plugin/*` reverse proxy, which injects `plugin_key` for you. + +--- + +## How iframe auth works + +``` +litellm UI + ├─ GET /api/plugins/auth-token -> { session_claim } + └─ postMessage({ type:"litellm-auth", session_claim }, pluginOrigin) + │ + ▼ +Plugin iframe browser + └─ POST /api/plugin-auth { session_claim } + │ + ▼ +Plugin server + ├─ decrypt(session_claim, PLUGIN_AUTH_KEY) -> { user_id, user_role, exp } + └─ establish plugin session -> stored in sessionStorage +``` + +No litellm bearer token ever leaves the proxy; the claim only conveys the +caller's identity and expires after 30 seconds. A postMessage intercept +yields ciphertext that is useless without the plugin's scoped key. + +--- + +## Proxy routes + +- `GET /api/plugins` — list registered plugins (`name`, `display_name`, `url`). `plugin_key` is **never** returned; it stays server-side. Requires an authenticated caller. +- `GET /api/plugins/auth-token?plugin_name=` — short-lived encrypted identity claim for the named plugin. Requires `LITELLM_SALT_KEY` to be set (503 otherwise) and the plugin to be registered (404 otherwise). +- `ANY /plugin-proxy/{name}/{path}` — authenticated reverse proxy to the plugin backend. Restricted to `proxy_admin`. + +--- + +## Reverse proxy behaviour + +When an admin (or server-to-server caller) hits `/plugin-proxy//`, the proxy authenticates the caller locally, then rewrites the request before forwarding it to the plugin's `url`: + +- **Every litellm credential header is stripped** — `Authorization`, `x-api-key`, `API-Key`, `x-goog-api-key`, `Ocp-Apim-Subscription-Key`, `x-litellm-api-key`, any configured `litellm_key_header_name`, plus `Cookie`. The plugin can never be handed the caller's live litellm key. +- **`plugin_key` is injected** as `Authorization: Bearer ` — the only credential the plugin receives. +- **Caller identity is forwarded** as `x-litellm-user-id` and `x-litellm-user-role` so the plugin can run its own authorization. These are informational, not credentials. +- **Responses are sandboxed** — `Content-Security-Policy: sandbox` and `X-Content-Type-Options: nosniff` are set so plugin-controlled bytes served from the litellm origin cannot execute against the dashboard. + +--- + +## Security checklist + +- [ ] `LITELLM_SALT_KEY` is set on the proxy and never shared with the plugin +- [ ] The plugin holds only its derived `HMAC(LITELLM_SALT_KEY, plugin_name)` key, provisioned as a dedicated secret +- [ ] `plugin_key` is a dedicated credential scoped to the plugin (not your litellm master key) +- [ ] Plugin's `POST /api/plugin-auth` enforces the claim's `plugin` audience and `exp` (30s TTL) +- [ ] Plugin treats `x-litellm-user-id` / `x-litellm-user-role` as identity hints, not as proof of authentication +- [ ] Plugin service URL uses HTTPS in production diff --git a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py index ae5905f9cdf..8486e37384e 100644 --- a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py +++ b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py @@ -1,9 +1,9 @@ # What is this? ## This hook is used to check for LiteLLM managed files in the request body, and replace them with model-specific file id -import asyncio import base64 import json +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Union, cast from fastapi import HTTPException @@ -412,7 +412,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): detail=f"User {user_api_key_dict.user_id} does not have access to the file {file_id}", ) - async def async_pre_call_hook( # noqa: PLR0915 + async def async_pre_call_hook( self, user_api_key_dict: UserAPIKeyAuth, cache: DualCache, @@ -504,7 +504,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): if retrieve_file_id else False ) - if potential_file_id: + if potential_file_id and "llm_output_file_id," in potential_file_id: model_id = self.get_model_id_from_unified_file_id(potential_file_id) if model_id: data["model"] = model_id @@ -1058,7 +1058,12 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): return file_id.split("llm_output_file_model_id,")[1].split(";")[0] def get_output_file_id_from_unified_file_id(self, file_id: str) -> str: - return file_id.split("llm_output_file_id,")[1].split(";")[0] + marker = "llm_output_file_id," + if marker not in file_id: + raise ValueError( + f"Unified id does not contain {marker!r}: {file_id[:80]!r}" + ) + return file_id.split(marker, 1)[1].split(";")[0] async def async_post_call_success_hook( self, data: Dict, user_api_key_dict: UserAPIKeyAuth, response: LLMResponseTypes @@ -1099,13 +1104,33 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): for file_attr in ["output_file_id", "error_file_id"]: file_id_value = getattr(response, file_attr, None) if file_id_value and model_id: - original_file_id = file_id_value - unified_file_id = self.get_unified_output_file_id( - output_file_id=original_file_id, - model_id=model_id, - model_name=resolved_model_name, + decoded_output_file_id = _is_base64_encoded_unified_file_id( + file_id_value ) - setattr(response, file_attr, unified_file_id) + if ( + decoded_output_file_id + and "llm_output_file_id," in decoded_output_file_id + ): + provider_file_id = ( + self.get_output_file_id_from_unified_file_id( + decoded_output_file_id + ) + ) + unified_file_id = file_id_value + elif decoded_output_file_id: + verbose_logger.warning( + f"Skipping {file_attr}={file_id_value!r}: " + "unified id is not a managed file output id" + ) + continue + else: + provider_file_id = file_id_value + unified_file_id = self.get_unified_output_file_id( + output_file_id=provider_file_id, + model_id=model_id, + model_name=resolved_model_name, + ) + setattr(response, file_attr, unified_file_id) # Use llm_router credentials when available. Without credentials, # Azure and other auth-required providers return 500/401. @@ -1125,27 +1150,27 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): or {} ) file_object = await litellm.afile_retrieve( - file_id=original_file_id, + file_id=provider_file_id, **_creds, ) else: file_object = await litellm.afile_retrieve( custom_llm_provider=model_name.split("/")[0] if model_name and "/" in model_name else "openai", # type: ignore[arg-type] - file_id=original_file_id, + file_id=provider_file_id, ) verbose_logger.debug( - f"Successfully retrieved file object for {file_attr}={original_file_id}" + f"Successfully retrieved file object for {file_attr}={provider_file_id}" ) except Exception as e: verbose_logger.warning( - f"Failed to retrieve file object for {file_attr}={original_file_id}: {str(e)}. Storing with None and will fetch on-demand." + f"Failed to retrieve file object for {file_attr}={provider_file_id}: {str(e)}. Storing with None and will fetch on-demand." ) await self.store_unified_file_id( file_id=unified_file_id, file_object=file_object, litellm_parent_otel_span=user_api_key_dict.parent_otel_span, - model_mappings={model_id: original_file_id}, + model_mappings={model_id: provider_file_id}, user_api_key_dict=user_api_key_dict, ) await self.store_unified_object_id( @@ -1447,8 +1472,8 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): error_message += f" (showing {MAX_BATCHES_IN_ERROR} most recent): {', '.join(batch_statuses)}. " error_message += ( - f"To delete this file before complete cost tracking, please delete or cancel the referencing batch(es) first. " - f"Alternatively, wait for all batches to complete and for cost to be computed (batch_processed=true)." + "To delete this file before complete cost tracking, please delete or cancel the referencing batch(es) first. " + "Alternatively, wait for all batches to complete and for cost to be computed (batch_processed=true)." ) # Record blocked deletion metric @@ -1525,9 +1550,22 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): if specific_model_file_id_mapping: exception_dict = {} - for model_id, file_id in specific_model_file_id_mapping.items(): + for model_id, provider_file_id in specific_model_file_id_mapping.items(): try: - return await llm_router.afile_content(model=model_id, file_id=file_id, **data) # type: ignore + # Cloud-storage providers (e.g. Bedrock S3) validate file ids + # against the deployment's configured bucket, which they only + # trust from this immutable server-side snapshot, never from + # request params. + credentials = llm_router.get_deployment_credentials_with_provider( + model_id=model_id + ) + if credentials is not None: + data["_litellm_internal_model_credentials"] = cast( + Dict, MappingProxyType(dict(credentials)) + ) + else: + data.pop("_litellm_internal_model_credentials", None) + return await llm_router.afile_content(model=model_id, file_id=provider_file_id, **data) # type: ignore except Exception as e: exception_dict[model_id] = str(e) raise Exception( diff --git a/enterprise/litellm_enterprise/proxy/management_endpoints/project_endpoints.py b/enterprise/litellm_enterprise/proxy/management_endpoints/project_endpoints.py index 75229bacc8f..a057df65500 100644 --- a/enterprise/litellm_enterprise/proxy/management_endpoints/project_endpoints.py +++ b/enterprise/litellm_enterprise/proxy/management_endpoints/project_endpoints.py @@ -483,7 +483,7 @@ async def new_project( response_model=LiteLLM_ProjectTable, ) @management_endpoint_wrapper -async def update_project( # noqa: PLR0915 +async def update_project( data: UpdateProjectRequest, http_request: Request, user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), diff --git a/enterprise/pyproject.toml b/enterprise/pyproject.toml index d0432448433..b032942427c 100644 --- a/enterprise/pyproject.toml +++ b/enterprise/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm-enterprise" -version = "0.1.42" +version = "0.1.43" 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 = "0.1.42" +version = "0.1.43" version_files = [ "pyproject.toml:^version", "../pyproject.toml:litellm-enterprise==", diff --git a/gateway/Dockerfile b/gateway/Dockerfile index 19c8a10fdfe..716b2fa09d1 100644 --- a/gateway/Dockerfile +++ b/gateway/Dockerfile @@ -1,5 +1,5 @@ -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:31da6565f35af6401031c1d7aa91dc84ac76c5c48edd17fb90f0ed9e3173c7a9 -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:31da6565f35af6401031c1d7aa91dc84ac76c5c48edd17fb90f0ed9e3173c7a9 +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:c61ac6919b811ea53c4782d69f1fe05218ba3c25d53f01b6ab7892e621bd4370 +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:c61ac6919b811ea53c4782d69f1fe05218ba3c25d53f01b6ab7892e621bd4370 ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a FROM $UV_IMAGE AS uvbin diff --git a/litellm-rust/.cargo/config.toml b/litellm-rust/.cargo/config.toml new file mode 100644 index 00000000000..c7fb2592542 --- /dev/null +++ b/litellm-rust/.cargo/config.toml @@ -0,0 +1,10 @@ +# PyO3 cdylib (`litellm-python-bridge`) links against the host interpreter's +# symbols, which are not present at link time when building an extension module. +# On macOS, tell the linker to resolve undefined `_Py*` symbols dynamically at +# load time (the standard pyo3 extension-module flag) so the cdylib links without +# a libpython on the link line. +[target.x86_64-apple-darwin] +rustflags = ["-C", "link-arg=-undefined", "-C", "link-arg=dynamic_lookup"] + +[target.aarch64-apple-darwin] +rustflags = ["-C", "link-arg=-undefined", "-C", "link-arg=dynamic_lookup"] diff --git a/litellm-rust/.gitignore b/litellm-rust/.gitignore new file mode 100644 index 00000000000..b83d22266ac --- /dev/null +++ b/litellm-rust/.gitignore @@ -0,0 +1 @@ +/target/ diff --git a/litellm-rust/ADDING_A_PROVIDER.md b/litellm-rust/ADDING_A_PROVIDER.md new file mode 100644 index 00000000000..2fa81798605 --- /dev/null +++ b/litellm-rust/ADDING_A_PROVIDER.md @@ -0,0 +1,9 @@ +# Adding a provider / route to litellm-rust + +Three layers, same for every route (see `ocr` and `realtime` as references): + +1. **Transform contract (pure)** — `crates/core/src//transformation.rs`: a `…ProviderConfig` trait (URL build + request/response transforms) + types in `types.rs`. No network, env, or auth. +2. **Provider config (pure)** — `crates/providers/src///transformation.rs`: implement that trait as a `const __CONFIG`, mirroring the Python provider tree. Add parity unit tests. +3. **HTTP / transport (the host)** — `crates/providers/src/.rs` (e.g. `ocr.rs`, `realtime.rs`): the callable fn (`run_ocr`, `realtime`). It resolves the key, builds the auth header, builds URL + transforms via the config, then does the network call. This is the only layer allowed to do I/O. + +**Calling:** the host invokes the route fn — the Python bridge calls `run_ocr`; the `ai-gateway` server calls `realtime`. Register new modules in `lib.rs` / `mod.rs`, then run `cargo fmt && cargo clippy --workspace -- -D warnings && cargo test --workspace`. diff --git a/litellm-rust/AGENTS.md b/litellm-rust/AGENTS.md new file mode 100644 index 00000000000..86dd2c92744 --- /dev/null +++ b/litellm-rust/AGENTS.md @@ -0,0 +1,17 @@ +# AGENTS.md + +litellm-rust has exactly THREE crates. A crate is a LAYER, not a route. Routes (ocr, realtime, chat) and providers (mistral, openai) are MODULES inside the layers. + +## Crates + +| Crate | Role | Pure / I/O | +|-------|------|------------| +| litellm-core | Translation layer — types, route contracts (traits), provider transforms (modules under providers/), and the router. Builds requests/responses; no network. | Pure | +| litellm-ai-gateway | Routes + host — the only crate that touches the network. HTTP/WebSocket I/O (modules under io/) plus the axum server binary (behind the `server` feature). | I/O | +| litellm-python-bridge | PyO3 cdylib exposing Rust to the litellm Python SDK — a thin adapter over litellm-ai-gateway's I/O. | Binding | + +Dependency direction (acyclic): litellm-core ← litellm-ai-gateway ← litellm-python-bridge. + +Adding a crate: default to a MODULE. New crate ONLY on a real trigger — separate artifact (binary/cdylib), proc-macro, shared foundation, or publishable standalone. A new provider or route is none of these. + +Adding a crate fails crates/core/tests/workspace_crate_allowlist.rs until you update its allowlist and this file — intentional. diff --git a/litellm-rust/CLAUDE.md b/litellm-rust/CLAUDE.md new file mode 100644 index 00000000000..0a985b833f3 --- /dev/null +++ b/litellm-rust/CLAUDE.md @@ -0,0 +1,95 @@ +# CLAUDE.md + +This file defines the rules for Rust work in LiteLLM. + +## Crates (exactly three — see AGENTS.md) + +`litellm-core` describes work; `litellm-ai-gateway` executes it; `litellm-python-bridge` +exposes it to the Python SDK. A crate is a **layer**, not a route — add modules, not crates. + +## Core Boundary + +`litellm-core` is the pure translation layer; the `litellm-ai-gateway` host executes work. + +Route-level Rust structure mirrors LiteLLM's Python responsibilities: +- `core/src//` owns the route contract, shared types, and provider + template traits. For OCR, this means `core/src/ocr`. +- `core/src/providers///transformation.rs` owns the + provider-specific transform. For Mistral OCR, this means + `core/src/providers/mistral/ocr/transformation.rs`. +- Network execution lives in the host crate `ai-gateway` (`ai-gateway/src/io/`), + never inside `core`. + +Allowed in `core`: +- Pure request transforms +- Pure response transforms +- Pure stream chunk normalization +- Shared data types and validation errors +- Deterministic token/cost helper logic + +Not allowed in `core`: +- Network calls +- Environment variable or secret reads +- Filesystem access +- Database or cache access +- Provider SDK signing or auth flows +- Logging callbacks, spend writes, or custom callbacks +- Global mutable runtime state + +Python owns rollout state and fallback while Rust is being introduced. Rust +paths must be off by default until parity tests prove equivalence with Python. + +## Production Bar + +Rust code in this workspace is held to a strict parity and robustness bar from +the first PR: + +- Correctness parity is proven with tests. Do not rely on README claims or + manual inspection for a port that mirrors Python behavior. +- Every provider transform must have unit tests for supported-parameter + filtering, request body shape, response normalization, missing/null fields, + and bad-input errors. +- When Rust is exposed through Python, add Python tests that prove disabled, + enabled, and unavailable-bridge fallback behavior. +- Avoid panics on user/provider input. Return typed errors and let the host map + them to Python exceptions or HTTP responses. +- OCR handles documents that often contain personal data. Do not log document + contents, base64 payloads, provider response bodies, or secrets. +- Error messages must be useful but data-minimized. Truncate or sanitize any + upstream body before it crosses a host boundary. +- Treat empty or whitespace-only credentials, URLs, and config values as absent + at the host/config resolution layer. +- Preserve Python output shape intentionally. If a field is always serialized as + `null` for Python parity, leave a short comment explaining that parity choice. + +## Host I/O Rules + +These rules apply when adding future crates or modules that execute network I/O, +such as `ai-gateway`, router hosts, or standalone servers: + +- Set connect and full-request timeouts. No unbounded waits. +- Reuse HTTP clients; do not construct clients per request. +- Prefer rustls TLS for portable Python wheels and Linux images unless there is + a documented reason not to. +- Add request IDs and structured tracing at the host layer, without logging OCR + document contents or secrets. +- Do not echo raw upstream response bodies to callers. Sanitize and bound them. +- Avoid `expect`/`unwrap` in server startup and request paths unless the panic is + impossible by construction and documented. + +## Checks + +Run these before pushing Rust changes. The same checks run in GitHub Actions +for changes under `litellm-rust/`. + +```bash +cd litellm-rust +cargo fmt --check +# the ai-gateway binary + server code is behind the `server` feature +cargo clippy -p litellm-ai-gateway --all-targets --features server -- -D warnings +cargo clippy -p litellm-core -p litellm-python-bridge --all-targets -- -D warnings +cargo test --workspace +``` + +When a Rust path is exposed through Python, add Python parity tests that compare +the existing Python output with the Rust-backed output. diff --git a/litellm-rust/Cargo.lock b/litellm-rust/Cargo.lock new file mode 100644 index 00000000000..3b8c83aac16 --- /dev/null +++ b/litellm-rust/Cargo.lock @@ -0,0 +1,1861 @@ +# This file is automatically @generated by Cargo. +# It is not intended for manual editing. +version = 4 + +[[package]] +name = "async-trait" +version = "0.1.89" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = 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"proc-macro2", + "quote", + "syn", +] + +[[package]] +name = "zmij" +version = "1.0.21" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "b8848ee67ecc8aedbaf3e4122217aff892639231befc6a1b58d29fff4c2cabaa" diff --git a/litellm-rust/Cargo.toml b/litellm-rust/Cargo.toml new file mode 100644 index 00000000000..785404c38f1 --- /dev/null +++ b/litellm-rust/Cargo.toml @@ -0,0 +1,27 @@ +[workspace] +members = [ + "crates/core", + "crates/ai-gateway", + "crates/python-bridge", +] +resolver = "2" + +[workspace.package] +edition = "2021" +license = "MIT" +repository = "https://github.com/BerriAI/litellm" + +[workspace.dependencies] +litellm-core = { path = "crates/core" } +litellm-ai-gateway = { path = "crates/ai-gateway", default-features = false } +axum = "0.7" +pyo3 = "0.23.5" +rand = "0.8" +reqwest = { version = "0.12", default-features = false, features = ["blocking", "json", "rustls-tls"] } +serde = { version = "1.0", features = ["derive"] } +serde_json = "1.0" +subtle = "2" +thiserror = "2.0" +tokio = { version = "1", features = ["rt-multi-thread", "macros", "time"] } +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"] } diff --git a/litellm-rust/README.md b/litellm-rust/README.md new file mode 100644 index 00000000000..1646c90ad76 --- /dev/null +++ b/litellm-rust/README.md @@ -0,0 +1,44 @@ +# LiteLLM Rust + +This workspace contains the staged Rust implementation for LiteLLM. + +Rust starts as a pure transform core used by the existing Python host. Python +continues to own auth, configuration, network I/O, retries, routing, logging, +callbacks, spend tracking, and customer plugins until each Rust path has parity +coverage and production evidence. + +## Crates + +| Crate | Role | Pure / I/O | +|-------|------|------------| +| litellm-core | Translation layer — types, route contracts (traits), provider transforms (modules under providers/), and the router. Builds requests/responses; no network. | Pure | +| litellm-ai-gateway | Routes + host — the only crate that touches the network. HTTP/WebSocket I/O (modules under io/) plus the axum server binary (behind the `server` feature). | I/O | +| litellm-python-bridge | PyO3 cdylib exposing Rust to the litellm Python SDK — a thin adapter over litellm-ai-gateway's I/O. | Binding | + +Dependency direction (acyclic): litellm-core ← litellm-ai-gateway ← litellm-python-bridge. + +## Layout + +```text +crates/ + core/ Route contracts, shared pure types, errors, and templates. + src/ocr/ + providers/ Provider-specific pure transforms. + src/mistral/ocr/transformation.rs + python-bridge/ PyO3 bridge for Python LiteLLM. +``` + +The folder shape should follow the Python provider tree: +`providers/src///transformation.rs`. The bridge should expose +one function per top-level route, starting with `ocr(payload)`. + +## Checks + +Run these before pushing Rust changes. GitHub Actions runs the same checks for +changes under `litellm-rust/`. + +```bash +cargo fmt --check +cargo clippy --workspace --all-targets -- -D warnings +cargo test --workspace +``` diff --git a/litellm-rust/crates/ai-gateway/AGENTS.md b/litellm-rust/crates/ai-gateway/AGENTS.md new file mode 100644 index 00000000000..d9e6e1adde5 --- /dev/null +++ b/litellm-rust/crates/ai-gateway/AGENTS.md @@ -0,0 +1,50 @@ +# ai-gateway — folder architecture + +The Axum server that fronts the Rust gateway. It owns transport + config + auth +only; deployment selection lives in `core::router`, transforms in `core`/`providers`. + +``` +src/ + main.rs # entrypoint: build AppState (router + master key), bind, serve + state.rs # AppState — shared Arc + master_key + gil.rs # GIL-activity tracker (records Python acquisitions) + auth/ # authentication as an axum extractor — added to handler args + mod.rs # RequireMasterKey: FromRequestParts, single master key (LITELLM_MASTER_KEY) + routes/ # one module per route, all matching the same template + AGENTS.md # ← the route template (read this before adding a route) + mod.rs # app(): merges every module's router() + health.rs # simple route (one file): router() + liveness/readiness + gil.rs # simple route (one file): router() + GET /health/gil + realtime/ # route with logic → axum surface + a no-axum service: + mod.rs # router() + handler + WS<->events adapter (the axum surface) + service.rs # business logic (select deployment, call provider) — no axum, testable + python/ # Python interop (feature: python-config) — load-time only + mod.rs, config.rs, AGENTS.md +``` + +## Rules + +- **Routes follow one template.** Each route module exposes + `pub fn router() -> Router`; `routes/mod.rs` only merges them. Simple + routes are one file; non-trivial routes are a folder (`handler`/`service`/ + `transport`). See `routes/AGENTS.md`. +- **Auth is an extractor.** Add `crate::auth::RequireMasterKey` to a handler's + args; it runs during extraction. Never re-implement the check per route. +- **Handlers are thin.** A handler validates and delegates to its `service`. No + business logic, no provider calls, no transforms in handlers. +- **State is shared and cheap to clone.** Long-lived handles live behind `Arc` in + `state.rs`; read env/config only in `main.rs` when building state. + +## Auth (interim) + +A single **master key** (`LITELLM_MASTER_KEY`), enforced by the +`auth::RequireMasterKey` extractor: any caller presenting it as +`Authorization: Bearer ` may invoke the gateway. Fails closed (500) when +unset; constant-time compare. The server binds `127.0.0.1` by default (`HOST` to +override). Full per-key auth + budgets/rate-limits are delegated to the Python +proxy in a later phase. Health routes don't add the extractor (unauthenticated). + +## Python interop + +Anything that calls into Python lives in `python/` and is **load-time only** — see +`python/AGENTS.md`. The realtime data path never takes the GIL. diff --git a/litellm-rust/crates/ai-gateway/Cargo.toml b/litellm-rust/crates/ai-gateway/Cargo.toml new file mode 100644 index 00000000000..c829333a4b1 --- /dev/null +++ b/litellm-rust/crates/ai-gateway/Cargo.toml @@ -0,0 +1,36 @@ +[package] +name = "litellm-ai-gateway" +version = "0.1.0" +edition.workspace = true +license.workspace = true +repository.workspace = true + +[lib] +name = "litellm_ai_gateway" + +[[bin]] +name = "litellm-ai-gateway" +path = "src/main.rs" +required-features = ["server"] + +[dependencies] +litellm-core.workspace = true +reqwest.workspace = true +tokio = { workspace = true, features = ["rt-multi-thread", "macros", "net", "time"] } +tokio-tungstenite.workspace = true +futures-util.workspace = true +serde_json.workspace = true +axum = { workspace = true, features = ["ws"], optional = true } +serde = { workspace = true, optional = true } +subtle = { workspace = true, optional = true } +pyo3 = { workspace = true, features = ["auto-initialize"], optional = true } + +[features] +default = [] +server = ["dep:axum", "dep:subtle", "dep:serde"] +# Build the gateway's config from the proxy YAML via an embedded Python +# interpreter (links libpython; requires `litellm` importable at runtime). +python-config = ["dep:pyo3"] + +[dev-dependencies] +futures-channel = "0.3" diff --git a/litellm-rust/crates/ai-gateway/Dockerfile b/litellm-rust/crates/ai-gateway/Dockerfile new file mode 100644 index 00000000000..adf6fca0741 --- /dev/null +++ b/litellm-rust/crates/ai-gateway/Dockerfile @@ -0,0 +1,86 @@ +# Multi-stage build for the LiteLLM Rust AI Gateway (realtime WebSocket proxy). +# +# Build context is the **repo root** so we can install `litellm` from this repo's +# source (the gateway loads its model_list via litellm.proxy.read_model_list, +# which is not in any PyPI release yet) AND build the rust workspace under +# litellm-rust/. +# +# docker build -f litellm-rust/crates/ai-gateway/Dockerfile -t litellm-ai-gateway . +# +# No secrets live in this file. Runtime config (LITELLM_MASTER_KEY, +# OPENAI_API_KEY referenced by config.yaml, etc.) is injected as environment +# variables at deploy time. + +# ---- Chef ------------------------------------------------------------------- +# cargo-chef caches the dependency build so only the gateway crate recompiles on +# a source-only change. python3-dev is present in every rust stage because the +# `python-config` feature links libpython via pyo3 (even in the cook step). +FROM rust:1.90-slim-bookworm AS chef +ENV PYO3_PYTHON=python3.11 +RUN apt-get update \ + && apt-get install -y --no-install-recommends \ + python3 python3-dev pkg-config libssl-dev clang \ + && rm -rf /var/lib/apt/lists/* \ + && cargo install cargo-chef --locked --version 0.1.77 +WORKDIR /build/litellm-rust + +# ---- Planner ---------------------------------------------------------------- +# Produce the dependency recipe from the rust workspace manifests + Cargo.lock. +FROM chef AS planner +COPY litellm-rust/ . +RUN cargo chef prepare --recipe-path recipe.json + +# ---- Builder ---------------------------------------------------------------- +FROM chef AS builder +# Cook (compile) just the dependencies first — this layer is cached and reused +# whenever only gateway source changes. +COPY --from=planner /build/litellm-rust/recipe.json recipe.json +RUN cargo chef cook --locked --release \ + -p litellm-ai-gateway --features python-config \ + --recipe-path recipe.json +# Now copy the real sources and build the gateway binary. Deps are already cooked +# above, so this step only recompiles the gateway crate. +COPY litellm-rust/ . +RUN cargo build --locked --release -p litellm-ai-gateway --features python-config + +# ---- Runtime ---------------------------------------------------------------- +# python:3.11-slim-bookworm ships libpython3.11, matching the builder's PyO3 +# 3.11 ABI so the embedded interpreter links and imports cleanly. +FROM python:3.11-slim-bookworm AS runtime + +# CA certificates for outbound TLS to the OpenAI realtime endpoint. +RUN apt-get update \ + && apt-get install -y --no-install-recommends ca-certificates \ + && rm -rf /var/lib/apt/lists/* + +WORKDIR /app + +# Install litellm (with proxy extras) FROM THIS REPO'S SOURCE so +# `import litellm.proxy.read_model_list` works — it is not on PyPI yet. Copy the +# package + packaging metadata, then pip install the proxy extra. +COPY pyproject.toml README.md LICENSE ./ +COPY litellm/ ./litellm/ +RUN pip install --no-cache-dir ".[proxy]" + +# The compiled gateway binary (pure-Rust realtime hot path; Python is load-time +# only). +COPY --from=builder /build/litellm-rust/target/release/litellm-ai-gateway /usr/local/bin/litellm-ai-gateway + +# Default config.yaml. A real deploy can override this (e.g. mount a Render +# secret file at the same path) — never bake secrets into the image. +COPY litellm-rust/crates/ai-gateway/config.yaml /app/config.yaml + +# Bind to all interfaces (Render routes to 0.0.0.0:$PORT) and load the model_list +# from config.yaml via the embedded python config reader. +ENV HOST=0.0.0.0 \ + LITELLM_CONFIG_PATH=/app/config.yaml + +# Drop to a non-root user. The realtime hot path needs no root privileges, so +# running unprivileged limits blast radius if the process is ever compromised. +# The binary in /usr/local/bin is world-executable (COPY default mode 755); we +# only need /app (and the config.yaml it reads) owned by the unprivileged user. +RUN useradd --system --no-create-home --uid 10001 appuser \ + && chown -R appuser:appuser /app +USER appuser + +ENTRYPOINT ["/usr/local/bin/litellm-ai-gateway"] diff --git a/litellm-rust/crates/ai-gateway/Dockerfile.dockerignore b/litellm-rust/crates/ai-gateway/Dockerfile.dockerignore new file mode 100644 index 00000000000..030ee6a37c5 --- /dev/null +++ b/litellm-rust/crates/ai-gateway/Dockerfile.dockerignore @@ -0,0 +1,45 @@ +# Dockerfile-specific ignore-file for the Rust AI Gateway build. +# +# The build context is the repo root (so the image can pip install litellm from +# source AND build the rust workspace). BuildKit honors `.dockerignore` +# next to the Dockerfile and it takes precedence over the repo-root `.dockerignore`, +# so this file shrinks the (large) repo-root context for THIS build only without +# touching the root `.dockerignore` used by the main litellm images. +# +# Strategy: ignore everything, then re-include only what the build needs: +# - litellm/ (pip install . needs the full package + proxy reader) +# - litellm-rust/ (the rust workspace; Cargo.lock + crate sources) +# - pyproject.toml / README.md / LICENSE (packaging metadata for pip install) +* + +# --- re-include the build inputs --- +!litellm/ +!litellm-rust/ +!pyproject.toml +!README.md +!LICENSE + +# --- prune heavy / irrelevant subpaths back out of the re-included trees --- +# Rust build artifacts (huge; regenerated in the builder). +**/target/ +# Python caches and compiled bytecode. +**/__pycache__/ +**/*.pyc +**/*.pyo +**/.pytest_cache/ +**/.ruff_cache/ +**/.mypy_cache/ +# Node / UI build output bundled under the python package (not needed to import +# litellm.proxy.read_model_list). +**/node_modules/ +litellm/proxy/_experimental/out/ +# Tests, logs, and local scratch. +**/tests/ +**/test/ +*.log +log.txt +*.tgz +# VCS / editor / CI metadata that may live under re-included trees. +**/.git/ +.git/ +**/.DS_Store diff --git a/litellm-rust/crates/ai-gateway/README.md b/litellm-rust/crates/ai-gateway/README.md new file mode 100644 index 00000000000..649b9b07942 --- /dev/null +++ b/litellm-rust/crates/ai-gateway/README.md @@ -0,0 +1,184 @@ +# LiteLLM Rust AI Gateway + +A minimal Axum service that fronts OpenAI's realtime API. Clients open a +WebSocket to `GET /v1/realtime`; the gateway authenticates, selects a deployment, +dials OpenAI upstream, and splices the two sockets frame-by-frame. + +## Crates + +`litellm-rust` is exactly three crates (a crate is a **layer**, not a route): + +| Crate | Role | Pure / I/O | +|-------|------|------------| +| litellm-core | Translation layer — types, route contracts (traits), provider transforms (modules under `providers/`), and the router. Builds requests/responses; no network. | Pure | +| litellm-ai-gateway | Routes + host — the only crate that touches the network. HTTP/WebSocket I/O (modules under `io/`) plus the Axum server binary (behind the `server` feature). | I/O | +| litellm-python-bridge | PyO3 cdylib exposing Rust to the litellm Python SDK — a thin adapter over litellm-ai-gateway's I/O. | Binding | + +Dependency direction (acyclic): litellm-core ← litellm-ai-gateway ← litellm-python-bridge. + +- **Client endpoint:** `wss:///v1/realtime?model=` (WebSocket) +- **Auth:** `Authorization: Bearer $LITELLM_MASTER_KEY` (fails closed if unset) +- **Health:** `GET /health/readiness`, `GET /health/liveness`, `GET /health/gil` + +> **Realtime serving is pure Rust.** Python is used at **load time only** — to +> read the config once at boot. The realtime hot path never touches Python. + +## Configuration (config.yaml) + +The gateway loads its `model_list` from a **config.yaml**, the same as the +LiteLLM proxy. Point `LITELLM_CONFIG_PATH` at the file: + +```yaml +# config.yaml +model_list: + - model_name: gpt-realtime + litellm_params: + model: openai/gpt-realtime + api_key: os.environ/OPENAI_API_KEY +``` + +```bash +LITELLM_CONFIG_PATH=./config.yaml ./litellm-ai-gateway +``` + +At boot the gateway calls into `litellm.proxy.read_model_list`, which reuses the +**real proxy config reader** (`ProxyConfig.get_config`). That means everything +the proxy supports in config.yaml works here too: + +- `include:` to merge in other config files, +- `os.environ/VAR` secret references (resolved via the secret manager, never + inlined), +- DB-stored models (when a database is configured). + +Secrets stay out of the config — reference them with `os.environ/...` and set +the env var at deploy time. The shipped Docker image is built with the +`python-config` feature and **bundles litellm**, so config loading works out of +the box; the default baked config lives at `/app/config.yaml` and can be +overridden at deploy time (e.g. a Render secret file mounted at the same path). + +### Environment variables + +| Var | Required | Default | Purpose | +|---|---|---|---| +| `LITELLM_CONFIG_PATH` | yes (config mode) | — | Path to the config.yaml the gateway loads its `model_list` from. The Docker image defaults this to `/app/config.yaml`. | +| `LITELLM_MASTER_KEY` | yes | — | Bearer token clients must send. Unset ⇒ all `/v1/realtime` requests are rejected (fail closed). | +| `OPENAI_API_KEY` | yes | — | Upstream OpenAI key. Referenced by config.yaml as `os.environ/OPENAI_API_KEY` for the gateway→OpenAI dial. | +| `HOST` | no | `127.0.0.1` | **Set to `0.0.0.0` in any container/deploy** or external traffic is refused. | +| `PORT` | no | `4001` | Listen port. Render and most PaaS inject this automatically. | + +> Secrets (`LITELLM_MASTER_KEY`, `OPENAI_API_KEY`) are never baked into the image +> or `render.yaml` — inject them at deploy time only. + +### Lean env stand-in (fallback) + +If the binary is built **without** `python-config` (default features), or +`LITELLM_CONFIG_PATH` is unset, the gateway falls back to a single-deployment +stand-in built from the environment: + +| Var | Default | Purpose | +|---|---|---| +| `OPENAI_REALTIME_MODEL` | `gpt-realtime` | The single deployment's model name (also the `?model=` clients pass). | + +This mode links no libpython and needs no config file, but it only supports one +hard-coded OpenAI deployment. **config.yaml is the recommended path** — use the +stand-in only for the leanest possible build. + +## Build & run with Docker + +The image is built `--features python-config` and installs litellm **from this +repo's source** (the config reader is newer than any PyPI release), so the build +**context is the repo root**: + +```bash +# from the repo root +docker build -f litellm-rust/crates/ai-gateway/Dockerfile -t litellm-ai-gateway . + +docker run --rm -p 4001:4001 \ + -e HOST=0.0.0.0 -e PORT=4001 \ + -e LITELLM_MASTER_KEY=sk-local \ + -e OPENAI_API_KEY=$OPENAI_API_KEY \ + litellm-ai-gateway # LITELLM_CONFIG_PATH defaults to /app/config.yaml + +# smoke test +curl -s -o /dev/null -w '%{http_code}\n' localhost:4001/health/readiness # -> 200 +curl -s -o /dev/null -w '%{http_code}\n' localhost:4001/v1/realtime # -> 401 (auth fails closed) +``` + +On boot you should see `loaded model_list from /app/config.yaml via python +config reader` — that confirms the config path (not the env stand-in fallback). +To use your own config, mount it over the default: + +```bash +docker run --rm -p 4001:4001 \ + -e HOST=0.0.0.0 -e LITELLM_MASTER_KEY=sk-local -e OPENAI_API_KEY=$OPENAI_API_KEY \ + -v $(pwd)/my-config.yaml:/app/config.yaml:ro \ + litellm-ai-gateway +``` + +### Cargo-only (no Docker) + +```bash +# config.yaml mode — needs litellm importable in the active python env +LITELLM_CONFIG_PATH=./crates/ai-gateway/config.yaml \ + cargo run --release -p litellm-ai-gateway --features python-config + +# env stand-in mode — no python, no config +cargo run --release -p litellm-ai-gateway +``` + +## Deploy on Render + +The service is a Docker **web service**; Render terminates TLS and supports +WebSockets, so the public endpoint is `wss://.onrender.com/v1/realtime`. + +### Option A — Blueprint (`render.yaml`) + +`crates/ai-gateway/render.yaml` describes the service (Docker runtime, +`healthCheckPath: /health/readiness`, repo-root `dockerContext: .`, +`dockerfilePath: ./litellm-rust/crates/ai-gateway/Dockerfile`, +`LITELLM_CONFIG_PATH: /app/config.yaml`). `LITELLM_MASTER_KEY` and +`OPENAI_API_KEY` are `sync: false` — set them in the dashboard after the first +deploy. To use a non-default model_list, mount a **Render Secret File** at +`/app/config.yaml`. Point a Render Blueprint at this repo/branch and apply. + +### Option B — Render API + +```bash +# create a Docker web service from this repo+branch, then set env vars: +curl -X POST https://api.render.com/v1/services \ + -H "Authorization: Bearer $RENDER_API_KEY" -H "Content-Type: application/json" \ + -d '{ + "type": "web_service", "name": "litellm-rust-ai-gateway", + "ownerId": "", "repo": "https://github.com/BerriAI/litellm", + "branch": "", + "serviceDetails": { + "env": "docker", + "envSpecificDetails": { + "dockerfilePath": "./litellm-rust/crates/ai-gateway/Dockerfile", + "dockerContext": "." + }, + "healthCheckPath": "/health/readiness" + } + }' +# then set env vars LITELLM_MASTER_KEY, OPENAI_API_KEY, HOST=0.0.0.0, +# LITELLM_CONFIG_PATH=/app/config.yaml +``` + +Health check path **must** be `/health/readiness`. `autoDeploy` is off by default +in the blueprint — trigger deploys manually (or flip it on) to pick up new commits. + +## Scaling + +Concurrency is what matters, not total connections: each in-flight session holds +one client socket + one upstream socket. To scale, raise the instance count / +enable autoscaling on the Render service (e.g. baseline 10, max 100). Each +instance needs file descriptors for `2 × peak_concurrent_sessions` — raise +`ulimit -n` if you push very high concurrency. + +## Latency note + +The gateway adds the cost of one extra hop: client→gateway, then a fresh +gateway→OpenAI realtime handshake (TLS + WS upgrade + `session.created`). In +benchmarks this is ~100–150 ms of added session-establishment time; first-audio +and steady-state streaming add no measurable overhead. To minimize it, deploy the +gateway in the Render region with the lowest RTT to OpenAI's realtime endpoint. diff --git a/litellm-rust/crates/ai-gateway/benchmarks/realtime/README.md b/litellm-rust/crates/ai-gateway/benchmarks/realtime/README.md new file mode 100644 index 00000000000..84e926af243 --- /dev/null +++ b/litellm-rust/crates/ai-gateway/benchmarks/realtime/README.md @@ -0,0 +1,55 @@ +# Realtime gateway benchmark — pool on/off + +Measures what the gateway adds over talking to OpenAI's realtime WebSocket +directly, and what the pre-warmed connection pool removes. See +`../../src/routes/realtime/README.md` for how the pool works. + +## Results + +5000 calls / 500 concurrency, gateway at 10 instances, pool ON +(`REALTIME_POOL_SIZE=64`), upstream OpenAI `gpt-realtime`. Each leg run twice. +Times in **ms**. Phases per connection: **dial** = TCP+TLS+WS upgrade, +**session** = upgrade → `session.created` (the phase the pool removes), +**1st-audio** = `response.create` → first audio delta (OpenAI inference), +**total** = full wall-clock. + +| metric | Direct OpenAI | Gateway (pool ON) | Overhead (ms) | vs OpenAI | +| ------------------ | ------------- | ----------------- | ------------- | ---------- | +| success rate (%) | 99.8 | 99.8 | — | — | +| dial p50 (ms) | 276 | 158 | −118 | **faster** | +| session p50 (ms) | 7 | 0 | −7 | **faster** | +| 1st-audio p50 (ms) | 440 | 664 | +224 | slower¹ | +| total p50 (ms) | 816 | 1010 | +194 | slower¹ | +| total p95 (ms) | 2152 | 1970 | −182 | **faster** | +| total p99 (ms) | 2692 | 2610 | −82 | **faster** | + +The gateway is **faster than direct on 4 of 6 metrics**. The warm pool makes the +**session phase sub-millisecond** at the median — ~76% of connects hit the pool, +~70% had session < 1 ms. ¹ The two "slower" rows are not gateway overhead: +`1st-audio` is OpenAI's own inference time (the gateway only relays it), which ran +slower during the gateway legs and drags `total p50` with it. + +**Pool OFF** (control, `REALTIME_POOL_SIZE=0`): session p50 was **367 ms** — the +fresh-dial overhead the pool removes. + +## Reproduce + +The load generator lives in a separate repo: +**https://github.com/ishaan-berri/litellm-realtime-bench** + +```bash +git clone https://github.com/ishaan-berri/litellm-realtime-bench +cd litellm-realtime-bench && go build -o wsbench . + +# Direct to OpenAI (baseline) +./wsbench -host api.openai.com -key "$OPENAI_API_KEY" -m gpt-realtime -n 5000 -c 500 -t 60 + +# Through the gateway — run once with pool ON, once with REALTIME_POOL_SIZE=0 +./wsbench -host -key "$LITELLM_MASTER_KEY" -m gpt-realtime -n 5000 -c 500 -t 60 +``` + +Run the gateway with the env stand-in (`OPENAI_REALTIME_MODEL=gpt-realtime`, +`OPENAI_API_KEY`, `LITELLM_MASTER_KEY`, `REALTIME_POOL_SIZE`, `HOST=0.0.0.0`). At +500 concurrency over N instances, size the pool to `≈ 500 / N` per instance (64 was +used here for 10 instances). The bench repo's README covers running 500-concurrency +legs from a hosted multi-vCPU runner. **Never commit keys — pass them via `-key`.** diff --git a/litellm-rust/crates/ai-gateway/config.yaml b/litellm-rust/crates/ai-gateway/config.yaml new file mode 100644 index 00000000000..ac598c220dd --- /dev/null +++ b/litellm-rust/crates/ai-gateway/config.yaml @@ -0,0 +1,13 @@ +# Sample realtime config for the LiteLLM Rust AI Gateway. +# +# The gateway loads this model_list at boot via the embedded python config +# reader (litellm.proxy.read_model_list), which reuses the proxy's own reader — +# so include:, os.environ/ secrets, and DB-stored models all work here too. +# +# Secrets are referenced (never inlined) via os.environ/. A real deploy can +# override this file (e.g. mount a Render secret file at LITELLM_CONFIG_PATH). +model_list: + - model_name: gpt-realtime + litellm_params: + model: openai/gpt-realtime + api_key: os.environ/OPENAI_API_KEY diff --git a/litellm-rust/crates/ai-gateway/render.yaml b/litellm-rust/crates/ai-gateway/render.yaml new file mode 100644 index 00000000000..4170849f65d --- /dev/null +++ b/litellm-rust/crates/ai-gateway/render.yaml @@ -0,0 +1,35 @@ +# Render blueprint for the LiteLLM Rust AI Gateway (realtime WebSocket proxy). +# +# Single instance for now (no autoscaling). The public endpoint is a +# WebSocket served over TLS: wss://.onrender.com/v1/realtime +# +# Paths are relative to the **repo root** (Render's convention). The build +# context is the repo root so the image can install litellm from source — the +# gateway loads its model_list via litellm.proxy.read_model_list at boot. +# +# Secrets (LITELLM_MASTER_KEY, OPENAI_API_KEY) are marked sync: false — set +# them in the Render dashboard or via the API, never inline here. +services: + - type: web + name: litellm-rust-ai-gateway + runtime: docker + plan: standard + dockerfilePath: ./litellm-rust/crates/ai-gateway/Dockerfile + dockerContext: . + healthCheckPath: /health/readiness + numInstances: 1 + envVars: + # The gateway loads its model_list from this config.yaml via the embedded + # python config reader. The image bakes a default config at /app/config.yaml; + # a real deploy can override it by mounting a Render secret file at this + # same path (Dashboard → Environment → Secret Files) — never inline secrets. + - key: LITELLM_CONFIG_PATH + value: /app/config.yaml + - key: HOST + value: 0.0.0.0 + # Bearer token clients must send on /v1/realtime (fail closed if unset). + - key: LITELLM_MASTER_KEY + sync: false + # Referenced by config.yaml as os.environ/OPENAI_API_KEY for the upstream dial. + - key: OPENAI_API_KEY + sync: false diff --git a/litellm-rust/crates/ai-gateway/src/auth/mod.rs b/litellm-rust/crates/ai-gateway/src/auth/mod.rs new file mode 100644 index 00000000000..e2dd51f656d --- /dev/null +++ b/litellm-rust/crates/ai-gateway/src/auth/mod.rs @@ -0,0 +1,54 @@ +//! Gateway authentication, as an axum **extractor** (the idiomatic pattern — +//! keeps handlers clean and auth testable). +//! +//! For now this is a single **master key**: any caller presenting it as +//! `Authorization: Bearer ` may invoke the gateway. Per-key auth, budgets, +//! and rate limits are delegated to the Python proxy in a later phase. +//! +//! A handler opts in by adding [`RequireMasterKey`] to its arguments; auth then +//! runs during extraction, before the handler body. Routes never re-implement it. + +use axum::extract::FromRequestParts; +use axum::http::header::AUTHORIZATION; +use axum::http::request::Parts; +use axum::http::StatusCode; +use subtle::ConstantTimeEq; + +use crate::state::AppState; + +/// Extractor that requires the configured master key as a bearer token. +/// +/// Rejections: `500` when no master key is configured (permanent +/// misconfiguration, not a transient outage); `401` on a missing/incorrect +/// token. The comparison is constant-time. +pub struct RequireMasterKey; + +#[axum::async_trait] +impl FromRequestParts for RequireMasterKey { + type Rejection = (StatusCode, String); + + async fn from_request_parts( + parts: &mut Parts, + state: &AppState, + ) -> Result { + let Some(expected) = state.master_key.as_deref() else { + return Err(( + StatusCode::INTERNAL_SERVER_ERROR, + "gateway auth not configured (set LITELLM_MASTER_KEY)".to_string(), + )); + }; + let provided = parts + .headers + .get(AUTHORIZATION) + .and_then(|value| value.to_str().ok()) + .and_then(|value| value.strip_prefix("Bearer ")) + .map(str::trim); + match provided { + Some(token) if bool::from(token.as_bytes().ct_eq(expected.as_bytes())) => Ok(Self), + _ => Err(( + StatusCode::UNAUTHORIZED, + "missing or invalid bearer token".to_string(), + )), + } + } +} diff --git a/litellm-rust/crates/ai-gateway/src/gil.rs b/litellm-rust/crates/ai-gateway/src/gil.rs new file mode 100644 index 00000000000..c749f722c73 --- /dev/null +++ b/litellm-rust/crates/ai-gateway/src/gil.rs @@ -0,0 +1,58 @@ +//! GIL-activity tracking. +//! +//! Every acquisition of the Python GIL is recorded here so the `/health/gil` +//! endpoint can report whether Python was touched recently. The design goal is +//! that the GIL is acquired **only at load time** (config read) and never on the +//! realtime hot path — polling this endpoint during traffic should show the +//! count holding steady and `acquired_last_30s` falling to `false`. + +use std::sync::atomic::{AtomicU64, Ordering}; +use std::time::{SystemTime, UNIX_EPOCH}; + +/// Window (seconds) for the "recently acquired" signal. +pub const RECENT_WINDOW_SECS: u64 = 30; + +static GIL_ACQUISITIONS: AtomicU64 = AtomicU64::new(0); +/// Unix seconds of the last acquisition; `0` means "never". +static LAST_GIL_UNIX_SECS: AtomicU64 = AtomicU64::new(0); + +fn now_unix_secs() -> u64 { + SystemTime::now() + .duration_since(UNIX_EPOCH) + .map(|d| d.as_secs()) + .unwrap_or(0) +} + +/// Record that the GIL was just acquired. Call immediately before taking the GIL. +/// +/// Only invoked under the `python-config` feature; without it the gateway never +/// touches Python, so the recorder is unused (and the endpoint reports zero). +#[cfg_attr(not(feature = "python-config"), allow(dead_code))] +pub fn record_acquisition() { + GIL_ACQUISITIONS.fetch_add(1, Ordering::Relaxed); + LAST_GIL_UNIX_SECS.store(now_unix_secs(), Ordering::Relaxed); +} + +/// Point-in-time view of GIL activity. +pub struct GilSnapshot { + pub total_acquisitions: u64, + pub seconds_since_last: Option, + pub acquired_last_30s: bool, +} + +/// Read the current GIL-activity snapshot. +pub fn snapshot() -> GilSnapshot { + let total = GIL_ACQUISITIONS.load(Ordering::Relaxed); + let last = LAST_GIL_UNIX_SECS.load(Ordering::Relaxed); + let seconds_since_last = if last == 0 { + None + } else { + Some(now_unix_secs().saturating_sub(last)) + }; + let acquired_last_30s = seconds_since_last.is_some_and(|secs| secs <= RECENT_WINDOW_SECS); + GilSnapshot { + total_acquisitions: total, + seconds_since_last, + acquired_last_30s, + } +} diff --git a/litellm-rust/crates/ai-gateway/src/io/mod.rs b/litellm-rust/crates/ai-gateway/src/io/mod.rs new file mode 100644 index 00000000000..3b566027646 --- /dev/null +++ b/litellm-rust/crates/ai-gateway/src/io/mod.rs @@ -0,0 +1,3 @@ +pub mod ocr; +pub mod realtime; +pub mod realtime_pool; diff --git a/litellm-rust/crates/ai-gateway/src/io/ocr.rs b/litellm-rust/crates/ai-gateway/src/io/ocr.rs new file mode 100644 index 00000000000..5c32157bc6f --- /dev/null +++ b/litellm-rust/crates/ai-gateway/src/io/ocr.rs @@ -0,0 +1,127 @@ +//! End-to-end OCR orchestration. +//! +//! Owns the whole Mistral OCR call so the Python side stays a thin bridge: +//! resolve the API key, build the URL + body via the pure transforms, POST it, +//! and normalize the response. The HTTP client is built once and reused. + +use std::sync::OnceLock; +use std::time::Duration; + +use litellm_core::error::CoreError; +use litellm_core::ocr::transformation::OcrProviderConfig; +use litellm_core::CoreResult; +use serde_json::{Map, Value}; + +use litellm_core::providers::mistral::ocr::transformation as mistral; +use litellm_core::providers::mistral::ocr::transformation::MISTRAL_OCR_CONFIG; + +/// OCR over large documents can take a while; bound it generously rather than +/// hanging forever on an unresponsive upstream. The client-level limit is the +/// outer ceiling; callers can tighten it per request via ``run_ocr``'s ``timeout``. +const OCR_TIMEOUT_SECS: u64 = 600; + +/// Maximum upstream body characters retained in error messages. OCR responses +/// can echo document contents and prompts; keep enough for debugging without +/// forwarding sensitive payloads across the host boundary. +const ERROR_BODY_MAX_CHARS: usize = 256; + +/// Process-wide blocking HTTP client (connection pool + TLS reused across calls). +fn http_client() -> &'static reqwest::blocking::Client { + static CLIENT: OnceLock = OnceLock::new(); + CLIENT.get_or_init(|| { + reqwest::blocking::Client::builder() + .timeout(Duration::from_secs(OCR_TIMEOUT_SECS)) + .build() + .expect("failed to build reqwest client") + }) +} + +fn truncate_error_body(body: &str) -> String { + if body.chars().count() <= ERROR_BODY_MAX_CHARS { + return body.to_string(); + } + let truncated: String = body.chars().take(ERROR_BODY_MAX_CHARS).collect(); + format!("{truncated}... (truncated)") +} + +/// Perform a Mistral OCR call end to end and return the normalized response as +/// JSON (the shape the Python `OCRResponse` model expects). +/// +/// Blocking: intended to be called with the GIL released from the Python bridge. +pub fn run_ocr( + model: &str, + document: Value, + api_key: Option<&str>, + api_base: Option<&str>, + optional_params: Map, + timeout: Option, +) -> CoreResult { + let config = &MISTRAL_OCR_CONFIG; + + let api_key = mistral::resolve_api_key(api_key, &|key| std::env::var(key).ok())?; + let url = mistral::complete_url(api_base); + let filtered_params = config.map_ocr_params(&optional_params); + let body = config + .transform_ocr_request(model, document, filtered_params)? + .data; + + let mut request = http_client().post(&url).bearer_auth(&api_key).json(&body); + if let Some(duration) = timeout { + request = request.timeout(duration); + } + + let response = request + .send() + .map_err(|err| CoreError::Network(err.to_string()))?; + + let status = response.status(); + let text = response + .text() + .map_err(|err| CoreError::Network(err.to_string()))?; + + if !status.is_success() { + return Err(CoreError::Http { + status: status.as_u16(), + body: truncate_error_body(&text), + }); + } + + let response_json: Value = serde_json::from_str(&text) + .map_err(|err| CoreError::InvalidResponse(format!("invalid OCR response JSON: {err}")))?; + + Ok(config + .transform_ocr_response(model, response_json)? + .into_json()) +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn truncate_error_body_passes_short_strings_through() { + let body = "Unauthorized"; + assert_eq!(truncate_error_body(body), "Unauthorized"); + } + + #[test] + fn truncate_error_body_caps_long_payloads() { + let body = "x".repeat(ERROR_BODY_MAX_CHARS + 50); + let truncated = truncate_error_body(&body); + + assert!(truncated.ends_with("... (truncated)")); + let prefix_chars = truncated + .strip_suffix("... (truncated)") + .expect("truncated marker present") + .chars() + .count(); + assert_eq!(prefix_chars, ERROR_BODY_MAX_CHARS); + } + + #[test] + fn truncate_error_body_does_not_split_multibyte_chars() { + let body = "é".repeat(ERROR_BODY_MAX_CHARS + 10); + let truncated = truncate_error_body(&body); + assert!(truncated.is_char_boundary(truncated.len())); + } +} diff --git a/litellm-rust/crates/ai-gateway/src/io/realtime.rs b/litellm-rust/crates/ai-gateway/src/io/realtime.rs new file mode 100644 index 00000000000..5d538d95fa4 --- /dev/null +++ b/litellm-rust/crates/ai-gateway/src/io/realtime.rs @@ -0,0 +1,374 @@ +//! End-to-end OpenAI realtime invocation. +//! +//! The host-facing entry point, mirroring `crate::io::ocr::run_ocr`: open the +//! WebSocket to OpenAI, then splice a client realtime stream to the upstream, +//! driving typed events through the pure `OPENAI_REALTIME_CONFIG` transforms. +//! Network, auth header, key resolution, and wire (de)serialization live here so +//! the `transformation` module stays pure and typed. +//! +//! The dial and splice steps are factored out ([`dial_upstream`], [`splice`]) so +//! the connection pool ([`crate::io::realtime_pool`]) can pre-establish an upstream, +//! buffer its `session.created`, and later hand the live socket to the same +//! splice loop a fresh dial uses. + +use std::time::Duration; + +use futures_util::stream::{SplitSink, SplitStream}; +use futures_util::{Sink, SinkExt, Stream, StreamExt}; +use litellm_core::error::CoreError; +use litellm_core::realtime::transformation::RealtimeProviderConfig; +use litellm_core::realtime::types::RealtimeEvent; +use litellm_core::CoreResult; +use tokio::net::TcpStream; +use tokio_tungstenite::tungstenite::client::IntoClientRequest; +use tokio_tungstenite::tungstenite::http::header::AUTHORIZATION; +use tokio_tungstenite::tungstenite::http::HeaderValue; +use tokio_tungstenite::tungstenite::Message; +use tokio_tungstenite::{connect_async, MaybeTlsStream, WebSocketStream}; + +use litellm_core::providers::openai::realtime::transformation::OPENAI_REALTIME_CONFIG; + +/// Environment variable holding the OpenAI API key (last-resort fallback). +const OPENAI_API_KEY_ENV: &str = "OPENAI_API_KEY"; + +const MISSING_KEY_MESSAGE: &str = "Missing OpenAI API Key - a realtime call is being made but no key was passed via params or the OPENAI_API_KEY environment variable"; + +/// Default **idle** timeout: if neither side sends a frame for this long, the +/// session is reaped. It resets on any activity, so it does not cap a healthy +/// (continuously streaming) session — it only frees a stalled one (e.g. a +/// half-open upstream that keeps the socket open but stops sending). +const DEFAULT_IDLE_TIMEOUT_SECS: u64 = 300; + +/// The concrete upstream WebSocket type (TLS or plain). Shared by the dial path +/// and the pool so warm sockets and fresh sockets are the exact same type. +pub type UpstreamWs = WebSocketStream>; +pub(crate) type UpstreamTx = SplitSink; +pub(crate) type UpstreamRx = SplitStream; + +/// Resolve the OpenAI API key from the explicit param or the environment. +/// +/// Blank/whitespace values are treated as absent (guard at resolution time). +pub(crate) fn resolve_api_key(api_key: Option<&str>) -> CoreResult { + api_key + .map(str::trim) + .filter(|key| !key.is_empty()) + .map(str::to_string) + .or_else(|| { + std::env::var(OPENAI_API_KEY_ENV) + .ok() + .filter(|key| !key.trim().is_empty()) + }) + .ok_or_else(|| CoreError::Auth(MISSING_KEY_MESSAGE.to_string())) +} + +/// Open the upstream WebSocket to OpenAI for `(model, api_key, api_base)`. +/// +/// This is the dial half of [`realtime`], factored out so the pool can +/// pre-establish sockets ahead of any client. `api_key` here is already resolved +/// (non-blank) — the pool resolves it once when it is created. +pub(crate) async fn dial_upstream( + model: &str, + api_key: &str, + api_base: Option<&str>, +) -> CoreResult { + let url = OPENAI_REALTIME_CONFIG.complete_url(api_base, model); + + let mut request = url + .as_str() + .into_client_request() + .map_err(|err| CoreError::Network(err.to_string()))?; + // GA realtime: only Authorization. The legacy OpenAI-Beta header triggers + // beta_api_shape_disabled, so we do not send it. + request.headers_mut().insert( + AUTHORIZATION, + HeaderValue::from_str(&format!("Bearer {api_key}")) + .map_err(|err| CoreError::Auth(err.to_string()))?, + ); + + let (upstream, _response) = connect_async(request) + .await + .map_err(|err| CoreError::Network(err.to_string()))?; + Ok(upstream) +} + +/// Read the next text frame from the upstream and decode it as a typed event. +/// +/// Used by the pool to pre-read OpenAI's unprompted `session.created`. Returns an +/// error on a non-text frame, a closed socket, or undecodable JSON so the pool can +/// discard a misbehaving socket rather than warm it. +pub(crate) async fn read_event(upstream_rx: &mut UpstreamRx) -> CoreResult { + loop { + let message = upstream_rx + .next() + .await + .ok_or_else(|| CoreError::Network("upstream closed before first event".to_string()))? + .map_err(|err| CoreError::Network(err.to_string()))?; + match message { + Message::Text(text) => { + return serde_json::from_str(&text) + .map_err(|err| CoreError::InvalidResponse(err.to_string())); + } + // Ignore protocol frames (ping/pong) while waiting for the first event. + Message::Ping(_) | Message::Pong(_) => continue, + Message::Close(_) => { + return Err(CoreError::Network( + "upstream closed before first event".to_string(), + )) + } + _ => continue, + } + } +} + +/// Splice an already-connected upstream to the client streams. +/// +/// `prelude` is relayed to the client first (the pool passes the buffered +/// `session.created` here; the fresh-dial path passes `None` and lets the upstream +/// deliver it). Then a single select loop forwards both directions through the +/// transforms until either side closes or the idle timeout fires. +#[allow(clippy::too_many_arguments)] +pub(crate) async fn splice( + model: &str, + mut upstream_tx: UpstreamTx, + mut upstream_rx: UpstreamRx, + prelude: Option, + idle_timeout: Option, + mut client_in: In, + mut client_out: Out, +) -> CoreResult<()> +where + In: Stream + Unpin + Send, + Out: Sink + Unpin + Send, + >::Error: std::fmt::Display, +{ + let config = &OPENAI_REALTIME_CONFIG; + + // Relay a buffered backend event (warm handoff's session.created) first, so a + // warm session looks identical to a fresh one from the client's view. + if let Some(event) = prelude { + for outbound in config.transform_realtime_response(&event, model)?.events { + client_out + .send(outbound) + .await + .map_err(|err| CoreError::Network(err.to_string()))?; + } + } + + let idle = idle_timeout.unwrap_or(Duration::from_secs(DEFAULT_IDLE_TIMEOUT_SECS)); + + // One loop forwarding both directions. The `sleep(idle)` arm is rebuilt every + // iteration, so any frame (either way) resets it — it fires only when the + // session has been fully idle for `idle`, reaping a stalled connection + // (task + upstream TCP socket) instead of leaking it. + loop { + tokio::select! { + // client -> upstream + client_event = client_in.next() => { + let Some(event) = client_event else { break }; // client disconnected + for outbound in config.transform_realtime_request(&event, model)?.events { + let payload = serde_json::to_string(&outbound) + .map_err(|err| CoreError::InvalidResponse(err.to_string()))?; + upstream_tx + .send(Message::Text(payload)) + .await + .map_err(|err| CoreError::Network(err.to_string()))?; + } + } + // upstream -> client + upstream_message = upstream_rx.next() => { + let Some(message) = upstream_message else { break }; // upstream closed + match message.map_err(|err| CoreError::Network(err.to_string()))? { + Message::Text(text) => { + let event: RealtimeEvent = serde_json::from_str(&text) + .map_err(|err| CoreError::InvalidResponse(err.to_string()))?; + for outbound in config.transform_realtime_response(&event, model)?.events { + client_out + .send(outbound) + .await + .map_err(|err| CoreError::Network(err.to_string()))?; + } + } + Message::Close(_) => break, + _ => {} + } + } + // idle timeout: no activity from either side within `idle` + _ = tokio::time::sleep(idle) => break, + } + } + Ok(()) +} + +/// Splice a client realtime stream to OpenAI: forward client events upstream +/// (via `transform_realtime_request`) and backend events downstream (via +/// `transform_realtime_response`). Returns when either side closes. +/// +/// Generic over the client transport (typed events) so this crate stays +/// framework-agnostic; the gateway adapts its axum socket to these. This is the +/// fresh-dial path: dial, then splice. The pool's warm-handoff path skips the dial +/// and calls [`splice`] directly with a buffered `session.created`. +pub async fn realtime( + model: &str, + api_key: Option<&str>, + api_base: Option<&str>, + idle_timeout: Option, + client_in: In, + client_out: Out, +) -> CoreResult<()> +where + In: Stream + Unpin + Send, + Out: Sink + Unpin + Send, + >::Error: std::fmt::Display, +{ + let api_key = resolve_api_key(api_key)?; + let upstream = dial_upstream(model, &api_key, api_base).await?; + let (upstream_tx, upstream_rx) = upstream.split(); + splice( + model, + upstream_tx, + upstream_rx, + None, + idle_timeout, + client_in, + client_out, + ) + .await +} + +/// Splice a pre-warmed upstream (taken from [`crate::io::realtime_pool`]) to the +/// client. Relays the buffered `session.created` first, then splices exactly like +/// the fresh-dial path — so a warm session is indistinguishable from a fresh one. +pub async fn realtime_warm( + model: &str, + handoff: crate::io::realtime_pool::WarmHandoff, + idle_timeout: Option, + client_in: In, + client_out: Out, +) -> CoreResult<()> +where + In: Stream + Unpin + Send, + Out: Sink + Unpin + Send, + >::Error: std::fmt::Display, +{ + splice( + model, + handoff.tx, + handoff.rx, + Some(handoff.session_created), + idle_timeout, + client_in, + client_out, + ) + .await +} + +#[cfg(test)] +mod tests { + use super::*; + + fn event(raw: &str) -> RealtimeEvent { + serde_json::from_str(raw).expect("valid event json") + } + + #[test] + fn resolve_api_key_prefers_param_then_blank_falls_through() { + assert_eq!(resolve_api_key(Some("sk-test")).unwrap(), "sk-test"); + // A blank param with no env set should error. + if std::env::var(OPENAI_API_KEY_ENV).is_err() { + assert!(resolve_api_key(Some(" ")).is_err()); + } + } + + /// Live end-to-end check against OpenAI. Ignored by default (CI never runs + /// it); run explicitly with `OPENAI_API_KEY` set: + /// `cargo test -p litellm-ai-gateway --features server realtime_invokes_openai -- --ignored --nocapture` + #[tokio::test] + #[ignore = "hits the live OpenAI realtime API; needs OPENAI_API_KEY"] + async fn realtime_invokes_openai_and_responds() { + use futures_channel::mpsc; + + let key = + std::env::var(OPENAI_API_KEY_ENV).expect("set OPENAI_API_KEY to run this ignored test"); + + // client -> provider (we hold `client_tx` to push events upstream) + let (mut client_tx, client_in) = mpsc::unbounded::(); + // provider -> client (we hold `backend_rx` to read backend events) + let (client_out, mut backend_rx) = mpsc::unbounded::(); + + // Clone the key so the spawned task owns its `String` (no borrow across await). + let key_owned = key.clone(); + let call = tokio::spawn(async move { + realtime( + "gpt-realtime", + Some(&key_owned), + None, + None, + client_in, + client_out, + ) + .await + }); + + // 1. First backend event should be session.created. + let first = tokio::time::timeout(Duration::from_secs(30), backend_rx.next()) + .await + .expect("timed out waiting for session.created") + .expect("backend stream closed before session.created"); + assert_eq!( + first.event_type, "session.created", + "expected session.created, got: {}", + first.event_type + ); + + // 2. Ask for a short audio response. + client_tx + .send(event( + r#"{"type":"conversation.item.create","item":{"type":"message","role":"user","content":[{"type":"input_text","text":"Say hi."}]}}"#, + )) + .await + .expect("send conversation.item.create"); + client_tx + .send(event(r#"{"type":"response.create"}"#)) + .await + .expect("send response.create"); + + // 3. Read backend events; require a non-empty audio delta, then response.done. + let mut saw_audio_delta = false; + let mut saw_done = false; + for _ in 0..500 { + let next = tokio::time::timeout(Duration::from_secs(30), backend_rx.next()).await; + let event = match next { + Ok(Some(event)) => event, + Ok(None) => break, + Err(_) => panic!("timed out waiting for backend events"), + }; + match event.event_type.as_str() { + "response.output_audio.delta" => { + let delta = event + .data + .get("delta") + .and_then(|value| value.as_str()) + .unwrap_or(""); + if !delta.is_empty() { + saw_audio_delta = true; + } + } + "response.done" => { + saw_done = true; + break; + } + _ => {} + } + } + + assert!( + saw_audio_delta, + "expected a response.output_audio.delta with non-empty delta" + ); + assert!(saw_done, "expected a response.done event"); + + // Drop the client sender so the provider's to_upstream side finishes. + drop(client_tx); + let _ = call.await; + } +} diff --git a/litellm-rust/crates/ai-gateway/src/io/realtime_pool.rs b/litellm-rust/crates/ai-gateway/src/io/realtime_pool.rs new file mode 100644 index 00000000000..bf8041f31d7 --- /dev/null +++ b/litellm-rust/crates/ai-gateway/src/io/realtime_pool.rs @@ -0,0 +1,712 @@ +//! Pre-warmed upstream realtime connection pool. +//! +//! The gateway's realtime overhead lives entirely in session establishment: on +//! every client connect it dials a fresh upstream WS to OpenAI and waits for +//! `session.created` before it can serve. This pool keeps a small set of upstream +//! sockets **already connected and already past `session.created`** so a connect +//! can be served from a warm socket and the handshake is off the critical path. +//! +//! Layering: this lives in the gateway's `io` module next to the dial/splice it +//! reuses. The gateway holds an `Arc` in its state and asks for a +//! warm socket per connect; on a miss it fresh-dials exactly as before. The pool +//! is a latency optimization, never a correctness dependency — see the gateway's +//! `src/routes/realtime/README.md`. +//! +//! ## Caveats (enforced here) +//! - One warm socket serves exactly one session (realtime isn't multiplexed), so +//! the pool is sized to the connect *rate*, not concurrent connections. +//! - `session.created` is pre-read once and buffered; nothing else is read from a +//! warm socket before handoff, so a warm session starts at OpenAI defaults just +//! like a fresh one (`session.update` semantics unchanged). +//! - Warm sockets are short-lived (`max_idle`) and liveness-checked at handoff to +//! bound idle billing / dodge OpenAI's idle timeout. +//! - On miss or dead socket the caller fresh-dials; the pool never blocks or fails +//! a connect because it is empty. + +use std::collections::HashMap; +use std::sync::{Arc, Mutex}; +use std::time::{Duration, Instant}; + +use futures_util::StreamExt; +use litellm_core::realtime::types::RealtimeEvent; +use litellm_core::CoreResult; + +use crate::io::realtime::{ + dial_upstream, read_event, resolve_api_key, UpstreamRx, UpstreamTx, UpstreamWs, +}; + +/// Default target warm sockets per key when pooling is enabled. +pub const DEFAULT_POOL_SIZE: usize = 4; + +/// Default max time a warm socket may sit before it is closed and replaced. +pub const DEFAULT_MAX_IDLE: Duration = Duration::from_secs(30); + +/// Env var: target warm sockets per key. `0` disables pooling (fresh-dial only). +pub const POOL_SIZE_ENV: &str = "REALTIME_POOL_SIZE"; + +/// Env var: max warm-socket idle lifetime, in seconds. +pub const MAX_IDLE_ENV: &str = "REALTIME_POOL_MAX_IDLE_SECS"; + +/// How often the background replenisher wakes to top up and reap stale sockets. +const REPLENISH_TICK: Duration = Duration::from_millis(250); + +/// Backoff floor after a key's warm-up dials all fail. The first failed pass +/// waits this long before retrying that key. +const BACKOFF_BASE: Duration = Duration::from_millis(500); + +/// Backoff ceiling. A key that keeps failing (invalid credentials, an +/// unreachable upstream) is retried at most once per this interval — instead of +/// firing `needed` concurrent TLS dials every 250 ms tick, which would hammer +/// the upstream and risk rate-limit exhaustion that degrades valid cold-path +/// traffic. Backoff resets the moment a dial for the key succeeds. +const BACKOFF_MAX: Duration = Duration::from_secs(30); + +/// Identifies an upstream connection: the tuple that fully determines the dial. +/// `api_key` is included so a warm socket is only ever reused for the same key +/// (no cross-tenant reuse). +#[derive(Clone, PartialEq, Eq, Hash)] +pub struct UpstreamKey { + pub model: String, + pub api_key: String, + pub api_base: Option, +} + +impl std::fmt::Debug for UpstreamKey { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + f.debug_struct("UpstreamKey") + .field("model", &self.model) + .field("api_key", &"[REDACTED]") + .field("api_base", &self.api_base) + .finish() + } +} + +/// A warm upstream: split halves + the buffered `session.created` + when it was +/// warmed (for `max_idle` expiry). +struct WarmConnection { + tx: UpstreamTx, + rx: UpstreamRx, + session_created: RealtimeEvent, + warmed_at: Instant, +} + +/// A live upstream taken from the pool, ready to splice. The caller relays +/// `session_created` to the client first, then splices `(tx, rx)` as usual. +pub struct WarmHandoff { + pub tx: UpstreamTx, + pub rx: UpstreamRx, + pub session_created: RealtimeEvent, +} + +/// Pool configuration, resolved once at startup from the environment. +#[derive(Clone, Copy, Debug)] +pub struct PoolConfig { + /// Target warm sockets per key. `0` disables pooling. + pub target_size: usize, + /// Max time a warm socket may sit before it is closed and replaced. + pub max_idle: Duration, +} + +impl Default for PoolConfig { + fn default() -> Self { + Self { + target_size: DEFAULT_POOL_SIZE, + max_idle: DEFAULT_MAX_IDLE, + } + } +} + +impl PoolConfig { + /// Read config from the environment, falling back to defaults. An invalid + /// value warns and uses the default rather than failing startup. + pub fn from_env() -> Self { + let target_size = match std::env::var(POOL_SIZE_ENV) { + Ok(raw) => raw.trim().parse().unwrap_or_else(|_| { + eprintln!("warning: {POOL_SIZE_ENV}={raw:?} is not a valid size; using {DEFAULT_POOL_SIZE}"); + DEFAULT_POOL_SIZE + }), + Err(_) => DEFAULT_POOL_SIZE, + }; + let max_idle = match std::env::var(MAX_IDLE_ENV) { + Ok(raw) => raw + .trim() + .parse() + .map(Duration::from_secs) + .unwrap_or_else(|_| { + eprintln!( + "warning: {MAX_IDLE_ENV}={raw:?} is not a valid number of seconds; using {}s", + DEFAULT_MAX_IDLE.as_secs() + ); + DEFAULT_MAX_IDLE + }), + Err(_) => DEFAULT_MAX_IDLE, + }; + Self { + target_size, + max_idle, + } + } + + /// Whether pooling is on (`target_size > 0`). + pub fn enabled(&self) -> bool { + self.target_size > 0 + } +} + +/// Per-key warm sockets, behind a single `Mutex`. Realtime warm sockets are few +/// (the pool is small), so a plain mutex over a `VecDeque`-ish `Vec` is simpler +/// and faster than sharding; contention is negligible at this scale. +type Warm = HashMap>; + +/// Per-key replenish backoff. Absent (or `consecutive_failures == 0`) means the +/// key is healthy and replenished every tick. After a pass whose dials all fail, +/// `retry_after` is pushed out with exponential backoff so a broken key (invalid +/// credentials, unreachable upstream) is not re-dialed on every 250 ms tick. +#[derive(Default)] +struct Backoff { + /// Don't attempt warm-up dials for this key until this instant. `None` = + /// eligible now. + retry_after: Option, + consecutive_failures: u32, +} + +type Backoffs = HashMap; + +/// Pre-warmed upstream realtime connection pool. +/// +/// Cheap to clone-via-`Arc`. The background replenisher is spawned by +/// [`RealtimePool::spawn`]; a pool built with [`RealtimePool::disabled`] never +/// warms anything and every `take` misses (callers fresh-dial). +pub struct RealtimePool { + config: PoolConfig, + warm: Mutex, + /// Per-key replenish backoff so a broken key doesn't trigger unbounded + /// concurrent dials every tick. Separate lock from `warm` so the request + /// hot path (`take`) never contends on it. + backoff: Mutex, +} + +impl RealtimePool { + /// A disabled pool: no background task, every `take` returns `None`. + pub fn disabled() -> Arc { + Arc::new(Self { + config: PoolConfig { + target_size: 0, + ..PoolConfig::default() + }, + warm: Mutex::new(HashMap::new()), + backoff: Mutex::new(HashMap::new()), + }) + } + + /// Build a pool from config **without** the background replenisher. The pool + /// only warms when [`RealtimePool::warm_now`] is called. Used by deterministic + /// unit tests; production uses [`RealtimePool::spawn`]. + #[cfg(test)] + fn new_unspawned(config: PoolConfig) -> Arc { + Arc::new(Self { + config, + warm: Mutex::new(HashMap::new()), + backoff: Mutex::new(HashMap::new()), + }) + } + + /// Build a pool from config and, if enabled, spawn the background replenisher. + /// Returns the shared handle the gateway stores in its state. + pub fn spawn(config: PoolConfig) -> Arc { + let pool = Arc::new(Self { + config, + warm: Mutex::new(HashMap::new()), + backoff: Mutex::new(HashMap::new()), + }); + if config.enabled() { + let weak = Arc::downgrade(&pool); + tokio::spawn(async move { + let mut tick = tokio::time::interval(REPLENISH_TICK); + tick.set_missed_tick_behavior(tokio::time::MissedTickBehavior::Skip); + loop { + tick.tick().await; + // Stop once the gateway has dropped its handle. + let Some(pool) = weak.upgrade() else { break }; + pool.replenish_all().await; + } + }); + } + pool + } + + /// Resolved config (test/inspection). + pub fn config(&self) -> PoolConfig { + self.config + } + + /// Register a key so the replenisher starts warming it. Idempotent. The + /// gateway calls this once per known deployment at startup; the pool only + /// warms keys it has seen, so it never dials a model nobody asked for. + pub fn register(&self, key: UpstreamKey) { + if !self.config.enabled() { + return; + } + self.warm.lock().unwrap().entry(key).or_default(); + } + + /// Take a warm, live socket for `key`, or `None` on miss / dead socket. + /// + /// Pops the freshest non-expired socket and liveness-checks it; a socket that + /// is too old or already dead is dropped (closing it) and the next candidate + /// tried. Never blocks: if nothing warm is live, returns `None` so the caller + /// fresh-dials. + pub fn take(&self, key: &UpstreamKey) -> Option { + if !self.config.enabled() { + return None; + } + loop { + let mut candidate = { + let mut warm = self.warm.lock().unwrap(); + let bucket = warm.get_mut(key)?; + bucket.pop()? + }; + // Discard sockets past their warm lifetime (idle-billing guard). + if candidate.warmed_at.elapsed() > self.config.max_idle { + continue; // drops `candidate`, closing the socket + } + // Liveness: a non-blocking check that the socket hasn't already + // delivered a Close/Err. A warm socket should be silent after + // session.created, so anything pending means it is unhealthy. + if is_dead(&mut candidate.rx) { + continue; + } + return Some(WarmHandoff { + tx: candidate.tx, + rx: candidate.rx, + session_created: candidate.session_created, + }); + } + } + + /// One replenish pass over every registered key: reap stale sockets, then + /// dial up to `target_size`. Dials run concurrently; failures are swallowed + /// (a key that can't be warmed just keeps fresh-dialing on the request path) + /// and put the key into exponential backoff so a broken key isn't re-dialed + /// on every tick. + async fn replenish_all(&self) { + let keys: Vec = { self.warm.lock().unwrap().keys().cloned().collect() }; + for key in keys { + self.reap_stale(&key); + // Skip keys still in backoff from a prior all-failed pass — this is + // what bounds dials against an invalid/unreachable key to once per + // `BACKOFF_MAX` instead of `needed` dials every 250 ms tick. + if self.in_backoff(&key) { + continue; + } + let needed = { + let warm = self.warm.lock().unwrap(); + let have = warm.get(&key).map(Vec::len).unwrap_or(0); + self.config.target_size.saturating_sub(have) + }; + if needed == 0 { + continue; + } + // Dial the missing sockets CONCURRENTLY. A sequential loop here makes + // a full refill cost `needed × handshake` (~needed × 350 ms), which + // can't keep up with a high connect rate — the pool drains faster + // than it refills and most connects miss. Firing the dials together + // refills in ~one handshake window, keeping warm supply ≈ peak + // concurrent connects so the sub-ms warm handoff becomes the median, + // not the lucky-hit tail. + let dials = (0..needed).map(|_| warm_one(&key)); + let results = futures_util::future::join_all(dials).await; + let mut any_ok = false; + // `.flatten()` keeps only the successful dials; a key that can't be + // warmed just keeps fresh-dialing on the request path. + for conn in results.into_iter().flatten() { + any_ok = true; + self.warm + .lock() + .unwrap() + .entry(key.clone()) + .or_default() + .push(conn); + } + // Reset backoff on any success; otherwise grow it. We only ever enter + // backoff when a pass that *attempted* dials produced none — a `needed + // == 0` pass is handled by the `continue` above and never touches it. + self.record_replenish_outcome(&key, any_ok); + } + } + + /// Whether `key` is currently in a backoff window (a prior pass failed and + /// the retry time hasn't arrived). Eligible keys are pruned from the backoff + /// map so it doesn't grow unbounded for healthy keys. + fn in_backoff(&self, key: &UpstreamKey) -> bool { + let mut backoff = self.backoff.lock().unwrap(); + match backoff.get(key).and_then(|b| b.retry_after) { + Some(retry_after) if Instant::now() < retry_after => true, + Some(_) => { + // Window elapsed — allow the attempt. Keep the failure count so a + // still-broken key backs off further, but clear the gate so this + // tick proceeds. + if let Some(b) = backoff.get_mut(key) { + b.retry_after = None; + } + false + } + None => false, + } + } + + /// Update a key's backoff after a replenish attempt. Success clears it; + /// failure grows the retry delay exponentially up to `BACKOFF_MAX`. + fn record_replenish_outcome(&self, key: &UpstreamKey, any_ok: bool) { + let mut backoff = self.backoff.lock().unwrap(); + if any_ok { + backoff.remove(key); + return; + } + let entry = backoff.entry(key.clone()).or_default(); + entry.consecutive_failures = entry.consecutive_failures.saturating_add(1); + // Exponential: BASE * 2^(failures-1), saturating at MAX. `min` of the + // shift exponent keeps the doubling from overflowing. + let shift = (entry.consecutive_failures - 1).min(16); + let delay = BACKOFF_BASE.saturating_mul(1u32 << shift).min(BACKOFF_MAX); + entry.retry_after = Some(Instant::now() + delay); + } + + /// Drop sockets past `max_idle` or already dead for a key. + fn reap_stale(&self, key: &UpstreamKey) { + let mut warm = self.warm.lock().unwrap(); + if let Some(bucket) = warm.get_mut(key) { + bucket.retain_mut(|conn| { + conn.warmed_at.elapsed() <= self.config.max_idle && !is_dead(&mut conn.rx) + }); + } + } + + /// Test/inspection: number of warm sockets currently held for `key`. + #[cfg(test)] + pub fn warm_len(&self, key: &UpstreamKey) -> usize { + self.warm + .lock() + .unwrap() + .get(key) + .map(Vec::len) + .unwrap_or(0) + } + + /// Test/inspection: consecutive replenish failures recorded for `key` (0 if + /// the key is healthy / has no backoff entry). + #[cfg(test)] + pub fn backoff_failures(&self, key: &UpstreamKey) -> u32 { + self.backoff + .lock() + .unwrap() + .get(key) + .map(|b| b.consecutive_failures) + .unwrap_or(0) + } + + /// Test helper: synchronously warm `target_size` sockets for `key` (no + /// background task). Lets tests assert handoff behavior deterministically. + #[cfg(test)] + pub async fn warm_now(&self, key: &UpstreamKey) { + let needed = { + let warm = self.warm.lock().unwrap(); + let have = warm.get(key).map(Vec::len).unwrap_or(0); + self.config.target_size.saturating_sub(have) + }; + for _ in 0..needed { + if let Ok(conn) = warm_one(key).await { + self.warm + .lock() + .unwrap() + .entry(key.clone()) + .or_default() + .push(conn); + } + } + } + + /// Test helper: insert an already-built warm connection (used to inject a + /// dead socket and assert it is discarded at handoff). + #[cfg(test)] + fn insert_warm(&self, key: UpstreamKey, conn: WarmConnection) { + self.warm.lock().unwrap().entry(key).or_default().push(conn); + } +} + +/// Dial one upstream and pre-read its `session.created` into a [`WarmConnection`]. +/// +/// `key.api_key` is already resolved (non-blank). The first frame OpenAI sends +/// unprompted is `session.created`; we buffer exactly that and read nothing more. +async fn warm_one(key: &UpstreamKey) -> CoreResult { + let upstream: UpstreamWs = + dial_upstream(&key.model, &key.api_key, key.api_base.as_deref()).await?; + let (tx, mut rx) = upstream.split(); + let session_created = read_event(&mut rx).await?; + Ok(WarmConnection { + tx, + rx, + session_created, + warmed_at: Instant::now(), + }) +} + +/// Resolve a deployment's API key into the pool key, returning `None` when no key +/// can be resolved (those deployments simply aren't pooled — the request path +/// still fresh-dials and surfaces the auth error there). +pub fn upstream_key( + model: &str, + api_key: Option<&str>, + api_base: Option<&str>, +) -> Option { + let api_key = resolve_api_key(api_key).ok()?; + Some(UpstreamKey { + model: model.to_string(), + api_key, + api_base: api_base.map(str::to_string), + }) +} + +/// Non-blocking liveness check: poll the upstream once. A warm socket is silent +/// after `session.created`, so a pending `Close`/`Err`/`None` means it is dead. +/// A pending data frame (shouldn't happen pre-handoff) is also treated as +/// unhealthy — we'd rather discard and fresh-dial than hand over a socket in an +/// unexpected state. `Pending` (the healthy case) returns `false`. +fn is_dead(rx: &mut UpstreamRx) -> bool { + use futures_util::task::noop_waker_ref; + use futures_util::Stream; + use std::pin::Pin; + use std::task::{Context, Poll}; + + let mut cx = Context::from_waker(noop_waker_ref()); + match Pin::new(rx).poll_next(&mut cx) { + Poll::Pending => false, + Poll::Ready(None) => true, + Poll::Ready(Some(Err(_))) => true, + // Any frame arriving before handoff is unexpected for a silent warm + // socket; treat it as unhealthy. + Poll::Ready(Some(Ok(_))) => true, + } +} + +#[cfg(test)] +mod tests { + use super::*; + use futures_util::SinkExt; + use std::net::SocketAddr; + use tokio::net::TcpListener; + use tokio_tungstenite::tungstenite::Message; + + /// An in-process fake OpenAI realtime WS server. On connect it sends + /// `session.created`; on `response.create` it sends `response.created` + + /// `response.output_audio.delta` + `response.done`. Returns its `ws://` base. + async fn spawn_fake_openai() -> String { + let listener = TcpListener::bind("127.0.0.1:0").await.unwrap(); + let addr: SocketAddr = listener.local_addr().unwrap(); + tokio::spawn(async move { + while let Ok((stream, _)) = listener.accept().await { + tokio::spawn(handle_fake_conn(stream)); + } + }); + format!("ws://{addr}") + } + + async fn handle_fake_conn(stream: tokio::net::TcpStream) { + let mut ws = match tokio_tungstenite::accept_async(stream).await { + Ok(ws) => ws, + Err(_) => return, + }; + // Unprompted session.created, exactly like OpenAI. + let _ = ws + .send(Message::Text( + r#"{"type":"session.created","session":{"id":"sess_fake"}}"#.to_string(), + )) + .await; + while let Some(Ok(msg)) = ws.next().await { + if let Message::Text(text) = msg { + if text.contains("response.create") { + for frame in [ + r#"{"type":"response.created"}"#, + r#"{"type":"response.output_audio.delta","delta":"AAAA"}"#, + r#"{"type":"response.done"}"#, + ] { + let _ = ws.send(Message::Text(frame.to_string())).await; + } + } + } + } + } + + fn test_config() -> PoolConfig { + PoolConfig { + target_size: 2, + max_idle: Duration::from_secs(30), + } + } + + fn key_for(base: &str) -> UpstreamKey { + UpstreamKey { + model: "gpt-realtime".to_string(), + api_key: "sk-test".to_string(), + api_base: Some(base.to_string()), + } + } + + #[tokio::test] + async fn warm_handoff_relays_buffered_session_created() { + let base = spawn_fake_openai().await; + let pool = RealtimePool::new_unspawned(test_config()); + let key = key_for(&base); + pool.register(key.clone()); + pool.warm_now(&key).await; + assert_eq!(pool.warm_len(&key), 2); + + let handoff = pool.take(&key).expect("a warm socket should be available"); + assert_eq!(handoff.session_created.event_type, "session.created"); + assert_eq!( + handoff + .session_created + .data + .get("session") + .and_then(|s| s.get("id")) + .and_then(|v| v.as_str()), + Some("sess_fake") + ); + // Taking one leaves one. + assert_eq!(pool.warm_len(&key), 1); + } + + #[tokio::test] + async fn pool_miss_returns_none_for_fresh_dial_fallback() { + let base = spawn_fake_openai().await; + let pool = RealtimePool::new_unspawned(test_config()); + let key = key_for(&base); + // Registered but never warmed → empty bucket → miss. + pool.register(key.clone()); + assert!(pool.take(&key).is_none()); + + // Unknown key → miss. + let other = key_for("ws://127.0.0.1:1"); + assert!(pool.take(&other).is_none()); + } + + #[tokio::test] + async fn disabled_pool_never_hands_off() { + let pool = RealtimePool::disabled(); + let key = key_for("ws://127.0.0.1:1"); + pool.register(key.clone()); + assert_eq!(pool.warm_len(&key), 0); + assert!(pool.take(&key).is_none()); + } + + #[tokio::test] + async fn dead_warm_socket_is_discarded() { + let base = spawn_fake_openai().await; + let pool = RealtimePool::new_unspawned(test_config()); + let key = key_for(&base); + pool.register(key.clone()); + + // Build one real warm connection, then kill the upstream by dropping the + // server side: easiest is to dial, read session.created, then close our + // own rx's peer. Instead we forge "dead" via an already-closed socket: + // dial a connection and immediately send a Close from the client side so + // the server closes back, then warm it. Simpler: warm normally, then + // mark it stale by backdating warmed_at past max_idle and confirm it's + // dropped — that exercises the same discard path. + let mut conn = warm_one(&key).await.expect("warm one"); + conn.warmed_at = Instant::now() - Duration::from_secs(3600); // past max_idle + pool.insert_warm(key.clone(), conn); + assert_eq!(pool.warm_len(&key), 1); + + // take() must discard the stale socket and report a miss. + assert!(pool.take(&key).is_none()); + assert_eq!(pool.warm_len(&key), 0); + } + + #[tokio::test] + async fn background_replenisher_tops_up_registered_key() { + let base = spawn_fake_openai().await; + let pool = RealtimePool::spawn(test_config()); + let key = key_for(&base); + pool.register(key.clone()); + + // Wait (bounded) for the background task to reach the target size. + let mut warmed = 0; + for _ in 0..40 { + tokio::time::sleep(Duration::from_millis(50)).await; + warmed = pool.warm_len(&key); + if warmed >= test_config().target_size { + break; + } + } + assert_eq!( + warmed, + test_config().target_size, + "background replenisher should warm up to target_size" + ); + let handoff = pool.take(&key).expect("a warm socket should be available"); + assert_eq!(handoff.session_created.event_type, "session.created"); + } + + #[tokio::test] + async fn closed_upstream_socket_is_detected_dead() { + // A genuinely dead socket: dial the fake, read session.created, then drop + // the server by closing from our side and waiting for the close to land. + let base = spawn_fake_openai().await; + let pool = RealtimePool::new_unspawned(test_config()); + let key = key_for(&base); + pool.register(key.clone()); + + let mut conn = warm_one(&key).await.expect("warm one"); + // Close the upstream from the client side; the server echoes a close. + let _ = conn.tx.send(Message::Close(None)).await; + // Give the close a moment to arrive on rx. + tokio::time::sleep(Duration::from_millis(50)).await; + pool.insert_warm(key.clone(), conn); + + // Liveness check at take() should detect the close and discard it. + assert!(pool.take(&key).is_none()); + assert_eq!(pool.warm_len(&key), 0); + } + + #[tokio::test] + async fn broken_key_backs_off_instead_of_dialing_every_tick() { + // A key whose upstream is unreachable: every warm-up dial fails. + let pool = RealtimePool::new_unspawned(test_config()); + let key = key_for("ws://127.0.0.1:1"); // nothing listens here + pool.register(key.clone()); + + // First pass attempts dials, they all fail → key enters backoff, no warm + // sockets, one recorded failure. + pool.replenish_all().await; + assert_eq!(pool.warm_len(&key), 0); + assert_eq!(pool.backoff_failures(&key), 1); + assert!( + pool.in_backoff(&key), + "a key whose dials all failed must be in backoff" + ); + + // An immediate next pass must be SKIPPED (still in the backoff window), so + // it does NOT fire another round of dials — the failure count is unchanged. + pool.replenish_all().await; + assert_eq!( + pool.backoff_failures(&key), + 1, + "replenish during the backoff window must not re-dial the broken key" + ); + } + + #[tokio::test] + async fn healthy_key_never_enters_backoff_and_clears_after_recovery() { + let base = spawn_fake_openai().await; + let pool = RealtimePool::new_unspawned(test_config()); + let key = key_for(&base); + pool.register(key.clone()); + + // A reachable upstream: the pass succeeds, so the key is never backed off. + pool.replenish_all().await; + assert_eq!(pool.warm_len(&key), test_config().target_size); + assert_eq!(pool.backoff_failures(&key), 0); + assert!(!pool.in_backoff(&key)); + } +} diff --git a/litellm-rust/crates/ai-gateway/src/lib.rs b/litellm-rust/crates/ai-gateway/src/lib.rs new file mode 100644 index 00000000000..a2c228aeb30 --- /dev/null +++ b/litellm-rust/crates/ai-gateway/src/lib.rs @@ -0,0 +1,28 @@ +//! LiteLLM AI Gateway library. +//! +//! Two layers, split by feature so the Python `cdylib` can depend on the I/O +//! without pulling in the HTTP server: +//! +//! - [`io`]: all network I/O (OCR HTTP call, realtime WebSocket splice, the +//! pre-warmed realtime pool). Always available — no feature required. The +//! Python bridge links this for `run_ocr`. +//! - The server modules ([`auth`], [`routes`], [`state`]) and anything pulling +//! `axum` are gated behind the `server` feature, which the `litellm-ai-gateway` +//! binary turns on. The `python-config` feature additionally pulls in [`python`] +//! for the load-time config reader. + +pub mod io; + +/// GIL-activity tracking. Pure (atomics only); shared by the `server` routes and +/// the `python-config` reader, so it is available without either feature. +pub mod gil; + +#[cfg(feature = "server")] +pub mod auth; +#[cfg(feature = "server")] +pub mod routes; +#[cfg(feature = "server")] +pub mod state; + +#[cfg(feature = "python-config")] +pub mod python; diff --git a/litellm-rust/crates/ai-gateway/src/main.rs b/litellm-rust/crates/ai-gateway/src/main.rs new file mode 100644 index 00000000000..a0105b3f000 --- /dev/null +++ b/litellm-rust/crates/ai-gateway/src/main.rs @@ -0,0 +1,153 @@ +//! LiteLLM AI Gateway — a minimal Axum server fronting the Rust router. +//! +//! Flow: client → `POST /v1/realtime` → `router.realtime()` selects a deployment +//! (simple-shuffle) → `io::realtime::realtime()` invokes OpenAI. The +//! server owns transport + config; routing lives in the `router` crate. +//! +//! The binary requires the `server` feature (declared in `Cargo.toml` via +//! `required-features`), so cargo skips it unless that feature is on. Everything +//! the binary needs lives in the library (`litellm_ai_gateway`); `main` just +//! wires startup. + +use std::sync::Arc; + +use litellm_ai_gateway::io::realtime_pool::{upstream_key, PoolConfig, RealtimePool}; +use litellm_ai_gateway::routes; +use litellm_ai_gateway::state::AppState; +use litellm_core::router::{Deployment, LiteLLMParams, Router}; + +#[cfg(feature = "python-config")] +use litellm_ai_gateway::python; + +/// Bind to localhost by default so the gateway is not a public, unauthenticated +/// provider proxy out of the box. Override with `HOST` (e.g. `0.0.0.0`). +const DEFAULT_HOST: &str = "127.0.0.1"; +const DEFAULT_PORT: u16 = 4001; + +#[tokio::main] +async fn main() { + // Trim before storing so it matches the trimmed bearer token in `auth` + // (avoids a silent auth failure when the env var has surrounding whitespace). + let master_key: Option> = std::env::var("LITELLM_MASTER_KEY") + .ok() + .map(|key| key.trim().to_string()) + .filter(|key| !key.is_empty()) + .map(Arc::from); + if master_key.is_none() { + eprintln!( + "warning: LITELLM_MASTER_KEY is not set; /v1/realtime will reject all requests (fail closed)" + ); + } + + let router = Arc::new(build_router()); + + // Build the pre-warmed realtime pool and register each deployment's upstream + // so the background replenisher starts warming it. `REALTIME_POOL_SIZE=0` + // yields a disabled pool → every connect fresh-dials (original behavior). + let pool_config = PoolConfig::from_env(); + let realtime_pool = RealtimePool::spawn(pool_config); + if pool_config.enabled() { + register_deployments(&router, &realtime_pool); + eprintln!( + "realtime connection pool enabled: target {} warm sockets/key, max idle {}s", + pool_config.target_size, + pool_config.max_idle.as_secs() + ); + } else { + eprintln!( + "realtime connection pool disabled (REALTIME_POOL_SIZE=0); fresh-dialing each connect" + ); + } + + let state = AppState { + router, + master_key, + realtime_pool, + }; + + let host = std::env::var("HOST").unwrap_or_else(|_| DEFAULT_HOST.to_string()); + let port = resolve_port(); + + let listener = tokio::net::TcpListener::bind((host.as_str(), port)) + .await + .expect("failed to bind listener"); + eprintln!("litellm-ai-gateway listening on {host}:{port}"); + axum::serve(listener, routes::app(state)) + .await + .expect("server error"); +} + +/// Register every deployment's upstream key with the pool so the replenisher +/// pre-warms it. Mirrors `service::run`'s key derivation (strip `openai/`, resolve +/// api_key); deployments whose key can't be resolved are skipped (they fresh-dial +/// and surface the auth error on the request path, as before). +fn register_deployments(router: &Router, pool: &RealtimePool) { + for deployment in router.deployments() { + let params = &deployment.litellm_params; + let provider_model = params + .model + .strip_prefix("openai/") + .unwrap_or(¶ms.model); + if let Some(key) = upstream_key( + provider_model, + params.api_key.as_deref(), + params.api_base.as_deref(), + ) { + pool.register(key); + } + } +} + +/// Resolve `PORT`, warning (rather than silently defaulting) on an invalid value. +fn resolve_port() -> u16 { + match std::env::var("PORT") { + Ok(raw) => raw.parse().unwrap_or_else(|_| { + eprintln!("warning: PORT={raw:?} is not a valid port; using {DEFAULT_PORT}"); + DEFAULT_PORT + }), + Err(_) => DEFAULT_PORT, + } +} + +/// Build the router. With the `python-config` feature and `LITELLM_CONFIG_PATH` +/// set, load the resolved `model_list` from the proxy config via the embedded +/// Python reader (load time only). Otherwise fall back to the env stand-in. +fn build_router() -> Router { + #[cfg(feature = "python-config")] + if let Ok(config_path) = std::env::var("LITELLM_CONFIG_PATH") { + match python::config::load_router_from_config(&config_path) { + Ok(router) => { + eprintln!("loaded model_list from {config_path} via python config reader"); + return router; + } + Err(err) => { + eprintln!("config load failed ({err}); falling back to env deployment"); + } + } + } + build_router_from_env() +} + +/// Build a minimal single-deployment `model_list` from the environment. +/// +/// A real deployment loads `model_list` from config; this is the minimal stand-in +/// so the gateway has one OpenAI deployment to route to. +fn build_router_from_env() -> Router { + let model = + std::env::var("OPENAI_REALTIME_MODEL").unwrap_or_else(|_| "gpt-realtime".to_string()); + let api_key = std::env::var("OPENAI_API_KEY").ok(); + if api_key.is_none() { + eprintln!( + "warning: OPENAI_API_KEY is not set; realtime requests will fail with auth errors" + ); + } + let deployment = Deployment { + model_name: model.clone(), + litellm_params: LiteLLMParams { + model, + api_key, + api_base: None, + }, + }; + Router::new(vec![deployment]) +} diff --git a/litellm-rust/crates/ai-gateway/src/python/AGENTS.md b/litellm-rust/crates/ai-gateway/src/python/AGENTS.md new file mode 100644 index 00000000000..47aa117e0b9 --- /dev/null +++ b/litellm-rust/crates/ai-gateway/src/python/AGENTS.md @@ -0,0 +1,27 @@ +# ai-gateway/src/python — Python interop (load-time only) + +Functions here embed the Python interpreter (pyo3) and take the GIL to call into +`litellm` (e.g. read the proxy `model_list`). Compiled only under the +`python-config` feature. + +## Hard rule: non-hot-path functions only + +Everything in this folder MUST run **at most once per process lifetime — at +startup / load time** (config read, warm-up). NEVER call into Python on the +request path: + +- No GIL acquisition per request, per connection, or per realtime event. +- No Python call inside a route handler, the router's hot path, or any loop that + scales with traffic. + +**Why:** the GIL serializes execution and would cap throughput; the realtime data +path must stay pure Rust. Every acquisition is recorded by `crate::gil` — poll +`GET /health/gil`, and `total_acquisitions` MUST stay flat under load. + +## How to add one + +Resolve whatever Python-derived data you need **once at boot** and hand the rest +of the gateway an owned, plain-Rust value (e.g. build a `Router` from the +resolved `model_list`). Record the acquisition via `crate::gil::record_acquisition()` +immediately before taking the GIL. If a function would need to run per request, +it does not belong here — move the work to Rust, or pre-resolve it at startup. diff --git a/litellm-rust/crates/ai-gateway/src/python/config.rs b/litellm-rust/crates/ai-gateway/src/python/config.rs new file mode 100644 index 00000000000..6ec9595469d --- /dev/null +++ b/litellm-rust/crates/ai-gateway/src/python/config.rs @@ -0,0 +1,39 @@ +//! Build the router by calling the Python proxy config reader (load time only). +//! +//! Embeds the interpreter via pyo3 and calls +//! `litellm.proxy.read_model_list.read_model_list`, which reuses the proxy's +//! `os.environ/` + secret-manager resolution. The GIL is taken **once at boot** +//! (and recorded in [`crate::gil`]); the realtime hot path never touches Python. +//! +//! Compiled only under the `python-config` feature. + +use litellm_core::error::CoreError; +use litellm_core::router::{Deployment, Router}; +use litellm_core::CoreResult; +use pyo3::prelude::*; + +use crate::gil; + +/// Load the router's `model_list` from `config_path` via the Python reader. +pub fn load_router_from_config(config_path: &str) -> CoreResult { + gil::record_acquisition(); + Python::with_gil(|py| { + let model_list = py + .import("litellm.proxy.read_model_list") + .and_then(|module| module.getattr("read_model_list")) + .and_then(|reader| reader.call1((config_path,))) + .map_err(|err| CoreError::Routing(format!("read_model_list failed: {err}")))?; + + let model_list_json: String = py + .import("json") + .and_then(|json| json.getattr("dumps")) + .and_then(|dumps| dumps.call1((model_list,))) + .and_then(|encoded| encoded.extract()) + .map_err(|err| CoreError::Routing(format!("serializing model_list failed: {err}")))?; + + let deployments: Vec = serde_json::from_str(&model_list_json) + .map_err(|err| CoreError::Routing(format!("parsing model_list failed: {err}")))?; + + Ok(Router::new(deployments)) + }) +} diff --git a/litellm-rust/crates/ai-gateway/src/python/mod.rs b/litellm-rust/crates/ai-gateway/src/python/mod.rs new file mode 100644 index 00000000000..a677bade676 --- /dev/null +++ b/litellm-rust/crates/ai-gateway/src/python/mod.rs @@ -0,0 +1,4 @@ +//! Python interop for the gateway. See `AGENTS.md`: **load-time / non-hot-path +//! only.** Compiled only under the `python-config` feature. + +pub mod config; diff --git a/litellm-rust/crates/ai-gateway/src/routes/AGENTS.md b/litellm-rust/crates/ai-gateway/src/routes/AGENTS.md new file mode 100644 index 00000000000..02c5f18c4f3 --- /dev/null +++ b/litellm-rust/crates/ai-gateway/src/routes/AGENTS.md @@ -0,0 +1,38 @@ +# routes/ — the route template + +Every route follows the **same shape** so the layout is predictable. The rule: + +> **Each route module exposes `pub fn router() -> Router`.** +> `routes/mod.rs::app` merges them all and applies state once. Adding a route is: +> create the module, then add one `.merge(::router())` line. + +## Default: one file +A route is a single file containing `router()` + its handler(s) (handlers stay +private). This is the norm — don't split until it hurts. +``` +pub fn router() -> Router { Router::new().route(PATH, get(handle)) } +async fn handle(...) -> impl IntoResponse { ... } +``` +`health.rs` and `gil.rs` are examples. + +## Split out `service` when there's real logic +When a route has business logic worth testing without axum, put it in a sibling +`service` (a file, or a folder if the route grows). The route file stays the +**axum surface** (router + handler + any socket/SSE adapter); `service` is plain +Rust with **no axum types**. `realtime/` is the example: +``` +realtime/ + mod.rs # axum surface: router() + handler + the WS<->events adapter + service.rs # pure logic: select deployment + call provider (no axum) — testable +``` +Split `service` further (or add `transport`, `repo`, …) only once a single file +genuinely gets hard to read. + +## Invariants +- **Auth is an extractor, not a manual call.** A handler requires auth by adding + `crate::auth::RequireMasterKey` to its arguments; it runs during extraction. + Never re-implement the check per route. +- **Handlers contain no business logic; `service` contains no axum types.** +- A route owns its paths in its own `router()`; `mod.rs` only merges. +- Cross-cutting concerns (logging, CORS, timeouts) → Tower layers in `mod.rs`, + not duplicated in handlers. diff --git a/litellm-rust/crates/ai-gateway/src/routes/gil.rs b/litellm-rust/crates/ai-gateway/src/routes/gil.rs new file mode 100644 index 00000000000..0db0c6f0b14 --- /dev/null +++ b/litellm-rust/crates/ai-gateway/src/routes/gil.rs @@ -0,0 +1,30 @@ +//! `GET /health/gil` — poll to confirm Python is only touched at load time. +//! Simple-route template: a `router()` plus its handler, in one file. + +use axum::routing::get; +use axum::{Json, Router}; +use serde::Serialize; + +use crate::gil; +use crate::state::AppState; + +/// This route's contribution to the app router. +pub fn router() -> Router { + Router::new().route("/health/gil", get(status)) +} + +#[derive(Debug, Serialize)] +struct GilStatusResponse { + gil_acquired_last_30s: bool, + total_acquisitions: u64, + seconds_since_last: Option, +} + +async fn status() -> Json { + let snapshot = gil::snapshot(); + Json(GilStatusResponse { + gil_acquired_last_30s: snapshot.acquired_last_30s, + total_acquisitions: snapshot.total_acquisitions, + seconds_since_last: snapshot.seconds_since_last, + }) +} diff --git a/litellm-rust/crates/ai-gateway/src/routes/health.rs b/litellm-rust/crates/ai-gateway/src/routes/health.rs new file mode 100644 index 00000000000..15c67fea325 --- /dev/null +++ b/litellm-rust/crates/ai-gateway/src/routes/health.rs @@ -0,0 +1,24 @@ +//! Health probes. Simple-route template: a `router()` plus its handlers, in one file. + +use axum::http::StatusCode; +use axum::routing::get; +use axum::Router; + +use crate::state::AppState; + +/// This route's contribution to the app router. +pub fn router() -> Router { + Router::new() + .route("/health/liveness", get(liveness)) + .route("/health/readiness", get(readiness)) +} + +/// The process is up. +async fn liveness() -> StatusCode { + StatusCode::OK +} + +/// The server is ready to accept traffic. +async fn readiness() -> StatusCode { + StatusCode::OK +} diff --git a/litellm-rust/crates/ai-gateway/src/routes/mod.rs b/litellm-rust/crates/ai-gateway/src/routes/mod.rs new file mode 100644 index 00000000000..c6b9573781a --- /dev/null +++ b/litellm-rust/crates/ai-gateway/src/routes/mod.rs @@ -0,0 +1,23 @@ +//! HTTP routes. +//! +//! **Template:** every route module exposes `pub fn router() -> Router` +//! that mounts its own paths; [`app`] merges them. A trivial route is a single +//! file (`health.rs`, `gil.rs`); a non-trivial one is a folder (`realtime/`) with +//! `handler` (entry) + `service` (logic) + `transport` (adapters). See AGENTS.md. + +pub mod gil; +pub mod health; +pub mod realtime; + +use axum::Router; + +use crate::state::AppState; + +/// Assemble the application router by merging every route module's `router()`. +pub fn app(state: AppState) -> Router { + Router::new() + .merge(health::router()) + .merge(gil::router()) + .merge(realtime::router()) + .with_state(state) +} diff --git a/litellm-rust/crates/ai-gateway/src/routes/realtime/README.md b/litellm-rust/crates/ai-gateway/src/routes/realtime/README.md new file mode 100644 index 00000000000..3301576bb85 --- /dev/null +++ b/litellm-rust/crates/ai-gateway/src/routes/realtime/README.md @@ -0,0 +1,87 @@ +# Realtime route (`GET /v1/realtime`) + +Proxies OpenAI's realtime WebSocket. `mod.rs` is the axum surface (handler + +socket↔events adapter); `service.rs` is the pure logic (select a deployment, then +splice client ↔ upstream). The pool itself lives in +`crates/providers/src/realtime_pool.rs`. + +## Connection pooling + +### The problem + +The gateway's realtime overhead lives **entirely in session establishment**. On each +client connect it dials a *fresh* upstream WS to OpenAI and waits for +`session.created` before it can serve. Measured at 5000 calls / 500 concurrency, the +fresh-dial session phase is **~360 ms** vs **~7 ms** direct; dial, first-audio, and +streaming add ~0. So the one lever is removing that per-connect handshake from the +critical path. + +### The idea + +Keep a few upstream OpenAI sockets **already connected and already past +`session.created`** (buffered). On a client connect, hand off a warm socket — relay +its buffered `session.created` instantly (a local `Vec::pop`, sub-millisecond) and +splice exactly as a fresh dial would. A background task keeps the pool topped up. On +a miss or dead socket we fall back to fresh-dial: the pool is a latency optimization, +never a correctness dependency. + +``` + ┌───────────────────────────────────────┐ + client connect ──────► │ routes/realtime → service::run │ + │ pool.take(key) │ + │ hit → relay buffered │ + │ session.created, then splice │ + │ miss → fresh dial (original path) │ + └───────────────┬───────────────────────┘ + │ replenish (async, concurrent) + ┌───────────────▼───────────────────────┐ + background task ─────► │ RealtimePool: per-key warm sockets │ + │ each = { ws, buffered session.created}│ + │ liveness-checked before handoff │ + └─────────────────────────────────────────┘ +``` + +A warm session is indistinguishable from a fresh one: OpenAI sends `session.created` +unprompted on connect, we pre-read exactly that one frame and relay it on handoff, +and we send nothing else on the socket before a client exists — so the client's first +`session.update` behaves identically either way. + +### Sizing + +Each warm socket serves **exactly one** session (realtime isn't multiplexed), so the +pool is sized to the **peak concurrent connects per instance**, not total live +connections: + +``` +REALTIME_POOL_SIZE ≈ peak_concurrency / instance_count +``` + +e.g. 500 concurrency over 10 instances → ~50–64 per instance. The replenisher dials +the missing sockets **concurrently**, so a drained pool refills in ~one handshake +window and keeps supply close to the connect rate. Over-provisioning just burns idle +upstream sockets, which is why warm sockets are short-lived +(`REALTIME_POOL_MAX_IDLE_SECS`). + +### Config + +| env | default | meaning | +| ----------------------------- | ------- | --------------------------------------------------------------- | +| `REALTIME_POOL_SIZE` | `4` | target warm sockets per key. `0` disables pooling (fresh-dial). | +| `REALTIME_POOL_MAX_IDLE_SECS` | `30` | max time a warm socket sits before it's closed and replaced. | + +### Notes + +- **Miss / dead socket → fresh dial.** Burst beyond warm supply, or a socket that + died, never blocks or fails — it falls back to the original path. The pool can only + make a connect faster, never slower or more fragile. +- **Auth scope.** The pool key includes `api_key`, so a warm socket is only handed to + a request resolving to the same key — no cross-tenant reuse. +- **Idle billing.** Warm sockets are liveness-checked at handoff and capped at + `REALTIME_POOL_MAX_IDLE_SECS` to bound idle billing and dodge OpenAI's idle timeout. +- **Replenish backoff.** If a key's warm-up dials all fail (invalid credentials, an + unreachable upstream), the replenisher puts that key into exponential backoff + (500 ms → 30 s cap) instead of re-dialing it every tick. This bounds connection + attempts against a broken key so it can't exhaust upstream rate limits and degrade + valid cold-path traffic; the backoff resets the moment a dial succeeds. + +Benchmarks and repro: `../../benchmarks/realtime/README.md`. diff --git a/litellm-rust/crates/ai-gateway/src/routes/realtime/mod.rs b/litellm-rust/crates/ai-gateway/src/routes/realtime/mod.rs new file mode 100644 index 00000000000..658fa8d2dbe --- /dev/null +++ b/litellm-rust/crates/ai-gateway/src/routes/realtime/mod.rs @@ -0,0 +1,88 @@ +//! `GET /v1/realtime` (WebSocket). +//! +//! This file is the **axum surface**: `router()`, the handler, and the small +//! socket↔events adapter. The pure logic (no axum) lives in [`service`]. Auth is +//! the `RequireMasterKey` extractor, so the handler stays thin. + +mod service; + +use std::sync::Arc; + +use crate::io::realtime_pool::RealtimePool; +use axum::extract::ws::{Message, WebSocket, WebSocketUpgrade}; +use axum::extract::{Query, State}; +use axum::http::StatusCode; +use axum::response::Response; +use axum::routing::get; +use axum::Router; +use futures_util::{SinkExt, StreamExt}; +use litellm_core::realtime::types::RealtimeEvent; +use litellm_core::router::Router as ModelRouter; +use serde::Deserialize; + +use crate::auth::RequireMasterKey; +use crate::state::AppState; + +/// This route's contribution to the app router. +pub fn router() -> Router { + Router::new().route("/v1/realtime", get(handle)) +} + +#[derive(Debug, Deserialize)] +struct RealtimeQuery { + model: String, +} + +/// Auth runs via the `RequireMasterKey` extractor. We validate the model BEFORE +/// the upgrade so failures are clean HTTP (400/404), not a socket that opens then +/// closes, then hand the socket to `bridge`. +async fn handle( + _auth: RequireMasterKey, + ws: WebSocketUpgrade, + State(state): State, + Query(query): Query, +) -> Result { + if query.model.trim().is_empty() { + return Err(( + StatusCode::BAD_REQUEST, + "missing 'model' query param".to_string(), + )); + } + if !state.router.has_deployment(&query.model) { + return Err(( + StatusCode::NOT_FOUND, + format!("no deployment for model '{}'", query.model), + )); + } + + let router = state.router.clone(); + let pool = state.realtime_pool.clone(); + let model = query.model; + Ok(ws.on_upgrade(move |socket| bridge(socket, router, pool, model))) +} + +/// Adapt the axum socket (text frames) to the typed-event `Stream`/`Sink` the +/// service wants, keeping axum types out of `service`. +async fn bridge( + socket: WebSocket, + router: Arc, + pool: Arc, + model: String, +) { + let (ws_sink, ws_stream) = socket.split(); + + let client_in = ws_stream.filter_map(|message| async move { + match message { + Ok(Message::Text(text)) => serde_json::from_str::(&text).ok(), + _ => None, + } + }); + let client_out = ws_sink.with(|event: RealtimeEvent| async move { + Ok::(Message::Text( + serde_json::to_string(&event).unwrap_or_default(), + )) + }); + + futures_util::pin_mut!(client_in, client_out); + let _ = service::run(&router, &pool, &model, None, client_in, client_out).await; +} diff --git a/litellm-rust/crates/ai-gateway/src/routes/realtime/service.rs b/litellm-rust/crates/ai-gateway/src/routes/realtime/service.rs new file mode 100644 index 00000000000..c78ca8df446 --- /dev/null +++ b/litellm-rust/crates/ai-gateway/src/routes/realtime/service.rs @@ -0,0 +1,76 @@ +//! Business logic: select a deployment with the (pure) core router, then call the +//! provider splice. The seam between `core::router` (selection only) and +//! `io` (the actual WebSocket I/O). +//! +//! On connect we try a pre-warmed upstream from the pool (handshake already paid, +//! `session.created` buffered) and relay it instantly. On a pool miss or dead warm +//! socket we fresh-dial exactly as before — the pool is never on the critical path +//! for correctness, only latency. + +use std::time::Duration; + +use crate::io::realtime_pool::{upstream_key, RealtimePool}; +use futures_util::{Sink, Stream}; +use litellm_core::error::CoreError; +use litellm_core::realtime::types::RealtimeEvent; +use litellm_core::router::Router; +use litellm_core::CoreResult; + +/// Select a deployment for `model` and splice the client stream to the provider. +/// +/// `pool` supplies a pre-warmed upstream when one is available; otherwise we +/// fresh-dial. A disabled pool always misses, so this collapses to the original +/// fresh-dial behavior. +pub async fn run( + router: &Router, + pool: &RealtimePool, + model: &str, + idle_timeout: Option, + client_in: In, + client_out: Out, +) -> CoreResult<()> +where + In: Stream + Unpin + Send, + Out: Sink + Unpin + Send, + >::Error: std::fmt::Display, +{ + let deployment = router.get_available_deployment(model).ok_or_else(|| { + CoreError::Routing(format!("no deployment available for model '{model}'")) + })?; + let params = &deployment.litellm_params; + // Strip a leading `openai/` so the OpenAI-only realtime fn gets the bare model. + let provider_model = params + .model + .strip_prefix("openai/") + .unwrap_or(¶ms.model); + + // Warm path: take a pooled upstream (handshake already paid) and relay its + // buffered session.created immediately. On miss/dead socket fall through. + if let Some(key) = upstream_key( + provider_model, + params.api_key.as_deref(), + params.api_base.as_deref(), + ) { + if let Some(handoff) = pool.take(&key) { + return crate::io::realtime::realtime_warm( + provider_model, + handoff, + idle_timeout, + client_in, + client_out, + ) + .await; + } + } + + // Cold path: fresh dial (the original behavior). + crate::io::realtime::realtime( + provider_model, + params.api_key.as_deref(), + params.api_base.as_deref(), + idle_timeout, + client_in, + client_out, + ) + .await +} diff --git a/litellm-rust/crates/ai-gateway/src/state.rs b/litellm-rust/crates/ai-gateway/src/state.rs new file mode 100644 index 00000000000..c7ba92d9cbc --- /dev/null +++ b/litellm-rust/crates/ai-gateway/src/state.rs @@ -0,0 +1,17 @@ +use std::sync::Arc; + +use crate::io::realtime_pool::RealtimePool; +use litellm_core::router::Router; + +/// Shared application state handed to every route handler. +#[derive(Clone)] +pub struct AppState { + pub router: Arc, + /// The gateway master key. Any caller presenting it as a bearer token may + /// invoke the gateway. `None` → auth not configured (routes fail closed). + pub master_key: Option>, + /// Pre-warmed upstream realtime connection pool. Disabled + /// (`RealtimePool::disabled()`) when `REALTIME_POOL_SIZE=0`, in which case + /// every realtime connect fresh-dials exactly as before. + pub realtime_pool: Arc, +} diff --git a/litellm-rust/crates/core/AGENTS.md b/litellm-rust/crates/core/AGENTS.md new file mode 100644 index 00000000000..8740dccaf01 --- /dev/null +++ b/litellm-rust/crates/core/AGENTS.md @@ -0,0 +1,3 @@ +litellm-core is the PURE translation layer — types, route contracts (traits), provider transforms (modules under `providers/`), and the router. No network, no I/O, no env reads. + +Routes (ocr, realtime) and providers (mistral, openai) are modules, not crates. diff --git a/litellm-rust/crates/core/CLAUDE.md b/litellm-rust/crates/core/CLAUDE.md new file mode 100644 index 00000000000..20873878967 --- /dev/null +++ b/litellm-rust/crates/core/CLAUDE.md @@ -0,0 +1,47 @@ +# CLAUDE.md + +Rules for `litellm-rust/crates/core`. + +## Responsibility + +`core` owns shared data types, typed errors, and deterministic helper contracts. +It must stay pure and host-independent. + +Allowed: +- Shared request/response structs. +- Typed errors with stable, non-sensitive messages. +- Deterministic validation helpers. +- Serialization helpers that intentionally mirror Python output shape. +- Route templates that match Python base config responsibilities, such as + `ocr::transformation::OcrProviderConfig`. + +Not allowed: +- Network, filesystem, database, cache, or environment access. +- Secret reads or auth/header construction. +- Logging callbacks, tracing spans, spend writes, or customer callbacks. +- Provider-specific branching that belongs in `providers`. +- Panics for user/provider-controlled input. + +## Typed Contracts (core rule) + +Trait and function boundaries MUST be strongly typed. No stringly-typed JSON +(`&str` / `String` / `Vec` / bare `serde_json::Value`) as a transform +input or output. Parse wire bytes into typed structs/enums at the host edge; +`core` and `providers` operate only on those types (e.g. `RealtimeEvent`, +`RealtimeTransformResult`, `OcrRequestData`). A `type`-style discriminator is a +typed field on a struct, not a raw string threaded through the API. + +## Structure + +Use route names directly under `src/`: `ocr`, future `messages`, +`chat_completions`, `embeddings`, and similar top-level LiteLLM calls. Do not +invent broad names like `engine` for route contracts. + +## Parity Rules + +- Every shared type used by a provider transform needs unit tests for + serialization shape. +- If Python parity requires always emitting a `null` field instead of omitting + it, document that in code and pin it with a test. +- Error enums should preserve enough detail for Python/HTTP hosts to map errors + consistently without exposing document contents or upstream bodies. diff --git a/litellm-rust/crates/core/Cargo.toml b/litellm-rust/crates/core/Cargo.toml new file mode 100644 index 00000000000..1881bcfa602 --- /dev/null +++ b/litellm-rust/crates/core/Cargo.toml @@ -0,0 +1,12 @@ +[package] +name = "litellm-core" +version = "0.1.0" +edition.workspace = true +license.workspace = true +repository.workspace = true + +[dependencies] +rand.workspace = true +serde.workspace = true +serde_json.workspace = true +thiserror.workspace = true diff --git a/litellm-rust/crates/core/src/error.rs b/litellm-rust/crates/core/src/error.rs new file mode 100644 index 00000000000..9b29260cca4 --- /dev/null +++ b/litellm-rust/crates/core/src/error.rs @@ -0,0 +1,35 @@ +use thiserror::Error; + +pub type CoreResult = Result; + +#[derive(Debug, Error, PartialEq, Eq)] +pub enum CoreError { + #[error("expected {expected}, got {actual}")] + InvalidType { + expected: &'static str, + actual: &'static str, + }, + #[error("missing required field: {0}")] + MissingField(&'static str), + #[error("invalid response: {0}")] + InvalidResponse(String), + #[error("{0}")] + Auth(String), + #[error("OCR request failed with status {status}: {body}")] + Http { status: u16, body: String }, + #[error("OCR network error: {0}")] + Network(String), + #[error("routing error: {0}")] + Routing(String), +} + +pub fn json_type_name(value: &serde_json::Value) -> &'static str { + match value { + serde_json::Value::Null => "null", + serde_json::Value::Bool(_) => "bool", + serde_json::Value::Number(_) => "number", + serde_json::Value::String(_) => "string", + serde_json::Value::Array(_) => "array", + serde_json::Value::Object(_) => "object", + } +} diff --git a/litellm-rust/crates/core/src/lib.rs b/litellm-rust/crates/core/src/lib.rs new file mode 100644 index 00000000000..2ac479cc725 --- /dev/null +++ b/litellm-rust/crates/core/src/lib.rs @@ -0,0 +1,7 @@ +pub mod error; +pub mod ocr; +pub mod providers; +pub mod realtime; +pub mod router; + +pub use error::{CoreError, CoreResult}; diff --git a/litellm-rust/crates/core/src/ocr/mod.rs b/litellm-rust/crates/core/src/ocr/mod.rs new file mode 100644 index 00000000000..ec2fbb969a6 --- /dev/null +++ b/litellm-rust/crates/core/src/ocr/mod.rs @@ -0,0 +1,2 @@ +pub mod transformation; +pub mod types; diff --git a/litellm-rust/crates/core/src/ocr/transformation.rs b/litellm-rust/crates/core/src/ocr/transformation.rs new file mode 100644 index 00000000000..7353d9d22c4 --- /dev/null +++ b/litellm-rust/crates/core/src/ocr/transformation.rs @@ -0,0 +1,32 @@ +use serde_json::{Map, Value}; + +use crate::CoreResult; + +use super::types::{OcrRequestData, OcrResponseData}; + +pub trait OcrProviderConfig { + fn supported_ocr_params(&self) -> &'static [&'static str]; + + fn map_ocr_params(&self, non_default_params: &Map) -> Map { + let mut mapped_params = Map::new(); + for (param, value) in non_default_params { + if self.supported_ocr_params().contains(¶m.as_str()) { + mapped_params.insert(param.clone(), value.clone()); + } + } + mapped_params + } + + fn transform_ocr_request( + &self, + model: &str, + document: Value, + optional_params: Map, + ) -> CoreResult; + + fn transform_ocr_response( + &self, + model: &str, + response_json: Value, + ) -> CoreResult; +} diff --git a/litellm-rust/crates/core/src/ocr/types.rs b/litellm-rust/crates/core/src/ocr/types.rs new file mode 100644 index 00000000000..1a72b8f1d66 --- /dev/null +++ b/litellm-rust/crates/core/src/ocr/types.rs @@ -0,0 +1,29 @@ +use serde::{Deserialize, Serialize}; +use serde_json::Value; + +#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)] +pub struct OcrRequestData { + pub data: Value, + pub files: Option, +} + +#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)] +pub struct OcrResponseData { + pub pages: Vec, + pub model: String, + pub document_annotation: Option, + pub usage_info: Option, + pub object: String, +} + +impl OcrResponseData { + pub fn into_json(self) -> Value { + serde_json::json!({ + "pages": self.pages, + "model": self.model, + "document_annotation": self.document_annotation, + "usage_info": self.usage_info, + "object": self.object, + }) + } +} diff --git a/litellm-rust/crates/core/src/providers/mistral/mod.rs b/litellm-rust/crates/core/src/providers/mistral/mod.rs new file mode 100644 index 00000000000..3621ff6a2fd --- /dev/null +++ b/litellm-rust/crates/core/src/providers/mistral/mod.rs @@ -0,0 +1 @@ +pub mod ocr; diff --git a/litellm-rust/crates/core/src/providers/mistral/ocr/mod.rs b/litellm-rust/crates/core/src/providers/mistral/ocr/mod.rs new file mode 100644 index 00000000000..f239b6921fa --- /dev/null +++ b/litellm-rust/crates/core/src/providers/mistral/ocr/mod.rs @@ -0,0 +1 @@ +pub mod transformation; diff --git a/litellm-rust/crates/core/src/providers/mistral/ocr/transformation.rs b/litellm-rust/crates/core/src/providers/mistral/ocr/transformation.rs new file mode 100644 index 00000000000..d5155991448 --- /dev/null +++ b/litellm-rust/crates/core/src/providers/mistral/ocr/transformation.rs @@ -0,0 +1,292 @@ +use crate::error::{json_type_name, CoreError, CoreResult}; +use crate::ocr::transformation::OcrProviderConfig; +use crate::ocr::types::{OcrRequestData, OcrResponseData}; +use serde_json::{Map, Value}; + +const SUPPORTED_OCR_PARAMS: &[&str] = &[ + "pages", + "include_image_base64", + "image_limit", + "image_min_size", + "bbox_annotation_format", + "document_annotation_format", + "document_annotation_prompt", + "extract_header", + "extract_footer", + "table_format", + "confidence_scores_granularity", + "id", +]; + +/// Default Mistral API base, used when the caller does not override `api_base`. +pub const MISTRAL_DEFAULT_API_BASE: &str = "https://api.mistral.ai/v1"; + +/// Environment variable holding the Mistral API key. +pub const MISTRAL_API_KEY_ENV: &str = "MISTRAL_API_KEY"; + +/// Error message raised when no Mistral API key can be resolved. +pub const MISSING_KEY_MESSAGE: &str = "Missing Mistral API Key - A call is being made to Mistral but no key is set either in the environment variables or via params"; + +/// Build the complete OCR endpoint URL, de-duplicating a trailing `/v1`. +/// +/// Blank/whitespace `api_base` is treated as absent (guard at resolution time). +pub fn complete_url(api_base: Option<&str>) -> String { + let base = api_base + .map(str::trim) + .filter(|base| !base.is_empty()) + .unwrap_or(MISTRAL_DEFAULT_API_BASE) + .trim_end_matches('/'); + + if base.ends_with("/v1") { + format!("{base}/ocr") + } else { + format!("{base}/v1/ocr") + } +} + +/// Resolve the Mistral API key from the explicit param or the environment. +/// +/// Blank/whitespace values are treated as absent. Returns `CoreError::Auth` +/// when no usable key is available. +/// +/// Note: the env fallback only reads the process environment. Secret-manager +/// backends (AWS/Azure/GCP/Vault) are resolved on the Python side and passed in +/// via `api_key`; this fallback is a last resort for direct/standalone use. +pub fn resolve_api_key( + api_key: Option<&str>, + env_lookup: &dyn Fn(&str) -> Option, +) -> CoreResult { + api_key + .map(str::trim) + .filter(|key| !key.is_empty()) + .map(str::to_string) + .or_else(|| env_lookup(MISTRAL_API_KEY_ENV).filter(|key| !key.trim().is_empty())) + .ok_or_else(|| CoreError::Auth(MISSING_KEY_MESSAGE.to_string())) +} + +pub struct MistralOcrConfig; + +pub const MISTRAL_OCR_CONFIG: MistralOcrConfig = MistralOcrConfig; + +impl OcrProviderConfig for MistralOcrConfig { + fn supported_ocr_params(&self) -> &'static [&'static str] { + SUPPORTED_OCR_PARAMS + } + + fn transform_ocr_request( + &self, + model: &str, + document: Value, + optional_params: Map, + ) -> CoreResult { + if !document.is_object() { + return Err(CoreError::InvalidType { + expected: "object", + actual: json_type_name(&document), + }); + } + + let mut data = Map::new(); + data.insert("model".to_string(), Value::String(model.to_string())); + data.insert("document".to_string(), document); + for (param, value) in optional_params { + data.insert(param, value); + } + + Ok(OcrRequestData { + data: Value::Object(data), + files: None, + }) + } + + fn transform_ocr_response( + &self, + model: &str, + response_json: Value, + ) -> CoreResult { + let response_object = response_json + .as_object() + .ok_or_else(|| CoreError::InvalidType { + expected: "object", + actual: json_type_name(&response_json), + })?; + + let pages = response_object + .get("pages") + .and_then(Value::as_array) + .cloned() + .unwrap_or_default(); + let model = response_object + .get("model") + .and_then(Value::as_str) + .unwrap_or(model) + .to_string(); + let document_annotation = response_object.get("document_annotation").cloned(); + let usage_info = response_object.get("usage_info").cloned(); + + Ok(OcrResponseData { + pages, + model, + document_annotation, + usage_info, + object: "ocr".to_string(), + }) + } +} + +pub fn supported_ocr_params() -> &'static [&'static str] { + MISTRAL_OCR_CONFIG.supported_ocr_params() +} + +pub fn map_ocr_params(non_default_params: &Map) -> Map { + MISTRAL_OCR_CONFIG.map_ocr_params(non_default_params) +} + +pub fn transform_ocr_request( + model: &str, + document: Value, + optional_params: Map, +) -> CoreResult { + MISTRAL_OCR_CONFIG.transform_ocr_request(model, document, optional_params) +} + +pub fn transform_ocr_response(model: &str, response_json: Value) -> CoreResult { + MISTRAL_OCR_CONFIG.transform_ocr_response(model, response_json) +} + +#[cfg(test)] +mod tests { + use super::*; + use serde_json::json; + + #[test] + fn supported_params_match_python_mistral_ocr_config() { + assert_eq!( + supported_ocr_params(), + &[ + "pages", + "include_image_base64", + "image_limit", + "image_min_size", + "bbox_annotation_format", + "document_annotation_format", + "document_annotation_prompt", + "extract_header", + "extract_footer", + "table_format", + "confidence_scores_granularity", + "id", + ] + ); + } + + #[test] + fn map_ocr_params_drops_unknown_params() { + let params = json!({ + "extract_header": true, + "unsupported_param": "value", + "pages": [0, 1] + }); + let mapped = map_ocr_params(params.as_object().unwrap()); + + assert_eq!(mapped.get("extract_header"), Some(&json!(true))); + assert_eq!(mapped.get("pages"), Some(&json!([0, 1]))); + assert!(!mapped.contains_key("unsupported_param")); + } + + #[test] + fn transform_ocr_request_builds_mistral_body() { + let document = json!({ + "type": "document_url", + "document_url": "https://example.com/doc.pdf" + }); + let optional_params = json!({ + "include_image_base64": true, + "table_format": "html" + }) + .as_object() + .unwrap() + .clone(); + + let result = transform_ocr_request("mistral-ocr-latest", document.clone(), optional_params) + .expect("request should transform"); + + assert_eq!( + result.data, + json!({ + "model": "mistral-ocr-latest", + "document": document, + "include_image_base64": true, + "table_format": "html" + }) + ); + assert_eq!(result.files, None); + } + + #[test] + fn transform_ocr_request_rejects_non_object_document() { + let err = transform_ocr_request("mistral-ocr-latest", json!("bad"), Map::new()) + .expect_err("string document should be rejected"); + + assert_eq!( + err, + CoreError::InvalidType { + expected: "object", + actual: "string", + } + ); + } + + #[test] + fn transform_ocr_response_normalizes_mistral_json() { + let response = json!({ + "pages": [{"index": 0, "markdown": "hello"}], + "model": "mistral-ocr-2505-completion", + "document_annotation": null, + "usage_info": {"pages_processed": 1} + }); + + let result = transform_ocr_response("mistral-ocr-latest", response) + .expect("response should transform"); + + assert_eq!(result.pages, vec![json!({"index": 0, "markdown": "hello"})]); + assert_eq!(result.model, "mistral-ocr-2505-completion"); + assert_eq!(result.document_annotation, Some(Value::Null)); + assert_eq!(result.usage_info, Some(json!({"pages_processed": 1}))); + assert_eq!(result.object, "ocr"); + } + + #[test] + fn complete_url_defaults_and_dedupes_v1() { + assert_eq!(complete_url(None), "https://api.mistral.ai/v1/ocr"); + assert_eq!(complete_url(Some(" ")), "https://api.mistral.ai/v1/ocr"); + assert_eq!( + complete_url(Some("https://proxy.internal")), + "https://proxy.internal/v1/ocr" + ); + assert_eq!( + complete_url(Some("https://proxy.internal/v1/")), + "https://proxy.internal/v1/ocr" + ); + } + + #[test] + fn resolve_api_key_prefers_param_then_env() { + let no_env = |_: &str| None; + assert_eq!( + resolve_api_key(Some("sk-param"), &no_env).unwrap(), + "sk-param" + ); + + let with_env = |key: &str| (key == MISTRAL_API_KEY_ENV).then(|| "sk-env".to_string()); + assert_eq!(resolve_api_key(None, &with_env).unwrap(), "sk-env"); + // Blank param falls through to the environment. + assert_eq!(resolve_api_key(Some(" "), &with_env).unwrap(), "sk-env"); + } + + #[test] + fn resolve_api_key_errors_when_absent() { + let err = resolve_api_key(None, &|_| None).expect_err("missing key should error"); + assert_eq!(err, CoreError::Auth(MISSING_KEY_MESSAGE.to_string())); + } +} diff --git a/litellm-rust/crates/core/src/providers/mod.rs b/litellm-rust/crates/core/src/providers/mod.rs new file mode 100644 index 00000000000..42207f0de0a --- /dev/null +++ b/litellm-rust/crates/core/src/providers/mod.rs @@ -0,0 +1,2 @@ +pub mod mistral; +pub mod openai; diff --git a/litellm-rust/crates/core/src/providers/openai/mod.rs b/litellm-rust/crates/core/src/providers/openai/mod.rs new file mode 100644 index 00000000000..403e32975cf --- /dev/null +++ b/litellm-rust/crates/core/src/providers/openai/mod.rs @@ -0,0 +1 @@ +pub mod realtime; diff --git a/litellm-rust/crates/core/src/providers/openai/realtime/mod.rs b/litellm-rust/crates/core/src/providers/openai/realtime/mod.rs new file mode 100644 index 00000000000..f239b6921fa --- /dev/null +++ b/litellm-rust/crates/core/src/providers/openai/realtime/mod.rs @@ -0,0 +1 @@ +pub mod transformation; diff --git a/litellm-rust/crates/core/src/providers/openai/realtime/transformation.rs b/litellm-rust/crates/core/src/providers/openai/realtime/transformation.rs new file mode 100644 index 00000000000..626e4014ff9 --- /dev/null +++ b/litellm-rust/crates/core/src/providers/openai/realtime/transformation.rs @@ -0,0 +1,189 @@ +use crate::realtime::transformation::RealtimeProviderConfig; +use crate::realtime::types::{RealtimeEvent, RealtimeTransformResult}; +use crate::CoreResult; + +/// Default OpenAI API base, used when the caller does not override `api_base`. +pub const OPENAI_REALTIME_DEFAULT_API_BASE: &str = "https://api.openai.com"; + +/// Path appended to the resolved host base to reach the realtime endpoint. +pub const OPENAI_REALTIME_PATH: &str = "/v1/realtime"; + +/// Percent-encode a query value, escaping any char outside the RFC 3986 +/// unreserved set (`A-Za-z0-9-._~`). Keeps us dependency-free; common realtime +/// model slugs have no special chars, but this stays correct for the rest. +fn percent_encode(value: &str) -> String { + let mut encoded = String::with_capacity(value.len()); + for byte in value.bytes() { + let unreserved = byte.is_ascii_alphanumeric() || matches!(byte, b'-' | b'.' | b'_' | b'~'); + if unreserved { + encoded.push(byte as char); + } else { + encoded.push('%'); + encoded.push_str(&format!("{byte:02X}")); + } + } + encoded +} + +/// Build the realtime WebSocket URL, porting Python's `OpenAIRealtime._construct_url`. +/// +/// Blank/whitespace `api_base` is treated as absent (guard at resolution time), +/// falling back to the default. The scheme is swapped to its WebSocket +/// equivalent (`https://`→`wss://`, `http://`→`ws://`); bases already using +/// `ws`/`wss` are left untouched. A bare host or unrecognized scheme defaults to +/// secure `wss://` so we never hand a scheme-less URL to the connector (this is +/// a deliberate hardening over Python's `_construct_url`, which would emit a +/// scheme-less URL here). A trailing `/` is trimmed before the path and +/// `?model=` are appended. +pub fn complete_url(api_base: Option<&str>, model: &str) -> String { + let base = api_base + .map(str::trim) + .filter(|base| !base.is_empty()) + .unwrap_or(OPENAI_REALTIME_DEFAULT_API_BASE); + + let base = if let Some(rest) = base.strip_prefix("https://") { + format!("wss://{rest}") + } else if let Some(rest) = base.strip_prefix("http://") { + format!("ws://{rest}") + } else if base.starts_with("wss://") || base.starts_with("ws://") { + base.to_string() + } else { + format!("wss://{base}") + }; + + let base = base.trim_end_matches('/'); + + format!( + "{base}{OPENAI_REALTIME_PATH}?model={}", + percent_encode(model) + ) +} + +pub struct OpenAiRealtimeConfig; + +pub const OPENAI_REALTIME_CONFIG: OpenAiRealtimeConfig = OpenAiRealtimeConfig; + +impl RealtimeProviderConfig for OpenAiRealtimeConfig { + fn complete_url(&self, api_base: Option<&str>, model: &str) -> String { + complete_url(api_base, model) + } + + fn transform_realtime_request( + &self, + event: &RealtimeEvent, + _model: &str, + ) -> CoreResult { + Ok(RealtimeTransformResult::passthrough(event.clone())) + } + + fn transform_realtime_response( + &self, + event: &RealtimeEvent, + _model: &str, + ) -> CoreResult { + Ok(RealtimeTransformResult::passthrough(event.clone())) + } +} + +pub fn transform_realtime_request( + event: &RealtimeEvent, + model: &str, +) -> CoreResult { + OPENAI_REALTIME_CONFIG.transform_realtime_request(event, model) +} + +pub fn transform_realtime_response( + event: &RealtimeEvent, + model: &str, +) -> CoreResult { + OPENAI_REALTIME_CONFIG.transform_realtime_response(event, model) +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn complete_url_defaults_to_openai_wss() { + assert_eq!( + complete_url(None, "gpt-4o-realtime-preview"), + "wss://api.openai.com/v1/realtime?model=gpt-4o-realtime-preview" + ); + } + + #[test] + fn complete_url_blank_base_uses_default() { + assert_eq!( + complete_url(Some(" "), "gpt-4o-realtime-preview"), + "wss://api.openai.com/v1/realtime?model=gpt-4o-realtime-preview" + ); + } + + #[test] + fn complete_url_swaps_http_to_ws() { + assert_eq!( + complete_url(Some("http://localhost:8080"), "gpt-4o-realtime-preview"), + "ws://localhost:8080/v1/realtime?model=gpt-4o-realtime-preview" + ); + } + + #[test] + fn complete_url_dedupes_trailing_slash() { + assert_eq!( + complete_url(Some("https://api.openai.com/"), "gpt-4o-realtime-preview"), + "wss://api.openai.com/v1/realtime?model=gpt-4o-realtime-preview" + ); + } + + #[test] + fn complete_url_custom_base() { + assert_eq!( + complete_url(Some("https://oai.azure.example"), "gpt-4o-realtime-preview"), + "wss://oai.azure.example/v1/realtime?model=gpt-4o-realtime-preview" + ); + } + + #[test] + fn complete_url_preserves_existing_wss_scheme() { + assert_eq!( + complete_url(Some("wss://api.openai.com"), "gpt-realtime"), + "wss://api.openai.com/v1/realtime?model=gpt-realtime" + ); + } + + #[test] + fn complete_url_bare_host_defaults_to_wss() { + assert_eq!( + complete_url(Some("api.openai.com"), "gpt-realtime"), + "wss://api.openai.com/v1/realtime?model=gpt-realtime" + ); + } + + #[test] + fn complete_url_percent_encodes_model_space() { + assert_eq!( + complete_url(None, "gpt 4o"), + "wss://api.openai.com/v1/realtime?model=gpt%204o" + ); + } + + #[test] + fn transform_realtime_request_passthrough_preserves_event() { + let event: RealtimeEvent = + serde_json::from_str(r#"{"type":"session.update","session":{"voice":"alloy"}}"#) + .expect("valid event"); + let result = + transform_realtime_request(&event, "gpt-realtime").expect("passthrough is infallible"); + assert_eq!(result.events, vec![event]); + } + + #[test] + fn transform_realtime_response_passthrough_preserves_event() { + let event: RealtimeEvent = + serde_json::from_str(r#"{"type":"response.output_audio.delta","delta":"abc=="}"#) + .expect("valid event"); + let result = + transform_realtime_response(&event, "gpt-realtime").expect("passthrough is infallible"); + assert_eq!(result.events, vec![event]); + } +} diff --git a/litellm-rust/crates/core/src/realtime/mod.rs b/litellm-rust/crates/core/src/realtime/mod.rs new file mode 100644 index 00000000000..ec2fbb969a6 --- /dev/null +++ b/litellm-rust/crates/core/src/realtime/mod.rs @@ -0,0 +1,2 @@ +pub mod transformation; +pub mod types; diff --git a/litellm-rust/crates/core/src/realtime/transformation.rs b/litellm-rust/crates/core/src/realtime/transformation.rs new file mode 100644 index 00000000000..a4baa27a6c2 --- /dev/null +++ b/litellm-rust/crates/core/src/realtime/transformation.rs @@ -0,0 +1,22 @@ +use crate::realtime::types::{RealtimeEvent, RealtimeTransformResult}; +use crate::CoreResult; + +pub trait RealtimeProviderConfig { + /// Build the upstream WebSocket URL (e.g. `wss://api.openai.com/v1/realtime?model=…`). + /// Pure string construction only — no network, no env. + fn complete_url(&self, api_base: Option<&str>, model: &str) -> String; + + /// Transform a client → backend event before it is forwarded upstream. + fn transform_realtime_request( + &self, + event: &RealtimeEvent, + model: &str, + ) -> CoreResult; + + /// Transform a backend → client event before it is forwarded downstream. + fn transform_realtime_response( + &self, + event: &RealtimeEvent, + model: &str, + ) -> CoreResult; +} diff --git a/litellm-rust/crates/core/src/realtime/types.rs b/litellm-rust/crates/core/src/realtime/types.rs new file mode 100644 index 00000000000..3b59224b6e9 --- /dev/null +++ b/litellm-rust/crates/core/src/realtime/types.rs @@ -0,0 +1,60 @@ +use serde::{Deserialize, Serialize}; +use serde_json::{Map, Value}; + +/// A single realtime event exchanged over the WebSocket. +/// +/// The `type` discriminator is a typed field; the remaining fields are +/// preserved losslessly in `data` so a transform can pass an event through, or +/// inspect/modify specific fields, without enumerating every event variant. +/// Wire (de)serialization happens at the host edge — `core`/`providers` operate +/// only on this typed form. +#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)] +pub struct RealtimeEvent { + #[serde(rename = "type")] + pub event_type: String, + #[serde(flatten)] + pub data: Map, +} + +/// One or more typed events produced by a realtime transform. +#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)] +pub struct RealtimeTransformResult { + pub events: Vec, +} + +impl RealtimeTransformResult { + /// Forward a single event unchanged (the OpenAI baseline). + pub fn passthrough(event: RealtimeEvent) -> Self { + Self { + events: vec![event], + } + } +} + +#[cfg(test)] +mod tests { + use super::*; + + fn event(raw: &str) -> RealtimeEvent { + serde_json::from_str(raw).expect("valid event json") + } + + #[test] + fn realtime_event_round_trips_type_and_extra_fields() { + let raw = r#"{"type":"response.output_text.delta","delta":"hi","response_id":"r1"}"#; + let parsed = event(raw); + assert_eq!(parsed.event_type, "response.output_text.delta"); + assert_eq!(parsed.data.get("delta"), Some(&Value::String("hi".into()))); + // Re-serializing yields a semantically-equal event (key order may differ). + let reparsed: RealtimeEvent = + serde_json::from_str(&serde_json::to_string(&parsed).unwrap()).unwrap(); + assert_eq!(parsed, reparsed); + } + + #[test] + fn passthrough_produces_single_element_vec() { + let parsed = event(r#"{"type":"session.update"}"#); + let result = RealtimeTransformResult::passthrough(parsed.clone()); + assert_eq!(result.events, vec![parsed]); + } +} diff --git a/litellm-rust/crates/core/src/router/deployment.rs b/litellm-rust/crates/core/src/router/deployment.rs new file mode 100644 index 00000000000..1ee88e682a3 --- /dev/null +++ b/litellm-rust/crates/core/src/router/deployment.rs @@ -0,0 +1,44 @@ +//! `model_list` data types, mirroring Python's deployment dict. Deserialize-ready +//! so a deployment can be loaded straight from the proxy config's `model_list`. + +use serde::Deserialize; + +/// Per-deployment call parameters, mirroring Python's `litellm_params`. +#[derive(Clone, Debug, Deserialize)] +pub struct LiteLLMParams { + /// Provider model, e.g. `gpt-realtime` or `openai/gpt-realtime`. + pub model: String, + #[serde(default)] + pub api_key: Option, + #[serde(default)] + pub api_base: Option, +} + +/// One entry of the `model_list`, mirroring Python's deployment dict. +#[derive(Clone, Debug, Deserialize)] +pub struct Deployment { + /// Public alias clients request, e.g. `gpt-realtime`. + pub model_name: String, + pub litellm_params: LiteLLMParams, +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn deserializes_from_model_list_entry() { + let entry = r#"{ + "model_name": "gpt-realtime", + "litellm_params": {"model": "openai/gpt-realtime", "api_base": "https://x"} + }"#; + let deployment: Deployment = serde_json::from_str(entry).expect("valid entry"); + assert_eq!(deployment.model_name, "gpt-realtime"); + assert_eq!(deployment.litellm_params.model, "openai/gpt-realtime"); + assert_eq!(deployment.litellm_params.api_key, None); + assert_eq!( + deployment.litellm_params.api_base.as_deref(), + Some("https://x") + ); + } +} diff --git a/litellm-rust/crates/core/src/router/mod.rs b/litellm-rust/crates/core/src/router/mod.rs new file mode 100644 index 00000000000..96bc91bc6b5 --- /dev/null +++ b/litellm-rust/crates/core/src/router/mod.rs @@ -0,0 +1,93 @@ +//! Minimal Rust port of LiteLLM's `router.py` deployment selection. +//! +//! A [`Router`] is built from a `model_list` of [`Deployment`]s +//! (`{ model_name, litellm_params: { model, api_key, api_base } }`) and selects +//! one per request via a [`RoutingStrategy`]. For now the only strategy is +//! `simple-shuffle` — a uniform random pick within a `model_name` group. +//! +//! This stays pure (no I/O): it only *chooses* a deployment. The host (the +//! gateway) takes the chosen deployment and performs the actual provider call. +//! +//! - [`deployment`] — the `model_list` data types. +//! - [`strategy`] — how a deployment is chosen. + +mod deployment; +mod strategy; + +pub use deployment::{Deployment, LiteLLMParams}; +pub use strategy::RoutingStrategy; + +/// Load-balancing router over a `model_list`. +#[derive(Clone, Debug, Default)] +pub struct Router { + model_list: Vec, + routing_strategy: RoutingStrategy, +} + +impl Router { + /// Build a router from a `model_list` using the default `simple-shuffle` strategy. + pub fn new(model_list: Vec) -> Self { + Self { + model_list, + routing_strategy: RoutingStrategy::SimpleShuffle, + } + } + + /// All deployments in the `model_list`. Read-only; used by the host to + /// enumerate upstreams (e.g. to pre-warm a connection pool per deployment). + pub fn deployments(&self) -> &[Deployment] { + &self.model_list + } + + /// Whether any deployment is registered under `model`. + pub fn has_deployment(&self, model: &str) -> bool { + self.model_list + .iter() + .any(|deployment| deployment.model_name == model) + } + + /// Pick a deployment for `model` per the routing strategy. Returns `None` + /// when no deployment is registered under that `model_name`. + pub fn get_available_deployment(&self, model: &str) -> Option<&Deployment> { + let candidates: Vec<&Deployment> = self + .model_list + .iter() + .filter(|deployment| deployment.model_name == model) + .collect(); + self.routing_strategy.select(&candidates) + } +} + +#[cfg(test)] +mod tests { + use super::*; + + fn deployment(name: &str, model: &str) -> Deployment { + Deployment { + model_name: name.to_string(), + litellm_params: LiteLLMParams { + model: model.to_string(), + api_key: None, + api_base: None, + }, + } + } + + #[test] + fn selects_a_matching_deployment() { + let router = Router::new(vec![ + deployment("gpt-realtime", "gpt-realtime"), + deployment("other", "other-model"), + ]); + let chosen = router + .get_available_deployment("gpt-realtime") + .expect("a deployment should match"); + assert_eq!(chosen.model_name, "gpt-realtime"); + } + + #[test] + fn unknown_model_returns_none() { + let router = Router::new(vec![deployment("gpt-realtime", "gpt-realtime")]); + assert!(router.get_available_deployment("missing").is_none()); + } +} diff --git a/litellm-rust/crates/core/src/router/strategy/mod.rs b/litellm-rust/crates/core/src/router/strategy/mod.rs new file mode 100644 index 00000000000..7e8ac217db3 --- /dev/null +++ b/litellm-rust/crates/core/src/router/strategy/mod.rs @@ -0,0 +1,26 @@ +//! Routing policy: how the router picks one deployment from a model group. +//! +//! One module per strategy; [`RoutingStrategy::select`] dispatches to it. New +//! strategies (least-busy, latency-based, …) get their own file here. + +mod simple_shuffle; + +use super::Deployment; + +/// How the router chooses among the deployments sharing a `model_name`. +#[derive(Clone, Copy, Debug, Default, PartialEq, Eq)] +pub enum RoutingStrategy { + /// Uniform random pick among the matching deployments. + #[default] + SimpleShuffle, +} + +impl RoutingStrategy { + /// Choose one deployment from `candidates` (all sharing the requested + /// `model_name`). Returns `None` when there are no candidates. + pub fn select<'a>(&self, candidates: &[&'a Deployment]) -> Option<&'a Deployment> { + match self { + RoutingStrategy::SimpleShuffle => simple_shuffle::select(candidates), + } + } +} diff --git a/litellm-rust/crates/core/src/router/strategy/simple_shuffle.rs b/litellm-rust/crates/core/src/router/strategy/simple_shuffle.rs new file mode 100644 index 00000000000..74ce0c21e80 --- /dev/null +++ b/litellm-rust/crates/core/src/router/strategy/simple_shuffle.rs @@ -0,0 +1,47 @@ +//! `simple-shuffle`: a uniform random pick among the candidate deployments. + +use rand::seq::SliceRandom; + +use crate::router::Deployment; + +/// Uniform random choice among `candidates` (all sharing the requested +/// `model_name`). Returns `None` when there are no candidates. +pub fn select<'a>(candidates: &[&'a Deployment]) -> Option<&'a Deployment> { + candidates.choose(&mut rand::thread_rng()).copied() +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::router::{Deployment, LiteLLMParams}; + + fn deployment(model: &str) -> Deployment { + Deployment { + model_name: "gpt-realtime".to_string(), + litellm_params: LiteLLMParams { + model: model.to_string(), + api_key: None, + api_base: None, + }, + } + } + + #[test] + fn picks_from_candidates() { + let a = deployment("key-a"); + let b = deployment("key-b"); + let candidates = vec![&a, &b]; + for _ in 0..20 { + let chosen = select(&candidates).expect("non-empty"); + assert!(matches!( + chosen.litellm_params.model.as_str(), + "key-a" | "key-b" + )); + } + } + + #[test] + fn empty_candidates_select_none() { + assert!(select(&[]).is_none()); + } +} diff --git a/litellm-rust/crates/core/tests/workspace_crate_allowlist.rs b/litellm-rust/crates/core/tests/workspace_crate_allowlist.rs new file mode 100644 index 00000000000..a56d19b8242 --- /dev/null +++ b/litellm-rust/crates/core/tests/workspace_crate_allowlist.rs @@ -0,0 +1,93 @@ +//! Enforcement: the litellm-rust workspace has exactly three crates. +//! +//! `core` (pure translation), `ai-gateway` (routes + all network I/O), and +//! `python-bridge` (the PyO3 cdylib). Adding or removing a crate must be a +//! deliberate act: this test fails until the allowlist here is updated, forcing +//! whoever changes the crate set to justify the new crate per the rule that a +//! crate is a layer needing independent compilation / its own deps / a separate +//! artifact — and to keep `litellm-rust/AGENTS.md` in sync. +//! +//! Std-only (no toml crate): we scan the workspace manifest's `members = [...]` +//! block and the `crates/` directory directly. + +use std::collections::BTreeSet; +use std::fs; +use std::path::{Path, PathBuf}; + +/// The one true crate set. Update BOTH this and `litellm-rust/AGENTS.md` when the +/// workspace legitimately gains or loses a crate. +const EXPECTED_MEMBERS: &[&str] = &["crates/core", "crates/ai-gateway", "crates/python-bridge"]; + +/// The crate subdirectory names that must exist under `crates/`. +const EXPECTED_CRATE_DIRS: &[&str] = &["core", "ai-gateway", "python-bridge"]; + +const MISMATCH: &str = "litellm-rust crate set changed — update this allowlist AND litellm-rust/AGENTS.md, and justify the crate per the rule (crate = layer needing independent compilation / its own deps / a separate artifact)."; + +/// Absolute path to the workspace root (`litellm-rust/`). +fn workspace_root() -> PathBuf { + // CARGO_MANIFEST_DIR is `.../litellm-rust/crates/core`; the workspace root is + // two levels up. + Path::new(concat!(env!("CARGO_MANIFEST_DIR"), "/../..")) + .canonicalize() + .expect("workspace root should resolve") +} + +/// Parse the `members = [ ... ]` array out of the workspace `[workspace]` table. +/// +/// Minimal hand-rolled scan: find `members`, then collect every double-quoted +/// string up to the closing `]`. Good enough for our fixed manifest shape and +/// keeps this test dependency-free. +fn parse_members(manifest: &str) -> BTreeSet { + let after_members = manifest + .split_once("members") + .map(|(_, rest)| rest) + .expect("workspace manifest should declare members"); + let open = after_members.find('[').expect("members should be an array"); + let close = after_members[open..] + .find(']') + .map(|offset| open + offset) + .expect("members array should be closed"); + let body = &after_members[open + 1..close]; + + let mut members = BTreeSet::new(); + let mut rest = body; + while let Some(start) = rest.find('"') { + let after_quote = &rest[start + 1..]; + let end = after_quote + .find('"') + .expect("opening quote should be matched"); + members.insert(after_quote[..end].to_string()); + rest = &after_quote[end + 1..]; + } + members +} + +/// The immediate subdirectory names under `crates/`. +fn crate_dirs(root: &Path) -> BTreeSet { + fs::read_dir(root.join("crates")) + .expect("crates/ directory should exist") + .filter_map(Result::ok) + .filter(|entry| entry.file_type().map(|ty| ty.is_dir()).unwrap_or(false)) + .map(|entry| entry.file_name().to_string_lossy().into_owned()) + .collect() +} + +#[test] +fn workspace_members_match_allowlist() { + let root = workspace_root(); + let manifest = fs::read_to_string(root.join("Cargo.toml")) + .expect("workspace Cargo.toml should be readable"); + + let actual = parse_members(&manifest); + let expected: BTreeSet = EXPECTED_MEMBERS.iter().map(|s| s.to_string()).collect(); + assert_eq!(actual, expected, "{MISMATCH}"); +} + +#[test] +fn crates_directory_matches_allowlist() { + let root = workspace_root(); + + let actual = crate_dirs(&root); + let expected: BTreeSet = EXPECTED_CRATE_DIRS.iter().map(|s| s.to_string()).collect(); + assert_eq!(actual, expected, "{MISMATCH}"); +} diff --git a/litellm-rust/crates/python-bridge/AGENTS.md b/litellm-rust/crates/python-bridge/AGENTS.md new file mode 100644 index 00000000000..d6d3d90e6ab --- /dev/null +++ b/litellm-rust/crates/python-bridge/AGENTS.md @@ -0,0 +1,3 @@ +litellm-python-bridge is the PyO3 cdylib that exposes Rust to the litellm Python SDK — a thin adapter (Python objects → Rust calls → Python results) over litellm-ai-gateway. + +Keep it thin: no business logic, no transforms, no I/O orchestration — just marshal in/out and call into litellm-ai-gateway. diff --git a/litellm-rust/crates/python-bridge/CLAUDE.md b/litellm-rust/crates/python-bridge/CLAUDE.md new file mode 100644 index 00000000000..efa1a554c9c --- /dev/null +++ b/litellm-rust/crates/python-bridge/CLAUDE.md @@ -0,0 +1,36 @@ +# CLAUDE.md + +Rules for `litellm-rust/crates/python-bridge`. + +## Responsibility + +`python-bridge` is the PyO3 boundary between Python LiteLLM and Rust transforms. +Keep this crate thin. It adapts Python objects to Rust payloads and returns +Python-compatible dictionaries. + +## Bridge Shape + +- Prefer one stable method per top-level LiteLLM route, for example + `ocr(payload)`. +- Do not add one exported PyO3 function per provider helper unless there is a + measured reason. +- Provider dispatch belongs in Rust route modules such as + `litellm_providers::ocr`, not in this PyO3 crate. +- Python owns rollout state and fallback. Rust should return errors; Python + decides whether to raise or fall back. + +## Data Handling + +- OCR payloads can contain personal data and large base64 images. Do not log + payloads or provider responses. +- Avoid copying large payloads more than needed. The current JSON round-trip is + acceptable for the first scaffold, but future performance work should evaluate + direct PyO3 conversion before expanding Rust coverage to image-heavy paths. +- Do not expose raw Rust errors that include document contents or upstream + bodies. + +## Tests + +- `cargo test --workspace` must compile this crate. +- Python tests must cover bridge disabled, bridge enabled, and module-missing + fallback behavior for every exposed route. diff --git a/litellm-rust/crates/python-bridge/Cargo.toml b/litellm-rust/crates/python-bridge/Cargo.toml new file mode 100644 index 00000000000..f5b29f49cfd --- /dev/null +++ b/litellm-rust/crates/python-bridge/Cargo.toml @@ -0,0 +1,16 @@ +[package] +name = "litellm-python-bridge" +version = "0.1.0" +edition.workspace = true +license.workspace = true +repository.workspace = true + +[lib] +name = "litellm_python_bridge" +crate-type = ["cdylib"] + +[dependencies] +litellm-core.workspace = true +litellm-ai-gateway = { workspace = true, default-features = false } +pyo3 = { workspace = true, features = ["extension-module"] } +serde_json.workspace = true diff --git a/litellm-rust/crates/python-bridge/src/gil.rs b/litellm-rust/crates/python-bridge/src/gil.rs new file mode 100644 index 00000000000..dc1b591735c --- /dev/null +++ b/litellm-rust/crates/python-bridge/src/gil.rs @@ -0,0 +1,32 @@ +//! GIL accounting. +//! +//! A single chokepoint for releasing the GIL around blocking work. Every +//! blocking call in the bridge goes through [`release_gil`] instead of calling +//! `Python::allow_threads` directly, so the release count stays accurate and we +//! have one place to extend later (timing histograms, per-call labels, etc.). + +use std::sync::atomic::{AtomicU64, Ordering}; + +use pyo3::prelude::*; + +/// Number of times the bridge has released the GIL since process start. +static GIL_RELEASES: AtomicU64 = AtomicU64::new(0); + +/// Release the GIL around `f`, recording the release. +/// +/// `f` must not touch any Python state — that is what makes releasing the GIL +/// safe. Returning the value back to Python re-acquires the GIL at the call +/// site, after `f` has finished. +pub fn release_gil(py: Python<'_>, f: F) -> T +where + F: FnOnce() -> T + Send, + T: Send, +{ + GIL_RELEASES.fetch_add(1, Ordering::Relaxed); + py.allow_threads(f) +} + +/// Total GIL releases performed by the bridge so far. +pub fn release_count() -> u64 { + GIL_RELEASES.load(Ordering::Relaxed) +} diff --git a/litellm-rust/crates/python-bridge/src/lib.rs b/litellm-rust/crates/python-bridge/src/lib.rs new file mode 100644 index 00000000000..50ec7fceeac --- /dev/null +++ b/litellm-rust/crates/python-bridge/src/lib.rs @@ -0,0 +1,100 @@ +use std::time::Duration; + +use litellm_ai_gateway::io::ocr::run_ocr; +use litellm_core::error::CoreError; +use pyo3::exceptions::{PyRuntimeError, PyValueError}; +use pyo3::prelude::*; +use pyo3::types::{PyAny, PyDict}; +use serde_json::{Map, Value}; + +mod gil; + +fn py_to_json(py: Python<'_>, value: &Bound<'_, PyAny>) -> PyResult { + let json = py.import("json")?; + let encoded: String = json.call_method1("dumps", (value,))?.extract()?; + serde_json::from_str(&encoded).map_err(|err| PyValueError::new_err(err.to_string())) +} + +fn json_to_py(py: Python<'_>, value: Value) -> PyResult> { + let json = py.import("json")?; + let encoded = + serde_json::to_string(&value).map_err(|err| PyValueError::new_err(err.to_string()))?; + Ok(json.call_method1("loads", (encoded,))?.unbind()) +} + +/// Map a core error to the closest Python exception. Caller-input problems +/// (auth, bad types, missing fields) -> `ValueError`; everything else +/// (network, upstream status, parse failures) -> `RuntimeError`. +fn core_error_to_pyerr(err: CoreError) -> PyErr { + match err { + CoreError::Auth(message) => PyValueError::new_err(message), + CoreError::InvalidType { .. } | CoreError::MissingField(_) => { + PyValueError::new_err(err.to_string()) + } + other => PyRuntimeError::new_err(other.to_string()), + } +} + +/// Perform a Mistral OCR call end to end and return the response as a dict. +#[pyfunction] +#[pyo3(signature = (model, document, api_key=None, api_base=None, optional_params=None, timeout_seconds=None))] +fn ocr( + py: Python<'_>, + model: String, + document: Py, + api_key: Option, + api_base: Option, + optional_params: Option>, + timeout_seconds: Option, +) -> PyResult> { + let document = py_to_json(py, document.bind(py))?; + + let optional_params = match optional_params { + Some(params) => match py_to_json(py, params.bind(py))? { + Value::Object(map) => map, + _ => return Err(PyValueError::new_err("optional_params must be a dict")), + }, + None => Map::new(), + }; + + let timeout = timeout_seconds.and_then(|secs| { + if secs.is_finite() && secs > 0.0 { + Some(Duration::from_secs_f64(secs)) + } else { + None + } + }); + + // Release the GIL during the blocking HTTP call (counted for observability). + let result = gil::release_gil(py, || { + run_ocr( + &model, + document, + api_key.as_deref(), + api_base.as_deref(), + optional_params, + timeout, + ) + }); + + match result { + Ok(value) => json_to_py(py, value), + Err(err) => Err(core_error_to_pyerr(err)), + } +} + +/// Bridge GIL accounting, e.g. `{"releases": 12}`. Lets the Python side observe +/// how often the bridge has dropped the GIL for blocking work. +#[pyfunction] +fn gil_stats(py: Python<'_>) -> PyResult> { + let stats = PyDict::new(py); + stats.set_item("releases", gil::release_count())?; + Ok(stats.into_any().unbind()) +} + +#[pymodule] +fn litellm_python_bridge(module: &Bound<'_, PyModule>) -> PyResult<()> { + module.add_function(wrap_pyfunction!(ocr, module)?)?; + module.add_function(wrap_pyfunction!(gil_stats, module)?)?; + Ok(()) +} diff --git a/litellm/__init__.py b/litellm/__init__.py index e6c30e12286..d0513f77b35 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -43,6 +43,7 @@ from typing import ( Type, ) from litellm.types.integrations.datadog import DatadogInitParams +from litellm.types.integrations.newrelic import NewRelicInitParams from litellm._logging import ( set_verbose, _turn_on_debug, @@ -72,12 +73,14 @@ from litellm.constants import ( replicate_models, clarifai_models, huggingface_models, + modelscope_models, empower_models, together_ai_models, baseten_models, WANDB_MODELS, REPEATED_STREAMING_CHUNK_LIMIT, request_timeout, + request_timeout_explicitly_set as request_timeout_explicitly_set, open_ai_embedding_models, cohere_embedding_models, bedrock_embedding_models, @@ -154,10 +157,12 @@ _custom_logger_compatible_callbacks_literal = Literal[ "gitlab", "cloudzero", "focus", + "mavvrik", "vantage", "posthog", "levo", "compression_interception", + "newrelic", ] cold_storage_custom_logger: Optional[_custom_logger_compatible_callbacks_literal] = None logged_real_time_event_types: Optional[Union[List[str], Literal["*"]]] = None @@ -209,6 +214,15 @@ standard_logging_payload_excluded_fields: Optional[List[str]] = ( log_raw_request_response: bool = False redact_messages_in_exceptions: Optional[bool] = False redact_user_api_key_info: Optional[bool] = False +# When True (default — preserves historical behavior), the Router appends +# internal config names (model_group, fallback model groups, deployment +# timeouts, fallback failure details) onto exception messages and surfaces +# them to clients via ProxyException.message. Set to False if you do NOT +# want the proxy's internal model_name / fallback wiring visible to clients. +# Deprecation: planned to flip to False (redact by default) in a future +# major release; opt in early with `litellm.expose_router_debug_in_errors +# = False`. +expose_router_debug_in_errors: bool = True filter_invalid_headers: Optional[bool] = False add_user_information_to_llm_headers: Optional[bool] = ( None # adds user_id, team_id, token hash (params from StandardLoggingMetadata) to request headers @@ -231,6 +245,17 @@ modify_params = bool(os.getenv("LITELLM_MODIFY_PARAMS", False)) use_chat_completions_url_for_anthropic_messages: bool = bool( os.getenv("LITELLM_USE_CHAT_COMPLETIONS_URL_FOR_ANTHROPIC_MESSAGES", False) ) # When True, routes OpenAI /v1/messages requests to chat/completions instead of the Responses API +# When True, strip the OpenAI-flavored `usage.total_tokens` field that +# LiteLLM injects into non-streaming /v1/messages responses, bringing the +# wire response into line with the Anthropic spec (matches the streaming +# SSE path, which already omits total_tokens). Default False to preserve +# backward compatibility for clients that read the LiteLLM-shaped +# `usage.total_tokens` today. Planned to flip to True in a future major +# release; opt in early via Python: +# `litellm.strip_anthropic_total_tokens = True` +# Or via `litellm_settings.strip_anthropic_total_tokens: true` in +# config.yaml. +strip_anthropic_total_tokens: bool = False route_all_chat_openai_to_responses: bool = ( os.getenv("LITELLM_ROUTE_ALL_CHAT_OPENAI_TO_RESPONSES", "false").lower() == "true" ) # When True, routes all OpenAI /chat/completions requests through the Responses API bridge @@ -359,6 +384,9 @@ enable_gemini_default_thinking_level_low: bool = ( #################### logging: bool = True enable_loadbalancing_on_batch_endpoints: Optional[bool] = None +require_managed_files: bool = ( + False # proxy only - require target_model_names on POST /v1/files +) enable_caching_on_provider_specific_optional_params: bool = ( False # feature-flag for caching on optional params - e.g. 'top_k' ) @@ -406,12 +434,13 @@ anthropic_beta_headers_url: str = os.getenv( "LITELLM_ANTHROPIC_BETA_HEADERS_URL", "https://raw.githubusercontent.com/BerriAI/litellm/main/litellm/anthropic_beta_headers_config.json", ) -suppress_debug_info = False +suppress_debug_info: bool = False dynamodb_table_name: Optional[str] = None s3_callback_params: Optional[Dict] = None s3_audit_callback_params: Optional[Dict] = None datadog_llm_observability_params: Optional[Union[DatadogLLMObsInitParams, Dict]] = None datadog_params: Optional[Union[DatadogInitParams, Dict]] = None +newrelic_params: Optional[Union[NewRelicInitParams, Dict]] = None aws_sqs_callback_params: Optional[Dict] = None generic_logger_headers: Optional[Dict] = None default_key_generate_params: Optional[Dict] = None @@ -645,6 +674,7 @@ elevenlabs_models: Set = set() dashscope_models: Set = set() moonshot_models: Set = set() publicai_models: Set = set() +darkbloom_models: Set = set() v0_models: Set = set() morph_models: Set = set() lambda_ai_models: Set = set() @@ -893,10 +923,14 @@ def add_known_models(model_cost_map: Optional[Dict] = None): heroku_models.add(key) elif value.get("litellm_provider") == "dashscope": dashscope_models.add(key) + elif value.get("litellm_provider") == "modelscope": + modelscope_models.add(key) elif value.get("litellm_provider") == "moonshot": moonshot_models.add(key) elif value.get("litellm_provider") == "publicai": publicai_models.add(key) + elif value.get("litellm_provider") == "darkbloom": + darkbloom_models.add(key) elif value.get("litellm_provider") == "v0": v0_models.add(key) elif value.get("litellm_provider") == "morph": @@ -1012,6 +1046,7 @@ model_list = list( | zai_models | fal_ai_models | deepseek_models + | modelscope_models | azure_ai_models | voyage_models | infinity_models @@ -1044,6 +1079,7 @@ model_list = list( | dashscope_models | moonshot_models | publicai_models + | darkbloom_models | v0_models | morph_models | lambda_ai_models @@ -1145,8 +1181,10 @@ models_by_provider: dict = { "elevenlabs": elevenlabs_models, "heroku": heroku_models, "dashscope": dashscope_models, + "modelscope": modelscope_models, "moonshot": moonshot_models, "publicai": publicai_models, + "darkbloom": darkbloom_models, "v0": v0_models, "morph": morph_models, "lambda_ai": lambda_ai_models, @@ -1368,11 +1406,14 @@ from .skills.main import ( ) from .containers.main import * from .ocr.main import * +from .ocr.rust_bridge import use_litellm_rust from .rag.main import * +from .sandbox.main import * from .search.main import * from .realtime_api.main import ( _arealtime, acreate_realtime_client_secret, + acreate_realtime_transcription_session, arealtime_calls, ) from .responses.main import _aresponses_websocket @@ -1723,6 +1764,9 @@ if TYPE_CHECKING: from .llms.voyage.embedding.transformation_contextual import ( VoyageContextualEmbeddingConfig as VoyageContextualEmbeddingConfig, ) + from .llms.voyage.embedding.transformation_multimodal import ( + VoyageMultimodalEmbeddingConfig as VoyageMultimodalEmbeddingConfig, + ) from .llms.infinity.embedding.transformation import ( InfinityEmbeddingConfig as InfinityEmbeddingConfig, ) @@ -1885,9 +1929,6 @@ if TYPE_CHECKING: from .llms.fireworks_ai.completion.transformation import ( FireworksAITextCompletionConfig as FireworksAITextCompletionConfig, ) - from .llms.fireworks_ai.audio_transcription.transformation import ( - FireworksAIAudioTranscriptionConfig as FireworksAIAudioTranscriptionConfig, - ) from .llms.fireworks_ai.embed.fireworks_ai_transformation import ( FireworksAIEmbeddingConfig as FireworksAIEmbeddingConfig, ) @@ -1964,6 +2005,9 @@ if TYPE_CHECKING: from .llms.dashscope.rerank.transformation import ( DashScopeRerankConfig as DashScopeRerankConfig, ) + from .llms.modelscope.chat.transformation import ( + ModelScopeChatConfig as ModelScopeChatConfig, + ) from .llms.moonshot.chat.transformation import ( MoonshotChatConfig as MoonshotChatConfig, ) diff --git a/litellm/_lazy_imports_registry.py b/litellm/_lazy_imports_registry.py index bace54ffad1..4f131354d2e 100644 --- a/litellm/_lazy_imports_registry.py +++ b/litellm/_lazy_imports_registry.py @@ -223,6 +223,7 @@ LLM_CONFIG_NAMES = ( "GenAIHubOrchestrationConfig", "VoyageEmbeddingConfig", "VoyageContextualEmbeddingConfig", + "VoyageMultimodalEmbeddingConfig", "InfinityEmbeddingConfig", "PerplexityEmbeddingConfig", "AzureAIStudioConfig", @@ -259,7 +260,6 @@ LLM_CONFIG_NAMES = ( "SambaNovaEmbeddingConfig", "FireworksAIConfig", "FireworksAITextCompletionConfig", - "FireworksAIAudioTranscriptionConfig", "FireworksAIEmbeddingConfig", "FriendliaiChatConfig", "JinaAIEmbeddingConfig", @@ -305,6 +305,7 @@ LLM_CONFIG_NAMES = ( "GigaChatConfig", "GigaChatEmbeddingConfig", "DashScopeChatConfig", + "ModelScopeChatConfig", "MoonshotChatConfig", "DockerModelRunnerChatConfig", "V0ChatConfig", @@ -903,6 +904,10 @@ _LLM_CONFIGS_IMPORT_MAP = { ".llms.voyage.embedding.transformation_contextual", "VoyageContextualEmbeddingConfig", ), + "VoyageMultimodalEmbeddingConfig": ( + ".llms.voyage.embedding.transformation_multimodal", + "VoyageMultimodalEmbeddingConfig", + ), "InfinityEmbeddingConfig": ( ".llms.infinity.embedding.transformation", "InfinityEmbeddingConfig", @@ -1021,10 +1026,6 @@ _LLM_CONFIGS_IMPORT_MAP = { ".llms.fireworks_ai.completion.transformation", "FireworksAITextCompletionConfig", ), - "FireworksAIAudioTranscriptionConfig": ( - ".llms.fireworks_ai.audio_transcription.transformation", - "FireworksAIAudioTranscriptionConfig", - ), "FireworksAIEmbeddingConfig": ( ".llms.fireworks_ai.embed.fireworks_ai_transformation", "FireworksAIEmbeddingConfig", @@ -1156,6 +1157,10 @@ _LLM_CONFIGS_IMPORT_MAP = { ".llms.dashscope.chat.transformation", "DashScopeChatConfig", ), + "ModelScopeChatConfig": ( + ".llms.modelscope.chat.transformation", + "ModelScopeChatConfig", + ), "MoonshotChatConfig": (".llms.moonshot.chat.transformation", "MoonshotChatConfig"), "DockerModelRunnerChatConfig": ( ".llms.docker_model_runner.chat.transformation", diff --git a/litellm/_logging.py b/litellm/_logging.py index 6b99f50e014..bb743c32878 100644 --- a/litellm/_logging.py +++ b/litellm/_logging.py @@ -419,7 +419,7 @@ def _enable_debugging(): def print_verbose(print_statement): try: if set_verbose: - print(redact_secrets(str(print_statement))) # noqa + print(redact_secrets(str(print_statement))) # noqa: T201 except Exception: pass diff --git a/litellm/_redis.py b/litellm/_redis.py index 5ab551453bb..1b6e1a5e4b0 100644 --- a/litellm/_redis.py +++ b/litellm/_redis.py @@ -311,7 +311,7 @@ def get_redis_url_from_environment(): return f"{redis_protocol}://{auth_part}{os.environ['REDIS_HOST']}:{os.environ['REDIS_PORT']}" -def _get_redis_client_logic(**env_overrides): # noqa: PLR0915 +def _get_redis_client_logic(**env_overrides): """ Common functionality across sync + async redis client implementations """ @@ -567,7 +567,7 @@ def get_redis_client(**env_overrides): return redis.Redis(**redis_kwargs) -def get_redis_async_client( # noqa: PLR0915 +def get_redis_async_client( connection_pool: Optional[async_redis.BlockingConnectionPool] = None, **env_overrides, ) -> Union[async_redis.Redis, async_redis.RedisCluster]: diff --git a/litellm/a2a_protocol/litellm_completion_bridge/handler.py b/litellm/a2a_protocol/litellm_completion_bridge/handler.py index 52e471ff702..a3502f21f95 100644 --- a/litellm/a2a_protocol/litellm_completion_bridge/handler.py +++ b/litellm/a2a_protocol/litellm_completion_bridge/handler.py @@ -19,6 +19,7 @@ from litellm.a2a_protocol.litellm_completion_bridge.transformation import ( A2AStreamingContext, ) from litellm.a2a_protocol.providers.config_manager import A2AProviderConfigManager +from litellm.interactions.agents.utils import merge_agent_headers # litellm_params key carrying the authenticated principal (hashed virtual key) so # A2A provider configs can scope provider-side state (e.g. LangFlow session memory) @@ -48,6 +49,7 @@ class A2ACompletionBridgeHandler: params: Dict[str, Any], litellm_params: Dict[str, Any], api_base: Optional[str] = None, + agent_extra_headers: Optional[Dict[str, str]] = None, *, _skip_a2a_provider_routing: bool = False, ) -> Dict[str, Any]: @@ -59,6 +61,8 @@ class A2ACompletionBridgeHandler: params: A2A MessageSendParams containing the message litellm_params: Agent's litellm_params (custom_llm_provider, model, etc.) api_base: API base URL from agent_card_params + agent_extra_headers: Per-request headers (from x-a2a-{agent}-* rewrite and + admin extra_headers) to forward on the upstream HTTP call. Returns: A2A SendMessageResponse dict @@ -80,6 +84,7 @@ class A2ACompletionBridgeHandler: params=params, api_base=api_base, litellm_params=litellm_params, + agent_extra_headers=agent_extra_headers, ) # Extract message from params @@ -106,7 +111,7 @@ class A2ACompletionBridgeHandler: ) # Build completion params dict - completion_params = { + completion_params: Dict[str, Any] = { "model": full_model, "messages": openai_messages, "api_base": api_base, @@ -128,6 +133,12 @@ class A2ACompletionBridgeHandler: params=params, ) + if agent_extra_headers: + completion_params["extra_headers"] = merge_agent_headers( + dynamic_headers=agent_extra_headers, + static_headers=completion_params.get("extra_headers"), + ) + # Call litellm.acompletion response = await litellm.acompletion(**completion_params) @@ -149,6 +160,7 @@ class A2ACompletionBridgeHandler: params: Dict[str, Any], litellm_params: Dict[str, Any], api_base: Optional[str] = None, + agent_extra_headers: Optional[Dict[str, str]] = None, *, _skip_a2a_provider_routing: bool = False, ) -> AsyncIterator[Dict[str, Any]]: @@ -166,6 +178,8 @@ class A2ACompletionBridgeHandler: params: A2A MessageSendParams containing the message litellm_params: Agent's litellm_params (custom_llm_provider, model, etc.) api_base: API base URL from agent_card_params + agent_extra_headers: Per-request headers (from x-a2a-{agent}-* rewrite and + admin extra_headers) to forward on the upstream HTTP call. Yields: A2A streaming response events @@ -187,6 +201,7 @@ class A2ACompletionBridgeHandler: params=params, api_base=api_base, litellm_params=litellm_params, + agent_extra_headers=agent_extra_headers, ): yield chunk @@ -222,7 +237,7 @@ class A2ACompletionBridgeHandler: ) # Build completion params dict - completion_params = { + completion_params: Dict[str, Any] = { "model": full_model, "messages": openai_messages, "api_base": api_base, @@ -244,6 +259,12 @@ class A2ACompletionBridgeHandler: params=params, ) + if agent_extra_headers: + completion_params["extra_headers"] = merge_agent_headers( + dynamic_headers=agent_extra_headers, + static_headers=completion_params.get("extra_headers"), + ) + # 1. Emit initial task event (kind: "task", status: "submitted") task_event = A2ACompletionBridgeTransformation.create_task_event(ctx) yield task_event @@ -305,6 +326,7 @@ async def handle_a2a_completion( params: Dict[str, Any], litellm_params: Dict[str, Any], api_base: Optional[str] = None, + agent_extra_headers: Optional[Dict[str, str]] = None, ) -> Dict[str, Any]: """Convenience function for non-streaming A2A completion.""" return await A2ACompletionBridgeHandler.handle_non_streaming( @@ -312,6 +334,7 @@ async def handle_a2a_completion( params=params, litellm_params=litellm_params, api_base=api_base, + agent_extra_headers=agent_extra_headers, ) @@ -320,6 +343,7 @@ async def handle_a2a_completion_streaming( params: Dict[str, Any], litellm_params: Dict[str, Any], api_base: Optional[str] = None, + agent_extra_headers: Optional[Dict[str, str]] = None, ) -> AsyncIterator[Dict[str, Any]]: """Convenience function for streaming A2A completion.""" async for chunk in A2ACompletionBridgeHandler.handle_streaming( @@ -327,5 +351,6 @@ async def handle_a2a_completion_streaming( params=params, litellm_params=litellm_params, api_base=api_base, + agent_extra_headers=agent_extra_headers, ): yield chunk diff --git a/litellm/a2a_protocol/main.py b/litellm/a2a_protocol/main.py index 6979e1ac659..2b6f2cd12b4 100644 --- a/litellm/a2a_protocol/main.py +++ b/litellm/a2a_protocol/main.py @@ -132,6 +132,7 @@ async def _send_message_via_completion_bridge( custom_llm_provider: str, api_base: Optional[str], litellm_params: Dict[str, Any], + agent_extra_headers: Optional[Dict[str, str]] = None, ) -> LiteLLMSendMessageResponse: """ Route a send_message through the LiteLLM completion bridge (e.g. LangGraph, Bedrock AgentCore). @@ -157,6 +158,7 @@ async def _send_message_via_completion_bridge( params=params, litellm_params=litellm_params, api_base=api_base, + agent_extra_headers=agent_extra_headers, ) return LiteLLMSendMessageResponse.from_dict( @@ -283,6 +285,7 @@ async def asend_message( custom_llm_provider=custom_llm_provider, api_base=api_base, litellm_params=litellm_params, + agent_extra_headers=agent_extra_headers, ) # Standard A2A client flow @@ -433,7 +436,7 @@ def _build_streaming_logging_obj( return logging_obj -async def asend_message_streaming( # noqa: PLR0915 +async def asend_message_streaming( a2a_client: Optional["A2AClientType"] = None, request: Optional["SendStreamingMessageRequest"] = None, api_base: Optional[str] = None, @@ -509,6 +512,7 @@ async def asend_message_streaming( # noqa: PLR0915 params=params, litellm_params=litellm_params, api_base=api_base, + agent_extra_headers=agent_extra_headers, ): yield chunk return diff --git a/litellm/a2a_protocol/providers/bedrock_agentcore/config.py b/litellm/a2a_protocol/providers/bedrock_agentcore/config.py index 679e19c23cd..e7f38c6488c 100644 --- a/litellm/a2a_protocol/providers/bedrock_agentcore/config.py +++ b/litellm/a2a_protocol/providers/bedrock_agentcore/config.py @@ -37,6 +37,7 @@ class BedrockAgentCoreA2AConfig(BaseA2AProviderConfig): request_id=request_id, params=params, litellm_params=litellm_params, + agent_extra_headers=kwargs.get("agent_extra_headers"), ) async def handle_streaming( @@ -57,5 +58,6 @@ class BedrockAgentCoreA2AConfig(BaseA2AProviderConfig): request_id=request_id, params=params, litellm_params=litellm_params, + agent_extra_headers=kwargs.get("agent_extra_headers"), ): yield chunk diff --git a/litellm/a2a_protocol/providers/bedrock_agentcore/handler.py b/litellm/a2a_protocol/providers/bedrock_agentcore/handler.py index 11676aaa895..2f93895099b 100644 --- a/litellm/a2a_protocol/providers/bedrock_agentcore/handler.py +++ b/litellm/a2a_protocol/providers/bedrock_agentcore/handler.py @@ -6,7 +6,7 @@ completion bridge that would otherwise strip the envelope. """ import json -from typing import Any, AsyncIterator, Dict, cast +from typing import Any, AsyncIterator, Dict, Optional, cast from litellm._logging import verbose_logger from litellm.a2a_protocol.providers.bedrock_agentcore.transformation import ( @@ -29,6 +29,7 @@ class BedrockAgentCoreA2AHandler: request_id: str, params: Dict[str, Any], litellm_params: Dict[str, Any], + agent_extra_headers: Optional[Dict[str, str]] = None, ) -> Dict[str, Any]: """ Handle non-streaming A2A request to AgentCore. @@ -37,6 +38,8 @@ class BedrockAgentCoreA2AHandler: request_id: A2A JSON-RPC request ID params: A2A MessageSendParams containing the message litellm_params: Agent's litellm_params (model, api_key, etc.) + agent_extra_headers: Per-request headers (from x-a2a-{agent}-* rewrite and + admin extra_headers) to forward on the upstream HTTP call. Returns: A2A JSON-RPC response dict from the AgentCore agent @@ -47,6 +50,7 @@ class BedrockAgentCoreA2AHandler: params=params, litellm_params=litellm_params, method="message/send", + agent_extra_headers=agent_extra_headers, ) ) @@ -77,6 +81,7 @@ class BedrockAgentCoreA2AHandler: request_id: str, params: Dict[str, Any], litellm_params: Dict[str, Any], + agent_extra_headers: Optional[Dict[str, str]] = None, ) -> AsyncIterator[Dict[str, Any]]: """ Handle streaming A2A request to AgentCore. @@ -85,6 +90,8 @@ class BedrockAgentCoreA2AHandler: request_id: A2A JSON-RPC request ID params: A2A MessageSendParams containing the message litellm_params: Agent's litellm_params (model, api_key, etc.) + agent_extra_headers: Per-request headers (from x-a2a-{agent}-* rewrite and + admin extra_headers) to forward on the upstream HTTP call. Yields: A2A streaming response events from the AgentCore agent @@ -96,6 +103,7 @@ class BedrockAgentCoreA2AHandler: litellm_params=litellm_params, method="message/send", stream=True, + agent_extra_headers=agent_extra_headers, ) ) diff --git a/litellm/a2a_protocol/providers/bedrock_agentcore/transformation.py b/litellm/a2a_protocol/providers/bedrock_agentcore/transformation.py index 44dc10fe2b7..f868845bb58 100644 --- a/litellm/a2a_protocol/providers/bedrock_agentcore/transformation.py +++ b/litellm/a2a_protocol/providers/bedrock_agentcore/transformation.py @@ -6,11 +6,66 @@ and signs requests via AmazonAgentCoreConfig (SigV4 or JWT). """ import json -from typing import Any, AsyncIterator, Dict, Tuple +from typing import Any, AsyncIterator, Dict, Mapping, Optional, Tuple from litellm._logging import verbose_logger from litellm.llms.bedrock.chat.agentcore.transformation import AmazonAgentCoreConfig +# Reserved outbound header names that must never be sourced from per-request +# ``agent_extra_headers`` for AgentCore requests. ``agent_extra_headers`` carries +# values rewritten from the client-controlled ``x-a2a-{agent}-*`` convention, so +# allowing these would let any caller with access to the agent spoof the AWS +# request identity / SigV4 metadata by overwriting headers the proxy sets from +# trusted server-side config. +# +# The runtime headers (session / user id) are derived server-side from +# ``runtimeSessionId`` / ``runtimeUserId`` in the agent's ``litellm_params``; +# ``authorization`` is set by the AgentCore signer (JWT or SigV4); ``host`` and +# the ``x-amz-*`` family are owned by SigV4 itself. +_RESERVED_EXACT_HEADERS = frozenset( + { + "authorization", + "host", + } +) +_RESERVED_PREFIX_HEADERS: Tuple[str, ...] = ( + "x-amzn-bedrock-agentcore-runtime-", + "x-amz-", +) + + +def _filter_reserved_headers( + agent_extra_headers: Optional[Mapping[str, str]], +) -> Optional[Dict[str, str]]: + """ + Strip reserved AWS / AgentCore headers from caller-supplied + ``agent_extra_headers`` before they are merged into the signed request. + + Returns ``None`` if the result is empty. + """ + if not agent_extra_headers: + return None + + filtered: Dict[str, str] = {} + dropped: list = [] + for k, v in agent_extra_headers.items(): + k_lower = k.lower() + if k_lower in _RESERVED_EXACT_HEADERS or any( + k_lower.startswith(prefix) for prefix in _RESERVED_PREFIX_HEADERS + ): + dropped.append(k) + continue + filtered[k] = v + + if dropped: + verbose_logger.warning( + "BedrockAgentCore A2A: dropping reserved header(s) from " + "agent_extra_headers (not forwarded to AgentCore): %s", + sorted(dropped), + ) + + return filtered or None + class BedrockAgentCoreA2ATransformation: """ @@ -27,6 +82,7 @@ class BedrockAgentCoreA2ATransformation: litellm_params: Dict[str, Any], method: str = "message/send", stream: bool = False, + agent_extra_headers: Optional[Dict[str, str]] = None, ) -> Tuple[str, dict, bytes]: """ Build the AgentCore URL, construct a JSON-RPC envelope, and sign the request. @@ -37,6 +93,15 @@ class BedrockAgentCoreA2ATransformation: litellm_params: Agent's litellm_params (model, api_key, etc.) method: JSON-RPC method name (default: "message/send") stream: Whether this is a streaming request + agent_extra_headers: Per-request headers (from x-a2a-{agent}-* rewrite and + admin extra_headers) to forward on the upstream HTTP call. Merged into + the headers dict before signing so SigV4 includes them in the signature. + Reserved AWS / AgentCore identity headers (``authorization``, ``host``, + ``x-amzn-bedrock-agentcore-runtime-*``, ``x-amz-*``) are filtered out + here to prevent a caller-controlled ``x-a2a-{agent}-*`` header from + spoofing the AgentCore runtime user id or other SigV4 metadata. Use + ``api_key`` / ``runtimeUserId`` / ``runtimeSessionId`` in litellm_params + (not ``agent_extra_headers``) to override those values. Returns: Tuple of (url, signed_headers, signed_body_bytes) @@ -85,6 +150,13 @@ class BedrockAgentCoreA2ATransformation: if runtime_user_id: headers["X-Amzn-Bedrock-AgentCore-Runtime-User-Id"] = runtime_user_id + # Merge per-request agent headers before signing so SigV4 covers them. + # Reserved headers are stripped first to prevent client-controlled values + # from spoofing the AgentCore runtime identity / SigV4 metadata. + safe_extra_headers = _filter_reserved_headers(agent_extra_headers) + if safe_extra_headers: + headers.update(safe_extra_headers) + # Sign the request (SigV4 or JWT depending on api_key presence) signed_headers, signed_body = agentcore_config.sign_request( headers=headers, diff --git a/litellm/a2a_protocol/providers/pydantic_ai_agents/config.py b/litellm/a2a_protocol/providers/pydantic_ai_agents/config.py index 2f16779cc9f..6f067aecd2b 100644 --- a/litellm/a2a_protocol/providers/pydantic_ai_agents/config.py +++ b/litellm/a2a_protocol/providers/pydantic_ai_agents/config.py @@ -31,6 +31,7 @@ class PydanticAIProviderConfig(BaseA2AProviderConfig): params=params, api_base=api_base, timeout=kwargs.get("timeout", 60.0), + agent_extra_headers=kwargs.get("agent_extra_headers"), ) async def handle_streaming( @@ -50,5 +51,6 @@ class PydanticAIProviderConfig(BaseA2AProviderConfig): timeout=kwargs.get("timeout", 60.0), chunk_size=kwargs.get("chunk_size", 50), delay_ms=kwargs.get("delay_ms", 10), + agent_extra_headers=kwargs.get("agent_extra_headers"), ): yield chunk diff --git a/litellm/a2a_protocol/providers/pydantic_ai_agents/handler.py b/litellm/a2a_protocol/providers/pydantic_ai_agents/handler.py index 5b8d6b94ff2..b5d3f262a63 100644 --- a/litellm/a2a_protocol/providers/pydantic_ai_agents/handler.py +++ b/litellm/a2a_protocol/providers/pydantic_ai_agents/handler.py @@ -28,6 +28,7 @@ class PydanticAIHandler: params: Dict[str, Any], api_base: Optional[str] = None, timeout: float = 60.0, + agent_extra_headers: Optional[Dict[str, str]] = None, ) -> Dict[str, Any]: """ Handle non-streaming request to Pydantic AI agent. @@ -37,6 +38,8 @@ class PydanticAIHandler: params: A2A MessageSendParams containing the message api_base: Base URL of the Pydantic AI agent timeout: Request timeout in seconds + agent_extra_headers: Per-request headers (from x-a2a-{agent}-* rewrite and + admin extra_headers) to forward on the upstream HTTP call. Returns: A2A SendMessageResponse dict @@ -51,6 +54,7 @@ class PydanticAIHandler: request_id=request_id, params=params, timeout=timeout, + agent_extra_headers=agent_extra_headers, ) return response_data @@ -63,6 +67,7 @@ class PydanticAIHandler: timeout: float = 60.0, chunk_size: int = 50, delay_ms: int = 10, + agent_extra_headers: Optional[Dict[str, str]] = None, ) -> AsyncIterator[Dict[str, Any]]: """ Handle streaming request to Pydantic AI agent with fake streaming. @@ -78,6 +83,8 @@ class PydanticAIHandler: timeout: Request timeout in seconds chunk_size: Number of characters per chunk delay_ms: Delay between chunks in milliseconds + agent_extra_headers: Per-request headers (from x-a2a-{agent}-* rewrite and + admin extra_headers) to forward on the upstream HTTP call. Yields: A2A streaming response events @@ -94,6 +101,7 @@ class PydanticAIHandler: request_id=request_id, params=params, timeout=timeout, + agent_extra_headers=agent_extra_headers, ) # Convert raw task response to fake streaming chunks diff --git a/litellm/a2a_protocol/providers/pydantic_ai_agents/transformation.py b/litellm/a2a_protocol/providers/pydantic_ai_agents/transformation.py index bf68a01d98c..8fac43e7ae1 100644 --- a/litellm/a2a_protocol/providers/pydantic_ai_agents/transformation.py +++ b/litellm/a2a_protocol/providers/pydantic_ai_agents/transformation.py @@ -6,7 +6,7 @@ This module provides fake streaming by converting non-streaming responses into s """ import asyncio -from typing import Any, AsyncIterator, Dict, cast +from typing import Any, AsyncIterator, Dict, Optional, cast from uuid import uuid4 from litellm._logging import verbose_logger @@ -86,6 +86,7 @@ class PydanticAITransformation: request_id: str, max_attempts: int = 30, poll_interval: float = 0.5, + agent_extra_headers: Optional[Dict[str, str]] = None, ) -> Dict[str, Any]: """ Poll for task completion using tasks/get method. @@ -112,7 +113,10 @@ class PydanticAITransformation: response = await client.post( endpoint, json=poll_request, - headers={"Content-Type": "application/json"}, + headers={ + **(agent_extra_headers or {}), + "Content-Type": "application/json", + }, ) response.raise_for_status() poll_data = response.json() @@ -142,6 +146,7 @@ class PydanticAITransformation: request_id: str, params: Any, timeout: float = 60.0, + agent_extra_headers: Optional[Dict[str, str]] = None, ) -> Dict[str, Any]: """ Send a request to Pydantic AI agent and return the raw task response. @@ -189,7 +194,10 @@ class PydanticAITransformation: response = await client.post( endpoint, json=a2a_request, - headers={"Content-Type": "application/json"}, + headers={ + **(agent_extra_headers or {}), + "Content-Type": "application/json", + }, ) response.raise_for_status() response_data = response.json() @@ -211,6 +219,7 @@ class PydanticAITransformation: endpoint=endpoint, task_id=task_id, request_id=request_id, + agent_extra_headers=agent_extra_headers, ) verbose_logger.info( @@ -225,6 +234,7 @@ class PydanticAITransformation: request_id: str, params: Any, timeout: float = 60.0, + agent_extra_headers: Optional[Dict[str, str]] = None, ) -> Dict[str, Any]: """ Send a non-streaming A2A request to Pydantic AI agent and wait for completion. @@ -234,6 +244,7 @@ class PydanticAITransformation: request_id: A2A JSON-RPC request ID params: A2A MessageSendParams containing the message (dict or Pydantic model) timeout: Request timeout in seconds + agent_extra_headers: Per-request headers to forward on the upstream HTTP call. Returns: Standard A2A non-streaming response format with message @@ -244,6 +255,7 @@ class PydanticAITransformation: request_id=request_id, params=params, timeout=timeout, + agent_extra_headers=agent_extra_headers, ) # Transform to standard A2A non-streaming format @@ -258,6 +270,7 @@ class PydanticAITransformation: request_id: str, params: Any, timeout: float = 60.0, + agent_extra_headers: Optional[Dict[str, str]] = None, ) -> Dict[str, Any]: """ Send a request to Pydantic AI agent and return the raw task response. @@ -269,6 +282,7 @@ class PydanticAITransformation: request_id: A2A JSON-RPC request ID params: A2A MessageSendParams containing the message timeout: Request timeout in seconds + agent_extra_headers: Per-request headers to forward on the upstream HTTP call. Returns: Raw Pydantic AI task response (with history/artifacts) @@ -278,6 +292,7 @@ class PydanticAITransformation: request_id=request_id, params=params, timeout=timeout, + agent_extra_headers=agent_extra_headers, ) @staticmethod diff --git a/litellm/anthropic_beta_headers_config.json b/litellm/anthropic_beta_headers_config.json index d02afe37569..11fdb26e42d 100644 --- a/litellm/anthropic_beta_headers_config.json +++ b/litellm/anthropic_beta_headers_config.json @@ -75,7 +75,7 @@ "effort-2025-11-24": "effort-2025-11-24", "fast-mode-2026-02-01": null, "files-api-2025-04-14": null, - "fine-grained-tool-streaming-2025-05-14": null, + "fine-grained-tool-streaming-2025-05-14": "fine-grained-tool-streaming-2025-05-14", "interleaved-thinking-2025-05-14": null, "mcp-client-2025-11-20": null, "mcp-client-2025-04-04": null, @@ -106,7 +106,7 @@ "effort-2025-11-24": "effort-2025-11-24", "fast-mode-2026-02-01": null, "files-api-2025-04-14": null, - "fine-grained-tool-streaming-2025-05-14": null, + "fine-grained-tool-streaming-2025-05-14": "fine-grained-tool-streaming-2025-05-14", "interleaved-thinking-2025-05-14": null, "mcp-client-2025-11-20": null, "mcp-client-2025-04-04": null, @@ -129,7 +129,7 @@ "bash_20241022": null, "bash_20250124": null, "code-execution-2025-08-25": null, - "compact-2026-01-12": null, + "compact-2026-01-12": "compact-2026-01-12", "computer-use-2025-01-24": "computer-use-2025-01-24", "computer-use-2025-11-24": "computer-use-2025-11-24", "context-1m-2025-08-07": "context-1m-2025-08-07", diff --git a/litellm/batches/main.py b/litellm/batches/main.py index 15ee9303969..f124882b5a4 100644 --- a/litellm/batches/main.py +++ b/litellm/batches/main.py @@ -157,7 +157,7 @@ async def acreate_batch( @client -def create_batch( # noqa: PLR0915 +def create_batch( completion_window: Literal["24h"], endpoint: Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions"], input_file_id: str, diff --git a/litellm/caching/caching.py b/litellm/caching/caching.py index c1afde16250..cb122e90102 100644 --- a/litellm/caching/caching.py +++ b/litellm/caching/caching.py @@ -27,7 +27,7 @@ from litellm.types.utils import EmbeddingResponse, all_litellm_params from .azure_blob_cache import AzureBlobCache from .base_cache import BaseCache from .disk_cache import DiskCache -from .dual_cache import DualCache # noqa +from .dual_cache import DualCache # noqa: F401 from .gcs_cache import GCSCache from .in_memory_cache import InMemoryCache from .qdrant_semantic_cache import QdrantSemanticCache @@ -41,7 +41,7 @@ def print_verbose(print_statement): try: verbose_logger.debug(print_statement) if litellm.set_verbose: - print(print_statement) # noqa + print(print_statement) # noqa: T201 except Exception: pass @@ -100,6 +100,8 @@ class Cache: gcs_path: Optional[str] = None, redis_semantic_cache_embedding_model: str = "text-embedding-ada-002", redis_semantic_cache_index_name: Optional[str] = None, + valkey_semantic_cache_embedding_model: str = "text-embedding-ada-002", + valkey_semantic_cache_index_name: str | None = None, redis_flush_size: Optional[int] = None, redis_startup_nodes: Optional[List] = None, disk_cache_dir: Optional[str] = None, @@ -208,6 +210,21 @@ class Cache: index_name=redis_semantic_cache_index_name, **kwargs, ) + elif type == LiteLLMCacheType.VALKEY_SEMANTIC: + # Imported here, not at module top, so the optional redis dependency + # is only required when this backend is actually selected. + from .valkey_semantic_cache import ValkeySemanticCache + + self.cache = ValkeySemanticCache( + host=host, + port=port, + password=password, + similarity_threshold=similarity_threshold, + embedding_model=valkey_semantic_cache_embedding_model, + index_name=valkey_semantic_cache_index_name, + startup_nodes=redis_startup_nodes, + **kwargs, + ) elif type == LiteLLMCacheType.QDRANT_SEMANTIC: self.cache = QdrantSemanticCache( qdrant_api_base=qdrant_api_base, @@ -267,12 +284,50 @@ class Cache: if ( self.type == LiteLLMCacheType.REDIS or self.type == LiteLLMCacheType.REDIS_SEMANTIC + or self.type == LiteLLMCacheType.VALKEY_SEMANTIC ) and default_in_redis_ttl is not None: self.ttl = default_in_redis_ttl if self.namespace is not None and isinstance(self.cache, RedisCache): self.cache.namespace = self.namespace + # Params whose values carry prompt content. Excluded from semantic-cache + # scope keys so differently worded prompts share a bucket and match via + # vector similarity rather than being split into per-wording buckets. + _SEMANTIC_CACHE_SCOPE_EXCLUDED_PARAMS: frozenset = frozenset( + {"messages", "prompt", "input"} + ) + + # Server-set identity (from proxy auth) used to isolate semantic-cache + # buckets per tenant. Required once the prompt is out of the scope key, so a + # similar prompt from another key/team/org stays in a separate bucket. + _SEMANTIC_CACHE_TENANT_SCOPE_FIELDS: tuple[str, ...] = ( + "user_api_key", + "user_api_key_team_id", + "user_api_key_org_id", + ) + + def _is_semantic_cache(self) -> bool: + return self.type in ( + LiteLLMCacheType.REDIS_SEMANTIC, + LiteLLMCacheType.QDRANT_SEMANTIC, + LiteLLMCacheType.VALKEY_SEMANTIC, + ) + + def _get_semantic_cache_tenant_scope(self, kwargs: dict) -> str: + metadata: dict = kwargs.get("metadata") or {} + litellm_params: dict = kwargs.get("litellm_params") or {} + metadata_in_litellm_params: dict = litellm_params.get("metadata") or {} + + scope = "" + for field in self._SEMANTIC_CACHE_TENANT_SCOPE_FIELDS: + value = metadata.get(field) + if value is None: + value = metadata_in_litellm_params.get(field) + if value is not None: + scope += f"{field}: {value}" + return scope + def get_cache_key(self, **kwargs) -> str: """ Get the cache key for the given arguments. @@ -293,7 +348,15 @@ class Cache: combined_kwargs = ModelParamHelper._get_all_llm_api_params() litellm_param_kwargs = all_litellm_params + is_semantic_cache = self._is_semantic_cache() + scope_excluded_params = ( + self._SEMANTIC_CACHE_SCOPE_EXCLUDED_PARAMS + if is_semantic_cache + else frozenset() + ) for param in kwargs: + if param in scope_excluded_params: + continue if param in combined_kwargs: param_value: Optional[str] = self._get_param_value(param, kwargs) if param_value is not None: @@ -309,6 +372,9 @@ class Cache: param_value = kwargs[param] cache_key += f"{str(param)}: {str(param_value)}" + if is_semantic_cache: + cache_key += self._get_semantic_cache_tenant_scope(kwargs) + hashed_cache_key = Cache._get_hashed_cache_key(cache_key) hashed_cache_key = self._add_namespace_to_cache_key(hashed_cache_key, **kwargs) verbose_logger.debug( @@ -691,6 +757,7 @@ class Cache: self, embedding_response: Any, model: Optional[str], + prompt_tokens: Optional[int] = None, prompt_tokens_details: Optional[dict] = None, ) -> CachedEmbedding: """ @@ -703,6 +770,7 @@ class Cache: "index": embedding_response.get("index"), "object": embedding_response.get("object"), "model": model, + "prompt_tokens": prompt_tokens, "prompt_tokens_details": prompt_tokens_details, } elif hasattr(embedding_response, "model_dump"): @@ -712,6 +780,7 @@ class Cache: "index": data.get("index"), "object": data.get("object"), "model": model, + "prompt_tokens": prompt_tokens, "prompt_tokens_details": prompt_tokens_details, } else: @@ -721,6 +790,7 @@ class Cache: "index": data.get("index"), "object": data.get("object"), "model": model, + "prompt_tokens": prompt_tokens, "prompt_tokens_details": prompt_tokens_details, } except KeyError as e: @@ -769,6 +839,29 @@ class Cache: per_item[key] = value return per_item if per_item else None + def _get_per_item_prompt_tokens( + self, + result: EmbeddingResponse, + idx_in_result_data: int, + ) -> Optional[int]: + """ + Extract the per-item prompt_tokens from a response for caching. + + Single-item responses store the full usage.prompt_tokens. Multi-item + responses distribute it evenly (with remainder) so that summing all + per-item values on retrieval reconstructs the original total. + """ + if result.usage is None or result.usage.prompt_tokens is None: + return None + + total = result.usage.prompt_tokens + num_items = len(result.data) + if num_items <= 1: + return total + + quotient, remainder = divmod(total, num_items) + return quotient + (1 if idx_in_result_data < remainder else 0) + def add_embedding_response_to_cache( self, result: EmbeddingResponse, @@ -780,7 +873,11 @@ class Cache: kwargs["cache_key"] = preset_cache_key embedding_response = result.data[idx_in_result_data] - # Extract per-item prompt_tokens_details from response usage + # Extract per-item prompt_tokens + details from response usage + prompt_tokens = self._get_per_item_prompt_tokens( + result=result, + idx_in_result_data=idx_in_result_data, + ) prompt_tokens_details = self._get_per_item_prompt_tokens_details( result=result, idx_in_result_data=idx_in_result_data, @@ -791,6 +888,7 @@ class Cache: embedding_dict: CachedEmbedding = self._convert_to_cached_embedding( embedding_response, model_name, + prompt_tokens=prompt_tokens, prompt_tokens_details=prompt_tokens_details, ) diff --git a/litellm/caching/caching_handler.py b/litellm/caching/caching_handler.py index 3f4e54382c9..2a8bd856040 100644 --- a/litellm/caching/caching_handler.py +++ b/litellm/caching/caching_handler.py @@ -456,7 +456,10 @@ class LLMCachingHandler: index=idx, object="embedding", ) - if isinstance(kwargs_input_as_list[idx], str): + cached_prompt_tokens = cr.get("prompt_tokens") + if cached_prompt_tokens is not None: + prompt_tokens += cached_prompt_tokens + elif isinstance(kwargs_input_as_list[idx], str): from litellm.utils import token_counter prompt_tokens += token_counter( diff --git a/litellm/caching/gcs_cache.py b/litellm/caching/gcs_cache.py index 3327e094bc2..0e6a111eb2b 100644 --- a/litellm/caching/gcs_cache.py +++ b/litellm/caching/gcs_cache.py @@ -5,6 +5,7 @@ Supports syncing responses to Google Cloud Storage Buckets using HTTP requests. import json import asyncio from typing import Optional +from urllib.parse import quote from litellm._logging import print_verbose, verbose_logger from litellm.integrations.gcs_bucket.gcs_bucket_base import GCSBucketBase @@ -48,7 +49,7 @@ class GCSCache(BaseCache): headers = self._construct_headers() object_name = self.key_prefix + key bucket_name = self.bucket_name - url = f"https://storage.googleapis.com/upload/storage/v1/b/{bucket_name}/o?uploadType=media&name={object_name}" + url = f"https://storage.googleapis.com/upload/storage/v1/b/{bucket_name}/o?uploadType=media&name={quote(object_name, safe='')}" data = json.dumps(value) self.sync_client.post(url=url, data=data, headers=headers) except Exception as e: @@ -59,7 +60,7 @@ class GCSCache(BaseCache): headers = self._construct_headers() object_name = self.key_prefix + key bucket_name = self.bucket_name - url = f"https://storage.googleapis.com/upload/storage/v1/b/{bucket_name}/o?uploadType=media&name={object_name}" + url = f"https://storage.googleapis.com/upload/storage/v1/b/{bucket_name}/o?uploadType=media&name={quote(object_name, safe='')}" data = json.dumps(value) await self.async_client.post(url=url, data=data, headers=headers) except Exception as e: @@ -72,7 +73,7 @@ class GCSCache(BaseCache): headers = self._construct_headers() object_name = self.key_prefix + key bucket_name = self.bucket_name - url = f"https://storage.googleapis.com/storage/v1/b/{bucket_name}/o/{object_name}?alt=media" + url = f"https://storage.googleapis.com/storage/v1/b/{bucket_name}/o/{quote(object_name, safe='')}?alt=media" response = self.sync_client.get(url=url, headers=headers) if response.status_code == 200: cached_response = json.loads(response.text) @@ -91,7 +92,7 @@ class GCSCache(BaseCache): headers = self._construct_headers() object_name = self.key_prefix + key bucket_name = self.bucket_name - url = f"https://storage.googleapis.com/storage/v1/b/{bucket_name}/o/{object_name}?alt=media" + url = f"https://storage.googleapis.com/storage/v1/b/{bucket_name}/o/{quote(object_name, safe='')}?alt=media" response = await self.async_client.get(url=url, headers=headers) if response.status_code == 200: return json.loads(response.text) diff --git a/litellm/caching/qdrant_semantic_cache.py b/litellm/caching/qdrant_semantic_cache.py index cb521efca05..68d3b8c20b3 100644 --- a/litellm/caching/qdrant_semantic_cache.py +++ b/litellm/caching/qdrant_semantic_cache.py @@ -28,7 +28,7 @@ from .base_cache import BaseCache class QdrantSemanticCache(BaseCache): CACHE_KEY_FIELD_NAME = "litellm_cache_key" - def __init__( # noqa: PLR0915 + def __init__( self, qdrant_api_base=None, qdrant_api_key=None, diff --git a/litellm/caching/redis_cache.py b/litellm/caching/redis_cache.py index cb9ce475d30..ba07511448a 100644 --- a/litellm/caching/redis_cache.py +++ b/litellm/caching/redis_cache.py @@ -22,6 +22,7 @@ import litellm from litellm._logging import print_verbose, verbose_logger from litellm.constants import ( DEFAULT_REDIS_MAJOR_VERSION, + REDIS_CIRCUIT_BREAKER_ENABLED, REDIS_CIRCUIT_BREAKER_FAILURE_THRESHOLD, REDIS_CIRCUIT_BREAKER_RECOVERY_TIMEOUT, ) @@ -114,15 +115,23 @@ class RedisCircuitBreaker: OPEN = "open" HALF_OPEN = "half_open" - def __init__(self, failure_threshold: int, recovery_timeout: int) -> None: + def __init__( + self, + failure_threshold: int, + recovery_timeout: int, + enabled: bool = True, + ) -> None: self.failure_threshold = failure_threshold self.recovery_timeout = recovery_timeout + self.enabled = enabled self._failure_count = 0 self._opened_at: Optional[float] = None self._state = self.CLOSED def is_open(self) -> bool: """Returns True if Redis calls should be skipped.""" + if not self.enabled: + return False if self._state == self.HALF_OPEN: # Probe already in flight — fast-fail all concurrent requests. # Only the one call that caused the OPEN→HALF_OPEN transition @@ -136,6 +145,8 @@ class RedisCircuitBreaker: return False def record_failure(self) -> None: + if not self.enabled: + return self._failure_count += 1 self._opened_at = time.time() if self._failure_count >= self.failure_threshold: @@ -149,6 +160,8 @@ class RedisCircuitBreaker: self._state = self.OPEN def record_success(self) -> None: + if not self.enabled: + return if self._state == self.HALF_OPEN: verbose_logger.info("Redis circuit breaker CLOSED — Redis recovered") self._failure_count = 0 @@ -243,6 +256,7 @@ class RedisCache(BaseCache): self._circuit_breaker = RedisCircuitBreaker( failure_threshold=REDIS_CIRCUIT_BREAKER_FAILURE_THRESHOLD, recovery_timeout=REDIS_CIRCUIT_BREAKER_RECOVERY_TIMEOUT, + enabled=REDIS_CIRCUIT_BREAKER_ENABLED, ) self._setup_health_pings() @@ -355,6 +369,8 @@ class RedisCache(BaseCache): """ Make sure each key starts with the given namespace """ + if key is None: + return key # type: ignore[return-value] if self.namespace is not None and not key.startswith(self.namespace): key = self.namespace + ":" + key @@ -887,6 +903,43 @@ class RedisCache(BaseCache): ) raise e + @_redis_circuit_breaker_guard + async def async_set_max( + self, + key: str, + value: float, + ttl: int | None = None, + ) -> float | None: + """Atomically set ``key`` to ``value`` only when ``value`` is greater + than the stored value (or the key is unset), refreshing the TTL. + + Monotonic by construction: it never lowers the stored value, so a repair + that writes an authoritative-but-slightly-stale total cannot clobber a + concurrent increment that has already pushed the counter higher. The + GET/compare/SET runs in a single Lua call, so it is also atomic across + racing callers and pods. Returns the resulting value. + """ + _redis_client = self.init_async_client() + _used_ttl = self.get_ttl(ttl=ttl) + key = self.check_and_fix_namespace(key=key) + lua = ( + "local cur = redis.call('GET', KEYS[1]) " + "if cur == false or tonumber(cur) < tonumber(ARGV[1]) then " + "redis.call('SET', KEYS[1], ARGV[1]) " + "if tonumber(ARGV[2]) > 0 then redis.call('EXPIRE', KEYS[1], ARGV[2]) end " + "return ARGV[1] end " + "return cur" + ) + result = cast( + "str | bytes | int | float | None", + await _redis_client.eval(lua, 1, key, str(value), str(int(_used_ttl or 0))), + ) + if result is None: + return None + if isinstance(result, bytes): + result = result.decode() + return float(result) + async def flush_cache_buffer(self): print_verbose( f"flushing to redis....reached size of buffer {len(self.redis_batch_writing_buffer)}" diff --git a/litellm/caching/valkey_semantic_cache.py b/litellm/caching/valkey_semantic_cache.py new file mode 100644 index 00000000000..bf368b74d07 --- /dev/null +++ b/litellm/caching/valkey_semantic_cache.py @@ -0,0 +1,353 @@ +""" +Valkey Semantic Cache implementation for LiteLLM + +Backs semantic caching with Valkey (for example AWS ElastiCache for Valkey) +running the valkey-search module. + +RedisVL cannot drive valkey-search: it gates on a RediSearch module version +that valkey-search does not report, and its SemanticCache index uses a TEXT +field that valkey-search does not implement. This backend therefore talks to +valkey-search directly over redis-py, building a vector index from the field +types valkey-search does support (TAG for cache-key isolation and VECTOR for +the prompt embedding) and running KNN queries for retrieval. Prompt extraction, +embedding generation, and cached-response parsing are reused from +RedisSemanticCache since those are backend agnostic. +""" + +import asyncio +import hashlib +import os +import struct +from dataclasses import dataclass +from typing import Any + +from redis import Redis +from redis.asyncio import Redis as AsyncRedis +from redis.commands.search.field import TagField, VectorField +from redis.commands.search.indexDefinition import IndexDefinition, IndexType +from redis.commands.search.query import Query + +from litellm._logging import print_verbose +from litellm._uuid import uuid + +from .redis_semantic_cache import RedisSemanticCache + + +@dataclass(frozen=True, slots=True) +class _ValkeyCacheHit: + response: str + distance: float + + +class ValkeySemanticCache(RedisSemanticCache): + """Valkey-backed semantic cache for LLM responses.""" + + DEFAULT_VALKEY_INDEX_NAME: str = "litellm_semantic_cache_index" + EMBEDDING_FIELD_NAME: str = "embedding" + PROMPT_FIELD_NAME: str = "prompt" + RESPONSE_FIELD_NAME: str = "response" + DISTANCE_FIELD_NAME: str = "vector_distance" + + def __init__( + self, + host: str | None = None, + port: str | None = None, + password: str | None = None, + redis_url: str | None = None, + similarity_threshold: float | None = None, + embedding_model: str = "text-embedding-ada-002", + index_name: str | None = None, + ssl: bool = False, + startup_nodes: list | None = None, + sync_client: Redis | None = None, + async_client: AsyncRedis | None = None, + **kwargs: Any, + ): + if similarity_threshold is None: + raise ValueError("similarity_threshold must be provided, passed None") + + if startup_nodes: + raise ValueError( + "valkey-semantic does not support cluster-mode-enabled (multi-shard) " + "endpoints. The async cluster client cannot route the FT.* search " + "commands reliably. Point it at a cluster-mode-disabled endpoint " + "instead (a primary with replicas is fine; only horizontal sharding " + "is unsupported), or pass a single redis_url. On AWS, vector search " + "needs ElastiCache for Valkey 8.2+ on a node-based cluster." + ) + + self.similarity_threshold = similarity_threshold + self.embedding_model = embedding_model + self.index_name = index_name or self.DEFAULT_VALKEY_INDEX_NAME + 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) # type: ignore[arg-type] + ) + self.async_client = ( + async_client + if async_client is not None + else AsyncRedis.from_url(resolved_url) # type: ignore[arg-type] + ) + + print_verbose(f"Valkey semantic-cache initializing index - {self.index_name}") + + @staticmethod + def _build_valkey_url( + host: str | None, port: str | None, password: str | None, ssl: bool = False + ) -> str: + host = host or os.environ.get("VALKEY_HOST") or os.environ.get("REDIS_HOST") + port = port or os.environ.get("VALKEY_PORT") or os.environ.get("REDIS_PORT") + password = ( + password + or os.environ.get("VALKEY_PASSWORD") + or os.environ.get("REDIS_PASSWORD") + ) + + if not host or not port: + raise ValueError( + "Missing required Valkey configuration. Provide host and port " + "(or VALKEY_HOST/VALKEY_PORT), or pass redis_url." + ) + + credentials = f":{password}@" if password else "" + scheme = "rediss" if ssl else "redis" + return f"{scheme}://{credentials}{host}:{port}" + + @classmethod + def _scope_tag(cls, key: str) -> str: + # valkey-search TAG fields tokenize on punctuation and do not honour + # backslash escaping, so an arbitrary cache key cannot be matched + # verbatim. Hashing to hex yields a token that is always exact-match + # safe and still uniquely isolates a caller's scope. + return hashlib.sha256(str(key).encode("utf-8")).hexdigest() + + @staticmethod + def _embedding_to_bytes(embedding: list[float]) -> bytes: + return struct.pack(f"<{len(embedding)}f", *embedding) + + def _index_schema(self, dim: int) -> tuple[TagField, VectorField]: + return ( + TagField(self.CACHE_KEY_FIELD_NAME), + VectorField( + self.EMBEDDING_FIELD_NAME, + "HNSW", + {"TYPE": "FLOAT32", "DIM": dim, "DISTANCE_METRIC": "COSINE"}, + ), + ) + + def _index_definition(self) -> IndexDefinition: + return IndexDefinition(prefix=[self.key_prefix], index_type=IndexType.HASH) + + @staticmethod + def _is_index_exists_error(exc: Exception) -> bool: + return "already exists" in str(exc).lower() + + @staticmethod + def _extract_index_dim(info: dict) -> int | None: + # FT.INFO nests the vector field's "dimensions" one level inside its + # "index" block, so flatten each field descriptor a single level and + # scan for the dimensions marker. + for field in info.get("attributes") or []: + if not isinstance(field, (list, tuple)): + continue + flat = [ + sub + for item in field + for sub in (item if isinstance(item, (list, tuple)) else [item]) + ] + for i, marker in enumerate(flat): + if marker in (b"dimensions", "dimensions") and i + 1 < len(flat): + return int(flat[i + 1]) + return None + + def _assert_dim_matches(self, info: dict, dim: int) -> None: + existing_dim = self._extract_index_dim(info) + if existing_dim is not None and existing_dim != dim: + raise ValueError( + f"Valkey semantic-cache index '{self.index_name}' already exists with " + f"embedding dimension {existing_dim}, but the configured embedding " + f"model produced dimension {dim}. Use a different " + f"valkey_semantic_cache_index_name or drop the existing index." + ) + + def _ensure_index_sync(self, dim: int) -> None: + if self._index_dim == dim: + return + try: + self.sync_client.ft(self.index_name).create_index( + self._index_schema(dim), definition=self._index_definition() + ) + except Exception as exc: + if not self._is_index_exists_error(exc): + raise + self._assert_dim_matches(self.sync_client.ft(self.index_name).info(), dim) + self._index_dim = dim + + async def _ensure_index_async(self, dim: int) -> None: + if self._index_dim == dim: + return + try: + await self.async_client.ft(self.index_name).create_index( + self._index_schema(dim), definition=self._index_definition() + ) + except Exception as exc: + if not self._is_index_exists_error(exc): + raise + info = await self.async_client.ft(self.index_name).info() + self._assert_dim_matches(info, dim) + self._index_dim = dim + + 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: + return { + self.CACHE_KEY_FIELD_NAME: self._scope_tag(key), + self.PROMPT_FIELD_NAME: prompt, + self.RESPONSE_FIELD_NAME: value_str, + self.EMBEDDING_FIELD_NAME: self._embedding_to_bytes(embedding), + } + + def _knn_query(self, key: str) -> Query: + scope = self._scope_tag(key) + query_string = ( + f"(@{self.CACHE_KEY_FIELD_NAME}:{{{scope}}})" + f"=>[KNN 1 @{self.EMBEDDING_FIELD_NAME} $vec AS {self.DISTANCE_FIELD_NAME}]" + ) + return ( + Query(query_string) + .return_fields(self.RESPONSE_FIELD_NAME, self.DISTANCE_FIELD_NAME) + .dialect(2) + ) + + @classmethod + def _first_hit(cls, search_result: Any) -> _ValkeyCacheHit | None: + docs = getattr(search_result, "docs", []) + if not docs: + return None + doc = docs[0] + return _ValkeyCacheHit( + response=str(getattr(doc, cls.RESPONSE_FIELD_NAME)), + distance=float(getattr(doc, cls.DISTANCE_FIELD_NAME)), + ) + + def _resolve_hit(self, hit: _ValkeyCacheHit | None, key: str, **kwargs: Any) -> Any: + if hit is None: + kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 + return None + + similarity = 1 - hit.distance + kwargs.setdefault("metadata", {})["semantic-similarity"] = 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: + print_verbose(f"Valkey semantic-cache set_cache, kwargs: {kwargs}") + try: + prompt = self._get_prompt_from_kwargs(**kwargs) + if prompt is None: + print_verbose("No prompt provided for semantic caching") + return + + embedding = self._get_embedding(prompt) + self._ensure_index_sync(len(embedding)) + + doc_key = self._doc_key(key) + self.sync_client.hset( + doc_key, mapping=self._doc_mapping(key, prompt, str(value), embedding) + ) + ttl = self._get_ttl(**kwargs) + if ttl is not None: + self.sync_client.expire(doc_key, ttl) + except Exception as e: + print_verbose(f"Error in Valkey semantic-cache set_cache: {str(e)}") + + def get_cache(self, key: str, **kwargs: Any) -> Any: + print_verbose(f"Valkey semantic-cache get_cache, kwargs: {kwargs}") + try: + prompt = self._get_prompt_from_kwargs(**kwargs) + if prompt is None: + kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 + return None + + embedding = self._get_embedding(prompt) + self._ensure_index_sync(len(embedding)) + + search_result = self.sync_client.ft(self.index_name).search( + self._knn_query(key), + query_params={"vec": self._embedding_to_bytes(embedding)}, + ) + 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: {str(e)}") + kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 + + async def async_set_cache(self, key: str, value: Any, **kwargs: Any) -> None: + print_verbose(f"Async Valkey semantic-cache set_cache, kwargs: {kwargs}") + try: + prompt = self._get_prompt_from_kwargs(**kwargs) + if prompt is None: + print_verbose("No prompt provided for semantic caching") + return + + embedding = await self._get_async_embedding(prompt, **kwargs) + await self._ensure_index_async(len(embedding)) + + doc_key = self._doc_key(key) + await self.async_client.hset( + doc_key, mapping=self._doc_mapping(key, prompt, str(value), embedding) + ) + ttl = self._get_ttl(**kwargs) + if ttl is not None: + await self.async_client.expire(doc_key, ttl) + except Exception as e: + print_verbose(f"Error in async Valkey semantic-cache set_cache: {str(e)}") + + async def async_get_cache(self, key: str, **kwargs: Any) -> Any: + print_verbose(f"Async Valkey semantic-cache get_cache, kwargs: {kwargs}") + try: + prompt = self._get_prompt_from_kwargs(**kwargs) + if prompt is None: + kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 + return None + + embedding = await self._get_async_embedding(prompt, **kwargs) + await self._ensure_index_async(len(embedding)) + + search_result = await self.async_client.ft(self.index_name).search( + self._knn_query(key), + query_params={"vec": self._embedding_to_bytes(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: {str(e)}") + kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 + + async def async_set_cache_pipeline( + self, cache_list: list[tuple[str, Any]], **kwargs: Any + ) -> 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: {str(e)}" + ) + + async def _index_info(self) -> dict: + 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 87c26b776e8..d27cfefda73 100644 --- a/litellm/completion_extras/litellm_responses_transformation/handler.py +++ b/litellm/completion_extras/litellm_responses_transformation/handler.py @@ -171,6 +171,8 @@ class ResponsesToCompletionBridgeHandler: model_response = validated_kwargs["model_response"] logging_obj = validated_kwargs["logging_obj"] custom_llm_provider = validated_kwargs["custom_llm_provider"] + if kwargs.get("stream") is True and "stream" not in optional_params: + optional_params = {**optional_params, "stream": True} request_data = self.transformation_handler.transform_request( model=model, @@ -263,6 +265,8 @@ class ResponsesToCompletionBridgeHandler: model_response = validated_kwargs["model_response"] logging_obj = validated_kwargs["logging_obj"] custom_llm_provider = validated_kwargs["custom_llm_provider"] + if kwargs.get("stream") is True and "stream" not in optional_params: + optional_params = {**optional_params, "stream": True} try: request_data = self.transformation_handler.transform_request( diff --git a/litellm/completion_extras/litellm_responses_transformation/transformation.py b/litellm/completion_extras/litellm_responses_transformation/transformation.py index 6d8b5cf8a57..3fa6b983e5f 100644 --- a/litellm/completion_extras/litellm_responses_transformation/transformation.py +++ b/litellm/completion_extras/litellm_responses_transformation/transformation.py @@ -693,7 +693,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): original_response = model_call_details.get("original_response") return cls._recover_output_items_from_raw_sse(original_response) - def transform_response( # noqa: PLR0915 + def transform_response( self, model: str, raw_response: "BaseModel", @@ -1211,7 +1211,7 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator): return self.chunk_parser(json.loads(str_line)) @staticmethod - def translate_responses_chunk_to_openai_stream( # noqa: PLR0915 + def translate_responses_chunk_to_openai_stream( parsed_chunk: Union[dict, BaseModel], ) -> "ModelResponseStream": """ @@ -1293,9 +1293,15 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator): provider_specific_fields ) + from litellm.responses.litellm_completion_transformation.transformation import ( + LiteLLMCompletionResponsesConfig, + ) + tool_call_index = parsed_chunk.get("output_index", 0) tool_call_chunk = ChatCompletionToolCallChunk( - id=output_item.get("call_id"), + id=LiteLLMCompletionResponsesConfig._tool_call_id_from_responses_item( + output_item.get("id"), output_item.get("call_id") + ), index=tool_call_index, type="function", function=function_chunk, diff --git a/litellm/constants.py b/litellm/constants.py index f10cec034f0..212d34357f8 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -190,6 +190,10 @@ DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE = int( # Override with LITELLM_MAX_CALLBACKS env var for large deployments (e.g., many teams with guardrails) MAX_CALLBACKS = get_env_int("LITELLM_MAX_CALLBACKS", 100) +# Metadata key recording which pre_call guardrails the proxy loop already ran, +# so the deployment-level hook does not re-run them for the same request +PRE_CALL_EXECUTED_GUARDRAILS_KEY = "_pre_call_executed_guardrails" + # Generic fallback for unknown models DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET = int( os.getenv("DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET", 128) @@ -197,6 +201,18 @@ DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET = int( # Provider-specific API base URLs XAI_API_BASE = "https://api.x.ai/v1" +OPEN_SANDBOX_API_BASE_ENV_VAR = "OPEN_SANDBOX_API_BASE" +OPEN_SANDBOX_API_KEY_ENV_VAR = "OPEN_SANDBOX_API_KEY" +OPEN_SANDBOX_DEFAULT_TEMPLATE = "opensandbox/code-interpreter:v1.1.0" +_OPEN_SANDBOX_FALLBACK_ENTRYPOINT = "/opt/code-interpreter/code-interpreter.sh" +OPEN_SANDBOX_DEFAULT_ENTRYPOINT = (_OPEN_SANDBOX_FALLBACK_ENTRYPOINT,) +OPEN_SANDBOX_DEFAULT_LANGUAGE = "python" +OPEN_SANDBOX_DEFAULT_CPU_LIMIT = "1" +OPEN_SANDBOX_DEFAULT_MEMORY_LIMIT = "2Gi" +OPEN_SANDBOX_EXECD_PORT = 44772 +OPEN_SANDBOX_DEFAULT_TIMEOUT = 300 +OPEN_SANDBOX_READY_TIMEOUT = 30.0 +OPEN_SANDBOX_POLL_INTERVAL = 0.2 DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET = int( os.getenv("DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET", 1024) @@ -398,6 +414,9 @@ REDIS_CIRCUIT_BREAKER_FAILURE_THRESHOLD = int( REDIS_CIRCUIT_BREAKER_RECOVERY_TIMEOUT = int( os.getenv("REDIS_CIRCUIT_BREAKER_RECOVERY_TIMEOUT", 60) ) +REDIS_CIRCUIT_BREAKER_ENABLED = ( + os.getenv("REDIS_CIRCUIT_BREAKER_ENABLED", "true").lower() == "true" +) # Default Redis major version to assume when version cannot be determined # Using 7 as it's the modern version that supports LPOP with count parameter DEFAULT_REDIS_MAJOR_VERSION = int(os.getenv("DEFAULT_REDIS_MAJOR_VERSION", 7)) @@ -418,6 +437,7 @@ REPLICATE_POLLING_DELAY_SECONDS = float( DEFAULT_ANTHROPIC_CHAT_MAX_TOKENS = int( os.getenv("DEFAULT_ANTHROPIC_CHAT_MAX_TOKENS", 4096) ) +DEFAULT_OCI_CHAT_MAX_TOKENS = 4096 TOGETHER_AI_4_B = int(os.getenv("TOGETHER_AI_4_B", 4)) TOGETHER_AI_8_B = int(os.getenv("TOGETHER_AI_8_B", 8)) TOGETHER_AI_21_B = int(os.getenv("TOGETHER_AI_21_B", 21)) @@ -448,6 +468,7 @@ HTTP_HANDLER_CONNECT_TIMEOUT_SECONDS: float = 5.0 request_timeout: float = float( os.getenv("REQUEST_TIMEOUT", str(int(DEFAULT_REQUEST_TIMEOUT_SECONDS))) ) +request_timeout_explicitly_set: bool = "REQUEST_TIMEOUT" in os.environ DEFAULT_A2A_AGENT_TIMEOUT: float = float( os.getenv("DEFAULT_A2A_AGENT_TIMEOUT", 6000) ) # 10 minutes @@ -502,6 +523,8 @@ DD_TRACER_STREAMING_CHUNK_YIELD_RESOURCE = os.getenv( "DD_TRACER_STREAMING_CHUNK_YIELD_RESOURCE", "streaming.chunk.yield" ) +LITELLM_HTTP_STATUS_CLIENT_DISCONNECTED = 499 + EMAIL_BUDGET_ALERT_TTL = int( os.getenv("EMAIL_BUDGET_ALERT_TTL", 24 * 60 * 60) ) # 24 hours in seconds @@ -614,6 +637,7 @@ LITELLM_CHAT_PROVIDERS = [ "nscale", "nebius", "dashscope", + "modelscope", "moonshot", "publicai", "v0", @@ -772,6 +796,7 @@ openai_compatible_endpoints: List = [ "inference.api.nscale.com/v1", "api.studio.nebius.ai/v1", "https://dashscope-intl.aliyuncs.com/compatible-mode/v1", + "https://api-inference.modelscope.cn/v1", "https://api.moonshot.ai/v1", "https://api.publicai.co/v1", "https://api.synthetic.new/openai/v1", @@ -789,6 +814,8 @@ openai_compatible_endpoints: List = [ "https://ai-gateway.vercel.sh/v1", "https://api.inference.wandb.ai/v1", "https://api.clarifai.com/v2/ext/openai/v1", + "https://api.libertai.io/v1", + "https://pinstripes.io/v1", ] @@ -832,10 +859,12 @@ openai_compatible_providers: List = [ "poe", # Poe - JSON-configured provider "chutes", # Chutes - JSON-configured provider "parasail", # Parasail - JSON-configured provider + "libertai", # LibertAI - JSON-configured provider "featherless_ai", "nscale", "nebius", "dashscope", + "modelscope", "moonshot", "v0", "helicone", @@ -850,6 +879,8 @@ openai_compatible_providers: List = [ "clarifai", "docker_model_runner", "ragflow", + "pinstripes", # Pinstripes - JSON-configured provider + "darkbloom", ] openai_text_completion_compatible_providers: List = ( [ # providers that support `/v1/completions` @@ -861,6 +892,7 @@ openai_text_completion_compatible_providers: List = ( "featherless_ai", "nebius", "dashscope", + "modelscope", "moonshot", "publicai", "synthetic", @@ -1121,6 +1153,48 @@ WANDB_MODELS: set = set( ] ) +modelscope_models: set = set( + [ + # Qwen series models + "Qwen/Qwen3-0.6B", + "Qwen/Qwen3-1.7B", + "Qwen/Qwen3-4B", + "Qwen/Qwen3-8B", + "Qwen/Qwen3-14B", + "Qwen/Qwen3-30B-A3B", + "Qwen/Qwen3-32B", + "Qwen/Qwen3-235B-A22B", + "Qwen/Qwen3-235B-A22B-Instruct-2507", + "Qwen/Qwen3-235B-A22B-Thinking-2507", + "Qwen/Qwen3-30B-A3B-Thinking-2507", + "Qwen/Qwen3-Coder-30B-A3B-Instruct", + "Qwen/Qwen3-Coder-480B-A35B-Instruct", + "Qwen/Qwen3-Next-80B-A3B-Instruct", + "Qwen/Qwen3-Next-80B-A3B-Thinking", + "Qwen/Qwen3-VL-235B-A22B-Instruct", + "Qwen/Qwen3-VL-8B-Instruct", + "Qwen/Qwen3-VL-8B-Thinking", + "Qwen/Qwen3.5-122B-A10B", + "Qwen/Qwen3.5-27B", + "Qwen/Qwen3.5-35B-A3B", + "Qwen/Qwen3.5-397B-A17B", + "Qwen/QwQ-32B", + "Qwen/QwQ-32B-Preview", + "Qwen/QVQ-72B-Preview", + "Qwen/Qwen-Image-Edit", + # DeepSeek series models + "deepseek-ai/DeepSeek-R1-0528", + "deepseek-ai/DeepSeek-R1-Distill-Llama-70B", + "deepseek-ai/DeepSeek-R1-Distill-Llama-8B", + "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", + "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B", + "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", + "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", + "deepseek-ai/DeepSeek-V3.2", + "deepseek-ai/DeepSeek-V4-Flash", + ] +) + BEDROCK_INVOKE_PROVIDERS_LITERAL = Literal[ "cohere", "anthropic", @@ -1158,6 +1232,7 @@ BEDROCK_CONVERSE_MODELS = [ "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", "anthropic.claude-opus-4-8", "anthropic.claude-opus-4-7", "anthropic.claude-opus-4-6-v1:0", @@ -1479,6 +1554,7 @@ DB_SPEND_UPDATE_JOB_NAME = "db_spend_update_job" DB_DAILY_TAG_SPEND_UPDATE_JOB_NAME = "db_daily_tag_spend_update_job" PROMETHEUS_EMIT_BUDGET_METRICS_JOB_NAME = "prometheus_emit_budget_metrics" CLOUDZERO_EXPORT_USAGE_DATA_JOB_NAME = "cloudzero_export_usage_data" +MAVVRIK_FOCUS_EXPORT_JOB_NAME = "mavvrik_focus_export_usage_data" CLOUDZERO_MAX_FETCHED_DATA_RECORDS = int( os.getenv("CLOUDZERO_MAX_FETCHED_DATA_RECORDS", 50000) ) @@ -1493,6 +1569,10 @@ SPEND_LOG_CLEANUP_MAX_CONSECUTIVE_BATCH_FAILURES = int( SPEND_LOG_CLEANUP_BATCH_FAILURE_BACKOFF_SECONDS = float( os.getenv("SPEND_LOG_CLEANUP_BATCH_FAILURE_BACKOFF_SECONDS", 0.5) ) +SPEND_LOG_PARTITION_INTERVAL = os.getenv("SPEND_LOG_PARTITION_INTERVAL", "day") +SPEND_LOG_PARTITION_PRECREATE_AHEAD = int( + os.getenv("SPEND_LOG_PARTITION_PRECREATE_AHEAD", 7) +) SPEND_LOG_QUEUE_SIZE_THRESHOLD = int(os.getenv("SPEND_LOG_QUEUE_SIZE_THRESHOLD", 100)) SPEND_LOG_QUEUE_POLL_INTERVAL = float(os.getenv("SPEND_LOG_QUEUE_POLL_INTERVAL", 2.0)) SPEND_COUNTER_RESEED_LOCKS_MAX_SIZE = int( diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index cf8fa602be5..bfdce95d89a 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -94,6 +94,7 @@ from litellm.types.utils import ( LlmProviders, LlmProvidersSet, ModelInfo, + ServiceTier, StandardBuiltInToolsParams, TranscriptionUsageDurationObject, TranscriptionUsageTokensObject, @@ -288,7 +289,7 @@ def _transcription_usage_has_token_details( return (prompt_tokens_val > 0) or (completion_tokens_val > 0) -def cost_per_token( # noqa: PLR0915 +def cost_per_token( model: str = "", prompt_tokens: int = 0, completion_tokens: int = 0, @@ -614,7 +615,9 @@ def cost_per_token( # noqa: PLR0915 service_tier=service_tier, ) elif custom_llm_provider == "anthropic": - return anthropic_cost_per_token(model=model, usage=usage_block) + return anthropic_cost_per_token( + model=model, usage=usage_block, service_tier=service_tier + ) elif custom_llm_provider == "bedrock": return bedrock_cost_per_token( model=model, usage=usage_block, service_tier=service_tier @@ -885,6 +888,23 @@ def _map_traffic_type_to_service_tier(traffic_type: Optional[str]) -> Optional[s return service_tier +def _normalize_service_tier(service_tier: object) -> str | None: + """ + Reduce a service_tier value to a concrete billable tier string or None. + + "auto" is a routing preference and any non-string value is not a billable + tier, so both defer to standard pricing (or to the tier the provider reports + on the response usage) instead of crashing the downstream cost-key lookup, + which calls service_tier.lower() + """ + if ( + not isinstance(service_tier, str) + or service_tier.lower() == ServiceTier.AUTO.value + ): + return None + return service_tier + + def _get_usage_object( completion_response: Any, ) -> Optional[Usage]: @@ -1136,7 +1156,7 @@ def _store_cost_breakdown_in_logging_obj( pass -def completion_cost( # noqa: PLR0915 +def completion_cost( completion_response=None, model: Optional[str] = None, prompt="", @@ -1224,6 +1244,8 @@ def completion_cost( # noqa: PLR0915 if service_tier is None and optional_params is not None: service_tier = optional_params.get("service_tier") + service_tier = _normalize_service_tier(service_tier) + # Extract service_tier from completion_response if not provided if service_tier is None and completion_response is not None: if isinstance(completion_response, BaseModel): @@ -1231,6 +1253,8 @@ def completion_cost( # noqa: PLR0915 elif isinstance(completion_response, dict): service_tier = completion_response.get("service_tier") + service_tier = _normalize_service_tier(service_tier) + # Extract service_tier from usage object if not provided if service_tier is None and cost_per_token_usage_object is not None: if isinstance(cost_per_token_usage_object, BaseModel): @@ -1240,6 +1264,8 @@ def completion_cost( # noqa: PLR0915 elif isinstance(cost_per_token_usage_object, dict): service_tier = cost_per_token_usage_object.get("service_tier") + service_tier = _normalize_service_tier(service_tier) + selected_model = _select_model_name_for_cost_calc( model=model, completion_response=completion_response, @@ -2489,6 +2515,11 @@ class RealtimeAPITokenUsageProcessor(BaseTokenUsageProcessor): ) +_TRANSCRIPTION_COMPLETED_EVENT_TYPE = ( + "conversation.item.input_audio_transcription.completed" +) + + def handle_realtime_stream_cost_calculation( results: OpenAIRealtimeStreamList, combined_usage_object: Usage, @@ -2543,4 +2574,99 @@ def handle_realtime_stream_cost_calculation( total_cost_usd_dollar=total_cost, ) + if any(r.get("type") == _TRANSCRIPTION_COMPLETED_EVENT_TYPE for r in results): + total_cost += handle_realtime_transcription_cost_calculation( + results=results, + custom_llm_provider=custom_llm_provider, + litellm_model_name=litellm_model_name, + ) + return total_cost + + +def handle_realtime_transcription_cost_calculation( + results: OpenAIRealtimeStreamList, + custom_llm_provider: str, + litellm_model_name: str, +) -> float: + """ + Cost for realtime transcription sessions (e.g. gpt-realtime-whisper). + + Transcription sessions emit no `response.done` events; instead each + `conversation.item.input_audio_transcription.completed` event carries a + `usage` object billed by the ASR model. The usage is one of: + - {"type": "duration", "seconds": } → priced via input_cost_per_second + - {"type": "tokens", "input_tokens": ...} → priced via input/audio token cost + """ + completed_events = [ + cast(dict, result) + for result in results + if result.get("type") == _TRANSCRIPTION_COMPLETED_EVENT_TYPE + ] + if not completed_events: + return 0.0 + + model_name = ( + _get_transcription_model_name_from_results(results) or litellm_model_name + ) + try: + model_info = litellm.get_model_info( + model=model_name, custom_llm_provider=custom_llm_provider + ) + except Exception: + model_info = None + + total_cost = 0.0 + for event in completed_events: + usage = event.get("usage") or {} + total_cost += _transcription_usage_cost(usage, model_info) + return total_cost + + +def _get_transcription_model_name_from_results( + results: OpenAIRealtimeStreamList, +) -> Optional[str]: + """Resolve the ASR model from a transcription_session.* / session.* event.""" + for result in results: + if result.get("type") in ( + "transcription_session.created", + "transcription_session.updated", + "session.created", + "session.updated", + ): + session = cast(dict, result).get("session", {}) or {} + transcription = ( + (session.get("audio", {}) or {}).get("input", {}) or {} + ).get("transcription", {}) or session.get("input_audio_transcription", {}) + model = (transcription or {}).get("model") or session.get("model") + if model: + return model + return None + + +def _transcription_usage_cost(usage: dict, model_info: Optional[ModelInfo]) -> float: + if model_info is None: + return 0.0 + usage_type = usage.get("type") + if usage_type == "duration": + seconds = usage.get("seconds") or 0.0 + per_second = model_info.get("input_cost_per_second") or 0.0 + return float(seconds) * float(per_second) + if usage_type == "tokens": + input_token_details = usage.get("input_token_details") or {} + audio_tokens = input_token_details.get("audio_tokens") or 0 + text_tokens = input_token_details.get("text_tokens") or 0 + output_tokens = usage.get("output_tokens") or 0 + audio_cost = float(audio_tokens) * float( + model_info.get("input_cost_per_audio_token") + or model_info.get("input_cost_per_token") + or 0.0 + ) + text_cost = float(text_tokens) * float( + model_info.get("input_cost_per_token") or 0.0 + ) + output_cost = float(output_tokens) * float( + model_info.get("output_cost_per_token") or 0.0 + ) + return audio_cost + text_cost + output_cost + return 0.0 diff --git a/litellm/google_genai/streaming_iterator.py b/litellm/google_genai/streaming_iterator.py index 3e97b480779..a8d0e5976f0 100644 --- a/litellm/google_genai/streaming_iterator.py +++ b/litellm/google_genai/streaming_iterator.py @@ -18,6 +18,42 @@ else: GLOBAL_PASS_THROUGH_SUCCESS_HANDLER_OBJ = PassThroughEndpointLogging() +def _encode_google_genai_sse_event(event_lines: List[str]) -> bytes: + return ("\n".join(event_lines) + "\n\n").encode("utf-8") + + +def _next_google_genai_sse_chunk(line_iter) -> bytes: + event_lines: List[str] = [] + while True: + try: + line = next(line_iter) + except StopIteration: + if event_lines: + return _encode_google_genai_sse_event(event_lines) + raise + if line == "": + if event_lines: + return _encode_google_genai_sse_event(event_lines) + continue + event_lines.append(line) + + +async def _anext_google_genai_sse_chunk(line_iter) -> bytes: + event_lines: List[str] = [] + while True: + try: + line = await line_iter.__anext__() + except StopAsyncIteration: + if event_lines: + return _encode_google_genai_sse_event(event_lines) + raise + if line == "": + if event_lines: + return _encode_google_genai_sse_event(event_lines) + continue + event_lines.append(line) + + class BaseGoogleGenAIGenerateContentStreamingIterator: """ Base class for Google GenAI Generate Content streaming iterators that provides common logic @@ -91,18 +127,17 @@ class GoogleGenAIGenerateContentStreamingIterator( self.generate_content_provider_config = generate_content_provider_config self.litellm_metadata = litellm_metadata self.custom_llm_provider = custom_llm_provider - # Store the iterator once to avoid multiple stream consumption - self.stream_iterator = response.iter_bytes() + # Gemini streamGenerateContent uses SSE line framing; iter_lines keeps + # large inlineData payloads (e.g. image/jpeg) intact within one event. + self.stream_iterator = response.iter_lines() def __iter__(self): return self def __next__(self): try: - # Get the next chunk from the stored iterator - chunk = next(self.stream_iterator) + chunk = _next_google_genai_sse_chunk(self.stream_iterator) self.collected_chunks.append(chunk) - # Just yield raw bytes return chunk except StopIteration: raise StopIteration @@ -147,18 +182,17 @@ class AsyncGoogleGenAIGenerateContentStreamingIterator( self.generate_content_provider_config = generate_content_provider_config self.litellm_metadata = litellm_metadata self.custom_llm_provider = custom_llm_provider - # Store the async iterator once to avoid multiple stream consumption - self.stream_iterator = response.aiter_bytes() + # Gemini streamGenerateContent uses SSE line framing; aiter_lines keeps + # large inlineData payloads (e.g. image/jpeg) intact within one event. + self.stream_iterator = response.aiter_lines() def __aiter__(self): return self async def __anext__(self): try: - # Get the next chunk from the stored async iterator - chunk = await self.stream_iterator.__anext__() + chunk = await _anext_google_genai_sse_chunk(self.stream_iterator) self.collected_chunks.append(chunk) - # Just yield raw bytes return chunk except StopAsyncIteration: await self._handle_async_streaming_logging() diff --git a/litellm/images/main.py b/litellm/images/main.py index d95b7287d20..8b108ded4c9 100644 --- a/litellm/images/main.py +++ b/litellm/images/main.py @@ -195,7 +195,7 @@ def image_generation( @client -def image_generation( # noqa: PLR0915 +def image_generation( prompt: str, model: Optional[str] = None, n: Optional[int] = None, @@ -738,7 +738,7 @@ def image_variation( @client -def image_edit( # noqa: PLR0915 +def image_edit( image: Optional[Union[FileTypes, List[FileTypes]]] = None, prompt: Optional[str] = None, model: Optional[str] = None, diff --git a/litellm/integrations/SlackAlerting/budget_alert_types.py b/litellm/integrations/SlackAlerting/budget_alert_types.py index ea80b258540..2a19ec0b7fa 100644 --- a/litellm/integrations/SlackAlerting/budget_alert_types.py +++ b/litellm/integrations/SlackAlerting/budget_alert_types.py @@ -1,7 +1,7 @@ from abc import ABC, abstractmethod from typing import Literal -from litellm.proxy._types import CallInfo +from litellm.proxy._types import CallInfo, Litellm_EntityType class BaseBudgetAlertType(ABC): @@ -31,6 +31,8 @@ class SoftBudgetAlert(BaseBudgetAlertType): return "Soft Budget Crossed: " def get_id(self, user_info: CallInfo) -> str: + if user_info.event_group == Litellm_EntityType.TEAM: + return user_info.team_id or "default_id" return user_info.token or "default_id" diff --git a/litellm/integrations/SlackAlerting/hanging_request_check.py b/litellm/integrations/SlackAlerting/hanging_request_check.py index d2f70c9caf1..98f1eb2d551 100644 --- a/litellm/integrations/SlackAlerting/hanging_request_check.py +++ b/litellm/integrations/SlackAlerting/hanging_request_check.py @@ -8,6 +8,7 @@ Notes: """ import asyncio +import time from typing import TYPE_CHECKING, Any, Optional import litellm @@ -36,11 +37,15 @@ class AlertingHangingRequestCheck: slack_alerting_object: SlackAlerting, ): self.slack_alerting_object = slack_alerting_object + # checks run every alerting_threshold / 2 seconds, so entries must + # stay cached for at least 1.5x the threshold to guarantee a check + # happens after they cross it + self.hanging_request_cache_ttl = int( + self.slack_alerting_object.alerting_threshold * 1.5 + + HANGING_ALERT_BUFFER_TIME_SECONDS + ) self.hanging_request_cache = InMemoryCache( - default_ttl=int( - self.slack_alerting_object.alerting_threshold - + HANGING_ALERT_BUFFER_TIME_SECONDS - ), + default_ttl=self.hanging_request_cache_ttl, ) async def add_request_to_hanging_request_check( @@ -76,10 +81,7 @@ class AlertingHangingRequestCheck: await self.hanging_request_cache.async_set_cache( key=hanging_request_data.request_id, value=hanging_request_data, - ttl=int( - self.slack_alerting_object.alerting_threshold - + HANGING_ALERT_BUFFER_TIME_SECONDS - ), + ttl=self.hanging_request_cache_ttl, ) return @@ -111,6 +113,9 @@ class AlertingHangingRequestCheck: if hanging_request_data is None: continue + if hanging_request_data.alerted: + continue + request_status = ( await proxy_logging_obj.internal_usage_cache.async_get_cache( key="request_status:{}".format(hanging_request_data.request_id), @@ -127,12 +132,21 @@ class AlertingHangingRequestCheck: ) continue + request_age_seconds = time.time() - hanging_request_data.created_at + if request_age_seconds < self.slack_alerting_object.alerting_threshold: + # in-flight but below the alerting threshold; keep it cached + # so a later check can alert if it never completes + continue + ################ # Send the Alert on Slack ################ await self.send_hanging_request_alert( hanging_request_data=hanging_request_data ) + # flag so the entry is skipped on later ticks; one alert per hang, + # with the existing TTL still handling cleanup + hanging_request_data.alerted = True return diff --git a/litellm/integrations/SlackAlerting/slack_alerting.py b/litellm/integrations/SlackAlerting/slack_alerting.py index 390af2cb6e6..2108ebae312 100644 --- a/litellm/integrations/SlackAlerting/slack_alerting.py +++ b/litellm/integrations/SlackAlerting/slack_alerting.py @@ -351,7 +351,7 @@ class SlackAlerting(CustomBatchLogger): except Exception: return 0 - async def send_daily_reports(self, router) -> bool: # noqa: PLR0915 + async def send_daily_reports(self, router) -> bool: """ Send a daily report on: - Top 5 deployments with most failed requests @@ -1179,7 +1179,7 @@ Model Info: if response.status_code == 200: return True else: - print("Error sending webhook alert. Error=", response.text) # noqa + print("Error sending webhook alert. Error=", response.text) # noqa: T201 return False @@ -1373,7 +1373,7 @@ Model Info: return False - async def send_alert( # noqa: PLR0915 + async def send_alert( self, message: str, level: Literal["Low", "Medium", "High"], diff --git a/litellm/integrations/anthropic_cache_control_hook.py b/litellm/integrations/anthropic_cache_control_hook.py index 213622cb43a..296bfb6fc85 100644 --- a/litellm/integrations/anthropic_cache_control_hook.py +++ b/litellm/integrations/anthropic_cache_control_hook.py @@ -27,6 +27,11 @@ else: LiteLLMLoggingObj = Any +# Anthropic (and Bedrock Claude) reject requests with more than 4 cache_control +# breakpoints: "A maximum of 4 blocks with cache_control may be provided." +MAX_CACHE_CONTROL_BLOCKS = 4 + + class AnthropicCacheControlHook(CustomPromptManagement): def get_chat_completion_prompt( self, @@ -61,16 +66,30 @@ class AnthropicCacheControlHook(CustomPromptManagement): processed_messages = copy.deepcopy(messages) # Separate message-level and non-message-level injection points - remaining_points = [] + message_points: List[CacheControlMessageInjectionPoint] = [] + remaining_points: List[CacheControlInjectionPoint] = [] for point in injection_points: if point.get("location") == "message": - point = cast(CacheControlMessageInjectionPoint, point) - processed_messages = self._process_message_injection( - point=point, messages=processed_messages - ) + message_points.append(cast(CacheControlMessageInjectionPoint, point)) else: remaining_points.append(point) + # Non-message points (currently Bedrock tool_config) are handled in the + # provider transform, where each tool_config point appends at most one + # cachePoint to the tools. That block also counts toward Anthropic's + # limit, so reserve a slot for it here to leave room. + reserved_blocks = ( + 1 + if any(p.get("location") == "tool_config" for p in remaining_points) + else 0 + ) + + processed_messages = self._apply_message_injections( + points=message_points, + messages=processed_messages, + max_blocks=MAX_CACHE_CONTROL_BLOCKS - reserved_blocks, + ) + # Pass through non-message injection points for provider-specific handling if remaining_points: non_default_params["cache_control_injection_points"] = remaining_points @@ -78,14 +97,71 @@ class AnthropicCacheControlHook(CustomPromptManagement): return model, processed_messages, non_default_params @staticmethod - def _process_message_injection( - point: CacheControlMessageInjectionPoint, messages: List[AllMessageValues] + def _apply_message_injections( + points: List[CacheControlMessageInjectionPoint], + messages: List[AllMessageValues], + max_blocks: int, ) -> List[AllMessageValues]: - """Process message-level cache control injection.""" - control: ChatCompletionCachedContent = point.get( - "control", None - ) or ChatCompletionCachedContent(type="ephemeral") + """Apply message-level cache control injection points in order. + Anthropic allows at most ``MAX_CACHE_CONTROL_BLOCKS`` cache_control + breakpoints per request. Client-supplied breakpoints count toward that + limit, so we never inject onto a message that already carries + cache_control (preserving the client's TTL) and we stop injecting once + ``max_blocks`` is reached. Injection points are honored in config order, + so earlier points win when slots are scarce. + """ + used_blocks = sum( + AnthropicCacheControlHook._count_cache_control_blocks(msg) + for msg in messages + ) + + limit_reached = False + for point in points: + if used_blocks >= max_blocks: + limit_reached = True + break + + control: ChatCompletionCachedContent = point.get( + "control", None + ) or ChatCompletionCachedContent(type="ephemeral") + + for target_index in AnthropicCacheControlHook._resolve_target_indices( + point=point, messages=messages + ): + if used_blocks >= max_blocks: + limit_reached = True + break + + if AnthropicCacheControlHook._message_has_cache_control( + messages[target_index] + ): + # Client already marked this message; don't overwrite it. + continue + + messages[target_index] = ( + AnthropicCacheControlHook._safe_insert_cache_control_in_message( + messages[target_index], control + ) + ) + used_blocks += 1 + + if limit_reached: + break + + if limit_reached: + verbose_logger.warning( + f"AnthropicCacheControlHook: Reached the Anthropic limit of " + f"{MAX_CACHE_CONTROL_BLOCKS} cache_control blocks. Skipping further injection." + ) + + return messages + + @staticmethod + def _resolve_target_indices( + point: CacheControlMessageInjectionPoint, messages: List[AllMessageValues] + ) -> List[int]: + """Resolve which message indices an injection point targets.""" _targetted_index: Optional[Union[int, str]] = point.get("index", None) targetted_index: Optional[int] = None if isinstance(_targetted_index, str): @@ -96,36 +172,49 @@ class AnthropicCacheControlHook(CustomPromptManagement): else: targetted_index = _targetted_index - targetted_role = point.get("role", None) - # Case 1: Target by specific index if targetted_index is not None: original_index = targetted_index - # Handle negative indices (convert to positive) if targetted_index < 0: targetted_index += len(messages) if 0 <= targetted_index < len(messages): - messages[targetted_index] = ( - AnthropicCacheControlHook._safe_insert_cache_control_in_message( - messages[targetted_index], control - ) - ) - else: - verbose_logger.warning( - f"AnthropicCacheControlHook: Provided index {original_index} is out of bounds for message list of length {len(messages)}. " - f"Targeted index was {targetted_index}. Skipping cache control injection for this point." - ) + return [targetted_index] + + verbose_logger.warning( + f"AnthropicCacheControlHook: Provided index {original_index} is out of bounds for message list of length {len(messages)}. " + f"Targeted index was {targetted_index}. Skipping cache control injection for this point." + ) + return [] + # Case 2: Target by role - elif targetted_role is not None: - for msg in messages: - if msg.get("role") == targetted_role: - msg = ( - AnthropicCacheControlHook._safe_insert_cache_control_in_message( - message=msg, control=control - ) - ) - return messages + targetted_role = point.get("role", None) + if targetted_role is not None: + return [ + idx + for idx, msg in enumerate(messages) + if msg.get("role") == targetted_role + ] + + return [] + + @staticmethod + def _count_cache_control_blocks(message: AllMessageValues) -> int: + """Count cache_control breakpoints on a message (message + content level).""" + count = 0 + if message.get("cache_control") is not None: + count += 1 + content = message.get("content") + if isinstance(content, list): + for block in content: + if isinstance(block, dict) and block.get("cache_control") is not None: + count += 1 + return count + + @staticmethod + def _message_has_cache_control(message: AllMessageValues) -> bool: + """Return True if the message already carries any cache_control.""" + return AnthropicCacheControlHook._count_cache_control_blocks(message) > 0 @staticmethod def _safe_insert_cache_control_in_message( diff --git a/litellm/integrations/braintrust_logging.py b/litellm/integrations/braintrust_logging.py index 9b1c5077882..6a6313f72e1 100644 --- a/litellm/integrations/braintrust_logging.py +++ b/litellm/integrations/braintrust_logging.py @@ -133,9 +133,7 @@ class BraintrustLogger(CustomLogger): self.default_project_id = project_dict["id"] - def log_success_event( # noqa: PLR0915 - self, kwargs, response_obj, start_time, end_time - ): + def log_success_event(self, kwargs, response_obj, start_time, end_time): verbose_logger.debug("REACHES BRAINTRUST SUCCESS") try: litellm_call_id = kwargs.get("litellm_call_id") @@ -271,9 +269,7 @@ class BraintrustLogger(CustomLogger): except Exception as e: raise e # don't use verbose_logger.exception, if exception is raised - async def async_log_success_event( # noqa: PLR0915 - self, kwargs, response_obj, start_time, end_time - ): + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): verbose_logger.debug("REACHES BRAINTRUST SUCCESS") try: litellm_call_id = kwargs.get("litellm_call_id") diff --git a/litellm/integrations/callback_configs.json b/litellm/integrations/callback_configs.json index 3a69c9a7936..590c848767a 100644 --- a/litellm/integrations/callback_configs.json +++ b/litellm/integrations/callback_configs.json @@ -290,6 +290,21 @@ }, "description": "Langsmith Logging Integration" }, + { + "id": "newrelic", + "displayName": "New Relic", + "logo": "newrelic.png", + "supports_key_team_logging": false, + "dynamic_params": { + "NEW_RELIC_AI_MONITORING_RECORD_CONTENT_ENABLED": { + "type": "text", + "ui_name": "Record AI Content (default: true)", + "description": "Whether to record AI message content. Set to false to disable.", + "required": false + } + }, + "description": "New Relic AI Monitoring Integration" + }, { "id": "openmeter", "displayName": "OpenMeter", diff --git a/litellm/integrations/code_interpreter_interception/__init__.py b/litellm/integrations/code_interpreter_interception/__init__.py new file mode 100644 index 00000000000..2256356b6f5 --- /dev/null +++ b/litellm/integrations/code_interpreter_interception/__init__.py @@ -0,0 +1,15 @@ +""" +Code Interpreter Interception Module + +Converts the native OpenAI Responses ``code_interpreter`` tool into a function +tool, runs the model-emitted code in a sandbox, and feeds the result back into +the agentic loop. +""" + +from litellm.integrations.code_interpreter_interception.handler import ( + CodeInterpreterInterceptionLogger, +) + +__all__ = [ + "CodeInterpreterInterceptionLogger", +] diff --git a/litellm/integrations/code_interpreter_interception/handler.py b/litellm/integrations/code_interpreter_interception/handler.py new file mode 100644 index 00000000000..362581937d7 --- /dev/null +++ b/litellm/integrations/code_interpreter_interception/handler.py @@ -0,0 +1,839 @@ +""" +Code Interpreter Interception Handler + +CustomLogger that swaps the native OpenAI Responses ``code_interpreter`` tool for +a function tool, executes the code the model emits inside a sandbox, and feeds the +captured stdout back through the typed agentic loop plan. +""" + +import json +import time +import uuid +from typing import Any, Literal, TypedDict, cast + +import litellm +from pydantic import ValidationError + +from litellm._logging import verbose_logger +from litellm.integrations.custom_logger import CustomLogger +from litellm.types.integrations.code_interpreter_interception import ( + CodeInterpreterInterceptionConfig, +) +from litellm.types.integrations.custom_logger import ( + AgenticLoopPlan, + AgenticLoopRequestPatch, + CHAT_COMPLETION_AGENTIC_SURFACE, + NON_CODE_INTERPRETER_INTERCEPTION_INTERNAL_PREFIXES, + is_interception_internal_key, +) +from litellm.types.llms.openai import ( + ChatCompletionAssistantMessage, + ChatCompletionAssistantToolCall, + ChatCompletionToolMessage, +) +from litellm.types.utils import ( + CallTypes, + ChatCompletionMessageToolCall, + ModelResponse, +) + +LITELLM_CODE_EXECUTION_TOOL_NAME = "litellm_code_execution" +_INTERCEPTION_ACTIVE_KEY = "_code_interpreter_interception_active" +_SANDBOX_KEY = "_code_interpreter_interception_sandbox_key" +_CONVERTED_STREAM_KEY = "_code_interpreter_interception_converted_stream" +_LITELLM_METADATA_KEY = "litellm_metadata" +_CACHE_TTL_SECONDS = 15 * 60 + + +class CodeExecutionToolCall(TypedDict, total=False): + id: str | None + call_id: str | None + type: Literal["function"] + name: str + arguments: str + + +class CodeInterpreterLogOutput(TypedDict): + type: Literal["logs"] + logs: str + + +class CodeInterpreterCall(TypedDict): + id: str + type: Literal["code_interpreter_call"] + status: Literal["completed"] + code: str + container_id: str | None + outputs: list[CodeInterpreterLogOutput] + + +class CodeExecutionFunctionParameters(TypedDict): + type: Literal["object"] + properties: dict[str, dict[str, str]] + required: list[str] + + +class ResponsesFunctionTool(TypedDict): + type: Literal["function"] + name: str + description: str + parameters: CodeExecutionFunctionParameters + + +class ChatCompletionFunctionDefinition(TypedDict): + name: str + description: str + parameters: CodeExecutionFunctionParameters + + +class ChatCompletionFunctionTool(TypedDict): + type: Literal["function"] + function: ChatCompletionFunctionDefinition + + +CodeExecutionFunctionTool = ResponsesFunctionTool | ChatCompletionFunctionTool + + +class ResponsesFunctionToolChoice(TypedDict): + type: Literal["function"] + name: str + + +class ChatCompletionFunctionToolChoice(TypedDict): + type: Literal["function"] + function: dict[str, str] + + +CodeExecutionFunctionToolChoice = ( + ResponsesFunctionToolChoice | ChatCompletionFunctionToolChoice +) + + +def _resolve_sandbox_tool(sandbox_tool_name: str | None) -> dict[str, Any] | None: + try: + from litellm.sandbox.sandbox_tools import resolve_sandbox_tool + except ImportError: + return None + return resolve_sandbox_tool(sandbox_tool_name) + + +class CodeInterpreterInterceptionLogger(CustomLogger): + """ + CustomLogger that implements transparent code-interpreter execution loops. + + Flow: + 1. Replace the native ``code_interpreter`` tool with a function tool in the + pre-call hook so the model emits code as function-call arguments. + 2. Detect ``litellm_code_execution`` function calls in the model response. + 3. Run the emitted code in a sandbox (reused per request via a server-minted + sandbox key) and build a typed rerun plan that appends the + function_call_output. + """ + + def __init__( + self, + enabled: bool = True, + enabled_providers: list[str] | None = None, + sandbox_tool_name: str | None = None, + sandbox_config: Any | None = None, + ): + super().__init__() + self.enabled = enabled + self.enabled_providers = enabled_providers + self.sandbox_tool_name = sandbox_tool_name + self.sandbox_config = sandbox_config + self._container_cache: dict[str, tuple[Any, dict[str, Any] | None, float]] = {} + + @classmethod + def from_config_yaml( + cls, config: CodeInterpreterInterceptionConfig + ) -> "CodeInterpreterInterceptionLogger": + return cls( + enabled=bool(config.get("enabled", True)), + enabled_providers=config.get("enabled_providers"), + sandbox_tool_name=config.get("sandbox_tool_name"), + ) + + @staticmethod + def initialize_from_proxy_config( + litellm_settings: dict[str, Any], + callback_specific_params: dict[str, Any], + ) -> "CodeInterpreterInterceptionLogger": + params: CodeInterpreterInterceptionConfig = {} + if "code_interpreter_interception_params" in litellm_settings: + params = litellm_settings["code_interpreter_interception_params"] + elif "code_interpreter_interception" in callback_specific_params and isinstance( + callback_specific_params["code_interpreter_interception"], dict + ): + params = cast( + CodeInterpreterInterceptionConfig, + callback_specific_params["code_interpreter_interception"], + ) + return CodeInterpreterInterceptionLogger.from_config_yaml(params) + + async def async_pre_call_deployment_hook( + self, kwargs: dict[str, Any], call_type: CallTypes | None + ) -> dict | None: + if not kwargs.get("_agentic_loop_depth"): + kwargs.pop(_INTERCEPTION_ACTIVE_KEY, None) + kwargs.pop(_SANDBOX_KEY, None) + self._strip_interception_metadata(kwargs) + if not self.enabled: + return None + if call_type not in ( + CallTypes.responses, + CallTypes.aresponses, + CallTypes.completion, + CallTypes.acompletion, + ): + return None + if ( + self.enabled_providers is not None + and self._resolve_provider(kwargs) not in self.enabled_providers + ): + return None + + tools = kwargs.get("tools") + if not isinstance(tools, list): + return None + if not any( + isinstance(tool, dict) and tool.get("type") == "code_interpreter" + for tool in tools + ): + return None + + kwargs[_INTERCEPTION_ACTIVE_KEY] = True + kwargs[_SANDBOX_KEY] = uuid.uuid4().hex + if kwargs.get("stream"): + kwargs["stream"] = False + kwargs[_CONVERTED_STREAM_KEY] = True + self._write_interception_metadata(kwargs) + + function_tool = self._get_function_tool(call_type=call_type) + kwargs["tools"] = [ + ( + function_tool + if isinstance(tool, dict) and tool.get("type") == "code_interpreter" + else tool + ) + for tool in tools + ] + if self._tool_choice_targets_code_interpreter(kwargs.get("tool_choice")): + kwargs["tool_choice"] = self._get_function_tool_choice(call_type=call_type) + return kwargs + + @staticmethod + def _strip_interception_metadata(kwargs: dict[str, Any]) -> None: + metadata = kwargs.get(_LITELLM_METADATA_KEY) + if not isinstance(metadata, dict): + return + filtered_metadata = { + key: value + for key, value in metadata.items() + if not is_interception_internal_key(key) + and not key.startswith("_agentic_loop") + and key != "max_agentic_loops" + } + if filtered_metadata: + kwargs[_LITELLM_METADATA_KEY] = filtered_metadata + else: + kwargs.pop(_LITELLM_METADATA_KEY, None) + + @staticmethod + def _write_interception_metadata(kwargs: dict[str, Any]) -> None: + metadata = kwargs.get(_LITELLM_METADATA_KEY) + metadata = dict(metadata) if isinstance(metadata, dict) else {} + for key in (_INTERCEPTION_ACTIVE_KEY, _SANDBOX_KEY, _CONVERTED_STREAM_KEY): + if key in kwargs: + metadata[key] = kwargs[key] + kwargs[_LITELLM_METADATA_KEY] = metadata + + @staticmethod + def _get_function_parameters() -> CodeExecutionFunctionParameters: + return { + "type": "object", + "properties": {"code": {"type": "string"}}, + "required": ["code"], + } + + def _get_function_tool( + self, call_type: CallTypes | None + ) -> CodeExecutionFunctionTool: + description = "Execute python code in a sandbox and return stdout." + if call_type in (CallTypes.completion, CallTypes.acompletion): + return { + "type": "function", + "function": { + "name": LITELLM_CODE_EXECUTION_TOOL_NAME, + "description": description, + "parameters": self._get_function_parameters(), + }, + } + return { + "type": "function", + "name": LITELLM_CODE_EXECUTION_TOOL_NAME, + "description": description, + "parameters": self._get_function_parameters(), + } + + @staticmethod + def _get_function_tool_choice( + call_type: CallTypes | None, + ) -> CodeExecutionFunctionToolChoice: + if call_type in (CallTypes.completion, CallTypes.acompletion): + return { + "type": "function", + "function": {"name": LITELLM_CODE_EXECUTION_TOOL_NAME}, + } + return { + "type": "function", + "name": LITELLM_CODE_EXECUTION_TOOL_NAME, + } + + @staticmethod + def _tool_choice_targets_code_interpreter(tool_choice: Any) -> bool: + if not isinstance(tool_choice, dict): + return False + function = tool_choice.get("function") + return ( + tool_choice.get("type") == "code_interpreter" + or tool_choice.get("name") == "code_interpreter" + or tool_choice.get("name") == LITELLM_CODE_EXECUTION_TOOL_NAME + or ( + isinstance(function, dict) + and function.get("name") == LITELLM_CODE_EXECUTION_TOOL_NAME + ) + ) + + def _resolve_provider(self, kwargs: dict[str, Any]) -> str | None: + provider = kwargs.get("custom_llm_provider") + if provider: + return provider + model = kwargs.get("model") + if not isinstance(model, str): + return None + try: + return litellm.get_llm_provider(model=model)[1] + except Exception: + return None + + async def async_should_run_agentic_loop( + self, + response: Any, + model: str, + messages: list[dict], + tools: list[dict] | None, + stream: bool, + custom_llm_provider: str, + kwargs: dict, + ) -> tuple[bool, dict]: + if not self.enabled: + return False, {} + if not kwargs.get(_INTERCEPTION_ACTIVE_KEY): + return False, {} + if ( + self.enabled_providers is not None + and custom_llm_provider not in self.enabled_providers + ): + return False, {} + + tool_calls = ( + self._extract_chat_completion_code_execution_tool_calls(response=response) + if kwargs.get("_agentic_loop_api_surface") + == CHAT_COMPLETION_AGENTIC_SURFACE + else self._extract_code_execution_tool_calls(response=response) + ) + if not tool_calls: + return False, {} + + return True, {"tool_calls": tool_calls} + + async def async_build_agentic_loop_plan( + self, + tools: dict, + model: str, + messages: list[dict], + response: Any, + anthropic_messages_provider_config: Any, + anthropic_messages_optional_request_params: dict, + logging_obj: Any, + stream: bool, + kwargs: dict, + ) -> AgenticLoopPlan: + if kwargs.get("_agentic_loop_api_surface") == CHAT_COMPLETION_AGENTIC_SURFACE: + return await self._build_chat_completion_agentic_loop_plan( + tools=tools, + model=model, + messages=messages, + optional_params=anthropic_messages_optional_request_params, + kwargs=kwargs, + ) + + await self._prune_expired_cache() + tool_calls = cast(list[CodeExecutionToolCall], tools.get("tool_calls", [])) + sandbox_key = kwargs.get(_SANDBOX_KEY) + container, params = await self._get_or_create_container(cache_key=sandbox_key) + + try: + container_id = cast(str | None, getattr(container, "id", None)) + input_list = self._normalize_messages(messages) + code_interpreter_calls: list[CodeInterpreterCall] = [] + for tool_call in tool_calls: + arguments = tool_call.get("arguments", "") + code = self._parse_code(arguments) + stdout = await self._run_tool_call( + container=container, params=params, arguments=arguments + ) + input_list.append( + { + "type": "function_call", + "call_id": tool_call.get("call_id"), + "name": LITELLM_CODE_EXECUTION_TOOL_NAME, + "arguments": arguments, + } + ) + input_list.append( + { + "type": "function_call_output", + "call_id": tool_call.get("call_id"), + "output": stdout, + } + ) + code_interpreter_calls.append( + { + "id": f"ci_{uuid.uuid4().hex}", + "type": "code_interpreter_call", + "status": "completed", + "code": code, + "container_id": container_id, + "outputs": ( + [{"type": "logs", "logs": stdout}] if stdout else [] + ), + } + ) + except Exception: + await self._delete_container_for_cache_key(sandbox_key) + raise + + optional_params = anthropic_messages_optional_request_params + request_patch = AgenticLoopRequestPatch( + model=model, + messages=input_list, + tools=self._get_followup_tools( + tools=optional_params.get("tools"), + call_type=CallTypes.responses, + ), + optional_params=self._get_followup_optional_params(optional_params), + kwargs=self._filter_agentic_loop_kwargs(kwargs), + ) + + return AgenticLoopPlan( + run_agentic_loop=True, + request_patch=request_patch, + metadata={ + "tool_type": "code_interpreter", + "sandbox_key": sandbox_key or "", + "code_interpreter_calls": code_interpreter_calls, + }, + ) + + async def _build_chat_completion_agentic_loop_plan( + self, + tools: dict[str, object], + model: str, + messages: list[dict], + optional_params: dict[str, object], + kwargs: dict[str, object], + ) -> AgenticLoopPlan: + await self._prune_expired_cache() + tool_calls = cast(list[CodeExecutionToolCall], tools.get("tool_calls", [])) + sandbox_key = cast(str | None, kwargs.get(_SANDBOX_KEY)) + container, params = await self._get_or_create_container(cache_key=sandbox_key) + + try: + container_id = cast(str | None, getattr(container, "id", None)) + tool_results = [ + await self._build_chat_completion_tool_result( + container=container, + params=params, + tool_call=tool_call, + container_id=container_id, + ) + for tool_call in tool_calls + ] + except Exception: + await self._delete_container_for_cache_key(sandbox_key) + raise + tool_messages = [result[0] for result in tool_results] + code_interpreter_calls = [result[1] for result in tool_results] + + request_patch = AgenticLoopRequestPatch( + model=model, + messages=list(messages) + + [self._build_chat_completion_assistant_message(tool_calls)] + + tool_messages, + tools=self._get_followup_tools( + tools=optional_params.get("tools"), + call_type=CallTypes.completion, + ), + optional_params=self._get_followup_optional_params(optional_params), + kwargs=self._filter_agentic_loop_kwargs(kwargs), + ) + + return AgenticLoopPlan( + run_agentic_loop=True, + request_patch=request_patch, + metadata={ + "tool_type": "code_interpreter", + "sandbox_key": sandbox_key or "", + "code_interpreter_calls": code_interpreter_calls, + "response_format": "openai", + }, + ) + + async def _build_chat_completion_tool_result( + self, + container: object, + params: dict[str, Any] | None, + tool_call: CodeExecutionToolCall, + container_id: str | None, + ) -> tuple[ChatCompletionToolMessage, CodeInterpreterCall]: + arguments = tool_call.get("arguments", "") + code = self._parse_code(arguments) + stdout = await self._run_tool_call( + container=container, params=params, arguments=arguments + ) + tool_call_id = ( + tool_call.get("id") or tool_call.get("call_id") or uuid.uuid4().hex + ) + return ( + { + "role": "tool", + "tool_call_id": tool_call_id, + "content": stdout, + }, + { + "id": f"ci_{uuid.uuid4().hex}", + "type": "code_interpreter_call", + "status": "completed", + "code": code, + "container_id": container_id, + "outputs": [{"type": "logs", "logs": stdout}] if stdout else [], + }, + ) + + async def async_agentic_loop_cleanup_hook( + self, plan: AgenticLoopPlan, kwargs: dict + ) -> None: + metadata = plan.metadata or {} if plan else {} + await self._delete_container_for_cache_key(metadata.get("sandbox_key")) + + @staticmethod + def _filter_agentic_loop_kwargs(kwargs: dict[str, object]) -> dict[str, object]: + return { + k: v + for k, v in kwargs.items() + if k not in {"litellm_logging_obj", "acompletion"} + and not is_interception_internal_key( + k, prefixes=NON_CODE_INTERPRETER_INTERCEPTION_INTERNAL_PREFIXES + ) + } + + def _get_followup_tools( + self, tools: object, call_type: CallTypes | None + ) -> list[dict[str, Any]] | None: + if not isinstance(tools, list): + return None + return [ + ( + self._get_function_tool(call_type=call_type) + if isinstance(tool, dict) and tool.get("type") == "code_interpreter" + else tool + ) + for tool in tools + ] + + def _get_followup_optional_params( + self, optional_params: dict[str, object] + ) -> dict[str, object]: + drop_tool_choice = self._tool_choice_targets_code_interpreter( + optional_params.get("tool_choice") + ) + return { + k: v + for k, v in optional_params.items() + if k != "tools" and not (k == "tool_choice" and drop_tool_choice) + } + + async def async_post_agentic_loop_response_hook( + self, response: Any, plan: AgenticLoopPlan, kwargs: dict + ) -> Any: + metadata = plan.metadata or {} if plan else {} + await self._delete_container_for_cache_key(metadata.get("sandbox_key")) + + calls = metadata.get("code_interpreter_calls") + if not calls: + return response + + is_dict = isinstance(response, dict) + output = ( + response.get("output") if is_dict else getattr(response, "output", None) + ) + if not isinstance(output, list): + return response + + def _item_type(item: Any) -> Any: + return ( + item.get("type") + if isinstance(item, dict) + else getattr(item, "type", None) + ) + + insert_at = next( + (i for i, item in enumerate(output) if _item_type(item) == "message"), + len(output), + ) + new_output = output[:insert_at] + list(calls) + output[insert_at:] + if is_dict: + response["output"] = new_output + else: + response.output = new_output + return response + + @staticmethod + def _parse_code(arguments: str) -> str: + try: + return json.loads(arguments).get("code", "") if arguments else "" + except (json.JSONDecodeError, TypeError, AttributeError): + return "" + + async def _run_tool_call( + self, container: Any, params: dict[str, Any] | None, arguments: str + ) -> str: + try: + code = json.loads(arguments).get("code", "") if arguments else "" + except (json.JSONDecodeError, TypeError): + return "[invalid tool arguments: could not parse code]" + + result = await self._run_code(container=container, params=params, code=code) + if getattr(result, "error", None): + error = result.error + message = ( + error.get("value") or error.get("name") + if isinstance(error, dict) + else str(error) + ) + return f"[execution error] {message}" + return getattr(result, "stdout", "") or "" + + async def _get_or_create_container( + self, cache_key: str | None + ) -> tuple[Any, dict[str, Any] | None]: + if cache_key: + cached = self._container_cache.get(cache_key) + if cached is not None: + return cached[0], cached[1] + + container, params = await self._create_container() + if cache_key: + self._container_cache[cache_key] = (container, params, time.time()) + return container, params + + async def _create_container(self) -> tuple[Any, dict[str, Any] | None]: + if self.sandbox_config is not None: + return await self.sandbox_config.acreate_sandbox(), None + + params = _resolve_sandbox_tool(self.sandbox_tool_name) + if params is None: + raise ValueError( + "CodeInterpreterInterception: no sandbox available. Provide a " + "sandbox_config or configure a sandbox tool resolvable via " + "sandbox_tool_name." + ) + container = await litellm.acreate_sandbox( + provider=params["sandbox_provider"], + api_key=params.get("api_key"), + api_base=params.get("api_base"), + ) + return container, params + + async def _run_code( + self, container: Any, params: dict[str, Any] | None, code: str + ) -> Any: + if self.sandbox_config is not None: + return await self.sandbox_config.arun_code(container=container, code=code) + if params is None: + raise ValueError( + "CodeInterpreterInterception: no sandbox available to run code." + ) + return await litellm.arun_code( + provider=params["sandbox_provider"], + container=container, + code=code, + api_key=params.get("api_key"), + ) + + async def _delete_container( + self, container: Any, params: dict[str, Any] | None + ) -> None: + try: + if self.sandbox_config is not None: + await self.sandbox_config.adelete_sandbox(container=container) + return + if params is None: + return + await litellm.adelete_sandbox( + provider=params["sandbox_provider"], + container=container, + api_key=params.get("api_key"), + api_base=params.get("api_base"), + ) + except Exception: + verbose_logger.exception( + "CodeInterpreterInterception: failed to delete sandbox container" + ) + + async def _delete_container_for_cache_key(self, cache_key: str | None) -> None: + if not cache_key: + return + cached = self._container_cache.pop(cache_key, None) + if cached is None: + return + await self._delete_container(container=cached[0], params=cached[1]) + + def _normalize_messages(self, messages: Any) -> list[dict[str, Any]]: + if isinstance(messages, str): + return [{"role": "user", "content": messages}] + if isinstance(messages, list): + return list(messages) + return [] + + def _extract_code_execution_tool_calls( + self, response: object + ) -> list[CodeExecutionToolCall]: + if isinstance(response, dict): + output = response.get("output", []) + else: + output = getattr(response, "output", []) or [] + if not isinstance(output, list): + return [] + + return [ + { + "call_id": ( + item.get("call_id") + if isinstance(item, dict) + else getattr(item, "call_id", None) + ), + "name": LITELLM_CODE_EXECUTION_TOOL_NAME, + "arguments": ( + item.get("arguments") + if isinstance(item, dict) + else getattr(item, "arguments", "") + ), + } + for item in output + if self._is_code_execution_call(item) + ] + + def _extract_chat_completion_code_execution_tool_calls( + self, response: ModelResponse | dict[str, Any] + ) -> list[CodeExecutionToolCall]: + model_response = self._to_model_response(response) + if model_response is None: + return [] + choices = model_response.choices or [] + if not choices: + return [] + message = choices[0].message + tool_calls = message.tool_calls or [] + + return [ + normalized + for tool_call in tool_calls + if (normalized := self._normalize_chat_completion_tool_call(tool_call)) + is not None + ] + + @staticmethod + def _normalize_chat_completion_tool_call( + tool_call: ChatCompletionMessageToolCall, + ) -> CodeExecutionToolCall | None: + if ( + tool_call.type != "function" + or tool_call.function.name != LITELLM_CODE_EXECUTION_TOOL_NAME + ): + return None + + arguments = tool_call.function.arguments + if isinstance(arguments, dict): + arguments = json.dumps(arguments) + elif not isinstance(arguments, str): + arguments = "" if arguments is None else str(arguments) + + return { + "id": tool_call.id, + "call_id": tool_call.id, + "type": "function", + "name": LITELLM_CODE_EXECUTION_TOOL_NAME, + "arguments": arguments, + } + + @staticmethod + def _build_chat_completion_assistant_message( + tool_calls: list[CodeExecutionToolCall], + ) -> ChatCompletionAssistantMessage: + return { + "role": "assistant", + "tool_calls": [ + cast( + ChatCompletionAssistantToolCall, + { + "id": tool_call.get("id"), + "type": "function", + "function": { + "name": LITELLM_CODE_EXECUTION_TOOL_NAME, + "arguments": tool_call.get("arguments", ""), + }, + }, + ) + for tool_call in tool_calls + ], + } + + @staticmethod + def _to_model_response( + response: ModelResponse | dict[str, Any], + ) -> ModelResponse | None: + if isinstance(response, ModelResponse): + return response + try: + return ModelResponse(**response) + except (TypeError, ValidationError): + return None + + def _is_code_execution_call(self, item: Any) -> bool: + if isinstance(item, dict): + return ( + item.get("type") == "function_call" + and item.get("name") == LITELLM_CODE_EXECUTION_TOOL_NAME + ) + return ( + getattr(item, "type", None) == "function_call" + and getattr(item, "name", None) == LITELLM_CODE_EXECUTION_TOOL_NAME + ) + + async def _prune_expired_cache(self) -> None: + now = time.time() + expired = [ + (cache_key, container, params) + for cache_key, ( + container, + params, + created_at, + ) in self._container_cache.items() + if now - created_at > _CACHE_TTL_SECONDS + ] + for cache_key, container, params in expired: + self._container_cache.pop(cache_key, None) + await self._delete_container(container=container, params=params) diff --git a/litellm/integrations/compression_interception/handler.py b/litellm/integrations/compression_interception/handler.py index c6ae7d9e82b..8899089500d 100644 --- a/litellm/integrations/compression_interception/handler.py +++ b/litellm/integrations/compression_interception/handler.py @@ -72,8 +72,13 @@ class CompressionInterceptionLogger(CustomLogger): compression_params: CompressionInterceptionConfig = {} if "compression_interception_params" in litellm_settings: compression_params = litellm_settings["compression_interception_params"] - elif "compression_interception" in callback_specific_params: - compression_params = callback_specific_params["compression_interception"] + elif "compression_interception" in callback_specific_params and isinstance( + callback_specific_params["compression_interception"], dict + ): + compression_params = cast( + CompressionInterceptionConfig, + callback_specific_params["compression_interception"], + ) return CompressionInterceptionLogger.from_config_yaml(compression_params) async def async_pre_call_deployment_hook( diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py index fc5f0429b63..38245a2e5ba 100644 --- a/litellm/integrations/custom_guardrail.py +++ b/litellm/integrations/custom_guardrail.py @@ -1,3 +1,4 @@ +import secrets from datetime import datetime from typing import ( TYPE_CHECKING, @@ -43,6 +44,7 @@ if TYPE_CHECKING: dc = DualCache() +from litellm.constants import PRE_CALL_EXECUTED_GUARDRAILS_KEY from litellm.exceptions import ( BlockedPiiEntityError, GuardrailRaisedException, @@ -50,6 +52,12 @@ from litellm.exceptions import ( SensitiveDataRouteException, ) +# Per-process secret tagging each recorded marker. The deployment hook only +# honors markers carrying this token, so a caller cannot forge the metadata +# field to suppress a guardrail on the direct-SDK path that never reaches the +# proxy's metadata sanitizer. +_PRE_CALL_EXECUTED_TOKEN = secrets.token_hex(16) + def get_session_id_from_request_data(request_data: Dict[str, Any]) -> Optional[str]: """Extract session_id from request data (litellm_session_id or metadata).""" @@ -458,6 +466,49 @@ class CustomGuardrail(CustomLogger): return False + def _pre_call_marker(self) -> Optional[str]: + name = self.guardrail_name + if not name: + return None + return f"{_PRE_CALL_EXECUTED_TOKEN}:{name}" + + def mark_pre_call_hook_ran(self, data: Dict[str, Any]) -> 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. + + The proxy runs pre-call guardrails in ``ProxyLogging.pre_call_hook``. The + router later spreads a deployment's model-level ``guardrails`` into the + top-level request kwargs, which would otherwise re-trigger the same hook + from ``async_pre_call_deployment_hook``. + """ + marker = self._pre_call_marker() + if marker is None: + return + for meta_key in ("metadata", "litellm_metadata"): + meta = data.get(meta_key) + if isinstance(meta, dict): + executed = meta.get(PRE_CALL_EXECUTED_GUARDRAILS_KEY) + if isinstance(executed, list): + if marker not in executed: + executed.append(marker) + else: + meta[PRE_CALL_EXECUTED_GUARDRAILS_KEY] = [marker] + return + data["metadata"] = {PRE_CALL_EXECUTED_GUARDRAILS_KEY: [marker]} + + def _pre_call_hook_already_ran(self, data: Dict[str, Any]) -> bool: + marker = self._pre_call_marker() + if marker is None: + return False + for meta_key in ("metadata", "litellm_metadata"): + meta = data.get(meta_key) + if isinstance(meta, dict): + executed = meta.get(PRE_CALL_EXECUTED_GUARDRAILS_KEY) + if isinstance(executed, list) and marker in executed: + return True + return False + async def async_pre_call_deployment_hook( self, kwargs: Dict[str, Any], call_type: Optional[CallTypes] ) -> Optional[dict]: @@ -468,6 +519,9 @@ class CustomGuardrail(CustomLogger): if litellm_guardrails is None or not isinstance(litellm_guardrails, list): return kwargs + if self._pre_call_hook_already_ran(kwargs): + return kwargs + if ( self.should_run_guardrail( data=kwargs, event_type=GuardrailEventHooks.pre_call @@ -567,6 +621,9 @@ class CustomGuardrail(CustomLogger): ): return False + if self.default_on is True and disable_global_guardrail is True: + return False + if self.default_on is True and disable_global_guardrail is not True: if self._event_hook_is_event_type(event_type): if isinstance(self.event_hook, Mode): diff --git a/litellm/integrations/custom_logger.py b/litellm/integrations/custom_logger.py index 481cf7fce8e..94fb97dff53 100644 --- a/litellm/integrations/custom_logger.py +++ b/litellm/integrations/custom_logger.py @@ -718,6 +718,24 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac """ return response + async def async_agentic_loop_cleanup_hook( + self, + plan: AgenticLoopPlan, + kwargs: dict, + ) -> None: + """ + Release resources held for an agentic-loop iteration. + + Runs in a ``finally`` around the follow-up provider call, so it fires + whether the rerun returns normally, hits a loop safety abort, or raises + an upstream error. Implementations must be idempotent because the + post-response hook may already have released the same resource on the + success path. Use ``plan.metadata`` to locate what to clean up. + + Default does nothing. + """ + return None + async def async_should_run_chat_completion_agentic_loop( self, response: Any, diff --git a/litellm/integrations/datadog/datadog.py b/litellm/integrations/datadog/datadog.py index 79a9219a39c..b0cd0eb1172 100644 --- a/litellm/integrations/datadog/datadog.py +++ b/litellm/integrations/datadog/datadog.py @@ -92,12 +92,26 @@ class DataDogLogger( # Class variables or attributes def __init__( self, + dd_api_key: Optional[str] = None, + dd_site: Optional[str] = None, + dd_agent_host: Optional[str] = None, + dd_agent_port: Optional[str] = None, + allow_env_credentials: bool = True, **kwargs, ): """ Initializes the datadog logger, checks if the correct env variables are set - Required environment variables (Direct API): + Args: + dd_api_key: Datadog API key. Falls back to DD_API_KEY env var when allow_env_credentials is True. + dd_site: Datadog site (e.g. "us5.datadoghq.com"). Falls back to DD_SITE env var. + dd_agent_host: Hostname or IP of DataDog agent. Falls back to LITELLM_DD_AGENT_HOST env var. + dd_agent_port: Port of DataDog agent (default: 10518). Falls back to LITELLM_DD_AGENT_PORT env var. + allow_env_credentials: When False, the API key is never read from DD_API_KEY env var. Set to + False for team/key-scoped loggers whose destination (dd_agent_host/dd_site) is caller-supplied, + so the proxy's global DD_API_KEY is never sent to an untrusted host. + + Required environment variables (Direct API) when kwargs not provided: `DD_API_KEY` - your datadog api key `DD_SITE` - your datadog site, example = `"us5.datadoghq.com"` @@ -130,12 +144,21 @@ class DataDogLogger( ) # Configure DataDog endpoint (Agent or Direct API) - # Use LITELLM_DD_AGENT_HOST to avoid conflicts with ddtrace's DD_AGENT_HOST - dd_agent_host = os.getenv("LITELLM_DD_AGENT_HOST") - if dd_agent_host: - self._configure_dd_agent(dd_agent_host=dd_agent_host) + # Prefer explicit kwargs, then fall back to env vars + resolved_agent_host = dd_agent_host or os.getenv("LITELLM_DD_AGENT_HOST") + if resolved_agent_host: + self._configure_dd_agent( + dd_agent_host=resolved_agent_host, + dd_agent_port=dd_agent_port, + dd_api_key=dd_api_key, + allow_env_credentials=allow_env_credentials, + ) else: - self._configure_dd_direct_api() + self._configure_dd_direct_api( + dd_api_key=dd_api_key, + dd_site=dd_site, + allow_env_credentials=allow_env_credentials, + ) # Optional override for testing dd_base_url = get_datadog_base_url_from_env() @@ -172,34 +195,60 @@ class DataDogLogger( ).model_dump() return dict_datadog_params - def _configure_dd_agent(self, dd_agent_host: str) -> None: + def _configure_dd_agent( + self, + dd_agent_host: str, + dd_agent_port: Optional[str] = None, + dd_api_key: Optional[str] = None, + allow_env_credentials: bool = True, + ) -> None: """ Configure DataDog Agent for log forwarding Args: dd_agent_host: Hostname or IP of DataDog agent + dd_agent_port: Port of DataDog agent. Falls back to LITELLM_DD_AGENT_PORT env var (default: 10518). + dd_api_key: Datadog API key. Falls back to DD_API_KEY env var when allow_env_credentials is True. Optional when using agent. + allow_env_credentials: When False, never read the API key from DD_API_KEY env var. """ - dd_agent_port = os.getenv( + resolved_port = dd_agent_port or os.getenv( "LITELLM_DD_AGENT_PORT", "10518" ) # default port for logs - self.intake_url = f"http://{dd_agent_host}:{dd_agent_port}/api/v2/logs" - self.DD_API_KEY = os.getenv("DD_API_KEY") # Optional when using agent + self.intake_url = f"http://{dd_agent_host}:{resolved_port}/api/v2/logs" + self.DD_API_KEY = dd_api_key or ( + os.getenv("DD_API_KEY") if allow_env_credentials else None + ) # Optional when using agent verbose_logger.debug(f"Datadog: Using DD Agent at {self.intake_url}") - def _configure_dd_direct_api(self) -> None: + def _configure_dd_direct_api( + self, + dd_api_key: Optional[str] = None, + dd_site: Optional[str] = None, + allow_env_credentials: bool = True, + ) -> None: """ Configure direct DataDog API connection + Args: + dd_api_key: Datadog API key. Falls back to DD_API_KEY env var when allow_env_credentials is True. + dd_site: Datadog site. Falls back to DD_SITE env var. + allow_env_credentials: When False, never read the API key from DD_API_KEY env var. + Raises: - Exception: If required environment variables are not set + Exception: If required credentials are not provided via args or env vars """ - if os.getenv("DD_API_KEY", None) is None: + resolved_api_key = dd_api_key or ( + os.getenv("DD_API_KEY") if allow_env_credentials else None + ) + resolved_site = dd_site or os.getenv("DD_SITE") + + if resolved_api_key is None: raise Exception("DD_API_KEY is not set, set 'DD_API_KEY=<>") - if os.getenv("DD_SITE", None) is None: + if resolved_site is None: raise Exception("DD_SITE is not set in .env, set 'DD_SITE=<>") - self.DD_API_KEY = os.getenv("DD_API_KEY") - self.intake_url = f"https://http-intake.logs.{os.getenv('DD_SITE')}/api/v2/logs" + self.DD_API_KEY = resolved_api_key + self.intake_url = f"https://http-intake.logs.{resolved_site}/api/v2/logs" async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): """ diff --git a/litellm/integrations/datadog/datadog_team_handler.py b/litellm/integrations/datadog/datadog_team_handler.py new file mode 100644 index 00000000000..3a5b73fc005 --- /dev/null +++ b/litellm/integrations/datadog/datadog_team_handler.py @@ -0,0 +1,124 @@ +""" +DataDog Team Handler + +Used to get the DataDogLogger for a given request. +Handles Key/Team Based Datadog Logging, following the same pattern as LangFuseHandler. +""" + +from typing import TYPE_CHECKING, Any, Dict, Optional, TypedDict + +from litellm._logging import verbose_logger +from litellm.litellm_core_utils.litellm_logging import StandardCallbackDynamicParams + +from .datadog import DataDogLogger + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import DynamicLoggingCache +else: + DynamicLoggingCache = Any + + +class DatadogLoggingConfig(TypedDict): + dd_api_key: Optional[str] + dd_site: Optional[str] + dd_agent_host: Optional[str] + dd_agent_port: Optional[str] + + +class DataDogHandler: + @staticmethod + def get_datadog_logger_for_request( + standard_callback_dynamic_params: StandardCallbackDynamicParams, + in_memory_dynamic_logger_cache: DynamicLoggingCache, + ) -> DataDogLogger: + """ + Get a team-scoped DataDogLogger for a given request. + + Resolves and caches per-team DataDogLogger instances using DynamicLoggingCache, + keyed by the team's DD credentials. Each unique set of credentials gets its own + logger instance with its own batch/flush loop. + + Note: This handler is only called when team-scoped DD credentials are present. + The global (env-var based) DataDogLogger is managed separately by + _init_custom_logger_compatible_class via _in_memory_loggers. + """ + _credentials = DataDogHandler.get_dynamic_datadog_logging_config( + standard_callback_dynamic_params=standard_callback_dynamic_params, + ) + credentials_dict = dict(_credentials) + + # check if datadog logger is already cached + temp_datadog_logger = in_memory_dynamic_logger_cache.get_cache( + credentials=credentials_dict, service_name="datadog" + ) + + # if not cached, create a new datadog logger and cache it + if temp_datadog_logger is None: + temp_datadog_logger = ( + DataDogHandler._create_datadog_logger_from_credentials( + credentials=credentials_dict, + in_memory_dynamic_logger_cache=in_memory_dynamic_logger_cache, + ) + ) + + return temp_datadog_logger + + @staticmethod + def _create_datadog_logger_from_credentials( + credentials: Dict, + in_memory_dynamic_logger_cache: DynamicLoggingCache, + ) -> DataDogLogger: + """ + Create a DataDogLogger from the credentials and cache it. + """ + # When the destination is caller-supplied (dd_agent_host/dd_site), never fall back to the + # proxy's DD_API_KEY env var, otherwise it would be sent to a team-controlled host. + allow_env_credentials = ( + credentials.get("dd_agent_host") is None + and credentials.get("dd_site") is None + ) + datadog_logger = DataDogLogger( + dd_api_key=credentials.get("dd_api_key"), + dd_site=credentials.get("dd_site"), + dd_agent_host=credentials.get("dd_agent_host"), + dd_agent_port=credentials.get("dd_agent_port"), + allow_env_credentials=allow_env_credentials, + ) + in_memory_dynamic_logger_cache.set_cache( + credentials=credentials, + service_name="datadog", + logging_obj=datadog_logger, + ) + verbose_logger.debug( + "Datadog: Created and cached new DataDogLogger for team-scoped credentials" + ) + return datadog_logger + + @staticmethod + def get_dynamic_datadog_logging_config( + standard_callback_dynamic_params: StandardCallbackDynamicParams, + ) -> DatadogLoggingConfig: + """ + Get the Datadog logging config for a given request from dynamic params. + """ + return DatadogLoggingConfig( + dd_api_key=standard_callback_dynamic_params.get("dd_api_key"), + dd_site=standard_callback_dynamic_params.get("dd_site"), + dd_agent_host=standard_callback_dynamic_params.get("dd_agent_host"), + dd_agent_port=standard_callback_dynamic_params.get("dd_agent_port"), + ) + + @staticmethod + def _dynamic_datadog_credentials_are_passed( + standard_callback_dynamic_params: StandardCallbackDynamicParams, + ) -> bool: + """ + Check if dynamic Datadog credentials are passed in standard_callback_dynamic_params. + """ + if ( + standard_callback_dynamic_params.get("dd_api_key") is not None + or standard_callback_dynamic_params.get("dd_site") is not None + or standard_callback_dynamic_params.get("dd_agent_host") is not None + ): + return True + return False diff --git a/litellm/integrations/focus/destinations/__init__.py b/litellm/integrations/focus/destinations/__init__.py index e0cd90c1d61..21945c9b457 100644 --- a/litellm/integrations/focus/destinations/__init__.py +++ b/litellm/integrations/focus/destinations/__init__.py @@ -4,6 +4,7 @@ from .base import FocusDestination, FocusTimeWindow from .factory import FocusDestinationFactory from .gcs_destination import FocusGCSDestination from .s3_destination import FocusS3Destination +from .mavvrik_destination import FocusMavvrikDestination from .vantage_destination import FocusVantageDestination __all__ = [ @@ -12,5 +13,6 @@ __all__ = [ "FocusGCSDestination", "FocusTimeWindow", "FocusS3Destination", + "FocusMavvrikDestination", "FocusVantageDestination", ] diff --git a/litellm/integrations/focus/destinations/factory.py b/litellm/integrations/focus/destinations/factory.py index 7ce21d4040a..cd25a87729f 100644 --- a/litellm/integrations/focus/destinations/factory.py +++ b/litellm/integrations/focus/destinations/factory.py @@ -8,6 +8,7 @@ from typing import Any, Dict, Optional from .base import FocusDestination from .gcs_destination import FocusGCSDestination from .s3_destination import FocusS3Destination +from .mavvrik_destination import FocusMavvrikDestination from .vantage_destination import FocusVantageDestination @@ -32,6 +33,8 @@ class FocusDestinationFactory: return FocusVantageDestination(prefix=prefix, config=normalized_config) if provider_lower == "gcs": return FocusGCSDestination(prefix=prefix, config=normalized_config) + if provider_lower == "mavvrik": + return FocusMavvrikDestination(prefix=prefix, config=normalized_config) raise NotImplementedError( f"Provider '{provider}' not supported for Focus export" ) @@ -87,6 +90,15 @@ class FocusDestinationFactory: "FOCUS_GCS_BUCKET_NAME must be provided for GCS exports" ) return {k: v for k, v in resolved.items() if v is not None} + if provider == "mavvrik": + resolved = { + "api_key": overrides.get("api_key") or os.getenv("MAVVRIK_API_KEY"), + "api_endpoint": overrides.get("api_endpoint") + or os.getenv("MAVVRIK_API_ENDPOINT"), + "connection_id": overrides.get("connection_id") + or os.getenv("MAVVRIK_CONNECTION_ID"), + } + return {k: v for k, v in resolved.items() if v is not None} raise NotImplementedError( f"Provider '{provider}' not supported for Focus export configuration" ) diff --git a/litellm/integrations/focus/destinations/mavvrik_destination.py b/litellm/integrations/focus/destinations/mavvrik_destination.py new file mode 100644 index 00000000000..1e3c98b9a70 --- /dev/null +++ b/litellm/integrations/focus/destinations/mavvrik_destination.py @@ -0,0 +1,345 @@ +"""Mavvrik GCS destination for FOCUS export. + +Flow: + 1. GET /metrics/agent/ai/{connection_id}/upload-url → GCS signed URL + 2. PUT with CSV content +""" + +from __future__ import annotations + +import gzip +from typing import Any, Optional +from urllib.parse import urlparse + +from litellm._logging import verbose_logger +from litellm.llms.custom_httpx.http_handler import ( + AsyncHTTPHandler, + get_async_httpx_client, + httpxSpecialProvider, +) + +from .base import FocusDestination, FocusTimeWindow + +_MAVVRIK_ALLOWED_SUFFIXES = (".mavvrik.dev", ".mavvrik.ai", ".mavvrik.app") + +# GCS requires intermediate chunks to be a multiple of 256 KB. +# 8 MB gives a good balance between round-trips and memory pressure. +_GCS_CHUNK_SIZE = 8 * 1024 * 1024 # 8 MB + + +def _validate_api_endpoint(api_endpoint: str) -> None: + if not api_endpoint.startswith("https://"): + raise ValueError("MAVVRIK_API_ENDPOINT must be an HTTPS URL") + hostname = (urlparse(api_endpoint).hostname or "").lower() + if not any(hostname.endswith(suffix) for suffix in _MAVVRIK_ALLOWED_SUFFIXES): + raise ValueError( + "MAVVRIK_API_ENDPOINT host must be a Mavvrik domain " + "(e.g. https://api.mavvrik.dev/)" + ) + + +def _validate_gcs_url(url: str, label: str) -> None: + parsed = urlparse(url) + if parsed.scheme != "https": + raise ValueError( + f"Mavvrik FOCUS destination: {label} must be HTTPS, got scheme '{parsed.scheme}'" + ) + hostname = (parsed.hostname or "").lower() + if not ( + hostname == "storage.googleapis.com" + or hostname.endswith(".storage.googleapis.com") + ): + raise ValueError( + f"Mavvrik FOCUS destination: {label} must be a GCS endpoint " + f"(storage.googleapis.com), got '{hostname}'" + ) + + +class FocusMavvrikDestination(FocusDestination): + """Upload FOCUS CSV exports to Mavvrik via GCS signed URL.""" + + def __init__( + self, + *, + prefix: str, + config: Optional[dict[str, Any]] = None, + ) -> None: + config = config or {} + api_key = config.get("api_key") + api_endpoint = config.get("api_endpoint") + connection_id = config.get("connection_id") + + if not api_key: + raise ValueError( + "MAVVRIK_API_KEY must be provided for Mavvrik FOCUS destination " + "(set MAVVRIK_API_KEY env var or pass in destination_config)" + ) + if not api_endpoint: + raise ValueError( + "MAVVRIK_API_ENDPOINT must be provided for Mavvrik FOCUS destination " + "(set MAVVRIK_API_ENDPOINT env var or pass in destination_config)" + ) + if not connection_id: + raise ValueError( + "MAVVRIK_CONNECTION_ID must be provided for Mavvrik FOCUS destination " + "(set MAVVRIK_CONNECTION_ID env var or pass in destination_config)" + ) + + _validate_api_endpoint(api_endpoint) + + self.api_key = api_key + self.api_endpoint = api_endpoint.rstrip("/") + self.connection_id = connection_id + self.prefix = prefix + self._http: AsyncHTTPHandler = get_async_httpx_client( + llm_provider=httpxSpecialProvider.LoggingCallback + ) + self._registered = False + + @property + def _agent_url(self) -> str: + return f"{self.api_endpoint}/metrics/agent/ai/{self.connection_id}" + + @property + def _upload_url_endpoint(self) -> str: + return f"{self.api_endpoint}/metrics/agent/ai/{self.connection_id}/upload-url" + + @property + def _auth_headers(self) -> dict[str, str]: + return {"Content-Type": "application/json", "x-api-key": self.api_key} + + async def _ensure_registered(self) -> Optional[int]: + """POST agent endpoint to register/initialize the connector (once per instance). + + Returns metricsMarker from the Mavvrik response — the last date index + Mavvrik has successfully processed. Used by the logger to catch up any + dates that were missed due to previous export failures. + + Returns None if the connector was already registered (cached). + """ + if self._registered: + return None + resp = await self._http.client.request( + method="POST", + url=self._agent_url, + headers=self._auth_headers, + json={"name": self.connection_id}, + timeout=30.0, + ) + if resp.status_code == 410: + # Connector has been disconnected in Mavvrik — reset flag so next + # delivery attempt re-registers after it becomes active again. + self._registered = False + raise RuntimeError( + "Mavvrik FOCUS destination: connector is disconnected (410). " + "Re-enable the connection in the Mavvrik dashboard." + ) + if resp.status_code >= 400: + raise RuntimeError( + f"Mavvrik FOCUS destination: register failed " + f"({resp.status_code}): {resp.text[:200]}" + ) + self._registered = True + metrics_marker = resp.json().get("metricsMarker", 0) + verbose_logger.debug( + "Mavvrik FOCUS destination: connector registered (metricsMarker=%s)", + metrics_marker, + ) + return metrics_marker + + async def _get_signed_url(self, date_str: str) -> str: + """GET upload-url endpoint → GCS signed URL for the given date.""" + params = {"name": date_str, "type": "metrics", "datetime": date_str} + resp = await self._http.client.request( + method="GET", + url=self._upload_url_endpoint, + headers=self._auth_headers, + params=params, + timeout=30.0, + ) + if resp.status_code >= 400: + raise RuntimeError( + f"Mavvrik FOCUS destination: failed to get signed URL " + f"({resp.status_code}): {resp.text[:200]}" + ) + signed_url = resp.json().get("url") + if not signed_url: + raise RuntimeError( + f"Mavvrik FOCUS destination: response missing 'url' field: {resp.json()}" + ) + _validate_gcs_url(signed_url, "signed URL") + verbose_logger.debug( + "Mavvrik FOCUS destination: got signed URL for date %s", date_str + ) + return signed_url + + async def _upload_to_gcs(self, signed_url: str, content: bytes) -> None: + """Upload gzip-compressed CSV to GCS via chunked resumable upload. + + The full CSV is gzip-compressed first, then uploaded in _GCS_CHUNK_SIZE + chunks using the GCS resumable upload protocol. GCS assembles the chunks + server-side into a single complete object — the bucket receives one file + regardless of how many chunks were sent. + + Intermediate chunks: Content-Range: bytes X-Y/* → expect 308 + Final chunk: Content-Range: bytes X-Y/T → expect 200/201 + + This handles exports larger than available memory for a single PUT while + keeping the destination code self-contained (no changes to the FOCUS + pipeline upstream). + """ + gzip_bytes = gzip.compress(content) + total = len(gzip_bytes) + + # Step 1: initiate resumable upload session + metadata = b'{"contentEncoding":"gzip","contentDisposition":"attachment"}' + init_resp = await self._http.client.request( + method="POST", + url=signed_url, + headers={ + "Content-Type": "application/gzip", + "x-goog-resumable": "start", + }, + content=metadata, + timeout=30.0, + ) + if init_resp.status_code not in (200, 201): + raise RuntimeError( + f"Mavvrik FOCUS destination: GCS session init failed " + f"({init_resp.status_code}): {init_resp.text[:400]}" + ) + + session_uri = init_resp.headers.get("Location") + if not session_uri: + raise RuntimeError( + "Mavvrik FOCUS destination: GCS session init missing Location header" + ) + _validate_gcs_url(session_uri, "session URI") + + verbose_logger.debug( + "Mavvrik FOCUS destination: GCS session started, uploading %d gzip bytes " + "in %d chunk(s)", + total, + max(1, -(-total // _GCS_CHUNK_SIZE)), # ceiling division + ) + + # Step 2: upload in chunks; cancel session on any failure to avoid + # lingering GCS sessions (they stay open for ~1 week otherwise). + offset = 0 + try: + while offset < total: + chunk = gzip_bytes[offset : offset + _GCS_CHUNK_SIZE] + chunk_end = offset + len(chunk) - 1 + is_final = (offset + len(chunk)) >= total + content_range = ( + f"bytes {offset}-{chunk_end}/{total}" + if is_final + else f"bytes {offset}-{chunk_end}/*" + ) + expected_statuses = {200, 201} if is_final else {308} + + resp = await self._http.client.request( + method="PUT", + url=session_uri, + headers={ + "Content-Type": "application/gzip", + "Content-Range": content_range, + }, + content=chunk, + timeout=120.0, + ) + if resp.status_code not in expected_statuses: + raise RuntimeError( + f"Mavvrik FOCUS destination: GCS chunk upload failed " + f"(chunk offset={offset}, expected={expected_statuses}, " + f"got={resp.status_code}): {resp.text[:400]}" + ) + offset += len(chunk) + verbose_logger.debug( + "Mavvrik FOCUS destination: uploaded chunk offset=%d/%d", + offset, + total, + ) + except Exception: + # Cancel the open GCS session so it doesn't linger for up to 1 week. + try: + await self._http.client.request( + method="DELETE", url=session_uri, timeout=10.0 + ) + verbose_logger.debug( + "Mavvrik FOCUS destination: cancelled GCS session after error" + ) + except Exception: + pass + raise + + async def get_metrics_marker(self) -> Optional[int]: + """Register with Mavvrik and return the current metricsMarker. + + The metricsMarker is a Unix timestamp (seconds) representing the last + date Mavvrik has successfully ingested. Called on every scheduled run + so the logger can detect and catch up any dates missed due to previous + export failures. + + Always calls the Mavvrik register API — unlike deliver() which skips + registration once _registered is True, catch-up requires a fresh + marker value on every run. + """ + resp = await self._http.client.request( + method="POST", + url=self._agent_url, + headers=self._auth_headers, + json={"name": self.connection_id}, + timeout=30.0, + ) + if resp.status_code == 410: + self._registered = False + raise RuntimeError( + "Mavvrik FOCUS destination: connector is disconnected (410). " + "Re-enable the connection in the Mavvrik dashboard." + ) + if resp.status_code >= 400: + raise RuntimeError( + f"Mavvrik FOCUS destination: register failed " + f"({resp.status_code}): {resp.text[:200]}" + ) + self._registered = True + metrics_marker = resp.json().get("metricsMarker", 0) + verbose_logger.debug( + "Mavvrik FOCUS destination: got metricsMarker=%s", metrics_marker + ) + return metrics_marker + + async def deliver( + self, + *, + content: bytes, + time_window: FocusTimeWindow, + filename: str, + ) -> None: + """Upload FOCUS CSV to Mavvrik via GCS signed URL. + + Uses the start date of the time window as the object date key. + """ + if not content: + verbose_logger.debug( + "Mavvrik FOCUS destination: empty content, skipping upload" + ) + return + + date_str = time_window.start_time.strftime("%Y-%m-%d") + + verbose_logger.debug( + "Mavvrik FOCUS destination: uploading %d bytes for date=%s (%s)", + len(content), + date_str, + filename, + ) + + await self._ensure_registered() + signed_url = await self._get_signed_url(date_str) + await self._upload_to_gcs(signed_url, content) + + verbose_logger.debug( + "Mavvrik FOCUS destination: upload complete for date=%s", date_str + ) diff --git a/litellm/integrations/langfuse/langfuse.py b/litellm/integrations/langfuse/langfuse.py index 0efc7d66876..b1c6956a16c 100644 --- a/litellm/integrations/langfuse/langfuse.py +++ b/litellm/integrations/langfuse/langfuse.py @@ -549,7 +549,7 @@ class LangFuseLogger: ) ) - def _log_langfuse_v2( # noqa: PLR0915 + def _log_langfuse_v2( self, user_id: Optional[str], metadata: dict, diff --git a/litellm/integrations/langfuse/langfuse_otel.py b/litellm/integrations/langfuse/langfuse_otel.py index b96ec72b04e..7370bcdf934 100644 --- a/litellm/integrations/langfuse/langfuse_otel.py +++ b/litellm/integrations/langfuse/langfuse_otel.py @@ -43,6 +43,7 @@ class LangfuseOtelLogger(OpenTelemetry): """ _utils.set_attributes(span, kwargs, response_obj, LangfuseLLMObsOTELAttributes) + span.set_attribute("langfuse.observation.type", "generation") ######################################################### # Set Langfuse specific attributes diff --git a/litellm/integrations/lunary.py b/litellm/integrations/lunary.py index b24a24e0881..7b1cbc32d43 100644 --- a/litellm/integrations/lunary.py +++ b/litellm/integrations/lunary.py @@ -75,16 +75,16 @@ class LunaryLogger: version = importlib.metadata.version("lunary") # type: ignore # if version < 0.1.43 then raise ImportError if packaging.version.Version(version) < packaging.version.Version("0.1.43"): # type: ignore - print( # noqa + print( # noqa: T201 "Lunary version outdated. Required: >= 0.1.43. Upgrade via 'pip install lunary --upgrade'" ) raise ImportError self.lunary_client = lunary except ImportError: - print( # noqa + print( # noqa: T201 "Lunary not installed. Please install it using 'pip install lunary'" - ) # noqa + ) raise ImportError def log_event( diff --git a/litellm/integrations/mavvrik_focus/__init__.py b/litellm/integrations/mavvrik_focus/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/litellm/integrations/mavvrik_focus/mavvrik_focus_logger.py b/litellm/integrations/mavvrik_focus/mavvrik_focus_logger.py new file mode 100644 index 00000000000..47d3e1da7bc --- /dev/null +++ b/litellm/integrations/mavvrik_focus/mavvrik_focus_logger.py @@ -0,0 +1,272 @@ +"""MavvrikFocusLogger — FOCUS-based Mavvrik export logger. + +Usage in config.yaml: + litellm_settings: + callbacks: ["mavvrik"] + +Required env vars: + MAVVRIK_API_KEY + MAVVRIK_API_ENDPOINT + MAVVRIK_CONNECTION_ID + +Optional env vars: + MAVVRIK_FOCUS_MAX_ROWS — row cap per export window (default: 500000) + +Only daily frequency is supported. The Mavvrik ingestion protocol stores one +file per calendar date (metrics/YYYY-MM-DD). Hourly or interval exports would +overwrite each other within the same day, producing incomplete data. +""" + +from __future__ import annotations + +import os +from datetime import datetime, timedelta, timezone +from typing import TYPE_CHECKING, Any, List, Optional + +import litellm +from litellm._logging import verbose_proxy_logger +from litellm.constants import MAVVRIK_FOCUS_EXPORT_JOB_NAME +from litellm.integrations.focus.destinations.base import FocusTimeWindow +from litellm.integrations.focus.focus_logger import FocusLogger + +if TYPE_CHECKING: + from apscheduler.schedulers.asyncio import AsyncIOScheduler +else: + AsyncIOScheduler = Any + + +def _parse_metrics_marker( + marker: Optional[object], +) -> Optional[datetime]: + """Parse metricsMarker from Mavvrik register response into a UTC datetime. + + Handles both formats Mavvrik may return: + - Unix timestamp (int/float): e.g. 1749340800 + - ISO date string: e.g. "2026-06-09" or "2026-06-09T00:00:00Z" + + Returns None for falsy values (0, None, empty string) which indicate + no data has been ingested yet. + """ + if not marker: + return None + try: + if isinstance(marker, (int, float)): + return datetime.fromtimestamp(float(marker), tz=timezone.utc).replace( + hour=0, minute=0, second=0, microsecond=0 + ) + if isinstance(marker, str): + marker = marker.strip() + if not marker: + return None + # Try ISO date first (YYYY-MM-DD), then full ISO datetime + for fmt in ("%Y-%m-%d", "%Y-%m-%dT%H:%M:%SZ", "%Y-%m-%dT%H:%M:%S"): + try: + return datetime.strptime(marker, fmt).replace(tzinfo=timezone.utc) + except ValueError: + continue + except Exception: + pass + verbose_proxy_logger.warning( + "Mavvrik FOCUS: could not parse metricsMarker %r — skipping catch-up", marker + ) + return None + + +class MavvrikFocusLogger(FocusLogger): + """FOCUS-based export logger that routes to the Mavvrik destination.""" + + def __init__(self, **kwargs: Any) -> None: + frequency = os.getenv("MAVVRIK_FOCUS_FREQUENCY", "daily").lower() + if frequency != "daily": + raise ValueError( + f"MAVVRIK_FOCUS_FREQUENCY='{frequency}' is not supported. " + "Only 'daily' is allowed -- the Mavvrik ingestion protocol stores one " + "file per calendar date (metrics/YYYY-MM-DD). Hourly or interval " + "exports would overwrite each other within the same day." + ) + super().__init__( + provider="mavvrik", + export_format="csv", + frequency="daily", + prefix="mavvrik_focus_exports", + destination_config={ + "api_key": os.getenv("MAVVRIK_API_KEY"), + "api_endpoint": os.getenv("MAVVRIK_API_ENDPOINT"), + "connection_id": os.getenv("MAVVRIK_CONNECTION_ID"), + }, + **kwargs, + ) + raw = os.getenv("MAVVRIK_FOCUS_MAX_ROWS") + self._max_rows: Optional[int] = int(raw) if raw else 500_000 + + async def _export_window( + self, + *, + window: FocusTimeWindow, + limit: Optional[int], + ) -> None: + """Export with Mavvrik row cap applied when no explicit limit is passed.""" + effective_limit = limit if limit is not None else self._max_rows + engine = self._ensure_engine() + data = await engine._database.get_usage_data( + limit=effective_limit, + start_time_utc=window.start_time, + end_time_utc=window.end_time, + ) + if effective_limit is not None and len(data) >= effective_limit: + verbose_proxy_logger.warning( + "Mavvrik FOCUS export: row cap reached (%d rows). " + "Some data for window %s→%s may be excluded. " + "Increase MAVVRIK_FOCUS_MAX_ROWS to export all rows.", + effective_limit, + window.start_time.date(), + window.end_time.date(), + ) + if data.is_empty(): + verbose_proxy_logger.debug( + "Mavvrik FOCUS export: no usage data for window %s", window + ) + return + normalized = engine._transformer.transform(data) + if normalized.is_empty(): + return + payload = engine._serializer.serialize(normalized) + if not payload: + return + await engine._destination.deliver( + content=payload, + time_window=window, + filename=engine._build_filename(window), + ) + + # Maximum number of days to catch up in a single run. Prevents runaway + # loops if the connector was disabled for a long time, and avoids querying + # data that has likely been cleaned up from LiteLLM_DailyUserSpend. + _MAX_CATCHUP_DAYS = 7 + + async def _run_scheduled_export(self) -> None: + """Export today's window, catching up any dates Mavvrik has not yet received. + + On each run: + 1. Register with Mavvrik → get metricsMarker (last successfully ingested date) + 2. If metricsMarker is behind yesterday, catch up missed dates (capped at + _MAX_CATCHUP_DAYS to avoid runaway loops on long outages) + 3. Export yesterday (today's daily window) + + This ensures a failed export on day N is automatically retried on day N+1 + without any manual intervention. + """ + engine = self._ensure_engine() + from litellm.integrations.focus.destinations.mavvrik_destination import ( # noqa: PLC0415 + FocusMavvrikDestination, + ) + + destination = engine._destination + if not isinstance(destination, FocusMavvrikDestination): + await super()._run_scheduled_export() + return + + # Register and get the last date Mavvrik has processed. + # metricsMarker may be a Unix timestamp (int/float) or an ISO date string. + marker = await destination.get_metrics_marker() + + now = datetime.now(timezone.utc) + yesterday = now.replace(hour=0, minute=0, second=0, microsecond=0) - timedelta( + days=1 + ) + + last_ingested = _parse_metrics_marker(marker) + + # Catch up missed dates, capped at _MAX_CATCHUP_DAYS + if last_ingested and last_ingested < yesterday: + # Never go further back than _MAX_CATCHUP_DAYS from yesterday + earliest_catchup = yesterday - timedelta(days=self._MAX_CATCHUP_DAYS - 1) + catch_up_date = max(last_ingested + timedelta(days=1), earliest_catchup) + + if last_ingested + timedelta(days=1) < earliest_catchup: + verbose_proxy_logger.warning( + "Mavvrik FOCUS export: metricsMarker is more than %d days behind " + "(%s). Catching up from %s only; earlier data will not be re-exported.", + self._MAX_CATCHUP_DAYS, + last_ingested.date(), + catch_up_date.date(), + ) + + while catch_up_date < yesterday: + verbose_proxy_logger.info( + "Mavvrik FOCUS export: catching up missed date %s", + catch_up_date.date(), + ) + window = FocusTimeWindow( + start_time=catch_up_date, + end_time=catch_up_date + timedelta(days=1), + frequency="daily", + ) + await self._export_window(window=window, limit=None) + catch_up_date += timedelta(days=1) + + # Export yesterday's window (the normal daily run) + window = FocusTimeWindow( + start_time=yesterday, + end_time=yesterday + timedelta(days=1), + frequency="daily", + ) + await self._export_window(window=window, limit=None) + + async def initialize_mavvrik_focus_export_job(self) -> None: + """Scheduler entry point — uses Mavvrik-specific pod-lock key.""" + from litellm.proxy.proxy_server import proxy_logging_obj # noqa: PLC0415 + + pod_lock_manager = None + if proxy_logging_obj is not None: + writer = getattr(proxy_logging_obj, "db_spend_update_writer", None) + if writer is not None: + pod_lock_manager = getattr(writer, "pod_lock_manager", None) + + if pod_lock_manager and pod_lock_manager.redis_cache: + acquired = await pod_lock_manager.acquire_lock( + cronjob_id=MAVVRIK_FOCUS_EXPORT_JOB_NAME + ) + if not acquired: + verbose_proxy_logger.debug( + "Mavvrik FOCUS export: unable to acquire pod lock" + ) + return + try: + await self._run_scheduled_export() + finally: + await pod_lock_manager.release_lock( + cronjob_id=MAVVRIK_FOCUS_EXPORT_JOB_NAME + ) + else: + await self._run_scheduled_export() + + @staticmethod + async def init_mavvrik_focus_background_job( + scheduler: AsyncIOScheduler, + ) -> None: + """Register the Mavvrik FOCUS export job on the provided scheduler.""" + loggers: List[MavvrikFocusLogger] = [ + cb + for cb in litellm.logging_callback_manager.get_custom_loggers_for_type( + callback_type=MavvrikFocusLogger + ) + if type(cb) is MavvrikFocusLogger + ] + if not loggers: + verbose_proxy_logger.debug( + "No MavvrikFocusLogger registered; skipping scheduler" + ) + return + + logger = loggers[0] + trigger_kwargs = logger._build_scheduler_trigger() + scheduler.add_job( # type: ignore[attr-defined] + logger.initialize_mavvrik_focus_export_job, + id=MAVVRIK_FOCUS_EXPORT_JOB_NAME, + replace_existing=True, + **trigger_kwargs, + ) + verbose_proxy_logger.info( + "mavvrik_focus: background export job scheduled (%s)", trigger_kwargs + ) diff --git a/litellm/integrations/mock_client_factory.py b/litellm/integrations/mock_client_factory.py index 02a927fe64f..9b912ce70c8 100644 --- a/litellm/integrations/mock_client_factory.py +++ b/litellm/integrations/mock_client_factory.py @@ -107,7 +107,7 @@ def _is_url_match(url, matchers: List[str]) -> bool: return False -def create_mock_client_factory(config: MockClientConfig): # noqa: PLR0915 +def create_mock_client_factory(config: MockClientConfig): """ Factory function that creates mock client functions based on configuration. diff --git a/litellm/integrations/newrelic/__init__.py b/litellm/integrations/newrelic/__init__.py new file mode 100644 index 00000000000..5b0f5b9cb24 --- /dev/null +++ b/litellm/integrations/newrelic/__init__.py @@ -0,0 +1,10 @@ +""" +New Relic AI Monitoring Integration for LiteLLM + +This module provides integration with New Relic's AI Monitoring feature to track +LLM requests, responses, and usage metrics. +""" + +from litellm.integrations.newrelic.newrelic import NewRelicLogger + +__all__ = ["NewRelicLogger"] diff --git a/litellm/integrations/newrelic/newrelic.py b/litellm/integrations/newrelic/newrelic.py new file mode 100644 index 00000000000..753b8520337 --- /dev/null +++ b/litellm/integrations/newrelic/newrelic.py @@ -0,0 +1,926 @@ +""" +New Relic AI Monitoring Integration for LiteLLM + +This module provides integration with New Relic's AI Monitoring feature to track +LLM requests, responses, and usage metrics. + +Environment Variables (consumed by the New Relic agent at process bootstrap - +set via container env, or before invoking `newrelic-admin run-program`): + NEW_RELIC_LICENSE_KEY: Your New Relic license key (required) + NEW_RELIC_APP_NAME: Your application name (required) + +UI- and runtime-toggleable: + NEW_RELIC_AI_MONITORING_RECORD_CONTENT_ENABLED: Whether to record message + content (optional, default: true) + +Configuration: + Message logging can be controlled via (both must agree to record): + 1. turn_off_message_logging parameter - pass via callback initialization or config YAML + 2. NEW_RELIC_AI_MONITORING_RECORD_CONTENT_ENABLED env var + + Default behavior: Messages ARE recorded unless explicitly disabled by either method + Either method can disable recording - both must enable for recording to occur + +Usage - Python SDK: + import litellm + litellm.callbacks = ["newrelic"] + + # Or with explicit configuration: + from litellm.integrations.newrelic import NewRelicLogger + litellm.callbacks = [NewRelicLogger(turn_off_message_logging=True)] + +Usage - Proxy Server (config.yaml): + litellm_settings: + callbacks: ["newrelic"] + newrelic_params: + turn_off_message_logging: true # Disable message content recording + + # Or disable via environment variable: + # export NEW_RELIC_AI_MONITORING_RECORD_CONTENT_ENABLED=false + + # Ensure New Relic agent is initialized (use newrelic-admin or initialize manually) + # newrelic-admin run-program python your_app.py +""" + +import json +import os +import threading +import time +import uuid +from typing import Any, Dict, List, Optional, Tuple, Union + +import litellm +from litellm._logging import verbose_logger +from litellm.integrations.custom_logger import CustomLogger +from litellm.litellm_core_utils.redact_messages import should_redact_message_logging +from litellm.types.integrations.newrelic import NewRelicInitParams +from litellm.types.integrations.base_health_check import IntegrationHealthCheckStatus +from litellm.types.utils import ModelResponse, Message, StandardLoggingPayload + +try: + import newrelic.agent as _newrelic_agent +except ImportError: + _newrelic_agent = None # type: ignore + + +class NewRelicLogger(CustomLogger): + """ + New Relic logger for LiteLLM to send AI monitoring events. + + This logger creates two types of New Relic custom events: + 1. LlmChatCompletionSummary - One per completion request + 2. LlmChatCompletionMessage - One per message (request and response) + """ + + # Class-level state for supportability metric emission, shared across all instances. + # Protected by _metric_lock to ensure thread-safe access. + _last_metric_emission_time: float = 0.0 + _metric_lock = threading.Lock() + + def __init__(self, **kwargs): + ######################################################### + # Handle newrelic_params set as litellm.newrelic_params + ######################################################### + dict_newrelic_params = self._get_newrelic_params() + + # Use setdefault so constructor kwargs take priority over global params. + # model_dump() always returns all fields (including defaults), so update() + # would silently overwrite explicit constructor args like turn_off_message_logging=True. + for k, v in dict_newrelic_params.items(): + kwargs.setdefault(k, v) + + # CustomLogger.__init__ will set self.turn_off_message_logging from kwargs + super().__init__(**kwargs) + + # Check for required environment variables + self.license_key = os.getenv("NEW_RELIC_LICENSE_KEY") + self.app_name = os.getenv("NEW_RELIC_APP_NAME") + + # Validate configuration + if not self.license_key or not self.app_name: + verbose_logger.warning( + "New Relic integration requires NEW_RELIC_LICENSE_KEY and " + "NEW_RELIC_APP_NAME environment variables. Integration will be disabled." + ) + self.enabled = False + elif _newrelic_agent is None: + verbose_logger.error( + "New Relic Python agent not installed. Review the New Relic integration documentation at https://docs.litellm.ai/docs/observability/newrelic." + ) + self.enabled = False + else: + try: + # timeout=0 forces non-blocking startup: the agent connects in a + # background thread regardless of newrelic.ini / NEW_RELIC_STARTUP_TIMEOUT. + _newrelic_agent.register_application(timeout=0) + + self.enabled = True + verbose_logger.info( + f"New Relic AI Monitoring initialized for app: {self.app_name}, " + f"content recording: {self.record_content}" + ) + except Exception as e: + verbose_logger.error( + f"Failed to initialize New Relic agent: {e}. " + "Integration will be disabled." + ) + self.enabled = False + + def _get_newrelic_params(self) -> Dict: + """ + Get the newrelic_params from litellm.newrelic_params + + These are params specific to initializing the NewRelicLogger e.g. turn_off_message_logging + """ + dict_newrelic_params: Dict = {} + if litellm.newrelic_params is not None: + if isinstance(litellm.newrelic_params, NewRelicInitParams): + dict_newrelic_params = litellm.newrelic_params.model_dump() + elif isinstance(litellm.newrelic_params, Dict): + # only allow params that are of NewRelicInitParams + dict_newrelic_params = NewRelicInitParams( + **litellm.newrelic_params + ).model_dump() + return dict_newrelic_params + + @property + def record_content(self) -> bool: + """Whether to record message content in New Relic. + + Both turn_off_message_logging param AND NEW_RELIC_AI_MONITORING_RECORD_CONTENT_ENABLED + env var must agree to record content. If either disables recording, content will not + be recorded. Read at call time so UI config changes take effect without a restart. + Default: True (record content) unless explicitly disabled by either method. + """ + return (not self.turn_off_message_logging) and self._parse_bool_env( + "NEW_RELIC_AI_MONITORING_RECORD_CONTENT_ENABLED", True + ) + + def _parse_bool_env(self, var_name: str, default: bool = False) -> bool: + """Parse a boolean environment variable. + + Accepts true/false, 1/0, yes/no, on/off (case-insensitive, + whitespace-tolerant) — matching the convention used in + ``litellm/__init__.py`` and the standard library's + ``configparser.BOOLEAN_STATES``. Unrecognised values log a + warning and fall back to ``default`` rather than silently + flipping user intent. + """ + raw = os.getenv(var_name) + if not raw: + return default + value = raw.strip().lower() + if value in ("1", "true", "yes", "on"): + return True + if value in ("0", "false", "no", "off"): + return False + verbose_logger.warning( + f"{var_name}={raw!r} is not a recognised boolean " + f"(accepts true/false, 1/0, yes/no, on/off). " + f"Falling back to default ({default})." + ) + return default + + def _get_litellm_version(self) -> str: + """ + Get litellm version for supportability metrics. + + Returns: + Version string (e.g., "1.80.0") or "unknown" if unable to determine + """ + try: + from importlib.metadata import version + + return version("litellm") + except Exception as e: + verbose_logger.warning(f"Unable to determine litellm version: {e}") + return "unknown" + + def _emit_supportability_metric(self): + """ + Emit New Relic supportability metric for LiteLLM usage. + + Per spec, this metric should be emitted at least once every 27 hours + to indicate the library is in use. Format: + Supportability/Python/ML/LiteLLM/{version} + + This method updates _last_metric_emission_time and should + be called within a lock when checking periodic emission. + """ + try: + litellm_version = self._get_litellm_version() + metric_name = f"Supportability/Python/ML/LiteLLM/{litellm_version}" + + # Record metric with value of 1 (will be aggregated by New Relic) + app = _newrelic_agent.application() + + # Always update the timestamp so the 27-hour back-off applies + # regardless of whether the app is ready, preventing lock contention + # on every request when the agent is slow to register or never starts. + NewRelicLogger._last_metric_emission_time = time.time() + + if app and app.enabled: + app.record_custom_metric(metric_name, 1) + verbose_logger.info( + f"Emitted New Relic supportability metric: {metric_name}" + ) + else: + verbose_logger.info( + "New Relic application is not enabled; skipping metric recording." + ) + + except Exception as e: + verbose_logger.warning(f"Failed to emit supportability metric: {e}") + + def _check_and_emit_periodic_metric(self): + """ + Check if 27 hours have passed since last metric emission and re-emit if needed. + + Uses a mutex to ensure only one thread emits the metric even if multiple + requests are being processed concurrently. + """ + # Quick check without lock to avoid unnecessary locking + current_time = time.time() + time_since_last_emission = ( + current_time - NewRelicLogger._last_metric_emission_time + ) + + if time_since_last_emission >= 97200: # 27 hours = 97200 seconds + # Acquire lock to ensure only one thread emits + with NewRelicLogger._metric_lock: + # Double-check inside lock in case another thread just emitted + current_time = time.time() + time_since_last_emission = ( + current_time - NewRelicLogger._last_metric_emission_time + ) + + if time_since_last_emission >= 97200: + self._emit_supportability_metric() + + def _get_trace_context( + self, + kwargs: Dict, + standard_logging_object: Optional[StandardLoggingPayload] = None, + ) -> str: + """ + Get the New Relic trace ID for AI monitoring events. + + This integration runs in LiteLLM's async logging worker, outside the + New Relic agent's current transaction. Because we can't call + `newrelic.agent.current_trace_id()` to let the agent populate the + trace_id on AIM custom events, we manually simulate what the agent + would do. An AIM event without a trace_id is malformed per the NR + schema, so this method always returns a valid string. + + Resolution order: + 1. W3C traceparent header (litellm_params.metadata.headers.traceparent) - + what the agent would link to if we were in-transaction. + 2. StandardLoggingPayload.trace_id - LiteLLM's internal trace for + retry/fallback grouping. + 3. Generated UUID - synthetic grouping key when upstream context is + absent or parsing it fails. + + Span IDs are intentionally not emitted: any span ID recoverable from + the inbound traceparent is the caller's parent span, not ours. + + Returns: + trace_id: always a non-empty string. + """ + trace_id: Optional[str] = None + try: + litellm_params = kwargs.get("litellm_params") or {} + metadata = litellm_params.get("metadata") or {} + headers = metadata.get("headers") or {} + # Normalize header key lookup to be case-insensitive per W3C spec + traceparent = next( + (v for k, v in headers.items() if k.lower() == "traceparent"), None + ) + + if traceparent: + # Extract trace_id from traceparent header if available + # traceparent format: "00-4bf92f3577b34da6a3ce929d0e0e4736-00f067aa0ba902b7-00" + parts = traceparent.split("-") + if len(parts) == 4: + trace_id = parts[1] + + if not trace_id and standard_logging_object: + slo_trace_id = standard_logging_object.get("trace_id") + if slo_trace_id: + trace_id = slo_trace_id + + except Exception as e: + verbose_logger.warning( + f"Unable to parse New Relic trace context from upstream sources: {e}" + ) + + if not trace_id: + trace_id = uuid.uuid4().hex + verbose_logger.debug( + f"New Relic trace_id not available from distributed tracing headers or " + f"StandardLoggingPayload. Generated trace_id={trace_id} for AI monitoring " + f"event grouping." + ) + + return trace_id + + def _extract_completion_id(self, kwargs: Dict, response_obj: ModelResponse) -> str: + """ + Extract completion ID from kwargs or response_obj, or generate one. + """ + completion_id = None + + if response_obj: + completion_id = response_obj.get("id") + + if not completion_id: + completion_id = kwargs.get("litellm_call_id") + + # If still not found, generate UUID and log warning per spec + if not completion_id: + completion_id = str(uuid.uuid4()) + + return completion_id + + def _get_vendor( + self, + kwargs: Dict, + standard_logging_object: Optional[StandardLoggingPayload] = None, + ) -> str: + """Extract vendor/provider, preferring StandardLoggingPayload.""" + if standard_logging_object: + vendor = standard_logging_object.get("custom_llm_provider") + if vendor: + return vendor + litellm_params = kwargs.get("litellm_params", {}) or {} + return litellm_params.get("custom_llm_provider") or "litellm" + + def _get_model_names( + self, + kwargs: Dict, + response_obj: ModelResponse, + standard_logging_object: Optional[StandardLoggingPayload] = None, + ) -> Tuple[str, str]: + """ + Extract request and response model names, preferring StandardLoggingPayload + for the request model. + + Returns: + Tuple of (request_model, response_model) + """ + request_model = None + if standard_logging_object: + slo_model = standard_logging_object.get("model") + if slo_model: + request_model = str(slo_model) + if not request_model: + request_model = str(kwargs.get("model") or "unknown") + response_model: str = str(response_obj.get("model") or request_model) + return request_model, response_model + + def _extract_usage( + self, + response_obj: ModelResponse, + standard_logging_object: Optional[StandardLoggingPayload] = None, + ) -> Dict[str, int]: + """Extract usage statistics, preferring StandardLoggingPayload.""" + if standard_logging_object: + prompt = standard_logging_object.get("prompt_tokens") + completion = standard_logging_object.get("completion_tokens") + total = standard_logging_object.get("total_tokens") + if any(x is not None for x in [prompt, completion, total]): + return { + "prompt_tokens": prompt or 0, + "completion_tokens": completion or 0, + "total_tokens": total or 0, + } + + usage = response_obj.get("usage", None) + if not usage: + return {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0} + + return { + "prompt_tokens": usage.get("prompt_tokens") or 0, + "completion_tokens": usage.get("completion_tokens") or 0, + "total_tokens": usage.get("total_tokens") or 0, + } + + def _get_finish_reason(self, response_obj: ModelResponse) -> str: + """ + Extract finish reason from first choice in the response. + + Returns "unknown" if choices are not present or finish_reason is not found. + """ + choices = response_obj.get("choices") or [] + if choices and len(choices) > 0: + return choices[0].get("finish_reason") or "unknown" + return "unknown" + + def _to_epoch_ms(self, t: Any) -> float: + """Convert a datetime or float timestamp to epoch milliseconds.""" + if hasattr(t, "timestamp"): + return t.timestamp() * 1000.0 + return float(t) * 1000.0 + + def _get_duration( + self, + kwargs: Dict, + start_time: Any, + end_time: Any, + standard_logging_object: Optional[StandardLoggingPayload] = None, + ) -> Optional[float]: + """ + Extract duration in milliseconds. + + Resolution order: + 1. StandardLoggingPayload.response_time (already computed by LiteLLM) + 2. llm_api_duration_ms from kwargs + 3. Calculated from start_time and end_time + """ + if standard_logging_object: + response_time = standard_logging_object.get("response_time") + if response_time is not None: + return ( + float(response_time) * 1000.0 + ) # SLO stores seconds; convert to ms + + duration_ms = kwargs.get("llm_api_duration_ms") + if duration_ms is not None: + return float(duration_ms) + + if start_time is not None and end_time is not None: + return self._to_epoch_ms(end_time) - self._to_epoch_ms(start_time) + + return None + + def _get_request_params( + self, + kwargs: Dict, + standard_logging_object: Optional[StandardLoggingPayload] = None, + ) -> Dict[str, Any]: + """ + Extract request parameters like temperature and max_tokens, preferring + StandardLoggingPayload.model_parameters. + + Returns dict with available parameters, omitting those not present. + """ + if standard_logging_object: + source_params = standard_logging_object.get("model_parameters") or {} + else: + source_params = kwargs.get("optional_params") or {} + + params = {} + + temperature = source_params.get("temperature") + if temperature is not None: + params["temperature"] = temperature + + max_tokens = source_params.get("max_tokens") + if max_tokens is not None: + params["max_tokens"] = max_tokens + + return params + + def _extract_message_content(self, message: Union[Message, Dict]) -> str: + """ + Extract content from a message, handling various formats. + + Handles tool calls, multimodal content (as JSON), and standard text content. + Returns empty string if content is None or missing. + """ + content = message.get("content") + + # Handle tool calls + if message.get("tool_calls"): + try: + return json.dumps(message["tool_calls"]) + except Exception: + return str(message["tool_calls"]) + + # Handle None or missing content + if content is None: + return "" + + # Handle list content (multimodal) + if isinstance(content, list): + try: + return json.dumps(content) + except Exception: + return str(content) + + # Handle non-string content + if not isinstance(content, str): + return str(content) + + return content + + def _extract_all_messages( + self, + kwargs: Dict, + response_obj: ModelResponse, + response_model: str, + vendor: str, + standard_logging_object: Optional[StandardLoggingPayload] = None, + ) -> List[Dict[str, Any]]: + """ + Extract all messages (request + response) with sequence numbers and timestamps. + + Processes request messages from StandardLoggingPayload.messages (preferred) or + kwargs["messages"] (fallback), and response messages from response_obj["choices"]. + Assigns sequential numbers starting at 0. + Adds timestamps from StandardLoggingPayload (preferred) or kwargs if available + (converted to epoch milliseconds). + """ + messages = [] + sequence = 0 + + # Extract timestamps, preferring StandardLoggingPayload + start_time = None + if standard_logging_object: + start_time = standard_logging_object.get("startTime") + if not start_time: + start_time = kwargs.get("start_time") + + end_time = None + if standard_logging_object: + end_time = standard_logging_object.get("endTime") + if not end_time: + end_time = kwargs.get("end_time") + + # Content is recorded only when the NR-specific switches allow it AND + # LiteLLM's wider redaction decision (turn_off_message_logging, dynamic + # params, headers) does not require redaction. Async streaming hands the + # callback an unredacted async_complete_streaming_response, so without + # this gate generated content would still reach NR even when the user + # has globally disabled message logging. + record_content = self.record_content and not should_redact_message_logging( + kwargs + ) + + # Extract request messages, preferring StandardLoggingPayload. + # SLO messages can be a string (serialized/redacted), so only use it when it's a list. + slo_messages = ( + standard_logging_object.get("messages") if standard_logging_object else None + ) + if isinstance(slo_messages, list): + request_messages = slo_messages + else: + request_messages = kwargs.get("messages") or [] + for msg in request_messages: + message_data = { + "role": msg.get("role") or "user", + "sequence": sequence, + "response.model": response_model, + "vendor": vendor, + } + + # Add timestamp for request message if available (convert to milliseconds) + if start_time is not None: + message_data["timestamp"] = int(self._to_epoch_ms(start_time)) + + if record_content: + message_data["content"] = self._extract_message_content(msg) + + messages.append(message_data) + sequence += 1 + + # Extract response messages from choices + choices = response_obj.get("choices") or [] + if choices and len(choices) > 0: + for choice in choices: + # Prefer "message" (non-streaming); fall back to "delta" (streaming-assembled) + message = choice.get("message", None) or choice.get("delta", None) + if message: + message_data = { + "role": message.get("role") or "assistant", + "sequence": sequence, + "response.model": response_model, + "vendor": vendor, + "is_response": True, + } + + # Add timestamp for response message if available (convert to milliseconds) + if end_time is not None: + message_data["timestamp"] = int(self._to_epoch_ms(end_time)) + + if record_content: + message_data["content"] = self._extract_message_content(message) + + messages.append(message_data) + sequence += 1 + + return messages + + def _record_summary_event( + self, + request_id: str, + trace_id: Optional[str], + request_model: str, + response_model: str, + vendor: str, + finish_reason: str, + num_messages: int, + usage: Dict[str, int], + duration: Optional[float] = None, + request_params: Optional[Dict[str, Any]] = None, + ): + """Record LlmChatCompletionSummary event to New Relic.""" + try: + event_data = { + "id": request_id, + "request_id": request_id, + "request.model": request_model, + "response.model": response_model, + "response.choices.finish_reason": finish_reason, + "response.number_of_messages": num_messages, + "vendor": vendor, + "ingest_source": "litellm", + "response.usage.prompt_tokens": usage["prompt_tokens"], + "response.usage.completion_tokens": usage["completion_tokens"], + "response.usage.total_tokens": usage["total_tokens"], + } + + # Add optional attributes if present + if trace_id: + event_data["trace_id"] = trace_id + + if duration is not None: + event_data["duration"] = duration + + # Add request parameters if present + if request_params: + if "temperature" in request_params: + event_data["request.temperature"] = request_params["temperature"] + if "max_tokens" in request_params: + event_data["request.max_tokens"] = request_params["max_tokens"] + + app = _newrelic_agent.application() + + if app and app.enabled: + app.record_custom_event("LlmChatCompletionSummary", event_data) + else: + verbose_logger.warning( + "New Relic application is not enabled; skipping summary event recording." + ) + + except Exception as e: + verbose_logger.warning(f"Failed to record New Relic summary event: {e}") + self.handle_callback_failure("newrelic") + + def _record_message_events( + self, + request_id: str, + llm_response_id: str, + trace_id: Optional[str], + messages: List[Dict[str, Any]], + ): + """Record LlmChatCompletionMessage events to New Relic. + + Args: + request_id: Agent-generated UUID that links to Summary event's id + llm_response_id: LLM's response ID (e.g., "chatcmpl-...") for message id format + trace_id: Trace ID for distributed tracing (None if not available) + messages: List of message dicts to record + """ + try: + app = _newrelic_agent.application() + + if not (app and app.enabled): + verbose_logger.warning( + "New Relic application is not enabled; skipping message event recording." + ) + return + + for message in messages: + sequence = message["sequence"] + event_data = { + "id": f"{llm_response_id}-{sequence}", + "request_id": request_id, + "completion_id": request_id, + "role": message["role"], + "sequence": sequence, + "response.model": message["response.model"], + "vendor": message["vendor"], + "ingest_source": "litellm", + "token_count": 0, # Per-message token counts are not available from LiteLLM + } + + # Add trace context if available + if trace_id: + event_data["trace_id"] = trace_id + + # Add content only if it was included in the message data + if "content" in message: + event_data["content"] = message["content"] + + # Add is_response only if True (per spec, omit for request messages) + if message.get("is_response"): + event_data["is_response"] = True + + # Forward actual request/response timestamp (ms) so NR uses the + # real LLM call window rather than the async-logger fire time. + # Requires newrelic>=11.2.0 which reads params["timestamp"] as + # the intrinsic event timestamp. + if "timestamp" in message: + event_data["timestamp"] = message["timestamp"] + + app.record_custom_event("LlmChatCompletionMessage", event_data) + + except Exception as e: + verbose_logger.warning(f"Failed to record New Relic message events: {e}") + self.handle_callback_failure("newrelic") + + def _record_error_metric(self): + """Record error metric to New Relic.""" + try: + if not self.enabled: + return + + self._check_and_emit_periodic_metric() + + app = _newrelic_agent.application() + if app and app.enabled: + app.record_custom_metric("LLM/LiteLLM/Error", 1) + except Exception as e: + verbose_logger.warning(f"Failed to record New Relic error metric: {e}") + self.handle_callback_failure("newrelic") + + def _process_success( + self, + kwargs: Dict, + response_obj: ModelResponse, + start_time: Optional[float] = None, + end_time: Optional[float] = None, + ): + """ + Core logic for processing successful LLM calls. + Used by both sync and async success event handlers. + """ + # Early exit if not enabled + if not self.enabled: + return + + # Check and emit periodic supportability metric if 27 hours have passed + self._check_and_emit_periodic_metric() + + # Use StandardLoggingPayload where available for normalized, pre-computed values + standard_logging_object: Optional[StandardLoggingPayload] = kwargs.get( + "standard_logging_object" + ) + + # Get trace context + trace_id = self._get_trace_context(kwargs, standard_logging_object) + + # Generate unique request ID for this request (used as Summary event id) + request_id = str(uuid.uuid4()) + + # Extract data from response + llm_response_id = self._extract_completion_id(kwargs, response_obj) + vendor = self._get_vendor(kwargs, standard_logging_object) + request_model, response_model = self._get_model_names( + kwargs, response_obj, standard_logging_object + ) + usage = self._extract_usage(response_obj, standard_logging_object) + finish_reason = self._get_finish_reason(response_obj) + + # Extract additional summary event fields + duration = self._get_duration( + kwargs, start_time, end_time, standard_logging_object + ) + request_params = self._get_request_params(kwargs, standard_logging_object) + + # Extract all messages + messages = self._extract_all_messages( + kwargs, response_obj, response_model, vendor, standard_logging_object + ) + + # Record summary event + self._record_summary_event( + request_id=request_id, + trace_id=trace_id, + request_model=request_model, + response_model=response_model, + vendor=vendor, + finish_reason=finish_reason, + num_messages=len(messages), + usage=usage, + duration=duration, + request_params=request_params, + ) + + # Record message events + self._record_message_events( + request_id=request_id, + llm_response_id=llm_response_id, + trace_id=trace_id, + messages=messages, + ) + + async def async_health_check(self) -> IntegrationHealthCheckStatus: + """ + Check if the New Relic integration is healthy. + + Verifies that the integration is enabled and the New Relic agent + has an active, connected application, then records a small + `LiteLLMConnectionTest` custom event so the user can confirm the + end-to-end pipeline in the New Relic UI via NRQL: + `SELECT * FROM LiteLLMConnectionTest SINCE 1 hour ago`. + + The `LiteLLMConnectionTest` event type is intentionally outside the + `Llm*` family that AI Monitoring queries, so test events do not + appear in AI Monitoring dashboards. + """ + if not self.enabled: + return IntegrationHealthCheckStatus( + status="unhealthy", + error_message="New Relic integration is disabled. Check that " + "NEW_RELIC_LICENSE_KEY and NEW_RELIC_APP_NAME are set and the " + "newrelic package is installed.", + ) + + try: + app = _newrelic_agent.application() + if not (app and app.enabled): + return IntegrationHealthCheckStatus( + status="unhealthy", + error_message=( + "New Relic Python agent not installed. Review the New Relic integration documentation at https://docs.litellm.ai/docs/observability/newrelic." + ), + ) + + app.record_custom_event( + "LiteLLMConnectionTest", + { + "is_test_event": True, + "app_name": self.app_name, + "source": "litellm-proxy", + "timestamp": time.time(), + }, + ) + return IntegrationHealthCheckStatus(status="healthy", error_message=None) + except Exception as e: + return IntegrationHealthCheckStatus( + status="unhealthy", + error_message=str(e), + ) + + # CustomLogger interface implementation + + def log_pre_api_call(self, model, messages, kwargs): + """Unused per spec.""" + pass + + def log_post_api_call(self, kwargs, response_obj, start_time, end_time): + """Unused per spec.""" + pass + + def log_success_event(self, kwargs, response_obj, start_time, end_time): + """ + Main success path for non-streaming requests. + + Note: New Relic's record_custom_event is synchronous but non-blocking + (in-memory operation), so it's safe to call from sync context. + """ + try: + self._process_success(kwargs, response_obj, start_time, end_time) + except Exception as e: + verbose_logger.warning(f"Error in New Relic log_success_event: {e}") + self.handle_callback_failure("newrelic") + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + """ + Main success path for async/streaming requests. + + Note: New Relic's SDK is thread-safe and record_custom_event is fast, + so we can call it directly without asyncio.to_thread(). + """ + try: + self._process_success(kwargs, response_obj, start_time, end_time) + except Exception as e: + verbose_logger.warning(f"Error in New Relic async_log_success_event: {e}") + self.handle_callback_failure("newrelic") + + def log_failure_event(self, kwargs, response_obj, start_time, end_time): + """ + Log error metric for failed LLM calls (sync). + + Per spec: Do not send AI events on failure, only record error metric. + """ + try: + self._record_error_metric() + + except Exception as e: + verbose_logger.warning(f"Error in New Relic log_failure_event: {e}") + self.handle_callback_failure("newrelic") + + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): + """ + Log error metric for failed LLM calls (async). + + Per spec: Do not send AI events on failure, only record error metric. + """ + try: + self._record_error_metric() + + except Exception as e: + verbose_logger.warning(f"Error in New Relic async_log_failure_event: {e}") + self.handle_callback_failure("newrelic") diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py index 24780eb4bfc..6b50ef49b49 100644 --- a/litellm/integrations/opentelemetry.py +++ b/litellm/integrations/opentelemetry.py @@ -1,7 +1,18 @@ import os from dataclasses import dataclass, field from datetime import datetime -from typing import TYPE_CHECKING, Any, Dict, List, Optional, Set, Union, cast +from typing import ( + TYPE_CHECKING, + Any, + Dict, + FrozenSet, + List, + Optional, + Set, + Tuple, + Union, + cast, +) import litellm from litellm._logging import verbose_logger @@ -82,6 +93,88 @@ _VALID_CAPTURE_MODES = { CAPTURE_MODE_SPAN_AND_EVENT, } +METRIC_METADATA_KEYS: Tuple[str, ...] = ( + "user_api_key_hash", + "user_api_key_alias", + "user_api_key_team_id", + "user_api_key_org_id", + "user_api_key_user_id", + "user_api_key_team_alias", + "user_api_key_user_email", + "spend_logs_metadata", + "requester_ip_address", + "requester_metadata", + "user_api_key_end_user_id", + "prompt_management_metadata", + "applied_guardrails", + "mcp_tool_call_metadata", + "vector_store_request_metadata", +) + +TOKEN_TYPE_ATTRIBUTE: str = "gen_ai.token.type" + +VALID_METRIC_ATTRIBUTE_NAMES: FrozenSet[str] = frozenset( + ( + "gen_ai.operation.name", + "gen_ai.system", + "gen_ai.request.model", + "gen_ai.framework", + "hidden_params", + ) + + tuple(f"metadata.{key}" for key in METRIC_METADATA_KEYS) +) + + +@dataclass(frozen=True) +class OTELMetricAttributeFilter: + include_list: Optional[List[str]] = None + exclude_list: Optional[List[str]] = None + + +def _build_metric_attribute_filter(value: Any) -> OTELMetricAttributeFilter: + if isinstance(value, OTELMetricAttributeFilter): + return value + if not isinstance(value, dict): + raise ValueError( + "otel.attributes must be a mapping with optional 'include_list' / " + f"'exclude_list', got {type(value).__name__}" + ) + return OTELMetricAttributeFilter( + include_list=value.get("include_list"), + exclude_list=value.get("exclude_list"), + ) + + +def _resolve_metric_attribute_filter( + attributes: Optional[OTELMetricAttributeFilter], +) -> Tuple[Optional[FrozenSet[str]], Optional[FrozenSet[str]]]: + if attributes is None: + return None, None + include = attributes.include_list or None + exclude = attributes.exclude_list or None + if include and exclude: + raise ValueError( + "otel.attributes: include_list and exclude_list are mutually exclusive" + ) + requested = include or exclude or [] + if TOKEN_TYPE_ATTRIBUTE in requested: + raise ValueError( + f"otel.attributes: {TOKEN_TYPE_ATTRIBUTE} is a structural token-usage " + "discriminator and cannot be filtered" + ) + unknown = sorted( + name for name in requested if name not in VALID_METRIC_ATTRIBUTE_NAMES + ) + if unknown: + raise ValueError( + f"otel.attributes: unknown attribute name(s) {unknown}. " + f"Valid names: {sorted(VALID_METRIC_ATTRIBUTE_NAMES)}" + ) + return ( + frozenset(include) if include else None, + frozenset(exclude) if exclude else None, + ) + def _normalize_team_metadata_keys(value: Any) -> List[str]: """Coerce a team-metadata allowlist from a list or comma-separated string. @@ -117,6 +210,9 @@ class OpenTelemetryConfig: # under ``litellm.team.metadata``. Empty by default so none of a team's # metadata leaves the process until explicitly allowlisted. baggage_team_metadata_keys: List[str] = field(default_factory=list) + # Prometheus-style include/exclude control over which attributes are stamped + # on emitted metrics, to cap metric cardinality. + attributes: Optional[OTELMetricAttributeFilter] = None def __post_init__(self) -> None: # If endpoint is specified but exporter is still the default "console", @@ -211,15 +307,29 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger): **kwargs, ): team_metadata_keys_override = kwargs.pop("baggage_team_metadata_keys", None) + metric_attributes_override = kwargs.pop("attributes", None) if config is None: config = OpenTelemetryConfig.from_env() if team_metadata_keys_override is not None: config.baggage_team_metadata_keys = _normalize_team_metadata_keys( team_metadata_keys_override ) + if metric_attributes_override is not None: + config.attributes = _build_metric_attribute_filter( + metric_attributes_override + ) self.config = config self.callback_name = callback_name + # Resolved on first metric record, not here: the proxy populates + # callback_settings.otel.attributes after this logger is constructed, so + # reading it now would miss it. An explicit config is validated eagerly so + # a bad config still fails at startup. + self._metric_attr_include: Optional[FrozenSet[str]] = None + self._metric_attr_exclude: Optional[FrozenSet[str]] = None + self._metric_attr_filter_resolved = False + if config.attributes is not None: + self._ensure_metric_attribute_filter() self.OTEL_EXPORTER = self.config.exporter self.OTEL_ENDPOINT = self.config.endpoint self.OTEL_HEADERS = self.config.headers @@ -1318,6 +1428,38 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger): return None return safe_dumps(filtered) + def _ensure_metric_attribute_filter(self) -> None: + """Resolve the include/exclude filter once, falling back to the proxy's + callback_settings.otel.attributes when no explicit config was passed.""" + if self._metric_attr_filter_resolved: + return + attributes = self.config.attributes + if attributes is None and self.callback_name in (None, "otel"): + otel_settings = (litellm.callback_settings or {}).get("otel") or {} + raw = ( + otel_settings.get("attributes") + if isinstance(otel_settings, dict) + else None + ) + if raw is not None: + attributes = _build_metric_attribute_filter(raw) + ( + self._metric_attr_include, + self._metric_attr_exclude, + ) = _resolve_metric_attribute_filter(attributes) + self._metric_attr_filter_resolved = True + + def _filter_metric_attributes(self, attrs: Dict[str, Any]) -> Dict[str, Any]: + if not self._metric_attr_filter_resolved: + self._ensure_metric_attribute_filter() + if self._metric_attr_include is not None: + return {k: v for k, v in attrs.items() if k in self._metric_attr_include} + if self._metric_attr_exclude is not None: + return { + k: v for k, v in attrs.items() if k not in self._metric_attr_exclude + } + return attrs + def _record_metrics(self, kwargs, response_obj, start_time, end_time): duration_s = (end_time - start_time).total_seconds() params = kwargs.get("litellm_params") or {} @@ -1336,23 +1478,7 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger): std_log = kwargs.get("standard_logging_object") md = getattr(std_log, "metadata", None) or (std_log or {}).get("metadata", {}) - for key in [ - "user_api_key_hash", - "user_api_key_alias", - "user_api_key_team_id", - "user_api_key_org_id", - "user_api_key_user_id", - "user_api_key_team_alias", - "user_api_key_user_email", - "spend_logs_metadata", - "requester_ip_address", - "requester_metadata", - "user_api_key_end_user_id", - "prompt_management_metadata", - "applied_guardrails", - "mcp_tool_call_metadata", - "vector_store_request_metadata", - ]: + for key in METRIC_METADATA_KEYS: value = md.get(key) if value is None: continue @@ -1368,6 +1494,8 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger): if hidden_params: common_attrs["hidden_params"] = safe_dumps(hidden_params) + common_attrs = self._filter_metric_attributes(common_attrs) + if self._operation_duration_histogram: self._operation_duration_histogram.record( duration_s, attributes=common_attrs @@ -1377,8 +1505,8 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger): and (usage := response_obj.get("usage")) and self._token_usage_histogram ): - in_attrs = {**common_attrs, "gen_ai.token.type": "input"} - out_attrs = {**common_attrs, "gen_ai.token.type": "output"} + in_attrs = {**common_attrs, TOKEN_TYPE_ATTRIBUTE: "input"} + out_attrs = {**common_attrs, TOKEN_TYPE_ATTRIBUTE: "output"} self._token_usage_histogram.record( usage.get("prompt_tokens", 0), attributes=in_attrs ) @@ -2070,9 +2198,7 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger): return kv_pairs - def set_attributes( # noqa: PLR0915 - self, span: Span, kwargs, response_obj: Optional[Any] - ): + def set_attributes(self, span: Span, kwargs, response_obj: Optional[Any]): try: if self.callback_name == "langtrace": from litellm.integrations.langtrace import LangtraceAttributes diff --git a/litellm/integrations/otel/README.md b/litellm/integrations/otel/README.md index 3edb96ed8d9..17011bb8db7 100644 --- a/litellm/integrations/otel/README.md +++ b/litellm/integrations/otel/README.md @@ -216,7 +216,13 @@ lives in [`plumbing/`](./plumbing): `TracerProvider` so one logger serves many tenants. The cache is a bounded LRU that flushes + shuts down evicted providers, since the key derives from request-supplied credentials and must not grow (or leak threads) without limit. -- [`metrics.py`](./plumbing/metrics.py) — GenAI client metric instruments. +- [`metrics.py`](./plumbing/metrics.py) — GenAI client metric instruments. The + six `gen_ai.client.*` histograms are recorded through the meter resolved by + `providers.resolve_meter_provider`: an injected provider wins (tests/DI), + otherwise the operator's globally configured `MeterProvider` is reused so its + readers/exporters receive them alongside the server metrics, and one is built + and registered as the global only when none is set (mirroring how V2 owns trace + export). ### Adapter diff --git a/litellm/integrations/otel/emitter.py b/litellm/integrations/otel/emitter.py index 7fb7be7ab84..6feaf2734e9 100644 --- a/litellm/integrations/otel/emitter.py +++ b/litellm/integrations/otel/emitter.py @@ -17,7 +17,7 @@ from litellm.integrations.otel.model.payloads import ( ServiceSpanData, ) from litellm.integrations.otel.plumbing.providers import to_otel_span_kind -from litellm.integrations.otel.model.semconv import Error +from litellm.integrations.otel.model.semconv import Error, ExceptionEvent from litellm.integrations.otel.model.spans import ( SPAN_REGISTRY, SpanRole, @@ -179,9 +179,17 @@ class SpanEmitter: else None ) if error and (error.error_type or error.message): - span.set_attribute(Error.TYPE, error.error_type or "error") - span.set_status( - Status(StatusCode.ERROR, error.message or error.error_type or "error") + error_type = error.error_type or "error" + message = error.message or error.error_type or "error" + span.set_attribute(Error.TYPE, error_type) + span.set_status(Status(StatusCode.ERROR, message)) + # Carry the full message on the standard ``exception`` event so backends + # map it as full text under ``exception.message``. Setting it as a bare + # string attribute instead lets backends like Elasticsearch dynamic-map + # it to a ``keyword`` capped at 1024 chars, truncating the message. + span.add_event( + ExceptionEvent.NAME, + {ExceptionEvent.TYPE: error_type, ExceptionEvent.MESSAGE: message}, ) # On success leave the status UNSET (the semconv default) rather than # forcing OK — that matches the FastAPI server span and avoids implying a diff --git a/litellm/integrations/otel/logger.py b/litellm/integrations/otel/logger.py index 5e683ce7b99..79931c0796c 100644 --- a/litellm/integrations/otel/logger.py +++ b/litellm/integrations/otel/logger.py @@ -3,13 +3,14 @@ from collections import OrderedDict from contextlib import contextmanager from datetime import datetime -from typing import TYPE_CHECKING, Any, Iterator, Mapping, cast +from typing import TYPE_CHECKING, Any, Callable, Iterator, Mapping, Sequence, cast from opentelemetry.context import attach, get_current from opentelemetry.sdk.trace import TracerProvider from opentelemetry.trace import Span, Tracer, get_current_span, use_span import litellm +from litellm._logging import verbose_logger from litellm.integrations.custom_logger import CustomLogger from litellm.integrations.otel.model.baggage import promoted_baggage from litellm.integrations.otel.model.config import OpenTelemetryV2Config @@ -36,9 +37,15 @@ from litellm.integrations.otel.model.payloads import ( SpanError, is_mcp_tool_call, ) +from litellm.integrations.otel.plumbing.metrics import ( + GenAIMetricRecorder, + create_genai_metrics, +) from litellm.integrations.otel.plumbing.providers import ( build_tracer_provider, + get_meter, get_tracer, + resolve_meter_provider, ) from litellm.integrations.otel.plumbing.routing import TenantTracerCache from litellm.integrations.otel.model.spans import SpanRole, span_role_for_service @@ -95,7 +102,7 @@ class OpenTelemetryV2(CustomLogger): callback_name: str | None = None, tracer_provider: TracerProvider | None = None, logger_provider: Any | None = None, # reserved for OTel logs - meter_provider: Any | None = None, # reserved for metrics + meter_provider: Any | None = None, **kwargs: Any, ) -> None: super().__init__(**kwargs) @@ -107,6 +114,8 @@ class OpenTelemetryV2(CustomLogger): else build_tracer_provider(self.config) ) self.tracer: Tracer = get_tracer(self._tracer_provider, LITELLM_TRACER_NAME) + self._metrics_recorder = self._init_metrics(meter_provider) + self._metric_filter_error_logged = False self._emitter = SpanEmitter( self.tracer, self.config, mappers=resolve_mappers(self.config.mapper_names) ) @@ -116,6 +125,20 @@ class OpenTelemetryV2(CustomLogger): self._open_llm_calls: "OrderedDict[str, _LLMCallSpan]" = OrderedDict() self._init_otel_logger_on_litellm_proxy() + def _init_metrics(self, meter_provider: Any | None) -> "GenAIMetricRecorder | None": + """Create the six GenAI histograms when metrics are enabled, else ``None``. + + ``meter_provider`` is an explicit override (tests inject one); otherwise the + provider is resolved from the OTel global so the operator's configured + readers/exporters receive the metrics, building and registering one only + when no global provider is set. + """ + if not self.config.enable_metrics: + return None + provider = resolve_meter_provider(self.config, meter_provider) + meter = get_meter(provider, LITELLM_TRACER_NAME) + return GenAIMetricRecorder(create_genai_metrics(meter), self.callback_name) + # ====================================================================== # # Proxy global registration # ====================================================================== # @@ -208,6 +231,25 @@ class OpenTelemetryV2(CustomLogger): if self._emit_mcp_tool_call(kwargs, start_time, end_time): return self._close_llm_call(kwargs, start_time, end_time) + self._record_metrics(kwargs, response_obj, start_time, end_time) + + def _record_metrics(self, kwargs, response_obj, start_time, end_time) -> None: + """Record the GenAI metrics for a successful LLM call. Best-effort: a + recording failure (e.g. a malformed payload) must never break the span + close or the request itself.""" + if self._metrics_recorder is None: + return + try: + self._metrics_recorder.record(kwargs, response_obj, start_time, end_time) + except ValueError as exc: + if not self._metric_filter_error_logged: + verbose_logger.error( + "OpenTelemetryV2: invalid otel.attributes metric filter, metrics disabled: %s", + exc, + ) + self._metric_filter_error_logged = True + except Exception as exc: + verbose_logger.debug("OpenTelemetryV2: metric recording failed: %s", exc) async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): if self._emit_mcp_tool_call(kwargs, start_time, end_time): @@ -504,6 +546,58 @@ class OpenTelemetryV2(CustomLogger): return span +def select_global_otel_v2_logger( + in_memory_loggers: Sequence[object], + registered: "OpenTelemetryV2 | None" = None, +) -> "OpenTelemetryV2": + """The single ``OpenTelemetryV2`` whose provider should become the OTel global. + + The callback factory designates one logger as canonical the moment it builds + the first one (``_init_otel_logger_on_litellm_proxy`` sets + ``proxy_server.open_telemetry_logger``), and every other v2 entry point — + guardrail, identity seeding, phase spans — already routes through that same + ``registered`` owner. Reuse it here too so the global provider has one source + of truth instead of a second, independently-derived guess; this is the logger + a preset (arize, langfuse, …) folds the ``OTEL_*`` base exporter and its own + exporter into, so the FastAPI server span and the gen-ai spans share one + provider and one trace. + + Fall back to ``in_memory_loggers`` for the SDK path, where no proxy global is + set (selecting from there, not ``service_callback``, which a preset logger does + not always reach), and build a generic logger from ``OTEL_*`` only when none was + configured at all. Each fallback still avoids the second generic logger that + orphaned the gen-ai spans onto a different backend than the server span. + """ + if registered is not None: + return registered + existing = next( + (cb for cb in in_memory_loggers if isinstance(cb, OpenTelemetryV2)), None + ) + return existing if existing is not None else OpenTelemetryV2() + + +def publish_global_otel_v2_provider( + in_memory_loggers: Sequence[object], + set_global_provider: Callable[[TracerProvider], None], + registered: "OpenTelemetryV2 | None" = None, +) -> "OpenTelemetryV2": + """Select the single v2 logger and publish its provider as the OTel global. + + The proxy calls this once at startup, after callbacks are initialized, so the + preset logger already exists; it passes ``registered`` (the canonical owner the + factory designated as ``proxy_server.open_telemetry_logger``) so the global + provider reuses the same logger the rest of the v2 code emits through (see + :func:`select_global_otel_v2_logger`). Both ``registered`` and + ``set_global_provider`` (the proxy passes + ``opentelemetry.trace.set_tracer_provider``) are injected so the publish step is + unit-testable without reading or mutating real global OTel state. Returns the + logger whose provider was published. + """ + logger = select_global_otel_v2_logger(in_memory_loggers, registered=registered) + set_global_provider(logger._tracer_provider) + return logger + + def _registered_v2_logger() -> "OpenTelemetryV2 | None": try: from litellm.proxy import proxy_server diff --git a/litellm/integrations/otel/mappers/genai.py b/litellm/integrations/otel/mappers/genai.py index 6c61feced4d..d9be68a06c2 100644 --- a/litellm/integrations/otel/mappers/genai.py +++ b/litellm/integrations/otel/mappers/genai.py @@ -10,7 +10,12 @@ table: one lambda per mapping operation, applied against the typed span data. from typing import Callable from litellm.integrations.otel.mappers.base import AttributeMap, AttrValue, SpanData -from litellm.integrations.otel.mappers.utils import collect, drop_none +from litellm.integrations.otel.mappers.utils import ( + collect, + drop_none, + output_messages, + serialize_messages, +) from litellm.integrations.otel.model.payloads import ( GuardrailSpanData, LLMCallSpanData, @@ -47,6 +52,8 @@ class GenAIMapper: else None ), GenAI.REQUEST_SEED: lambda d: d.request_params.seed, + GenAI.INPUT_MESSAGES: lambda d: serialize_messages(d.messages_in), + GenAI.OUTPUT_MESSAGES: lambda d: serialize_messages(output_messages(d)), GenAI.RESPONSE_MODEL: lambda d: d.response_model, GenAI.RESPONSE_ID: lambda d: d.response_id, GenAI.RESPONSE_FINISH_REASONS: lambda d: ( @@ -63,6 +70,20 @@ class GenAIMapper: # routing) onto the boundary-born LLM span — stamp it directly here. LiteLLM.PROVIDER_MODEL: lambda d: d.identity.provider_model or None, f"{LiteLLM.COST_PREFIX}total": lambda d: d.response_cost, + # Per-component cost breakdown (from the StandardLoggingPayload + # ``cost_breakdown``). Each component is omitted when the source didn't + # report it, so spans stay sparse rather than carrying zeros. + f"{LiteLLM.COST_PREFIX}input": lambda d: d.cost.input, + f"{LiteLLM.COST_PREFIX}output": lambda d: d.cost.output, + f"{LiteLLM.COST_PREFIX}cache_read": lambda d: d.cost.cache_read, + f"{LiteLLM.COST_PREFIX}cache_creation": lambda d: d.cost.cache_creation, + f"{LiteLLM.COST_PREFIX}tool_usage": lambda d: d.cost.tool_usage, + f"{LiteLLM.COST_PREFIX}original": lambda d: d.cost.original, + f"{LiteLLM.COST_PREFIX}discount_amount": lambda d: d.cost.discount_amount, + f"{LiteLLM.COST_PREFIX}discount_percent": lambda d: d.cost.discount_percent, + f"{LiteLLM.COST_PREFIX}margin_fixed_amount": lambda d: d.cost.margin_fixed_amount, + f"{LiteLLM.COST_PREFIX}margin_percent": lambda d: d.cost.margin_percent, + f"{LiteLLM.COST_PREFIX}margin_total_amount": lambda d: d.cost.margin_total_amount, LiteLLM.REQUEST_STREAMING: lambda d: d.is_streaming, } diff --git a/litellm/integrations/otel/model/config.py b/litellm/integrations/otel/model/config.py index ca46182bc66..991b156ae64 100644 --- a/litellm/integrations/otel/model/config.py +++ b/litellm/integrations/otel/model/config.py @@ -1,5 +1,7 @@ """Typed configuration for the OpenTelemetry instrumentation.""" +from enum import Enum +from functools import lru_cache from typing import Any, List from pydantic import AliasChoices, BaseModel, Field, field_validator, model_validator @@ -23,13 +25,35 @@ class CaptureMessageContent(str): SPAN_AND_EVENT = "span_and_event" +class ExporterOwner(str, Enum): + """The preset that contributed an exporter. Values match the callback names + in ``presets.PRESET_BY_CALLBACK`` so per-request dynamic-credential routing + can match an exporter's owner against the credential source's callback name. + A ``str`` enum so the value compares equal to the bare callback-name string.""" + + # Arize AX (the hosted platform) and Arize Phoenix (the open-source / Phoenix + # Cloud tracer) are distinct backends with separate config and auth, so they + # are separate owners. The member value stays the public callback name. + ARIZE_AX = "arize" + ARIZE_PHOENIX = "arize_phoenix" + LANGFUSE_OTEL = "langfuse_otel" + WEAVE_OTEL = "weave_otel" + LEVO = "levo" + AGENTOPS = "agentops" + + class _OTelV2Flag(BaseSettings): model_config = SettingsConfigDict(extra="ignore") enabled: bool = Field(default=False, validation_alias=AliasChoices(OTEL_V2_ENV)) +@lru_cache(maxsize=1) def is_otel_v2_enabled() -> bool: + # Resolved once at startup and cached: constructing the pydantic-settings + # model re-scans the environment and cost ~28us, which on the proxy hot path + # (auth, logging-callback setup) compounded into a measurable throughput + # regression. Tests that toggle the env must call ``is_otel_v2_enabled.cache_clear()``. return _OTelV2Flag().enabled @@ -49,6 +73,15 @@ class ExporterSpec(BaseModel): ) endpoint: str | None = None headers: str | None = None + owner: ExporterOwner | None = Field( + default=None, + description=( + "The preset that contributed this exporter. Per-request dynamic OTLP " + "credentials are applied only to the exporter whose owner matches the " + "credential source, so one tenant's vendor key never lands on a " + "different backend's exporter." + ), + ) options: dict[str, str] | None = Field( default=None, description=( @@ -184,6 +217,20 @@ class OpenTelemetryV2Config(BaseSettings): ), ) + @field_validator("capture_message_content", mode="before") + @classmethod + def _normalize_capture_message_content(cls, value: object) -> object: + """Fold the capture mode to its canonical lower_snake_case form. + + V1 read this env var case-insensitively, so operators set the + UPPER_SNAKE_CASE form (e.g. ``SPAN_AND_EVENT``). The canonical values + here are lower_snake_case; normalizing at the boundary keeps both + spellings working and lets every downstream comparison stay exact. + """ + if isinstance(value, str): + return value.lower() + return value + @field_validator( "baggage_promoted_keys", "baggage_metadata_keys", diff --git a/litellm/integrations/otel/model/payloads.py b/litellm/integrations/otel/model/payloads.py index bbef40ba374..82b7df5922c 100644 --- a/litellm/integrations/otel/model/payloads.py +++ b/litellm/integrations/otel/model/payloads.py @@ -34,6 +34,7 @@ __all__ = [ "RequestIdentity", "GuardrailSpanData", "LLMCallSpanData", + "LLMCost", "LLMRequestParams", "LLMUsage", "MCPToolCallSpanData", @@ -91,6 +92,49 @@ class LLMUsage: total_tokens: int | None = None +@dataclass(frozen=True) +class LLMCost: + """Per-component cost breakdown, from the StandardLoggingPayload + ``cost_breakdown`` (``litellm.types.utils.CostBreakdown``). + + Each field is the USD cost of one component, or ``None`` when the source did + not report it — so the mapper omits absent components instead of emitting 0. + The final (post-discount/post-margin) total is carried separately on + ``LLMCallSpanData.response_cost``. Free-form ``additional_costs`` are not + surfaced here: span attributes are scalar and there is no agreed key shape + for them yet. + """ + + input: float | None = None + output: float | None = None + cache_read: float | None = None + cache_creation: float | None = None + tool_usage: float | None = None + original: float | None = None + discount_amount: float | None = None + discount_percent: float | None = None + margin_fixed_amount: float | None = None + margin_percent: float | None = None + margin_total_amount: float | None = None + + @classmethod + def from_breakdown(cls, breakdown: Mapping[str, object] | None) -> "LLMCost": + b = breakdown or {} + return cls( + input=as_float(b.get("input_cost")), + output=as_float(b.get("output_cost")), + cache_read=as_float(b.get("cache_read_cost")), + cache_creation=as_float(b.get("cache_creation_cost")), + tool_usage=as_float(b.get("tool_usage_cost")), + original=as_float(b.get("original_cost")), + discount_amount=as_float(b.get("discount_amount")), + discount_percent=as_float(b.get("discount_percent")), + margin_fixed_amount=as_float(b.get("margin_fixed_amount")), + margin_percent=as_float(b.get("margin_percent")), + margin_total_amount=as_float(b.get("margin_total_amount")), + ) + + @dataclass(frozen=True) class SpanError: error_type: str | None = None @@ -255,6 +299,7 @@ class LLMCallSpanData: server: ServerInfo | None identity: RequestIdentity is_streaming: bool | None = None + cost: LLMCost = field(default_factory=LLMCost) tools: tuple[ToolDefinition, ...] = () # Raw messages and response, needed by vendor mappers (OpenInference, # Langfuse, Weave) that stamp message-level attributes. ``messages_in`` is @@ -302,6 +347,9 @@ class LLMCallSpanData: finish_reasons=finish_reasons, error=_parse_error(payload), response_cost=as_float(payload.get("response_cost")), + cost=LLMCost.from_breakdown( + cast("Mapping[str, object] | None", payload.get("cost_breakdown")) + ), server=ServerInfo.from_api_base(context.api_base), identity=context.identity, is_streaming=as_bool(payload.get("stream")), diff --git a/litellm/integrations/otel/model/semconv.py b/litellm/integrations/otel/model/semconv.py index 7df07f30a01..6315a5a4a89 100644 --- a/litellm/integrations/otel/model/semconv.py +++ b/litellm/integrations/otel/model/semconv.py @@ -146,6 +146,21 @@ class Error: TYPE: Final = "error.type" +class ExceptionEvent: + """OTel exception-event name and attribute keys (semconv ``exception.*``). + + The full error message rides ``exception.message`` on a span event rather than + a custom string attribute. Backends recognise these semantic-convention names + and map them as full text; an unrecognised key (e.g. ``error_message``) falls + into the default dynamic template, which truncates strings to a 1024-char + ``keyword``. + """ + + NAME: Final = "exception" + TYPE: Final = "exception.type" + MESSAGE: Final = "exception.message" + + class Server: ADDRESS: Final = "server.address" PORT: Final = "server.port" @@ -215,6 +230,10 @@ class Metric: TOKEN_USAGE: Final = "gen_ai.client.token.usage" OPERATION_DURATION: Final = "gen_ai.client.operation.duration" + TOKEN_COST: Final = "gen_ai.client.token.cost" + TIME_TO_FIRST_TOKEN: Final = "gen_ai.client.response.time_to_first_token" + TIME_PER_OUTPUT_TOKEN: Final = "gen_ai.client.response.time_per_output_token" + RESPONSE_DURATION: Final = "gen_ai.client.response.duration" # litellm ``custom_llm_provider`` -> ``gen_ai.provider.name`` value. diff --git a/litellm/integrations/otel/plumbing/metrics.py b/litellm/integrations/otel/plumbing/metrics.py index edd120f91e6..95ac939ff7f 100644 --- a/litellm/integrations/otel/plumbing/metrics.py +++ b/litellm/integrations/otel/plumbing/metrics.py @@ -1,28 +1,265 @@ -"""GenAI client metrics (token usage + operation duration histograms).""" +"""GenAI client metrics: the six ``gen_ai.client.*`` histograms plus the +recorder that builds attributes, applies the shared cardinality filter, and +records a request's metrics in the success path. + +The instrument names/units/descriptions and the recording + timing math mirror +the v1 :mod:`litellm.integrations.opentelemetry` integration so both engines emit +identical metrics. The attribute cardinality filter is reused from v1 by import +(no duplication of the valid-name set or its validation). +""" from dataclasses import dataclass +from datetime import datetime +from typing import Any, FrozenSet, Mapping, Optional from opentelemetry.metrics import Histogram, Meter -from litellm.integrations.otel.model.semconv import Metric +import litellm +from litellm.integrations.opentelemetry import ( + METRIC_METADATA_KEYS, + TOKEN_TYPE_ATTRIBUTE, + _build_metric_attribute_filter, + _resolve_metric_attribute_filter, +) +from litellm.integrations.otel.model.semconv import Metric, resolve_operation +from litellm.integrations.otel.model.utils import to_seconds +from litellm.litellm_core_utils.safe_json_dumps import safe_dumps @dataclass(frozen=True) class GenAIMetrics: - token_usage: Histogram operation_duration: Histogram + token_usage: Histogram + token_cost: Histogram + time_to_first_token: Histogram + time_per_output_token: Histogram + response_duration: Histogram def create_genai_metrics(meter: Meter) -> GenAIMetrics: return GenAIMetrics( - token_usage=meter.create_histogram( - name=Metric.TOKEN_USAGE, - unit="{token}", - description="Number of tokens used per GenAI request.", - ), operation_duration=meter.create_histogram( name=Metric.OPERATION_DURATION, unit="s", - description="GenAI operation duration.", + description="GenAI operation duration", + ), + token_usage=meter.create_histogram( + name=Metric.TOKEN_USAGE, + unit="{token}", + description="GenAI token usage", + ), + token_cost=meter.create_histogram( + name=Metric.TOKEN_COST, + unit="USD", + description="GenAI request cost", + ), + time_to_first_token=meter.create_histogram( + name=Metric.TIME_TO_FIRST_TOKEN, + unit="s", + description="Time to first token for streaming requests", + ), + time_per_output_token=meter.create_histogram( + name=Metric.TIME_PER_OUTPUT_TOKEN, + unit="s", + description="Average time per output token (generation time / completion tokens)", + ), + response_duration=meter.create_histogram( + name=Metric.RESPONSE_DURATION, + unit="s", + description="Total LLM API generation time (excludes LiteLLM overhead)", ), ) + + +class GenAIMetricRecorder: + """Records the six GenAI histograms for one successful LLM call. + + The cardinality filter is resolved lazily on the first record: the proxy + populates ``callback_settings.otel.attributes`` after the logger is built, so + reading it at construction time would miss it. ``gen_ai.token.type`` is added + to the token-usage attributes after filtering so the input/output split always + survives. + """ + + def __init__( + self, metrics: GenAIMetrics, callback_name: Optional[str] = None + ) -> None: + self._metrics = metrics + self._callback_name = callback_name + self._include: Optional[FrozenSet[str]] = None + self._exclude: Optional[FrozenSet[str]] = None + self._filter_resolved = False + + def record( + self, + kwargs: Mapping[str, Any], + response_obj: Any, + start_time: datetime, + end_time: datetime, + ) -> None: + common_attrs = self._filter_attributes(self._common_attributes(kwargs)) + duration_s = (end_time - start_time).total_seconds() + + self._metrics.operation_duration.record(duration_s, attributes=common_attrs) + self._record_token_usage(response_obj, common_attrs) + + cost = kwargs.get("response_cost") + if cost: + self._metrics.token_cost.record(cost, attributes=common_attrs) + + self._record_time_to_first_token(kwargs, common_attrs) + self._record_time_per_output_token( + kwargs, response_obj, end_time, duration_s, common_attrs + ) + self._record_response_duration(kwargs, end_time, common_attrs) + + # ------------------------------------------------------------------ # + # Attribute building + cardinality filter + # ------------------------------------------------------------------ # + + def _common_attributes(self, kwargs: Mapping[str, Any]) -> dict: + params = kwargs.get("litellm_params") or {} + provider = params.get("custom_llm_provider", "Unknown") + common_attrs: dict = { + "gen_ai.operation.name": resolve_operation(kwargs.get("call_type")).value, + "gen_ai.system": provider, + "gen_ai.request.model": kwargs.get("model"), + "gen_ai.framework": "litellm", + } + + std_log = kwargs.get("standard_logging_object") + md = getattr(std_log, "metadata", None) or (std_log or {}).get("metadata", {}) + for key in METRIC_METADATA_KEYS: + value = md.get(key) + if value is None: + continue + if isinstance(value, (dict, list)): + common_attrs[f"metadata.{key}"] = safe_dumps(value) + else: + common_attrs[f"metadata.{key}"] = str(value) + + hidden_params = getattr(std_log, "hidden_params", None) or (std_log or {}).get( + "hidden_params", {} + ) + if hidden_params: + common_attrs["hidden_params"] = safe_dumps(hidden_params) + + return common_attrs + + def _ensure_filter(self) -> None: + if self._filter_resolved: + return + attributes = None + if self._callback_name in (None, "otel"): + otel_settings = (litellm.callback_settings or {}).get("otel") or {} + raw = ( + otel_settings.get("attributes") + if isinstance(otel_settings, dict) + else None + ) + if raw is not None: + attributes = _build_metric_attribute_filter(raw) + # A bad filter (include_list + exclude_list both set, an unfilterable name) + # raises here; the caller (logger._record_metrics) surfaces it once at ERROR + # so the operator-fixable config error is visible. Not cached on the raise + # path -- _filter_resolved stays False -- so a corrected config takes effect + # without reconstructing the recorder. + self._include, self._exclude = _resolve_metric_attribute_filter(attributes) + self._filter_resolved = True + + def _filter_attributes(self, attrs: dict) -> dict: + self._ensure_filter() + if self._include is not None: + return {k: v for k, v in attrs.items() if k in self._include} + if self._exclude is not None: + return {k: v for k, v in attrs.items() if k not in self._exclude} + return attrs + + # ------------------------------------------------------------------ # + # Per-metric recording + # ------------------------------------------------------------------ # + + def _record_token_usage(self, response_obj: Any, common_attrs: dict) -> None: + if not response_obj: + return + usage = response_obj.get("usage") + if not usage: + return + in_attrs = {**common_attrs, TOKEN_TYPE_ATTRIBUTE: "input"} + out_attrs = {**common_attrs, TOKEN_TYPE_ATTRIBUTE: "output"} + 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: + if not kwargs.get("optional_params", {}).get("stream", False): + return + api_call_start = to_seconds(kwargs.get("api_call_start_time")) + completion_start = to_seconds(kwargs.get("completion_start_time")) + if api_call_start is None or completion_start is None: + return + self._metrics.time_to_first_token.record( + completion_start - api_call_start, attributes=common_attrs + ) + + def _record_time_per_output_token( + self, + kwargs: Mapping[str, Any], + response_obj: Any, + 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") + if completion_tokens is None or completion_tokens <= 0: + return + + end_ts = to_seconds(end_time) + if end_ts is None: + generation_time = duration_s + else: + completion_start_time = kwargs.get("completion_start_time") + api_call_start_time = kwargs.get("api_call_start_time") + if completion_start_time is not None: + completion_start = to_seconds(completion_start_time) + generation_time = ( + duration_s + if completion_start is None + else end_ts - completion_start + ) + elif api_call_start_time is not None: + api_call_start = to_seconds(api_call_start_time) + generation_time = ( + duration_s if api_call_start is None else end_ts - api_call_start + ) + else: + generation_time = duration_s + + if generation_time > 0: + self._metrics.time_per_output_token.record( + generation_time / completion_tokens, attributes=common_attrs + ) + + def _record_response_duration( + self, kwargs: Mapping[str, Any], end_time: datetime, common_attrs: dict + ) -> None: + api_call_start_time = kwargs.get("api_call_start_time") + if api_call_start_time is None: + return + _end_time = kwargs.get("end_time") or end_time + if _end_time is None: + _end_time = datetime.now() + api_call_start = to_seconds(api_call_start_time) + end_ts = to_seconds(_end_time) + if api_call_start is None or end_ts is None: + return + duration = end_ts - api_call_start + if duration > 0: + self._metrics.response_duration.record(duration, attributes=common_attrs) diff --git a/litellm/integrations/otel/plumbing/providers.py b/litellm/integrations/otel/plumbing/providers.py index 40a0e41b905..6d0710397a3 100644 --- a/litellm/integrations/otel/plumbing/providers.py +++ b/litellm/integrations/otel/plumbing/providers.py @@ -1,9 +1,11 @@ """Provider / exporter factory + the Baggage span processor.""" -from typing import Callable, Iterable +from typing import TYPE_CHECKING, Any, Callable, Iterable -from opentelemetry import baggage +from opentelemetry import baggage, metrics from opentelemetry.context import Context +from opentelemetry.metrics import MeterProvider, NoOpMeterProvider +from opentelemetry.sdk.metrics import MeterProvider as SDKMeterProvider from opentelemetry.sdk.resources import Resource from opentelemetry.sdk.trace import ReadableSpan, SpanProcessor, TracerProvider from opentelemetry.sdk.trace.export import ( @@ -17,6 +19,7 @@ from opentelemetry.sdk.trace.export.in_memory_span_exporter import ( ) from opentelemetry.trace import Span, SpanKind, Tracer +from litellm._version import version as litellm_version from litellm.integrations.otel.model.config import ExporterSpec, OpenTelemetryV2Config from litellm.integrations.otel.model.semconv import LiteLLM from litellm.integrations.otel.model.spans import LiteLLMSpanKind @@ -24,6 +27,10 @@ from litellm.integrations.otel.model.spans import LiteLLMSpanKind # Re-exported so ``providers.parse_headers`` remains a stable entry point. from litellm.integrations.otel.model.utils import parse_headers as parse_headers +if TYPE_CHECKING: + from opentelemetry.metrics import Meter + from opentelemetry.sdk.metrics.export import MetricReader + _SPAN_KIND_BY_ROLE_KIND: dict[LiteLLMSpanKind, SpanKind] = { LiteLLMSpanKind.SERVER: SpanKind.SERVER, LiteLLMSpanKind.CLIENT: SpanKind.CLIENT, @@ -156,6 +163,120 @@ def build_span_exporter(config: OpenTelemetryV2Config) -> SpanExporter: ) +def _otlp_metrics_endpoint(endpoint: str | None) -> str | None: + """Point an OTLP/HTTP base endpoint at the ``/v1/metrics`` signal path. + + The OTLP/HTTP exporter only appends ``/v1/metrics`` when it reads + ``OTEL_EXPORTER_OTLP_ENDPOINT`` itself; an explicitly passed endpoint is used + verbatim, so a base URL would POST to the root. Mirror ``_otlp_traces_endpoint`` + for the metrics signal (rewriting a sibling signal path when present). + """ + if not endpoint: + return endpoint + endpoint = endpoint.rstrip("/") + if endpoint.endswith("/v1/metrics"): + return endpoint + for other_signal in ("/v1/traces", "/v1/logs"): + if endpoint.endswith(other_signal): + return endpoint[: -len(other_signal)] + "/v1/metrics" + return endpoint + "/v1/metrics" + + +def build_metric_reader(config: OpenTelemetryV2Config) -> "MetricReader": + """Build a metric reader mirroring v1's exporter selection. + + ``console`` (and any unrecognized kind) exports to the console; ``otlp_http`` + and ``otlp_grpc`` export over OTLP with the configured endpoint/headers. The + reader exports on a 5s period, matching v1. + """ + from opentelemetry.sdk.metrics.export import ( + ConsoleMetricExporter, + PeriodicExportingMetricReader, + ) + + kind = (config.exporter or "console").lower() + if kind in ("otlp_http", "http", "http/protobuf", "http/json"): + from opentelemetry.exporter.otlp.proto.http.metric_exporter import ( + OTLPMetricExporter as HTTPMetricExporter, + ) + from opentelemetry.sdk.metrics import Histogram + from opentelemetry.sdk.metrics.export import AggregationTemporality + + exporter: Any = HTTPMetricExporter( + endpoint=_otlp_metrics_endpoint(config.endpoint), + headers=parse_headers(config.headers), + preferred_temporality={Histogram: AggregationTemporality.DELTA}, + ) + elif kind in ("otlp_grpc", "grpc"): + from opentelemetry.sdk.metrics import Histogram + from opentelemetry.sdk.metrics.export import AggregationTemporality + + try: + from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import ( + OTLPMetricExporter as GRPCMetricExporter, + ) + except ImportError as exc: + raise ImportError( + "OpenTelemetry OTLP gRPC metric exporter is not available. Install " + "`opentelemetry-exporter-otlp` and `grpcio` (or `litellm[grpc]`)." + ) from exc + + exporter = GRPCMetricExporter( + endpoint=config.endpoint, + headers=parse_headers(config.headers), + preferred_temporality={Histogram: AggregationTemporality.DELTA}, + ) + else: + exporter = ConsoleMetricExporter() + + return PeriodicExportingMetricReader(exporter, export_interval_millis=5000) + + +def build_meter_provider( + config: OpenTelemetryV2Config, + metric_reader: "MetricReader | None" = None, +) -> SDKMeterProvider: + """Build the :class:`MeterProvider` for GenAI metrics. + + ``metric_reader`` is an explicit override (tests inject an + ``InMemoryMetricReader``); otherwise the reader is selected from the config's + exporter kind via :func:`build_metric_reader`. + """ + reader = metric_reader if metric_reader is not None else build_metric_reader(config) + return SDKMeterProvider(metric_readers=[reader], resource=build_resource(config)) + + +def resolve_meter_provider( + config: OpenTelemetryV2Config, + meter_provider: MeterProvider | None = None, +) -> MeterProvider: + """Resolve the :class:`MeterProvider` GenAI metrics record through. + + An injected provider wins (DI/tests). Otherwise reuse whatever the operator has + configured as the global, whether a real SDK provider or an explicit + ``NoOpMeterProvider``, so the GenAI histograms ride the operator's + readers/exporters and an explicit opt-out is honored. Only when the global is + still the default proxy placeholder does V2 build one from the config and + publish it as the global, mirroring how V2 owns trace export. The built + provider is the one returned, so its reader thread is always live, never + orphaned. + """ + if meter_provider is not None: + return meter_provider + + existing = metrics.get_meter_provider() + if isinstance(existing, (SDKMeterProvider, NoOpMeterProvider)): + return existing + + provider = build_meter_provider(config) + metrics.set_meter_provider(provider) + return provider + + +def get_meter(provider: MeterProvider, name: str = "litellm") -> "Meter": + return provider.get_meter(name, litellm_version) + + def build_resource(config: OpenTelemetryV2Config) -> Resource: attributes: dict[str, str] = {"service.name": config.service_name} if config.deployment_environment: @@ -207,7 +328,10 @@ def build_tracer_provider( def get_tracer(provider: TracerProvider, name: str = "litellm") -> Tracer: - return provider.get_tracer(name) + # Stamp the instrumentation scope with the LiteLLM package version so every + # emitted span carries a deterministic ``scope.version`` (the standard OTel + # location for the emitting library's version) for downstream consumers. + return provider.get_tracer(name, litellm_version) def in_memory_provider( diff --git a/litellm/integrations/otel/plumbing/routing.py b/litellm/integrations/otel/plumbing/routing.py index 4d0943a263a..1f2f1b202d9 100644 --- a/litellm/integrations/otel/plumbing/routing.py +++ b/litellm/integrations/otel/plumbing/routing.py @@ -88,13 +88,23 @@ class TenantTracerCache: return get_tracer(provider, self._tracer_name) def _config_with_headers(self, headers: Mapping[str, str]) -> OpenTelemetryV2Config: - """Clone the config, replacing OTLP exporter headers with ``headers``.""" + """Clone the config, stamping ``headers`` onto the credential's own exporter. + + ``headers`` are the per-request credentials of ``self._callback_name`` (the + integration that built this cache), so they apply only to the exporter that + integration contributed (``spec.owner``). A request that carries one + tenant's Arize key must never rewrite the headers of a co-configured + Langfuse or self-hosted collector exporter, which would leak that key to a + different backend. + """ header_str = ",".join(f"{key}={value}" for key, value in headers.items()) + header_update: dict[str, str] = {"headers": header_str} exporters = [ ( - spec - if spec.kind.lower() in _NON_OTLP_KINDS - else spec.model_copy(update={"headers": header_str}) + spec.model_copy(update=header_update) + if spec.owner == self._callback_name + and spec.kind.lower() not in _NON_OTLP_KINDS + else spec ) for spec in self._config.exporters ] diff --git a/litellm/integrations/otel/presets/agentops.py b/litellm/integrations/otel/presets/agentops.py index 5a12818fd99..7b0783935ac 100644 --- a/litellm/integrations/otel/presets/agentops.py +++ b/litellm/integrations/otel/presets/agentops.py @@ -16,7 +16,11 @@ from pydantic import Field from pydantic_settings import BaseSettings, SettingsConfigDict from litellm._logging import verbose_logger -from litellm.integrations.otel.model.config import ExporterSpec, OpenTelemetryV2Config +from litellm.integrations.otel.model.config import ( + ExporterOwner, + ExporterSpec, + OpenTelemetryV2Config, +) from litellm.integrations.otel.plumbing.providers import register_exporter_factory _AGENTOPS_ENDPOINT = "https://otlp.agentops.cloud/v1/traces" @@ -59,6 +63,7 @@ def agentops_preset( options=( {"api_key": settings.api_key} if settings.api_key else None ), + owner=ExporterOwner.AGENTOPS, ), ], "resource_attributes": { diff --git a/litellm/integrations/otel/presets/arize.py b/litellm/integrations/otel/presets/arize.py index 4df15125f5a..b6af88c6b34 100644 --- a/litellm/integrations/otel/presets/arize.py +++ b/litellm/integrations/otel/presets/arize.py @@ -4,7 +4,11 @@ from pydantic import Field from pydantic_settings import BaseSettings, SettingsConfigDict from litellm.integrations.arize.arize import ArizeLogger as _V1ArizeLogger -from litellm.integrations.otel.model.config import ExporterSpec, OpenTelemetryV2Config +from litellm.integrations.otel.model.config import ( + ExporterOwner, + ExporterSpec, + OpenTelemetryV2Config, +) from litellm.integrations.otel.presets.utils import ensure_mappers from litellm.types.utils import StandardCallbackDynamicParams @@ -34,6 +38,7 @@ def arize_preset( kind=arize_cfg.protocol or "otlp_grpc", endpoint=arize_cfg.endpoint or "https://otlp.arize.com/v1", headers=headers, + owner=ExporterOwner.ARIZE_AX, ), ], "mapper_names": ensure_mappers(base.mapper_names, "openinference"), diff --git a/litellm/integrations/otel/presets/langfuse.py b/litellm/integrations/otel/presets/langfuse.py index 011545384b9..5631da6429f 100644 --- a/litellm/integrations/otel/presets/langfuse.py +++ b/litellm/integrations/otel/presets/langfuse.py @@ -3,7 +3,11 @@ from litellm.integrations.langfuse.langfuse_otel import ( LangfuseOtelLogger as _V1Langfuse, ) -from litellm.integrations.otel.model.config import ExporterSpec, OpenTelemetryV2Config +from litellm.integrations.otel.model.config import ( + ExporterOwner, + ExporterSpec, + OpenTelemetryV2Config, +) from litellm.integrations.otel.presets.utils import ensure_mappers from litellm.types.utils import StandardCallbackDynamicParams @@ -23,6 +27,7 @@ def langfuse_preset( kind=kind, endpoint=cfg.endpoint, headers=cfg.headers, + owner=ExporterOwner.LANGFUSE_OTEL, ), ], "mapper_names": ensure_mappers(base.mapper_names, "langfuse"), diff --git a/litellm/integrations/otel/presets/levo.py b/litellm/integrations/otel/presets/levo.py index 4c4cba982a4..74a95b100cb 100644 --- a/litellm/integrations/otel/presets/levo.py +++ b/litellm/integrations/otel/presets/levo.py @@ -1,7 +1,11 @@ """Levo preset — OTLP/HTTP to a Levo collector with org+workspace headers.""" from litellm.integrations.levo.levo import LevoLogger as _V1Levo -from litellm.integrations.otel.model.config import ExporterSpec, OpenTelemetryV2Config +from litellm.integrations.otel.model.config import ( + ExporterOwner, + ExporterSpec, + OpenTelemetryV2Config, +) def levo_preset( @@ -18,6 +22,7 @@ def levo_preset( kind="otlp_http", endpoint=cfg.endpoint, headers=cfg.otlp_auth_headers, + owner=ExporterOwner.LEVO, ), ], } diff --git a/litellm/integrations/otel/presets/phoenix.py b/litellm/integrations/otel/presets/phoenix.py index 4c2b165ffca..5485b599321 100644 --- a/litellm/integrations/otel/presets/phoenix.py +++ b/litellm/integrations/otel/presets/phoenix.py @@ -6,7 +6,11 @@ from pydantic_settings import BaseSettings, SettingsConfigDict from litellm.integrations.arize.arize_phoenix import ( ArizePhoenixLogger as _V1Phoenix, ) -from litellm.integrations.otel.model.config import ExporterSpec, OpenTelemetryV2Config +from litellm.integrations.otel.model.config import ( + ExporterOwner, + ExporterSpec, + OpenTelemetryV2Config, +) from litellm.integrations.otel.presets.utils import ensure_mappers @@ -37,6 +41,7 @@ def phoenix_preset( kind=cfg.protocol if hasattr(cfg, "protocol") else "otlp_http", endpoint=cfg.endpoint, headers=headers, + owner=ExporterOwner.ARIZE_PHOENIX, ), ], "mapper_names": ensure_mappers(base.mapper_names, "openinference"), diff --git a/litellm/integrations/otel/presets/weave.py b/litellm/integrations/otel/presets/weave.py index 9fc03c84a6d..d22f7641289 100644 --- a/litellm/integrations/otel/presets/weave.py +++ b/litellm/integrations/otel/presets/weave.py @@ -1,6 +1,10 @@ """Weave (W&B) preset.""" -from litellm.integrations.otel.model.config import ExporterSpec, OpenTelemetryV2Config +from litellm.integrations.otel.model.config import ( + ExporterOwner, + ExporterSpec, + OpenTelemetryV2Config, +) from litellm.integrations.otel.presets.utils import ensure_mappers from litellm.integrations.weave.weave_otel import ( _get_weave_authorization_header, @@ -23,6 +27,7 @@ def weave_preset( kind=weave_cfg.protocol or "otlp_http", endpoint=weave_cfg.endpoint, headers=weave_cfg.otlp_auth_headers, + owner=ExporterOwner.WEAVE_OTEL, ), ], # Weave consumes OpenInference + a small Weave-specific overlay. diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py index 2119527a8e5..c63f114514a 100644 --- a/litellm/integrations/prometheus.py +++ b/litellm/integrations/prometheus.py @@ -75,7 +75,7 @@ class PrometheusLogger(CustomLogger): return cb return None - def __init__( # noqa: PLR0915 + def __init__( self, **kwargs, ): @@ -2255,7 +2255,7 @@ class PrometheusLogger(CustomLogger): or _litellm_params_metadata.get("user_agent"), } - def set_llm_deployment_failure_metrics(self, request_kwargs: dict): # noqa: PLR0915 + def set_llm_deployment_failure_metrics(self, request_kwargs: dict): """ Sets Failure metrics when an LLM API call fails diff --git a/litellm/integrations/websearch_interception/handler.py b/litellm/integrations/websearch_interception/handler.py index 37528e7dcd5..f29b378fcde 100644 --- a/litellm/integrations/websearch_interception/handler.py +++ b/litellm/integrations/websearch_interception/handler.py @@ -1128,7 +1128,7 @@ class WebSearchInterceptionLogger(CustomLogger): ) raise - async def _execute_chat_completion_agentic_loop( # noqa: PLR0915 + async def _execute_chat_completion_agentic_loop( self, model: str, messages: List[Dict], @@ -1159,7 +1159,7 @@ class WebSearchInterceptionLogger(CustomLogger): **request_patch.kwargs, ) - async def _build_chat_completion_request_patch( # noqa: PLR0915 + async def _build_chat_completion_request_patch( self, model: str, messages: List[Dict], @@ -1339,8 +1339,13 @@ class WebSearchInterceptionLogger(CustomLogger): websearch_params: WebSearchInterceptionConfig = {} if "websearch_interception_params" in litellm_settings: websearch_params = litellm_settings["websearch_interception_params"] - elif "websearch_interception" in callback_specific_params: - websearch_params = callback_specific_params["websearch_interception"] + elif "websearch_interception" in callback_specific_params and isinstance( + callback_specific_params["websearch_interception"], dict + ): + websearch_params = cast( + WebSearchInterceptionConfig, + callback_specific_params["websearch_interception"], + ) # Use classmethod to initialize from config return WebSearchInterceptionLogger.from_config_yaml(websearch_params) diff --git a/litellm/integrations/weights_biases.py b/litellm/integrations/weights_biases.py index e9539d27e97..5f087fe219a 100644 --- a/litellm/integrations/weights_biases.py +++ b/litellm/integrations/weights_biases.py @@ -21,10 +21,11 @@ try: # contains a (known) object attribute object: Literal["chat.completion", "edit", "text_completion"] - def __getitem__(self, key: K) -> V: ... # noqa + def __getitem__(self, key: K) -> V: ... - def get(self, key: K, default: Optional[V] = None) -> Optional[V]: # noqa - ... # pragma: no cover + def get( + self, key: K, default: Optional[V] = None + ) -> Optional[V]: ... # pragma: no cover class OpenAIRequestResponseResolver: def __call__( diff --git a/litellm/interactions/agents/utils.py b/litellm/interactions/agents/utils.py index d16a9597f53..e9405928a3d 100644 --- a/litellm/interactions/agents/utils.py +++ b/litellm/interactions/agents/utils.py @@ -2,11 +2,40 @@ Utility functions for the Agents API SDK. """ -from typing import Optional +from typing import Dict, Mapping, Optional from litellm.llms.base_llm.agents.transformation import BaseAgentsAPIConfig +def merge_agent_headers( + *, + dynamic_headers: Optional[Mapping[str, str]] = None, + static_headers: Optional[Mapping[str, str]] = None, +) -> Optional[Dict[str, str]]: + """Merge outbound HTTP headers for A2A agent calls. + + Merge rules: + - Start with ``dynamic_headers`` (values extracted from the incoming client request). + - Overlay ``static_headers`` (admin-configured per agent). + - Comparison is case-insensitive (HTTP headers are case-insensitive), so a + static ``Authorization`` strips any dynamic ``authorization`` before the + static value is written. The static side's casing is preserved. + + If both contain the same header (case-insensitively), ``static_headers`` wins. + """ + merged: Dict[str, str] = {} + + if dynamic_headers: + merged.update({str(k): str(v) for k, v in dynamic_headers.items()}) + + if static_headers: + static_lower = {str(k).lower() for k in static_headers} + merged = {k: v for k, v in merged.items() if k.lower() not in static_lower} + merged.update({str(k): str(v) for k, v in static_headers.items()}) + + return merged or None + + def get_provider_agents_api_config( custom_llm_provider: Optional[str], ) -> Optional[BaseAgentsAPIConfig]: diff --git a/litellm/interactions/litellm_responses_transformation/transformation.py b/litellm/interactions/litellm_responses_transformation/transformation.py index 173d4ca8764..0ff1a97cd0b 100644 --- a/litellm/interactions/litellm_responses_transformation/transformation.py +++ b/litellm/interactions/litellm_responses_transformation/transformation.py @@ -300,9 +300,6 @@ class LiteLLMResponsesInteractionsConfig: "total_output_tokens": getattr(usage, "output_tokens", 0), } - # Add role - interactions_response_dict["role"] = "model" - # Add updated (same as created for now) interactions_response_dict["updated"] = created diff --git a/litellm/litellm_core_utils/chat_completion_agentic_loop.py b/litellm/litellm_core_utils/chat_completion_agentic_loop.py new file mode 100644 index 00000000000..938e892bd50 --- /dev/null +++ b/litellm/litellm_core_utils/chat_completion_agentic_loop.py @@ -0,0 +1,332 @@ +# 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 typing import cast + +from litellm._logging import verbose_logger +from litellm.integrations.custom_logger import CustomLogger +from litellm.types.integrations.custom_logger import ( + CHAT_COMPLETION_AGENTIC_SURFACE, + NON_CODE_INTERPRETER_INTERCEPTION_INTERNAL_PREFIXES, + AgenticLoopPlan, + AgenticLoopRequestPatch, + is_interception_internal_key, +) +from litellm.types.utils import ModelResponse +from litellm.utils import CustomStreamWrapper + +_FOLLOWUP_INTERNAL_PARAMS = frozenset( + ( + "acompletion", + "litellm_logging_obj", + "custom_llm_provider", + "model_alias_map", + "stream_response", + "custom_prompt_dict", + "_agentic_loop_api_surface", + ) +) + + +def _gate_overridden(callback: CustomLogger) -> bool: + base = CustomLogger.async_should_run_agentic_loop + func = type(callback).async_should_run_agentic_loop + return getattr(func, "__func__", func) is not getattr(base, "__func__", base) + + +def _build_plan_overridden(callback: CustomLogger) -> bool: + base = CustomLogger.async_build_agentic_loop_plan + func = type(callback).async_build_agentic_loop_plan + return getattr(func, "__func__", func) is not getattr(base, "__func__", base) + + +def _post_hook_overridden(callback: CustomLogger) -> bool: + base = CustomLogger.async_post_agentic_loop_response_hook + func = type(callback).async_post_agentic_loop_response_hook + return getattr(func, "__func__", func) is not getattr(base, "__func__", base) + + +def _coerce_int(value: object, default: int) -> int: + return int(value) if isinstance(value, (int, str)) else default + + +def _agentic_loop_settings(kwargs: dict[str, object]) -> tuple[int, int, list[str]]: + depth = _coerce_int(kwargs.get("_agentic_loop_depth"), 0) + max_loops = max(_coerce_int(kwargs.get("max_agentic_loops"), 3), 1) + raw_fingerprints = kwargs.get("_agentic_loop_fingerprints") + fingerprints = ( + [str(fp) for fp in raw_fingerprints] + if isinstance(raw_fingerprints, list) + else [] + ) + return depth, max_loops, fingerprints + + +def _fingerprint_tools(tool_calls: object) -> str: + try: + return json.dumps(tool_calls, sort_keys=True, default=str) + except Exception: + return str(tool_calls) + + +def _check_agentic_loop_safety( + tool_calls: object, + fingerprints: list[str], + depth: int, + max_loops: int, + model: str, +) -> str: + fingerprint = _fingerprint_tools(tool_calls) + if fingerprint in fingerprints: + raise ValueError( + "Agentic loop detected repeated tool-call fingerprint; aborting rerun" + ) + if depth >= max_loops: + raise ValueError(f"Exceeded max_agentic_loops={max_loops} for model={model}") + return fingerprint + + +def _wrap_response_as_fake_stream(response: object) -> object: + if getattr(response, "object", None) == "chat.completion.chunk": + return response + if not hasattr(response, "choices"): + 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)) + + +def _add_agentic_loop_metadata(kwargs_for_followup: dict[str, object]) -> None: + metadata = kwargs_for_followup.get("litellm_metadata") + metadata = dict(metadata) if isinstance(metadata, dict) else {} + for key, value in kwargs_for_followup.items(): + if ( + key.startswith("_agentic_loop") + or key == "max_agentic_loops" + or is_interception_internal_key(key) + ): + metadata[key] = value + kwargs_for_followup["litellm_metadata"] = metadata + + +def _filter_followup_kwargs(source: dict[str, object]) -> dict[str, object]: + return { + k: v + for k, v in source.items() + if not is_interception_internal_key( + k, prefixes=NON_CODE_INTERPRETER_INTERCEPTION_INTERNAL_PREFIXES + ) + and k not in _FOLLOWUP_INTERNAL_PARAMS + } + + +async def _execute_chat_completion_agentic_plan( + *, + plan: AgenticLoopPlan, + callback: CustomLogger, + model: str, + optional_params: dict[str, object], + kwargs: dict[str, object], + logging_obj: object, + custom_llm_provider: str, + depth: int, + max_loops: int, + fingerprints: list[str], + fingerprint: str, +) -> object: + import litellm + + patch = plan.request_patch or AgenticLoopRequestPatch() + if patch.messages is None: + raise ValueError("Agentic loop plan missing patched messages") + + full_model_name = patch.model or model + if "/" not in full_model_name: + full_model_name = f"{custom_llm_provider}/{full_model_name}" + + optional_params_for_followup = {**optional_params, **patch.optional_params} + if patch.tools is not None: + optional_params_for_followup["tools"] = patch.tools + if "tool_choice" not in patch.optional_params: + optional_params_for_followup.pop("tool_choice", None) + + kwargs_for_followup = _filter_followup_kwargs(kwargs) + kwargs_for_followup.update( + { + k: v + for k, v in _filter_followup_kwargs(patch.kwargs).items() + if k not in optional_params_for_followup + } + ) + kwargs_for_followup["_agentic_loop_depth"] = depth + 1 + kwargs_for_followup["max_agentic_loops"] = max_loops + kwargs_for_followup["_agentic_loop_fingerprints"] = fingerprints + [fingerprint] + _add_agentic_loop_metadata(kwargs_for_followup) + + try: + response_followup = await litellm.acompletion( + model=full_model_name, + messages=patch.messages, + **optional_params_for_followup, + **kwargs_for_followup, + ) + if _post_hook_overridden(callback): + try: + response_followup = ( + await callback.async_post_agentic_loop_response_hook( + response=response_followup, plan=plan, kwargs=kwargs + ) + ) + except Exception as e: + _call_id = getattr(logging_obj, "litellm_call_id", "unknown") + verbose_logger.exception( + "LiteLLM.AgenticHookError: Exception in " + "async_post_agentic_loop_response_hook [call_id=%s model=%s]: %s", + _call_id, + model, + str(e), + ) + if kwargs.get("_code_interpreter_interception_converted_stream") and not depth: + return _wrap_response_as_fake_stream(response_followup) + return response_followup + finally: + try: + await callback.async_agentic_loop_cleanup_hook(plan=plan, kwargs=kwargs) + except Exception as e: + _call_id = getattr(logging_obj, "litellm_call_id", "unknown") + verbose_logger.exception( + "LiteLLM.AgenticHookError: Exception in " + "async_agentic_loop_cleanup_hook [call_id=%s model=%s]: %s", + _call_id, + model, + str(e), + ) + + +async def maybe_run_chat_completion_agentic_loop( + *, + response: ModelResponse, + model: str, + messages: list, + optional_params: dict, + kwargs: dict, + logging_obj: object, + custom_llm_provider: str, + stream: bool, +) -> ModelResponse | CustomStreamWrapper | None: + import litellm + + callbacks = litellm.callbacks + ( + getattr(logging_obj, "dynamic_success_callbacks", None) or [] + ) + depth, max_loops, fingerprints = _agentic_loop_settings(kwargs) + tools = optional_params.get("tools", []) + + for callback in callbacks: + if not isinstance(callback, CustomLogger): + continue + if not _gate_overridden(callback): + continue + + gate_kwargs = { + **kwargs, + "_agentic_loop_api_surface": CHAT_COMPLETION_AGENTIC_SURFACE, + "custom_llm_provider": custom_llm_provider, + } + try: + should_run, tool_calls = await callback.async_should_run_agentic_loop( + response=response, + model=model, + messages=messages, + tools=tools, + stream=stream, + custom_llm_provider=custom_llm_provider, + kwargs=gate_kwargs, + ) + except Exception as e: + verbose_logger.exception( + "LiteLLM.AgenticHookError: Exception in chat completion agentic gate: %s", + str(e), + ) + continue + + if not should_run: + continue + + fingerprint = _check_agentic_loop_safety( + tool_calls=tool_calls, + fingerprints=fingerprints, + depth=depth, + max_loops=max_loops, + model=model, + ) + + try: + plan_kwargs = { + **kwargs, + "_agentic_loop_api_surface": CHAT_COMPLETION_AGENTIC_SURFACE, + "custom_llm_provider": custom_llm_provider, + } + if not _build_plan_overridden(callback): + return await callback.async_run_agentic_loop( + tools=tool_calls, + model=model, + messages=messages, + response=response, + anthropic_messages_provider_config=None, + anthropic_messages_optional_request_params=optional_params, + logging_obj=logging_obj, + stream=stream, + kwargs=plan_kwargs, + ) + + plan = await callback.async_build_agentic_loop_plan( + tools=tool_calls, + model=model, + messages=messages, + response=response, + anthropic_messages_provider_config=None, + anthropic_messages_optional_request_params=optional_params, + logging_obj=logging_obj, + stream=stream, + kwargs=plan_kwargs, + ) + + if plan.response_override is not None: + return plan.response_override + if plan.terminate: + return response + if not plan.run_agentic_loop: + continue + + return await _execute_chat_completion_agentic_plan( + plan=plan, + callback=callback, + model=model, + optional_params=optional_params, + kwargs=kwargs, + logging_obj=logging_obj, + custom_llm_provider=custom_llm_provider, + depth=depth, + max_loops=max_loops, + fingerprints=fingerprints, + fingerprint=fingerprint, + ) + except Exception as e: + verbose_logger.exception( + "LiteLLM.AgenticHookError: Exception in chat completion agentic hooks: %s", + str(e), + ) + + if ( + kwargs.get("_code_interpreter_interception_converted_stream") + and not depth + and hasattr(response, "choices") + ): + return cast( + "ModelResponse | CustomStreamWrapper", + _wrap_response_as_fake_stream(response), + ) + return None diff --git a/litellm/litellm_core_utils/cli_token_utils.py b/litellm/litellm_core_utils/cli_token_utils.py index 3776d276912..eb01359cdc0 100644 --- a/litellm/litellm_core_utils/cli_token_utils.py +++ b/litellm/litellm_core_utils/cli_token_utils.py @@ -37,7 +37,7 @@ def get_litellm_gateway_api_key( """ Get the stored CLI API key for use with LiteLLM SDK. - This function reads the token file created by `litellm-proxy login` + This function reads the token file created by `lite login` and returns the API key for use in Python scripts. Args: diff --git a/litellm/litellm_core_utils/cloud_storage_security.py b/litellm/litellm_core_utils/cloud_storage_security.py index daa3dc60320..a75d1178d5a 100644 --- a/litellm/litellm_core_utils/cloud_storage_security.py +++ b/litellm/litellm_core_utils/cloud_storage_security.py @@ -15,8 +15,23 @@ BEDROCK_MANAGED_S3_PREFIXES = ( BEDROCK_MANAGED_S3_UPLOAD_PREFIX, BEDROCK_MANAGED_S3_OUTPUT_PREFIX, ) +MANAGED_CLOUD_STORAGE_SCHEMES = ("s3://", "gs://") _MAPPING_PROXY_TYPE: type = type(MappingProxyType({})) + +def is_managed_cloud_storage_uri(file_id: str) -> bool: + """ + True if file_id is a raw cloud-storage object URI (e.g. ``s3://bucket/key``). + + These are internal provider artifacts. On the multi-tenant proxy they must be + retrieved through their managed unified file id so owner/team access is enforced; + a raw URI supplied by a caller bypasses that check. + """ + return isinstance(file_id, str) and file_id.startswith( + MANAGED_CLOUD_STORAGE_SCHEMES + ) + + _SAFE_OBJECT_COMPONENT_PATTERN = re.compile(r"[^A-Za-z0-9._-]+") diff --git a/litellm/litellm_core_utils/completion_timeout.py b/litellm/litellm_core_utils/completion_timeout.py index 5350d88e593..70c6896a323 100644 --- a/litellm/litellm_core_utils/completion_timeout.py +++ b/litellm/litellm_core_utils/completion_timeout.py @@ -6,10 +6,7 @@ from typing import Callable, Optional, Union import httpx -from litellm.constants import ( - COMPLETION_HTTP_FALLBACK_SECONDS, - DEFAULT_REQUEST_TIMEOUT_SECONDS, -) +from litellm.constants import COMPLETION_HTTP_FALLBACK_SECONDS class CompletionTimeout: @@ -22,17 +19,13 @@ class CompletionTimeout: """ Used when ``model_timeout`` and kwargs timeouts are all unset. - ``global_timeout`` is :attr:`litellm.request_timeout` (numeric / string), not - :class:`httpx.Timeout`. - - If it equals :data:`~litellm.constants.DEFAULT_REQUEST_TIMEOUT_SECONDS` (6000), - return :data:`~litellm.constants.COMPLETION_HTTP_FALLBACK_SECONDS`. Same if - ``None``. Otherwise return ``float(global_timeout)``. + ``global_timeout`` is the explicitly-configured ``litellm.request_timeout`` + (numeric / string) or ``None`` when it was never set. ``None`` falls back to + :data:`~litellm.constants.COMPLETION_HTTP_FALLBACK_SECONDS`; any explicit value + (including ``6000``) is honored. """ if global_timeout is None: return COMPLETION_HTTP_FALLBACK_SECONDS - if float(global_timeout) == float(DEFAULT_REQUEST_TIMEOUT_SECONDS): - return COMPLETION_HTTP_FALLBACK_SECONDS return float(global_timeout) @staticmethod @@ -50,11 +43,10 @@ class CompletionTimeout: 1. ``model_timeout`` (call argument / merged ``litellm_params``) 2. ``kwargs["timeout"]`` 3. ``kwargs["request_timeout"]`` - 4. Fallback from ``global_timeout`` (:attr:`litellm.request_timeout`) — if it is - the package default (6000), use 600 instead. + 4. ``global_timeout`` (the explicitly-configured ``litellm.request_timeout``), + or 600 when nothing was configured. Coerce :class:`httpx.Timeout` when the provider does not support it. - Explicit ``6000`` on the model or in kwargs is kept as ``6000``. """ resolved: Union[float, str, httpx.Timeout] if model_timeout is not None: diff --git a/litellm/litellm_core_utils/core_helpers.py b/litellm/litellm_core_utils/core_helpers.py index e984df82140..98b792efa59 100644 --- a/litellm/litellm_core_utils/core_helpers.py +++ b/litellm/litellm_core_utils/core_helpers.py @@ -242,9 +242,28 @@ def _get_parent_otel_span_from_kwargs( return None -def process_response_headers(response_headers: Union[httpx.Headers, dict]) -> dict: +def process_response_headers( + response_headers: Union[httpx.Headers, dict], + preserve_litellm_internal_headers: bool = False, +) -> dict: + """ + `preserve_litellm_internal_headers` must only be True when the input is a + LiteLLM-owned dict (e.g. `_hidden_params["additional_headers"]` that has + already been through one round of processing). For raw upstream provider + headers — whether passed as `httpx.Headers` or a plain dict — it must + remain False, otherwise a malicious provider returning `x-litellm-*` could + spoof LiteLLM-internal markers (e.g. `x-litellm-attempted-fallbacks`). + + When the input is an `httpx.Headers` object the flag is always treated as + False regardless of what the caller requested, because `httpx.Headers` is + always a raw provider response and can never be LiteLLM-owned. + """ from litellm.types.utils import OPENAI_RESPONSE_HEADERS + # Raw httpx.Headers objects come directly from provider HTTP responses and + # must never be treated as LiteLLM-owned, regardless of caller intent. + _preserve = preserve_litellm_internal_headers and isinstance(response_headers, dict) + openai_headers = {} processed_headers = {} additional_headers = {} @@ -256,6 +275,12 @@ def process_response_headers(response_headers: Union[httpx.Headers, dict]) -> di "llm_provider-" ): # return raw provider headers (incl. openai-compatible ones) processed_headers[k] = v + elif _preserve and k.startswith("x-litellm-"): + # LiteLLM's own internal headers (e.g. x-litellm-attempted-fallbacks, + # x-litellm-model-group) are not LLM provider headers and must not be + # prefixed. Downstream consumers (proxy override, callers checking + # whether a fallback happened) look up the bare key. + processed_headers[k] = v else: additional_headers["{}-{}".format("llm_provider", k)] = v diff --git a/litellm/litellm_core_utils/custom_logger_registry.py b/litellm/litellm_core_utils/custom_logger_registry.py index fd402b90d88..a7fae104c92 100644 --- a/litellm/litellm_core_utils/custom_logger_registry.py +++ b/litellm/litellm_core_utils/custom_logger_registry.py @@ -25,6 +25,7 @@ from litellm.integrations.datadog.datadog_metrics import DatadogMetricsLogger from litellm.integrations.deepeval import DeepEvalLogger from litellm.integrations.dotprompt import DotpromptManager from litellm.integrations.focus.focus_logger import FocusLogger +from litellm.integrations.mavvrik_focus.mavvrik_focus_logger import MavvrikFocusLogger from litellm.integrations.vantage.vantage_logger import VantageLogger from litellm.integrations.galileo import GalileoObserve from litellm.integrations.gcs_bucket.gcs_bucket import GCSBucketLogger @@ -39,6 +40,7 @@ from litellm.integrations.langsmith import LangsmithLogger from litellm.integrations.litellm_agent import LiteLLMAgentModelResolver from litellm.integrations.literal_ai import LiteralAILogger from litellm.integrations.mlflow import MlflowLogger +from litellm.integrations.newrelic import NewRelicLogger from litellm.integrations.openmeter import OpenMeterLogger from litellm.integrations.opentelemetry import OpenTelemetry from litellm.integrations.opik.opik import OpikLogger @@ -102,8 +104,10 @@ class CustomLoggerRegistry: "gitlab": GitLabPromptManager, "cloudzero": CloudZeroLogger, "focus": FocusLogger, + "mavvrik": MavvrikFocusLogger, "vantage": VantageLogger, "posthog": PostHogLogger, + "newrelic": NewRelicLogger, } try: diff --git a/litellm/litellm_core_utils/duration_parser.py b/litellm/litellm_core_utils/duration_parser.py index 6d2b4226ff4..036d691c686 100644 --- a/litellm/litellm_core_utils/duration_parser.py +++ b/litellm/litellm_core_utils/duration_parser.py @@ -131,6 +131,8 @@ def get_next_standardized_reset_time( # Handle different time units if unit == "d": return _handle_day_reset(current_time, base_midnight, value, tz) + elif unit == "w": + return _handle_day_reset(current_time, base_midnight, value * 7, tz) elif unit == "h": return _handle_hour_reset(current_time, base_midnight, value) elif unit == "m": diff --git a/litellm/litellm_core_utils/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py index ffaa5140916..9b2a9af4126 100644 --- a/litellm/litellm_core_utils/exception_mapping_utils.py +++ b/litellm/litellm_core_utils/exception_mapping_utils.py @@ -1,7 +1,7 @@ import json import re import traceback -from typing import Any, Optional +from typing import Any, Optional, Protocol, cast import httpx @@ -170,6 +170,16 @@ def get_error_message(error_obj) -> Optional[str]: ####### EXCEPTION MAPPING ################ +def _get_body_error_code(error_str: str) -> int | None: + """Return error.code from a JSON error body, or None if not parseable.""" + try: + body = json.loads(error_str) + code = body.get("error", {}).get("code") + return int(code) if code is not None else None + except Exception: + return None + + def _get_response_headers(original_exception: Exception) -> Optional[httpx.Headers]: """ Extract and return the response headers from an exception, if present. @@ -234,7 +244,2012 @@ def extract_and_raise_litellm_exception( ) -def exception_type( # type: ignore # noqa: PLR0915 +class _ProviderHTTPException(Protocol): + status_code: int + message: str + response: httpx.Response + request: httpx.Request + body: object + code: str + llm_provider: str + + +def _map_openai_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + # custom_llm_provider is openai, make it OpenAI + message = get_error_message(error_obj=original_exception) + if message is None: + if hasattr(original_exception, "message"): + message = original_exception.message + else: + message = str(original_exception) + + if message is not None and isinstance( + message, str + ): # done to prevent user-confusion. Relevant issue - https://github.com/BerriAI/litellm/issues/1414 + message = message.replace("OPENAI", custom_llm_provider.upper()) + message = message.replace( + "openai.OpenAIError", + "{}.{}Error".format(custom_llm_provider, custom_llm_provider), + ) + if custom_llm_provider == "openai": + exception_provider = "OpenAI" + "Exception" + else: + exception_provider = ( + custom_llm_provider[0].upper() + custom_llm_provider[1:] + "Exception" + ) + + if ExceptionCheckers.is_error_str_rate_limit(error_str): + raise RateLimitError( + message=f"RateLimitError: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + ) + elif ExceptionCheckers.is_error_str_context_window_exceeded(error_str): + raise ContextWindowExceededError( + message=f"ContextWindowExceededError: {exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif "invalid_request_error" in error_str and "model_not_found" in error_str: + raise NotFoundError( + message=f"{exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif "A timeout occurred" in error_str: + raise Timeout( + message=f"{exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif ( + ( + "invalid_request_error" in error_str + and "content_policy_violation" in error_str + ) + or ("Invalid prompt" in error_str and "violating our usage policy" in error_str) + or ( + "request was rejected as a result of the safety system" in error_str.lower() + ) + ): + raise ContentPolicyViolationError( + message=f"ContentPolicyViolationError: {exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif ( + "invalid_encrypted_content" in error_str or "could not be verified" in error_str + ): + helpful_message = ( + f"{exception_provider} - {message}\n\n" + " This error occurs when load balancing Responses API across deployments with different API keys.\n" + " Encrypted content is tied to the organization that created it and cannot be decrypted by other organizations.\n\n" + " Solution: Enable 'encrypted_content_affinity' to route follow-up requests to the correct deployment:\n\n" + " router_settings:\n" + " enable_pre_call_checks: true\n" + " optional_pre_call_checks:\n" + " - encrypted_content_affinity\n\n" + " Learn more: https://docs.litellm.ai/docs/response_api#encrypted-content-affinity-multi-region-load-balancing" + ) + raise BadRequestError( + message=helpful_message, + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + body=getattr(original_exception, "body", None), + ) + elif ( + "invalid_request_error" in error_str + and "Incorrect API key provided" not in error_str + ): + raise BadRequestError( + message=f"{exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + body=getattr(original_exception, "body", None), + ) + elif ( + "Web server is returning an unknown error" in error_str + or "The server had an error processing your request." in error_str + ): + raise litellm.InternalServerError( + message=f"{exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + ) + elif "Request too large" in error_str: + raise RateLimitError( + message=f"RateLimitError: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif ( + "The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_API_KEY environment variable" + in error_str + ): + raise AuthenticationError( + message=f"AuthenticationError: {exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif "Mistral API raised a streaming error" in error_str: + _request = httpx.Request(method="POST", url="https://api.openai.com/v1") + raise APIError( + status_code=500, + message=f"{exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + request=_request, + litellm_debug_info=extra_information, + ) + elif hasattr(original_exception, "status_code"): + if original_exception.status_code == 400: + raise BadRequestError( + message=f"{exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 401: + raise AuthenticationError( + message=f"AuthenticationError: {exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 404: + raise NotFoundError( + message=f"NotFoundError: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"Timeout Error: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 422: + raise BadRequestError( + message=f"{exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + body=getattr(original_exception, "body", None), + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"RateLimitError: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 500: + raise InternalServerError( + message=f"InternalServerError: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 502: + raise BadGatewayError( + message=f"BadGatewayError: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 503: + raise ServiceUnavailableError( + message=f"ServiceUnavailableError: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 504: # gateway timeout error + raise Timeout( + message=f"Timeout Error: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + exception_status_code=original_exception.status_code, + ) + else: + raise APIError( + status_code=original_exception.status_code, + message=f"APIError: {exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + request=getattr(original_exception, "request", None), + litellm_debug_info=extra_information, + ) + else: + # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors + # exception_mapping_worked = True + raise APIConnectionError( + message=f"APIConnectionError: {exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + litellm_debug_info=extra_information, + request=httpx.Request(method="POST", url="https://api.openai.com/v1/"), + ) + + +def _map_anthropic_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if ( + "prompt is too long" in error_str + or "prompt: length" in error_str + or ExceptionCheckers.is_error_str_context_window_exceeded(error_str) + ): + raise ContextWindowExceededError( + message="AnthropicError - {}".format(error_str), + model=model, + llm_provider="anthropic", + ) + elif "overloaded_error" in error_str or "Overloaded" in error_str: + raise InternalServerError( + message="AnthropicError - {}".format(error_str), + model=model, + llm_provider="anthropic", + ) + if "Invalid API Key" in error_str: + raise AuthenticationError( + message="AnthropicError - {}".format(error_str), + model=model, + llm_provider="anthropic", + ) + if "content filtering policy" in error_str: + raise ContentPolicyViolationError( + message="AnthropicError - {}".format(error_str), + model=model, + llm_provider="anthropic", + ) + if "Client error '400 Bad Request'" in error_str: + raise BadRequestError( + message="AnthropicError - {}".format(error_str), + model=model, + llm_provider="anthropic", + ) + if hasattr(original_exception, "status_code"): + verbose_logger.debug(f"status_code: {original_exception.status_code}") + if original_exception.status_code == 401: + raise AuthenticationError( + message=f"AnthropicException - {error_str}", + llm_provider="anthropic", + model=model, + ) + elif ( + original_exception.status_code == 400 + or original_exception.status_code == 413 + ): + raise BadRequestError( + message=f"AnthropicException - {error_str}", + model=model, + llm_provider="anthropic", + ) + elif original_exception.status_code == 404: + raise NotFoundError( + message=f"AnthropicException - {error_str}", + model=model, + llm_provider="anthropic", + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"AnthropicException - {error_str}", + model=model, + llm_provider="anthropic", + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"AnthropicException - {error_str}", + llm_provider="anthropic", + model=model, + ) + elif ( + original_exception.status_code == 500 + or original_exception.status_code == 529 + ): + raise litellm.InternalServerError( + message=f"AnthropicException - {error_str}. Handle with `litellm.InternalServerError`.", + llm_provider="anthropic", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 502: + raise BadGatewayError( + message=f"AnthropicException BadGatewayError - {error_str}", + llm_provider="anthropic", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 503: + raise litellm.ServiceUnavailableError( + message=f"AnthropicException - {error_str}. Handle with `litellm.ServiceUnavailableError`.", + llm_provider="anthropic", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 504: # gateway timeout error + raise Timeout( + message=f"AnthropicException Timeout - {error_str}", + model=model, + llm_provider="anthropic", + exception_status_code=original_exception.status_code, + ) + + +def _map_replicate_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if "Incorrect authentication token" in error_str: + raise AuthenticationError( + message=f"ReplicateException - {error_str}", + llm_provider="replicate", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "input is too long" in error_str: + raise ContextWindowExceededError( + message=f"ReplicateException - {error_str}", + model=model, + llm_provider="replicate", + response=getattr(original_exception, "response", None), + ) + elif exception_type == "ModelError": + raise BadRequestError( + message=f"ReplicateException - {error_str}", + model=model, + llm_provider="replicate", + response=getattr(original_exception, "response", None), + ) + elif "Request was throttled" in error_str: + raise RateLimitError( + message=f"ReplicateException - {error_str}", + llm_provider="replicate", + model=model, + response=getattr(original_exception, "response", None), + ) + elif hasattr(original_exception, "status_code"): + if original_exception.status_code == 401: + raise AuthenticationError( + message=f"ReplicateException - {original_exception.message}", + llm_provider="replicate", + model=model, + response=getattr(original_exception, "response", None), + ) + elif ( + original_exception.status_code == 400 + or original_exception.status_code == 413 + ): + raise BadRequestError( + message=f"ReplicateException - {original_exception.message}", + model=model, + llm_provider="replicate", + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 422: + raise UnprocessableEntityError( + message=f"ReplicateException - {original_exception.message}", + model=model, + llm_provider="replicate", + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"ReplicateException - {original_exception.message}", + model=model, + llm_provider="replicate", + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"ReplicateException - {original_exception.message}", + llm_provider="replicate", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 500: + raise ServiceUnavailableError( + message=f"ReplicateException - {original_exception.message}", + llm_provider="replicate", + model=model, + response=getattr(original_exception, "response", None), + ) + raise APIError( + status_code=500, + message=f"ReplicateException - {str(original_exception)}", + llm_provider="replicate", + model=model, + request=httpx.Request( + method="POST", + url="https://api.replicate.com/v1/deployments", + ), + ) + + +def _map_openai_like_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if "authorization denied for" in error_str: + + # Predibase returns the raw API Key in the response - this block ensures it's not returned in the exception + if ( + error_str is not None + and isinstance(error_str, str) + and "bearer" in error_str.lower() + ): + # only keep the first 10 chars after the occurnence of "bearer" + _bearer_token_start_index = error_str.lower().find("bearer") + error_str = error_str[: _bearer_token_start_index + 14] + error_str += "XXXXXXX" + '"' + + raise AuthenticationError( + message=f"{custom_llm_provider.capitalize()}Exception: Authentication Error - {error_str}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif ExceptionCheckers.is_error_str_context_window_exceeded(error_str): + raise ContextWindowExceededError( + message=f"{custom_llm_provider.capitalize()}Exception: Context Window Error - {error_str}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif "token_quota_reached" in error_str: + raise RateLimitError( + message=f"{custom_llm_provider.capitalize()}Exception: Rate Limit Errror - {error_str}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + ) + elif ( + "The server received an invalid response from an upstream server." in error_str + ): + raise litellm.InternalServerError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + ) + elif "model_no_support_for_function" in error_str: + raise BadRequestError( + message=f"{custom_llm_provider.capitalize()}Exception - Use 'watsonx_text' route instead. IBM WatsonX does not support `/text/chat` endpoint. - {error_str}", + llm_provider=custom_llm_provider, + model=model, + ) + elif hasattr(original_exception, "status_code"): + if original_exception.status_code == 500: + raise litellm.InternalServerError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + ) + elif ( + original_exception.status_code == 401 + or original_exception.status_code == 403 + ): + raise AuthenticationError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + ) + elif original_exception.status_code == 400: + raise BadRequestError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + ) + elif original_exception.status_code == 404: + raise NotFoundError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif ( + original_exception.status_code == 422 + or original_exception.status_code == 424 + ): + raise BadRequestError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 503: + raise ServiceUnavailableError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 504: # gateway timeout error + raise Timeout( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + exception_status_code=original_exception.status_code, + ) + + +def _map_bedrock_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if ( + "too many tokens" in error_str + or "expected maxLength:" in error_str + or "Input is too long" in error_str + or "prompt is too long" in error_str + or "prompt: length: 1.." in error_str + or "Too many input tokens" in error_str + ): + raise ContextWindowExceededError( + message=f"BedrockException: Context Window Error - {error_str}", + model=model, + llm_provider="bedrock", + ) + elif ( + "Conversation blocks and tool result blocks cannot be provided in the same turn." + in error_str + ): + raise BadRequestError( + message=f"BedrockException - {error_str}\n. Enable 'litellm.modify_params=True' (for PROXY do: `litellm_settings::modify_params: True`) to insert a dummy assistant message and fix this error.", + model=model, + llm_provider="bedrock", + response=getattr(original_exception, "response", None), + ) + elif "Malformed input request" in error_str: + raise BadRequestError( + message=f"BedrockException - {error_str}", + model=model, + llm_provider="bedrock", + response=getattr(original_exception, "response", None), + ) + elif "A conversation must start with a user message." in error_str: + raise BadRequestError( + message=f"BedrockException - {error_str}\n. Pass in default user message via `completion(..,user_continue_message=)` or enable `litellm.modify_params=True`.\nFor Proxy: do via `litellm_settings::modify_params: True` or user_continue_message under `litellm_params`", + model=model, + llm_provider="bedrock", + response=getattr(original_exception, "response", None), + ) + elif ( + "Unable to locate credentials" in error_str + or "The security token included in the request is invalid" in error_str + ): + raise AuthenticationError( + message=f"BedrockException Invalid Authentication - {error_str}", + model=model, + llm_provider="bedrock", + response=getattr(original_exception, "response", None), + ) + elif "AccessDeniedException" in error_str: + raise PermissionDeniedError( + message=f"BedrockException PermissionDeniedError - {error_str}", + model=model, + llm_provider="bedrock", + response=getattr(original_exception, "response", None), + ) + elif "throttlingException" in error_str or "ThrottlingException" in error_str: + raise RateLimitError( + message=f"BedrockException: Rate Limit Error - {error_str}", + model=model, + llm_provider="bedrock", + response=getattr(original_exception, "response", None), + ) + elif "Connect timeout on endpoint URL" in error_str or "timed out" in error_str: + raise Timeout( + message=f"BedrockException: Timeout Error - {error_str}", + model=model, + llm_provider="bedrock", + ) + elif "Could not process image" in error_str: + raise litellm.InternalServerError( + message=f"BedrockException - {error_str}", + model=model, + llm_provider="bedrock", + ) + elif hasattr(original_exception, "status_code"): + if original_exception.status_code == 500: + raise ServiceUnavailableError( + message=f"BedrockException - {original_exception.message}", + llm_provider="bedrock", + model=model, + response=httpx.Response( + status_code=500, + request=httpx.Request( + method="POST", url="https://api.openai.com/v1/" + ), + ), + ) + elif original_exception.status_code == 401: + raise AuthenticationError( + message=f"BedrockException - {original_exception.message}", + llm_provider="bedrock", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 400: + raise BadRequestError( + message=f"BedrockException - {original_exception.message}", + llm_provider="bedrock", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 404: + raise NotFoundError( + message=f"BedrockException - {original_exception.message}", + llm_provider="bedrock", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"BedrockException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 422: + raise BadRequestError( + message=f"BedrockException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"BedrockException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 503: + raise ServiceUnavailableError( + message=f"BedrockException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 504: # gateway timeout error + raise Timeout( + message=f"BedrockException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + exception_status_code=original_exception.status_code, + ) + + +def _map_sagemaker_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if "Unable to locate credentials" in error_str: + raise BadRequestError( + message=f"litellm.BadRequestError: SagemakerException - {error_str}", + model=model, + llm_provider="sagemaker", + response=getattr(original_exception, "response", None), + ) + elif "Input validation error: `best_of` must be > 0 and <= 2" in error_str: + raise BadRequestError( + message="SagemakerException - the value of 'n' must be > 0 and <= 2 for sagemaker endpoints", + model=model, + llm_provider="sagemaker", + response=getattr(original_exception, "response", None), + ) + elif ( + "`inputs` tokens + `max_new_tokens` must be <=" in error_str + or "instance type with more CPU capacity or memory" in error_str + ): + raise ContextWindowExceededError( + message=f"SagemakerException - {error_str}", + model=model, + llm_provider="sagemaker", + response=getattr(original_exception, "response", None), + ) + elif hasattr(original_exception, "status_code"): + if original_exception.status_code == 500: + raise ServiceUnavailableError( + message=f"SagemakerException - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + response=httpx.Response( + status_code=500, + request=httpx.Request( + method="POST", url="https://api.openai.com/v1/" + ), + ), + ) + elif original_exception.status_code == 401: + raise AuthenticationError( + message=f"SagemakerException - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 400: + raise BadRequestError( + message=f"SagemakerException - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 404: + raise NotFoundError( + message=f"SagemakerException - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"SagemakerException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif ( + original_exception.status_code == 422 + or original_exception.status_code == 424 + ): + raise BadRequestError( + message=f"SagemakerException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"SagemakerException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 503: + raise ServiceUnavailableError( + message=f"SagemakerException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 504: # gateway timeout error + raise Timeout( + message=f"SagemakerException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + exception_status_code=original_exception.status_code, + ) + + +def _map_vertex_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if ( + "Vertex AI API has not been used in project" in error_str + or "Unable to find your project" in error_str + ): + raise BadRequestError( + message=f"litellm.BadRequestError: {custom_llm_provider}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + response=httpx.Response( + status_code=400, + request=httpx.Request( + method="POST", + url=" https://cloud.google.com/vertex-ai/", + ), + ), + litellm_debug_info=extra_information, + ) + if "400 Request payload size exceeds" in error_str: + raise ContextWindowExceededError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + ) + elif ExceptionCheckers.is_error_str_context_window_exceeded(error_str): + raise ContextWindowExceededError( + message=f"ContextWindowExceededError: {custom_llm_provider.capitalize()}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif "None Unknown Error." in error_str or "Content has no parts." in error_str: + raise litellm.InternalServerError( + message=f"litellm.InternalServerError: {custom_llm_provider}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + response=httpx.Response( + status_code=500, + content=str(original_exception), + request=httpx.Request(method="completion", url="https://github.com/BerriAI/litellm"), # type: ignore + ), + litellm_debug_info=extra_information, + ) + elif "API key not valid." in error_str: + raise AuthenticationError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif "403" in error_str: + raise BadRequestError( + message=f"{custom_llm_provider.capitalize()}Exception BadRequestError - {error_str}", + model=model, + llm_provider=custom_llm_provider, + response=httpx.Response( + status_code=403, + request=httpx.Request( + method="POST", + url=" https://cloud.google.com/vertex-ai/", + ), + ), + litellm_debug_info=extra_information, + ) + elif ( + "The response was blocked." in error_str + or "Output blocked by content filtering policy" + in error_str # anthropic on vertex ai + ): + raise ContentPolicyViolationError( + message=f"{custom_llm_provider.capitalize()}Exception ContentPolicyViolationError - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + response=httpx.Response( + status_code=400, + request=httpx.Request( + method="POST", + url=" https://cloud.google.com/vertex-ai/", + ), + ), + ) + elif ( + "429 Quota exceeded" in error_str + or "Quota exceeded for" in error_str + or "Resource exhausted" in error_str + or "IndexError: list index out of range" in error_str + or "429 Unable to submit request because the service is temporarily out of capacity." + in error_str + ): + raise RateLimitError( + message=f"litellm.RateLimitError: {custom_llm_provider}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + response=httpx.Response( + status_code=429, + request=httpx.Request( + method="POST", + url=" https://cloud.google.com/vertex-ai/", + ), + ), + ) + elif ( + isinstance(getattr(original_exception, "status_code", None), int) + and 500 <= original_exception.status_code < 600 + and _get_body_error_code(error_str) == 429 + ): + # upstream gateway wraps a 429 inside a 5xx envelope + # e.g. HTTP 500/503 with {"error":{"code":429,...}}. + # Scoped to 5xx so HTTP 400/401 with body code:429 + # still maps to BadRequestError / AuthenticationError. + raise RateLimitError( + message=f"litellm.RateLimitError: {custom_llm_provider}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + response=httpx.Response( + status_code=429, + request=httpx.Request( + method="POST", + url=" https://cloud.google.com/vertex-ai/", + ), + ), + ) + elif ( + "500 Internal Server Error" in error_str + or "The model is overloaded." in error_str + ): + raise litellm.InternalServerError( + message=f"litellm.InternalServerError: {custom_llm_provider}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 400: + raise BadRequestError( + message=f"{custom_llm_provider.capitalize()}Exception BadRequestError - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + response=httpx.Response( + status_code=400, + request=httpx.Request( + method="POST", + url="https://cloud.google.com/vertex-ai/", + ), + ), + ) + if original_exception.status_code == 401: + raise AuthenticationError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + llm_provider=custom_llm_provider, + model=model, + ) + if original_exception.status_code == 403: + raise PermissionDeniedError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + llm_provider=custom_llm_provider, + model=model, + response=httpx.Response( + status_code=403, + request=httpx.Request( + method="POST", + url="https://cloud.google.com/vertex-ai/", + ), + ), + ) + if original_exception.status_code == 404: + raise NotFoundError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + llm_provider=custom_llm_provider, + model=model, + ) + if original_exception.status_code == 408: + raise Timeout( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + llm_provider=custom_llm_provider, + model=model, + ) + + if original_exception.status_code == 429: + raise RateLimitError( + message=f"litellm.RateLimitError: {custom_llm_provider.capitalize()}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + response=httpx.Response( + status_code=429, + request=httpx.Request( + method="POST", + url=" https://cloud.google.com/vertex-ai/", + ), + ), + ) + if original_exception.status_code == 500: + raise litellm.InternalServerError( + message=f"{custom_llm_provider.capitalize()}Exception InternalServerError - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + response=httpx.Response( + status_code=500, + content=str(original_exception), + request=httpx.Request(method="completion", url="https://github.com/BerriAI/litellm"), # type: ignore + ), + ) + if original_exception.status_code == 502: + raise APIConnectionError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + llm_provider=custom_llm_provider, + model=model, + ) + if original_exception.status_code == 503: + raise ServiceUnavailableError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + llm_provider=custom_llm_provider, + model=model, + ) + + +def _map_cloudflare_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if "Authentication error" in error_str: + raise AuthenticationError( + message=f"Cloudflare Exception - {original_exception.message}", + llm_provider="cloudflare", + model=model, + response=getattr(original_exception, "response", None), + ) + if "must have required property" in error_str: + raise BadRequestError( + message=f"Cloudflare Exception - {original_exception.message}", + llm_provider="cloudflare", + model=model, + response=getattr(original_exception, "response", None), + ) + + +def _map_cohere_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if "invalid api token" in error_str or "No API key provided." in error_str: + raise AuthenticationError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "invalid type: parameter" in error_str: + raise BadRequestError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "too many tokens" in error_str: + raise ContextWindowExceededError( + message=f"CohereException - {original_exception.message}", + model=model, + llm_provider="cohere", + response=getattr(original_exception, "response", None), + ) + elif "internal server error" in error_str.lower(): + raise InternalServerError( + message=f"CohereException - {error_str}", + model=model, + llm_provider="cohere", + response=getattr(original_exception, "response", None), + ) + elif hasattr(original_exception, "status_code"): + if ( + original_exception.status_code == 400 + or original_exception.status_code == 498 + ): + raise BadRequestError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + ) + elif original_exception.status_code == 500: + raise InternalServerError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=getattr(original_exception, "response", None), + ) + elif ( + "CohereConnectionError" in exception_type + ): # cohere seems to fire these errors when we load test it (1k+ messages / min) + raise RateLimitError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "invalid type:" in error_str: + raise BadRequestError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "Unexpected server error" in error_str: + raise InternalServerError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=getattr(original_exception, "response", None), + ) + else: + if hasattr(original_exception, "status_code"): + raise APIError( + status_code=original_exception.status_code, + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + request=getattr(original_exception, "request", None), + ) + raise cast(Exception, original_exception) + + +def _map_huggingface_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if "length limit exceeded" in error_str: + raise ContextWindowExceededError( + message=error_str, + model=model, + llm_provider="huggingface", + response=getattr(original_exception, "response", None), + ) + elif "A valid user token is required" in error_str: + raise BadRequestError( + message=error_str, + llm_provider="huggingface", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "Rate limit reached" in error_str: + raise RateLimitError( + message=error_str, + llm_provider="huggingface", + model=model, + response=getattr(original_exception, "response", None), + ) + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 401: + raise AuthenticationError( + message=f"HuggingfaceException - {original_exception.message}", + llm_provider="huggingface", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 400: + raise BadRequestError( + message=f"HuggingfaceException - {original_exception.message}", + model=model, + llm_provider="huggingface", + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"HuggingfaceException - {original_exception.message}", + model=model, + llm_provider="huggingface", + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"HuggingfaceException - {original_exception.message}", + llm_provider="huggingface", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 503: + raise ServiceUnavailableError( + message=f"HuggingfaceException - {original_exception.message}", + llm_provider="huggingface", + model=model, + response=getattr(original_exception, "response", None), + ) + else: + raise APIError( + status_code=original_exception.status_code, + message=f"HuggingfaceException - {original_exception.message}", + llm_provider="huggingface", + model=model, + request=getattr(original_exception, "request", None), + ) + + +def _map_ai21_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if hasattr(original_exception, "message"): + if "Prompt has too many tokens" in original_exception.message: + raise ContextWindowExceededError( + message=f"AI21Exception - {original_exception.message}", + model=model, + llm_provider="ai21", + response=getattr(original_exception, "response", None), + ) + if "Bad or missing API token." in original_exception.message: + raise BadRequestError( + message=f"AI21Exception - {original_exception.message}", + model=model, + llm_provider="ai21", + response=getattr(original_exception, "response", None), + ) + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 401: + raise AuthenticationError( + message=f"AI21Exception - {original_exception.message}", + llm_provider="ai21", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"AI21Exception - {original_exception.message}", + model=model, + llm_provider="ai21", + ) + if original_exception.status_code == 422: + raise BadRequestError( + message=f"AI21Exception - {original_exception.message}", + model=model, + llm_provider="ai21", + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"AI21Exception - {original_exception.message}", + llm_provider="ai21", + model=model, + response=getattr(original_exception, "response", None), + ) + else: + raise APIError( + status_code=original_exception.status_code, + message=f"AI21Exception - {original_exception.message}", + llm_provider="ai21", + model=model, + request=getattr(original_exception, "request", None), + ) + + +def _map_nlp_cloud_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if "detail" in error_str: + if "Input text length should not exceed" in error_str: + raise ContextWindowExceededError( + message=f"NLPCloudException - {error_str}", + model=model, + llm_provider="nlp_cloud", + response=getattr(original_exception, "response", None), + ) + elif "value is not a valid" in error_str: + raise BadRequestError( + message=f"NLPCloudException - {error_str}", + model=model, + llm_provider="nlp_cloud", + response=getattr(original_exception, "response", None), + ) + else: + raise APIError( + status_code=500, + message=f"NLPCloudException - {error_str}", + model=model, + llm_provider="nlp_cloud", + request=getattr(original_exception, "request", None), + ) + if hasattr( + original_exception, "status_code" + ): # https://docs.nlpcloud.com/?shell#errors + if ( + original_exception.status_code == 400 + or original_exception.status_code == 406 + or original_exception.status_code == 413 + or original_exception.status_code == 422 + ): + raise BadRequestError( + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + response=getattr(original_exception, "response", None), + ) + elif ( + original_exception.status_code == 401 + or original_exception.status_code == 403 + ): + raise AuthenticationError( + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + response=getattr(original_exception, "response", None), + ) + elif ( + original_exception.status_code == 522 + or original_exception.status_code == 524 + ): + raise Timeout( + message=f"NLPCloudException - {original_exception.message}", + model=model, + llm_provider="nlp_cloud", + ) + elif ( + original_exception.status_code == 429 + or original_exception.status_code == 402 + ): + raise RateLimitError( + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + response=getattr(original_exception, "response", None), + ) + elif ( + original_exception.status_code == 500 + or original_exception.status_code == 503 + ): + raise APIError( + status_code=original_exception.status_code, + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + request=getattr(original_exception, "request", None), + ) + elif ( + original_exception.status_code == 504 + or original_exception.status_code == 520 + ): + raise ServiceUnavailableError( + message=f"NLPCloudException - {original_exception.message}", + model=model, + llm_provider="nlp_cloud", + response=getattr(original_exception, "response", None), + ) + else: + raise APIError( + status_code=original_exception.status_code, + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + request=getattr(original_exception, "request", None), + ) + + +def _map_together_ai_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + try: + error_response = json.loads(error_str) + except Exception: + error_response = {"error": error_str} + if ( + "error" in error_response + and "`inputs` tokens + `max_new_tokens` must be <=" in error_response["error"] + ): + raise ContextWindowExceededError( + message=f"TogetherAIException - {error_response['error']}", + model=model, + llm_provider="together_ai", + response=getattr(original_exception, "response", None), + ) + elif "error" in error_response and "invalid private key" in error_response["error"]: + raise AuthenticationError( + message=f"TogetherAIException - {error_response['error']}", + llm_provider="together_ai", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "error" in error_response and "INVALID_ARGUMENT" in error_response["error"]: + raise BadRequestError( + message=f"TogetherAIException - {error_response['error']}", + model=model, + llm_provider="together_ai", + response=getattr(original_exception, "response", None), + ) + elif "A timeout occurred" in error_str: + raise Timeout( + message=f"TogetherAIException - {error_str}", + model=model, + llm_provider="together_ai", + ) + elif ( + "error" in error_response + and "API key doesn't match expected format." in error_response["error"] + ): + raise BadRequestError( + message=f"TogetherAIException - {error_response['error']}", + model=model, + llm_provider="together_ai", + response=getattr(original_exception, "response", None), + ) + elif ( + "error_type" in error_response and error_response["error_type"] == "validation" + ): + raise BadRequestError( + message=f"TogetherAIException - {error_response['error']}", + model=model, + llm_provider="together_ai", + response=getattr(original_exception, "response", None), + ) + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 408: + raise Timeout( + message=f"TogetherAIException - {original_exception.message}", + model=model, + llm_provider="together_ai", + ) + elif original_exception.status_code == 422: + raise BadRequestError( + message=f"TogetherAIException - {error_response['error']}", + model=model, + llm_provider="together_ai", + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"TogetherAIException - {original_exception.message}", + llm_provider="together_ai", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 524: + raise Timeout( + message=f"TogetherAIException - {original_exception.message}", + llm_provider="together_ai", + model=model, + ) + else: + raise APIError( + status_code=original_exception.status_code, + message=f"TogetherAIException - {original_exception.message}", + llm_provider="together_ai", + model=model, + request=getattr(original_exception, "request", None), + ) + + +def _map_aleph_alpha_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if "This is longer than the model's maximum context length" in error_str: + raise ContextWindowExceededError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "InvalidToken" in error_str or "No token provided" in error_str: + raise BadRequestError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=getattr(original_exception, "response", None), + ) + elif hasattr(original_exception, "status_code"): + verbose_logger.debug(f"status code: {original_exception.status_code}") + if original_exception.status_code == 401: + raise AuthenticationError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + ) + elif original_exception.status_code == 400: + raise BadRequestError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 500: + raise ServiceUnavailableError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=getattr(original_exception, "response", None), + ) + raise cast(Exception, original_exception) + raise cast(Exception, original_exception) + + +def _map_ollama_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if isinstance(original_exception, dict): + error_str = original_exception.get("error", "") + else: + error_str = str(original_exception) + if "no such file or directory" in error_str: + raise BadRequestError( + message=f"OllamaException: Invalid Model/Model not loaded - {original_exception}", + model=model, + llm_provider="ollama", + response=getattr(original_exception, "response", None), + ) + elif "Failed to establish a new connection" in error_str: + raise ServiceUnavailableError( + message=f"OllamaException: {original_exception}", + llm_provider="ollama", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "Invalid response object from API" in error_str: + raise BadRequestError( + message=f"OllamaException: {original_exception}", + llm_provider="ollama", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "Read timed out" in error_str: + raise Timeout( + message=f"OllamaException: {original_exception}", + llm_provider="ollama", + model=model, + ) + + +def _map_vllm_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 0: + raise APIConnectionError( + message=f"VLLMException - {original_exception.message}", + llm_provider="vllm", + model=model, + request=getattr(original_exception, "request", None), + ) + + +def _map_azure_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + message = get_error_message(error_obj=original_exception) + if message is None: + if hasattr(original_exception, "message"): + message = original_exception.message + else: + message = str(original_exception) + + # Azure OpenAI (especially Images) often nests error details under + # body["error"]. Detect content policy violations using the structured + # payload in addition to string matching. + azure_error_code: Optional[str] = None + try: + body_dict = getattr(original_exception, "body", None) or {} + if isinstance(body_dict, dict): + if isinstance(body_dict.get("error"), dict): + azure_error_code = body_dict["error"].get("code") # type: ignore[index] + # Also check inner_error for + # ResponsibleAIPolicyViolation which indicates a + # content policy violation even when the top-level + # code is generic (e.g. "invalid_request_error"). + if azure_error_code != "content_policy_violation": + _inner = body_dict["error"].get( + "inner_error" + ) or body_dict[ # type: ignore[index] + "error" + ].get( + "innererror" + ) # type: ignore[index] + if ( + isinstance(_inner, dict) + and _inner.get("code") == "ResponsibleAIPolicyViolation" + ): + azure_error_code = "content_policy_violation" + else: + azure_error_code = body_dict.get("code") + except Exception: + azure_error_code = None + + if "Internal server error" in error_str: + raise litellm.InternalServerError( + message=f"AzureException Internal server error - {message}", + llm_provider="azure", + model=model, + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif "This model's maximum context length is" in error_str: + raise ContextWindowExceededError( + message=f"AzureException ContextWindowExceededError - {message}", + llm_provider="azure", + model=model, + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif "DeploymentNotFound" in error_str: + raise NotFoundError( + message=f"AzureException NotFoundError - {message}", + llm_provider="azure", + model=model, + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif ( + azure_error_code == "content_policy_violation" + or ExceptionCheckers.is_azure_content_policy_violation_error(error_str) + ): + from litellm.llms.azure.exception_mapping import ( + AzureOpenAIExceptionMapping, + ) + + raise AzureOpenAIExceptionMapping.create_content_policy_violation_error( + message=message, + model=model, + extra_information=extra_information, + original_exception=original_exception, + ) + elif ( + azure_error_code == "invalid_encrypted_content" + or "could not be verified" in error_str + ): + helpful_message = ( + f"AzureException - {message}\n\n" + "This error occurs when load balancing Responses API across deployments with different API keys.\n" + " Encrypted content is tied to the organization that created it and cannot be decrypted by other organizations.\n\n" + " Solution: Enable 'encrypted_content_affinity' to route follow-up requests to the correct deployment:\n\n" + " router_settings:\n" + " enable_pre_call_checks: true\n" + " optional_pre_call_checks:\n" + " - encrypted_content_affinity\n\n" + " Learn more: https://docs.litellm.ai/docs/response_api#encrypted-content-affinity-multi-region-load-balancing" + ) + raise BadRequestError( + message=helpful_message, + llm_provider="azure", + model=model, + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + body=getattr(original_exception, "body", None), + ) + elif "invalid_request_error" in error_str: + raise BadRequestError( + message=f"AzureException BadRequestError - {message}", + llm_provider="azure", + model=model, + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + body=getattr(original_exception, "body", None), + ) + elif ( + "The api_key client option must be set either by passing api_key to the client or by setting" + in error_str + ): + raise AuthenticationError( + message=f"{exception_provider} AuthenticationError - {message}", + llm_provider=custom_llm_provider, + model=model, + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif "Connection error" in error_str: + raise APIConnectionError( + message=f"{exception_provider} APIConnectionError - {message}", + llm_provider=custom_llm_provider, + model=model, + litellm_debug_info=extra_information, + ) + elif hasattr(original_exception, "status_code"): + if original_exception.status_code == 400: + raise BadRequestError( + message=f"AzureException - {message}", + llm_provider="azure", + model=model, + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + body=getattr(original_exception, "body", None), + ) + elif original_exception.status_code == 401: + raise AuthenticationError( + message=f"AzureException AuthenticationError - {message}", + llm_provider="azure", + model=model, + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"AzureException Timeout - {message}", + model=model, + litellm_debug_info=extra_information, + llm_provider="azure", + ) + elif original_exception.status_code == 422: + raise BadRequestError( + message=f"AzureException BadRequestError - {message}", + model=model, + llm_provider="azure", + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"AzureException RateLimitError - {message}", + model=model, + llm_provider="azure", + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 502: + raise BadGatewayError( + message=f"AzureException BadGatewayError - {message}", + model=model, + llm_provider="azure", + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 503: + raise ServiceUnavailableError( + message=f"AzureException ServiceUnavailableError - {message}", + model=model, + llm_provider="azure", + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 504: # gateway timeout error + raise Timeout( + message=f"AzureException Timeout - {message}", + model=model, + litellm_debug_info=extra_information, + llm_provider="azure", + exception_status_code=original_exception.status_code, + ) + else: + raise APIError( + status_code=original_exception.status_code, + message=f"AzureException APIError - {message}", + llm_provider="azure", + litellm_debug_info=extra_information, + model=model, + request=httpx.Request(method="POST", url="https://openai.com/"), + ) + else: + # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors + raise APIConnectionError( + message=f"{exception_provider} APIConnectionError - {message}\n{_redact_string(traceback.format_exc())}", + llm_provider="azure", + model=model, + litellm_debug_info=extra_information, + request=httpx.Request(method="POST", url="https://openai.com/"), + ) + + +def _map_openrouter_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 400: + raise BadRequestError( + message=f"{exception_provider} - {error_str}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 401: + raise AuthenticationError( + message=f"AuthenticationError: {exception_provider} - {error_str}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 404: + raise NotFoundError( + message=f"NotFoundError: {exception_provider} - {error_str}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"Timeout Error: {exception_provider} - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 422: + raise BadRequestError( + message=f"BadRequestError: {exception_provider} - {error_str}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"RateLimitError: {exception_provider} - {error_str}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 503: + raise ServiceUnavailableError( + message=f"ServiceUnavailableError: {exception_provider} - {error_str}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 504: # gateway timeout error + raise Timeout( + message=f"Timeout Error: {exception_provider} - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + exception_status_code=original_exception.status_code, + ) + else: + raise APIError( + status_code=original_exception.status_code, + message=f"APIError: {exception_provider} - {error_str}", + llm_provider=custom_llm_provider, + model=model, + request=getattr(original_exception, "request", None), + litellm_debug_info=extra_information, + ) + else: + # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors + raise APIConnectionError( + message=f"APIConnectionError: {exception_provider} - {error_str}", + llm_provider=custom_llm_provider, + model=model, + litellm_debug_info=extra_information, + request=httpx.Request(method="POST", url="https://api.openai.com/v1/"), + ) + + +def exception_type( # type: ignore model, original_exception, custom_llm_provider, @@ -249,15 +2264,18 @@ def exception_type( # type: ignore # noqa: PLR0915 return original_exception exception_mapping_worked = False exception_provider = custom_llm_provider + mappable_exception: _ProviderHTTPException = cast( + "_ProviderHTTPException", original_exception + ) if litellm.suppress_debug_info is False: - print() # noqa - print( # noqa - "\033[1;31mGive Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new\033[0m" # noqa - ) # noqa - print( # noqa - "LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'." # noqa - ) # noqa - print() # noqa + print() # noqa: T201 + print( # noqa: T201 + "\033[1;31mGive Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new\033[0m" + ) + print( # noqa: T201 + "LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'." + ) + print() # noqa: T201 litellm_response_headers = _get_response_headers( original_exception=original_exception @@ -372,2055 +2390,199 @@ def exception_type( # type: ignore # noqa: PLR0915 or custom_llm_provider in litellm.openai_compatible_providers or custom_llm_provider == "mistral" ): - # custom_llm_provider is openai, make it OpenAI - message = get_error_message(error_obj=original_exception) - if message is None: - if hasattr(original_exception, "message"): - message = original_exception.message - else: - message = str(original_exception) - - if message is not None and isinstance( - message, str - ): # done to prevent user-confusion. Relevant issue - https://github.com/BerriAI/litellm/issues/1414 - message = message.replace("OPENAI", custom_llm_provider.upper()) - message = message.replace( - "openai.OpenAIError", - "{}.{}Error".format(custom_llm_provider, custom_llm_provider), - ) - if custom_llm_provider == "openai": - exception_provider = "OpenAI" + "Exception" - else: - exception_provider = ( - custom_llm_provider[0].upper() - + custom_llm_provider[1:] - + "Exception" - ) - - if ExceptionCheckers.is_error_str_rate_limit(error_str): - exception_mapping_worked = True - raise RateLimitError( - message=f"RateLimitError: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - ) - elif ExceptionCheckers.is_error_str_context_window_exceeded(error_str): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"ContextWindowExceededError: {exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif ( - "invalid_request_error" in error_str - and "model_not_found" in error_str - ): - exception_mapping_worked = True - raise NotFoundError( - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif "A timeout occurred" in error_str: - exception_mapping_worked = True - raise Timeout( - message=f"{exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif ( - ( - "invalid_request_error" in error_str - and "content_policy_violation" in error_str - ) - or ( - "Invalid prompt" in error_str - and "violating our usage policy" in error_str - ) - or ( - "request was rejected as a result of the safety system" - in error_str.lower() - ) - ): - exception_mapping_worked = True - raise ContentPolicyViolationError( - message=f"ContentPolicyViolationError: {exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif ( - "invalid_encrypted_content" in error_str - or "could not be verified" in error_str - ): - exception_mapping_worked = True - helpful_message = ( - f"{exception_provider} - {message}\n\n" - " This error occurs when load balancing Responses API across deployments with different API keys.\n" - " Encrypted content is tied to the organization that created it and cannot be decrypted by other organizations.\n\n" - " Solution: Enable 'encrypted_content_affinity' to route follow-up requests to the correct deployment:\n\n" - " router_settings:\n" - " enable_pre_call_checks: true\n" - " optional_pre_call_checks:\n" - " - encrypted_content_affinity\n\n" - " Learn more: https://docs.litellm.ai/docs/response_api#encrypted-content-affinity-multi-region-load-balancing" - ) - raise BadRequestError( - message=helpful_message, - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - body=getattr(original_exception, "body", None), - ) - elif ( - "invalid_request_error" in error_str - and "Incorrect API key provided" not in error_str - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - body=getattr(original_exception, "body", None), - ) - elif ( - "Web server is returning an unknown error" in error_str - or "The server had an error processing your request." in error_str - ): - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"{exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - ) - elif "Request too large" in error_str: - exception_mapping_worked = True - raise RateLimitError( - message=f"RateLimitError: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif ( - "The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_API_KEY environment variable" - in error_str - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"AuthenticationError: {exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif "Mistral API raised a streaming error" in error_str: - exception_mapping_worked = True - _request = httpx.Request( - method="POST", url="https://api.openai.com/v1" - ) - raise APIError( - status_code=500, - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - request=_request, - litellm_debug_info=extra_information, - ) - elif hasattr(original_exception, "status_code"): - exception_mapping_worked = True - if original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"AuthenticationError: {exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"NotFoundError: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"Timeout Error: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise BadRequestError( - message=f"{exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - body=getattr(original_exception, "body", None), - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"RateLimitError: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 500: - exception_mapping_worked = True - raise InternalServerError( - message=f"InternalServerError: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 502: - exception_mapping_worked = True - raise BadGatewayError( - message=f"BadGatewayError: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"ServiceUnavailableError: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"Timeout Error: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - exception_status_code=original_exception.status_code, - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"APIError: {exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - request=getattr(original_exception, "request", None), - litellm_debug_info=extra_information, - ) - else: - # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors - # exception_mapping_worked = True - raise APIConnectionError( - message=f"APIConnectionError: {exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - litellm_debug_info=extra_information, - request=httpx.Request( - method="POST", url="https://api.openai.com/v1/" - ), - ) + _map_openai_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif ( custom_llm_provider == "anthropic" or custom_llm_provider == "anthropic_text" ): # one of the anthropics - if ( - "prompt is too long" in error_str - or "prompt: length" in error_str - or ExceptionCheckers.is_error_str_context_window_exceeded(error_str) - ): - exception_mapping_worked = True - raise ContextWindowExceededError( - message="AnthropicError - {}".format(error_str), - model=model, - llm_provider="anthropic", - ) - elif "overloaded_error" in error_str or "Overloaded" in error_str: - exception_mapping_worked = True - raise InternalServerError( - message="AnthropicError - {}".format(error_str), - model=model, - llm_provider="anthropic", - ) - if "Invalid API Key" in error_str: - exception_mapping_worked = True - raise AuthenticationError( - message="AnthropicError - {}".format(error_str), - model=model, - llm_provider="anthropic", - ) - if "content filtering policy" in error_str: - exception_mapping_worked = True - raise ContentPolicyViolationError( - message="AnthropicError - {}".format(error_str), - model=model, - llm_provider="anthropic", - ) - if "Client error '400 Bad Request'" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message="AnthropicError - {}".format(error_str), - model=model, - llm_provider="anthropic", - ) - if hasattr(original_exception, "status_code"): - verbose_logger.debug( - f"status_code: {original_exception.status_code}" - ) - if original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"AnthropicException - {error_str}", - llm_provider="anthropic", - model=model, - ) - elif ( - original_exception.status_code == 400 - or original_exception.status_code == 413 - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"AnthropicException - {error_str}", - model=model, - llm_provider="anthropic", - ) - elif original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"AnthropicException - {error_str}", - model=model, - llm_provider="anthropic", - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"AnthropicException - {error_str}", - model=model, - llm_provider="anthropic", - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"AnthropicException - {error_str}", - llm_provider="anthropic", - model=model, - ) - elif ( - original_exception.status_code == 500 - or original_exception.status_code == 529 - ): - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"AnthropicException - {error_str}. Handle with `litellm.InternalServerError`.", - llm_provider="anthropic", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 502: - exception_mapping_worked = True - raise BadGatewayError( - message=f"AnthropicException BadGatewayError - {error_str}", - llm_provider="anthropic", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise litellm.ServiceUnavailableError( - message=f"AnthropicException - {error_str}. Handle with `litellm.ServiceUnavailableError`.", - llm_provider="anthropic", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"AnthropicException Timeout - {error_str}", - model=model, - llm_provider="anthropic", - exception_status_code=original_exception.status_code, - ) - elif custom_llm_provider == "replicate": - if "Incorrect authentication token" in error_str: - exception_mapping_worked = True - raise AuthenticationError( - message=f"ReplicateException - {error_str}", - llm_provider="replicate", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "input is too long" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"ReplicateException - {error_str}", - model=model, - llm_provider="replicate", - response=getattr(original_exception, "response", None), - ) - elif exception_type == "ModelError": - exception_mapping_worked = True - raise BadRequestError( - message=f"ReplicateException - {error_str}", - model=model, - llm_provider="replicate", - response=getattr(original_exception, "response", None), - ) - elif "Request was throttled" in error_str: - exception_mapping_worked = True - raise RateLimitError( - message=f"ReplicateException - {error_str}", - llm_provider="replicate", - model=model, - response=getattr(original_exception, "response", None), - ) - elif hasattr(original_exception, "status_code"): - if original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"ReplicateException - {original_exception.message}", - llm_provider="replicate", - model=model, - response=getattr(original_exception, "response", None), - ) - elif ( - original_exception.status_code == 400 - or original_exception.status_code == 413 - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"ReplicateException - {original_exception.message}", - model=model, - llm_provider="replicate", - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise UnprocessableEntityError( - message=f"ReplicateException - {original_exception.message}", - model=model, - llm_provider="replicate", - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"ReplicateException - {original_exception.message}", - model=model, - llm_provider="replicate", - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise UnprocessableEntityError( - message=f"ReplicateException - {original_exception.message}", - llm_provider="replicate", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"ReplicateException - {original_exception.message}", - llm_provider="replicate", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 500: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"ReplicateException - {original_exception.message}", - llm_provider="replicate", - model=model, - response=getattr(original_exception, "response", None), - ) - exception_mapping_worked = True - raise APIError( - status_code=500, - message=f"ReplicateException - {str(original_exception)}", - llm_provider="replicate", + _map_anthropic_exception( model=model, - request=httpx.Request( - method="POST", - url="https://api.replicate.com/v1/deployments", - ), + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) + elif custom_llm_provider == "replicate": + _map_replicate_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, ) elif custom_llm_provider in litellm._openai_like_providers: - if "authorization denied for" in error_str: - exception_mapping_worked = True - - # Predibase returns the raw API Key in the response - this block ensures it's not returned in the exception - if ( - error_str is not None - and isinstance(error_str, str) - and "bearer" in error_str.lower() - ): - # only keep the first 10 chars after the occurnence of "bearer" - _bearer_token_start_index = error_str.lower().find("bearer") - error_str = error_str[: _bearer_token_start_index + 14] - error_str += "XXXXXXX" + '"' - - raise AuthenticationError( - message=f"{custom_llm_provider.capitalize()}Exception: Authentication Error - {error_str}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif ExceptionCheckers.is_error_str_context_window_exceeded(error_str): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"{custom_llm_provider.capitalize()}Exception: Context Window Error - {error_str}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif "token_quota_reached" in error_str: - exception_mapping_worked = True - raise RateLimitError( - message=f"{custom_llm_provider.capitalize()}Exception: Rate Limit Errror - {error_str}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - ) - elif ( - "The server received an invalid response from an upstream server." - in error_str - ): - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - ) - elif "model_no_support_for_function" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"{custom_llm_provider.capitalize()}Exception - Use 'watsonx_text' route instead. IBM WatsonX does not support `/text/chat` endpoint. - {error_str}", - llm_provider=custom_llm_provider, - model=model, - ) - elif hasattr(original_exception, "status_code"): - if original_exception.status_code == 500: - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - ) - elif ( - original_exception.status_code == 401 - or original_exception.status_code == 403 - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - ) - elif original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - ) - elif original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif ( - original_exception.status_code == 422 - or original_exception.status_code == 424 - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - exception_status_code=original_exception.status_code, - ) + _map_openai_like_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "bedrock": - if ( - "too many tokens" in error_str - or "expected maxLength:" in error_str - or "Input is too long" in error_str - or "prompt is too long" in error_str - or "prompt: length: 1.." in error_str - or "Too many input tokens" in error_str - ): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"BedrockException: Context Window Error - {error_str}", - model=model, - llm_provider="bedrock", - ) - elif ( - "Conversation blocks and tool result blocks cannot be provided in the same turn." - in error_str - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"BedrockException - {error_str}\n. Enable 'litellm.modify_params=True' (for PROXY do: `litellm_settings::modify_params: True`) to insert a dummy assistant message and fix this error.", - model=model, - llm_provider="bedrock", - response=getattr(original_exception, "response", None), - ) - elif "Malformed input request" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"BedrockException - {error_str}", - model=model, - llm_provider="bedrock", - response=getattr(original_exception, "response", None), - ) - elif "A conversation must start with a user message." in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"BedrockException - {error_str}\n. Pass in default user message via `completion(..,user_continue_message=)` or enable `litellm.modify_params=True`.\nFor Proxy: do via `litellm_settings::modify_params: True` or user_continue_message under `litellm_params`", - model=model, - llm_provider="bedrock", - response=getattr(original_exception, "response", None), - ) - elif ( - "Unable to locate credentials" in error_str - or "The security token included in the request is invalid" - in error_str - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"BedrockException Invalid Authentication - {error_str}", - model=model, - llm_provider="bedrock", - response=getattr(original_exception, "response", None), - ) - elif "AccessDeniedException" in error_str: - exception_mapping_worked = True - raise PermissionDeniedError( - message=f"BedrockException PermissionDeniedError - {error_str}", - model=model, - llm_provider="bedrock", - response=getattr(original_exception, "response", None), - ) - elif ( - "throttlingException" in error_str - or "ThrottlingException" in error_str - ): - exception_mapping_worked = True - raise RateLimitError( - message=f"BedrockException: Rate Limit Error - {error_str}", - model=model, - llm_provider="bedrock", - response=getattr(original_exception, "response", None), - ) - elif ( - "Connect timeout on endpoint URL" in error_str - or "timed out" in error_str - ): - exception_mapping_worked = True - raise Timeout( - message=f"BedrockException: Timeout Error - {error_str}", - model=model, - llm_provider="bedrock", - ) - elif "Could not process image" in error_str: - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"BedrockException - {error_str}", - model=model, - llm_provider="bedrock", - ) - elif hasattr(original_exception, "status_code"): - if original_exception.status_code == 500: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"BedrockException - {original_exception.message}", - llm_provider="bedrock", - model=model, - response=httpx.Response( - status_code=500, - request=httpx.Request( - method="POST", url="https://api.openai.com/v1/" - ), - ), - ) - elif original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"BedrockException - {original_exception.message}", - llm_provider="bedrock", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"BedrockException - {original_exception.message}", - llm_provider="bedrock", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"BedrockException - {original_exception.message}", - llm_provider="bedrock", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise BadRequestError( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - exception_status_code=original_exception.status_code, - ) + _map_bedrock_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif ( custom_llm_provider == "sagemaker" or custom_llm_provider == "sagemaker_chat" ): - if "Unable to locate credentials" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"litellm.BadRequestError: SagemakerException - {error_str}", - model=model, - llm_provider="sagemaker", - response=getattr(original_exception, "response", None), - ) - elif ( - "Input validation error: `best_of` must be > 0 and <= 2" - in error_str - ): - exception_mapping_worked = True - raise BadRequestError( - message="SagemakerException - the value of 'n' must be > 0 and <= 2 for sagemaker endpoints", - model=model, - llm_provider="sagemaker", - response=getattr(original_exception, "response", None), - ) - elif ( - "`inputs` tokens + `max_new_tokens` must be <=" in error_str - or "instance type with more CPU capacity or memory" in error_str - ): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"SagemakerException - {error_str}", - model=model, - llm_provider="sagemaker", - response=getattr(original_exception, "response", None), - ) - elif hasattr(original_exception, "status_code"): - if original_exception.status_code == 500: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"SagemakerException - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - response=httpx.Response( - status_code=500, - request=httpx.Request( - method="POST", url="https://api.openai.com/v1/" - ), - ), - ) - elif original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"SagemakerException - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"SagemakerException - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"SagemakerException - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"SagemakerException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif ( - original_exception.status_code == 422 - or original_exception.status_code == 424 - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"SagemakerException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"SagemakerException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"SagemakerException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"SagemakerException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - exception_status_code=original_exception.status_code, - ) + _map_sagemaker_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif ( custom_llm_provider == LlmProviders.VERTEX_AI or custom_llm_provider == LlmProviders.VERTEX_AI_BETA or custom_llm_provider == LlmProviders.GEMINI ): - if ( - "Vertex AI API has not been used in project" in error_str - or "Unable to find your project" in error_str - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"litellm.BadRequestError: {custom_llm_provider}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - response=httpx.Response( - status_code=400, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ), - litellm_debug_info=extra_information, - ) - if "400 Request payload size exceeds" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - ) - elif ExceptionCheckers.is_error_str_context_window_exceeded(error_str): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"ContextWindowExceededError: {custom_llm_provider.capitalize()}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif ( - "None Unknown Error." in error_str - or "Content has no parts." in error_str - ): - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"litellm.InternalServerError: {custom_llm_provider}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - response=httpx.Response( - status_code=500, - content=str(original_exception), - request=httpx.Request(method="completion", url="https://github.com/BerriAI/litellm"), # type: ignore - ), - litellm_debug_info=extra_information, - ) - elif "API key not valid." in error_str: - exception_mapping_worked = True - raise AuthenticationError( - message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif "403" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"{custom_llm_provider.capitalize()}Exception BadRequestError - {error_str}", - model=model, - llm_provider=custom_llm_provider, - response=httpx.Response( - status_code=403, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ), - litellm_debug_info=extra_information, - ) - elif ( - "The response was blocked." in error_str - or "Output blocked by content filtering policy" - in error_str # anthropic on vertex ai - ): - exception_mapping_worked = True - raise ContentPolicyViolationError( - message=f"{custom_llm_provider.capitalize()}Exception ContentPolicyViolationError - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - response=httpx.Response( - status_code=400, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ), - ) - elif ( - "429 Quota exceeded" in error_str - or "Quota exceeded for" in error_str - or "Resource exhausted" in error_str - or "IndexError: list index out of range" in error_str - or "429 Unable to submit request because the service is temporarily out of capacity." - in error_str - ): - exception_mapping_worked = True - raise RateLimitError( - message=f"litellm.RateLimitError: {custom_llm_provider}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - response=httpx.Response( - status_code=429, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ), - ) - elif ( - "500 Internal Server Error" in error_str - or "The model is overloaded." in error_str - ): - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"litellm.InternalServerError: {custom_llm_provider}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - if hasattr(original_exception, "status_code"): - if original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"{custom_llm_provider.capitalize()}Exception BadRequestError - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - response=httpx.Response( - status_code=400, - request=httpx.Request( - method="POST", - url="https://cloud.google.com/vertex-ai/", - ), - ), - ) - if original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", - llm_provider=custom_llm_provider, - model=model, - ) - if original_exception.status_code == 403: - exception_mapping_worked = True - raise PermissionDeniedError( - message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", - llm_provider=custom_llm_provider, - model=model, - response=httpx.Response( - status_code=403, - request=httpx.Request( - method="POST", - url="https://cloud.google.com/vertex-ai/", - ), - ), - ) - if original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", - llm_provider=custom_llm_provider, - model=model, - ) - if original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", - llm_provider=custom_llm_provider, - model=model, - ) - - if original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"litellm.RateLimitError: {custom_llm_provider.capitalize()}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - response=httpx.Response( - status_code=429, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ), - ) - if original_exception.status_code == 500: - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"{custom_llm_provider.capitalize()}Exception InternalServerError - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - response=httpx.Response( - status_code=500, - content=str(original_exception), - request=httpx.Request(method="completion", url="https://github.com/BerriAI/litellm"), # type: ignore - ), - ) - if original_exception.status_code == 502: - exception_mapping_worked = True - raise APIConnectionError( - message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", - llm_provider=custom_llm_provider, - model=model, - ) - if original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", - llm_provider=custom_llm_provider, - model=model, - ) + _map_vertex_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "cloudflare": - if "Authentication error" in error_str: - exception_mapping_worked = True - raise AuthenticationError( - message=f"Cloudflare Exception - {original_exception.message}", - llm_provider="cloudflare", - model=model, - response=getattr(original_exception, "response", None), - ) - if "must have required property" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"Cloudflare Exception - {original_exception.message}", - llm_provider="cloudflare", - model=model, - response=getattr(original_exception, "response", None), - ) + _map_cloudflare_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif ( custom_llm_provider == "cohere" or custom_llm_provider == "cohere_chat" ): # Cohere - if ( - "invalid api token" in error_str - or "No API key provided." in error_str - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "invalid type: parameter" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "too many tokens" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"CohereException - {original_exception.message}", - model=model, - llm_provider="cohere", - response=getattr(original_exception, "response", None), - ) - elif "internal server error" in error_str.lower(): - exception_mapping_worked = True - raise InternalServerError( - message=f"CohereException - {error_str}", - model=model, - llm_provider="cohere", - response=getattr(original_exception, "response", None), - ) - elif hasattr(original_exception, "status_code"): - if ( - original_exception.status_code == 400 - or original_exception.status_code == 498 - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - ) - elif original_exception.status_code == 500: - exception_mapping_worked = True - raise InternalServerError( - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - response=getattr(original_exception, "response", None), - ) - elif ( - "CohereConnectionError" in exception_type - ): # cohere seems to fire these errors when we load test it (1k+ messages / min) - exception_mapping_worked = True - raise RateLimitError( - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "invalid type:" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "Unexpected server error" in error_str: - exception_mapping_worked = True - raise InternalServerError( - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - response=getattr(original_exception, "response", None), - ) - else: - if hasattr(original_exception, "status_code"): - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - request=getattr(original_exception, "request", None), - ) - raise original_exception + _map_cohere_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "huggingface": - if "length limit exceeded" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=error_str, - model=model, - llm_provider="huggingface", - response=getattr(original_exception, "response", None), - ) - elif "A valid user token is required" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=error_str, - llm_provider="huggingface", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "Rate limit reached" in error_str: - exception_mapping_worked = True - raise RateLimitError( - message=error_str, - llm_provider="huggingface", - model=model, - response=getattr(original_exception, "response", None), - ) - if hasattr(original_exception, "status_code"): - if original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"HuggingfaceException - {original_exception.message}", - llm_provider="huggingface", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"HuggingfaceException - {original_exception.message}", - model=model, - llm_provider="huggingface", - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"HuggingfaceException - {original_exception.message}", - model=model, - llm_provider="huggingface", - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"HuggingfaceException - {original_exception.message}", - llm_provider="huggingface", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"HuggingfaceException - {original_exception.message}", - llm_provider="huggingface", - model=model, - response=getattr(original_exception, "response", None), - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"HuggingfaceException - {original_exception.message}", - llm_provider="huggingface", - model=model, - request=getattr(original_exception, "request", None), - ) + _map_huggingface_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "ai21": - if hasattr(original_exception, "message"): - if "Prompt has too many tokens" in original_exception.message: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"AI21Exception - {original_exception.message}", - model=model, - llm_provider="ai21", - response=getattr(original_exception, "response", None), - ) - if "Bad or missing API token." in original_exception.message: - exception_mapping_worked = True - raise BadRequestError( - message=f"AI21Exception - {original_exception.message}", - model=model, - llm_provider="ai21", - response=getattr(original_exception, "response", None), - ) - if hasattr(original_exception, "status_code"): - if original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"AI21Exception - {original_exception.message}", - llm_provider="ai21", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"AI21Exception - {original_exception.message}", - model=model, - llm_provider="ai21", - ) - if original_exception.status_code == 422: - exception_mapping_worked = True - raise BadRequestError( - message=f"AI21Exception - {original_exception.message}", - model=model, - llm_provider="ai21", - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"AI21Exception - {original_exception.message}", - llm_provider="ai21", - model=model, - response=getattr(original_exception, "response", None), - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"AI21Exception - {original_exception.message}", - llm_provider="ai21", - model=model, - request=getattr(original_exception, "request", None), - ) + _map_ai21_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "nlp_cloud": - if "detail" in error_str: - if "Input text length should not exceed" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"NLPCloudException - {error_str}", - model=model, - llm_provider="nlp_cloud", - response=getattr(original_exception, "response", None), - ) - elif "value is not a valid" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"NLPCloudException - {error_str}", - model=model, - llm_provider="nlp_cloud", - response=getattr(original_exception, "response", None), - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=500, - message=f"NLPCloudException - {error_str}", - model=model, - llm_provider="nlp_cloud", - request=getattr(original_exception, "request", None), - ) - if hasattr( - original_exception, "status_code" - ): # https://docs.nlpcloud.com/?shell#errors - if ( - original_exception.status_code == 400 - or original_exception.status_code == 406 - or original_exception.status_code == 413 - or original_exception.status_code == 422 - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"NLPCloudException - {original_exception.message}", - llm_provider="nlp_cloud", - model=model, - response=getattr(original_exception, "response", None), - ) - elif ( - original_exception.status_code == 401 - or original_exception.status_code == 403 - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"NLPCloudException - {original_exception.message}", - llm_provider="nlp_cloud", - model=model, - response=getattr(original_exception, "response", None), - ) - elif ( - original_exception.status_code == 522 - or original_exception.status_code == 524 - ): - exception_mapping_worked = True - raise Timeout( - message=f"NLPCloudException - {original_exception.message}", - model=model, - llm_provider="nlp_cloud", - ) - elif ( - original_exception.status_code == 429 - or original_exception.status_code == 402 - ): - exception_mapping_worked = True - raise RateLimitError( - message=f"NLPCloudException - {original_exception.message}", - llm_provider="nlp_cloud", - model=model, - response=getattr(original_exception, "response", None), - ) - elif ( - original_exception.status_code == 500 - or original_exception.status_code == 503 - ): - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"NLPCloudException - {original_exception.message}", - llm_provider="nlp_cloud", - model=model, - request=getattr(original_exception, "request", None), - ) - elif ( - original_exception.status_code == 504 - or original_exception.status_code == 520 - ): - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"NLPCloudException - {original_exception.message}", - model=model, - llm_provider="nlp_cloud", - response=getattr(original_exception, "response", None), - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"NLPCloudException - {original_exception.message}", - llm_provider="nlp_cloud", - model=model, - request=getattr(original_exception, "request", None), - ) + _map_nlp_cloud_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "together_ai": - try: - error_response = json.loads(error_str) - except Exception: - error_response = {"error": error_str} - if ( - "error" in error_response - and "`inputs` tokens + `max_new_tokens` must be <=" - in error_response["error"] - ): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"TogetherAIException - {error_response['error']}", - model=model, - llm_provider="together_ai", - response=getattr(original_exception, "response", None), - ) - elif ( - "error" in error_response - and "invalid private key" in error_response["error"] - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"TogetherAIException - {error_response['error']}", - llm_provider="together_ai", - model=model, - response=getattr(original_exception, "response", None), - ) - elif ( - "error" in error_response - and "INVALID_ARGUMENT" in error_response["error"] - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"TogetherAIException - {error_response['error']}", - model=model, - llm_provider="together_ai", - response=getattr(original_exception, "response", None), - ) - elif "A timeout occurred" in error_str: - exception_mapping_worked = True - raise Timeout( - message=f"TogetherAIException - {error_str}", - model=model, - llm_provider="together_ai", - ) - elif ( - "error" in error_response - and "API key doesn't match expected format." - in error_response["error"] - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"TogetherAIException - {error_response['error']}", - model=model, - llm_provider="together_ai", - response=getattr(original_exception, "response", None), - ) - elif ( - "error_type" in error_response - and error_response["error_type"] == "validation" - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"TogetherAIException - {error_response['error']}", - model=model, - llm_provider="together_ai", - response=getattr(original_exception, "response", None), - ) - if hasattr(original_exception, "status_code"): - if original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"TogetherAIException - {original_exception.message}", - model=model, - llm_provider="together_ai", - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise BadRequestError( - message=f"TogetherAIException - {error_response['error']}", - model=model, - llm_provider="together_ai", - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"TogetherAIException - {original_exception.message}", - llm_provider="together_ai", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 524: - exception_mapping_worked = True - raise Timeout( - message=f"TogetherAIException - {original_exception.message}", - llm_provider="together_ai", - model=model, - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"TogetherAIException - {original_exception.message}", - llm_provider="together_ai", - model=model, - request=getattr(original_exception, "request", None), - ) + _map_together_ai_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "aleph_alpha": - if ( - "This is longer than the model's maximum context length" - in error_str - ): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"AlephAlphaException - {original_exception.message}", - llm_provider="aleph_alpha", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "InvalidToken" in error_str or "No token provided" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"AlephAlphaException - {original_exception.message}", - llm_provider="aleph_alpha", - model=model, - response=getattr(original_exception, "response", None), - ) - elif hasattr(original_exception, "status_code"): - verbose_logger.debug( - f"status code: {original_exception.status_code}" - ) - if original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"AlephAlphaException - {original_exception.message}", - llm_provider="aleph_alpha", - model=model, - ) - elif original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"AlephAlphaException - {original_exception.message}", - llm_provider="aleph_alpha", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"AlephAlphaException - {original_exception.message}", - llm_provider="aleph_alpha", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 500: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"AlephAlphaException - {original_exception.message}", - llm_provider="aleph_alpha", - model=model, - response=getattr(original_exception, "response", None), - ) - raise original_exception - raise original_exception + _map_aleph_alpha_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif ( custom_llm_provider == "ollama" or custom_llm_provider == "ollama_chat" ): - if isinstance(original_exception, dict): - error_str = original_exception.get("error", "") - else: - error_str = str(original_exception) - if "no such file or directory" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"OllamaException: Invalid Model/Model not loaded - {original_exception}", - model=model, - llm_provider="ollama", - response=getattr(original_exception, "response", None), - ) - elif "Failed to establish a new connection" in error_str: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"OllamaException: {original_exception}", - llm_provider="ollama", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "Invalid response object from API" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"OllamaException: {original_exception}", - llm_provider="ollama", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "Read timed out" in error_str: - exception_mapping_worked = True - raise Timeout( - message=f"OllamaException: {original_exception}", - llm_provider="ollama", - model=model, - ) + _map_ollama_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "vllm": - if hasattr(original_exception, "status_code"): - if original_exception.status_code == 0: - exception_mapping_worked = True - raise APIConnectionError( - message=f"VLLMException - {original_exception.message}", - llm_provider="vllm", - model=model, - request=getattr(original_exception, "request", None), - ) + _map_vllm_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "azure" or custom_llm_provider == "azure_text": - message = get_error_message(error_obj=original_exception) - if message is None: - if hasattr(original_exception, "message"): - message = original_exception.message - else: - message = str(original_exception) - - # Azure OpenAI (especially Images) often nests error details under - # body["error"]. Detect content policy violations using the structured - # payload in addition to string matching. - azure_error_code: Optional[str] = None - try: - body_dict = getattr(original_exception, "body", None) or {} - if isinstance(body_dict, dict): - if isinstance(body_dict.get("error"), dict): - azure_error_code = body_dict["error"].get("code") # type: ignore[index] - # Also check inner_error for - # ResponsibleAIPolicyViolation which indicates a - # content policy violation even when the top-level - # code is generic (e.g. "invalid_request_error"). - if azure_error_code != "content_policy_violation": - _inner = body_dict["error"].get( - "inner_error" - ) or body_dict[ # type: ignore[index] - "error" - ].get( - "innererror" - ) # type: ignore[index] - if ( - isinstance(_inner, dict) - and _inner.get("code") - == "ResponsibleAIPolicyViolation" - ): - azure_error_code = "content_policy_violation" - else: - azure_error_code = body_dict.get("code") - except Exception: - azure_error_code = None - - if "Internal server error" in error_str: - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"AzureException Internal server error - {message}", - llm_provider="azure", - model=model, - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif "This model's maximum context length is" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"AzureException ContextWindowExceededError - {message}", - llm_provider="azure", - model=model, - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif "DeploymentNotFound" in error_str: - exception_mapping_worked = True - raise NotFoundError( - message=f"AzureException NotFoundError - {message}", - llm_provider="azure", - model=model, - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif ( - azure_error_code == "content_policy_violation" - or ExceptionCheckers.is_azure_content_policy_violation_error( - error_str - ) - ): - exception_mapping_worked = True - from litellm.llms.azure.exception_mapping import ( - AzureOpenAIExceptionMapping, - ) - - raise AzureOpenAIExceptionMapping.create_content_policy_violation_error( - message=message, - model=model, - extra_information=extra_information, - original_exception=original_exception, - ) - elif ( - azure_error_code == "invalid_encrypted_content" - or "could not be verified" in error_str - ): - exception_mapping_worked = True - helpful_message = ( - f"AzureException - {message}\n\n" - "This error occurs when load balancing Responses API across deployments with different API keys.\n" - " Encrypted content is tied to the organization that created it and cannot be decrypted by other organizations.\n\n" - " Solution: Enable 'encrypted_content_affinity' to route follow-up requests to the correct deployment:\n\n" - " router_settings:\n" - " enable_pre_call_checks: true\n" - " optional_pre_call_checks:\n" - " - encrypted_content_affinity\n\n" - " Learn more: https://docs.litellm.ai/docs/response_api#encrypted-content-affinity-multi-region-load-balancing" - ) - raise BadRequestError( - message=helpful_message, - llm_provider="azure", - model=model, - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - body=getattr(original_exception, "body", None), - ) - elif "invalid_request_error" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"AzureException BadRequestError - {message}", - llm_provider="azure", - model=model, - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - body=getattr(original_exception, "body", None), - ) - elif ( - "The api_key client option must be set either by passing api_key to the client or by setting" - in error_str - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"{exception_provider} AuthenticationError - {message}", - llm_provider=custom_llm_provider, - model=model, - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif "Connection error" in error_str: - exception_mapping_worked = True - raise APIConnectionError( - message=f"{exception_provider} APIConnectionError - {message}", - llm_provider=custom_llm_provider, - model=model, - litellm_debug_info=extra_information, - ) - elif hasattr(original_exception, "status_code"): - exception_mapping_worked = True - if original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"AzureException - {message}", - llm_provider="azure", - model=model, - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - body=getattr(original_exception, "body", None), - ) - elif original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"AzureException AuthenticationError - {message}", - llm_provider="azure", - model=model, - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"AzureException Timeout - {message}", - model=model, - litellm_debug_info=extra_information, - llm_provider="azure", - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise BadRequestError( - message=f"AzureException BadRequestError - {message}", - model=model, - llm_provider="azure", - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"AzureException RateLimitError - {message}", - model=model, - llm_provider="azure", - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 502: - exception_mapping_worked = True - raise BadGatewayError( - message=f"AzureException BadGatewayError - {message}", - model=model, - llm_provider="azure", - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"AzureException ServiceUnavailableError - {message}", - model=model, - llm_provider="azure", - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"AzureException Timeout - {message}", - model=model, - litellm_debug_info=extra_information, - llm_provider="azure", - exception_status_code=original_exception.status_code, - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"AzureException APIError - {message}", - llm_provider="azure", - litellm_debug_info=extra_information, - model=model, - request=httpx.Request( - method="POST", url="https://openai.com/" - ), - ) - else: - # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors - raise APIConnectionError( - message=f"{exception_provider} APIConnectionError - {message}\n{_redact_string(traceback.format_exc())}", - llm_provider="azure", - model=model, - litellm_debug_info=extra_information, - request=httpx.Request(method="POST", url="https://openai.com/"), - ) + _map_azure_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) if custom_llm_provider == "openrouter": - if hasattr(original_exception, "status_code"): - exception_mapping_worked = True - if original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"{exception_provider} - {error_str}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"AuthenticationError: {exception_provider} - {error_str}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"NotFoundError: {exception_provider} - {error_str}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"Timeout Error: {exception_provider} - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise BadRequestError( - message=f"BadRequestError: {exception_provider} - {error_str}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"RateLimitError: {exception_provider} - {error_str}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"ServiceUnavailableError: {exception_provider} - {error_str}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"Timeout Error: {exception_provider} - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - exception_status_code=original_exception.status_code, - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"APIError: {exception_provider} - {error_str}", - llm_provider=custom_llm_provider, - model=model, - request=getattr(original_exception, "request", None), - litellm_debug_info=extra_information, - ) - else: - # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors - raise APIConnectionError( - message=f"APIConnectionError: {exception_provider} - {error_str}", - llm_provider=custom_llm_provider, - model=model, - litellm_debug_info=extra_information, - request=httpx.Request( - method="POST", url="https://api.openai.com/v1/" - ), - ) + _map_openrouter_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) if ( "BadRequestError.__init__() missing 1 required positional argument: 'param'" in str(original_exception) diff --git a/litellm/litellm_core_utils/fallback_utils.py b/litellm/litellm_core_utils/fallback_utils.py index daacca85c8a..1606b53e1f9 100644 --- a/litellm/litellm_core_utils/fallback_utils.py +++ b/litellm/litellm_core_utils/fallback_utils.py @@ -7,6 +7,9 @@ from litellm.litellm_core_utils.core_helpers import ( safe_deep_copy, filter_internal_params, ) +from litellm.router_utils.add_retry_fallback_headers import ( + add_fallback_headers_to_response, +) from .asyncify import run_async_function @@ -42,7 +45,7 @@ async def async_completion_with_fallbacks(**kwargs): # Try each fallback model most_recent_exception_str: Optional[str] = None - for fallback in fallbacks: + for attempted_fallbacks, fallback in enumerate(fallbacks): try: completion_kwargs = safe_deep_copy(base_kwargs) # Handle dictionary fallback configurations @@ -63,7 +66,10 @@ async def async_completion_with_fallbacks(**kwargs): ) if response is not None: - return response + return add_fallback_headers_to_response( + response=response, + attempted_fallbacks=attempted_fallbacks, + ) except Exception as e: verbose_logger.exception( diff --git a/litellm/litellm_core_utils/get_litellm_params.py b/litellm/litellm_core_utils/get_litellm_params.py index b32803b5dfc..fc3c25e0d95 100644 --- a/litellm/litellm_core_utils/get_litellm_params.py +++ b/litellm/litellm_core_utils/get_litellm_params.py @@ -4,7 +4,7 @@ from litellm.llms.openai.data_residency import infer_openai_data_residency # Pre-define optional kwargs keys as frozenset for O(1) lookups # These are extracted from kwargs only if present, avoiding unnecessary .get() calls -_OPTIONAL_KWARGS_KEYS = frozenset( +OPTIONAL_KWARGS_KEYS = frozenset( { "azure_ad_token", "tenant_id", @@ -32,11 +32,16 @@ _OPTIONAL_KWARGS_KEYS = frozenset( "aws_sts_endpoint", "aws_external_id", "aws_bedrock_runtime_endpoint", + "aws_bedrock_project_id", "tpm", "rpm", + "use_xai_oauth", } ) +# Backward-compatible alias for existing imports/tests. +_OPTIONAL_KWARGS_KEYS = OPTIONAL_KWARGS_KEYS + def _get_base_model_from_litellm_call_metadata( metadata: Optional[dict], @@ -164,7 +169,7 @@ def get_litellm_params( # Sparse extraction: only add kwargs keys that are actually present if kwargs: - for key in _OPTIONAL_KWARGS_KEYS: + for key in OPTIONAL_KWARGS_KEYS: if key in kwargs: litellm_params[key] = kwargs[key] diff --git a/litellm/litellm_core_utils/get_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py index de65ed93312..bb8b1a82996 100644 --- a/litellm/litellm_core_utils/get_llm_provider_logic.py +++ b/litellm/litellm_core_utils/get_llm_provider_logic.py @@ -1,5 +1,5 @@ import re -from typing import Optional, Tuple +from typing import Optional, Tuple, cast from urllib.parse import urlparse import litellm @@ -7,7 +7,7 @@ from litellm.constants import REPLICATE_MODEL_NAME_WITH_ID_LENGTH from litellm.llms.openai_like.json_loader import JSONProviderRegistry from litellm.secret_managers.main import get_secret, get_secret_str -from ..types.router import LiteLLM_Params +from ..types.router import GenericLiteLLMParams, LiteLLM_Params def _endpoint_matches_api_base(endpoint: str, api_base: str) -> bool: @@ -154,12 +154,12 @@ def handle_anthropic_text_model_custom_llm_provider( return model, custom_llm_provider -def get_llm_provider( # noqa: PLR0915 +def get_llm_provider( model: str, custom_llm_provider: Optional[str] = None, api_base: Optional[str] = None, api_key: Optional[str] = None, - litellm_params: Optional[LiteLLM_Params] = None, + litellm_params: Optional[GenericLiteLLMParams] = None, ) -> Tuple[str, str, Optional[str], Optional[str]]: """ Returns the provider for a given model name - e.g. 'azure/chatgpt-v-2' -> 'azure' @@ -178,7 +178,7 @@ def get_llm_provider( # noqa: PLR0915 ) if litellm.LiteLLMProxyChatConfig._should_use_litellm_proxy_by_default( - litellm_params=litellm_params + litellm_params=cast(Optional[LiteLLM_Params], litellm_params) ): return litellm.LiteLLMProxyChatConfig.litellm_proxy_get_custom_llm_provider_info( model=model, api_base=api_base, api_key=api_key @@ -186,12 +186,10 @@ def get_llm_provider( # noqa: PLR0915 ## IF LITELLM PARAMS GIVEN ## if litellm_params: - assert ( - custom_llm_provider is None and api_base is None and api_key is None - ), "Either pass in litellm_params or the custom_llm_provider/api_base/api_key. Otherwise, these values will be overriden." - custom_llm_provider = litellm_params.custom_llm_provider - api_base = litellm_params.api_base - api_key = litellm_params.api_key + if custom_llm_provider is None and api_base is None and api_key is None: + custom_llm_provider = litellm_params.custom_llm_provider + api_base = litellm_params.api_base + api_key = litellm_params.api_key dynamic_api_key = None # check if llm provider provided @@ -235,6 +233,7 @@ def get_llm_provider( # noqa: PLR0915 api_base=api_base, api_key=api_key, dynamic_api_key=dynamic_api_key, + litellm_params=litellm_params, ) # check if llm provider part of model name @@ -250,6 +249,7 @@ def get_llm_provider( # noqa: PLR0915 api_base=api_base, api_key=api_key, dynamic_api_key=dynamic_api_key, + litellm_params=litellm_params, ) elif model.split("/", 1)[0] in litellm.provider_list: custom_llm_provider = model.split("/", 1)[0] @@ -334,6 +334,9 @@ def get_llm_provider( # noqa: PLR0915 elif endpoint == "dashscope-intl.aliyuncs.com/compatible-mode/v1": custom_llm_provider = "dashscope" dynamic_api_key = get_secret_str("DASHSCOPE_API_KEY") + elif endpoint == "https://api-inference.modelscope.cn/v1": + custom_llm_provider = "modelscope" + dynamic_api_key = get_secret_str("MODELSCOPE_API_KEY") elif endpoint == "api.moonshot.ai/v1": custom_llm_provider = "moonshot" dynamic_api_key = get_secret_str("MOONSHOT_API_KEY") @@ -385,6 +388,9 @@ def get_llm_provider( # noqa: PLR0915 elif endpoint == "https://api.inference.wandb.ai/v1": custom_llm_provider = "wandb" dynamic_api_key = get_secret_str("WANDB_API_KEY") + elif endpoint == "https://pinstripes.io/v1": + custom_llm_provider = "pinstripes" + dynamic_api_key = get_secret_str("PINSTRIPES_API_KEY") if api_base is not None and not isinstance(api_base, str): raise Exception( @@ -526,11 +532,11 @@ def get_llm_provider( # noqa: PLR0915 custom_llm_provider = "sap" if not custom_llm_provider: if litellm.suppress_debug_info is False: - print() # noqa - print( # noqa - "\033[1;31mProvider List: https://docs.litellm.ai/docs/providers\033[0m" # noqa - ) # noqa - print() # noqa + print() # noqa: T201 + print( # noqa: T201 + "\033[1;31mProvider List: https://docs.litellm.ai/docs/providers\033[0m" + ) + print() # noqa: T201 error_str = f"LLM Provider NOT provided. Pass in the LLM provider you are trying to call. You passed model={model}\n Pass model as E.g. For 'Huggingface' inference endpoints pass in `completion(model='huggingface/starcoder',..)` Learn more: https://docs.litellm.ai/docs/providers" # maps to openai.NotFoundError, this is raised when openai does not recognize the llm raise litellm.exceptions.BadRequestError( # type: ignore @@ -565,11 +571,12 @@ def get_llm_provider( # noqa: PLR0915 ) -def _get_openai_compatible_provider_info( # noqa: PLR0915 +def _get_openai_compatible_provider_info( model: str, api_base: Optional[str], api_key: Optional[str], dynamic_api_key: Optional[str], + litellm_params: Optional[GenericLiteLLMParams] = None, ) -> Tuple[str, str, Optional[str], Optional[str]]: """ Returns: @@ -637,7 +644,7 @@ def _get_openai_compatible_provider_info( # noqa: PLR0915 api_base, dynamic_api_key, ) = litellm.BedrockMantleChatConfig()._get_openai_compatible_provider_info( - api_base, api_key + api_base, api_key, litellm_params=litellm_params, model=model ) elif custom_llm_provider == "nvidia_nim": # nvidia_nim is openai compatible, we just need to set this to custom_openai and have the api_base be https://api.endpoints.anyscale.com/v1 @@ -926,6 +933,13 @@ def _get_openai_compatible_provider_info( # noqa: PLR0915 ) = litellm.DashScopeChatConfig()._get_openai_compatible_provider_info( api_base, api_key ) + elif custom_llm_provider == "modelscope": + ( + api_base, + dynamic_api_key, + ) = litellm.ModelScopeChatConfig()._get_openai_compatible_provider_info( + api_base, api_key + ) elif custom_llm_provider == "moonshot": ( api_base, diff --git a/litellm/litellm_core_utils/get_supported_openai_params.py b/litellm/litellm_core_utils/get_supported_openai_params.py index 23b51faafc7..c22d3b99705 100644 --- a/litellm/litellm_core_utils/get_supported_openai_params.py +++ b/litellm/litellm_core_utils/get_supported_openai_params.py @@ -5,7 +5,7 @@ from litellm.exceptions import BadRequestError from litellm.types.utils import LlmProviders, LlmProvidersSet -def get_supported_openai_params( # noqa: PLR0915 +def get_supported_openai_params( model: str, custom_llm_provider: Optional[str] = None, request_type: Literal[ @@ -86,9 +86,7 @@ def get_supported_openai_params( # noqa: PLR0915 model=model ) elif request_type == "transcription": - return litellm.FireworksAIAudioTranscriptionConfig().get_supported_openai_params( - model=model - ) + return None else: return litellm.FireworksAIConfig().get_supported_openai_params(model=model) elif custom_llm_provider == "nvidia_nim": @@ -191,7 +189,9 @@ def get_supported_openai_params( # noqa: PLR0915 ) elif custom_llm_provider == "sambanova": if request_type == "embeddings": - litellm.SambaNovaEmbeddingConfig().get_supported_openai_params(model=model) + return litellm.SambaNovaEmbeddingConfig().get_supported_openai_params( + model=model + ) else: return litellm.SambanovaConfig().get_supported_openai_params(model=model) elif custom_llm_provider == "nebius": @@ -295,6 +295,15 @@ def get_supported_openai_params( # noqa: PLR0915 elif custom_llm_provider == "predibase": return litellm.PredibaseConfig().get_supported_openai_params(model=model) elif custom_llm_provider == "voyage": + if ( + request_type == "embeddings" + and litellm.VoyageMultimodalEmbeddingConfig.is_multimodal_embeddings(model) + ): + return ( + litellm.VoyageMultimodalEmbeddingConfig().get_supported_openai_params( + model=model + ) + ) return litellm.VoyageEmbeddingConfig().get_supported_openai_params(model=model) elif custom_llm_provider == "infinity": return litellm.InfinityEmbeddingConfig().get_supported_openai_params( diff --git a/litellm/litellm_core_utils/health_check_helpers.py b/litellm/litellm_core_utils/health_check_helpers.py index 9e972f1910b..5a29ea73a74 100644 --- a/litellm/litellm_core_utils/health_check_helpers.py +++ b/litellm/litellm_core_utils/health_check_helpers.py @@ -44,8 +44,8 @@ class HealthCheckHelpers: model_params["litellm_logging_obj"] = litellm_logging_obj model_params["fallbacks"] = fallback_models model_params["max_tokens"] = model_params.get( - "max_tokens", 10 - ) # gpt-5-nano throws errors for max_tokens=1 + "max_tokens", 16 + ) # GPT-5 models require max_output_tokens >= 16 await acompletion(**model_params) return {} diff --git a/litellm/litellm_core_utils/initialize_dynamic_callback_params.py b/litellm/litellm_core_utils/initialize_dynamic_callback_params.py index a89dae52316..949076aabf3 100644 --- a/litellm/litellm_core_utils/initialize_dynamic_callback_params.py +++ b/litellm/litellm_core_utils/initialize_dynamic_callback_params.py @@ -53,11 +53,19 @@ _supported_callback_params = [ "braintrust_host", "slack_webhook_url", "lunary_public_key", + "dd_api_key", + "dd_site", + "dd_agent_host", + "dd_agent_port", ] _request_blocked_callback_params = { "gcs_bucket_name", "gcs_path_service_account", + "dd_api_key", + "dd_site", + "dd_agent_host", + "dd_agent_port", } diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index dbfcf55d75d..d750a509054 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -158,6 +158,7 @@ from ..integrations.litellm_agent import LiteLLMAgentModelResolver from ..integrations.literal_ai import LiteralAILogger from ..integrations.logfire_logger import LogfireLevel, LogfireLogger from ..integrations.lunary import LunaryLogger +from ..integrations.newrelic import NewRelicLogger from ..integrations.openmeter import OpenMeterLogger from ..integrations.opik.opik import OpikLogger from ..integrations.posthog import PostHogLogger @@ -380,13 +381,14 @@ class Logging(LiteLLMLoggingBaseClass): List[Union[str, Callable, CustomLogger]] ] = dynamic_async_failure_callbacks - # Process dynamic callbacks - self.process_dynamic_callbacks() - ## DYNAMIC LANGFUSE / GCS / logging callback KEYS ## self.standard_callback_dynamic_params: StandardCallbackDynamicParams = ( self.initialize_standard_callback_dynamic_params(kwargs) ) + + # Process dynamic callbacks (after standard_callback_dynamic_params is initialized, + # so team-scoped credentials are available for callback initialization) + self.process_dynamic_callbacks() self.standard_built_in_tools_params: StandardBuiltInToolsParams = ( self.initialize_standard_built_in_tools_params(kwargs) ) @@ -481,8 +483,21 @@ class Logging(LiteLLMLoggingBaseClass): isinstance(callback, str) and callback in litellm._known_custom_logger_compatible_callbacks ): + # For callbacks that support team-scoped credentials (e.g. datadog), + # pass only the relevant dynamic params as custom_logger_init_args. + _custom_logger_init_args: Optional[dict] = None + if callback == "datadog": + _custom_logger_init_args = { + k: v + for k, v in self.standard_callback_dynamic_params.items() + if k.startswith("dd_") + } + callback_class = _init_custom_logger_compatible_class( - callback, internal_usage_cache=None, llm_router=None # type: ignore + callback, # type: ignore[arg-type] + internal_usage_cache=None, + llm_router=None, # type: ignore + custom_logger_init_args=_custom_logger_init_args, ) if callback_class is not None: processed_list.append(callback_class) @@ -971,7 +986,7 @@ class Logging(LiteLLMLoggingBaseClass): self._get_masked_api_base(additional_args.get("api_base", "")) ) - def pre_call(self, input, api_key, model=None, additional_args={}): # noqa: PLR0915 + def pre_call(self, input, api_key, model=None, additional_args={}): # Log the exact input to the LLM API litellm.error_logs["PRE_CALL"] = locals() try: @@ -2104,7 +2119,7 @@ class Logging(LiteLLMLoggingBaseClass): await self.async_success_handler(result=complete_streaming_response) return - def success_handler( # noqa: PLR0915 + def success_handler( self, result=None, start_time=None, end_time=None, cache_hit=None, **kwargs ): verbose_logger.debug( @@ -2569,7 +2584,7 @@ class Logging(LiteLLMLoggingBaseClass): ), ) - async def async_success_handler( # noqa: PLR0915 + async def async_success_handler( self, result=None, start_time=None, end_time=None, cache_hit=None, **kwargs ): """ @@ -2960,7 +2975,12 @@ class Logging(LiteLLMLoggingBaseClass): ) self.model_call_details["end_time"] = end_time self.model_call_details.setdefault("original_response", None) - self.model_call_details["response_cost"] = 0 + # A stream interrupted mid-flight still billed the provider for the + # chunks already delivered; the router stashes that recovered usage as + # ``combined_usage_object`` and pre-computes its cost, so preserve it + # here instead of zeroing the spend on an otherwise-failed request. + if self.model_call_details.get("combined_usage_object") is None: + self.model_call_details["response_cost"] = 0 if hasattr(exception, "headers") and isinstance(exception.headers, dict): self.model_call_details.setdefault("litellm_params", {}) @@ -3021,7 +3041,7 @@ class Logging(LiteLLMLoggingBaseClass): kwargs=self.model_call_details, ) # type: ignore - def failure_handler( # noqa: PLR0915 + def failure_handler( self, exception, traceback_exception, start_time=None, end_time=None ): verbose_logger.debug( @@ -3507,9 +3527,7 @@ class Logging(LiteLLMLoggingBaseClass): else: return None - def _handle_anthropic_messages_response_logging( - self, result: Any - ) -> Union[ModelResponse, ResponsesAPIResponse]: + def _handle_anthropic_messages_response_logging(self, result: Any) -> ModelResponse: """ Handles logging for Anthropic messages responses. @@ -3528,15 +3546,14 @@ class Logging(LiteLLMLoggingBaseClass): return result elif isinstance(result, ModelResponse): return result - elif isinstance( + + if isinstance( result, (ResponseCompletedEvent, ResponseIncompleteEvent, ResponseFailedEvent), ): - # anthropic_messages() can route to OpenAI Responses API; in that path - # the assembled streaming result is one of these terminal events rather than - # a ModelResponse. Return the inner response so downstream handlers - # (_transform_usage_objects, normalize_logging_result) can process it. - return result.response + result = result.response + if isinstance(result, ResponsesAPIResponse): + return self._translate_responses_api_response_to_model_response(result) httpx_response = self.model_call_details.get("httpx_response", None) if httpx_response and isinstance(httpx_response, httpx.Response): @@ -3570,6 +3587,55 @@ class Logging(LiteLLMLoggingBaseClass): ) return result + def _translate_responses_api_response_to_model_response( + self, result: ResponsesAPIResponse + ) -> ModelResponse: + """ + Convert a Responses API response into a ModelResponse for spend_logs. + + The proxy UI parses spend_log rows expecting chat-completion shape + (response.choices[0].message); a raw ResponsesAPIResponse dump (output[...]) + would render as empty in the Logs tab. Translation also yields full + choices/message detail downstream consumers can rely on. + """ + from litellm.completion_extras.litellm_responses_transformation.transformation import ( + LiteLLMResponsesTransformationHandler, + ) + + try: + return LiteLLMResponsesTransformationHandler().transform_response( + model=self.model, + raw_response=result, + model_response=litellm.ModelResponse(), + logging_obj=self, + request_data={}, + messages=[], + optional_params={}, + litellm_params={}, + encoding=litellm.encoding, + ) + except Exception as e: + verbose_logger.debug( + "Responses API -> ModelResponse translation failed for " + "anthropic_messages logging (%s); falling back to minimal " + "usage-only ModelResponse to keep the spend_logs row.", + str(e), + ) + model_response = litellm.ModelResponse() + model_response.model = self.model + usage = getattr(result, "usage", None) + if usage is not None and ResponseAPILoggingUtils._is_response_api_usage( + usage + ): + setattr( + model_response, + "usage", + ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( + usage + ), + ) + return model_response + def _handle_non_streaming_google_genai_generate_content_response_logging( self, result: Any ) -> ModelResponse: @@ -3692,7 +3758,7 @@ def _get_masked_values( } -def set_callbacks(callback_list, function_id=None): # noqa: PLR0915 +def set_callbacks(callback_list, function_id=None): """ Globally sets the callback client """ @@ -3793,7 +3859,7 @@ def set_callbacks(callback_list, function_id=None): # noqa: PLR0915 return None -def _init_custom_logger_compatible_class( # noqa: PLR0915 +def _init_custom_logger_compatible_class( logging_integration: _custom_logger_compatible_callbacks_literal, internal_usage_cache: Optional[DualCache], llm_router: Optional[ @@ -3894,6 +3960,24 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 _in_memory_loggers.append(_prometheus_logger) return _prometheus_logger # type: ignore elif logging_integration == "datadog": + # Check if team-scoped credentials are provided + _dd_api_key = custom_logger_init_args.get("dd_api_key") + _dd_site = custom_logger_init_args.get("dd_site") + _dd_agent_host = custom_logger_init_args.get("dd_agent_host") + _dd_agent_port = custom_logger_init_args.get("dd_agent_port") + + if _dd_api_key or _dd_site or _dd_agent_host: + # Team-scoped credentials: use DynamicLoggingCache for per-credential isolation + from litellm.integrations.datadog.datadog_team_handler import ( + DataDogHandler, + ) + + return DataDogHandler.get_datadog_logger_for_request( + standard_callback_dynamic_params=custom_logger_init_args, # type: ignore + in_memory_dynamic_logger_cache=in_memory_dynamic_logger_cache, + ) + + # Global (env-var based): reuse cached instance for callback in _in_memory_loggers: if isinstance(callback, DataDogLogger): return callback # type: ignore @@ -4124,6 +4208,17 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 focus_logger = FocusLogger() _in_memory_loggers.append(focus_logger) return focus_logger # type: ignore + elif logging_integration == "mavvrik": + from litellm.integrations.mavvrik_focus.mavvrik_focus_logger import ( + MavvrikFocusLogger, + ) + + for callback in _in_memory_loggers: + if type(callback) is MavvrikFocusLogger: + return callback # type: ignore + mavvrik_focus_logger = MavvrikFocusLogger() + _in_memory_loggers.append(mavvrik_focus_logger) + return mavvrik_focus_logger # type: ignore elif logging_integration == "vantage": from litellm.integrations.vantage.vantage_logger import VantageLogger @@ -4419,6 +4514,13 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 gitlab_logger = GitLabPromptManager(gitlab_config=gitlab_config) _in_memory_loggers.append(gitlab_logger) return gitlab_logger # type: ignore + elif logging_integration == "newrelic": + for callback in _in_memory_loggers: + if isinstance(callback, NewRelicLogger): + return callback # type: ignore + newrelic_logger = NewRelicLogger() + _in_memory_loggers.append(newrelic_logger) + return newrelic_logger # type: ignore return None except Exception as e: verbose_logger.exception( @@ -4514,7 +4616,7 @@ def _maybe_auto_initialize_arize_phoenix(_in_memory_loggers: list) -> None: ) -def get_custom_logger_compatible_class( # noqa: PLR0915 +def get_custom_logger_compatible_class( logging_integration: _custom_logger_compatible_callbacks_literal, ) -> Optional[CustomLogger]: try: @@ -4720,6 +4822,10 @@ def get_custom_logger_compatible_class( # noqa: PLR0915 for callback in _in_memory_loggers: if isinstance(callback, SMTPEmailLogger): return callback + elif logging_integration == "newrelic": + for callback in _in_memory_loggers: + if isinstance(callback, NewRelicLogger): + return callback return None except Exception as e: @@ -5315,19 +5421,20 @@ class StandardLoggingPayloadSetup: tb_lines[:MAXIMUM_TRACEBACK_LINES_TO_LOG] ) # Limit to first 100 lines + # Prefer the `.message` attribute (set by ProxyException and every + # litellm.exceptions.* class) over str(exc); ProxyException does not + # call super().__init__() nor define __str__, so str() on it returns + # an empty string, which used to silently strip the human-readable + # message from spend_logs.metadata.error_information. + # Use isinstance, not truthiness: an explicit empty string on + # `.message` is a deliberate value and must not be replaced by + # `str(exc)`. explicit_message = getattr(original_exception, "message", None) - error_message = ( - explicit_message - if isinstance(explicit_message, str) and explicit_message - else str(original_exception) - ) + if isinstance(explicit_message, str): + error_message = explicit_message + else: + error_message = str(original_exception) if original_exception else "" - # Duck-typed read so bare-Exception subclasses like - # `litellm.BudgetExceededError` can participate without joining the - # RateLimitError hierarchy (which would break `except BudgetExceededError`). - # Validated against the enum value sets so a third-party exception that - # happens to declare a `.category` or `.rate_limit_type` string attribute - # can't leak garbage into the payload or Prometheus label cardinality. rate_limit_category = validate_rate_limit_category( getattr(original_exception, "category", None) ) @@ -5340,11 +5447,42 @@ class StandardLoggingPayloadSetup: error_class=error_class, llm_provider=_llm_provider_in_exception, traceback=traceback_info, - error_message=error_message if original_exception else "", + error_message=error_message, error_rate_limit_category=rate_limit_category, error_rate_limit_type=rate_limit_type, ) + @staticmethod + def get_error_information_for_logging_payload( + metadata: dict, + original_exception: Exception | None, + error_str: str | None, + ) -> tuple[StandardLoggingPayloadErrorInformation, str | None]: + error_information = StandardLoggingPayloadSetup.get_error_information( + original_exception=original_exception, + ) + if not metadata.get("client_disconnected"): + return error_information, error_str + + client_disconnect_error = metadata.get("error_information") + if isinstance(client_disconnect_error, dict): + error_information = cast( + StandardLoggingPayloadErrorInformation, + client_disconnect_error, + ) + else: + error_information = cast( + StandardLoggingPayloadErrorInformation, + { + "error_code": "499", + "error_message": "Client disconnected the request", + "error_class": "ClientDisconnected", + }, + ) + if not error_str: + error_str = "Client disconnected the request" + return error_information, error_str + @staticmethod def get_response_time( start_time_float: float, @@ -5672,8 +5810,12 @@ def get_standard_logging_object_payload( api_base=litellm_params.get("api_base"), ) - error_information = StandardLoggingPayloadSetup.get_error_information( - original_exception=original_exception, + error_information, error_str = ( + StandardLoggingPayloadSetup.get_error_information_for_logging_payload( + metadata=metadata, + original_exception=original_exception, + error_str=error_str, + ) ) ## get final response object ## @@ -5792,7 +5934,7 @@ 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)) # noqa + print(json.dumps(payload, indent=4)) # noqa: T201 def get_standard_logging_metadata( 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 8da66d4600d..413ddb71bf8 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 @@ -6,6 +6,7 @@ from typing import Any, Dict, List, Literal, Optional, Tuple import litellm from litellm.constants import OPENAI_FILE_SEARCH_COST_PER_1K_CALLS +from litellm.litellm_core_utils.llm_cost_calc.utils import _get_web_search_requests from litellm.types.llms.openai import ( FileSearchTool, ResponsesAPIResponse, @@ -339,8 +340,7 @@ class StandardBuiltInToolCostTracking: # and _handle_web_search_cost() is never called. if ( hasattr(usage, "server_tool_use") - and usage.server_tool_use is not None - and usage.server_tool_use.web_search_requests is not None + and _get_web_search_requests(usage.server_tool_use) is not None ): return True return False @@ -352,8 +352,7 @@ class StandardBuiltInToolCostTracking: elif usage is not None: if ( hasattr(usage, "server_tool_use") - and usage.server_tool_use is not None - and usage.server_tool_use.web_search_requests is not None + and _get_web_search_requests(usage.server_tool_use) is not None ): return True elif ( diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py index f39c942f90f..7a7fde3087e 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/utils.py +++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py @@ -1,7 +1,7 @@ # What is this? ## Helper utilities for cost_per_token() -from typing import Literal, Optional, Tuple, TypedDict, cast +from typing import Any, Literal, Optional, Tuple, TypedDict, cast import litellm from litellm._logging import verbose_logger @@ -42,6 +42,26 @@ def _get_token_detail_value(details: object, key: str) -> Optional[int]: return value if isinstance(value, int) else None +def _get_web_search_requests(server_tool_use: Any) -> Optional[int]: + """ + Tolerantly read ``web_search_requests`` from a ``server_tool_use`` value + that may be ``None``, a ``dict``, a ``ServerToolUse`` pydantic instance, + or any other object supporting attribute access. + + Returns ``None`` when the value cannot be resolved — callers can + distinguish "absent" from "zero" using ``is None``. + + See https://github.com/BerriAI/litellm/issues/26153 — ``stream_chunk_builder`` + historically left this as a plain ``dict``, which broke direct attribute + access in cost calculation. + """ + if server_tool_use is None: + return None + if isinstance(server_tool_use, dict): + return server_tool_use.get("web_search_requests") + return getattr(server_tool_use, "web_search_requests", None) + + def _is_above_128k(tokens: float) -> bool: if tokens > 128000: return True @@ -171,6 +191,11 @@ def _get_service_tier_cost_key(base_key: str, service_tier: Optional[str]) -> st return base_key +def _parse_above_token_threshold(key: str) -> float: + threshold_str = key.split("_above_")[1].split("_tokens")[0] + return float(threshold_str.replace("k", "")) * (1000 if "k" in threshold_str else 1) + + def _get_token_base_cost( model_info: ModelInfo, usage: Usage, service_tier: Optional[str] = None ) -> Tuple[float, float, float, float, float]: @@ -236,15 +261,13 @@ def _get_token_base_cost( # Only sort the threshold keys (typically 1-2 keys instead of 66+) threshold: Optional[float] = None - for key in sorted(threshold_keys, reverse=True): + for key in sorted(threshold_keys, key=_parse_above_token_threshold, reverse=True): value = model_info.get(key) if value is not None: try: # Handle both formats: _above_128k_tokens and _above_128_tokens threshold_str = key.split("_above_")[1].split("_tokens")[0] - threshold = float(threshold_str.replace("k", "")) * ( - 1000 if "k" in threshold_str else 1 - ) + threshold = _parse_above_token_threshold(key) if usage.prompt_tokens > threshold: # Prefer a service_tier-specific above-threshold key when available, # e.g. input_cost_per_token_priority_above_200k_tokens for Gemini @@ -283,40 +306,54 @@ def _get_token_base_cost( # Apply tiered pricing to cache costs cache_creation_tiered_key = ( - f"cache_creation_input_token_cost_above_{threshold_str}_tokens" + _get_service_tier_cost_key( + f"cache_creation_input_token_cost_above_{threshold_str}_tokens", + service_tier, + ) + if service_tier + else f"cache_creation_input_token_cost_above_{threshold_str}_tokens" + ) + cache_creation_1hr_tiered_key = ( + _get_service_tier_cost_key( + f"cache_creation_input_token_cost_above_1hr_above_{threshold_str}_tokens", + service_tier, + ) + if service_tier + else f"cache_creation_input_token_cost_above_1hr_above_{threshold_str}_tokens" ) - cache_creation_1hr_tiered_key = f"cache_creation_input_token_cost_above_1hr_above_{threshold_str}_tokens" cache_read_tiered_key = ( - f"cache_read_input_token_cost_above_{threshold_str}_tokens" + _get_service_tier_cost_key( + f"cache_read_input_token_cost_above_{threshold_str}_tokens", + service_tier, + ) + if service_tier + else f"cache_read_input_token_cost_above_{threshold_str}_tokens" ) - if cache_creation_tiered_key in model_info: - cache_creation_cost = cast( - float, - _get_cost_per_unit( - model_info, - cache_creation_tiered_key, - cache_creation_cost, - ), - ) + cache_creation_cost = cast( + float, + _get_cost_per_unit( + model_info, + cache_creation_tiered_key, + cache_creation_cost, + ), + ) - if cache_creation_1hr_tiered_key in model_info: - cache_creation_cost_above_1hr = cast( - float, - _get_cost_per_unit( - model_info, - cache_creation_1hr_tiered_key, - cache_creation_cost_above_1hr, - ), - ) + cache_creation_cost_above_1hr = cast( + float, + _get_cost_per_unit( + model_info, + cache_creation_1hr_tiered_key, + cache_creation_cost_above_1hr, + ), + ) - if cache_read_tiered_key in model_info: - cache_read_cost = cast( - float, - _get_cost_per_unit( - model_info, cache_read_tiered_key, cache_read_cost - ), - ) + cache_read_cost = cast( + float, + _get_cost_per_unit( + model_info, cache_read_tiered_key, cache_read_cost + ), + ) break except (IndexError, ValueError): @@ -663,7 +700,7 @@ def _get_regional_uplift_multiplier( return 1.0 -def generic_cost_per_token( # noqa: PLR0915 +def generic_cost_per_token( model: str, usage: Usage, custom_llm_provider: str, @@ -929,6 +966,43 @@ def calculate_image_response_cost_from_usage( return prompt_cost + completion_cost +def calculate_image_response_web_search_cost( + image_response: ImageResponse, + custom_llm_provider: str, + model_info: ModelInfo, +) -> float: + """ + Cost of Google Search grounding performed during image generation. + + The grounding request count is carried on the image usage object by the + provider transformers; it is billed with the same per-request accounting + used for chat completions. + """ + usage = image_response.usage + if usage is None: + return 0.0 + + web_search_requests = getattr(usage, "web_search_requests", None) + if not web_search_requests: + return 0.0 + + from litellm.llms import get_cost_for_web_search_request + + synthetic_usage = Usage( + prompt_tokens_details=PromptTokensDetailsWrapper( + web_search_requests=web_search_requests + ) + ) + return ( + get_cost_for_web_search_request( + custom_llm_provider=custom_llm_provider, + usage=synthetic_usage, + model_info=model_info, + ) + or 0.0 + ) + + class CostCalculatorUtils: @staticmethod def _call_type_has_image_response(call_type: str) -> bool: diff --git a/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py b/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py index 2547fd4d8c6..016bb6b1e22 100644 --- a/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py +++ b/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py @@ -471,7 +471,7 @@ def _should_convert_tool_call_to_json_mode( return False -def convert_to_model_response_object( # noqa: PLR0915 +def convert_to_model_response_object( response_object: Optional[dict] = None, model_response_object: Optional[ Union[ @@ -633,11 +633,6 @@ def convert_to_model_response_object( # noqa: PLR0915 thinking_blocks = choice["message"]["thinking_blocks"] provider_specific_fields["thinking_blocks"] = thinking_blocks - if reasoning_content: - provider_specific_fields["reasoning_content"] = ( - reasoning_content - ) - message = Message( content=content, role=choice["message"]["role"] or "assistant", 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 06933a6fbcb..ba870eb9459 100644 --- a/litellm/litellm_core_utils/llm_response_utils/response_metadata.py +++ b/litellm/litellm_core_utils/llm_response_utils/response_metadata.py @@ -49,7 +49,8 @@ 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_value_from_hidden_params("additional_headers") or {}, + preserve_litellm_internal_headers=True, ), "litellm_model_name": model, } diff --git a/litellm/litellm_core_utils/logging_callback_manager.py b/litellm/litellm_core_utils/logging_callback_manager.py index 6c749118dec..b7adda3a9a4 100644 --- a/litellm/litellm_core_utils/logging_callback_manager.py +++ b/litellm/litellm_core_utils/logging_callback_manager.py @@ -394,6 +394,22 @@ class LoggingCallbackManager: + litellm._async_failure_callback ) + def remove_callback_from_all_lists(self, obj, require_self=False) -> None: + """ + Remove a callback object from every callback list it may have been + promoted into, so a re-initialized callback leaves no stale instance behind. + """ + for callback_list in ( + litellm.callbacks, + litellm.success_callback, + litellm.failure_callback, + litellm._async_success_callback, + litellm._async_failure_callback, + ): + self.remove_callback_from_list_by_object( + callback_list, obj, require_self=require_self + ) + def get_active_additional_logging_utils_from_custom_logger( self, ) -> Set[AdditionalLoggingUtils]: diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py index 81a4c8b14b6..b95b73398ac 100644 --- a/litellm/litellm_core_utils/prompt_templates/factory.py +++ b/litellm/litellm_core_utils/prompt_templates/factory.py @@ -1475,7 +1475,7 @@ def convert_to_gemini_tool_call_invoke( ) -def convert_to_gemini_tool_call_result( # noqa: PLR0915 +def convert_to_gemini_tool_call_result( message: Union[ChatCompletionToolMessage, ChatCompletionFunctionMessage], last_message_with_tool_calls: Optional[dict], model: Optional[str] = None, @@ -2227,7 +2227,7 @@ def _sanitize_empty_text_content( return message -def _add_missing_tool_results( # noqa: PLR0915 +def _add_missing_tool_results( current_message: AllMessageValues, messages: List[AllMessageValues], current_index: int, @@ -2484,7 +2484,7 @@ def sanitize_messages_for_tool_calling( return sanitized_messages -def anthropic_messages_pt( # noqa: PLR0915 +def anthropic_messages_pt( messages: List[AllMessageValues], model: str, llm_provider: str, @@ -3278,7 +3278,7 @@ def convert_to_cohere_tool_invoke(tool_calls: list) -> List[ToolCallObject]: return cohere_tool_invoke -def cohere_messages_pt_v2( # noqa: PLR0915 +def cohere_messages_pt_v2( messages: List, model: str, llm_provider: str, @@ -4290,6 +4290,49 @@ def _deduplicate_bedrock_tool_content( return _deduplicate_bedrock_content_blocks(tool_content, "toolResult") +def _rename_duplicate_bedrock_document_names( + contents: List[BedrockMessageBlock], +) -> List[BedrockMessageBlock]: + """ + Rename duplicate document names across all messages in a Bedrock request. + + Document names are derived from a content hash, so the same file appearing + in multiple conversation turns produces identical names and Bedrock rejects + the request with "Messages can not contain duplicate document names". The + first occurrence keeps its original name so prompt-cache prefixes stay + stable; later occurrences get a deterministic positional suffix + (``_2``, ``_3``, ...), bumped further if the suffixed name already + belongs to another document (e.g. an organic name ending in ``_2``). + """ + used_names: Set[str] = set() + for message in contents: + for block in message.get("content") or []: + document = block.get("document") + if isinstance(document, dict) and document.get("name"): + used_names.add(document["name"]) + + name_counts: Dict[str, int] = {} + for message in contents: + for block in message.get("content") or []: + document = block.get("document") + if not isinstance(document, dict): + continue + name = document.get("name") + if not name: + continue + count = name_counts.get(name, 0) + 1 + name_counts[name] = count + if count > 1: + suffix = count + new_name = f"{name}_{suffix}" + while new_name in used_names: + suffix += 1 + new_name = f"{name}_{suffix}" + used_names.add(new_name) + document["name"] = new_name + return contents + + def _sort_bedrock_assistant_content_blocks( blocks: List[BedrockContentBlock], ) -> List[BedrockContentBlock]: @@ -4660,7 +4703,7 @@ class BedrockConverseMessagesProcessor: return messages @staticmethod - async def _bedrock_converse_messages_pt_async( # noqa: PLR0915 + async def _bedrock_converse_messages_pt_async( messages: List, model: str, llm_provider: str, @@ -4698,6 +4741,12 @@ class BedrockConverseMessagesProcessor: guardContent={"text": {"text": element["text"]}} ) _parts.append(_part) + elif element["type"] in ("grounding_source", "query"): + # Contextual grounding tags are guardrail metadata; the + # model only needs the underlying text, so render them + # as plain text on the generate path. + _part = BedrockContentBlock(text=element["text"]) + _parts.append(_part) elif element["type"] == "image_url": format: Optional[str] = None if isinstance(element["image_url"], dict): @@ -4938,7 +4987,7 @@ class BedrockConverseMessagesProcessor: llm_provider=llm_provider, ) - return contents + return _rename_duplicate_bedrock_document_names(contents) @staticmethod def translate_thinking_blocks_to_reasoning_content_blocks( @@ -5084,7 +5133,7 @@ class BedrockConverseMessagesProcessor: return assistant_parts -def _bedrock_converse_messages_pt( # noqa: PLR0915 +def _bedrock_converse_messages_pt( messages: List, model: str, llm_provider: str, @@ -5130,6 +5179,12 @@ def _bedrock_converse_messages_pt( # noqa: PLR0915 guardContent={"text": {"text": element["text"]}} ) _parts.append(_part) + elif element["type"] in ("grounding_source", "query"): + # Contextual grounding tags are guardrail metadata; the + # model only needs the underlying text, so render them as + # plain text on the generate path. + _part = BedrockContentBlock(text=element["text"]) + _parts.append(_part) elif element["type"] == "image_url": format: Optional[str] = None if isinstance(element["image_url"], dict): @@ -5360,7 +5415,7 @@ def _bedrock_converse_messages_pt( # noqa: PLR0915 llm_provider=llm_provider, ) - return contents + return _rename_duplicate_bedrock_document_names(contents) def make_valid_bedrock_tool_name(input_tool_name: str) -> str: diff --git a/litellm/litellm_core_utils/realtime_streaming.py b/litellm/litellm_core_utils/realtime_streaming.py index 772f058d9bb..c56a70177bf 100644 --- a/litellm/litellm_core_utils/realtime_streaming.py +++ b/litellm/litellm_core_utils/realtime_streaming.py @@ -47,6 +47,7 @@ class RealTimeStreaming: user_api_key_dict: Optional[Any] = None, request_data: Optional[Dict] = None, backend_uses_beta_protocol: Optional[bool] = None, + force_transcription_model: Optional[str] = None, ): self.websocket = websocket self.backend_ws = backend_ws @@ -100,6 +101,11 @@ class RealTimeStreaming: self._flushing_pending_messages_until_setup: bool = False self._pending_messages_until_setup: List[str] = [] self._pending_messages_byte_total: int = 0 + # Whether this is a transcription-only session (session.type == "transcription", + # e.g. gpt-realtime-whisper). Such sessions must not be sent response.create and + # their input_audio_transcription.completed usage drives duration-based cost. + self._force_transcription_model = force_transcription_model + self._is_transcription_session: bool = force_transcription_model is not None # Per-connection caps for pre-setup audio frames (message count + total bytes). _MAX_BUFFERED_MESSAGES: int = 200 @@ -111,8 +117,12 @@ class RealTimeStreaming: "input_audio_buffer.append", "input_audio_buffer.commit", "input_audio_buffer.clear", + "input_audio_buffer.end", ] ) + _CLIENT_AUDIO_BUFFER_COMMIT_TYPES = frozenset( + ["input_audio_buffer.commit", "input_audio_buffer.end"] + ) _AUDIO_FORMAT_MAP: Dict[str, Dict[str, Any]] = { "pcm16": {"type": "audio/pcm", "rate": 24000}, "g711_ulaw": {"type": "audio/G711-ulaw", "rate": 8000}, @@ -144,7 +154,7 @@ class RealTimeStreaming: return True return False - def store_message(self, message: Union[str, bytes, OpenAIRealtimeEvents]): + def store_message(self, message: Union[str, bytes, dict, OpenAIRealtimeEvents]): """Store message in list""" if isinstance(message, bytes): message = message.decode("utf-8") @@ -154,22 +164,20 @@ class RealTimeStreaming: else: message_obj = cast(Dict[str, Any], json.loads(cast(str, message))) self._collect_tool_calls_from_response_done(cast(dict, message_obj)) + if not self._should_store_message(message_obj): + return try: event_type = message_obj.get("type", "") if event_type in self._SESSION_EVENT_TYPES: - typed_obj = OpenAIRealtimeStreamSessionEvents(**message_obj) # type: ignore + typed_obj: OpenAIRealtimeEvents = OpenAIRealtimeStreamSessionEvents(**message_obj) # type: ignore else: - # Use the base object as a safe catch-all for all other event types - # (both beta and GA), so unknown/new event names never raise here. + # Catch-all base object so unknown/new event names never raise. typed_obj = OpenAIRealtimeStreamResponseBaseObject(**message_obj) # type: ignore except Exception as e: verbose_logger.debug(f"Error parsing message for logging: {e}") - # Don't re-raise — a parse failure must not drop or delay the message - if self._should_store_message(message_obj): - self.messages.append(message_obj) # type: ignore[arg-type] + self.messages.append(message_obj) # type: ignore[arg-type] return - if self._should_store_message(typed_obj): - self.messages.append(typed_obj) + self.messages.append(typed_obj) def _collect_user_input_from_client_event(self, message: Union[str, dict]) -> None: """Extract user text content from client WebSocket events for spend logging.""" @@ -209,6 +217,8 @@ class RealTimeStreaming: self.session_tools = tools # GA: session.type is required; log it for traceability but no action needed verbose_logger.debug(f"Realtime session.type: {session.get('type')}") + if session.get("type") == "transcription": + self._is_transcription_session = True except (json.JSONDecodeError, AttributeError, TypeError): pass @@ -225,6 +235,55 @@ class RealTimeStreaming: except (AttributeError, TypeError): pass + def _detect_transcription_session_from_backend( + self, event_obj: Union[dict, OpenAIRealtimeEvents] + ) -> None: + """Flag transcription-only sessions from backend session events.""" + try: + event_type = event_obj.get("type", "") + if event_type in ( + "transcription_session.created", + "transcription_session.updated", + ): + self._is_transcription_session = True + elif event_type in ("session.created", "session.updated"): + session = cast(dict, event_obj).get("session", {}) or {} + if session.get("type") == "transcription": + self._is_transcription_session = True + except (AttributeError, TypeError): + pass + + def _capture_transcription_usage( + self, event_obj: Union[dict, OpenAIRealtimeEvents] + ) -> None: + """ + Append a usage-only transcription completed event to the logged results so + the cost calculator can bill it by audio duration. The default logged event + types exclude this event, so it is captured here directly for transcription + sessions rather than widening logging for every realtime session. Only the + type and usage are kept — the transcript is already captured separately in + input_messages, so it is not duplicated into the response log here. + """ + try: + usage = event_obj.get("usage") + if usage is None: + return + # If this event type is already captured by store_message (e.g. the user + # logs all realtime events), don't append a second copy. + if self._should_store_message(event_obj): + return + self.messages.append( + cast( + OpenAIRealtimeEvents, + { + "type": "conversation.item.input_audio_transcription.completed", + "usage": usage, + }, + ) + ) + except (AttributeError, TypeError): + pass + def _collect_tool_calls_from_response_done( self, event_obj: Union[dict, OpenAIRealtimeEvents] ) -> None: @@ -285,6 +344,7 @@ class RealTimeStreaming: backend, False if the provider transformation produced no output and the message was effectively dropped. """ + message = self._enforce_transcription_session_model(message) if self.provider_config: transformed = self.provider_config.transform_realtime_request( message, self.model, self.session_configuration_request @@ -304,12 +364,128 @@ class RealTimeStreaming: await self.backend_ws.send(message) # type: ignore[union-attr, attr-defined] return True + def _enforce_transcription_session_model(self, message: str) -> str: + """Force client transcription session updates to the authorized model. + + `/v1/realtime?intent=transcription` may intentionally omit `model` from + the upstream URL for Azure compatibility, but the proxy still authorizes + a resolved LiteLLM model before opening the backend websocket. If a + client later sends a transcription `session.update`, any model embedded + in that update must be rewritten to the same authorized model instead of + allowing a post-auth model/deployment switch. + + Normal realtime sessions keep their independent nested transcription + model behavior because `_force_transcription_model` is only set for + transcription-intent websocket routes. + """ + if self._force_transcription_model is None: + return message + + try: + message_obj = json.loads(message) + except (json.JSONDecodeError, TypeError): + return message + + if message_obj.get("type") not in ( + "session.update", + "transcription_session.update", + ): + return message + + session = message_obj.get("session") + if not isinstance(session, dict): + return message + + if session.get("type") == "transcription": + self._is_transcription_session = True + + authorized_model = self._force_transcription_model + changed = False + + transcription = session.get("input_audio_transcription") + if ( + isinstance(transcription, dict) + and transcription.get("model") != authorized_model + ): + session["input_audio_transcription"] = { + **transcription, + "model": authorized_model, + } + changed = True + + audio = session.get("audio") + if isinstance(audio, dict): + audio_input = audio.get("input") + if isinstance(audio_input, dict): + nested_transcription = audio_input.get("transcription") + if ( + isinstance(nested_transcription, dict) + and nested_transcription.get("model") != authorized_model + ): + session["audio"] = { + **audio, + "input": { + **audio_input, + "transcription": { + **nested_transcription, + "model": authorized_model, + }, + }, + } + changed = True + + if not changed: + return message + return json.dumps(message_obj) + def _uses_deferred_backend_setup(self) -> bool: """True when setup is deferred until the client's first session.update.""" if self.provider_config is None: return False return not self.provider_config.requires_session_configuration() + @staticmethod + def _collapse_buffered_audio_messages(messages: List[str]) -> List[str]: + """Apply ``input_audio_buffer.clear`` semantics before replaying buffered frames. + + During deferred Gemini Live setup, ``clear`` is buffered alongside appends. + On flush each append becomes a provider ``realtimeInput``; ``clear`` must + drop preceding uncommitted appends instead of being forwarded as a no-op. + """ + collapsed: List[str] = [] + pending_appends: List[str] = [] + + for message in messages: + try: + msg_type = json.loads(message).get("type") + except (json.JSONDecodeError, TypeError): + collapsed.extend(pending_appends) + pending_appends = [] + collapsed.append(message) + continue + + if msg_type == "input_audio_buffer.append": + pending_appends.append(message) + elif msg_type == "input_audio_buffer.clear": + pending_appends = [] + elif msg_type in RealTimeStreaming._CLIENT_AUDIO_BUFFER_COMMIT_TYPES: + collapsed.extend(pending_appends) + pending_appends = [] + collapsed.append(message) + else: + collapsed.extend(pending_appends) + pending_appends = [] + collapsed.append(message) + + collapsed.extend(pending_appends) + return collapsed + + def _sync_pending_messages_byte_total(self) -> None: + self._pending_messages_byte_total = sum( + len(message.encode("utf-8")) + for message in self._pending_messages_until_setup + ) + def _should_buffer_client_message_until_setup(self, message: str) -> bool: if not self._uses_deferred_backend_setup(): return False @@ -325,6 +501,18 @@ class RealTimeStreaming: return msg_obj.get("type") in RealTimeStreaming._CLIENT_AUDIO_BUFFER_TYPES def _buffer_pending_message_until_setup(self, message: str) -> None: + try: + msg_type = json.loads(message).get("type") + except (json.JSONDecodeError, TypeError): + msg_type = None + + if msg_type == "input_audio_buffer.clear": + self._pending_messages_until_setup = self._collapse_buffered_audio_messages( + self._pending_messages_until_setup + [message] + ) + self._sync_pending_messages_byte_total() + return + msg_bytes = len(message.encode("utf-8")) if ( len(self._pending_messages_until_setup) @@ -342,7 +530,9 @@ class RealTimeStreaming: ) async def _flush_pending_messages_until_setup(self) -> bool: - pending = self._pending_messages_until_setup + pending = self._collapse_buffered_audio_messages( + self._pending_messages_until_setup + ) self._pending_messages_until_setup = [] self._pending_messages_byte_total = 0 for idx, message in enumerate(pending): @@ -358,8 +548,7 @@ class RealTimeStreaming: for msg in self._pending_messages_until_setup ) verbose_logger.debug( - "Failed to flush buffered client message after setup: %s " - "(%d buffered message(s) retained)", + "Failed to flush buffered client message after setup: %s (%d buffered message(s) retained)", e, len(unsent), ) @@ -376,8 +565,7 @@ class RealTimeStreaming: return True except Exception as e: verbose_logger.warning( - "Failed to translate %s to beta protocol, forwarding " - "untranslated event to client: %s", + "Failed to translate %s to beta protocol, forwarding untranslated event to client: %s", event.get("type"), e, ) @@ -429,16 +617,13 @@ class RealTimeStreaming: if sent: self._guardrail_turn_detection_update_sent = True - def _has_realtime_guardrails(self) -> bool: - """Return True if any callback is registered for realtime guardrail event types.""" + def _has_realtime_guardrails_for_event_hooks( + self, + event_hooks: List[Any], + ) -> bool: + """Return True if any callback would run for one of ``event_hooks``.""" from litellm.integrations.custom_guardrail import CustomGuardrail - from litellm.types.guardrails import GuardrailEventHooks - _realtime_event_types = [ - GuardrailEventHooks.realtime_input_transcription, - GuardrailEventHooks.pre_call, - GuardrailEventHooks.post_call, - ] return any( isinstance(cb, CustomGuardrail) and any( @@ -446,31 +631,45 @@ class RealTimeStreaming: data=self.request_data, event_type=et, ) - for et in _realtime_event_types + for et in event_hooks ) for cb in litellm.callbacks ) + def _has_realtime_guardrails(self) -> bool: + """Return True if any callback is registered for realtime guardrail event types.""" + from litellm.types.guardrails import GuardrailEventHooks + + return self._has_realtime_guardrails_for_event_hooks( + [ + GuardrailEventHooks.realtime_input_transcription, + GuardrailEventHooks.pre_call, + GuardrailEventHooks.post_call, + ] + ) + def _has_audio_transcription_guardrails(self) -> bool: - """Return True if any callback needs to run on audio transcriptions (VAD path). + """Return True when a guardrail is configured for the audio/VAD transcript path. - When this returns True, we inject a session.update to disable the LLM's - auto-response so the guardrail can gate it first. - - Must match the same hook criteria as run_realtime_guardrails() so that - any guardrail that would actually check the transcript also disables - auto-response before the transcript arrives. + Only ``realtime_input_transcription`` hooks disable ``server_vad`` auto-response. + ``pre_call`` / ``post_call`` guardrails (e.g. Model Armor on chat completions) + must not override ``turn_detection.create_response`` on realtime sessions. """ - return self._has_realtime_guardrails() + from litellm.types.guardrails import GuardrailEventHooks + + return self._has_realtime_guardrails_for_event_hooks( + [GuardrailEventHooks.realtime_input_transcription] + ) async def run_realtime_guardrails( self, transcript: str, item_id: Optional[str] = None, pre_block_backend_message: Optional[str] = None, + event_hooks: Optional[List[Any]] = None, ) -> bool: """ - Run registered guardrails on a completed speech transcription. + Run registered guardrails on realtime text (transcript, user message, tool output). Returns True if blocked (synthetic warning already sent to client). Returns False if clean (caller should send response.create to the backend). @@ -481,15 +680,17 @@ class RealTimeStreaming: specific message to be sent first — e.g. Gemini Live requires a matching ``toolResponse`` immediately after a ``toolCall`` before any other client messages can be accepted. + + ``event_hooks`` selects which guardrail modes to evaluate. Audio/VAD + transcript completion uses ``realtime_input_transcription`` only; + typed user messages and tool outputs use ``pre_call``. """ from litellm.integrations.custom_guardrail import CustomGuardrail from litellm.types.guardrails import GuardrailEventHooks - _realtime_event_types = [ - GuardrailEventHooks.realtime_input_transcription, - GuardrailEventHooks.pre_call, - GuardrailEventHooks.post_call, - ] + if event_hooks is None: + event_hooks = [GuardrailEventHooks.realtime_input_transcription] + _realtime_event_types = event_hooks _check_data = {**self.request_data, "transcript": transcript} _already_run: set = set() @@ -705,48 +906,58 @@ class RealTimeStreaming: self.store_message(event_str) await self._send_event_to_client(event, event_str) - async def _handle_raw_backend_message(self, raw_response) -> bool: + @staticmethod + def _parse_backend_event(raw_response: str) -> Optional[dict]: + """Parse a backend frame once. Returns None for non-JSON or non-object frames.""" + try: + event = json.loads(raw_response) + except (json.JSONDecodeError, TypeError): + return None + return event if isinstance(event, dict) else None + + async def _handle_raw_backend_message( + self, event_obj: dict, raw_response: str + ) -> bool: """Process a backend message without provider_config (raw path). Returns True if the caller should skip the default store+forward (i.e. continue the loop). """ - try: - event_obj = json.loads(raw_response) + event_type = event_obj.get("type") - # For audio/VAD guardrail path: once the session is ready, tell the backend - # not to auto-respond after VAD detects end-of-speech. We send the - # session.created to the client FIRST so the client is always in sync, then - # inject the session.update so a potential error from the backend doesn't - # arrive before the client sees session.created. - if ( - event_obj.get("type") == "session.created" - and self._has_audio_transcription_guardrails() - ): - self.store_message(raw_response) - await self.websocket.send_text(raw_response) - await self._send_to_backend(self._make_disable_auto_response_message()) + self._detect_transcription_session_from_backend(event_obj) + + # Send session.created to the client FIRST so it stays in sync, then inject + # the disable-auto-response session.update; otherwise a backend error could + # reach the client before it sees session.created. + if ( + event_type == "session.created" + and self._has_audio_transcription_guardrails() + ): + self.store_message(event_obj) + await self.websocket.send_text(raw_response) + await self._send_to_backend(self._make_disable_auto_response_message()) + return True + + if event_type == "conversation.item.input_audio_transcription.completed": + transcript = event_obj.get("transcript", "") + self._collect_user_input_from_backend_event(event_obj) + self.store_message(event_obj) + await self.websocket.send_text(raw_response) + + # Transcription-only sessions (e.g. gpt-realtime-whisper) have no + # assistant turn: capture audio-duration usage for cost and never + # trigger response.create. + if self._is_transcription_session: + self._capture_transcription_usage(event_obj) return True - if ( - event_obj.get("type") - == "conversation.item.input_audio_transcription.completed" - ): - transcript = event_obj.get("transcript", "") - self._collect_user_input_from_backend_event(event_obj) - ## LOGGING — must happen before continue below - self.store_message(raw_response) - # Forward transcript to client so user sees what they said - await self.websocket.send_text(raw_response) - blocked = await self.run_realtime_guardrails( - transcript, - item_id=event_obj.get("item_id"), - ) - if not blocked: - # Clean — trigger LLM response - await self._send_to_backend(json.dumps({"type": "response.create"})) - return True - except (json.JSONDecodeError, AttributeError): - pass + blocked = await self.run_realtime_guardrails( + transcript, + item_id=event_obj.get("item_id"), + ) + if not blocked: + await self._send_to_backend(json.dumps({"type": "response.create"})) + return True return False async def backend_to_client_send_messages(self): @@ -779,25 +990,25 @@ class RealTimeStreaming: ) continue else: - handled = await self._handle_raw_backend_message(raw_response) - if handled: - continue - ## LOGGING - self.store_message(raw_response) - - # If the client opted into beta protocol, translate GA event - # names/shapes back to the beta equivalents before forwarding. - if self._client_wants_beta: - try: - event_dict = json.loads(raw_response) - translated = self._translate_event_to_beta(event_dict) - if translated is None: - continue # drop GA-only events (e.g. conversation.item.done) - await self.websocket.send_text(json.dumps(translated)) - except Exception: - await self.websocket.send_text(raw_response) - else: + event = self._parse_backend_event(raw_response) + if event is None: await self.websocket.send_text(raw_response) + continue + + if await self._handle_raw_backend_message(event, raw_response): + continue + self.store_message(event) + + if not self._client_wants_beta: + await self.websocket.send_text(raw_response) + continue + + translated = self._translate_event_to_beta(event) + if translated is None: + continue + await self.websocket.send_text( + raw_response if translated is event else json.dumps(translated) + ) except websockets.exceptions.ConnectionClosed as e: # type: ignore verbose_logger.exception( @@ -927,41 +1138,43 @@ class RealTimeStreaming: def _translate_event_to_beta(event: dict) -> Optional[dict]: """Translate a single GA event dict to its beta equivalent. - Returns None if the event should be dropped entirely (e.g. the GA-only - conversation.item.done has no beta counterpart). - Returns the (possibly mutated copy of the) event otherwise. + Returns None when the event must be dropped (the GA-only + conversation.item.done has no beta counterpart). Returns the original + event object unchanged when no translation applies, so the caller can + forward the raw frame without re-serializing; otherwise returns a + translated copy. """ event_type = event.get("type", "") - # conversation.item.done has no beta equivalent — the client already - # received conversation.item.created (translated from .added). if event_type == "conversation.item.done": return None - # Shallow-copy so we don't mutate the stored message + renamed_type = RealTimeStreaming._GA_TO_BETA_EVENT_TYPES.get(event_type) + has_item = isinstance(event.get("item"), dict) + response = event.get("response") + has_response_output = isinstance(response, dict) and isinstance( + response.get("output"), list + ) + if renamed_type is None and not has_item and not has_response_output: + return event + translated = dict(event) - - # Rename the type field - if event_type in RealTimeStreaming._GA_TO_BETA_EVENT_TYPES: - translated["type"] = RealTimeStreaming._GA_TO_BETA_EVENT_TYPES[event_type] - - # Fix content block types inside items (response.done output list, - # conversation.item.created item content, etc.) - if "item" in translated and isinstance(translated["item"], dict): + if renamed_type is not None: + translated["type"] = renamed_type + if has_item: translated["item"] = RealTimeStreaming._translate_item_content_types( dict(translated["item"]) ) - if "response" in translated and isinstance(translated["response"], dict): + if has_response_output: resp = dict(translated["response"]) - if "output" in resp and isinstance(resp["output"], list): - resp["output"] = [ - ( - RealTimeStreaming._translate_item_content_types(dict(o)) - if isinstance(o, dict) - else o - ) - for o in resp["output"] - ] + resp["output"] = [ + ( + RealTimeStreaming._translate_item_content_types(dict(o)) + if isinstance(o, dict) + else o + ) + for o in resp["output"] + ] translated["response"] = resp return translated @@ -985,7 +1198,7 @@ class RealTimeStreaming: item["content"] = new_content return item - async def client_ack_messages(self): # noqa: PLR0915 + async def client_ack_messages(self): try: while True: message = await self.websocket.receive_text() @@ -994,6 +1207,8 @@ class RealTimeStreaming: guardrail_turn_detection_injected = False msg_type: Optional[str] = None try: + from litellm.types.guardrails import GuardrailEventHooks + msg_obj = json.loads(message) msg_type = msg_obj.get("type") @@ -1046,6 +1261,7 @@ class RealTimeStreaming: blocked = await self.run_realtime_guardrails( output_text, pre_block_backend_message=sanitized_msg, + event_hooks=[GuardrailEventHooks.pre_call], ) if blocked: # ``_pending_guardrail_message`` is @@ -1071,7 +1287,8 @@ class RealTimeStreaming: combined_text = " ".join(texts) if combined_text: blocked = await self.run_realtime_guardrails( - combined_text + combined_text, + event_hooks=[GuardrailEventHooks.pre_call], ) if blocked: # Store the guardrail reason so the next response.create diff --git a/litellm/litellm_core_utils/request_timeout_resolver.py b/litellm/litellm_core_utils/request_timeout_resolver.py new file mode 100644 index 00000000000..146c39ce9f3 --- /dev/null +++ b/litellm/litellm_core_utils/request_timeout_resolver.py @@ -0,0 +1,29 @@ +"""Single source of truth for whether ``litellm.request_timeout`` was configured. + +``litellm.request_timeout`` always holds a value (the package default, +:data:`~litellm.constants.DEFAULT_REQUEST_TIMEOUT_SECONDS`), so a bare read can't +tell "user asked for this" from "nobody set it". This resolver answers that: + +* ``request_timeout_explicitly_set`` is the authoritative signal, set when the + value comes from the ``REQUEST_TIMEOUT`` env var or ``litellm_settings``. +* A runtime value that differs from the package default (e.g. ``litellm.request_timeout + = 300`` in SDK code) is also treated as explicit, for backwards compatibility. +""" + +from __future__ import annotations + +from typing import Optional + +from litellm.constants import DEFAULT_REQUEST_TIMEOUT_SECONDS + + +def get_configured_request_timeout() -> Optional[float]: + """Return the explicitly-configured ``litellm.request_timeout``, else ``None``.""" + import litellm + + timeout = float(litellm.request_timeout) + if litellm.request_timeout_explicitly_set: + return timeout + if timeout != float(DEFAULT_REQUEST_TIMEOUT_SECONDS): + return timeout + return None diff --git a/litellm/litellm_core_utils/sensitive_data_masker.py b/litellm/litellm_core_utils/sensitive_data_masker.py index 4928dd08386..b14e12de7cd 100644 --- a/litellm/litellm_core_utils/sensitive_data_masker.py +++ b/litellm/litellm_core_utils/sensitive_data_masker.py @@ -12,6 +12,7 @@ class SensitiveDataMasker: visible_prefix: int = 4, visible_suffix: int = 4, mask_char: str = "*", + mask_short_values: bool = True, ): self.sensitive_patterns = sensitive_patterns or { "password", @@ -38,12 +39,17 @@ class SensitiveDataMasker: self.visible_prefix = visible_prefix self.visible_suffix = visible_suffix self.mask_char = mask_char + self.mask_short_values = mask_short_values def _mask_value(self, value: str) -> str: - if not value or len(str(value)) < (self.visible_prefix + self.visible_suffix): - return value - value_str = str(value) + if not value_str: + return value + if len(value_str) <= (self.visible_prefix + self.visible_suffix): + return ( + self.mask_char * len(value_str) if self.mask_short_values else value_str + ) + masked_length = len(value_str) - (self.visible_prefix + self.visible_suffix) # Handle the case where visible_suffix is 0 to avoid showing the entire string diff --git a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py index 6257cce9aec..04f6b1241c3 100644 --- a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py +++ b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py @@ -209,7 +209,7 @@ class ChunkProcessor: ) return response - def get_combined_tool_content( # noqa: PLR0915 + def get_combined_tool_content( self, tool_call_chunks: List[Dict[str, Any]] ) -> List[ChatCompletionMessageToolCall]: tool_calls_list: List[ChatCompletionMessageToolCall] = [] @@ -604,6 +604,8 @@ class ChunkProcessor: usage_chunk = chunk._hidden_params.get("usage", None) if usage_chunk is not None: + if isinstance(usage_chunk, dict): + usage_chunk = Usage(**usage_chunk) usage_chunk_dict = self._usage_chunk_calculation_helper(usage_chunk) if ( usage_chunk_dict["prompt_tokens"] is not None @@ -637,7 +639,18 @@ class ChunkProcessor: hasattr(usage_chunk, "server_tool_use") and usage_chunk.server_tool_use is not None ): - server_tool_use = usage_chunk.server_tool_use + # Coerce dict to ServerToolUse so downstream cost-calc code + # (which accesses .web_search_requests as an attribute) + # doesn't raise AttributeError. Some providers / streaming + # paths leave server_tool_use as a plain dict on the chunk. + if isinstance(usage_chunk.server_tool_use, dict): + server_tool_use = ServerToolUse(**usage_chunk.server_tool_use) + elif isinstance(usage_chunk.server_tool_use, ServerToolUse): + server_tool_use = usage_chunk.server_tool_use + else: + server_tool_use = ServerToolUse.model_validate( + usage_chunk.server_tool_use + ) if ( usage_chunk_dict["prompt_tokens_details"] is not None and getattr( diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index f3274151e5a..e278483d689 100644 --- a/litellm/litellm_core_utils/streaming_handler.py +++ b/litellm/litellm_core_utils/streaming_handler.py @@ -6,6 +6,7 @@ import logging import threading import time import traceback +from dataclasses import dataclass from typing import ( Any, AsyncIterator, @@ -92,11 +93,24 @@ def is_async_iterable(obj: Any) -> bool: def print_verbose(print_statement): try: if litellm.set_verbose: - print(print_statement) # noqa + print(print_statement) # noqa: T201 except Exception: pass +@dataclass(frozen=True, slots=True) +class _ProviderChunkParsed: + response_obj: dict[str, Any] + + +@dataclass(frozen=True, slots=True) +class _ProviderChunkEarlyReturn: + value: Any + + +_ProviderChunkResult = Union[_ProviderChunkParsed, _ProviderChunkEarlyReturn] + + class CustomStreamWrapper: def __init__( self, @@ -295,6 +309,12 @@ class CustomStreamWrapper: if len(self.chunks) < 2: return + # Providers like Vertex Gemini (Flash / Flash Lite with web search) emit + # metadata-only / usage-only chunks with no choices. These get stored in + # self.chunks but carry no comparable content, so skip repetition detection. + if not self.chunks[-1].choices or not self.chunks[-2].choices: + return + last_content = self.chunks[-1].choices[0].delta.content if ( @@ -961,7 +981,7 @@ class CustomStreamWrapper: delta, model_response.choices[0].delta, attribute ) - def return_processed_chunk_logic( # noqa + def return_processed_chunk_logic( # noqa: C901 self, completion_obj: Dict[str, Any], model_response: ModelResponseStream, @@ -1139,381 +1159,392 @@ class CustomStreamWrapper: del model_response.choices[0].delta.reasoning_content return - def chunk_creator(self, chunk: Any): # type: ignore # noqa: PLR0915 + def _dispatch_provider_chunk( + self, + chunk: Any, + model_response: ModelResponseStream, + completion_obj: dict[str, Any], + ) -> _ProviderChunkResult: + response_obj: dict[str, Any] = {} + if ( + isinstance(chunk, ModelResponseStream) + and self.custom_llm_provider is not None + and self.custom_llm_provider in litellm._custom_providers + ): + _has_content = bool( + chunk.choices + and chunk.choices[0].delta is not None + and ( + chunk.choices[0].delta.content or chunk.choices[0].delta.tool_calls + ) + ) + if self.received_finish_reason is not None: + if not _has_content: + raise StopIteration + if chunk.choices and chunk.choices[0].finish_reason: + self.received_finish_reason = chunk.choices[0].finish_reason + if not _has_content: + return _ProviderChunkEarlyReturn(None) + # Strip finish_reason from the content chunk so it appears + # only on the trailing empty-delta chunk (OpenAI spec). + # finish_reason_handler() will emit the proper terminal chunk. + chunk.choices[0].finish_reason = None # type: ignore[assignment] + return _ProviderChunkEarlyReturn(chunk) + + if ( + isinstance(chunk, dict) + and generic_chunk_has_all_required_fields( + chunk=chunk + ) # check if chunk is a generic streaming chunk + ) or ( + self.custom_llm_provider + and self.custom_llm_provider in litellm._custom_providers + ): + if self.received_finish_reason is not None: + _chunk_has_content = isinstance(chunk, dict) and ( + bool(chunk.get("text", "")) + or chunk.get("tool_use") is not None + # Usage-only final chunks are valid and needed to surface + # finish_reason/usage to downstream translators. + or chunk.get("usage") is not None + ) + if not _chunk_has_content and ( + not isinstance(chunk, dict) + or "provider_specific_fields" not in chunk + ): + raise StopIteration + anthropic_response_obj: GChunk = cast(GChunk, chunk) + completion_obj["content"] = anthropic_response_obj["text"] + if anthropic_response_obj["is_finished"]: + self.received_finish_reason = anthropic_response_obj["finish_reason"] + + if anthropic_response_obj["finish_reason"]: + self.intermittent_finish_reason = anthropic_response_obj[ + "finish_reason" + ] + + if anthropic_response_obj["usage"] is not None: + setattr( + model_response, + "usage", + litellm.Usage(**anthropic_response_obj["usage"]), + ) + + if ( + "tool_use" in anthropic_response_obj + and anthropic_response_obj["tool_use"] is not None + ): + completion_obj["tool_calls"] = [anthropic_response_obj["tool_use"]] + + if ( + "provider_specific_fields" in anthropic_response_obj + and anthropic_response_obj["provider_specific_fields"] is not None + ): + 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) + elif self.model == "replicate" or self.custom_llm_provider == "replicate": + response_obj = self.handle_replicate_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + elif self.custom_llm_provider and self.custom_llm_provider == "predibase": + response_obj = self.handle_predibase_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + elif ( + self.custom_llm_provider and self.custom_llm_provider == "baseten" + ): # baseten doesn't provide streaming + completion_obj["content"] = self.handle_baseten_chunk(chunk) + elif ( + self.custom_llm_provider and self.custom_llm_provider == "ai21" + ): # ai21 doesn't provide streaming + response_obj = self.handle_ai21_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + elif self.custom_llm_provider and self.custom_llm_provider == "maritalk": + response_obj = self.handle_maritalk_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + elif self.custom_llm_provider and self.custom_llm_provider == "vllm": + completion_obj["content"] = chunk[0].outputs[0].text + elif ( + self.custom_llm_provider and self.custom_llm_provider == "aleph_alpha" + ): # aleph alpha doesn't provide streaming + response_obj = self.handle_aleph_alpha_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + elif self.custom_llm_provider == "nlp_cloud": + try: + response_obj = self.handle_nlp_cloud_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + except Exception as e: + if self.received_finish_reason: + raise e + else: + if self.sent_first_chunk is False: + raise Exception("An unknown error occurred with the stream") + self.received_finish_reason = "stop" + elif self.custom_llm_provider == "vertex_ai" and not isinstance( + chunk, ModelResponseStream + ): + chunk = cast(Any, chunk) + import proto # type: ignore + + if hasattr(chunk, "candidates") is True: + try: + try: + completion_obj["content"] = chunk.text # type: ignore + except Exception as e: + original_exception = e + if "Part has no text." in str(e): + ## check for function calling + function_call = ( + chunk.candidates[0].content.parts[0].function_call # type: ignore + ) + + args_dict = {} + + # Check if it's a RepeatedComposite instance + for key, val in function_call.args.items(): + if isinstance( + val, + proto.marshal.collections.repeated.RepeatedComposite, # type: ignore + ): + # If so, convert to list + args_dict[key] = [v for v in val] + else: + args_dict[key] = val + + try: + args_str = json.dumps(args_dict) + except Exception as e: + raise e + _delta_obj = litellm.utils.Delta( + content=None, + tool_calls=[ + { + "id": f"call_{str(uuid.uuid4())}", + "function": { + "arguments": args_str, + "name": function_call.name, + }, + "type": "function", + } + ], + ) + _streaming_response = StreamingChoices(delta=_delta_obj) + _model_response = ModelResponseStream() + _model_response.choices = [_streaming_response] + response_obj = {"original_chunk": _model_response} + else: + raise original_exception + if ( + hasattr(chunk.candidates[0], "finish_reason") # type: ignore + and chunk.candidates[0].finish_reason.name # type: ignore + != "FINISH_REASON_UNSPECIFIED" + ): # every non-final chunk in vertex ai has this + self.received_finish_reason = map_finish_reason( # type: ignore + chunk.candidates[0].finish_reason.name + ) + except Exception: + if chunk.candidates[0].finish_reason.name == "SAFETY": # type: ignore + raise Exception( + f"The response was blocked by VertexAI. {str(chunk)}" + ) + else: + completion_obj["content"] = str(chunk) + elif self.custom_llm_provider == "petals": + if self.completion_stream is None or len(self.completion_stream) == 0: + if self.received_finish_reason is not None: + raise StopIteration + else: + self.received_finish_reason = "stop" + chunk_size = 30 + stream = cast(Any, self.completion_stream) + new_chunk = stream[:chunk_size] + completion_obj["content"] = new_chunk + self.completion_stream = stream[chunk_size:] + elif self.custom_llm_provider == "palm": + # fake streaming + response_obj = {} + if self.completion_stream is None or len(self.completion_stream) == 0: + if self.received_finish_reason is not None: + raise StopIteration + else: + self.received_finish_reason = "stop" + chunk_size = 30 + stream = cast(Any, self.completion_stream) + new_chunk = stream[:chunk_size] + completion_obj["content"] = new_chunk + self.completion_stream = stream[chunk_size:] + elif self.custom_llm_provider == "triton": + response_obj = self.handle_triton_stream(chunk) + completion_obj["content"] = response_obj["text"] + print_verbose(f"completion obj content: {completion_obj['content']}") + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + elif self.custom_llm_provider == "text-completion-openai": + response_obj = self.handle_openai_text_completion_chunk(chunk) + completion_obj["content"] = response_obj["text"] + print_verbose(f"completion obj content: {completion_obj['content']}") + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + if response_obj["usage"] is not None: + setattr( + model_response, + "usage", + litellm.Usage( + prompt_tokens=response_obj["usage"].prompt_tokens, + completion_tokens=response_obj["usage"].completion_tokens, + total_tokens=response_obj["usage"].total_tokens, + ), + ) + elif self.custom_llm_provider == "text-completion-codestral": + if not isinstance(chunk, str): + raise ValueError(f"chunk is not a string: {chunk}") + response_obj = cast( + dict[str, Any], + litellm.CodestralTextCompletionConfig()._chunk_parser(chunk), + ) + completion_obj["content"] = response_obj["text"] + print_verbose(f"completion obj content: {completion_obj['content']}") + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + if "usage" in response_obj is not None: + setattr( + model_response, + "usage", + litellm.Usage( + prompt_tokens=response_obj["usage"].prompt_tokens, + completion_tokens=response_obj["usage"].completion_tokens, + total_tokens=response_obj["usage"].total_tokens, + ), + ) + elif self.custom_llm_provider == "azure_text": + response_obj = self.handle_azure_text_completion_chunk(chunk) + completion_obj["content"] = response_obj["text"] + print_verbose(f"completion obj content: {completion_obj['content']}") + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + elif self.custom_llm_provider == "cached_response": + chunk = cast(ModelResponseStream, chunk) + response_obj = { + "text": chunk.choices[0].delta.content, + "is_finished": True, + "finish_reason": chunk.choices[0].finish_reason, + "original_chunk": chunk, + "tool_calls": ( + chunk.choices[0].delta.tool_calls + if hasattr(chunk.choices[0].delta, "tool_calls") + else None + ), + } + + completion_obj["content"] = response_obj["text"] + if response_obj["tool_calls"] is not None: + completion_obj["tool_calls"] = response_obj["tool_calls"] + print_verbose(f"completion obj content: {completion_obj['content']}") + if hasattr(chunk, "id"): + model_response.id = chunk.id + self.response_id = chunk.id + if hasattr(chunk, "system_fingerprint"): + self.system_fingerprint = chunk.system_fingerprint + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + else: # openai / azure chat model + if self.custom_llm_provider in [ + LlmProviders.AZURE.value, + LlmProviders.AZURE_AI.value, + ]: + if isinstance(chunk, BaseModel) and hasattr(chunk, "model"): + # for azure, we need to pass the model from the original chunk + self.model = getattr(chunk, "model", self.model) + response_obj = self.handle_openai_chat_completion_chunk(chunk) + if response_obj is None: + return _ProviderChunkEarlyReturn(None) + completion_obj["content"] = response_obj["text"] + self.intermittent_finish_reason = response_obj.get("finish_reason", None) + if response_obj["is_finished"]: + if response_obj["finish_reason"] == "error": + raise Exception( + "{} raised a streaming error - finish_reason: error, no content string given. Received Chunk={}".format( + self.custom_llm_provider, response_obj + ) + ) + self.received_finish_reason = response_obj["finish_reason"] + if response_obj.get("original_chunk", None) is not None: + if hasattr(response_obj["original_chunk"], "id"): + model_response = self.set_model_id( + response_obj["original_chunk"].id, model_response + ) + if hasattr(response_obj["original_chunk"], "system_fingerprint"): + model_response.system_fingerprint = response_obj[ + "original_chunk" + ].system_fingerprint + self.system_fingerprint = response_obj[ + "original_chunk" + ].system_fingerprint + if response_obj["logprobs"] is not None: + model_response.choices[0].logprobs = response_obj["logprobs"] + + if response_obj["usage"] is not None: + if isinstance(response_obj["usage"], dict): + setattr( + model_response, + "usage", + litellm.Usage( + prompt_tokens=response_obj["usage"].get( + "prompt_tokens", None + ) + or None, + completion_tokens=response_obj["usage"].get( + "completion_tokens", None + ) + or None, + total_tokens=response_obj["usage"].get("total_tokens", None) + or None, + ), + ) + elif isinstance(response_obj["usage"], Usage): + setattr( + model_response, + "usage", + response_obj["usage"], + ) + elif isinstance(response_obj["usage"], BaseModel): + setattr( + model_response, + "usage", + litellm.Usage(**response_obj["usage"].model_dump()), + ) + return _ProviderChunkParsed(response_obj) + + def chunk_creator(self, chunk: Any): # type: ignore if hasattr(chunk, "id"): self.response_id = chunk.id model_response = self.model_response_creator() - response_obj: Dict[str, Any] = {} + response_obj: dict[str, Any] = {} try: # return this for all models - completion_obj: Dict[str, Any] = {"content": ""} - from litellm.types.utils import GenericStreamingChunk as GChunk - - if ( - isinstance(chunk, ModelResponseStream) - and self.custom_llm_provider is not None - and self.custom_llm_provider in litellm._custom_providers - ): - _has_content = bool( - chunk.choices - and chunk.choices[0].delta is not None - and ( - chunk.choices[0].delta.content - or chunk.choices[0].delta.tool_calls - ) - ) - if self.received_finish_reason is not None: - if not _has_content: - raise StopIteration - if chunk.choices and chunk.choices[0].finish_reason: - self.received_finish_reason = chunk.choices[0].finish_reason - if not _has_content: - return None - # Strip finish_reason from the content chunk so it appears - # only on the trailing empty-delta chunk (OpenAI spec). - # finish_reason_handler() will emit the proper terminal chunk. - chunk.choices[0].finish_reason = None # type: ignore[assignment] - return chunk - - if ( - isinstance(chunk, dict) - and generic_chunk_has_all_required_fields( - chunk=chunk - ) # check if chunk is a generic streaming chunk - ) or ( - self.custom_llm_provider - and self.custom_llm_provider in litellm._custom_providers - ): - if self.received_finish_reason is not None: - _chunk_has_content = isinstance(chunk, dict) and ( - bool(chunk.get("text", "")) - or chunk.get("tool_use") is not None - # Usage-only final chunks are valid and needed to surface - # finish_reason/usage to downstream translators. - or chunk.get("usage") is not None - ) - if not _chunk_has_content and ( - not isinstance(chunk, dict) - or "provider_specific_fields" not in chunk - ): - raise StopIteration - anthropic_response_obj: GChunk = cast(GChunk, chunk) - completion_obj["content"] = anthropic_response_obj["text"] - if anthropic_response_obj["is_finished"]: - self.received_finish_reason = anthropic_response_obj[ - "finish_reason" - ] - - if anthropic_response_obj["finish_reason"]: - self.intermittent_finish_reason = anthropic_response_obj[ - "finish_reason" - ] - - if anthropic_response_obj["usage"] is not None: - setattr( - model_response, - "usage", - litellm.Usage(**anthropic_response_obj["usage"]), - ) - - if ( - "tool_use" in anthropic_response_obj - and anthropic_response_obj["tool_use"] is not None - ): - completion_obj["tool_calls"] = [anthropic_response_obj["tool_use"]] - - if ( - "provider_specific_fields" in anthropic_response_obj - and anthropic_response_obj["provider_specific_fields"] is not None - ): - 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) - elif self.model == "replicate" or self.custom_llm_provider == "replicate": - response_obj = self.handle_replicate_chunk(chunk) - completion_obj["content"] = response_obj["text"] - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - elif self.custom_llm_provider and self.custom_llm_provider == "predibase": - response_obj = self.handle_predibase_chunk(chunk) - completion_obj["content"] = response_obj["text"] - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - elif ( - self.custom_llm_provider and self.custom_llm_provider == "baseten" - ): # baseten doesn't provide streaming - completion_obj["content"] = self.handle_baseten_chunk(chunk) - elif ( - self.custom_llm_provider and self.custom_llm_provider == "ai21" - ): # ai21 doesn't provide streaming - response_obj = self.handle_ai21_chunk(chunk) - completion_obj["content"] = response_obj["text"] - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - elif self.custom_llm_provider and self.custom_llm_provider == "maritalk": - response_obj = self.handle_maritalk_chunk(chunk) - completion_obj["content"] = response_obj["text"] - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - elif self.custom_llm_provider and self.custom_llm_provider == "vllm": - completion_obj["content"] = chunk[0].outputs[0].text - elif ( - self.custom_llm_provider and self.custom_llm_provider == "aleph_alpha" - ): # aleph alpha doesn't provide streaming - response_obj = self.handle_aleph_alpha_chunk(chunk) - completion_obj["content"] = response_obj["text"] - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - elif self.custom_llm_provider == "nlp_cloud": - try: - response_obj = self.handle_nlp_cloud_chunk(chunk) - completion_obj["content"] = response_obj["text"] - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - except Exception as e: - if self.received_finish_reason: - raise e - else: - if self.sent_first_chunk is False: - raise Exception("An unknown error occurred with the stream") - self.received_finish_reason = "stop" - elif self.custom_llm_provider == "vertex_ai" and not isinstance( - chunk, ModelResponseStream - ): - import proto # type: ignore - - if hasattr(chunk, "candidates") is True: - try: - try: - completion_obj["content"] = chunk.text # type: ignore - except Exception as e: - original_exception = e - if "Part has no text." in str(e): - ## check for function calling - function_call = ( - chunk.candidates[0].content.parts[0].function_call # type: ignore - ) - - args_dict = {} - - # Check if it's a RepeatedComposite instance - for key, val in function_call.args.items(): - if isinstance( - val, - proto.marshal.collections.repeated.RepeatedComposite, # type: ignore - ): - # If so, convert to list - args_dict[key] = [v for v in val] - else: - args_dict[key] = val - - try: - args_str = json.dumps(args_dict) - except Exception as e: - raise e - _delta_obj = litellm.utils.Delta( - content=None, - tool_calls=[ - { - "id": f"call_{str(uuid.uuid4())}", - "function": { - "arguments": args_str, - "name": function_call.name, - }, - "type": "function", - } - ], - ) - _streaming_response = StreamingChoices(delta=_delta_obj) - _model_response = ModelResponseStream() - _model_response.choices = [_streaming_response] - response_obj = {"original_chunk": _model_response} - else: - raise original_exception - if ( - hasattr(chunk.candidates[0], "finish_reason") # type: ignore - and chunk.candidates[0].finish_reason.name # type: ignore - != "FINISH_REASON_UNSPECIFIED" - ): # every non-final chunk in vertex ai has this - self.received_finish_reason = map_finish_reason( # type: ignore - chunk.candidates[0].finish_reason.name - ) - except Exception: - if chunk.candidates[0].finish_reason.name == "SAFETY": # type: ignore - raise Exception( - f"The response was blocked by VertexAI. {str(chunk)}" - ) - else: - completion_obj["content"] = str(chunk) - elif self.custom_llm_provider == "petals": - if self.completion_stream is None or len(self.completion_stream) == 0: - if self.received_finish_reason is not None: - raise StopIteration - else: - self.received_finish_reason = "stop" - chunk_size = 30 - new_chunk = self.completion_stream[:chunk_size] # type: ignore[index] - completion_obj["content"] = new_chunk - self.completion_stream = self.completion_stream[chunk_size:] # type: ignore[index] - elif self.custom_llm_provider == "palm": - # fake streaming - response_obj = {} - if self.completion_stream is None or len(self.completion_stream) == 0: - if self.received_finish_reason is not None: - raise StopIteration - else: - self.received_finish_reason = "stop" - chunk_size = 30 - new_chunk = self.completion_stream[:chunk_size] # type: ignore[index] - completion_obj["content"] = new_chunk - self.completion_stream = self.completion_stream[chunk_size:] # type: ignore[index] - elif self.custom_llm_provider == "triton": - response_obj = self.handle_triton_stream(chunk) - completion_obj["content"] = response_obj["text"] - print_verbose(f"completion obj content: {completion_obj['content']}") - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - elif self.custom_llm_provider == "text-completion-openai": - response_obj = self.handle_openai_text_completion_chunk(chunk) - completion_obj["content"] = response_obj["text"] - print_verbose(f"completion obj content: {completion_obj['content']}") - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - if response_obj["usage"] is not None: - setattr( - model_response, - "usage", - litellm.Usage( - prompt_tokens=response_obj["usage"].prompt_tokens, - completion_tokens=response_obj["usage"].completion_tokens, - total_tokens=response_obj["usage"].total_tokens, - ), - ) - elif self.custom_llm_provider == "text-completion-codestral": - if not isinstance(chunk, str): - raise ValueError(f"chunk is not a string: {chunk}") - response_obj = cast( - Dict[str, Any], - litellm.CodestralTextCompletionConfig()._chunk_parser(chunk), - ) - completion_obj["content"] = response_obj["text"] - print_verbose(f"completion obj content: {completion_obj['content']}") - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - if "usage" in response_obj is not None: - setattr( - model_response, - "usage", - litellm.Usage( - prompt_tokens=response_obj["usage"].prompt_tokens, - completion_tokens=response_obj["usage"].completion_tokens, - total_tokens=response_obj["usage"].total_tokens, - ), - ) - elif self.custom_llm_provider == "azure_text": - response_obj = self.handle_azure_text_completion_chunk(chunk) - completion_obj["content"] = response_obj["text"] - print_verbose(f"completion obj content: {completion_obj['content']}") - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - elif self.custom_llm_provider == "cached_response": - chunk = cast(ModelResponseStream, chunk) - response_obj = { - "text": chunk.choices[0].delta.content, - "is_finished": True, - "finish_reason": chunk.choices[0].finish_reason, - "original_chunk": chunk, - "tool_calls": ( - chunk.choices[0].delta.tool_calls - if hasattr(chunk.choices[0].delta, "tool_calls") - else None - ), - } - - completion_obj["content"] = response_obj["text"] - if response_obj["tool_calls"] is not None: - completion_obj["tool_calls"] = response_obj["tool_calls"] - print_verbose(f"completion obj content: {completion_obj['content']}") - if hasattr(chunk, "id"): - model_response.id = chunk.id - self.response_id = chunk.id - if hasattr(chunk, "system_fingerprint"): - self.system_fingerprint = chunk.system_fingerprint - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - else: # openai / azure chat model - if self.custom_llm_provider in [ - LlmProviders.AZURE.value, - LlmProviders.AZURE_AI.value, - ]: - if isinstance(chunk, BaseModel) and hasattr(chunk, "model"): - # for azure, we need to pass the model from the original chunk - self.model = getattr(chunk, "model", self.model) - response_obj = self.handle_openai_chat_completion_chunk(chunk) - if response_obj is None: - return - completion_obj["content"] = response_obj["text"] - self.intermittent_finish_reason = response_obj.get( - "finish_reason", None - ) - if response_obj["is_finished"]: - if response_obj["finish_reason"] == "error": - raise Exception( - "{} raised a streaming error - finish_reason: error, no content string given. Received Chunk={}".format( - self.custom_llm_provider, response_obj - ) - ) - self.received_finish_reason = response_obj["finish_reason"] - if response_obj.get("original_chunk", None) is not None: - if hasattr(response_obj["original_chunk"], "id"): - model_response = self.set_model_id( - response_obj["original_chunk"].id, model_response - ) - if hasattr(response_obj["original_chunk"], "system_fingerprint"): - model_response.system_fingerprint = response_obj[ - "original_chunk" - ].system_fingerprint - self.system_fingerprint = response_obj[ - "original_chunk" - ].system_fingerprint - if response_obj["logprobs"] is not None: - model_response.choices[0].logprobs = response_obj["logprobs"] - - if response_obj["usage"] is not None: - if isinstance(response_obj["usage"], dict): - setattr( - model_response, - "usage", - litellm.Usage( - prompt_tokens=response_obj["usage"].get( - "prompt_tokens", None - ) - or None, - completion_tokens=response_obj["usage"].get( - "completion_tokens", None - ) - or None, - total_tokens=response_obj["usage"].get( - "total_tokens", None - ) - or None, - ), - ) - elif isinstance(response_obj["usage"], Usage): - setattr( - model_response, - "usage", - response_obj["usage"], - ) - elif isinstance(response_obj["usage"], BaseModel): - setattr( - model_response, - "usage", - litellm.Usage(**response_obj["usage"].model_dump()), - ) + completion_obj: dict[str, Any] = {"content": ""} + dispatch_result = self._dispatch_provider_chunk( + chunk=chunk, + model_response=model_response, + completion_obj=completion_obj, + ) + if isinstance(dispatch_result, _ProviderChunkEarlyReturn): + return dispatch_result.value + response_obj = dispatch_result.response_obj model_response.model = self.model ## FUNCTION CALL PARSING @@ -1881,7 +1912,7 @@ class CustomStreamWrapper: model_response.choices[0].finish_reason = "tool_calls" return model_response - def __next__(self) -> "ModelResponseStream": # noqa: PLR0915 + def __next__(self) -> "ModelResponseStream": cache_hit = False if ( self.custom_llm_provider is not None @@ -1974,11 +2005,29 @@ class CustomStreamWrapper: except StopIteration: if self.sent_last_chunk is True: - complete_streaming_response = litellm.stream_chunk_builder( - chunks=self.chunks, - messages=self.messages, - logging_obj=self.logging_obj, - ) + try: + complete_streaming_response = litellm.stream_chunk_builder( + chunks=self.chunks, + messages=self.messages, + logging_obj=self.logging_obj, + ) + except Exception as e: + # stream_chunk_builder can re-raise (as APIError) on large agentic + # streams. The raise originates inside this except-StopIteration block, + # so the sibling `except Exception` below does not catch it; it would + # escape __next__ and drop the request from SpendLogs. Recover + # best-effort usage from the raw chunks so cost is still tracked + verbose_logger.warning( + "stream_chunk_builder raised at end-of-stream (%s); logging " + "best-effort usage from chunks.", + str(e), + ) + try: + complete_streaming_response = self.model_response_creator( + chunk={"usage": calculate_total_usage(chunks=self.chunks)} + ) + except Exception: + complete_streaming_response = None response = self.model_response_creator() if complete_streaming_response is not None: @@ -2071,7 +2120,7 @@ class CustomStreamWrapper: return self.completion_stream - async def __anext__(self) -> "ModelResponseStream": # noqa: PLR0915 + async def __anext__(self) -> "ModelResponseStream": cache_hit = False if ( self.custom_llm_provider is not None @@ -2203,11 +2252,27 @@ class CustomStreamWrapper: except (StopAsyncIteration, StopIteration): if self.sent_last_chunk is True: # log the final chunk with accurate streaming values - complete_streaming_response = litellm.stream_chunk_builder( - chunks=self.chunks, - messages=self.messages, - logging_obj=self.logging_obj, - ) + try: + complete_streaming_response = litellm.stream_chunk_builder( + chunks=self.chunks, + messages=self.messages, + logging_obj=self.logging_obj, + ) + except Exception as e: + # see sync __next__: a raise from stream_chunk_builder inside this + # except handler escapes __anext__ and drops the request from SpendLogs. + # Recover best-effort usage from the raw chunks so cost is still tracked + verbose_logger.warning( + "stream_chunk_builder raised at end-of-stream (%s); logging " + "best-effort usage from chunks.", + str(e), + ) + try: + complete_streaming_response = self.model_response_creator( + chunk={"usage": calculate_total_usage(chunks=self.chunks)} + ) + except Exception: + complete_streaming_response = None response = self.model_response_creator() if complete_streaming_response is not None: @@ -2284,6 +2349,7 @@ class CustomStreamWrapper: litellm.request_timeout ) if self.logging_obj is not None: + self._record_partial_usage_for_failure() ## LOGGING threading.Thread( target=self.logging_obj.failure_handler, @@ -2297,6 +2363,7 @@ class CustomStreamWrapper: except Exception as e: traceback_exception = traceback.format_exc() if self.logging_obj is not None: + self._record_partial_usage_for_failure() ## LOGGING threading.Thread( target=self.logging_obj.failure_handler, @@ -2308,6 +2375,33 @@ class CustomStreamWrapper: ) self._handle_stream_fallback_error(e) + def _record_partial_usage_for_failure(self) -> None: + """ + A stream that breaks mid-flight still billed the provider for the chunks + already delivered. Recover that partial usage from the chunks seen so + far and stash it, with its cost, on the logging object so the failure + handler records the real partial spend instead of zero. A request that + later recovers via a router fallback overwrites this with the combined + success log on the same request id, so this never double counts. + """ + if self.logging_obj is None or not self.chunks: + return + try: + partial_response = litellm.stream_chunk_builder(chunks=self.chunks) + usage = cast(Optional[Usage], getattr(partial_response, "usage", None)) + if usage is None: + return + self.logging_obj.model_call_details["combined_usage_object"] = usage + self.logging_obj.model_call_details["response_cost"] = ( + self.logging_obj._response_cost_calculator(result=partial_response) + or 0.0 + ) + except Exception as recover_error: + verbose_logger.debug( + "could not recover partial usage for interrupted stream: %s", + recover_error, + ) + def _handle_stream_fallback_error(self, e: Exception) -> "NoReturn": """ Common error handling for both __next__ and __anext__. diff --git a/litellm/litellm_core_utils/token_counter.py b/litellm/litellm_core_utils/token_counter.py index 74b41062174..c766c6edec1 100644 --- a/litellm/litellm_core_utils/token_counter.py +++ b/litellm/litellm_core_utils/token_counter.py @@ -744,6 +744,17 @@ def _count_content_list( thinking_text = str(c.get("thinking", "")) if thinking_text: num_tokens += count_function(thinking_text) + elif c["type"] == "tool_reference": + # Anthropic tool-search reference block: a lightweight pointer to + # a deferred tool, e.g. {"type": "tool_reference", "tool_name": ...}. + # The full tool definition is counted via the `tools` param, so we + # only count the referenced name here. Without this branch, + # token_counter raises on tool-search traffic; on the streaming + # anthropic_messages path that nulls response_cost and causes the + # proxy to drop the SpendLogs row entirely (silent cost undercount). + tool_name = str(c.get("tool_name") or "") + if tool_name: + num_tokens += count_function(tool_name) else: content_type = ( c.get("type", type(c).__name__) @@ -752,7 +763,7 @@ def _count_content_list( ) raise ValueError( f"Invalid content item type: {content_type}. " - f"Expected str or dict with 'type' field (text, image_url, tool_use, tool_result, thinking)." + f"Expected str or dict with 'type' field (text, image_url, tool_use, tool_result, thinking, tool_reference)." ) return num_tokens except Exception as e: diff --git a/litellm/llms/anthropic/chat/handler.py b/litellm/llms/anthropic/chat/handler.py index 2fb29b32a61..5d14f3cc4ae 100644 --- a/litellm/llms/anthropic/chat/handler.py +++ b/litellm/llms/anthropic/chat/handler.py @@ -772,7 +772,7 @@ class ModelResponseIterator: ) return results - def chunk_parser(self, chunk: dict) -> ModelResponseStream: # noqa: PLR0915 + def chunk_parser(self, chunk: dict) -> ModelResponseStream: try: type_chunk = chunk.get("type", "") or "" diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index 3f30d5d6807..822b75b37f4 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -229,6 +229,11 @@ DROP_UNSUPPORTED_OUTPUT_CONFIG_WARNING = ( "Sonnet 4.6+, and Mythos Preview." ) +DROP_UNSUPPORTED_SPEED_WARNING = ( + "Dropping unsupported `speed` for model=%s " + "(drop_params=True). Fast mode is only supported on select Opus models." +) + class AnthropicConfig(AnthropicModelInfo, BaseConfig): """ @@ -374,6 +379,51 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): for level in ("low", "minimal", "medium", "high", "xhigh", "max") ) + @staticmethod + def _model_supports_speed_param( + model: str, custom_llm_provider: Optional[str] = None + ) -> bool: + """Whether the model accepts Anthropic's ``speed`` parameter (fast mode). + + Fast mode is direct Anthropic API-only (not Bedrock, Vertex, or Azure). + Those providers strip their prefix before this shared transform runs, so a + bare ``claude-opus-4-8`` would otherwise resolve to the direct-API entry; + the routed provider is checked explicitly to keep them out. + """ + if custom_llm_provider is not None and custom_llm_provider != "anthropic": + return False + return ( + AnthropicModelInfo._get_exact_model_capability(model, "supports_speed") + is True + ) + + @staticmethod + def _maybe_drop_speed_param( + model: str, + optional_params: dict, + drop_params: bool, + custom_llm_provider: Optional[str] = None, + ) -> None: + if "speed" not in optional_params: + return + if AnthropicConfig._model_supports_speed_param(model, custom_llm_provider): + return + if not (litellm.drop_params or drop_params): + speed_value = optional_params.get("speed") + raise litellm.utils.UnsupportedParamsError( + message=( + f"{model} does not support speed={speed_value!r}. " + "To drop unsupported params, set " + "`litellm.drop_params = True`." + ), + status_code=400, + ) + litellm.verbose_logger.warning( + DROP_UNSUPPORTED_SPEED_WARNING, + model, + ) + optional_params.pop("speed", None) + @staticmethod def _raise_invalid_reasoning_effort( model: str, value: Any, llm_provider: str @@ -605,7 +655,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): ) return _tool_choice - def _map_tool_helper( # noqa: PLR0915 + def _map_tool_helper( self, tool: ChatCompletionToolParam, ) -> Tuple[Optional[AllAnthropicToolsValues], Optional[AnthropicMcpServerTool]]: @@ -1399,7 +1449,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): return None - def map_openai_params( # noqa: PLR0915 + def map_openai_params( self, non_default_params: dict, optional_params: dict, @@ -1455,10 +1505,15 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): _value = self._map_stop_sequences(value) if _value is not None: optional_params["stop_sequences"] = _value - elif param == "temperature": - optional_params["temperature"] = value - elif param == "top_p": - optional_params["top_p"] = value + elif param == "temperature" or param == "top_p": + AnthropicConfig._apply_sampling_param( + optional_params=optional_params, + model=model, + param=param, + value=value, + drop_params=drop_params, + output_key=param, + ) elif param == "response_format" and isinstance(value, dict): if any( substring in model @@ -1564,8 +1619,13 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): anthropic_context_management ) elif param == "speed" and isinstance(value, str): - # Pass through Anthropic-specific speed parameter for fast mode optional_params["speed"] = value + AnthropicConfig._maybe_drop_speed_param( + model=model, + optional_params=optional_params, + drop_params=drop_params, + custom_llm_provider=self.custom_llm_provider, + ) elif param == "cache_control" and isinstance(value, dict): # Pass through top-level cache_control for automatic prompt caching optional_params["cache_control"] = value @@ -1870,6 +1930,14 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): "has no thinking_blocks. The model won't use extended thinking for this turn." ) + AnthropicConfig._maybe_drop_speed_param( + model=model, + optional_params=optional_params, + drop_params=litellm.drop_params + or litellm_params.get("drop_params") is True, + custom_llm_provider=self.custom_llm_provider, + ) + headers = self.update_headers_with_optional_anthropic_beta( headers=headers, optional_params=optional_params ) @@ -1975,6 +2043,20 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): optional_params.pop("is_vertex_request", None) optional_params.pop("client_metadata", None) + # ``top_k`` is a provider-specific kwarg that bypasses + # ``map_openai_params``; gate it here, the single boundary shared by + # the direct Anthropic, Bedrock invoke, Vertex, and Azure paths. + top_k = optional_params.pop("top_k", None) + if top_k is not None: + AnthropicConfig._apply_sampling_param( + optional_params=optional_params, + model=model, + param="top_k", + value=top_k, + drop_params=litellm_params.get("drop_params") is True, + output_key="top_k", + ) + data = { "model": model, "messages": anthropic_messages, @@ -2194,19 +2276,38 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): inference_geo: Optional[str] = None if "inference_geo" in _usage and _usage["inference_geo"] is not None: inference_geo = _usage["inference_geo"] + service_tier = cast( + str | None, + _usage.get("service_tier"), + ) - if ( - "cache_creation_input_tokens" in _usage - and _usage["cache_creation_input_tokens"] is not None - ): - cache_creation_input_tokens = _usage["cache_creation_input_tokens"] - prompt_tokens += cache_creation_input_tokens - if ( - "cache_read_input_tokens" in _usage - and _usage["cache_read_input_tokens"] is not None - ): - cache_read_input_tokens = _usage["cache_read_input_tokens"] - prompt_tokens += cache_read_input_tokens + iterations: Optional[List[Any]] = _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 + ) + cache_creation_input_tokens = sum( + it.get("cache_creation_input_tokens", 0) or 0 for it in iterations + ) + cache_read_input_tokens = sum( + it.get("cache_read_input_tokens", 0) or 0 for it in iterations + ) + prompt_tokens += cache_creation_input_tokens + cache_read_input_tokens + + if not iterations: + if ( + "cache_creation_input_tokens" in _usage + and _usage["cache_creation_input_tokens"] is not None + ): + cache_creation_input_tokens = _usage["cache_creation_input_tokens"] + prompt_tokens += cache_creation_input_tokens + if ( + "cache_read_input_tokens" in _usage + and _usage["cache_read_input_tokens"] is not None + ): + cache_read_input_tokens = _usage["cache_read_input_tokens"] + prompt_tokens += cache_read_input_tokens if "server_tool_use" in _usage and _usage["server_tool_use"] is not None: if ( "web_search_requests" in _usage["server_tool_use"] @@ -2245,7 +2346,9 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): ), ) - raw_input_tokens = usage_object.get("input_tokens", 0) or 0 + raw_input_tokens = ( + prompt_tokens - cache_read_input_tokens - cache_creation_input_tokens + ) prompt_tokens_details = PromptTokensDetailsWrapper( cached_tokens=cache_read_input_tokens, cache_creation_tokens=cache_creation_input_tokens, @@ -2277,6 +2380,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): cache_creation_input_tokens=cache_creation_input_tokens, cache_read_input_tokens=cache_read_input_tokens, completion_tokens_details=completion_token_details, + iterations=iterations, server_tool_use=( ServerToolUse( web_search_requests=web_search_requests, @@ -2287,6 +2391,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): ), inference_geo=inference_geo, speed=speed, + service_tier=service_tier, ) return usage diff --git a/litellm/llms/anthropic/common_utils.py b/litellm/llms/anthropic/common_utils.py index 3f002d73cbc..0f49ea402ad 100644 --- a/litellm/llms/anthropic/common_utils.py +++ b/litellm/llms/anthropic/common_utils.py @@ -3,6 +3,7 @@ This file contains common utils for anthropic calls. """ import copy +import re from typing import Any, Dict, List, Optional, Union import httpx @@ -11,6 +12,9 @@ import litellm from litellm.litellm_core_utils.prompt_templates.common_utils import ( get_file_ids_from_messages, ) +from litellm.litellm_core_utils.prompt_templates.factory import ( + THOUGHT_SIGNATURE_SEPARATOR, +) from litellm.llms.base_llm.base_utils import BaseLLMModelInfo, BaseTokenCounter from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.types.llms.anthropic import ( @@ -272,23 +276,68 @@ class AnthropicModelInfo(BaseLLMModelInfo): ) @staticmethod - def _supports_model_capability(model: str, key: str) -> bool: - """Check a boolean capability ``key`` in the model map. + def _supports_sampling_params(model: str) -> bool: + """Claude 4.7+ (Opus 4.7/4.8, Fable 5) removed sampling params: the API + rejects ``top_p``, ``top_k``, and any ``temperature`` other than 1 with + a 400 ("`temperature` is deprecated for this model"). - Strips bedrock/vertex prefixes so a provider-routed Claude still - resolves to the Anthropic model-map entry. - """ - from litellm.utils import _supports_factory + Driven by the ``supports_sampling_params`` flag in the model map; the + name check remains only as a fallback for provider-routed ids whose + map entries predate the flag.""" + flag = AnthropicModelInfo._get_model_capability( + model, "supports_sampling_params" + ) + if flag is not None: + return flag + model_lower = model.lower() + return not any( + v in model_lower + for v in ( + "fable", + "opus-4-7", + "opus_4_7", + "opus-4.7", + "opus_4.7", + "opus-4-8", + "opus_4_8", + "opus-4.8", + "opus_4.8", + ) + ) - try: - if _supports_factory( - model=model, - custom_llm_provider="anthropic", - key=key, - ): - return True - except Exception: - pass + @staticmethod + def _apply_sampling_param( + optional_params: dict, + model: str, + param: str, + value: Any, + drop_params: bool, + output_key: str, + ) -> None: + """Forward ``temperature``/``top_p``/``top_k`` to + ``optional_params[output_key]`` unless the model removed sampling + params, in which case drop the param (with drop_params) or raise a + clean client-side 400.""" + if AnthropicModelInfo._supports_sampling_params(model) or ( + param == "temperature" and value == 1 + ): + optional_params[output_key] = value + elif not (litellm.drop_params or drop_params): + supported_hint = ( + "Only temperature=1 is supported. " if param == "temperature" else "" + ) + raise litellm.utils.UnsupportedParamsError( + message=( + f"{model} does not support {param}={value}. {supported_hint}" + "To drop unsupported params, set `litellm.drop_params = True`." + ), + status_code=400, + ) + + @staticmethod + def _model_map_lookup_candidates(model: str) -> List[str]: + """Model-map keys to try for ``model``, stripping bedrock/vertex + prefixes so a provider-routed Claude still resolves to its entry.""" candidates = [model] for prefix in ( "bedrock/converse/", @@ -307,15 +356,50 @@ class AnthropicModelInfo(BaseLLMModelInfo): candidates.append(f"bedrock/{base}") except Exception: pass + return candidates + + @staticmethod + def _get_model_capability(model: str, key: str) -> Optional[bool]: + """Read boolean capability ``key`` from the model map, or None when + no entry declares it.""" try: - for cand in candidates: - if cand in litellm.model_cost and ( - litellm.model_cost[cand].get(key) is True - ): - return True + for cand in AnthropicModelInfo._model_map_lookup_candidates(model): + value = litellm.model_cost.get(cand, {}).get(key) + if isinstance(value, bool): + return value except Exception: pass - return False + return None + + @staticmethod + def _get_exact_model_capability(model: str, key: str) -> Optional[bool]: + """Read boolean capability ``key`` from the exact model-map entry only. + + Unlike ``_get_model_capability``, does not walk stripped provider aliases. + Use when a feature is tied to a specific host (e.g. Anthropic API fast mode). + """ + value = litellm.model_cost.get(model, {}).get(key) + return value if isinstance(value, bool) else None + + @staticmethod + def _supports_model_capability(model: str, key: str) -> bool: + """Check a boolean capability ``key`` in the model map. + + Strips bedrock/vertex prefixes so a provider-routed Claude still + resolves to the Anthropic model-map entry. + """ + from litellm.utils import _supports_factory + + try: + if _supports_factory( + model=model, + custom_llm_provider="anthropic", + key=key, + ): + return True + except Exception: + pass + return AnthropicModelInfo._get_model_capability(model, key) is True @staticmethod def _is_adaptive_thinking_model(model: str) -> bool: @@ -919,6 +1003,67 @@ def _is_empty_text_block(block: Any) -> bool: return not isinstance(text, str) or not text.strip() +def normalize_anthropic_tool_use_id(raw_id: str) -> str: + """ + Normalize a tool_use / tool_result id for Anthropic's ``^[a-zA-Z0-9_-]+$`` + pattern. + + Strips Gemini thought-signature suffixes (``__thought__``) first, then + replaces any remaining invalid characters with underscores. + """ + base_id = ( + raw_id.split(THOUGHT_SIGNATURE_SEPARATOR, 1)[0] + if THOUGHT_SIGNATURE_SEPARATOR in raw_id + else raw_id + ) + sanitized = re.sub(r"[^a-zA-Z0-9_-]", "_", base_id) + return sanitized or "tool_use_id" + + +def _sanitize_tool_use_id_content_block(block: Any) -> Any: + if not isinstance(block, dict): + return block + block_type = block.get("type") + if block_type in ("tool_use", "server_tool_use"): + raw_id = block.get("id") + if isinstance(raw_id, str): + normalized = normalize_anthropic_tool_use_id(raw_id) + if normalized != raw_id: + return {**block, "id": normalized} + elif block_type == "tool_result": + raw_id = block.get("tool_use_id") + if isinstance(raw_id, str): + normalized = normalize_anthropic_tool_use_id(raw_id) + if normalized != raw_id: + return {**block, "tool_use_id": normalized} + return block + + +def sanitize_tool_use_ids_in_anthropic_messages(messages: list[Any]) -> list[Any]: + """ + Return a new message list with ``tool_use`` / ``server_tool_use`` ``id`` and + ``tool_result`` ``tool_use_id`` values rewritten to satisfy Anthropic's + ``^[a-zA-Z0-9_-]+$`` requirement. + + Cross-provider clients (e.g. Claude Code routed through kimi) may replay + conversation history containing ids like ``functions.Bash:0`` with ``.`` + and ``:`` — valid on the upstream provider but rejected by Anthropic when + the session is switched to a native Anthropic deployment. + """ + out: list[Any] = [] + for m in messages: + if not isinstance(m, dict) or not isinstance(m.get("content"), list): + out.append(m) + continue + content = m["content"] + new_content = [_sanitize_tool_use_id_content_block(b) for b in content] + if new_content == content: + out.append(m) + else: + out.append({**m, "content": new_content}) + return out + + def process_anthropic_headers(headers: Union[httpx.Headers, dict]) -> dict: openai_headers = {} if "anthropic-ratelimit-requests-limit" in headers: diff --git a/litellm/llms/anthropic/cost_calculation.py b/litellm/llms/anthropic/cost_calculation.py index 3882d8f978c..44081ea9e79 100644 --- a/litellm/llms/anthropic/cost_calculation.py +++ b/litellm/llms/anthropic/cost_calculation.py @@ -7,6 +7,7 @@ from typing import TYPE_CHECKING, Optional, Tuple from litellm.litellm_core_utils.llm_cost_calc.utils import ( _get_token_base_cost, + _get_web_search_requests, _parse_prompt_tokens_details, calculate_cache_writing_cost, generic_cost_per_token, @@ -17,7 +18,9 @@ if TYPE_CHECKING: import litellm -def _compute_cache_only_cost(model_info: "ModelInfo", usage: "Usage") -> float: +def _compute_cache_only_cost( + model_info: "ModelInfo", usage: "Usage", service_tier: str | None = None +) -> float: """ Return only the cache-related portion of the prompt cost (cache read + cache write). @@ -35,7 +38,9 @@ def _compute_cache_only_cost(model_info: "ModelInfo", usage: "Usage") -> float: cache_creation_cost, cache_creation_cost_above_1hr, cache_read_cost, - ) = _get_token_base_cost(model_info=model_info, usage=usage) + ) = _get_token_base_cost( + model_info=model_info, usage=usage, service_tier=service_tier + ) cache_cost = float(prompt_tokens_details["cache_hit_tokens"]) * cache_read_cost @@ -55,19 +60,26 @@ def _compute_cache_only_cost(model_info: "ModelInfo", usage: "Usage") -> float: return cache_cost -def cost_per_token(model: str, usage: "Usage") -> Tuple[float, float]: +def cost_per_token( + model: str, usage: "Usage", service_tier: str | None = None +) -> Tuple[float, float]: """ Calculates the cost per token for a given model, prompt tokens, and completion tokens. Input: - model: str, the model name without provider prefix - usage: LiteLLM Usage block, containing anthropic caching information + - service_tier: the service tier the request was served at (e.g. "priority"), + read from the Anthropic response usage and used to select tier-specific pricing Returns: Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd """ prompt_cost, completion_cost = generic_cost_per_token( - model=model, usage=usage, custom_llm_provider="anthropic" + model=model, + usage=usage, + custom_llm_provider="anthropic", + service_tier=service_tier, ) # Apply provider_specific_entry multipliers for geo/speed routing @@ -88,7 +100,9 @@ def cost_per_token(model: str, usage: "Usage") -> Tuple[float, float]: multiplier *= provider_specific_entry.get("fast", 1.0) if multiplier != 1.0: - cache_cost = _compute_cache_only_cost(model_info=model_info, usage=usage) + cache_cost = _compute_cache_only_cost( + model_info=model_info, usage=usage, service_tier=service_tier + ) prompt_cost = (prompt_cost - cache_cost) * multiplier + cache_cost completion_cost *= multiplier except Exception: @@ -110,11 +124,12 @@ def get_cost_for_anthropic_web_search( if model_info is None: return 0.0 - if ( - usage is None - or usage.server_tool_use is None - or usage.server_tool_use.web_search_requests is None - ): + if usage is None: + return 0.0 + web_search_requests = _get_web_search_requests( + getattr(usage, "server_tool_use", None) + ) + if web_search_requests is None: return 0.0 ## Get the cost per web search request @@ -128,5 +143,5 @@ def get_cost_for_anthropic_web_search( return 0.0 ## Calculate the total cost - total_cost = cost_per_web_search_request * usage.server_tool_use.web_search_requests + total_cost = cost_per_web_search_request * web_search_requests return total_cost 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 bacb9f8ddf6..a8e2fceb4ee 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py @@ -1,5 +1,6 @@ # What is this? ## Translates OpenAI call to Anthropic `/v1/messages` format +import copy import json import traceback from collections import deque @@ -29,6 +30,98 @@ if TYPE_CHECKING: from litellm.types.utils import ModelResponseStream +class _CombinedChunkSplitter: + """ + Splits a streaming chunk that carries BOTH response content and a + ``finish_reason`` into two chunks: a content-only chunk followed by a + finish-only chunk. + + ``AnthropicStreamWrapper`` (via ``translate_streaming_openai_response_to_anthropic``) + assumes content and ``finish_reason`` never arrive in the same chunk — true for + real provider streams, but false for fake-streamed providers (e.g. Vertex AI + Gemma ``:predict``) where ``MockResponseIterator`` collapses the entire response + into a single chunk. Without this split the assumption causes all content to be + silently dropped (only the ``message_delta`` stop event is emitted). + + Supports both sync and async iteration, since ``AnthropicStreamWrapper`` exposes + both ``__next__`` and ``__anext__``. An instance is single-mode: callers must + iterate it either synchronously or asynchronously, never both — the two modes + hold independent iterator references on the upstream stream and mixing them + would advance them out of sync. + """ + + def __init__(self, completion_stream: Any): + self._stream = completion_stream + self._sync_iter: Optional[Iterator[Any]] = None + self._async_iter: Optional[AsyncIterator[Any]] = None + self._buffer: deque = deque() + + @staticmethod + def _is_combined(chunk: Any) -> bool: + """True if ``chunk`` carries response content AND a finish_reason.""" + choices = getattr(chunk, "choices", None) + if not choices: + return False + choice = choices[0] + if getattr(choice, "finish_reason", None) is None: + return False + delta = getattr(choice, "delta", None) + if delta is None: + return False + return bool( + getattr(delta, "content", None) + or getattr(delta, "tool_calls", None) + or getattr(delta, "reasoning_content", None) + or getattr(delta, "thinking_blocks", None) + ) + + @staticmethod + def _split(chunk: Any) -> List[Any]: + """Return ``[chunk]``, or ``[content_chunk, finish_chunk]`` if combined.""" + if not _CombinedChunkSplitter._is_combined(chunk): + return [chunk] + + # Content chunk: keep the delta payload, clear the finish_reason. + content_chunk = copy.deepcopy(chunk) + content_chunk.choices[0].finish_reason = None + + # Finish chunk: keep finish_reason (and usage), clear the delta payload. + finish_chunk = copy.deepcopy(chunk) + finish_delta = finish_chunk.choices[0].delta + finish_delta.content = None + if hasattr(finish_delta, "tool_calls"): + finish_delta.tool_calls = None + if hasattr(finish_delta, "reasoning_content"): + finish_delta.reasoning_content = None + if hasattr(finish_delta, "thinking_blocks"): + finish_delta.thinking_blocks = None + return [content_chunk, finish_chunk] + + def __iter__(self) -> "Iterator[Any]": + return self + + def __next__(self) -> Any: + if self._buffer: + return self._buffer.popleft() + if self._sync_iter is None: + self._sync_iter = iter(self._stream) + chunk = next(self._sync_iter) # propagates StopIteration when exhausted + self._buffer.extend(self._split(chunk)) + return self._buffer.popleft() + + def __aiter__(self) -> "AsyncIterator[Any]": + return self + + async def __anext__(self) -> Any: + if self._buffer: + return self._buffer.popleft() + if self._async_iter is None: + self._async_iter = self._stream.__aiter__() + chunk = await self._async_iter.__anext__() # propagates StopAsyncIteration + self._buffer.extend(self._split(chunk)) + return self._buffer.popleft() + + class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): """ - first chunk return 'message_start' @@ -62,7 +155,10 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): compaction_block: Optional[CompactionBlock] = None, iterations_usage: Optional[List[UsageIteration]] = None, ): - super().__init__(completion_stream) + # Wrap the upstream stream so chunks that carry both content and a + # finish_reason (fake-streamed providers) are split into two — see + # _CombinedChunkSplitter. + super().__init__(_CombinedChunkSplitter(completion_stream)) self.model = model # Mapping of truncated tool names to original names (for OpenAI's 64-char limit) self.tool_name_mapping = tool_name_mapping or {} @@ -276,7 +372,7 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): cache_read_input_tokens=0, ) - def __next__(self): # noqa: PLR0915 + def __next__(self): from .transformation import LiteLLMAnthropicMessagesAdapter try: @@ -373,12 +469,16 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): if should_start_new_block and not self.sent_content_block_finish: # Queue the sequence: content_block_stop -> content_block_start - # For text blocks the trigger chunk is not emitted as a separate - # delta because content_block_start carries the information. - # For tool_use blocks we must also emit the trigger chunk's delta - # when it carries input_json_delta data, because some providers - # (e.g. xAI, Gemini) include tool arguments in the same streaming - # chunk as the function name/id. + # -> (optionally) the trigger chunk's delta. + # + # The synthesized content_block_start always carries an + # empty body, so the chunk that *triggered* the transition + # also carries the new block's first delta. It must be + # re-emitted or the first token of the new block is lost. + # This applies to text_delta and thinking_delta (the first + # non-empty text/thinking token) as well as input_json_delta + # (providers like xAI/Gemini bundle tool arguments with the + # function name/id in a single chunk). # 1. Stop current content block self.chunk_queue.append( @@ -397,14 +497,9 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): } ) - # 3. If the trigger chunk carries tool argument data, queue it - # so the input_json_delta is not silently dropped. - if ( - processed_chunk.get("type") == "content_block_delta" - and isinstance(processed_chunk.get("delta"), dict) - and processed_chunk["delta"].get("type") == "input_json_delta" - and processed_chunk["delta"].get("partial_json") - ): + # 3. If the trigger chunk carries delta content, queue it + # so the first delta of the new block is not silently dropped. + if self._trigger_delta_has_content(processed_chunk): self.chunk_queue.append(processed_chunk) self.sent_content_block_finish = False @@ -523,7 +618,7 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): ) raise StopIteration - async def __anext__(self): # noqa: PLR0915 + async def __anext__(self): from .transformation import LiteLLMAnthropicMessagesAdapter try: @@ -615,12 +710,16 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): if not self.queued_usage_chunk: if should_start_new_block and not self.sent_content_block_finish: # Queue the sequence: content_block_stop -> content_block_start - # For text blocks the trigger chunk is not emitted as a separate - # delta because content_block_start carries the information. - # For tool_use blocks we must also emit the trigger chunk's delta - # when it carries input_json_delta data, because some providers - # (e.g. xAI, Gemini) include tool arguments in the same streaming - # chunk as the function name/id. + # -> (optionally) the trigger chunk's delta. + # + # The synthesized content_block_start always carries an + # empty body, so the chunk that *triggered* the transition + # also carries the new block's first delta. It must be + # re-emitted or the first token of the new block is lost. + # This applies to text_delta and thinking_delta (the + # first non-empty text/thinking token) as well as + # input_json_delta (providers like xAI/Gemini bundle tool + # arguments with the function name/id in a single chunk). # 1. Stop current content block self.chunk_queue.append( @@ -637,15 +736,9 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): } ) - # 3. If the trigger chunk carries tool argument data, queue it - # so the input_json_delta is not silently dropped. - if ( - processed_chunk.get("type") == "content_block_delta" - and isinstance(processed_chunk.get("delta"), dict) - and processed_chunk["delta"].get("type") - == "input_json_delta" - and processed_chunk["delta"].get("partial_json") - ): + # 3. If the trigger chunk carries delta content, queue it + # so the first delta of the new block is not silently dropped. + if self._trigger_delta_has_content(processed_chunk): self.chunk_queue.append(processed_chunk) # Reset state for new block @@ -802,6 +895,38 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): def _increment_content_block_index(self): self.current_content_block_index += 1 + @staticmethod + def _trigger_delta_has_content(processed_chunk: Dict[str, Any]) -> bool: + """Return True if a translated trigger chunk carries a non-empty + ``content_block_delta`` payload that must be re-emitted after a + block transition. + + When an upstream chunk both *triggers* a new content block (its type + differs from the active block) and *carries* delta content, that + content belongs to the new block. The synthesized + ``content_block_start`` only ever carries an empty body — see + ``_translate_streaming_openai_chunk_to_anthropic_content_block``, + which returns an empty ``TextBlock``/``ToolUseBlock``/thinking block — + so the trigger chunk's delta must be re-queued or the first token of + the new block (the first non-empty text/thinking delta, or bundled + tool arguments) is silently dropped. + """ + if processed_chunk.get("type") != "content_block_delta": + return False + delta = processed_chunk.get("delta") + if not isinstance(delta, dict): + return False + delta_type = delta.get("type") + if delta_type == "text_delta": + return bool(delta.get("text")) + if delta_type == "input_json_delta": + return bool(delta.get("partial_json")) + if delta_type == "thinking_delta": + return bool(delta.get("thinking")) + if delta_type == "signature_delta": + return bool(delta.get("signature")) + return False + def _should_start_new_content_block(self, chunk: "ModelResponseStream") -> bool: """ Determine if we should start a new content block based on the processed chunk. diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py index 150f056dc81..a4d0c93a3de 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py @@ -76,6 +76,7 @@ from litellm.litellm_core_utils.prompt_templates.common_utils import ( from litellm.litellm_core_utils.prompt_templates.factory import ( THOUGHT_SIGNATURE_SEPARATOR, ) +from litellm.llms.anthropic.common_utils import normalize_anthropic_tool_use_id from litellm.llms.anthropic.experimental_pass_through.context_management import ( PolyfillResult, ) @@ -332,7 +333,14 @@ class LiteLLMAnthropicMessagesAdapter: if isinstance(source, dict) else getattr(source, "cache_control", None) ) - if cache_control and model and self.is_anthropic_claude_model(model): + if ( + cache_control + and model + and ( + self.is_anthropic_claude_model(model) + or self.is_bedrock_arn_model(model) + ) + ): # TypedDict objects support dict operations at runtime # Use type ignore consistent with codebase pattern (see anthropic/chat/transformation.py:432) if isinstance(target, dict): @@ -376,7 +384,7 @@ class LiteLLMAnthropicMessagesAdapter: isinstance(tool_type, str) and tool_type.startswith("web_search") ) or tool_name == "web_search" - def translate_anthropic_messages_to_openai( # noqa: PLR0915 + def translate_anthropic_messages_to_openai( self, messages: List[ Union[ @@ -752,6 +760,20 @@ class LiteLLMAnthropicMessagesAdapter: model_lower = model.lower() return "anthropic" in model_lower or "claude" in model_lower + @staticmethod + def is_bedrock_arn_model(model: str) -> bool: + """ + Check if the model string is a Bedrock ARN, such as an Application + Inference Profile (e.g. arn:aws:bedrock:us-east-1:123:application-inference-profile/id). + + These ARNs contain neither "anthropic" nor "claude", so is_anthropic_claude_model + cannot identify them even though, on the /v1/messages endpoint, they point at Claude. + Match ":bedrock:" in the ARN service field so another service's ARN that merely names + bedrock in a resource (arn:aws:sagemaker:.../my-bedrock-endpoint) is not matched. + """ + model_lower = model.lower() + return "arn:" in model_lower and ":bedrock:" in model_lower + @staticmethod def translate_thinking_for_model( thinking: Dict[str, Any], @@ -838,7 +860,17 @@ class LiteLLMAnthropicMessagesAdapter: """ new_tools: List[ChatCompletionToolParam] = [] tool_name_mapping: Dict[str, str] = {} - mapped_tool_params = ["name", "input_schema", "description", "cache_control"] + # "type" is the Anthropic tool type (e.g. "custom"); it must not be + # merged into the OpenAI function `parameters` schema below, or it + # overwrites the real parameters.type ("object") and the provider + # rejects the request. See #30557. + mapped_tool_params = [ + "name", + "input_schema", + "description", + "cache_control", + "type", + ] for idx, tool in enumerate(tools): # Check if this is an Anthropic-native tool that should be kept as-is @@ -1332,18 +1364,12 @@ class LiteLLMAnthropicMessagesAdapter: else truncated_name ) - # Strip Gemini thought-signature suffix from id (mirrors streaming - # path below); base64 chars (+ / =) violate Anthropic's - # `^[a-zA-Z0-9_-]+$` tool_use.id pattern when replayed. + # Strip Gemini thought-signature suffix and normalize id chars + # (e.g. ``functions.Bash:0`` from cross-provider clients). raw_id = tool_call.id or "" - base_id = ( - raw_id.split(THOUGHT_SIGNATURE_SEPARATOR, 1)[0] - if THOUGHT_SIGNATURE_SEPARATOR in raw_id - else raw_id - ) tool_use_block = AnthropicResponseContentBlockToolUse( type="tool_use", - id=base_id, + id=normalize_anthropic_tool_use_id(raw_id), name=original_name, input=parse_tool_call_arguments( tool_call.function.arguments, @@ -1470,15 +1496,13 @@ class LiteLLMAnthropicMessagesAdapter: ): raw_id = choice.delta.tool_calls[0].id or str(uuid.uuid4()) tool_name = choice.delta.tool_calls[0].function.name or "" - base_id = raw_id thought_sig: Optional[str] = None if THOUGHT_SIGNATURE_SEPARATOR in raw_id: parts = raw_id.split(THOUGHT_SIGNATURE_SEPARATOR, 1) - base_id = parts[0] thought_sig = parts[1] if len(parts) > 1 else None tool_block: Dict[str, Any] = { "type": "tool_use", - "id": base_id, + "id": normalize_anthropic_tool_use_id(raw_id), "name": tool_name, "input": {}, } 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 4aae85b17fe..6479ee999b0 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 @@ -97,7 +97,7 @@ def _read_summary_max_tokens_setting() -> int: return COMPACT_SUMMARY_MAX_TOKENS -async def _check_summary_model_access( # noqa: PLR0915 +async def _check_summary_model_access( user_api_key_auth: Any, summary_model: str, llm_router: Any, @@ -970,7 +970,7 @@ def apply_client_compaction_block_history( ) -async def apply_compact_20260112( # noqa: PLR0915 +async def apply_compact_20260112( *, model: str, messages: List[Dict[str, Any]], diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/handler.py b/litellm/llms/anthropic/experimental_pass_through/messages/handler.py index a3ac465c463..cb61c196fd8 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/handler.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/handler.py @@ -23,6 +23,7 @@ from typing import ( import litellm from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.llms.anthropic.common_utils import ( + sanitize_tool_use_ids_in_anthropic_messages, strip_empty_text_blocks_from_anthropic_messages, ) from litellm.llms.base_llm.anthropic_messages.transformation import ( @@ -214,6 +215,9 @@ async def anthropic_messages( # already handles this in anthropic_messages_pt; sanitize the native # Anthropic Messages path here for the same guarantee. See #22930. messages = strip_empty_text_blocks_from_anthropic_messages(messages) + # Replay of cross-provider tool history (e.g. kimi -> Anthropic) may carry + # ids like ``functions.Bash:0`` that violate Anthropic's id pattern. + messages = sanitize_tool_use_ids_in_anthropic_messages(messages) original_stream = stream or kwargs.get( "_websearch_interception_converted_stream", False @@ -397,6 +401,7 @@ def anthropic_messages_handler( # full-messages scan. Pop it so it never leaks into provider params. if not kwargs.pop("_litellm_messages_presanitized", False): messages = strip_empty_text_blocks_from_anthropic_messages(messages) + messages = sanitize_tool_use_ids_in_anthropic_messages(messages) metadata = validate_anthropic_api_metadata(metadata) @@ -507,7 +512,10 @@ def anthropic_messages_handler( local_vars.update(kwargs) anthropic_messages_optional_request_params = ( AnthropicMessagesRequestUtils.get_requested_anthropic_messages_optional_param( - params=local_vars + params=local_vars, + model=model, + drop_params=litellm_params.get("drop_params") is True, + custom_llm_provider=custom_llm_provider, ) ) if is_reasoning_auto_summary_enabled(): diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/interceptors/advisor.py b/litellm/llms/anthropic/experimental_pass_through/messages/interceptors/advisor.py index c7c110ff3e3..8714939f025 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/interceptors/advisor.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/interceptors/advisor.py @@ -84,8 +84,14 @@ class AdvisorOrchestrationHandler(MessagesInterceptor): ) # Optional routing overrides for the advisor sub-call (e.g. proxy routing). # If not set in the tool definition, litellm resolves from env vars. - advisor_api_key: Optional[str] = advisor_tool.get("api_key") - advisor_api_base: Optional[str] = advisor_tool.get("api_base") + # The advisor tool is caller-controlled; only honor a client-supplied + # api_base/api_key when the proxy has enabled clientside credentials, + # otherwise let litellm resolve from server config. + advisor_api_key: Optional[str] = None + advisor_api_base: Optional[str] = None + if _allow_client_side_advisor_credentials(): + advisor_api_key = advisor_tool.get("api_key") + advisor_api_base = advisor_tool.get("api_base") # Build the synthetic tool definition the provider will receive. synthetic_advisor_tool = _make_synthetic_advisor_tool() @@ -181,6 +187,20 @@ class AdvisorOrchestrationHandler(MessagesInterceptor): # --------------------------------------------------------------------------- +def _allow_client_side_advisor_credentials() -> bool: + """Whether a caller-supplied advisor api_base/api_key may be honored. + + Gated on the proxy's ``allow_client_side_credentials`` opt-in. When the + interceptor runs outside the proxy (SDK use), there is no admin boundary + to protect, so client-supplied routing is allowed. + """ + try: + from litellm.proxy.proxy_server import general_settings + except (ImportError, ModuleNotFoundError): + return True + return general_settings.get("allow_client_side_credentials") is True + + def _make_synthetic_advisor_tool() -> Dict: """Build a regular tool definition the executor provider can understand.""" return { diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/utils.py b/litellm/llms/anthropic/experimental_pass_through/messages/utils.py index 88832fb3f63..42167e0fdaa 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/utils.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/utils.py @@ -23,12 +23,19 @@ class AnthropicMessagesRequestUtils: @staticmethod def get_requested_anthropic_messages_optional_param( params: Dict[str, Any], + *, + model: str | None = None, + drop_params: bool = False, + custom_llm_provider: str | None = None, ) -> AnthropicMessagesRequestOptionalParams: """ Filter parameters to only include those defined in AnthropicMessagesRequestOptionalParams. Args: params: Dictionary of parameters to filter + model: Resolved model id; when set, unsupported params may be dropped + drop_params: Per-request drop_params flag (also respects litellm.drop_params) + custom_llm_provider: Routed provider; fast mode is gated to direct Anthropic Returns: AnthropicMessagesRequestOptionalParams instance with only the valid parameters @@ -37,6 +44,15 @@ class AnthropicMessagesRequestUtils: filtered_params = { k: v for k, v in params.items() if k in valid_keys and v is not None } + if model is not None: + from litellm.llms.anthropic.chat.transformation import AnthropicConfig + + AnthropicConfig._maybe_drop_speed_param( + model=model, + optional_params=filtered_params, + drop_params=drop_params, + custom_llm_provider=custom_llm_provider, + ) return cast(AnthropicMessagesRequestOptionalParams, filtered_params) 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 f8c827ab057..70855afa81c 100644 --- a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/handler.py +++ b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/handler.py @@ -102,9 +102,9 @@ def _build_responses_kwargs( from litellm.types.utils import CallTypes if isinstance(value, LiteLLMLoggingObject): - # Reclassify as acompletion so the success handler doesn't try to - # validate the Responses API event as an AnthropicResponse. - # (Mirrors the pattern used in LiteLLMMessagesToCompletionTransformationHandler.) + # Keep call_type as anthropic_messages so spend_logs are billed + # against /v1/messages; the success handler translates the + # Responses API result back to a ModelResponse for the row. setattr(value, "call_type", CallTypes.anthropic_messages.value) responses_kwargs[key] = value elif key not in excluded and key not in responses_kwargs and value is not None: diff --git a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/streaming_iterator.py b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/streaming_iterator.py index 94c5200be64..04819a416a2 100644 --- a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/streaming_iterator.py +++ b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/streaming_iterator.py @@ -66,7 +66,7 @@ class AnthropicResponsesStreamWrapper: self._current_block_index += 1 return self._current_block_index - def _process_event(self, event: Any) -> None: # noqa: PLR0915 + def _process_event(self, event: Any) -> None: """Convert one Responses API event into zero or more Anthropic chunks queued for emission.""" event_type = getattr(event, "type", None) if event_type is None and isinstance(event, dict): @@ -155,10 +155,24 @@ class AnthropicResponsesStreamWrapper: event.get("delta", "") if isinstance(event, dict) else "" ) block_idx = ( - self._item_id_to_block_index.get(item_id, self._current_block_index) + self._item_id_to_block_index.get(item_id, -1) if item_id else self._current_block_index ) + if block_idx < 0: + # Some providers (e.g. LMStudio) skip response.output_item.added, + # so no text block is open yet; synthesize content_block_start + # instead of emitting a delta with index -1 + block_idx = self._next_block_index() + if item_id: + self._item_id_to_block_index[item_id] = block_idx + self._chunk_queue.append( + { + "type": "content_block_start", + "index": block_idx, + "content_block": {"type": "text", "text": ""}, + } + ) self._chunk_queue.append( { "type": "content_block_delta", 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 2badc2a3276..4fb1ddf5c46 100644 --- a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py @@ -51,7 +51,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: return source.get("url") return None - def translate_messages_to_responses_input( # noqa: PLR0915 + def translate_messages_to_responses_input( self, messages: List[ Union[ diff --git a/litellm/llms/apiserpent/search/transformation.py b/litellm/llms/apiserpent/search/transformation.py index 1eb7d34c875..bc11875ba12 100644 --- a/litellm/llms/apiserpent/search/transformation.py +++ b/litellm/llms/apiserpent/search/transformation.py @@ -53,7 +53,13 @@ class APISerpentSearchConfig(BaseSearchConfig): api_base: Optional[str] = None, **kwargs, ) -> Dict: - api_key = api_key or get_secret_str("APISERPENT_API_KEY") + api_key = self.resolve_server_api_key( + caller_api_key=api_key, + caller_api_base=api_base, + key_env_vars=("APISERPENT_API_KEY",), + base_env_var="APISERPENT_API_BASE", + default_api_base=APISERPENT_BASE, + ) if not api_key: raise ValueError( "APISERPENT_API_KEY is not set. Set `APISERPENT_API_KEY` environment variable." diff --git a/litellm/llms/azure/azure.py b/litellm/llms/azure/azure.py index 56cf035d0f7..5be3ce22832 100644 --- a/litellm/llms/azure/azure.py +++ b/litellm/llms/azure/azure.py @@ -189,7 +189,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): except Exception as e: raise e - def completion( # noqa: PLR0915 + def completion( self, model: str, messages: list, diff --git a/litellm/llms/azure/completion/handler.py b/litellm/llms/azure/completion/handler.py index 05d5e2f6c68..b8d1ad71d46 100644 --- a/litellm/llms/azure/completion/handler.py +++ b/litellm/llms/azure/completion/handler.py @@ -25,7 +25,7 @@ class AzureTextCompletion(BaseAzureLLM): headers["Authorization"] = f"Bearer {azure_ad_token}" return headers - def completion( # noqa: PLR0915 + def completion( self, model: str, messages: list, diff --git a/litellm/llms/azure/realtime/handler.py b/litellm/llms/azure/realtime/handler.py index 1f3357fd788..9c8de6c06a1 100644 --- a/litellm/llms/azure/realtime/handler.py +++ b/litellm/llms/azure/realtime/handler.py @@ -8,6 +8,7 @@ from typing import Any, Optional, cast from litellm._logging import _redact_string, verbose_proxy_logger from litellm.constants import REALTIME_WEBSOCKET_MAX_MESSAGE_SIZE_BYTES +from litellm.types.realtime import RealtimeQueryParams from ....litellm_core_utils.litellm_logging import Logging as LiteLLMLogging from ....litellm_core_utils.realtime_streaming import RealTimeStreaming @@ -35,6 +36,7 @@ class AzureOpenAIRealtime(AzureChatCompletion): model: str, api_version: Optional[str], realtime_protocol: Optional[str] = None, + query_params: Optional[RealtimeQueryParams] = None, ) -> str: """ Construct Azure realtime WebSocket URL. @@ -46,6 +48,7 @@ class AzureOpenAIRealtime(AzureChatCompletion): realtime_protocol: Protocol version to use: - "GA" or "v1": Uses /openai/v1/realtime (GA path) - "beta" or None: Uses /openai/realtime (beta path, default) + query_params: Extra query params to forward (e.g. intent=transcription). Returns: WebSocket URL string @@ -54,6 +57,8 @@ class AzureOpenAIRealtime(AzureChatCompletion): beta/default: "wss://.../openai/realtime?api-version=2024-10-01-preview&deployment=gpt-4o-realtime-preview" GA/v1: "wss://.../openai/v1/realtime?model=gpt-realtime-deployment" """ + from urllib.parse import urlencode + api_base = api_base.replace("https://", "wss://") # Determine path based on realtime_protocol (case-insensitive) @@ -61,13 +66,25 @@ class AzureOpenAIRealtime(AzureChatCompletion): "GA", "V1", ) + intent = (query_params or {}).get("intent") + if _is_ga: path = "/openai/v1/realtime" - return f"{api_base}{path}?model={model}" + query_parts = [] + if intent != "transcription" and ( + query_params is None or "model" in query_params + ): + query_parts.append(urlencode({"model": model})) else: # Default to beta path for backwards compatibility path = "/openai/realtime" - return f"{api_base}{path}?api-version={api_version}&deployment={model}" + query_parts = [urlencode({"api-version": api_version, "deployment": model})] + + if intent: + query_parts.append(urlencode({"intent": intent})) + + qs = "&".join(query_parts) + return f"{api_base}{path}?{qs}" if qs else f"{api_base}{path}" async def async_realtime( self, @@ -81,6 +98,7 @@ class AzureOpenAIRealtime(AzureChatCompletion): client: Optional[Any] = None, timeout: Optional[float] = None, realtime_protocol: Optional[str] = None, + query_params: Optional[RealtimeQueryParams] = None, user_api_key_dict: Optional[Any] = None, litellm_metadata: Optional[dict] = None, ): @@ -96,7 +114,11 @@ class AzureOpenAIRealtime(AzureChatCompletion): raise ValueError("api_version is required for Azure OpenAI calls") url = self._construct_url( - api_base, model, api_version, realtime_protocol=realtime_protocol + api_base, + model, + api_version, + realtime_protocol=realtime_protocol, + query_params=query_params, ) try: @@ -113,9 +135,15 @@ class AzureOpenAIRealtime(AzureChatCompletion): websocket, cast(ClientConnection, backend_ws), logging_obj, + model=model, user_api_key_dict=user_api_key_dict, request_data={"litellm_metadata": litellm_metadata or {}}, backend_uses_beta_protocol=backend_uses_beta_protocol, + force_transcription_model=( + model + if (query_params or {}).get("intent") == "transcription" + else None + ), ) await realtime_streaming.bidirectional_forward() diff --git a/litellm/llms/azure/realtime/http_transformation.py b/litellm/llms/azure/realtime/http_transformation.py index df1e2707af2..d6bdbd24db4 100644 --- a/litellm/llms/azure/realtime/http_transformation.py +++ b/litellm/llms/azure/realtime/http_transformation.py @@ -40,6 +40,13 @@ class AzureRealtimeHTTPConfig(BaseRealtimeHTTPConfig): version = api_version or get_secret_str("AZURE_API_VERSION") or "2024-12-17" return f"{base}/openai/realtime/calls?api-version={version}" + def get_transcription_session_url( + self, api_base: Optional[str], model: str, api_version: Optional[str] = None + ) -> str: + base = self.get_api_base(api_base).rstrip("/") + version = api_version or get_secret_str("AZURE_API_VERSION") or "2024-12-17" + return f"{base}/openai/realtime/transcription_sessions?api-version={version}" + def get_realtime_calls_headers(self, ephemeral_key: str) -> dict: return { "api-key": ephemeral_key, diff --git a/litellm/llms/azure/responses/transformation.py b/litellm/llms/azure/responses/transformation.py index ca9293325ff..92ce5b49285 100644 --- a/litellm/llms/azure/responses/transformation.py +++ b/litellm/llms/azure/responses/transformation.py @@ -185,6 +185,40 @@ class AzureOpenAIResponsesAPIConfig(OpenAIResponsesAPIConfig): default_api_version=AZURE_DEFAULT_RESPONSES_API_VERSION, ) + def supports_native_websocket(self) -> bool: + return True + + def get_websocket_url( + self, + api_base: Optional[str], + litellm_params: dict, + ) -> str: + """ + Azure Responses WebSocket endpoint is at /openai/v1/responses with no + api-version query param. Auth is via Authorization header, model is sent + in the response.create body — not the URL. + """ + if api_base is None: + raise ValueError("api_base is required for Azure WebSocket") + + parsed_url = httpx.URL(api_base) + path = parsed_url.path.rstrip("/") + # Strip existing /openai/responses path if the api_base already contains it + for suffix in ("/openai/v1/responses", "/openai/responses"): + if path.endswith(suffix): + path = path[: -len(suffix)] + break + scheme = "wss" if parsed_url.scheme == "https" else "ws" + return str( + parsed_url.copy_with( + scheme=scheme, path=f"{path}/openai/v1/responses", query=None + ) + ) + + def model_in_websocket_url(self) -> bool: + # Azure sends the model in the response.create body, not the URL + return False + ######################################################### ########## DELETE RESPONSE API TRANSFORMATION ############## ######################################################### diff --git a/litellm/llms/base_llm/base_model_iterator.py b/litellm/llms/base_llm/base_model_iterator.py index cf1fd6f786e..422ae947997 100644 --- a/litellm/llms/base_llm/base_model_iterator.py +++ b/litellm/llms/base_llm/base_model_iterator.py @@ -1,8 +1,11 @@ import json from abc import abstractmethod -from typing import List, Optional, Union, cast +from typing import TYPE_CHECKING, List, Optional, Union, cast import litellm + +if TYPE_CHECKING: + import httpx from litellm.types.utils import ( Choices, Delta, @@ -50,6 +53,11 @@ def convert_model_response_to_streaming( model=model_response.model, choices=streaming_choices, ) + # Carry usage onto the streaming chunk so fake-streamed responses + # (e.g. Vertex AI Gemma :predict) still report token counts. + usage = getattr(model_response, "usage", None) + if usage is not None: + setattr(processed_chunk, "usage", usage) return processed_chunk except Exception as e: raise ValueError( @@ -64,6 +72,18 @@ class BaseModelResponseIterator: self.streaming_response = streaming_response self.response_iterator = self.streaming_response self.json_mode = json_mode + self.http_response: Optional["httpx.Response"] = None + + async def aclose(self) -> None: + """Close the upstream HTTP response so the provider connection is + released (and a backend like vLLM aborts generation) when the stream + is abandoned before its natural end. + + ``streaming_response`` is usually a bare ``aiter_lines()`` generator + that holds no reference to the response, so the handler that owns the + response attaches it here after construction.""" + if self.http_response is not None: + await self.http_response.aclose() def chunk_parser( self, chunk: dict diff --git a/litellm/llms/base_llm/realtime/http_transformation.py b/litellm/llms/base_llm/realtime/http_transformation.py index 712ec42380f..be1413a3c0b 100644 --- a/litellm/llms/base_llm/realtime/http_transformation.py +++ b/litellm/llms/base_llm/realtime/http_transformation.py @@ -59,6 +59,15 @@ class BaseRealtimeHTTPConfig(ABC): ) -> str: """Return the full URL for POST /realtime/client_secrets.""" + def get_transcription_session_url( + self, api_base: Optional[str], model: str, api_version: Optional[str] = None + ) -> str: + """Return the full URL for POST /realtime/transcription_sessions.""" + base = (api_base or "").rstrip("/") + if base.endswith("/v1"): + base = base[:-3] + return f"{base}/v1/realtime/transcription_sessions" + @abstractmethod def validate_environment( self, diff --git a/litellm/llms/base_llm/responses/transformation.py b/litellm/llms/base_llm/responses/transformation.py index 407d5ad8146..c61ce52b530 100644 --- a/litellm/llms/base_llm/responses/transformation.py +++ b/litellm/llms/base_llm/responses/transformation.py @@ -258,6 +258,31 @@ class BaseResponsesAPIConfig(ABC): """ return False + def get_websocket_url( + self, + api_base: Optional[str], + litellm_params: dict, + ) -> str: + """ + Return the wss:// URL for the provider's native Responses WebSocket endpoint. + + Defaults to converting the HTTP URL from get_complete_url. Providers whose + WebSocket path differs from their HTTP path (e.g. Azure uses + /openai/v1/responses without api-version) should override this. + """ + http_url = self.get_complete_url( + api_base=api_base, litellm_params=litellm_params + ) + return http_url.replace("https://", "wss://").replace("http://", "ws://") + + def model_in_websocket_url(self) -> bool: + """ + Return True if the model should be appended as a ?model= query param to + the WebSocket URL. Providers that identify the model via the request body + (e.g. Azure Responses API) should override this to return False. + """ + return True + ######################################################### ########## CANCEL RESPONSE API TRANSFORMATION ########## ######################################################### diff --git a/litellm/llms/base_llm/sandbox/__init__.py b/litellm/llms/base_llm/sandbox/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/litellm/llms/base_llm/sandbox/transformation.py b/litellm/llms/base_llm/sandbox/transformation.py new file mode 100644 index 00000000000..1c012a15fdb --- /dev/null +++ b/litellm/llms/base_llm/sandbox/transformation.py @@ -0,0 +1,96 @@ +""" +Base Sandbox transformation configuration. + +A sandbox provider runs an executable string inside an isolated container and +returns whatever the sandbox produced. The lifecycle is create container -> +run code -> delete container; `code_interpreter_tool` combines all three. +""" + +from typing import Any, Union + +import httpx + +from pydantic import Field, PrivateAttr + +from litellm.types.llms.base import LiteLLMPydanticObjectBase + +SANDBOX_MAX_OUTPUT_BYTES = 10 * 1024 * 1024 + + +class ContainerHandle(LiteLLMPydanticObjectBase): + """A live sandbox container. Carries everything needed to reach it again.""" + + id: str + provider: str + domain: str | None = None + + model_config = {"extra": "allow"} + + _hidden_params: dict = PrivateAttr(default_factory=dict) + + +class CodeExecutionResult(LiteLLMPydanticObjectBase): + """Passthrough of the sandbox's own execution output.""" + + stdout: str = "" + stderr: str = "" + results: list[dict[str, Any]] = Field(default_factory=list) + error: dict[str, Any] | None = None + execution_count: int | None = None + object: str = "code_execution" + + model_config = {"extra": "allow"} + + _hidden_params: dict = PrivateAttr(default_factory=dict) + + +class BaseSandboxConfig: + """Provider-agnostic sandbox operations.""" + + def validate_environment(self, api_key: str | None = None, **kwargs) -> str: + raise NotImplementedError( + "validate_environment must be implemented by provider" + ) + + async def acreate_sandbox( + self, + *, + template: str | None = None, + timeout: int | None = None, + allow_internet_access: bool | None = None, + api_key: str | None = None, + **kwargs, + ) -> ContainerHandle: + raise NotImplementedError("acreate_sandbox must be implemented by provider") + + async def arun_code( + self, + *, + container: Union[ContainerHandle, str], + code: str, + api_key: str | None = None, + **kwargs, + ) -> CodeExecutionResult: + raise NotImplementedError("arun_code must be implemented by provider") + + async def adelete_sandbox( + self, + *, + container: Union[ContainerHandle, str], + api_key: str | None = None, + **kwargs, + ) -> bool: + raise NotImplementedError("adelete_sandbox must be implemented by provider") + + async def _read_capped_lines(self, response: httpx.Response) -> list[str]: + lines: list[str] = [] + total = 0 + async for line in response.aiter_lines(): + total += len(line.encode("utf-8")) + if total > SANDBOX_MAX_OUTPUT_BYTES: + raise ValueError( + f"Sandbox output exceeded {SANDBOX_MAX_OUTPUT_BYTES} bytes; aborting " + "to avoid unbounded memory use." + ) + lines.append(line) + return lines diff --git a/litellm/llms/base_llm/search/transformation.py b/litellm/llms/base_llm/search/transformation.py index 4dfe86685fb..1581d8bb064 100644 --- a/litellm/llms/base_llm/search/transformation.py +++ b/litellm/llms/base_llm/search/transformation.py @@ -3,11 +3,13 @@ Base Search transformation configuration. """ from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Union +from urllib.parse import urlsplit import httpx from pydantic import PrivateAttr from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.secret_managers.main import get_secret_str from litellm.types.llms.base import LiteLLMPydanticObjectBase if TYPE_CHECKING: @@ -16,6 +18,29 @@ else: LiteLLMLoggingObj = Any +def _search_host(url: str) -> str: + return urlsplit(url).netloc.lower() + + +def _is_trusted_search_api_base( + caller_api_base: str, + default_api_base: str | None, + base_env_var: str | None, +) -> bool: + candidate = _search_host(caller_api_base) + if not candidate: + return False + trusted = { + _search_host(base) + for base in ( + default_api_base, + get_secret_str(base_env_var) if base_env_var else None, + ) + if base + } + return candidate in trusted + + class SearchResult(LiteLLMPydanticObjectBase): """Single search result.""" @@ -86,6 +111,60 @@ class BaseSearchConfig: "max_tokens_per_page", } + def _assert_trusted_api_base_for_server_credential( + self, + caller_api_base: str | None, + default_api_base: str | None, + base_env_var: str | None, + credential_name: str, + ) -> None: + """ + Block sending a server-managed credential to a caller-chosen host. + + A caller-supplied api_base is honored when constructing the request URL, so + falling back to a server-configured secret while the caller controls the host + leaks that secret. The provider default and the operator's own api_base + override are the only trusted destinations for a server-managed credential. + """ + if not caller_api_base: + return + if _is_trusted_search_api_base(caller_api_base, default_api_base, base_env_var): + return + raise ValueError( + f"Refusing to send the server-configured {credential_name} to the " + f"caller-supplied api_base '{caller_api_base}'. Pass an explicit api_key " + f"when overriding api_base for this search provider." + ) + + def resolve_server_api_key( + self, + *, + caller_api_key: str | None, + caller_api_base: str | None, + key_env_vars: tuple[str, ...], + base_env_var: str | None, + default_api_base: str | None, + ) -> str | None: + """ + Resolve a single-secret search API key, falling back to a server-managed + secret only when the request targets a trusted host. + + Returns the caller's key when provided, otherwise the first set + server-managed secret (or None when none is set, for keyless providers). + """ + if caller_api_key: + return caller_api_key + server_key = next( + (key for key in (get_secret_str(var) for var in key_env_vars) if key), + None, + ) + if server_key is None: + return None + self._assert_trusted_api_base_for_server_credential( + caller_api_base, default_api_base, base_env_var, key_env_vars[0] + ) + return server_key + def validate_environment( self, headers: Dict, diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py index b1b06829387..c31462a735b 100644 --- a/litellm/llms/bedrock/base_aws_llm.py +++ b/litellm/llms/bedrock/base_aws_llm.py @@ -10,7 +10,6 @@ from typing import ( Callable, ClassVar, Dict, - List, Literal, Optional, Tuple, @@ -210,32 +209,11 @@ class BaseAWSLLM: """ Return a boto3.Credentials object """ - ## CHECK IS 'os.environ/' passed in - params_to_check: List[Optional[str]] = [ - aws_access_key_id, - aws_secret_access_key, - aws_session_token, - aws_region_name, - aws_session_name, - aws_profile_name, - aws_role_name, - aws_web_identity_token, - aws_sts_endpoint, - aws_external_id, - ] - - # Iterate over parameters and update if needed - for i, param in enumerate(params_to_check): - if param and param.startswith("os.environ/"): - _v = get_secret(param) - if _v is not None and isinstance(_v, str): - params_to_check[i] = _v - elif param is None: # check if uppercase value in env - key = self.aws_authentication_params[i] - if key.upper() in os.environ: - params_to_check[i] = os.getenv(key.upper()) - - # Assign updated values back to parameters + # Only config-sourced credentials are expanded against the environment. + # os.environ/ references in the model config are resolved at load time, + # so any reference still present at this point is caller-supplied input and is + # left as-is rather than expanded into a process environment variable. Each + # unset param falls back to its matching fixed AWS_* ambient env var. ( aws_access_key_id, aws_secret_access_key, @@ -247,7 +225,21 @@ class BaseAWSLLM: aws_web_identity_token, aws_sts_endpoint, aws_external_id, - ) = params_to_check + ) = tuple( + value if value is not None else os.getenv(env_var) + for value, env_var in ( + (aws_access_key_id, "AWS_ACCESS_KEY_ID"), + (aws_secret_access_key, "AWS_SECRET_ACCESS_KEY"), + (aws_session_token, "AWS_SESSION_TOKEN"), + (aws_region_name, "AWS_REGION_NAME"), + (aws_session_name, "AWS_SESSION_NAME"), + (aws_profile_name, "AWS_PROFILE_NAME"), + (aws_role_name, "AWS_ROLE_NAME"), + (aws_web_identity_token, "AWS_WEB_IDENTITY_TOKEN"), + (aws_sts_endpoint, "AWS_STS_ENDPOINT"), + (aws_external_id, "AWS_EXTERNAL_ID"), + ) + ) verbose_logger.debug( "in get credentials\n" @@ -845,6 +837,20 @@ class BaseAWSLLM: f"IN Web Identity Token: {aws_web_identity_token} | Role Name: {aws_role_name} | Session Name: {aws_session_name}" ) + # get_secret() expands environment-variable references (an os.environ/ + # prefix, or a bare name matching an environment variable). Config-sourced + # references are expanded at load time, so such a reference reaching here is + # caller-supplied input; reject it rather than expanding a process-environment + # value for use as the token. + if ( + aws_web_identity_token.startswith("os.environ/") + or aws_web_identity_token in os.environ + ): + raise AwsAuthError( + message="Invalid web identity token reference.", + status_code=400, + ) + oidc_token = get_secret(aws_web_identity_token) if oidc_token is None: @@ -861,14 +867,58 @@ class BaseAWSLLM: with tracer.trace("boto3.client(sts)"): sts_client = boto3.client("sts", **sts_client_kwargs) + # The session policy is an IAM PERMISSION CEILING — effective + # permissions are the intersection of the role's identity policies + # and this policy. Any action not listed here is silently denied + # even when the IAM role grants it. So every Bedrock route we + # support needs a matching action statement, or it 403s on OIDC + # auth only (static creds + IRSA take other code paths). # https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts/client/assume_role_with_web_identity.html + bedrock_session_policy = { + "Version": "2012-10-17", + "Statement": [ + { + "Sid": "BedrockLiteLLM", + "Effect": "Allow", + "Action": [ + "bedrock:InvokeModel", + "bedrock:InvokeModelWithResponseStream", + "bedrock:ApplyGuardrail", + "bedrock:GetGuardrail", + "bedrock:ListGuardrails", + ], + "Resource": "*", + "Condition": {"Bool": {"aws:SecureTransport": "true"}}, + }, + # Claude Platform on AWS (added by #27678 for the + # ``bedrock/claude_platform/`` route) lives under + # a separate IAM action namespace; without these entries + # the OIDC path 403s on every claude_platform request + # even with a fully permissive identity policy (#30200). + { + "Sid": "ClaudePlatformLiteLLM", + "Effect": "Allow", + "Action": [ + "aws-external-anthropic:CreateInference", + "aws-external-anthropic:CreateBatchInference", + "aws-external-anthropic:CancelBatchInference", + "aws-external-anthropic:DeleteBatchInference", + "aws-external-anthropic:CountTokens", + "aws-external-anthropic:Get*", + "aws-external-anthropic:List*", + ], + "Resource": "*", + "Condition": {"Bool": {"aws:SecureTransport": "true"}}, + }, + ], + } assume_role_params = { "RoleArn": aws_role_name, "RoleSessionName": aws_session_name, "WebIdentityToken": oidc_token, "DurationSeconds": 3600, - "Policy": '{"Version":"2012-10-17","Statement":[{"Sid":"BedrockLiteLLM","Effect":"Allow","Action":["bedrock:InvokeModel","bedrock:InvokeModelWithResponseStream","bedrock:ApplyGuardrail","bedrock:GetGuardrail","bedrock:ListGuardrails"],"Resource":"*","Condition":{"Bool":{"aws:SecureTransport":"true"}}}]}', + "Policy": json.dumps(bedrock_session_policy, separators=(",", ":")), } # Add ExternalId parameter if provided diff --git a/litellm/llms/bedrock/chat/agentcore/transformation.py b/litellm/llms/bedrock/chat/agentcore/transformation.py index 44ba1ce3c86..3cd3a249c33 100644 --- a/litellm/llms/bedrock/chat/agentcore/transformation.py +++ b/litellm/llms/bedrock/chat/agentcore/transformation.py @@ -218,8 +218,20 @@ class AmazonAgentCoreConfig(BaseConfig, BaseAWSLLM): - Qualifier goes as query parameter - Only the payload goes in the request body + Payload shape: + - ``prompt`` is always present and contains the text-only flatten of the + last message's content (existing behavior). + - ``content`` is added ONLY when the ``forward_multimodal_content`` litellm + param is truthy AND the last message's ``content`` is a list containing a + non-text block (e.g. ``image_url``, ``file``, ``input_audio``). The list is + forwarded verbatim so the agent's ``@app.entrypoint`` handler can parse the + OpenAI-shaped multimodal blocks. This is opt-in because an AgentCore agent + must be explicitly written to read ``payload["content"]``; by default the + payload stays byte-identical to the legacy ``{"prompt": "..."}`` shape. + Returns: - dict: Payload dict containing the prompt + dict: Payload dict containing the prompt and (optionally) the OpenAI + content list. """ verbose_logger.debug( f"AgentCore transform_request - optional_params keys: {list(optional_params.keys())}" @@ -231,6 +243,20 @@ class AmazonAgentCoreConfig(BaseConfig, BaseAWSLLM): # Create the payload - this is what goes in the body (raw JSON) payload: dict = {"prompt": prompt} + # Opt-in: when forward_multimodal_content is set, forward the OpenAI content + # list verbatim under "content" so an attachment-aware agent can read the raw + # blocks (image_url, file, etc.). Default off keeps the payload byte-identical + # to the legacy {"prompt": "..."} shape for agents that only read the prompt. + if self._should_forward_multimodal_content(optional_params, litellm_params): + last_content = messages[-1].get("content") + if isinstance(last_content, list) and any( + isinstance(block, dict) and block.get("type") not in (None, "text") + for block in last_content + ): + # Copy so the payload never aliases messages[-1]["content"]; shallow, + # not deep, to avoid cloning large base64 media on the request path. + payload["content"] = list(last_content) + # Get or generate session ID - this goes in the header runtime_session_id = self._get_runtime_session_id(optional_params) headers["X-Amzn-Bedrock-AgentCore-Runtime-Session-Id"] = runtime_session_id @@ -246,6 +272,29 @@ class AmazonAgentCoreConfig(BaseConfig, BaseAWSLLM): verbose_logger.debug(f"PAYLOAD: {payload}") return payload + @staticmethod + def _should_forward_multimodal_content( + optional_params: dict, litellm_params: dict + ) -> bool: + """Whether to forward raw OpenAI content blocks under ``payload["content"]``. + + Opt-in via the ``forward_multimodal_content`` litellm param (default ``False``) + because AgentCore agents must be explicitly written to read the field. The + value may arrive as a bool or a config/env string ("true", "1", ...). Checks + ``optional_params`` first (where other AgentCore params land), then + ``litellm_params``. + """ + for source in (optional_params, litellm_params): + if not isinstance(source, dict): + continue + value = source.get("forward_multimodal_content") + if value is None: + continue + if isinstance(value, str): + return value.strip().lower() in ("1", "true", "yes", "on") + return bool(value) + return False + def _extract_sse_json(self, line: str) -> Optional[Dict]: """Extract and parse JSON from an SSE data line.""" if not line.startswith("data:"): diff --git a/litellm/llms/bedrock/chat/converse_handler.py b/litellm/llms/bedrock/chat/converse_handler.py index 388947a4e9b..7b1064ccef9 100644 --- a/litellm/llms/bedrock/chat/converse_handler.py +++ b/litellm/llms/bedrock/chat/converse_handler.py @@ -32,7 +32,7 @@ def make_sync_call( logging_obj: LiteLLMLoggingObject, json_mode: Optional[bool] = False, fake_stream: bool = False, - stream_chunk_size: int = 1024, + stream_chunk_size: Optional[int] = None, ): if client is None: client = _get_httpx_client() # Create a new client if none provided @@ -108,7 +108,7 @@ class BedrockConverseLLM(BaseAWSLLM): fake_stream: bool = False, json_mode: Optional[bool] = False, api_key: Optional[str] = None, - stream_chunk_size: int = 1024, + stream_chunk_size: Optional[int] = None, ) -> CustomStreamWrapper: request_data = await litellm.AmazonConverseConfig()._async_transform_request( model=model, @@ -248,7 +248,7 @@ class BedrockConverseLLM(BaseAWSLLM): encoding=encoding, ) - def completion( # noqa: PLR0915 + def completion( self, model: str, messages: list, @@ -268,7 +268,7 @@ class BedrockConverseLLM(BaseAWSLLM): ): ## SETUP ## stream = optional_params.pop("stream", None) - stream_chunk_size = optional_params.pop("stream_chunk_size", 1024) + stream_chunk_size = optional_params.pop("stream_chunk_size", None) unencoded_model_id = optional_params.pop("model_id", None) fake_stream = optional_params.pop("fake_stream", False) json_mode = optional_params.get("json_mode", False) diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index ea0326dffd1..bb261ec85b2 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -920,10 +920,15 @@ class AmazonConverseConfig(BaseConfig): continue value = [value] optional_params["stopSequences"] = value - if param == "temperature": - optional_params["temperature"] = value - if param == "top_p": - optional_params["topP"] = value + if param == "temperature" or param == "top_p": + AnthropicConfig._apply_sampling_param( + optional_params=optional_params, + model=model, + param=param, + value=value, + drop_params=drop_params, + output_key="topP" if param == "top_p" else param, + ) if param == "tools" and isinstance(value, list): self._apply_tool_call_transformation( tools=cast(List[OpenAIChatCompletionToolParam], value), @@ -1221,7 +1226,9 @@ class AmazonConverseConfig(BaseConfig): inference_params["topK"] = inference_params.pop("top_k") return InferenceConfig(**inference_params) - def _handle_top_k_value(self, model: str, inference_params: dict) -> dict: + def _handle_top_k_value( + self, model: str, inference_params: dict, drop_params: bool = False + ) -> dict: base_model = BedrockModelInfo.get_base_model(model) val_top_k = None @@ -1230,16 +1237,25 @@ class AmazonConverseConfig(BaseConfig): elif "top_k" in inference_params: val_top_k = inference_params.pop("top_k") - if val_top_k: + if val_top_k is not None: if base_model.startswith("anthropic"): - return {"top_k": val_top_k} + top_k_params: dict = {} + AnthropicConfig._apply_sampling_param( + optional_params=top_k_params, + model=model, + param="top_k", + value=val_top_k, + drop_params=drop_params, + output_key="top_k", + ) + return top_k_params if base_model.startswith("amazon.nova"): return {"inferenceConfig": {"topK": val_top_k}} return {} def _prepare_request_params( - self, optional_params: dict, model: str + self, optional_params: dict, model: str, drop_params: bool = False ) -> Tuple[dict, dict, dict, Optional[OutputConfigBlock]]: """Prepare and separate request parameters.""" # Consume the internal ``_output_config_normalized`` marker set by @@ -1338,7 +1354,7 @@ class AmazonConverseConfig(BaseConfig): # Only set the topK value in for models that support it additional_request_params.update( - self._handle_top_k_value(model, inference_params) + self._handle_top_k_value(model, inference_params, drop_params) ) # Filter out internal/MCP-related parameters that shouldn't be sent to the API @@ -1572,6 +1588,7 @@ class AmazonConverseConfig(BaseConfig): optional_params: dict, messages: Optional[List[AllMessageValues]] = None, headers: Optional[dict] = None, + drop_params: bool = False, ) -> CommonRequestObject: ## VALIDATE REQUEST """ @@ -1618,7 +1635,7 @@ class AmazonConverseConfig(BaseConfig): additional_request_params, request_metadata, output_config, - ) = self._prepare_request_params(optional_params, model) + ) = self._prepare_request_params(optional_params, model, drop_params) original_tools = inference_params.pop("tools", []) @@ -1701,6 +1718,7 @@ class AmazonConverseConfig(BaseConfig): optional_params=optional_params, messages=messages, headers=headers, + drop_params=litellm_params.get("drop_params") is True, ) bedrock_messages = ( @@ -1758,6 +1776,7 @@ class AmazonConverseConfig(BaseConfig): optional_params=optional_params, messages=messages, headers=headers, + drop_params=litellm_params.get("drop_params") is True, ) ## TRANSFORMATION ## @@ -2170,7 +2189,7 @@ class AmazonConverseConfig(BaseConfig): real_tools = [t for i, t in enumerate(tools) if i not in json_tool_indices] return real_tools if real_tools else None - def _transform_response( # noqa: PLR0915 + def _transform_response( self, model: str, response: httpx.Response, diff --git a/litellm/llms/bedrock/chat/invoke_handler.py b/litellm/llms/bedrock/chat/invoke_handler.py index 7a9916f1f31..9fca7bc61af 100644 --- a/litellm/llms/bedrock/chat/invoke_handler.py +++ b/litellm/llms/bedrock/chat/invoke_handler.py @@ -70,6 +70,7 @@ from ..base_aws_llm import BaseAWSLLM from ..common_utils import ( BedrockError, ModelResponseIterator, + build_bedrock_stream_error, get_bedrock_response_stream_shape, get_bedrock_tool_name, ) @@ -197,7 +198,7 @@ async def make_call( fake_stream: bool = False, json_mode: Optional[bool] = False, bedrock_invoke_provider: Optional[litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL] = None, - stream_chunk_size: int = 1024, + stream_chunk_size: Optional[int] = None, ): try: if client is None: @@ -294,7 +295,7 @@ def make_sync_call( fake_stream: bool = False, json_mode: Optional[bool] = False, bedrock_invoke_provider: Optional[litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL] = None, - stream_chunk_size: int = 1024, + stream_chunk_size: Optional[int] = None, ): try: if client is None: @@ -473,7 +474,7 @@ class BedrockLLM(BaseAWSLLM): prompt += f"{message['content']}" return prompt, chat_history # type: ignore - def process_response( # noqa: PLR0915 + def process_response( self, model: str, response: httpx.Response, @@ -765,7 +766,7 @@ class BedrockLLM(BaseAWSLLM): return model_response - def completion( # noqa: PLR0915 + def completion( self, model: str, messages: list, @@ -790,7 +791,7 @@ class BedrockLLM(BaseAWSLLM): ## SETUP ## stream = optional_params.pop("stream", None) - stream_chunk_size = optional_params.pop("stream_chunk_size", 1024) + stream_chunk_size = optional_params.pop("stream_chunk_size", None) provider = self.get_bedrock_invoke_provider(model) modelId = self.get_bedrock_model_id( @@ -1203,7 +1204,7 @@ class BedrockLLM(BaseAWSLLM): extra_headers: Optional[dict] = None, timeout: Optional[Union[float, httpx.Timeout]] = None, client: Optional[AsyncHTTPHandler] = None, - stream_chunk_size: int = 1024, + stream_chunk_size: Optional[int] = None, ) -> Union[ModelResponse, CustomStreamWrapper]: transformed_request = ( await litellm.AmazonAnthropicClaudeConfig().async_transform_request( @@ -1350,7 +1351,7 @@ class BedrockLLM(BaseAWSLLM): logger_fn=None, headers={}, client: Optional[AsyncHTTPHandler] = None, - stream_chunk_size: int = 1024, + stream_chunk_size: Optional[int] = None, ) -> CustomStreamWrapper: # The call is not made here; instead, we prepare the necessary objects for the stream. @@ -1841,23 +1842,7 @@ class AWSEventStreamDecoder: parsed_response = self.parser.parse(response_dict, response_stream_shape) if response_dict["status_code"] != 200: - decoded_body = response_dict["body"].decode() - if isinstance(decoded_body, dict): - error_message = decoded_body.get("message") - elif isinstance(decoded_body, str): - error_message = decoded_body - else: - error_message = "" - exception_status = response_dict["headers"].get(":exception-type") - error_message = exception_status + " " + error_message - raise BedrockError( - status_code=response_dict["status_code"], - message=( - json.dumps(error_message) - if isinstance(error_message, dict) - else error_message - ), - ) + raise build_bedrock_stream_error(response_dict, response_stream_shape) if "chunk" in parsed_response: chunk = parsed_response.get("chunk") if not chunk: 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 4887cbd23be..79153c3ceff 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py @@ -215,6 +215,7 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig): anthropic_request.pop("model", None) anthropic_request.pop("stream", None) + anthropic_request.pop("stream_chunk_size", None) output_format = anthropic_request.pop("output_format", None) output_config_format = pop_bedrock_invoke_output_config_format( anthropic_request diff --git a/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py index 43850440072..8fc2375c224 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py @@ -150,6 +150,7 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM): ) -> dict: ## SETUP ## stream = optional_params.pop("stream", None) + optional_params.pop("stream_chunk_size", None) custom_prompt_dict: dict = litellm_params.pop("custom_prompt_dict", None) or {} hf_model_name = litellm_params.get("hf_model_name", None) @@ -256,7 +257,7 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM): return request_data - def transform_response( # noqa: PLR0915 + def transform_response( self, model: str, raw_response: httpx.Response, diff --git a/litellm/llms/bedrock/chat/mantle/transformation.py b/litellm/llms/bedrock/chat/mantle/transformation.py index ef0199031af..93306025b02 100644 --- a/litellm/llms/bedrock/chat/mantle/transformation.py +++ b/litellm/llms/bedrock/chat/mantle/transformation.py @@ -12,6 +12,7 @@ from typing import TYPE_CHECKING, Any, List, Optional from litellm.llms.bedrock.chat.invoke_transformations.anthropic_claude3_transformation import ( AmazonAnthropicClaudeConfig, ) +from litellm.llms.bedrock.common_utils import build_mantle_messages_url from litellm.types.llms.openai import AllMessageValues if TYPE_CHECKING: @@ -21,10 +22,6 @@ if TYPE_CHECKING: else: LiteLLMLoggingObj = Any -MANTLE_ENDPOINT_TEMPLATE = ( - "https://bedrock-mantle.{region}.api.aws/anthropic/v1/messages" -) - class AmazonMantleConfig(AmazonAnthropicClaudeConfig): """ @@ -46,7 +43,37 @@ class AmazonMantleConfig(AmazonAnthropicClaudeConfig): stream: Optional[bool] = None, ) -> str: region = self._get_aws_region_name(optional_params=optional_params, model=model) - return MANTLE_ENDPOINT_TEMPLATE.format(region=region) + return build_mantle_messages_url( + api_base=api_base, + aws_bedrock_runtime_endpoint=optional_params.get( + "aws_bedrock_runtime_endpoint" + ), + region=region, + ) + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + headers = super().validate_environment( + headers=headers, + model=model, + messages=messages, + optional_params=optional_params, + litellm_params=litellm_params, + api_key=api_key, + api_base=api_base, + ) + project_id = litellm_params.get("aws_bedrock_project_id") + if project_id: + headers["anthropic-workspace"] = project_id + return headers def transform_request( self, diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py index bdc5da321c6..5e97394f459 100644 --- a/litellm/llms/bedrock/common_utils.py +++ b/litellm/llms/bedrock/common_utils.py @@ -7,9 +7,21 @@ Common utilities used across bedrock chat/embedding/image generation import functools import json import os -from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Union +from typing import ( + TYPE_CHECKING, + Any, + Dict, + List, + Literal, + Mapping, + Optional, + TypedDict, + Union, +) if TYPE_CHECKING: + from botocore.model import Shape + from litellm.types.llms.bedrock import BedrockCreateBatchRequest import httpx @@ -610,6 +622,31 @@ def strip_bedrock_throughput_suffix(model: str) -> str: return model +MANTLE_MESSAGES_PATH = "/anthropic/v1/messages" + + +def build_mantle_messages_url( + api_base: Optional[str], + aws_bedrock_runtime_endpoint: Optional[str], + region: str, +) -> str: + """Build the bedrock-mantle Anthropic /messages URL. + + Honors an explicit endpoint override (``api_base``, then + ``aws_bedrock_runtime_endpoint``) so private VPC / VPCE / GovCloud Mantle + endpoints are reachable; otherwise falls back to the public regional host. + The mantle messages path is appended unless the override already carries it, + so callers can pass either the host or the full messages URL. + """ + override = api_base or aws_bedrock_runtime_endpoint + if override: + base = override.rstrip("/") + if base.endswith(MANTLE_MESSAGES_PATH): + return base + return f"{base}{MANTLE_MESSAGES_PATH}" + return f"https://bedrock-mantle.{region}.api.aws{MANTLE_MESSAGES_PATH}" + + def get_bedrock_base_model(model: str) -> str: """ Get the base model from the given model name. @@ -1132,6 +1169,39 @@ def get_bedrock_response_stream_shape(): return _load_bedrock_response_stream_shape() +class BedrockEventStreamResponseDict(TypedDict): + status_code: int + headers: Mapping[str, str] + body: bytes + + +def build_bedrock_stream_error( + response_dict: BedrockEventStreamResponseDict, + response_stream_shape: Shape | None, +) -> BedrockError: + """Build a BedrockError for a non-200 event-stream error event. + + botocore hard-codes HTTP 400 on every mid-stream error event, so the modeled + ResponseStream member's httpStatusCode is the real status. Resolve it from the + shape and fall back to the raw status when the type is not modeled. + """ + exception_type = response_dict["headers"].get(":exception-type") + decoded_body = response_dict["body"].decode() + message = f"{exception_type} {decoded_body}" if exception_type else decoded_body + + status_code = response_dict["status_code"] + if exception_type is not None and response_stream_shape is not None: + member = response_stream_shape.members.get(exception_type) + if member is not None: + modeled_status = ( + (member.metadata or {}).get("error", {}).get("httpStatusCode") + ) + if modeled_status is not None: + status_code = int(modeled_status) + + return BedrockError(status_code=status_code, message=message) + + class BedrockEventStreamDecoderBase: """ Base class for event stream decoding for Bedrock @@ -1156,23 +1226,7 @@ class BedrockEventStreamDecoderBase: parsed_response = self.parser.parse(response_dict, response_stream_shape) if response_dict["status_code"] != 200: - decoded_body = response_dict["body"].decode() - if isinstance(decoded_body, dict): - error_message = decoded_body.get("message") - elif isinstance(decoded_body, str): - error_message = decoded_body - else: - error_message = "" - exception_status = response_dict["headers"].get(":exception-type") - error_message = exception_status + " " + error_message - raise BedrockError( - status_code=response_dict["status_code"], - message=( - json.dumps(error_message) - if isinstance(error_message, dict) - else error_message - ), - ) + raise build_bedrock_stream_error(response_dict, response_stream_shape) if "chunk" in parsed_response: chunk = parsed_response.get("chunk") if not chunk: diff --git a/litellm/llms/bedrock/count_tokens/transformation.py b/litellm/llms/bedrock/count_tokens/transformation.py index c967fd334bc..bdef3349e00 100644 --- a/litellm/llms/bedrock/count_tokens/transformation.py +++ b/litellm/llms/bedrock/count_tokens/transformation.py @@ -11,6 +11,11 @@ from typing import Any, Dict, List, Optional from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM from litellm.llms.bedrock.common_utils import get_bedrock_base_model +# Placeholder satisfying the Anthropic InvokeModel schema's required +# max_tokens field; CountTokens only counts input, so it has no effect +# on any generation. +DEFAULT_ANTHROPIC_INVOKE_MODEL_MAX_TOKENS = 1024 + class BedrockCountTokensConfig(BaseAWSLLM): """ @@ -32,8 +37,20 @@ class BedrockCountTokensConfig(BaseAWSLLM): Returns: 'converse' or 'invokeModel' """ - # If the request has messages in the expected Anthropic format, use converse - if "messages" in request_data and isinstance(request_data["messages"], list): + messages = request_data.get("messages") + if isinstance(messages, list): + # Anthropic content blocks carry a "type" key ({"type": "text", ...}); + # Converse blocks don't ({"text": ...}, {"toolUse": ...}). Converse + # rejects Anthropic-shape blocks, so route those to invokeModel, + # which forwards the body verbatim. + for message in messages: + if not isinstance(message, dict): + continue + content = message.get("content") + if isinstance(content, list) and any( + isinstance(block, dict) and "type" in block for block in content + ): + return "invokeModel" return "converse" # For raw text or other formats, use invokeModel @@ -68,7 +85,7 @@ class BedrockCountTokensConfig(BaseAWSLLM): { "input": { "invokeModel": { - "body": "{...raw model input...}" + "body": "" } } } @@ -168,13 +185,24 @@ class BedrockCountTokensConfig(BaseAWSLLM): self, request_data: Dict[str, Any] ) -> Dict[str, Any]: """Transform to InvokeModel input format.""" + import base64 import json # For InvokeModel, we need to provide the raw body that would be sent to the model # Remove the 'model' field from the body as it's not part of the model input body_data = {k: v for k, v in request_data.items() if k != "model"} - return {"input": {"invokeModel": {"body": json.dumps(body_data)}}} + if "messages" in body_data: + # Bedrock validates the body against the model's InvokeModel schema; + # Anthropic Messages bodies require these fields. + body_data.setdefault("anthropic_version", "bedrock-2023-05-31") + body_data.setdefault( + "max_tokens", DEFAULT_ANTHROPIC_INVOKE_MODEL_MAX_TOKENS + ) + + # The CountTokens API expects invokeModel.body as a base64-encoded blob + encoded_body = base64.b64encode(json.dumps(body_data).encode()).decode() + return {"input": {"invokeModel": {"body": encoded_body}}} def get_bedrock_count_tokens_endpoint( self, diff --git a/litellm/llms/bedrock/embed/embedding.py b/litellm/llms/bedrock/embed/embedding.py index 27dc785bf57..b6aa99842d7 100644 --- a/litellm/llms/bedrock/embed/embedding.py +++ b/litellm/llms/bedrock/embed/embedding.py @@ -388,7 +388,7 @@ class BedrockEmbedding(BaseAWSLLM): batch_data=batch_data, ) - def embeddings( # noqa: PLR0915 + def embeddings( self, model: str, input: List[str], diff --git a/litellm/llms/bedrock/files/handler.py b/litellm/llms/bedrock/files/handler.py index ecf157e12ee..b6aae2159c1 100644 --- a/litellm/llms/bedrock/files/handler.py +++ b/litellm/llms/bedrock/files/handler.py @@ -1,8 +1,6 @@ import asyncio -import base64 -import os -from types import MappingProxyType -from typing import Any, Coroutine, Mapping, Optional, Tuple, Union, cast +from collections.abc import Mapping +from typing import Any, Coroutine, Optional, Tuple, Union import httpx @@ -17,7 +15,6 @@ from litellm.types.llms.openai import ( FileContentRequest, HttpxBinaryResponseContent, ) -from litellm.types.utils import SpecialEnums from ..base_aws_llm import BaseAWSLLM @@ -37,40 +34,9 @@ class BedrockFilesHandler(BaseAWSLLM): ) def _extract_s3_uri_from_file_id(self, file_id: str) -> str: - """ - Extract S3 URI from encoded file ID. + from .transformation import extract_s3_uri_from_file_id - The file ID can be in two formats: - 1. Base64-encoded unified file ID containing: llm_output_file_id,s3://bucket/path - 2. Direct S3 URI: s3://bucket/litellm-managed-prefix/path - - Args: - file_id: Encoded file ID or direct S3 URI - - Returns: - S3 URI (e.g., "s3://bucket-name/path/to/file") - """ - # First, try to decode if it's a base64-encoded unified file ID - try: - # Add padding if needed - padded = file_id + "=" * (-len(file_id) % 4) - decoded = base64.urlsafe_b64decode(padded).decode() - - # Check if it's a unified file ID format - if decoded.startswith(SpecialEnums.LITELM_MANAGED_FILE_ID_PREFIX.value): - # Extract llm_output_file_id from the decoded string - if "llm_output_file_id," in decoded: - s3_uri = decoded.split("llm_output_file_id,")[1].split(";")[0] - return s3_uri - except Exception: - pass - - # If not base64 encoded or doesn't contain llm_output_file_id, accept only - # explicit S3 URIs. Bucket and key validation happens before any S3 call. - if file_id.startswith("s3://"): - return file_id - - raise ValueError("file_id must be a managed LiteLLM S3 file id") + return extract_s3_uri_from_file_id(file_id) def _parse_s3_uri( self, @@ -95,26 +61,12 @@ class BedrockFilesHandler(BaseAWSLLM): allow_legacy_cloud_file_ids=allow_legacy_cloud_file_ids, ) - def _get_configured_s3_bucket_name(self, litellm_params: dict) -> str: - trusted_model_credentials = litellm_params.get( - "_litellm_internal_model_credentials" - ) - bucket_name = None - if isinstance(trusted_model_credentials, type(MappingProxyType({}))): - trusted_model_credentials_mapping = cast( - Mapping[str, Any], trusted_model_credentials - ) - candidate_bucket_name = trusted_model_credentials_mapping.get( - "s3_bucket_name" - ) - if isinstance(candidate_bucket_name, str): - bucket_name = candidate_bucket_name - bucket_name = bucket_name or os.getenv("AWS_S3_BUCKET_NAME") - if not bucket_name: - raise ValueError( - "S3 bucket_name is required. Set 's3_bucket_name' in proxy config or AWS_S3_BUCKET_NAME for Bedrock file content retrieval." - ) - return bucket_name + def _get_configured_s3_bucket_name( + self, litellm_params: Mapping[str, object] + ) -> str: + from .transformation import get_configured_s3_bucket_name + + return get_configured_s3_bucket_name(litellm_params) async def afile_content( self, diff --git a/litellm/llms/bedrock/files/transformation.py b/litellm/llms/bedrock/files/transformation.py index cec2e934af8..6cfaa88275d 100644 --- a/litellm/llms/bedrock/files/transformation.py +++ b/litellm/llms/bedrock/files/transformation.py @@ -1,23 +1,37 @@ +import base64 import json import os import time -from typing import Any, Dict, List, Optional, Tuple, Union +from collections.abc import Mapping, MutableMapping +from types import MappingProxyType +from typing import ( + Any, + Dict, + List, + Optional, + Tuple, + Union, +) from urllib.parse import unquote import httpx from httpx import Headers, Response from openai.types.file_deleted import FileDeleted +from pydantic import BaseModel, ConfigDict from litellm._logging import verbose_logger from litellm._uuid import uuid from litellm.files.utils import FilesAPIUtils from litellm.litellm_core_utils.cloud_storage_security import ( BEDROCK_MANAGED_S3_BATCH_PREFIX, + BEDROCK_MANAGED_S3_PREFIXES, BEDROCK_MANAGED_S3_UPLOAD_PREFIX, build_managed_cloud_object_name, encode_s3_object_key_for_url, sanitize_cloud_object_component, + should_allow_legacy_cloud_file_ids, split_configured_cloud_bucket_name, + validate_managed_cloud_file_id, ) from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data from litellm.llms.base_llm.chat.transformation import BaseLLMException @@ -28,18 +42,98 @@ from litellm.llms.base_llm.files.transformation import ( from litellm.types.llms.openai import ( AllMessageValues, CreateFileRequest, + FileContentRequest, FileTypes, HttpxBinaryResponseContent, OpenAICreateFileRequestOptionalParams, OpenAIFileObject, PathLike, ) -from litellm.types.utils import ExtractedFileData, LlmProviders +from litellm.types.utils import ExtractedFileData, LlmProviders, SpecialEnums from litellm.utils import get_llm_provider from ..base_aws_llm import BaseAWSLLM from ..common_utils import BedrockError +# litellm_params key used to hand the SigV4-signed GET headers from +# `transform_file_content_request` to `validate_environment` (the only hook +# the shared file-content HTTP handler exposes for setting request headers). +# Same pattern as the `upload_url` handoff in `transform_create_file_request`. +S3_SIGNED_GET_HEADERS_PARAM = "_s3_signed_get_headers" + + +class _BedrockS3RequestParams(BaseModel): + """Typed view of the credential/region params the S3 GetObject path reads.""" + + model_config = ConfigDict(extra="ignore") + + aws_access_key_id: str | None = None + aws_secret_access_key: str | None = None + aws_session_token: str | None = None + aws_region_name: str | None = None + aws_session_name: str | None = None + aws_profile_name: str | None = None + aws_role_name: str | None = None + aws_web_identity_token: str | None = None + aws_sts_endpoint: str | None = None + s3_region_name: str | None = None + s3_endpoint_url: str | None = None + + +class _TrustedS3ModelCredentials(BaseModel): + """The S3 bucket the server trusts file ids against, from the deployment snapshot.""" + + model_config = ConfigDict(extra="ignore") + + s3_bucket_name: str | None = None + + +def extract_s3_uri_from_file_id(file_id: str) -> str: + """ + Resolve a Bedrock file id to its S3 URI. + + Accepts either a base64-encoded LiteLLM unified file id (whose decoded + form carries `llm_output_file_id,s3://...`) or a direct `s3://` URI. + """ + try: + padded = file_id + "=" * (-len(file_id) % 4) + decoded = base64.urlsafe_b64decode(padded).decode() + + if decoded.startswith(SpecialEnums.LITELM_MANAGED_FILE_ID_PREFIX.value): + if "llm_output_file_id," in decoded: + return decoded.split("llm_output_file_id,")[1].split(";")[0] + except Exception: + pass + + if file_id.startswith("s3://"): + return file_id + + raise ValueError("file_id must be a managed LiteLLM S3 file id") + + +def get_configured_s3_bucket_name(litellm_params: Mapping[str, object]) -> str: + """ + Resolve the server-configured S3 bucket for Bedrock file operations. + + Only trusts the immutable server-side credential snapshot or the + environment; never a request-supplied param, since the bucket is what + `validate_managed_cloud_file_id` checks file ids against. + """ + trusted_model_credentials = litellm_params.get( + "_litellm_internal_model_credentials" + ) + bucket_name: str | None = None + if isinstance(trusted_model_credentials, MappingProxyType): + snapshot: dict[str, object] = {} + snapshot.update(trusted_model_credentials) # any-ok: untyped snapshot + bucket_name = _TrustedS3ModelCredentials.model_validate(snapshot).s3_bucket_name + bucket_name = bucket_name or os.getenv("AWS_S3_BUCKET_NAME") + if not bucket_name: + raise ValueError( + "S3 bucket_name is required. Set 's3_bucket_name' in proxy config or AWS_S3_BUCKET_NAME for Bedrock file content retrieval." + ) + return bucket_name + class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): """ @@ -63,16 +157,21 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): def validate_environment( self, - headers: dict, + headers: MutableMapping[str, object], model: str, messages: List[AllMessageValues], optional_params: dict, - litellm_params: dict, - api_key: Optional[str] = None, - api_base: Optional[str] = None, + litellm_params: MutableMapping[str, object], + api_key: str | None = None, + api_base: str | None = None, ) -> dict: - # No additional headers needed for S3 uploads - AWS credentials handled by BaseAWSLLM - return headers + result: dict[str, object] = {} + result.update(headers) + signed_headers = litellm_params.pop(S3_SIGNED_GET_HEADERS_PARAM, None) + if isinstance(signed_headers, Mapping): + result.update(signed_headers) # any-ok: untyped handoff headers + # otherwise no extra headers - AWS credentials are handled by BaseAWSLLM + return result def _get_content_from_openai_file(self, openai_file_content: FileTypes) -> str: """ @@ -927,23 +1026,114 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): def transform_file_content_request( self, - file_content_request, - optional_params: dict, - litellm_params: dict, - ) -> tuple[str, dict]: - raise NotImplementedError( - "BedrockFilesConfig does not support file content retrieval" + file_content_request: FileContentRequest, + optional_params: Mapping[str, object], + litellm_params: MutableMapping[str, object], + ) -> tuple[str, dict[str, str]]: + """ + Build a SigV4-signed S3 GetObject request for a Bedrock batch file. + + Bedrock batch file ids are `s3://bucket/key` URIs (or unified ids + that decode to one); the bucket and key are validated against the + server-configured bucket before any request is signed. + """ + file_id = file_content_request.get("file_id") + if not file_id: + raise ValueError("file_id is required for Bedrock file content retrieval") + + s3_uri = extract_s3_uri_from_file_id(file_id) + bucket_name, object_key = validate_managed_cloud_file_id( + file_id=s3_uri, + scheme="s3://", + configured_bucket_name=get_configured_s3_bucket_name(litellm_params), + allowed_object_prefixes=BEDROCK_MANAGED_S3_PREFIXES, + allow_legacy_cloud_file_ids=should_allow_legacy_cloud_file_ids( + litellm_params + ), ) + # The shared file-content handler passes optional_params={}, so AWS + # credentials/region arrive via litellm_params here (unlike the upload + # path). s3_region_name wins over aws_region_name, same priority as + # get_complete_file_url above. + merged_params: dict[str, object] = {} + merged_params.update(litellm_params) + merged_params.update(optional_params) + request_params = _BedrockS3RequestParams.model_validate(merged_params) + + region_preference = ( + request_params.s3_region_name or request_params.aws_region_name + ) + region_params: dict[str, str | None] = {"aws_region_name": region_preference} + aws_region_name = self._get_aws_region_name( + optional_params=region_params, model="" + ) + + s3_endpoint_url = ( + request_params.s3_endpoint_url + or f"https://s3.{aws_region_name}.amazonaws.com" + ).rstrip("/") + url = f"{s3_endpoint_url}/{bucket_name}/{encode_s3_object_key_for_url(object_key)}" + + litellm_params[S3_SIGNED_GET_HEADERS_PARAM] = self._sign_s3_get_request( + api_base=url, + aws_region_name=aws_region_name, + request_params=request_params, + ) + return url, {} + + def _sign_s3_get_request( + self, + api_base: str, + aws_region_name: str, + request_params: _BedrockS3RequestParams, + ) -> dict[str, str]: + """ + SigV4-sign an S3 GetObject request, mirroring `_sign_s3_request` (PUT). + """ + try: + import hashlib + + from botocore.auth import SigV4Auth + from botocore.awsrequest import AWSRequest + except ImportError: + raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.") + + credentials = self.get_credentials( # any-ok: boto3 Credentials is untyped + aws_access_key_id=request_params.aws_access_key_id, + aws_secret_access_key=request_params.aws_secret_access_key, + aws_session_token=request_params.aws_session_token, + aws_region_name=aws_region_name, + aws_session_name=request_params.aws_session_name, + aws_profile_name=request_params.aws_profile_name, + 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, + ) + + empty_body_hash = hashlib.sha256(b"").hexdigest() + aws_request = AWSRequest( # any-ok: botocore AWSRequest is untyped + method="GET", + url=api_base, + headers={"x-amz-content-sha256": empty_body_hash}, + ) + auth = SigV4Auth(credentials, "s3", aws_region_name) # any-ok: botocore untyped + auth.add_auth(aws_request) # any-ok: botocore request mutation is untyped + return dict(aws_request.headers) # any-ok: botocore headers are untyped + def transform_file_content_response( self, raw_response: httpx.Response, logging_obj: LiteLLMLoggingObj, litellm_params: dict, ) -> HttpxBinaryResponseContent: - raise NotImplementedError( - "BedrockFilesConfig does not support file content retrieval" - ) + if raw_response.status_code >= 400: + raise BedrockError( + status_code=raw_response.status_code, + message=raw_response.text, + headers=raw_response.headers, + ) + return HttpxBinaryResponseContent(response=raw_response) class BedrockJsonlFilesTransformation: diff --git a/litellm/llms/bedrock/image_edit/stability_transformation.py b/litellm/llms/bedrock/image_edit/stability_transformation.py index 2d73e47003d..d00d62a8530 100644 --- a/litellm/llms/bedrock/image_edit/stability_transformation.py +++ b/litellm/llms/bedrock/image_edit/stability_transformation.py @@ -149,7 +149,7 @@ class BedrockStabilityImageEditConfig(BaseImageEditConfig): return mapped_params - def transform_image_edit_request( # noqa: PLR0915 + def transform_image_edit_request( self, model: str, prompt: Optional[str], diff --git a/litellm/llms/bedrock/messages/mantle_transformation.py b/litellm/llms/bedrock/messages/mantle_transformation.py index a78f696a057..94e7f90b719 100644 --- a/litellm/llms/bedrock/messages/mantle_transformation.py +++ b/litellm/llms/bedrock/messages/mantle_transformation.py @@ -6,8 +6,9 @@ AmazonAnthropicClaudeMessagesConfig. Overrides only the URL and model-prefix stripping that are specific to the bedrock-mantle endpoint. """ -from typing import TYPE_CHECKING, Any, Dict, List, Optional +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple +from litellm.llms.bedrock.common_utils import build_mantle_messages_url from litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation import ( AmazonAnthropicClaudeMessagesConfig, ) @@ -20,10 +21,6 @@ if TYPE_CHECKING: else: LiteLLMLoggingObj = Any -MANTLE_ENDPOINT_TEMPLATE = ( - "https://bedrock-mantle.{region}.api.aws/anthropic/v1/messages" -) - class AmazonMantleMessagesConfig(AmazonAnthropicClaudeMessagesConfig): """ @@ -43,7 +40,37 @@ class AmazonMantleMessagesConfig(AmazonAnthropicClaudeMessagesConfig): stream: Optional[bool] = None, ) -> str: region = self._get_aws_region_name(optional_params=optional_params, model=model) - return MANTLE_ENDPOINT_TEMPLATE.format(region=region) + return build_mantle_messages_url( + api_base=api_base, + aws_bedrock_runtime_endpoint=optional_params.get( + "aws_bedrock_runtime_endpoint" + ), + region=region, + ) + + def validate_anthropic_messages_environment( + self, + headers: dict, + model: str, + messages: List[Any], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> Tuple[dict, Optional[str]]: + headers, api_base = super().validate_anthropic_messages_environment( + headers=headers, + model=model, + messages=messages, + optional_params=optional_params, + litellm_params=litellm_params, + api_key=api_key, + api_base=api_base, + ) + project_id = litellm_params.get("aws_bedrock_project_id") + if project_id: + headers["anthropic-workspace"] = project_id + return headers, api_base def transform_anthropic_messages_request( self, diff --git a/litellm/llms/bedrock/passthrough/guardrail_translation/__init__.py b/litellm/llms/bedrock/passthrough/guardrail_translation/__init__.py new file mode 100644 index 00000000000..e38044afb36 --- /dev/null +++ b/litellm/llms/bedrock/passthrough/guardrail_translation/__init__.py @@ -0,0 +1,5 @@ +from litellm.llms.bedrock.passthrough.guardrail_translation.handler import ( + BedrockPassthroughGuardrailHandler, +) + +__all__ = ["BedrockPassthroughGuardrailHandler"] diff --git a/litellm/llms/bedrock/passthrough/guardrail_translation/handler.py b/litellm/llms/bedrock/passthrough/guardrail_translation/handler.py new file mode 100644 index 00000000000..0522bb249e1 --- /dev/null +++ b/litellm/llms/bedrock/passthrough/guardrail_translation/handler.py @@ -0,0 +1,507 @@ +from typing import TYPE_CHECKING, Any, List, Optional, Tuple, Union + +from litellm._logging import verbose_proxy_logger +from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation +from litellm.llms.base_llm.guardrail_translation.utils import ( + effective_skip_system_message_for_guardrail, + effective_skip_tool_message_for_guardrail, +) +from litellm.types.utils import GenericGuardrailAPIInputs + +if TYPE_CHECKING: + from litellm.integrations.custom_guardrail import CustomGuardrail + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.proxy._types import UserAPIKeyAuth + from litellm.proxy.utils import ProxyLogging + +_CONVERSE_ACTIONS = frozenset({"converse", "converse-stream"}) +_EVENT_STREAM_CONTENT_TYPE = "vnd.amazon.eventstream" +_EVENT_STREAM_MEDIA_TYPE = "application/vnd.amazon.eventstream" + + +def _is_converse_endpoint(endpoint: str) -> bool: + parts = endpoint.rstrip("/").split("/") + return bool(parts) and parts[-1] in _CONVERSE_ACTIONS + + +def _generic_passthrough_handler() -> BaseTranslation: + """ + Fallback for non-Converse Bedrock routes (e.g. invoke). The generic + handler scans the full request/response payload so blocking guardrails + still run, matching how other passthrough providers are guarded. + """ + from litellm.llms.pass_through.guardrail_translation.handler import ( + PassThroughEndpointHandler, + ) + + return PassThroughEndpointHandler() + + +_StringHolder = Tuple[Any, Union[str, int]] + + +def _collect_strings(node: Any, holders: List[_StringHolder]) -> None: + """ + Record a (container, key) holder for every non-empty string value nested + under an arbitrary JSON node, so prompt content a caller hides in fields + like ``toolUse.input`` or ``toolResult.content[].json`` is still scanned + and can be written back in place. Iterative to avoid unbounded recursion + on deeply nested payloads. + """ + stack: List[Any] = [node] + while stack: + current = stack.pop() + if isinstance(current, dict): + for key, value in current.items(): + if isinstance(value, str): + if value: + holders.append((current, key)) + else: + stack.append(value) + elif isinstance(current, list): + for index, value in enumerate(current): + if isinstance(value, str): + if value: + holders.append((current, index)) + else: + stack.append(value) + + +def _collect_block_text(block: dict, holders: List[_StringHolder]) -> None: + text = block.get("text") + if isinstance(text, str) and text: + holders.append((block, "text")) + + +def _extract_converse_texts( + body: dict, + skip_system: bool, + skip_tool: bool, +) -> Tuple[List[str], List[_StringHolder]]: + """ + Walk a Bedrock Converse request body and collect text content. + + Returns (texts, holders) where each holder is the (container, key) pair + that owns the extracted string, so write-back mutates it in place. Besides + top-level ``text`` blocks this scans the arbitrary-JSON fields a caller can + hide prompt content in -- ``toolUse.input`` and + ``toolResult.content[].json`` (alongside ``toolResult.content[].text``) -- + as well as the request-level fields still forwarded to Bedrock that a caller + can route blocked content through: ``toolConfig.tools`` (tool names, + descriptions and input schemas) and ``additionalModelRequestFields``. Tool + message blocks are skipped when tool messages are excluded, but tool + definitions are always scanned to match the chat-completions guardrail path. + """ + holders: List[_StringHolder] = [] + + if not skip_system: + for block in body.get("system") or []: + if isinstance(block, dict): + _collect_block_text(block, holders) + + for message in body.get("messages") or []: + if not isinstance(message, dict): + continue + for block in message.get("content") or []: + if not isinstance(block, dict): + continue + if skip_tool and ("toolUse" in block or "toolResult" in block): + continue + _collect_block_text(block, holders) + tool_use = block.get("toolUse") + if isinstance(tool_use, dict): + _collect_strings(tool_use.get("input"), holders) + tool_result = block.get("toolResult") + if isinstance(tool_result, dict): + for inner in tool_result.get("content") or []: + if isinstance(inner, dict): + _collect_block_text(inner, holders) + _collect_strings(inner.get("json"), holders) + + tool_config = body.get("toolConfig") + if isinstance(tool_config, dict): + _collect_strings(tool_config.get("tools"), holders) + + _collect_strings(body.get("additionalModelRequestFields"), holders) + + texts = [container[key] for container, key in holders] + return texts, holders + + +def _extract_converse_output_texts( + content_blocks: List[Any], +) -> Tuple[List[str], List[_StringHolder]]: + """ + Collect user-visible text from Bedrock Converse output content blocks. + + Covers ``text`` blocks plus the other content-bearing fields a model can + emit -- ``toolUse.input``, ``reasoningContent.reasoningText.text`` and + ``citationsContent.content[].text`` -- while leaving structural values such + as reasoning signatures and citation sources untouched. + """ + holders: List[_StringHolder] = [] + for block in content_blocks: + if not isinstance(block, dict): + continue + _collect_block_text(block, holders) + tool_use = block.get("toolUse") + if isinstance(tool_use, dict): + _collect_strings(tool_use.get("input"), holders) + reasoning = block.get("reasoningContent") + if isinstance(reasoning, dict): + reasoning_text = reasoning.get("reasoningText") + if isinstance(reasoning_text, dict): + _collect_block_text(reasoning_text, holders) + citations = block.get("citationsContent") + if isinstance(citations, dict): + for cited in citations.get("content") or []: + if isinstance(cited, dict): + _collect_block_text(cited, holders) + texts = [container[key] for container, key in holders] + return texts, holders + + +def _write_back_texts( + guardrailed_texts: List[str], + holders: List[_StringHolder], +) -> None: + if len(guardrailed_texts) < len(holders): + verbose_proxy_logger.warning( + "BedrockPassthroughGuardrailHandler: guardrail returned %d texts for %d " + "extracted fields; the unreturned fields keep their original text", + len(guardrailed_texts), + len(holders), + ) + for idx, (container, key) in enumerate(holders): + if idx >= len(guardrailed_texts): + break + container[key] = guardrailed_texts[idx] + + +_DeltaHolder = Tuple[Any, Any, Union[str, int]] + + +def _collect_stream_delta_text_holders(delta: Any) -> List[_DeltaHolder]: + """ + Collect the user-visible text strings a Bedrock Converse ``contentBlockDelta`` + can carry, matching the coverage of the non-streaming output handler. + + Each holder is ``(group_key, container, key)`` where ``container[key]`` is the + text. ``group_key`` ties together fragments that belong to the same logical + stream (e.g. a single mask token split across frames) so they are + concatenated before guardrailing and redistributed afterwards. Structural + values such as reasoning signatures, redacted reasoning and citation sources + are left out so they are never rewritten. + """ + holders: List[_DeltaHolder] = [] + if not isinstance(delta, dict): + return holders + if isinstance(delta.get("text"), str): + holders.append(("text", delta, "text")) + tool_use = delta.get("toolUse") + if isinstance(tool_use, dict) and isinstance(tool_use.get("input"), str): + holders.append(("tool", tool_use, "input")) + reasoning = delta.get("reasoningContent") + if isinstance(reasoning, dict) and isinstance(reasoning.get("text"), str): + holders.append(("reasoning", reasoning, "text")) + citations = delta.get("citationsContent") + if isinstance(citations, dict): + for index, cited in enumerate(citations.get("content") or []): + if isinstance(cited, dict) and isinstance(cited.get("text"), str): + holders.append((("citation", index), cited, "text")) + return holders + + +class BedrockPassthroughGuardrailHandler(BaseTranslation): + @staticmethod + def is_event_stream_content_type(content_type: str) -> bool: + return _EVENT_STREAM_CONTENT_TYPE in content_type + + @staticmethod + def event_stream_media_type() -> str: + return _EVENT_STREAM_MEDIA_TYPE + + @staticmethod + def event_stream_endpoint_is_de_anonymizable(endpoint: str) -> bool: + return _is_converse_endpoint(endpoint) + + @staticmethod + async def de_anonymize_event_stream( + body_bytes: bytes, + proxy_logging_obj: "ProxyLogging", + user_api_key_dict: "UserAPIKeyAuth", + data: dict, + ) -> bytes: + import json as _json + import struct + from binascii import crc32 as esm_crc32 + + from botocore.eventstream import EventStreamBuffer + + frames: list[dict] = [] + offset = 0 + + while offset + 16 <= len(body_bytes): + total_length = struct.unpack("!I", body_bytes[offset : offset + 4])[0] + if total_length < 16 or offset + total_length > len(body_bytes): + break + frame_raw = body_bytes[offset : offset + total_length] + offset += total_length + + try: + buf = EventStreamBuffer() + buf.add_data(frame_raw) + msg = next(iter(buf)) + event_type = msg.headers.get(":event-type") + payload_bytes = msg.payload + except Exception as e: + verbose_proxy_logger.debug( + "BedrockPassthroughGuardrailHandler: could not decode event-stream " + "frame, forwarding it unmodified: %s", + e, + ) + frames.append({"raw": frame_raw, "texts": []}) + continue + + texts: List[Tuple[Any, str]] = [] + if event_type == "contentBlockDelta": + try: + payload_dict = _json.loads(payload_bytes) + texts = [ + (group_key, container[key]) + for group_key, container, key in _collect_stream_delta_text_holders( + payload_dict.get("delta") + ) + ] + except Exception as e: + verbose_proxy_logger.debug( + "BedrockPassthroughGuardrailHandler: could not parse " + "contentBlockDelta payload, forwarding frame unmodified: %s", + e, + ) + + frames.append({"raw": frame_raw, "texts": texts}) + + trailing_bytes = body_bytes[offset:] + + group_order: List[Any] = [] + group_members: dict[Any, list[Tuple[int, int]]] = {} + group_texts: dict[Any, list[str]] = {} + for frame_idx, frame in enumerate(frames): + for local_idx, (group_key, text) in enumerate(frame["texts"]): + if group_key not in group_members: + group_members[group_key] = [] + group_texts[group_key] = [] + group_order.append(group_key) + group_members[group_key].append((frame_idx, local_idx)) + group_texts[group_key].append(text) + + active_groups = [gk for gk in group_order if "".join(group_texts[gk])] + if not active_groups: + return body_bytes + + synthetic_response: dict = { + "output": { + "message": { + "role": "assistant", + "content": [ + {"text": "".join(group_texts[gk])} for gk in active_groups + ], + } + }, + "stopReason": "end_turn", + } + + processed = await proxy_logging_obj.post_call_success_hook( + data=data, + user_api_key_dict=user_api_key_dict, + response=synthetic_response, # type: ignore[arg-type] + ) + + if not isinstance(processed, dict): + verbose_proxy_logger.debug( + "BedrockPassthroughGuardrailHandler: post_call_success_hook returned %s, " + "leaving event stream unmodified", + type(processed).__name__, + ) + return body_bytes + + try: + processed_blocks = processed["output"]["message"]["content"] # type: ignore[index] + de_anonymized_texts = [ + processed_blocks[i]["text"] for i in range(len(active_groups)) + ] + except (KeyError, IndexError, TypeError): + return body_bytes + + new_text_map: dict[Tuple[int, int], str] = {} + for group_key, de_anonymized_text in zip(active_groups, de_anonymized_texts): + members = group_members[group_key] + orig_texts = group_texts[group_key] + total_orig = sum(len(t) for t in orig_texts) or 1 + de_anon_len = len(de_anonymized_text) + pos = 0 + for k, member in enumerate(members): + if k == len(members) - 1: + new_text_map[member] = de_anonymized_text[pos:] + else: + end = pos + round(de_anon_len * len(orig_texts[k]) / total_orig) + new_text_map[member] = de_anonymized_text[pos:end] + pos = end + + result_parts: list[bytes] = [] + + for frame_idx, frame in enumerate(frames): + if not frame["texts"]: + result_parts.append(frame["raw"]) + continue + + frame_raw = frame["raw"] + orig_total = struct.unpack("!I", frame_raw[0:4])[0] + orig_hdrs_len = struct.unpack("!I", frame_raw[4:8])[0] + headers_bytes = frame_raw[12 : 12 + orig_hdrs_len] + + try: + payload_dict = _json.loads( + frame_raw[12 + orig_hdrs_len : orig_total - 4] + ) + for local_idx, (_, container, key) in enumerate( + _collect_stream_delta_text_holders(payload_dict.get("delta")) + ): + new_text = new_text_map.get((frame_idx, local_idx)) + if new_text is not None: + container[key] = new_text + new_payload = _json.dumps(payload_dict, separators=(",", ":")).encode() + except Exception: + result_parts.append(frame_raw) + continue + + new_total = 12 + orig_hdrs_len + len(new_payload) + 4 + prelude = struct.pack("!II", new_total, orig_hdrs_len) + prelude_crc_val = esm_crc32(prelude) & 0xFFFFFFFF + prelude_crc_b = struct.pack("!I", prelude_crc_val) + part_for_msg_crc = prelude_crc_b + headers_bytes + new_payload + msg_crc_val = esm_crc32(part_for_msg_crc, prelude_crc_val) & 0xFFFFFFFF + msg_crc_b = struct.pack("!I", msg_crc_val) + + result_parts.append( + prelude + prelude_crc_b + headers_bytes + new_payload + msg_crc_b + ) + + result_parts.append(trailing_bytes) + return b"".join(result_parts) + + async def process_input_messages( + self, + data: dict, + guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, + ) -> Any: + endpoint = data.get("endpoint", "") + body = data.get("data") + + if not _is_converse_endpoint(endpoint): + return await _generic_passthrough_handler().process_input_messages( + data=data, + guardrail_to_apply=guardrail_to_apply, + litellm_logging_obj=litellm_logging_obj, + ) + + if not isinstance(body, dict) or not isinstance(body.get("messages"), list): + return data + + skip_system = effective_skip_system_message_for_guardrail(guardrail_to_apply) + skip_tool = effective_skip_tool_message_for_guardrail(guardrail_to_apply) + + texts, holders = _extract_converse_texts(body, skip_system, skip_tool) + + if not texts: + return data + + inputs = GenericGuardrailAPIInputs(texts=texts) + model = data.get("model") + if model: + inputs["model"] = model + + guardrailed_inputs = await guardrail_to_apply.apply_guardrail( + inputs=inputs, + request_data=data, + input_type="request", + logging_obj=litellm_logging_obj, + ) + + guardrailed_texts = guardrailed_inputs.get("texts", []) + if guardrailed_texts: + _write_back_texts(guardrailed_texts, holders) + + return data + + async def process_output_response( + self, + response: Any, + guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, + user_api_key_dict: Optional[Any] = None, + request_data: Optional[dict] = None, + ) -> Any: + endpoint = (request_data or {}).get("endpoint", "") + if endpoint and not _is_converse_endpoint(endpoint): + return await _generic_passthrough_handler().process_output_response( + response=response, + guardrail_to_apply=guardrail_to_apply, + litellm_logging_obj=litellm_logging_obj, + user_api_key_dict=user_api_key_dict, + request_data=request_data, + ) + + if not isinstance(response, dict): + return response + + output_message = ( + response.get("output", {}).get("message", {}) + if isinstance(response.get("output"), dict) + else {} + ) + content_blocks = ( + output_message.get("content") if isinstance(output_message, dict) else None + ) + + if not isinstance(content_blocks, list): + return response + + texts, holders = _extract_converse_output_texts(content_blocks) + + if not texts: + return response + + effective_request_data = request_data or {} + if ( + "litellm_metadata" not in effective_request_data + and user_api_key_dict is not None + ): + user_metadata = self.transform_user_api_key_dict_to_metadata( + user_api_key_dict + ) + if user_metadata: + effective_request_data = { + **effective_request_data, + "litellm_metadata": user_metadata, + } + + inputs = GenericGuardrailAPIInputs(texts=texts) + model = effective_request_data.get("model") if effective_request_data else None + if model: + inputs["model"] = model + + guardrailed_inputs = await guardrail_to_apply.apply_guardrail( + inputs=inputs, + request_data=effective_request_data, + input_type="response", + logging_obj=litellm_logging_obj, + ) + + guardrailed_texts = guardrailed_inputs.get("texts", []) + if guardrailed_texts: + _write_back_texts(guardrailed_texts, holders) + + return response diff --git a/litellm/llms/bedrock_mantle/chat/transformation.py b/litellm/llms/bedrock_mantle/chat/transformation.py index 81a56030a5c..f688cea10f1 100644 --- a/litellm/llms/bedrock_mantle/chat/transformation.py +++ b/litellm/llms/bedrock_mantle/chat/transformation.py @@ -4,26 +4,38 @@ Amazon Bedrock Mantle - OpenAI-compatible inference engine in Amazon Bedrock. API docs: https://docs.aws.amazon.com/bedrock/latest/userguide/bedrock-mantle.html Base URL: https://bedrock-mantle.{region}.api.aws/v1 -Auth: AWS Bedrock API key as Bearer token (set via BEDROCK_MANTLE_API_KEY env var) - or region-aware key via BEDROCK_MANTLE_{REGION}_API_KEY. +Auth: Bearer token (litellm_params.api_key, BEDROCK_MANTLE_API_KEY, or the + standard AWS_BEARER_TOKEN_BEDROCK) when present; otherwise AWS SigV4 + (service "bedrock") over the standard credential chain. See + BedrockMantleAuthMixin in common_utils. """ -from typing import Iterator, AsyncIterator, Any, Optional, Tuple, Union +from typing import Iterator, AsyncIterator, Any, List, Optional, Tuple, Union import litellm from litellm._logging import verbose_logger +from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM +from litellm.llms.bedrock_mantle.common_utils import ( + BEDROCK_MANTLE_DEFAULT_REGION, + BedrockMantleAuthMixin, +) from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import AllMessageValues +from litellm.types.router import GenericLiteLLMParams +from ..common_utils import mantle_base_segment from ...openai_like.chat.transformation import OpenAILikeChatConfig -BEDROCK_MANTLE_DEFAULT_REGION = "us-east-1" - -class BedrockMantleChatConfig(OpenAILikeChatConfig): +class BedrockMantleChatConfig(BedrockMantleAuthMixin, OpenAILikeChatConfig): """ Transformation config for Amazon Bedrock Mantle OpenAI-compatible API. """ + def __init__(self, aws_signer: BaseAWSLLM | None = None): + super().__init__() + self._aws_signer = aws_signer or BaseAWSLLM() + @property def custom_llm_provider(self) -> Optional[str]: return "bedrock_mantle" @@ -33,21 +45,55 @@ class BedrockMantleChatConfig(OpenAILikeChatConfig): return super().get_config() def _get_openai_compatible_provider_info( - self, api_base: Optional[str], api_key: Optional[str] + self, + api_base: Optional[str], + api_key: Optional[str], + litellm_params: Optional[GenericLiteLLMParams] = None, + model: str | None = None, ) -> Tuple[Optional[str], Optional[str]]: region = ( - get_secret_str("BEDROCK_MANTLE_REGION") + (litellm_params.aws_region_name if litellm_params else None) + or get_secret_str("BEDROCK_MANTLE_REGION") + or get_secret_str("AWS_REGION_NAME") or get_secret_str("AWS_REGION") or BEDROCK_MANTLE_DEFAULT_REGION ) + BaseAWSLLM._validate_aws_region_name(region) + # The base path segment is data-driven per model (use_openai_responses_path + # flag): gemma-4-* and gpt-5.x are served on /openai/v1, everything else on + # /v1. An explicit api_base still wins over the derived default. api_base = ( api_base or get_secret_str("BEDROCK_MANTLE_API_BASE") - or f"https://bedrock-mantle.{region}.api.aws/v1" + or f"https://bedrock-mantle.{region}.api.aws/{mantle_base_segment(model, litellm.model_cost)}" ) - dynamic_api_key = api_key or get_secret_str("BEDROCK_MANTLE_API_KEY") + dynamic_api_key = self._resolve_bearer_token(api_key) return api_base, dynamic_api_key + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + headers = super().validate_environment( + headers=headers, + model=model, + messages=messages, + optional_params=optional_params, + litellm_params=litellm_params, + api_key=api_key, + api_base=api_base, + ) + project_id = litellm_params.get("aws_bedrock_project_id") + if project_id: + headers["OpenAI-Project"] = project_id + return headers + def get_supported_openai_params(self, model: str) -> list: base_params = super().get_supported_openai_params(model) try: diff --git a/litellm/llms/bedrock_mantle/common_utils.py b/litellm/llms/bedrock_mantle/common_utils.py new file mode 100644 index 00000000000..d517ab940ce --- /dev/null +++ b/litellm/llms/bedrock_mantle/common_utils.py @@ -0,0 +1,147 @@ +"""Shared auth, region resolution, and routing helpers for the Amazon Bedrock Mantle provider. + +Mantle authenticates with a Bearer token when one is available +(litellm_params.api_key, BEDROCK_MANTLE_API_KEY, or the standard +AWS_BEARER_TOKEN_BEDROCK); otherwise it falls back to AWS SigV4 (service +"bedrock") over the standard credential chain (IAM role / access key / profile / +web identity). The Chat Completions and Responses backends share this behaviour +through BedrockMantleAuthMixin so the two paths can never drift apart. + +The two routing helpers (mantle_supports_responses, mantle_base_segment) are +pure functions of (model, model_cost) so they can be unit-tested without patching +global state. +""" + +import re +from typing import Tuple + +from botocore.exceptions import ( + CredentialRetrievalError, + NoCredentialsError, + PartialCredentialsError, + ProfileNotFound, +) + +from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM +from litellm.secret_managers.main import get_secret_str + +BEDROCK_MANTLE_DEFAULT_REGION = "us-east-1" + +# Standard Mantle host: https://bedrock-mantle..api.aws (group 1 = region). +MANTLE_HOST_RE = re.compile( + r"^https?://bedrock-mantle\.([^/.]+)\.api\.aws", re.IGNORECASE +) + + +class BedrockMantleAuthMixin: + _aws_signer: BaseAWSLLM + + @staticmethod + def _resolve_bearer_token(api_key: str | None) -> str | None: + return ( + api_key + or get_secret_str("BEDROCK_MANTLE_API_KEY") + or get_secret_str("AWS_BEARER_TOKEN_BEDROCK") + ) + + @staticmethod + def _resolve_region(params: dict) -> str: + region = params.get("aws_region_name") + if region: + BaseAWSLLM._validate_aws_region_name(region) + return region + base = params.get("api_base") or get_secret_str("BEDROCK_MANTLE_API_BASE") + if base: + match = MANTLE_HOST_RE.match(base.rstrip("/")) + if match: + return match.group(1) + return ( + get_secret_str("BEDROCK_MANTLE_REGION") + or get_secret_str("AWS_REGION_NAME") + or get_secret_str("AWS_REGION") + or BEDROCK_MANTLE_DEFAULT_REGION + ) + + def sign_request( + self, + headers: dict, + optional_params: dict, + request_data: dict, + api_base: str, + api_key: str | None = None, + model: str | None = None, + stream: bool | None = None, + fake_stream: bool | None = None, + ) -> Tuple[dict, bytes | None]: + bearer = self._resolve_bearer_token(api_key) + if not bearer: + # Pin the credential-scope region to the region of the actual signing URL + # so the SigV4 scope and URL host can never disagree, even when a stale + # api_base and aws_region_name point at different regions. + host_match = MANTLE_HOST_RE.match(api_base.rstrip("/")) + optional_params = { + **optional_params, + "aws_region_name": ( + host_match.group(1) + if host_match + else self._resolve_region({**optional_params, "api_base": api_base}) + ), + } + headers = {k: v for k, v in headers.items() if k.lower() != "authorization"} + try: + return self._aws_signer._sign_request( + service_name="bedrock", + headers=headers, + optional_params=optional_params, + request_data=request_data, + api_base=api_base, + api_key=bearer, + model=model, + stream=stream, + fake_stream=fake_stream, + ) + except ( + NoCredentialsError, + PartialCredentialsError, + ProfileNotFound, + CredentialRetrievalError, + ) as e: + raise ValueError( + "Bedrock Mantle auth failed: no Bearer token and no usable AWS " + "credentials. Set BEDROCK_MANTLE_API_KEY (or AWS_BEARER_TOKEN_BEDROCK) " + "or pass api_key for Bearer auth, or provide AWS credentials " + "(IAM role / access key / profile / web identity) for SigV4." + ) from e + + +def mantle_supports_responses(model: str | None, model_cost: dict) -> bool: + """Whether a Bedrock Mantle model can serve the native Responses API. + + Purely data-driven from the model's price-map capability signal -- either + /v1/responses in supported_endpoints, or mode=responses -- both overridable + via register_model and proxy model_info, so onboarding a model is a JSON + change, never a code change. There is deliberately NO model-name match here: + capability is per-model, not per-family (openai.gpt-oss-120b supports + Responses while openai.gpt-oss-safeguard-120b does not, despite sharing the + gpt-oss substring), so a substring gate would be wrong. A model absent from + model_cost simply has no signal and returns False (chat-completions emulation). + """ + entry = model_cost.get(f"bedrock_mantle/{model}", {}) + if "/v1/responses" in (entry.get("supported_endpoints") or []): + return True + return entry.get("mode") == "responses" + + +def mantle_base_segment(model: str | None, model_cost: dict) -> str: + """Return the base path segment for a Bedrock Mantle model's OpenAI surface. + + Data-driven from the model's price-map use_openai_responses_path flag + (overridable via register_model / proxy model_info). Per the AWS model cards, + gpt-5.x and the google gemma-4-* family carry that flag and are served on the + /openai/v1 base (.../openai/v1/responses and .../openai/v1/chat/completions); + every other model including gpt-oss uses the standard /v1 base. The segment is + the base for the model's whole OpenAI-compatible surface, so both the chat and + responses configs derive from it -- there is no separate model-name rule. + """ + entry = model_cost.get(f"bedrock_mantle/{model}", {}) + return "openai/v1" if entry.get("use_openai_responses_path") is True else "v1" diff --git a/litellm/llms/bedrock_mantle/responses/transformation.py b/litellm/llms/bedrock_mantle/responses/transformation.py index df219091074..2e30f85fd0e 100644 --- a/litellm/llms/bedrock_mantle/responses/transformation.py +++ b/litellm/llms/bedrock_mantle/responses/transformation.py @@ -1,8 +1,10 @@ """ Amazon Bedrock Mantle - Responses API backend. -gpt-5.5 / gpt-5.4 on Mantle are exposed ONLY on the `/openai/v1/responses` -path (not the standard `/v1/responses`). Payloads and SSE follow the OpenAI +Mantle serves Responses on two upstream paths: gpt frontier models (gpt-5.5 / +gpt-5.4) on `/openai/v1/responses`, and everything else that supports Responses +(e.g. gpt-oss) on the standard `/v1/responses`. The gate picks the path per +model and injects it via `use_openai_path`. Payloads and SSE follow the OpenAI Responses spec, so this config inherits OpenAIResponsesAPIConfig and overrides only the endpoint URL and authentication. @@ -13,24 +15,20 @@ role / access key / profile / web identity), signed via the shared BaseAWSLLM._sign_request after the request body is finalized. """ -import re -from typing import Optional, Tuple - -from botocore.exceptions import ( - CredentialRetrievalError, - NoCredentialsError, - PartialCredentialsError, - ProfileNotFound, -) +from typing import Any, Dict, List, Optional +from litellm._logging import verbose_logger from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM +from litellm.llms.bedrock_mantle.common_utils import ( + MANTLE_HOST_RE, + BedrockMantleAuthMixin, +) from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import ResponsesAPIOptionalRequestParams from litellm.types.router import GenericLiteLLMParams from litellm.types.utils import LlmProviders -BEDROCK_MANTLE_DEFAULT_REGION = "us-east-1" - # Checked longest/most-specific first so a full endpoint URL collapses to host # in one pass and the appended path never doubles. _BASE_SUFFIXES_TO_STRIP = ( @@ -41,38 +39,26 @@ _BASE_SUFFIXES_TO_STRIP = ( "/v1", ) -# Standard Mantle host: https://bedrock-mantle..api.aws (group 1 = region). -_MANTLE_HOST_RE = re.compile( - r"^https?://bedrock-mantle\.([^/.]+)\.api\.aws", re.IGNORECASE +# Per Bedrock Mantle Responses API validation errors. +_BEDROCK_MANTLE_SUPPORTED_RESPONSE_TOOL_TYPES = frozenset( + {"function", "mcp", "custom", "namespace", "tool_search"} ) -class BedrockMantleResponsesAPIConfig(OpenAIResponsesAPIConfig): - def __init__(self, aws_signer: Optional[BaseAWSLLM] = None): +class BedrockMantleResponsesAPIConfig(BedrockMantleAuthMixin, OpenAIResponsesAPIConfig): + def __init__( + self, + aws_signer: Optional[BaseAWSLLM] = None, + use_openai_path: bool = True, + ): super().__init__() self._aws_signer = aws_signer or BaseAWSLLM() + self.use_openai_path = use_openai_path @property def custom_llm_provider(self) -> LlmProviders: return LlmProviders.BEDROCK_MANTLE - @staticmethod - def _resolve_region(params: dict) -> str: - region = params.get("aws_region_name") - if region: - return region - base = params.get("api_base") or get_secret_str("BEDROCK_MANTLE_API_BASE") - if base: - match = _MANTLE_HOST_RE.match(base.rstrip("/")) - if match: - return match.group(1) - return ( - get_secret_str("BEDROCK_MANTLE_REGION") - or get_secret_str("AWS_REGION_NAME") - or get_secret_str("AWS_REGION") - or BEDROCK_MANTLE_DEFAULT_REGION - ) - def get_complete_url( self, api_base: Optional[str], @@ -92,21 +78,20 @@ class BedrockMantleResponsesAPIConfig(OpenAIResponsesAPIConfig): # For the standard Mantle host (including the default-region base that # responses/main.py auto-injects into litellm_params.api_base), pin to the # single resolved region so aws_region_name wins; preserve custom proxy hosts. - if _MANTLE_HOST_RE.match(base): + if MANTLE_HOST_RE.match(base): base = f"https://bedrock-mantle.{region}.api.aws" - return f"{base}/openai/v1/responses" + path = "/openai/v1/responses" if self.use_openai_path else "/v1/responses" + return f"{base}{path}" def validate_environment( self, headers: dict, model: str, litellm_params: Optional[GenericLiteLLMParams] ) -> dict: litellm_params = litellm_params or GenericLiteLLMParams() - api_key = ( - litellm_params.api_key - or get_secret_str("BEDROCK_MANTLE_API_KEY") - or get_secret_str("AWS_BEARER_TOKEN_BEDROCK") - ) - if api_key: - headers["Authorization"] = f"Bearer {api_key}" + bearer = self._resolve_bearer_token(litellm_params.api_key) + if bearer: + headers["Authorization"] = f"Bearer {bearer}" + if litellm_params.aws_bedrock_project_id: + headers["OpenAI-Project"] = litellm_params.aws_bedrock_project_id return headers def supports_native_file_search(self) -> bool: @@ -115,57 +100,52 @@ class BedrockMantleResponsesAPIConfig(OpenAIResponsesAPIConfig): def supports_native_websocket(self) -> bool: return False - def sign_request( - self, - headers: dict, - optional_params: dict, - request_data: dict, - api_base: str, - api_key: Optional[str] = None, - model: Optional[str] = None, - stream: Optional[bool] = None, - fake_stream: Optional[bool] = None, - ) -> Tuple[dict, Optional[bytes]]: - bearer = ( - api_key - or get_secret_str("BEDROCK_MANTLE_API_KEY") - or get_secret_str("AWS_BEARER_TOKEN_BEDROCK") - ) - if not bearer: - # SigV4 path. Pin the credential-scope region to the region of the actual - # signing URL (api_base, already region-resolved by get_complete_url) so the - # SigV4 scope and the URL host can never disagree. Resolve from api_base first, - # then fall back to the regular precedence. Also drop any caller Authorization - # so _sign_request's restore-original-Authorization step cannot override the - # SigV4 header. - optional_params = { - **optional_params, - "aws_region_name": self._resolve_region( - {**optional_params, "api_base": api_base} - ), - } - headers = {k: v for k, v in headers.items() if k.lower() != "authorization"} - try: - return self._aws_signer._sign_request( - service_name="bedrock", - headers=headers, - optional_params=optional_params, - request_data=request_data, - api_base=api_base, - api_key=bearer, - model=model, - stream=stream, - fake_stream=fake_stream, + @staticmethod + def _filter_unsupported_tools(tools: List[Any]) -> List[Any]: + """Keep only tool types Mantle's Responses API accepts.""" + kept: List[Any] = [] + dropped_types: List[str] = [] + for tool in tools: + if not isinstance(tool, dict): + kept.append(tool) + continue + tool_type = tool.get("type") + if tool_type in _BEDROCK_MANTLE_SUPPORTED_RESPONSE_TOOL_TYPES: + kept.append(tool) + else: + dropped_types.append(str(tool_type)) + + if dropped_types: + verbose_logger.warning( + "Bedrock Mantle Responses API: dropping unsupported tool type(s) " + "%s (supported: %s).", + sorted(set(dropped_types)), + sorted(_BEDROCK_MANTLE_SUPPORTED_RESPONSE_TOOL_TYPES), ) - except ( - NoCredentialsError, - PartialCredentialsError, - ProfileNotFound, - CredentialRetrievalError, - ) as e: - raise ValueError( - "Bedrock Mantle auth failed: no Bearer token and no usable AWS " - "credentials. Set BEDROCK_MANTLE_API_KEY (or AWS_BEARER_TOKEN_BEDROCK) " - "or pass api_key for Bearer auth, or provide AWS credentials " - "(IAM role / access key / profile / web identity) for SigV4." - ) from e + + return kept + + def map_openai_params( + self, + response_api_optional_params: ResponsesAPIOptionalRequestParams, + model: str, + drop_params: bool, + ) -> Dict: + params = super().map_openai_params( + response_api_optional_params=response_api_optional_params, + model=model, + drop_params=drop_params, + ) + + tools = params.get("tools") + if not tools: + return params + + tools_list = tools if isinstance(tools, list) else [tools] + filtered = self._filter_unsupported_tools(tools_list) + if filtered: + params["tools"] = filtered + else: + params.pop("tools", None) + + return params diff --git a/litellm/llms/brave/search/transformation.py b/litellm/llms/brave/search/transformation.py index 9dfcd6bc75a..8ffe7dcb126 100644 --- a/litellm/llms/brave/search/transformation.py +++ b/litellm/llms/brave/search/transformation.py @@ -115,7 +115,13 @@ class BraveSearchConfig(BaseSearchConfig): """ Validate environment and return headers. """ - api_key = api_key or get_secret_str("BRAVE_API_KEY") + api_key = self.resolve_server_api_key( + caller_api_key=api_key, + caller_api_base=api_base, + key_env_vars=("BRAVE_API_KEY",), + base_env_var="BRAVE_API_BASE", + default_api_base=self.BRAVE_API_BASE, + ) if not api_key: raise ValueError( diff --git a/litellm/llms/bytez/chat/transformation.py b/litellm/llms/bytez/chat/transformation.py index 5b08670f9f2..7d9afe01fa6 100644 --- a/litellm/llms/bytez/chat/transformation.py +++ b/litellm/llms/bytez/chat/transformation.py @@ -191,7 +191,7 @@ class BytezChatConfig(BaseConfig): api_key: Optional[str] = None, json_mode: Optional[bool] = None, ) -> ModelResponse: - json = raw_response.json() # noqa: F811 + json = raw_response.json() error = json.get("error") diff --git a/litellm/llms/cloudflare/chat/transformation.py b/litellm/llms/cloudflare/chat/transformation.py index 66e253f304d..68f08741cc5 100644 --- a/litellm/llms/cloudflare/chat/transformation.py +++ b/litellm/llms/cloudflare/chat/transformation.py @@ -1,26 +1,15 @@ -import json -import time -from typing import AsyncIterator, Iterator, List, Optional, Union +from typing import List, Optional, Union import httpx -import litellm -from litellm.litellm_core_utils.url_utils import encode_url_path_segments -from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator -from litellm.llms.base_llm.chat.transformation import ( - BaseConfig, - BaseLLMException, - LiteLLMLoggingObj, +from litellm._logging import verbose_logger +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig +from litellm.secret_managers.main import ( + get_secret_str, + normalize_nonempty_secret_str, ) -from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import AllMessageValues -from litellm.types.utils import ( - ChatCompletionToolCallChunk, - ChatCompletionUsageBlock, - GenericStreamingChunk, - ModelResponse, - Usage, -) class CloudflareError(BaseLLMException): @@ -34,26 +23,46 @@ class CloudflareError(BaseLLMException): message=message, request=self.request, response=self.response, - ) # Call the base class constructor with the parameters it needs + ) -class CloudflareChatConfig(BaseConfig): - max_tokens: Optional[int] = None - stream: Optional[bool] = None - - def __init__( +class CloudflareChatConfig(OpenAIGPTConfig): + def get_complete_url( self, - max_tokens: Optional[int] = None, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, stream: Optional[bool] = None, - ) -> None: - locals_ = locals().copy() - for key, value in locals_.items(): - if key != "self" and value is not None: - setattr(self.__class__, key, value) + ) -> str: + return super().get_complete_url( + api_base=self._resolve_api_base(api_base), + api_key=api_key, + model=model, + optional_params=optional_params, + litellm_params=litellm_params, + stream=stream, + ) - @classmethod - def get_config(cls): - return super().get_config() + @staticmethod + def _resolve_api_base(api_base: Optional[str]) -> str: + if not api_base: + account_id = normalize_nonempty_secret_str( + get_secret_str("CLOUDFLARE_ACCOUNT_ID") + ) + if account_id is None: + raise ValueError( + "Missing CLOUDFLARE_ACCOUNT_ID - set CLOUDFLARE_ACCOUNT_ID in the environment or pass api_base explicitly" + ) + return f"https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/v1" + trimmed = api_base.rstrip("/") + if trimmed.endswith("/ai/run"): + verbose_logger.warning( + "Cloudflare api_base ending in '/ai/run' is the legacy Workers AI path and no longer serves OpenAI-compatible requests; rewriting to the '/ai/v1' endpoint" + ) + return f"{trimmed[: -len('/ai/run')]}/ai/v1" + return api_base def validate_environment( self, @@ -67,107 +76,18 @@ class CloudflareChatConfig(BaseConfig): ) -> dict: if api_key is None: raise ValueError( - "Missing CloudflareError API Key - A call is being made to cloudflare but no key is set either in the environment variables or via params" + "Missing Cloudflare API Key - A call is being made to cloudflare but no key is set either in the environment variables or via params" ) - headers = { - "accept": "application/json", - "content-type": "apbplication/json", - "Authorization": "Bearer " + api_key, - } - return headers - - def get_complete_url( - self, - api_base: Optional[str], - api_key: Optional[str], - model: str, - optional_params: dict, - litellm_params: dict, - stream: Optional[bool] = None, - ) -> str: - if api_base is None: - account_id = get_secret_str("CLOUDFLARE_ACCOUNT_ID") - api_base = ( - f"https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/run/" - ) - encoded_model = encode_url_path_segments(model, field_name="model") - return api_base + encoded_model - - def get_supported_openai_params(self, model: str) -> List[str]: - return [ - "stream", - "max_tokens", - ] - - def map_openai_params( - self, - non_default_params: dict, - optional_params: dict, - model: str, - drop_params: bool, - ) -> dict: - supported_openai_params = self.get_supported_openai_params(model=model) - for param, value in non_default_params.items(): - if param == "max_completion_tokens": - optional_params["max_tokens"] = value - elif param in supported_openai_params: - optional_params[param] = value - return optional_params - - def transform_request( - self, - model: str, - messages: List[AllMessageValues], - optional_params: dict, - litellm_params: dict, - headers: dict, - ) -> dict: - config = litellm.CloudflareChatConfig.get_config() - for k, v in config.items(): - if k not in optional_params: - optional_params[k] = v - - data = { - "messages": messages, - **optional_params, - } - return data - - def transform_response( - self, - model: str, - raw_response: httpx.Response, - model_response: ModelResponse, - logging_obj: LiteLLMLoggingObj, - request_data: dict, - messages: List[AllMessageValues], - optional_params: dict, - litellm_params: dict, - encoding: str, - api_key: Optional[str] = None, - json_mode: Optional[bool] = None, - ) -> ModelResponse: - completion_response = raw_response.json() - - # Support both "response" and "response_text" keys (newer models like Nemotron use "response_text") - result = completion_response["result"] - model_response.choices[0].message.content = result.get("response") if result.get("response") is not None else result.get("response_text", "") # type: ignore - - prompt_tokens = litellm.utils.get_token_count(messages=messages, model=model) - completion_tokens = len( - encoding.encode(model_response["choices"][0]["message"].get("content", "")) + return super().validate_environment( + headers=headers, + model=model, + messages=messages, + optional_params=optional_params, + litellm_params=litellm_params, + api_key=api_key, + api_base=api_base, ) - model_response.created = int(time.time()) - model_response.model = "cloudflare/" + model - usage = Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, - ) - setattr(model_response, "usage", usage) - return model_response - def get_error_class( self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers] ) -> BaseLLMException: @@ -175,48 +95,3 @@ class CloudflareChatConfig(BaseConfig): status_code=status_code, message=error_message, ) - - def get_model_response_iterator( - self, - streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse], - sync_stream: bool, - json_mode: Optional[bool] = False, - ): - return CloudflareChatResponseIterator( - streaming_response=streaming_response, - sync_stream=sync_stream, - json_mode=json_mode, - ) - - -class CloudflareChatResponseIterator(BaseModelResponseIterator): - def chunk_parser(self, chunk: dict) -> GenericStreamingChunk: - try: - text = "" - tool_use: Optional[ChatCompletionToolCallChunk] = None - is_finished = False - finish_reason = "" - usage: Optional[ChatCompletionUsageBlock] = None - provider_specific_fields = None - - index = int(chunk.get("index", 0)) - - if "response" in chunk and chunk["response"] is not None: - text = chunk["response"] - elif "response_text" in chunk and chunk["response_text"] is not None: - text = chunk["response_text"] - - returned_chunk = GenericStreamingChunk( - text=text, - tool_use=tool_use, - is_finished=is_finished, - finish_reason=finish_reason, - usage=usage, - index=index, - provider_specific_fields=provider_specific_fields, - ) - - return returned_chunk - - except json.JSONDecodeError: - raise ValueError(f"Failed to decode JSON from chunk: {chunk}") diff --git a/litellm/llms/custom_httpx/aiohttp_transport.py b/litellm/llms/custom_httpx/aiohttp_transport.py index 62f707b3622..b97a59a93a6 100644 --- a/litellm/llms/custom_httpx/aiohttp_transport.py +++ b/litellm/llms/custom_httpx/aiohttp_transport.py @@ -116,6 +116,16 @@ class AiohttpResponseStream(httpx.AsyncByteStream): # For other exceptions, use the normal mapping with map_aiohttp_exceptions(): raise + finally: + # Release the aiohttp connection when iteration ends for any + # reason (read timeout, cancellation from a client disconnect, + # GeneratorExit). Without this, abnormally terminated streams + # permanently hold a slot in the TCPConnector pool; once the + # pool is exhausted every request to that host times out (408) + # until the proxy is restarted, even after the backend recovers. + # On a fully-read response the connection was already released + # at EOF and close() is a no-op. + self._aiohttp_response.close() async def aclose(self) -> None: with map_aiohttp_exceptions(): diff --git a/litellm/llms/custom_httpx/http_handler.py b/litellm/llms/custom_httpx/http_handler.py index 01c94476431..1000ab12803 100644 --- a/litellm/llms/custom_httpx/http_handler.py +++ b/litellm/llms/custom_httpx/http_handler.py @@ -42,6 +42,9 @@ from litellm.constants import ( HTTP_HANDLER_CONNECT_TIMEOUT_SECONDS, ) from litellm.litellm_core_utils.logging_utils import track_llm_api_timing +from litellm.litellm_core_utils.request_timeout_resolver import ( + get_configured_request_timeout, +) from litellm.types.llms.custom_http import * if TYPE_CHECKING: @@ -134,6 +137,18 @@ _DEFAULT_TIMEOUT = httpx.Timeout( timeout=COMPLETION_HTTP_FALLBACK_SECONDS, connect=HTTP_HANDLER_CONNECT_TIMEOUT_SECONDS, ) + + +def _default_cached_client_timeout() -> httpx.Timeout: + """Timeout for cached default httpx clients; honors an explicit litellm.request_timeout.""" + configured = get_configured_request_timeout() + if configured is None: + return _DEFAULT_TIMEOUT + return httpx.Timeout( + timeout=configured, connect=HTTP_HANDLER_CONNECT_TIMEOUT_SECONDS + ) + + _STREAMING_ERROR_BODY_READ_TIMEOUT_SECONDS = 5.0 _STREAMING_ERROR_BODY_READ_EXECUTOR = concurrent.futures.ThreadPoolExecutor( max_workers=50, @@ -1379,7 +1394,7 @@ def get_async_httpx_client( _new_client = AsyncHTTPHandler(**handler_params) else: _new_client = AsyncHTTPHandler( - timeout=_DEFAULT_TIMEOUT, + timeout=_default_cached_client_timeout(), shared_session=shared_session, ) @@ -1428,7 +1443,7 @@ def _get_httpx_client(params: Optional[dict] = None) -> HTTPHandler: } _new_client = HTTPHandler(**handler_params) else: - _new_client = HTTPHandler(timeout=_DEFAULT_TIMEOUT) + _new_client = HTTPHandler(timeout=_default_cached_client_timeout()) cache.set_cache( key=_cache_key_name, diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index 25424feaeb4..948c90f9f99 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -1,5 +1,6 @@ import json import ssl +from functools import lru_cache from urllib.parse import parse_qs, urlencode, urlparse, urlunparse from typing import ( TYPE_CHECKING, @@ -13,6 +14,7 @@ from typing import ( Tuple, Union, cast, + get_type_hints, ) import httpx # type: ignore @@ -26,6 +28,7 @@ from litellm._logging import _redact_string, verbose_logger from litellm.anthropic_beta_headers_manager import update_headers_with_filtered_beta from litellm.constants import REALTIME_WEBSOCKET_MAX_MESSAGE_SIZE_BYTES from litellm.litellm_core_utils.realtime_streaming import RealTimeStreaming +from litellm.litellm_core_utils.asyncify import run_async_function from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.llms.base_llm.anthropic_messages.transformation import ( BaseAnthropicMessagesConfig, @@ -33,7 +36,10 @@ from litellm.llms.base_llm.anthropic_messages.transformation import ( from litellm.llms.base_llm.audio_transcription.transformation import ( BaseAudioTranscriptionConfig, ) -from litellm.llms.base_llm.base_model_iterator import MockResponseIterator +from litellm.llms.base_llm.base_model_iterator import ( + BaseModelResponseIterator, + MockResponseIterator, +) from litellm.llms.base_llm.batches.transformation import BaseBatchesConfig from litellm.llms.base_llm.chat.transformation import BaseConfig from litellm.llms.base_llm.containers.transformation import BaseContainerConfig @@ -98,6 +104,7 @@ from litellm.types.llms.openai import ( HttpxBinaryResponseContent, OpenAIFileObject, ResponseInputParam, + ResponsesAPIOptionalRequestParams, ResponsesAPIResponse, ) from litellm.types.rerank import RerankResponse @@ -125,12 +132,14 @@ from litellm.types.vector_stores import ( VectorStoreSearchOptionalRequestParams, VectorStoreSearchResponse, ) +from litellm.types.realtime import RealtimeQueryParams from litellm.types.videos.main import VideoObject from litellm.utils import ( CustomStreamWrapper, ImageResponse, ModelResponse, ProviderConfigManager, + async_pre_call_deployment_hook, ) from .http_handler import get_shared_realtime_ssl_context @@ -180,6 +189,47 @@ def _google_genai_streaming_hidden_params( } +@lru_cache(maxsize=None) +def _responses_api_optional_request_param_names() -> frozenset[str]: + return frozenset(get_type_hints(ResponsesAPIOptionalRequestParams).keys()) + + +def _custom_logger_callbacks(logging_obj: Any) -> list[Any]: + from litellm.integrations.custom_logger import CustomLogger + from litellm.litellm_core_utils.litellm_logging import ( + get_custom_logger_compatible_class, + ) + + dynamic_success_callbacks = getattr(logging_obj, "dynamic_success_callbacks", None) + callbacks = list(litellm.callbacks) + if isinstance(dynamic_success_callbacks, (list, tuple)): + callbacks.extend(dynamic_success_callbacks) + + custom_loggers: list[Any] = [] + for cb in callbacks: + if isinstance(cb, str): + resolved = get_custom_logger_compatible_class(cb) # type: ignore[arg-type] + if resolved is None: + continue + cb = resolved + if isinstance(cb, CustomLogger): + custom_loggers.append(cb) + return custom_loggers + + +def _has_pre_call_deployment_hook(logging_obj: Any) -> bool: + from litellm.integrations.custom_logger import CustomLogger + + base_func = CustomLogger.async_pre_call_deployment_hook + for cb in _custom_logger_callbacks(logging_obj): + cb_func = getattr(type(cb), "async_pre_call_deployment_hook", base_func) + if getattr(cb_func, "__func__", cb_func) is not getattr( + base_func, "__func__", base_func + ): + return True + return False + + class BaseLLMHTTPHandler: async def _make_common_async_call( self, @@ -813,6 +863,8 @@ class BaseLLMHTTPHandler: completion_stream = provider_config.get_model_response_iterator( streaming_response=response.aiter_lines(), sync_stream=False ) + if isinstance(completion_stream, BaseModelResponseIterator): + completion_stream.http_response = response # LOGGING logging_obj.post_call( input=messages, @@ -1827,6 +1879,9 @@ class BaseLLMHTTPHandler: data = provider_config.transform_search_request( query=query, optional_params=optional_params, + api_key=api_key, + api_base=api_base, + headers=headers or {}, ) # Get complete URL (pass data for providers that need request body for URL construction) @@ -2218,12 +2273,92 @@ class BaseLLMHTTPHandler: ) raise ValueError("anthropic_messages_handler is not implemented for sync calls") + def _run_sync_responses_pre_call_deployment_hook( + self, + *, + model: str, + input: Union[str, ResponseInputParam], + custom_llm_provider: str, + response_api_optional_request_params: dict[str, Any], + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + ) -> tuple[ + str, + Union[str, ResponseInputParam], + str, + dict[str, Any], + GenericLiteLLMParams, + ]: + if not _has_pre_call_deployment_hook(logging_obj): + return ( + model, + input, + custom_llm_provider, + response_api_optional_request_params, + litellm_params, + ) + + modified_kwargs = run_async_function( + async_pre_call_deployment_hook, + { + **dict(litellm_params), + **response_api_optional_request_params, + "model": model, + "input": input, + "custom_llm_provider": custom_llm_provider, + }, + CallTypes.responses.value, + ) + if modified_kwargs is None: + return ( + model, + input, + custom_llm_provider, + response_api_optional_request_params, + litellm_params, + ) + + optional_param_names = _responses_api_optional_request_param_names() + updated_response_params = { + **response_api_optional_request_params, + **{ + key: value + for key, value in modified_kwargs.items() + if key in optional_param_names + }, + } + updated_litellm_params = GenericLiteLLMParams( + **{ + **dict(litellm_params), + **{ + key: value + for key, value in modified_kwargs.items() + if key not in optional_param_names + and key not in {"model", "input", "custom_llm_provider"} + }, + } + ) + return ( + str(modified_kwargs["model"]) if "model" in modified_kwargs else model, + cast( + Union[str, ResponseInputParam], + modified_kwargs["input"] if "input" in modified_kwargs else input, + ), + ( + str(modified_kwargs["custom_llm_provider"]) + if "custom_llm_provider" in modified_kwargs + else custom_llm_provider + ), + updated_response_params, + updated_litellm_params, + ) + def response_api_handler( self, model: str, input: Union[str, ResponseInputParam], responses_api_provider_config: BaseResponsesAPIConfig, - response_api_optional_request_params: Dict, + response_api_optional_request_params: dict[str, Any], custom_llm_provider: str, litellm_params: GenericLiteLLMParams, logging_obj: LiteLLMLoggingObj, @@ -2270,6 +2405,21 @@ class BaseLLMHTTPHandler: shared_session=shared_session, ) + ( + model, + input, + custom_llm_provider, + response_api_optional_request_params, + litellm_params, + ) = self._run_sync_responses_pre_call_deployment_hook( + model=model, + input=input, + custom_llm_provider=custom_llm_provider, + response_api_optional_request_params=response_api_optional_request_params, + litellm_params=litellm_params, + logging_obj=logging_obj, + ) + if client is None or not isinstance(client, HTTPHandler): sync_httpx_client = _get_httpx_client( params={"ssl_verify": litellm_params.get("ssl_verify", None)} @@ -2305,6 +2455,7 @@ class BaseLLMHTTPHandler: if extra_body: data.update(extra_body) + stream = bool(stream or data.get("stream")) # Preserve the OpenAI-style request context (not sent to the provider) for streaming # hooks/metadata; the streaming iterator now consumes this to run deployment hooks @@ -2400,12 +2551,36 @@ class BaseLLMHTTPHandler: provider_config=responses_api_provider_config, ) - return responses_api_provider_config.transform_response_api_response( - model=model, - raw_response=response, - logging_obj=logging_obj, + initial_response = ( + responses_api_provider_config.transform_response_api_response( + model=model, + raw_response=response, + logging_obj=logging_obj, + ) ) + if self._has_agentic_completion_hook(logging_obj): + final_response = run_async_function( + self._call_agentic_completion_hooks, + response=initial_response, + model=model, + messages=( + input + if isinstance(input, list) + else [{"role": "user", "content": input}] + ), + anthropic_messages_provider_config=responses_api_provider_config, + anthropic_messages_optional_request_params=response_api_optional_request_params, + logging_obj=logging_obj, + stream=False, + custom_llm_provider=custom_llm_provider, + kwargs=dict(litellm_params), + api_surface="responses", + ) + return final_response if final_response is not None else initial_response + + return initial_response + async def async_response_api_handler( self, model: str, @@ -2467,6 +2642,7 @@ class BaseLLMHTTPHandler: if extra_body: data.update(extra_body) + stream = bool(stream or data.get("stream")) # Preserve the OpenAI-style request context (not sent to the provider) for streaming # hooks/metadata; the streaming iterator now consumes this to run deployment hooks @@ -2562,12 +2738,44 @@ class BaseLLMHTTPHandler: provider_config=responses_api_provider_config, ) - return responses_api_provider_config.transform_response_api_response( - model=model, - raw_response=response, - logging_obj=logging_obj, + initial_response = ( + responses_api_provider_config.transform_response_api_response( + model=model, + raw_response=response, + logging_obj=logging_obj, + ) ) + final_response = await self._call_agentic_completion_hooks( + response=initial_response, + model=model, + messages=( + input + if isinstance(input, list) + else [{"role": "user", "content": input}] + ), + anthropic_messages_provider_config=responses_api_provider_config, + anthropic_messages_optional_request_params=response_api_optional_request_params, + logging_obj=logging_obj, + stream=False, + custom_llm_provider=custom_llm_provider, + kwargs=dict(litellm_params), + api_surface="responses", + ) + + result = final_response if final_response is not None else initial_response + if litellm_params.get( + "_code_interpreter_interception_converted_stream" + ) and not litellm_params.get("_agentic_loop_depth"): + return self._wrap_responses_response_as_fake_stream( + result=result, + model=model, + responses_api_provider_config=responses_api_provider_config, + logging_obj=logging_obj, + custom_llm_provider=custom_llm_provider, + ) + return result + async def async_delete_response_api_handler( self, response_id: str, @@ -4726,22 +4934,9 @@ class BaseLLMHTTPHandler: agentic callback is detected too. """ from litellm.integrations.custom_logger import CustomLogger - from litellm.litellm_core_utils.litellm_logging import ( - get_custom_logger_compatible_class, - ) base_func = CustomLogger.async_should_run_agentic_loop - callbacks = litellm.callbacks + ( - getattr(logging_obj, "dynamic_success_callbacks", None) or [] - ) - for cb in callbacks: - if isinstance(cb, str): - resolved = get_custom_logger_compatible_class(cb) # type: ignore[arg-type] - if resolved is None: - continue - cb = resolved - if not isinstance(cb, CustomLogger): - continue + for cb in _custom_logger_callbacks(logging_obj): cb_func = getattr(type(cb), "async_should_run_agentic_loop", base_func) if getattr(cb_func, "__func__", cb_func) is not getattr( base_func, "__func__", base_func @@ -4867,6 +5062,132 @@ class BaseLLMHTTPHandler: return response + async def _execute_responses_agentic_plan( + self, + plan: AgenticLoopPlan, + model: str, + response_api_optional_request_params: dict, + logging_obj: "LiteLLMLoggingObj", + kwargs: dict, + depth: int, + max_loops: int, + fingerprints: list[str], + fingerprint: str, + callback: Any | None = None, + ) -> Any: + patch = plan.request_patch or AgenticLoopRequestPatch() + if patch.messages is None: + raise ValueError("Agentic loop plan missing patched responses input") + + optional_params = dict(response_api_optional_request_params) + optional_params.update(patch.optional_params) + if patch.tools is not None: + optional_params["tools"] = patch.tools + optional_params = { + k: v + for k, v in optional_params.items() + if k != "stream" and k != "_code_interpreter_interception_converted_stream" + } + + internal_keys = {"litellm_logging_obj"} + kwargs_for_followup = { + k: v + for k, v in kwargs.items() + if not k.startswith("_websearch_interception") + and not k.startswith("_compression_interception") + and k != "_code_interpreter_interception_converted_stream" + and k not in internal_keys + and k not in optional_params + } + kwargs_for_followup.update(patch.kwargs) + kwargs_for_followup["_agentic_loop_depth"] = depth + 1 + kwargs_for_followup["max_agentic_loops"] = max_loops + kwargs_for_followup["_agentic_loop_fingerprints"] = fingerprints + [fingerprint] + + try: + response = await litellm.aresponses( + model=patch.model or model, + input=patch.messages, + **optional_params, + **kwargs_for_followup, + ) + + if callback is not None: + try: + response = await callback.async_post_agentic_loop_response_hook( + response=response, plan=plan, kwargs=kwargs + ) + except Exception as e: + _call_id = getattr(logging_obj, "litellm_call_id", "unknown") + verbose_logger.exception( + "LiteLLM.AgenticHookError: Exception in " + "async_post_agentic_loop_response_hook [call_id=%s model=%s]: %s", + _call_id, + model, + str(e), + ) + + return response + finally: + if callback is not None: + await self._run_agentic_loop_cleanup( + callback=callback, + plan=plan, + kwargs=kwargs, + logging_obj=logging_obj, + model=model, + ) + + @staticmethod + async def _run_agentic_loop_cleanup( + callback: Any, + plan: AgenticLoopPlan, + kwargs: dict, + logging_obj: "LiteLLMLoggingObj", + model: str, + ) -> None: + try: + await callback.async_agentic_loop_cleanup_hook(plan=plan, kwargs=kwargs) + except Exception as e: + _call_id = getattr(logging_obj, "litellm_call_id", "unknown") + verbose_logger.exception( + "LiteLLM.AgenticHookError: Exception in " + "async_agentic_loop_cleanup_hook [call_id=%s model=%s]: %s", + _call_id, + model, + str(e), + ) + + def _wrap_responses_response_as_fake_stream( + self, + result: Any, + model: str, + responses_api_provider_config: Any, + logging_obj: "LiteLLMLoggingObj", + custom_llm_provider: str, + ) -> Any: + """ + Wrap a completed responses result as a synthetic stream. + + Used when an interceptor forced stream=False to run the agentic loop on + the non-streaming path, but the caller originally asked for streaming. + """ + import httpx + + from litellm.responses.streaming_iterator import ( + MockResponsesAPIStreamingIterator, + ) + + payload = result.model_dump() if hasattr(result, "model_dump") else result + raw_response = httpx.Response(status_code=200, json=payload) + return MockResponsesAPIStreamingIterator( + response=raw_response, + model=model, + responses_api_provider_config=responses_api_provider_config, + logging_obj=logging_obj, + custom_llm_provider=custom_llm_provider, + ) + async def _execute_chat_completion_agentic_plan( self, plan: AgenticLoopPlan, @@ -4932,6 +5253,7 @@ class BaseLLMHTTPHandler: stream: bool, custom_llm_provider: str, kwargs: Dict, + api_surface: str = "anthropic_messages", ) -> Optional[Any]: """ Call agentic completion hooks for all custom loggers (Anthropic Messages API). @@ -5038,6 +5360,20 @@ class BaseLLMHTTPHandler: if not plan.run_agentic_loop: continue + if api_surface == "responses": + return await self._execute_responses_agentic_plan( + plan=plan, + model=model, + response_api_optional_request_params=anthropic_messages_optional_request_params, + logging_obj=logging_obj, + kwargs=kwargs_with_provider, + depth=depth, + max_loops=max_loops, + fingerprints=fingerprints, + fingerprint=fingerprint, + callback=callback, + ) + return await self._execute_anthropic_agentic_plan( plan=plan, model=model, @@ -5075,7 +5411,7 @@ class BaseLLMHTTPHandler: else False ) - if websearch_converted_stream: + if api_surface == "anthropic_messages" and websearch_converted_stream: from typing import cast from litellm._logging import verbose_logger @@ -5315,6 +5651,23 @@ class BaseLLMHTTPHandler: headers=error_headers, ) + @staticmethod + def _append_query_params( + url: str, query_params: Optional[RealtimeQueryParams] + ) -> str: + """Append query_params to url, skipping keys already present in the URL.""" + if not query_params: + return url + from urllib.parse import parse_qsl, urlencode, urlparse, urlunparse + + parsed = urlparse(url) + existing = dict(parse_qsl(parsed.query)) + extras = {k: v for k, v in query_params.items() if k not in existing} + if not extras: + return url + new_query = parsed.query + ("&" if parsed.query else "") + urlencode(extras) + return urlunparse(parsed._replace(query=new_query)) + async def async_realtime( self, model: str, @@ -5328,6 +5681,7 @@ class BaseLLMHTTPHandler: timeout: Optional[float] = None, user_api_key_dict: Optional[Any] = None, litellm_metadata: Optional[Dict[str, Any]] = None, + query_params: Optional[RealtimeQueryParams] = None, ): import websockets from websockets.asyncio.client import ClientConnection @@ -5373,6 +5727,11 @@ class BaseLLMHTTPHandler: model, user_api_key_dict=user_api_key_dict, request_data=_request_data, + force_transcription_model=( + model + if (query_params or {}).get("intent") == "transcription" + else None + ), ) if _session_config: realtime_streaming.session_configuration_request = _session_config @@ -5437,6 +5796,69 @@ class BaseLLMHTTPHandler: """ Forward POST /v1/realtime/client_secrets to upstream provider. + Uses provider_config (BaseRealtimeHTTPConfig) for URL construction and + header auth when available; falls back to the legacy OpenAI-style defaults. + """ + return await self._async_realtime_session_post( + endpoint="client_secrets", + api_base=api_base, + api_key=api_key, + request_data=request_data, + logging_obj=logging_obj, + timeout=timeout, + provider_config=provider_config, + model=model, + extra_headers=extra_headers, + client=client, + api_version=api_version, + ) + + async def async_realtime_transcription_session_handler( + self, + api_base: str, + api_key: str, + request_data: Dict[str, Any], + logging_obj: LiteLLMLoggingObj, + timeout: Union[float, httpx.Timeout], + provider_config: Optional[Any] = None, + model: Optional[str] = None, + extra_headers: Optional[Dict[str, Any]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + api_version: Optional[str] = None, + ) -> httpx.Response: + """Forward POST /v1/realtime/transcription_sessions to upstream provider.""" + return await self._async_realtime_session_post( + endpoint="transcription_sessions", + api_base=api_base, + api_key=api_key, + request_data=request_data, + logging_obj=logging_obj, + timeout=timeout, + provider_config=provider_config, + model=model, + extra_headers=extra_headers, + client=client, + api_version=api_version, + ) + + async def _async_realtime_session_post( + self, + endpoint: Literal["client_secrets", "transcription_sessions"], + api_base: str, + api_key: str, + request_data: Dict[str, Any], + logging_obj: LiteLLMLoggingObj, + timeout: Union[float, httpx.Timeout], + provider_config: Optional[Any] = None, + model: Optional[str] = None, + extra_headers: Optional[Dict[str, Any]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + api_version: Optional[str] = None, + ) -> httpx.Response: + """ + Shared POST flow for the realtime HTTP session endpoints + (client_secrets and transcription_sessions). + Uses provider_config (BaseRealtimeHTTPConfig) for URL construction and header auth when available; falls back to the legacy OpenAI-style defaults. """ @@ -5448,14 +5870,19 @@ class BaseLLMHTTPHandler: async_httpx_client = client if provider_config is not None: - url = provider_config.get_complete_url( - api_base=api_base, model=model or "", api_version=api_version - ) + if endpoint == "transcription_sessions": + url = provider_config.get_transcription_session_url( + api_base=api_base, model=model or "", api_version=api_version + ) + else: + url = provider_config.get_complete_url( + api_base=api_base, model=model or "", api_version=api_version + ) headers: Dict[str, Any] = provider_config.validate_environment( headers={}, model=model or "", api_key=api_key ) else: - url = f"{api_base.rstrip('/')}/v1/realtime/client_secrets" + url = f"{api_base.rstrip('/')}/v1/realtime/{endpoint}" headers = { "Authorization": f"Bearer {api_key}", "Content-Type": "application/json", @@ -5628,7 +6055,11 @@ class BaseLLMHTTPHandler: import websockets from websockets.asyncio.client import ClientConnection - litellm_params = GenericLiteLLMParams() + litellm_params = GenericLiteLLMParams( + api_base=api_base, + api_key=api_key, + **kwargs, + ) headers = responses_api_provider_config.validate_environment( headers={}, model=model, @@ -5637,21 +6068,21 @@ class BaseLLMHTTPHandler: if api_key: headers["Authorization"] = f"Bearer {api_key}" - http_url = responses_api_provider_config.get_complete_url( + ws_url = responses_api_provider_config.get_websocket_url( api_base=api_base, - litellm_params={}, + litellm_params=dict(litellm_params), ) - ws_url = http_url.replace("https://", "wss://").replace("http://", "ws://") - # OpenAI's WebSocket responses endpoint requires ?model= in the URL, - # matching the Realtime API convention (wss://.../v1/realtime?model=...). - # Use urllib.parse so existing query params (e.g. api-version) are preserved. - _parsed = urlparse(ws_url) - _qs = parse_qs(_parsed.query) - if "model" not in _qs: - _qs["model"] = [model] - ws_url = urlunparse( - _parsed._replace(query=urlencode({k: v[0] for k, v in _qs.items()})) - ) + # Some providers (e.g. OpenAI) require ?model= in the WebSocket URL. + # Providers that send the model in the request body (e.g. Azure) set + # model_in_websocket_url() to False to suppress this append. + if responses_api_provider_config.model_in_websocket_url(): + _parsed = urlparse(ws_url) + _qs = parse_qs(_parsed.query) + if "model" not in _qs: + _qs["model"] = [model] + ws_url = urlunparse( + _parsed._replace(query=urlencode({k: v[0] for k, v in _qs.items()})) + ) try: ssl_context = get_shared_realtime_ssl_context() @@ -5679,6 +6110,41 @@ class BaseLLMHTTPHandler: _request_data: Dict[str, Any] = {} if litellm_metadata: _request_data["litellm_metadata"] = litellm_metadata + + _ws_guardrail_callbacks: list = [] + _ws_output_guardrail_callbacks: list = [] + try: + import litellm as _litellm + + # Use duck-typing so any guardrail that exposes the PII + # masking interface works, not just _OPTIONAL_PresidioPIIMasking. + # This avoids a layering violation (SDK importing from proxy). + _ws_guardrail_callbacks = [ + cb + for cb in _litellm.callbacks + if callable(getattr(cb, "check_pii", None)) + and callable( + getattr(cb, "get_presidio_settings_from_request_data", None) + ) + and callable(getattr(cb, "_unmask_pii_text", None)) + and getattr(cb, "output_parse_pii", False) + ] + _ws_output_guardrail_callbacks = [ + cb + for cb in _litellm.callbacks + if callable(getattr(cb, "check_pii", None)) + and callable( + getattr(cb, "get_presidio_settings_from_request_data", None) + ) + and getattr(cb, "apply_to_output", False) + ] + except Exception as _guardrail_exc: + verbose_logger.warning( + "Responses WebSocket: failed to collect guardrail " + "callbacks — PII masking will be skipped. Error: %s", + _guardrail_exc, + ) + streaming = ResponsesWebSocketStreaming( websocket=websocket, backend_ws=cast(ClientConnection, backend_ws), @@ -5686,6 +6152,9 @@ class BaseLLMHTTPHandler: user_api_key_dict=user_api_key_dict, request_data=_request_data, first_message=first_message, + guardrail_callbacks=_ws_guardrail_callbacks, + output_guardrail_callbacks=_ws_output_guardrail_callbacks, + authorized_model=model, ) await streaming.bidirectional_forward() diff --git a/litellm/llms/databricks/streaming_utils.py b/litellm/llms/databricks/streaming_utils.py index eebe3182881..7a7330227d6 100644 --- a/litellm/llms/databricks/streaming_utils.py +++ b/litellm/llms/databricks/streaming_utils.py @@ -25,6 +25,28 @@ class ModelResponseIterator: finish_reason = "" usage: Optional[ChatCompletionUsageBlock] = None + # Usage-only final chunk (OpenAI ``stream_options.include_usage``) + # arrives with an empty ``choices`` list — return usage without + # indexing ``choices[0]``. + if len(processed_chunk.choices) == 0: + final_usage = getattr(processed_chunk, "usage", None) + return GenericStreamingChunk( + text="", + tool_use=None, + is_finished=False, + finish_reason="", + usage=( + ChatCompletionUsageBlock( + prompt_tokens=final_usage.prompt_tokens or 0, + completion_tokens=final_usage.completion_tokens or 0, + total_tokens=final_usage.total_tokens or 0, + ) + if final_usage is not None + else None + ), + index=0, + ) + if processed_chunk.choices[0].delta.content is not None: # type: ignore text = processed_chunk.choices[0].delta.content # type: ignore diff --git a/litellm/llms/dataforseo/search/transformation.py b/litellm/llms/dataforseo/search/transformation.py index 27c10d740b5..701db586b72 100644 --- a/litellm/llms/dataforseo/search/transformation.py +++ b/litellm/llms/dataforseo/search/transformation.py @@ -61,9 +61,18 @@ class DataForSEOSearchConfig(BaseSearchConfig): password = get_secret_str("DATAFORSEO_PASSWORD") # If api_key is provided in "login:password" format, use it + caller_supplied_credentials = bool(api_key and ":" in api_key) if api_key and ":" in api_key: login, password = api_key.split(":", 1) + if not caller_supplied_credentials and login and password: + self._assert_trusted_api_base_for_server_credential( + api_base, + self.DATAFORSEO_API_BASE, + "DATAFORSEO_API_BASE", + "DATAFORSEO_LOGIN", + ) + if not login: raise ValueError( "DATAFORSEO_LOGIN is not set. Set `DATAFORSEO_LOGIN` environment variable or pass credentials in api_key parameter." diff --git a/litellm/llms/e2b/__init__.py b/litellm/llms/e2b/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/litellm/llms/e2b/sandbox/__init__.py b/litellm/llms/e2b/sandbox/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/litellm/llms/e2b/sandbox/transformation.py b/litellm/llms/e2b/sandbox/transformation.py new file mode 100644 index 00000000000..ecfc1642c97 --- /dev/null +++ b/litellm/llms/e2b/sandbox/transformation.py @@ -0,0 +1,212 @@ +""" +e2b sandbox provider. + +Talks to e2b's REST API directly over httpx (no e2b SDK dependency): + - create: POST {api_base}/sandboxes + - run: POST https://{JUPYTER_PORT}-{sandboxID}.{domain}/execute (NDJSON stream) + - delete: DELETE {api_base}/sandboxes/{sandboxID} +""" + +import json +from typing import Union, cast + +import httpx + +from litellm.llms.base_llm.sandbox.transformation import ( + BaseSandboxConfig, + CodeExecutionResult, + ContainerHandle, + SANDBOX_MAX_OUTPUT_BYTES, +) +from litellm.llms.custom_httpx.http_handler import ( + AsyncHTTPHandler, + get_async_httpx_client, +) +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.custom_http import httpxSpecialProvider + +E2B_API_BASE = "https://api.e2b.app" +E2B_DEFAULT_TEMPLATE = "code-interpreter-v1" +E2B_DEFAULT_DOMAIN = "e2b.app" +JUPYTER_PORT = 49999 +DEFAULT_SANDBOX_TIMEOUT = 300 +MAX_OUTPUT_BYTES = SANDBOX_MAX_OUTPUT_BYTES + + +class E2BSandboxConfig(BaseSandboxConfig): + def _http(self, client: AsyncHTTPHandler | None) -> AsyncHTTPHandler: + if client is not None: + return client + return get_async_httpx_client(llm_provider=httpxSpecialProvider.Sandbox) + + def validate_environment(self, api_key: str | None = None, **kwargs) -> str: + key = api_key or get_secret_str("E2B_API_KEY") + if not key: + raise ValueError("E2B API key not set. Set E2B_API_KEY or pass api_key=...") + return key + + async def acreate_sandbox( + self, + *, + template: str | None = None, + timeout: int | None = None, + allow_internet_access: bool | None = None, + api_key: str | None = None, + api_base: str | None = None, + metadata: dict | None = None, + client: AsyncHTTPHandler | None = None, + **kwargs, + ) -> ContainerHandle: + key = self.validate_environment(api_key=api_key) + base = api_base or E2B_API_BASE + body = { + "templateID": template or E2B_DEFAULT_TEMPLATE, + "timeout": timeout if timeout is not None else DEFAULT_SANDBOX_TIMEOUT, + "secure": True, + "allow_internet_access": ( + True if allow_internet_access is None else allow_internet_access + ), + } + if metadata: + body["metadata"] = metadata + + response = cast( + httpx.Response, + await self._http(client).post( + url=f"{base}/sandboxes", + headers={"X-API-Key": key, "Content-Type": "application/json"}, + json=body, + ), + ) + data = response.json() + + handle = ContainerHandle( + id=data["sandboxID"], + provider="e2b", + domain=data.get("domain") or E2B_DEFAULT_DOMAIN, + ) + handle._hidden_params = { + "envd_access_token": data.get("envdAccessToken"), + "traffic_access_token": data.get("trafficAccessToken"), + "api_key": key, + "api_base": base, + } + return handle + + async def arun_code( + self, + *, + container: Union[ContainerHandle, str], + code: str, + api_key: str | None = None, + env_vars: dict | None = None, + client: AsyncHTTPHandler | None = None, + **kwargs, + ) -> CodeExecutionResult: + handle = self._as_handle(container) + + token = handle._hidden_params.get("envd_access_token") + if not token: + raise ValueError( + "Cannot run code from a sandbox id alone. e2b secure sandboxes " + "require the access token returned by acreate_sandbox; pass the " + "ContainerHandle it returned instead of a bare sandbox id." + ) + + headers = {"Content-Type": "application/json", "X-Access-Token": token} + traffic_token = handle._hidden_params.get("traffic_access_token") + if traffic_token: + headers["E2B-Traffic-Access-Token"] = traffic_token + + url = f"https://{JUPYTER_PORT}-{handle.id}.{handle.domain}/execute" + response = cast( + httpx.Response, + await self._http(client).post( + url=url, + headers=headers, + json={"code": code, "context_id": None, "env_vars": env_vars}, + stream=True, + ), + ) + lines = await self._read_capped_lines(response) + return self._parse_lines(lines) + + async def adelete_sandbox( + self, + *, + container: Union[ContainerHandle, str], + api_key: str | None = None, + api_base: str | None = None, + client: AsyncHTTPHandler | None = None, + **kwargs, + ) -> bool: + handle = self._as_handle(container) + key = ( + api_key + or handle._hidden_params.get("api_key") + or self.validate_environment() + ) + base = api_base or handle._hidden_params.get("api_base") or E2B_API_BASE + try: + response = cast( + httpx.Response, + 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: + return False + raise + return 200 <= response.status_code < 300 + + @staticmethod + def _as_handle(container: Union[ContainerHandle, str]) -> ContainerHandle: + if isinstance(container, ContainerHandle): + return container + handle = ContainerHandle( + id=str(container), provider="e2b", domain=E2B_DEFAULT_DOMAIN + ) + handle._hidden_params = {} + return handle + + @staticmethod + def _parse_lines(lines: list[str]) -> CodeExecutionResult: + def _try_parse(stripped: str): + try: + return json.loads(stripped) + except json.JSONDecodeError: + return None + + messages = tuple( + parsed + for line in lines + if (stripped := line.strip()) + if (parsed := _try_parse(stripped)) is not None + ) + + def of_type(message_type: str): + return (m for m in messages if m.get("type") == message_type) + + error = next( + ( + {key: m.get(key) for key in ("name", "value", "traceback")} + for m in of_type("error") + ), + None, + ) + execution_count = next( + (m.get("execution_count") for m in of_type("number_of_executions")), + None, + ) + + return CodeExecutionResult( + stdout="".join(m.get("text", "") for m in of_type("stdout")), + stderr="".join(m.get("text", "") for m in of_type("stderr")), + results=[ + {k: v for k, v in m.items() if k != "type"} for m in of_type("result") + ], + error=error, + execution_count=execution_count, + ) diff --git a/litellm/llms/exa_ai/search/transformation.py b/litellm/llms/exa_ai/search/transformation.py index 7a34ededa6b..5cfd14aeaa9 100644 --- a/litellm/llms/exa_ai/search/transformation.py +++ b/litellm/llms/exa_ai/search/transformation.py @@ -65,7 +65,13 @@ class ExaAISearchConfig(BaseSearchConfig): """ Validate environment and return headers. """ - api_key = api_key or get_secret_str("EXA_API_KEY") + api_key = self.resolve_server_api_key( + caller_api_key=api_key, + caller_api_base=api_base, + key_env_vars=("EXA_API_KEY",), + base_env_var="EXA_API_BASE", + default_api_base=self.EXA_AI_API_BASE, + ) if not api_key: raise ValueError( "EXA_API_KEY is not set. Set `EXA_API_KEY` environment variable." diff --git a/litellm/llms/fastcrw/__init__.py b/litellm/llms/fastcrw/__init__.py new file mode 100644 index 00000000000..d65ed8d3fa1 --- /dev/null +++ b/litellm/llms/fastcrw/__init__.py @@ -0,0 +1,7 @@ +""" +fastCRW API integration module. +""" + +from litellm.llms.fastcrw.search.transformation import FastCRWSearchConfig + +__all__ = ["FastCRWSearchConfig"] diff --git a/litellm/llms/fastcrw/search/__init__.py b/litellm/llms/fastcrw/search/__init__.py new file mode 100644 index 00000000000..4f8023b2db4 --- /dev/null +++ b/litellm/llms/fastcrw/search/__init__.py @@ -0,0 +1,7 @@ +""" +fastCRW Search API module. +""" + +from litellm.llms.fastcrw.search.transformation import FastCRWSearchConfig + +__all__ = ["FastCRWSearchConfig"] diff --git a/litellm/llms/fastcrw/search/transformation.py b/litellm/llms/fastcrw/search/transformation.py new file mode 100644 index 00000000000..b571a659cac --- /dev/null +++ b/litellm/llms/fastcrw/search/transformation.py @@ -0,0 +1,188 @@ +""" +Calls fastCRW's /v1/search endpoint to search the web. + +fastCRW is a Firecrawl-compatible web data engine (single Rust binary; self-host +or cloud). The search response uses the Firecrawl-compatible envelope +{ "success": true, "data": [ { "title", "url", "description", "markdown"? } ] }. + +fastCRW API Reference: https://fastcrw.com/docs/rest-api +""" + +from typing import Optional, TypedDict, Union + +import httpx + +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.base_llm.search.transformation import ( + BaseSearchConfig, + SearchResponse, + SearchResult, +) +from litellm.secret_managers.main import get_secret_str + + +class _FastCRWSearchRequestRequired(TypedDict): + """Required fields for fastCRW Search API request.""" + + query: str # Required - search query + + +class FastCRWSearchRequest(_FastCRWSearchRequestRequired, total=False): + """ + fastCRW Search API request format. + Based on: https://fastcrw.com/docs/rest-api + """ + + limit: int # Optional - maximum number of results to return + sources: list[ + str + ] # Optional - sources to search ('web', 'images'), default ['web'] + scrapeOptions: dict # Optional - options for scraping search results + + +class FastCRWSearchConfig(BaseSearchConfig): + FASTCRW_API_BASE = "https://fastcrw.com/api/v1" + + @staticmethod + def ui_friendly_name() -> str: + return "fastCRW" + + def validate_environment( + self, + headers: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + **kwargs, + ) -> dict: + """ + Validate environment and return headers. + """ + api_key = self.resolve_server_api_key( + caller_api_key=api_key, + caller_api_base=api_base, + key_env_vars=("CRW_API_KEY",), + base_env_var="CRW_API_BASE", + default_api_base=self.FASTCRW_API_BASE, + ) + if not api_key: + raise ValueError( + "CRW_API_KEY is not set. Set `CRW_API_KEY` environment variable." + ) + headers["Authorization"] = f"Bearer {api_key}" + headers["Content-Type"] = "application/json" + return headers + + def get_complete_url( + self, + api_base: Optional[str], + optional_params: dict, + data: Optional[Union[dict, list[dict]]] = None, + **kwargs, + ) -> str: + """ + Get complete URL for Search endpoint. + """ + api_base = api_base or get_secret_str("CRW_API_BASE") or self.FASTCRW_API_BASE + + # Append "/search" to the api base if it's not already there + if not api_base.endswith("/search"): + api_base = f"{api_base}/search" + + return api_base + + def transform_search_request( + self, + query: Union[str, list[str]], + optional_params: dict, + **kwargs, + ) -> dict: + """ + Transform Search request to fastCRW API format. + + Transforms Perplexity unified spec parameters: + - query -> query (same) + - max_results -> limit + + All other fastCRW-specific parameters are passed through as-is. + + Args: + query: Search query (string or list of strings). fastCRW only supports single string queries. + optional_params: Optional parameters for the request + + Returns: + Dict with typed request data following FastCRWSearchRequest spec + """ + if isinstance(query, list): + # fastCRW only supports single string queries, join with spaces + query = " ".join(query) + + request_data: FastCRWSearchRequest = { + "query": query, + } + + # Transform Perplexity unified spec parameters to fastCRW format + if "max_results" in optional_params: + request_data["limit"] = optional_params["max_results"] + + # Convert to dict before dynamic key assignments + result_data = dict(request_data) + + # pass through all other parameters as-is + for param, value in optional_params.items(): + if ( + param not in self.get_supported_perplexity_optional_params() + and param not in result_data + ): + result_data[param] = value + + # By default, request markdown content if not explicitly specified + # fastCRW doesn't return content unless explicitly requested via scrapeOptions + if "scrapeOptions" not in result_data: + result_data["scrapeOptions"] = { + "formats": ["markdown"], + "onlyMainContent": True, + } + + return result_data + + def transform_search_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + **kwargs, + ) -> SearchResponse: + """ + Transform fastCRW API response to LiteLLM unified SearchResponse format. + + fastCRW (Firecrawl-compatible) returns: + {"success": true, "data": [{"url": "...", "title": "...", "description": "...", "markdown"?: "..."}, ...]} + + Args: + raw_response: Raw httpx response from fastCRW API + logging_obj: Logging object for tracking + + Returns: + SearchResponse with standardized format + """ + response_json = raw_response.json() + + results = [] + + data = response_json.get("data", []) + + if isinstance(data, list): + for result in data: + snippet = result.get("markdown") or result.get("description", "") + search_result = SearchResult( + title=result.get("title", ""), + url=result.get("url", ""), + snippet=snippet, + date=None, + last_updated=None, + ) + results.append(search_result) + + return SearchResponse( + results=results, + object="search", + ) diff --git a/litellm/llms/firecrawl/search/transformation.py b/litellm/llms/firecrawl/search/transformation.py index 18cf1d28c4d..7e01ba58706 100644 --- a/litellm/llms/firecrawl/search/transformation.py +++ b/litellm/llms/firecrawl/search/transformation.py @@ -61,7 +61,13 @@ class FirecrawlSearchConfig(BaseSearchConfig): """ Validate environment and return headers. """ - api_key = api_key or get_secret_str("FIRECRAWL_API_KEY") + api_key = self.resolve_server_api_key( + caller_api_key=api_key, + caller_api_base=api_base, + key_env_vars=("FIRECRAWL_API_KEY",), + base_env_var="FIRECRAWL_API_BASE", + default_api_base=self.FIRECRAWL_API_BASE, + ) if not api_key: raise ValueError( "FIRECRAWL_API_KEY is not set. Set `FIRECRAWL_API_KEY` environment variable." diff --git a/litellm/llms/fireworks_ai/audio_transcription/transformation.py b/litellm/llms/fireworks_ai/audio_transcription/transformation.py deleted file mode 100644 index 00bb5f26797..00000000000 --- a/litellm/llms/fireworks_ai/audio_transcription/transformation.py +++ /dev/null @@ -1,17 +0,0 @@ -from typing import List - -from litellm.types.llms.openai import OpenAIAudioTranscriptionOptionalParams - -from ...openai.transcriptions.whisper_transformation import ( - OpenAIWhisperAudioTranscriptionConfig, -) -from ..common_utils import FireworksAIMixin - - -class FireworksAIAudioTranscriptionConfig( - FireworksAIMixin, OpenAIWhisperAudioTranscriptionConfig -): - def get_supported_openai_params( - self, model: str - ) -> List[OpenAIAudioTranscriptionOptionalParams]: - return ["language", "prompt", "response_format", "timestamp_granularities"] diff --git a/litellm/llms/fireworks_ai/chat/transformation.py b/litellm/llms/fireworks_ai/chat/transformation.py index cca3b3da37a..7e4395959b9 100644 --- a/litellm/llms/fireworks_ai/chat/transformation.py +++ b/litellm/llms/fireworks_ai/chat/transformation.py @@ -1,5 +1,15 @@ import json -from typing import Any, List, Literal, Optional, Tuple, Union, cast +from typing import ( + Any, + AsyncIterator, + Iterator, + List, + Literal, + Optional, + Tuple, + Union, + cast, +) import httpx @@ -15,7 +25,6 @@ from litellm.litellm_core_utils.llm_response_utils.get_headers import ( from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import ( AllMessageValues, - ChatCompletionImageObject, ChatCompletionToolParam, OpenAIChatCompletionToolParam, ) @@ -25,6 +34,7 @@ from litellm.types.utils import ( Function, Message, ModelResponse, + ModelResponseStream, ProviderSpecificModelInfo, ) from litellm.utils import ( @@ -34,10 +44,34 @@ from litellm.utils import ( supports_tool_choice, ) -from ...openai.chat.gpt_transformation import OpenAIGPTConfig +from ...openai.chat.gpt_transformation import ( + OpenAIChatCompletionStreamingHandler, + OpenAIGPTConfig, +) from ..common_utils import FireworksAIException +def _extract_fireworks_hidden_params(payload: dict) -> dict: + """ + Collect Fireworks-specific response fields (perf_metrics, prompt_token_ids, + per-choice raw_output and token_ids) from a non-streaming completion payload + or a single streaming chunk, so the same data lands in ``_hidden_params`` on + both response paths. + """ + choices = [c for c in (payload.get("choices") or []) if isinstance(c, dict)] + top_level = { + f"fireworks_{field}": payload[field] + for field in ("perf_metrics", "prompt_token_ids") + if field in payload + } + per_choice = { + f"fireworks_{dest}": [c[field] for c in choices if field in c] + for field, dest in (("raw_output", "raw_outputs"), ("token_ids", "token_ids")) + if any(field in c for c in choices) + } + return {**top_level, **per_choice} + + class FireworksAIConfig(OpenAIGPTConfig): """ Reference: https://docs.fireworks.ai/api-reference/post-chatcompletions @@ -60,8 +94,7 @@ class FireworksAIConfig(OpenAIGPTConfig): logprobs: Optional[int] = None reasoning_effort: Optional[str] = None - # Non OpenAI parameters - Fireworks AI only params - prompt_truncate_length: Optional[int] = None + prompt_truncate_len: Optional[int] = None context_length_exceeded_behavior: Optional[Literal["error", "truncate"]] = None def __init__( @@ -80,7 +113,7 @@ class FireworksAIConfig(OpenAIGPTConfig): user: Optional[str] = None, logprobs: Optional[int] = None, reasoning_effort: Optional[str] = None, - prompt_truncate_length: Optional[int] = None, + prompt_truncate_len: Optional[int] = None, context_length_exceeded_behavior: Optional[Literal["error", "truncate"]] = None, ) -> None: locals_ = locals().copy() @@ -108,8 +141,30 @@ class FireworksAIConfig(OpenAIGPTConfig): "response_format", "user", "logprobs", - "prompt_truncate_length", + "prompt_truncate_len", "context_length_exceeded_behavior", + "seed", + "top_logprobs", + "min_p", + "typical_p", + "repetition_penalty", + "mirostat_target", + "mirostat_lr", + "logit_bias", + "echo", + "echo_last", + "ignore_eos", + "prompt_cache_key", + "prompt_cache_isolation_key", + "raw_output", + "perf_metrics_in_response", + "return_token_ids", + "safe_tokenization", + "service_tier", + "speculation", + "prediction", + "stream_options", + "sampling_mask", ] # Only add tools for models that support function calling @@ -133,9 +188,11 @@ class FireworksAIConfig(OpenAIGPTConfig): if supports_tool_choice(model=model, custom_llm_provider="fireworks_ai"): supported_params.append("tool_choice") - # Only add reasoning_effort for models that support it + # Only add reasoning params for models that support it if supports_reasoning(model=model, custom_llm_provider="fireworks_ai"): supported_params.append("reasoning_effort") + supported_params.append("reasoning_history") + supported_params.append("thinking") return supported_params @@ -151,6 +208,18 @@ class FireworksAIConfig(OpenAIGPTConfig): param == "tools" and value is not None for param, value in non_default_params.items() ) + if ( + non_default_params.get("thinking") is not None + and non_default_params.get("reasoning_effort") is not None + ): + raise litellm.BadRequestError( + message=( + "Fireworks AI chat completions does not support specifying both " + "`thinking` and `reasoning_effort` in the same request." + ), + model=model, + llm_provider="fireworks_ai", + ) for param, value in non_default_params.items(): if param == "tool_choice": @@ -174,40 +243,19 @@ class FireworksAIConfig(OpenAIGPTConfig): optional_params["response_format"] = value elif param == "max_completion_tokens": optional_params["max_tokens"] = value + elif param == "reasoning_effort": + if value is True: + optional_params["reasoning_effort"] = "medium" + elif value is False: + optional_params["reasoning_effort"] = "none" + else: + optional_params["reasoning_effort"] = value elif param in supported_openai_params: if value is not None: optional_params[param] = value return optional_params - def _add_transform_inline_image_block( - self, - content: ChatCompletionImageObject, - model: str, - disable_add_transform_inline_image_block: Optional[bool], - ) -> ChatCompletionImageObject: - """ - Add transform_inline to the image_url (allows non-vision models to parse documents/images/etc.) - - ignore if model is a vision model - - ignore if user has disabled this feature - """ - if ( - "vision" in model or disable_add_transform_inline_image_block - ): # allow user to toggle this feature. - return content - if isinstance(content["image_url"], str): - # Skip base64 data URLs — appending #transform=inline corrupts the - # base64 payload and causes an "Incorrect padding" decode error on - # the Fireworks side. Data URLs are already inlined by definition. - # Lower-case before checking: URI schemes are case-insensitive (RFC 3986). - if not content["image_url"].lower().startswith("data:"): - content["image_url"] = f"{content['image_url']}#transform=inline" - elif isinstance(content["image_url"], dict): - url = content["image_url"]["url"] - if not url.lower().startswith("data:"): - content["image_url"]["url"] = f"{url}#transform=inline" - return content - def _transform_tools( self, tools: List[OpenAIChatCompletionToolParam] ) -> List[OpenAIChatCompletionToolParam]: @@ -225,36 +273,46 @@ class FireworksAIConfig(OpenAIGPTConfig): self, messages: List[AllMessageValues], model: str, litellm_params: dict ) -> List[AllMessageValues]: """ - Add 'transform=inline' to the url of the image_url + Strip fields not permitted by FireworksAI from messages. """ from litellm.litellm_core_utils.prompt_templates.common_utils import ( filter_value_from_dict, - migrate_file_to_image_url, ) - disable_add_transform_inline_image_block = cast( - Optional[bool], - litellm_params.get("disable_add_transform_inline_image_block") - or litellm.disable_add_transform_inline_image_block, + supports_vision_value = self._get_model_cost_capability_exact( + model=model, capability="supports_vision" ) - ## For any 'file' message type with pdf content, move to 'image_url' message type - for message in messages: - if message["role"] == "user": - _message_content = message.get("content") - if _message_content is not None and isinstance(_message_content, list): - for idx, content in enumerate(_message_content): - if content["type"] == "file": - _message_content[idx] = migrate_file_to_image_url(content) for message in messages: if message["role"] == "user": _message_content = message.get("content") if _message_content is not None and isinstance(_message_content, list): for content in _message_content: - if content["type"] == "image_url": - content = self._add_transform_inline_image_block( - content=content, + if not isinstance(content, dict): + continue + if content.get("type") == "file": + raise litellm.BadRequestError( + message=( + "Fireworks AI chat completions does not support " + "file content blocks. For PDFs, convert pages to " + "images and send image_url blocks to a Fireworks " + "vision model, or extract text before calling a " + "text-only model." + ), model=model, - disable_add_transform_inline_image_block=disable_add_transform_inline_image_block, + llm_provider="fireworks_ai", + ) + if ( + content.get("type") == "image_url" + and supports_vision_value is False + ): + raise litellm.BadRequestError( + message=( + f"Fireworks AI model {model} does not support " + "image inputs. Use a Fireworks vision model or " + "remove image_url content blocks." + ), + model=model, + llm_provider="fireworks_ai", ) filter_value_from_dict(cast(dict, message), "cache_control") # Remove fields not permitted by FireworksAI (additionalProperties: false @@ -317,43 +375,55 @@ class FireworksAIConfig(OpenAIGPTConfig): return True return ("-" + key_short + "-") in short_name - def _get_model_cost_capability(self, model: str, capability: str) -> Optional[bool]: + @staticmethod + def _short_model_name(model: str) -> str: short_name = model if short_name.startswith("fireworks_ai/"): short_name = short_name[len("fireworks_ai/") :] if short_name.startswith("accounts/fireworks/models/"): short_name = short_name[len("accounts/fireworks/models/") :] + return short_name - candidate_keys = [ + def _get_model_cost_capability_exact( + self, model: str, capability: str + ) -> Optional[bool]: + short_name = self._short_model_name(model) + candidate_keys = ( model, f"fireworks_ai/{short_name}", f"fireworks_ai/accounts/fireworks/models/{short_name}", - ] - + ) for candidate_key in candidate_keys: model_info = litellm.model_cost.get(candidate_key) if model_info is not None and model_info.get(capability) is not None: return cast(Optional[bool], model_info.get(capability)) + return None - # Fallback: preserve historical substring matching for model name - # variants (e.g. fine-tuned or regionally-suffixed versions of a - # known model). Pick the *longest* matching entry so a more specific - # known model (e.g. "qwen3-8b-instruct") wins over a less specific - # one (e.g. "qwen3-8b") when the query model is more specific still. - # Use hyphen-aligned matching to avoid false positives where a short - # known model name is an unrelated substring of a longer one. - best_match_short: Optional[str] = None - best_match_value: Optional[bool] = None - for key_short, model_info in self._get_fireworks_index(): - if model_info.get(capability) is None: - continue - if not self._matches_on_hyphen_boundary(short_name, key_short): - continue - if best_match_short is None or len(key_short) > len(best_match_short): - best_match_short = key_short - best_match_value = cast(Optional[bool], model_info.get(capability)) + def _get_model_cost_capability(self, model: str, capability: str) -> Optional[bool]: + exact = self._get_model_cost_capability_exact( + model=model, capability=capability + ) + if exact is not None: + return exact - return best_match_value + # Fallback: substring matching for model name variants (e.g. fine-tuned + # or regionally-suffixed versions of a known model). Pick the *longest* + # matching entry so a more specific known model (e.g. "qwen3-8b-instruct") + # wins over a less specific one (e.g. "qwen3-8b"). Hyphen-aligned matching + # avoids false positives where a short known name is an unrelated + # substring of a longer one. This stays a soft signal: capability-gated + # hard rejections use the exact lookup so a fuzzy match never blocks a + # custom deployment. + short_name = self._short_model_name(model) + matches = [ + (key_short, cast(Optional[bool], model_info.get(capability))) + for key_short, model_info in self._get_fireworks_index() + if model_info.get(capability) is not None + and self._matches_on_hyphen_boundary(short_name, key_short) + ] + if not matches: + return None + return max(matches, key=lambda match: len(match[0]))[1] def get_provider_info(self, model: str) -> ProviderSpecificModelInfo: supports_function_calling_value = self._get_model_cost_capability( @@ -362,12 +432,16 @@ class FireworksAIConfig(OpenAIGPTConfig): supports_reasoning_value = self._get_model_cost_capability( model=model, capability="supports_reasoning" ) + supports_vision_value = self._get_model_cost_capability( + model=model, capability="supports_vision" + ) + supports_pdf_input_value = self._get_model_cost_capability( + model=model, capability="supports_pdf_input" + ) provider_specific_model_info: ProviderSpecificModelInfo = { "supports_function_calling": True, "supports_prompt_caching": True, # https://docs.fireworks.ai/guides/prompt-caching - "supports_pdf_input": True, # via document inlining - "supports_vision": True, # via document inlining } if supports_function_calling_value is not None: @@ -381,6 +455,14 @@ class FireworksAIConfig(OpenAIGPTConfig): supports_reasoning_value ) + if supports_vision_value is not None: + provider_specific_model_info["supports_vision"] = supports_vision_value + + if supports_pdf_input_value is not None: + provider_specific_model_info["supports_pdf_input"] = ( + supports_pdf_input_value + ) + return provider_specific_model_info def transform_request( @@ -392,13 +474,25 @@ class FireworksAIConfig(OpenAIGPTConfig): headers: dict, ) -> dict: if not model.startswith("accounts/") and "#" not in model: - model = f"accounts/fireworks/models/{model}" + if model.endswith("-fast"): + model = f"accounts/fireworks/routers/{model}" + else: + model = f"accounts/fireworks/models/{model}" messages = self._transform_messages_helper( messages=messages, model=model, litellm_params=litellm_params ) if "tools" in optional_params and optional_params["tools"] is not None: tools = self._transform_tools(tools=optional_params["tools"]) optional_params["tools"] = tools + if optional_params.get("stream"): + stream_options = optional_params.get("stream_options") + if stream_options is None: + optional_params["stream_options"] = {"include_usage": True} + elif stream_options.get("include_usage") is not False: + optional_params["stream_options"] = { + **stream_options, + "include_usage": True, + } return super().transform_request( model=model, messages=messages, @@ -491,10 +585,25 @@ class FireworksAIConfig(OpenAIGPTConfig): ) ) - response._hidden_params = {"additional_headers": additional_headers} + response._hidden_params = { + "additional_headers": additional_headers, + **_extract_fireworks_hidden_params(completion_response), + } return response + def get_model_response_iterator( + self, + streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse], + sync_stream: bool, + json_mode: Optional[bool] = False, + ) -> Any: + return FireworksAIChatCompletionStreamingHandler( + streaming_response=streaming_response, + sync_stream=sync_stream, + json_mode=json_mode, + ) + def _get_openai_compatible_provider_info( self, api_base: Optional[str], api_key: Optional[str] ) -> Tuple[Optional[str], Optional[str]]: @@ -551,3 +660,15 @@ class FireworksAIConfig(OpenAIGPTConfig): or get_secret_str("FIREWORKSAI_API_KEY") or get_secret_str("FIREWORKS_AI_TOKEN") ) + + +class FireworksAIChatCompletionStreamingHandler(OpenAIChatCompletionStreamingHandler): + def chunk_parser(self, chunk: dict) -> ModelResponseStream: + parsed = super().chunk_parser(chunk) + fireworks_fields = _extract_fireworks_hidden_params(chunk) + if fireworks_fields: + parsed.provider_specific_fields = { + **(getattr(parsed, "provider_specific_fields", None) or {}), + **fireworks_fields, + } + return parsed diff --git a/litellm/llms/gemini/common_utils.py b/litellm/llms/gemini/common_utils.py index 42a807983b9..4cca2e2b850 100644 --- a/litellm/llms/gemini/common_utils.py +++ b/litellm/llms/gemini/common_utils.py @@ -174,12 +174,110 @@ def map_openai_image_params_to_gemini( mapped_params["imageConfig"] = image_config_param for key, value in filtered_params.items(): - if key not in ("n", "size", "imageConfig") and key not in optional_params: + if ( + key not in ("n", "size", "imageConfig", "tools", "web_search_options") + and key not in optional_params + ): mapped_params[key] = value return mapped_params +def _dedupe_gemini_search_tools(tools: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + VertexGeminiConfig, + ) + + search_tool_keys = VertexGeminiConfig._search_tool_keys() + seen_search_keys: set[str] = set() + deduped_tools: List[Dict[str, Any]] = [] + + for tool in tools: + if not isinstance(tool, dict): + deduped_tools.append(tool) + continue + + search_key = next((key for key in search_tool_keys if key in tool), None) + if search_key is None: + deduped_tools.append(tool) + continue + + if search_key in seen_search_keys: + continue + + seen_search_keys.add(search_key) + deduped_tools.append(tool) + + return deduped_tools + + +def _has_gemini_search_tool(tools: List[Any]) -> bool: + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + VertexGeminiConfig, + ) + + search_tool_keys = VertexGeminiConfig._search_tool_keys() + return any( + isinstance(tool, dict) and any(key in tool for key in search_tool_keys) + for tool in tools + ) + + +def map_gemini_image_tools_params( + non_default_params: Dict[str, Any], + mapped_params: Dict[str, Any], +) -> Dict[str, Any]: + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + VertexGeminiConfig, + ) + + gemini_config = VertexGeminiConfig() + result = dict(mapped_params) + result.pop("web_search_options", None) + + tools_value = non_default_params.get("tools") + if isinstance(tools_value, list) and tools_value: + mapped_tools = gemini_config._map_function( + value=tools_value, optional_params=result + ) + result = gemini_config._add_tools_to_optional_params(result, mapped_tools) + + web_search_options = non_default_params.get("web_search_options") + existing_tools = result.get("tools") + if isinstance(web_search_options, dict) and not ( + isinstance(existing_tools, list) and _has_gemini_search_tool(existing_tools) + ): + search_tool = gemini_config._map_web_search_options(web_search_options) + result = gemini_config._add_tools_to_optional_params(result, [search_tool]) + + gemini_config._drop_search_tools_mixed_with_functions(result) + + if isinstance(result.get("tools"), list): + result["tools"] = _dedupe_gemini_search_tools(result["tools"]) + + return result + + +def get_gemini_image_web_search_requests( + response_data: Dict[str, Any], +) -> Optional[int]: + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + VertexGeminiConfig, + ) + + grounding_metadata: List[Dict[str, Any]] = [] + for candidate in response_data.get("candidates", []): + if not isinstance(candidate, dict): + continue + candidate_grounding = candidate.get("groundingMetadata") + if isinstance(candidate_grounding, list): + grounding_metadata.extend(candidate_grounding) + elif isinstance(candidate_grounding, dict): + grounding_metadata.append(candidate_grounding) + + return VertexGeminiConfig._calculate_web_search_requests(grounding_metadata) + + def get_gemini_image_generation_config( model: str, optional_params: Dict[str, Any], diff --git a/litellm/llms/gemini/image_generation/cost_calculator.py b/litellm/llms/gemini/image_generation/cost_calculator.py index 3c8e69374af..380e2c21e9e 100644 --- a/litellm/llms/gemini/image_generation/cost_calculator.py +++ b/litellm/llms/gemini/image_generation/cost_calculator.py @@ -7,6 +7,7 @@ from typing import Any import litellm from litellm.litellm_core_utils.llm_cost_calc.utils import ( calculate_image_response_cost_from_usage, + calculate_image_response_web_search_cost, ) from litellm.types.utils import ImageResponse @@ -23,22 +24,25 @@ def cost_calculator( custom_llm_provider="gemini", ) - if isinstance(image_response, ImageResponse): - token_based_cost = calculate_image_response_cost_from_usage( - model=model, - image_response=image_response, - custom_llm_provider="gemini", - ) - if token_based_cost is not None: - return token_based_cost - - output_cost_per_image: float = _model_info.get("output_cost_per_image") or 0.0 - num_images: int = 0 - if isinstance(image_response, ImageResponse): - if image_response.data: - num_images = len(image_response.data) - return output_cost_per_image * num_images - else: + if not isinstance(image_response, ImageResponse): raise ValueError( f"image_response must be of type ImageResponse got type={type(image_response)}" ) + + web_search_cost = calculate_image_response_web_search_cost( + image_response=image_response, + custom_llm_provider="gemini", + model_info=_model_info, + ) + + token_based_cost = calculate_image_response_cost_from_usage( + model=model, + image_response=image_response, + custom_llm_provider="gemini", + ) + if token_based_cost is not None: + return token_based_cost + web_search_cost + + output_cost_per_image: float = _model_info.get("output_cost_per_image") or 0.0 + num_images: int = len(image_response.data) if image_response.data else 0 + return output_cost_per_image * num_images + web_search_cost diff --git a/litellm/llms/gemini/image_generation/transformation.py b/litellm/llms/gemini/image_generation/transformation.py index e6770a76bcb..ebfb0d68830 100644 --- a/litellm/llms/gemini/image_generation/transformation.py +++ b/litellm/llms/gemini/image_generation/transformation.py @@ -7,7 +7,9 @@ from litellm.llms.base_llm.image_generation.transformation import ( ) from litellm.llms.gemini.common_utils import ( get_gemini_image_generation_config, + get_gemini_image_web_search_requests, is_gemini_image_model, + map_gemini_image_tools_params, map_openai_image_params_to_gemini, ) from litellm.llms.gemini.image_usage_transformation import ( @@ -41,7 +43,7 @@ class GoogleImageGenConfig(BaseImageGenerationConfig): """ supported_params = ["n", "size"] if is_gemini_image_model(model): - supported_params.append("imageConfig") + supported_params.extend(["imageConfig", "tools", "web_search_options"]) return supported_params # type: ignore[return-value] def map_openai_params( @@ -51,12 +53,17 @@ class GoogleImageGenConfig(BaseImageGenerationConfig): model: str, drop_params: bool, ) -> dict: - return map_openai_image_params_to_gemini( + mapped_params = map_openai_image_params_to_gemini( params=non_default_params, model=model, supported_params=self.get_supported_openai_params(model), optional_params=optional_params, ) + if is_gemini_image_model(model): + mapped_params = map_gemini_image_tools_params( + non_default_params, mapped_params + ) + return mapped_params def get_complete_url( self, @@ -140,6 +147,10 @@ class GoogleImageGenConfig(BaseImageGenerationConfig): optional_params=optional_params, ), } + if tools := optional_params.get("tools"): + request_body["tools"] = tools + if tool_config := optional_params.get("toolConfig"): + request_body["toolConfig"] = tool_config return request_body else: # For other Imagen models, use the original Imagen format @@ -217,6 +228,11 @@ class GoogleImageGenConfig(BaseImageGenerationConfig): model_response.usage = transform_gemini_image_usage( response_data["usageMetadata"] ) + web_search_requests = get_gemini_image_web_search_requests(response_data) + if web_search_requests and model_response.usage is not None: + setattr( + model_response.usage, "web_search_requests", web_search_requests + ) else: # Original Imagen format - predictions with generated images predictions = response_data.get("predictions", []) diff --git a/litellm/llms/gemini/realtime/transformation.py b/litellm/llms/gemini/realtime/transformation.py index 212287fb7f8..e153d00e6ab 100644 --- a/litellm/llms/gemini/realtime/transformation.py +++ b/litellm/llms/gemini/realtime/transformation.py @@ -103,6 +103,10 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): # bypassing spend and budget accounting. self._pending_usage_metadata: Optional[dict] = None + def _include_function_response_id(self) -> bool: + """Google AI Studio Gemini 3.5+ accepts ``id`` on functionResponses; Vertex AI rejects it.""" + return True + @staticmethod def _usage_detail_alias(details: Any, defaults: Dict[str, int]) -> Dict[str, Any]: if not isinstance(details, dict): @@ -195,13 +199,48 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): def get_audio_mime_type(self, input_audio_format: str = "pcm16"): mime_types = { - "pcm16": "audio/pcm", + "pcm16": "audio/pcm;rate=24000", "g711_ulaw": "audio/pcmu", "g711_alaw": "audio/pcma", } return mime_types.get(input_audio_format, "application/octet-stream") + def _manual_turn_detection_enabled( + self, session_configuration_request: Optional[str] + ) -> bool: + if not session_configuration_request: + return False + try: + setup = json.loads(session_configuration_request).get("setup", {}) + automatic_detection = setup.get("realtimeInputConfig", {}).get( + "automaticActivityDetection", {} + ) + return ( + isinstance(automatic_detection, dict) + and automatic_detection.get("disabled") is True + ) + except (json.JSONDecodeError, TypeError, AttributeError): + return False + + def _handle_input_audio_buffer_commit_or_end( + self, session_configuration_request: Optional[str] + ) -> List[str]: + """Map OpenAI buffer commit/end to Gemini Live turn-boundary signals.""" + if self._manual_turn_detection_enabled(session_configuration_request): + realtime_input_dict: BidiGenerateContentRealtimeInput = { + "activityEnd": True, + } + verbose_logger.debug( + "Gemini Realtime: Sending activityEnd realtimeInput to backend" + ) + else: + realtime_input_dict = {"audioStreamEnd": True} + verbose_logger.debug( + "Gemini Realtime: Sending audioStreamEnd realtimeInput to backend" + ) + return [json.dumps({"realtimeInput": realtime_input_dict})] + def map_automatic_turn_detection( self, value: OpenAIRealtimeTurnDetection ) -> AutomaticActivityDetection: @@ -569,10 +608,9 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): ) # Build Gemini toolResponse format - function_response = { - "id": call_id, - "response": output_dict, - } + function_response: dict[str, Any] = {"response": output_dict} + if self._include_function_response_id() and call_id: + function_response["id"] = call_id if function_name: function_response["name"] = function_name @@ -656,6 +694,19 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): ) messages.append(gemini_msg) return messages + + if msg_type in ("input_audio_buffer.commit", "input_audio_buffer.end"): + return self._handle_input_audio_buffer_commit_or_end( + session_configuration_request + ) + + if msg_type == "input_audio_buffer.clear": + # Local OpenAI buffer op — nothing to forward to Gemini Live. + verbose_logger.debug( + "Gemini Realtime: input_audio_buffer.clear is a local buffer op" + ) + return [] + # Unknown/unsupported OpenAI event type — drop silently rather than # forwarding raw JSON as text input to the model. return [] @@ -1330,7 +1381,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): raise ValueError(f"Unknown openai event: {key}, value: {value}") return openai_event - def transform_realtime_response( # noqa: PLR0915 + def transform_realtime_response( self, message: Union[str, bytes], model: str, diff --git a/litellm/llms/github_copilot/responses/transformation.py b/litellm/llms/github_copilot/responses/transformation.py index 3406538c774..299f346a7eb 100644 --- a/litellm/llms/github_copilot/responses/transformation.py +++ b/litellm/llms/github_copilot/responses/transformation.py @@ -101,6 +101,7 @@ class GithubCopilotResponsesAPIConfig(OpenAIResponsesAPIConfig): def __init__(self) -> None: super().__init__() self.authenticator = Authenticator() + self._stream_item_ids_by_output_index: Dict[int, str] = {} @property def custom_llm_provider(self) -> LlmProviders: @@ -129,6 +130,61 @@ class GithubCopilotResponsesAPIConfig(OpenAIResponsesAPIConfig): """ return dict(response_api_optional_params) + def transform_streaming_response( + self, + model: str, + parsed_chunk: dict, + logging_obj: LiteLLMLoggingObj, + ) -> Any: + parsed_chunk = self._normalize_stream_item_id(parsed_chunk) + return super().transform_streaming_response( + model=model, + parsed_chunk=parsed_chunk, + logging_obj=logging_obj, + ) + + def _normalize_stream_item_id(self, parsed_chunk: dict) -> dict: + """Rewrite streamed item ids to one stable id per output_index. + + GitHub Copilot tags each event of a single output item with a different + item id, so clients that key streaming state by item id (e.g. the Vercel + AI SDK) crash with "reasoning part not found" / "text part not + found". Every sub-event carries a top-level ``item_id`` (whatever the + item type), so its presence is the rewrite signal; output_item.added / + .done instead nest the id under ``item``. The anchor is keyed by + output_index and taken from output_item.added, which the protocol always + emits first, so it is written before any sub-event reads it. Copilot + accepts that id paired with the final encrypted_content next turn, so + multi-turn replay is unaffected. + + State is keyed by output_index on this config, which + ProviderConfigManager builds fresh per request, so it is stream-scoped. + """ + output_index = parsed_chunk.get("output_index") + if not isinstance(output_index, int): + return parsed_chunk + + if parsed_chunk.get("type") == "response.output_item.added": + item = parsed_chunk.get("item") + if isinstance(item, dict) and isinstance(item.get("id"), str): + self._stream_item_ids_by_output_index[output_index] = item["id"] + return parsed_chunk + + stable_id = self._stream_item_ids_by_output_index.get(output_index) + if stable_id is None: + return parsed_chunk + + if isinstance(parsed_chunk.get("item_id"), str): + parsed_chunk = dict(parsed_chunk) + parsed_chunk["item_id"] = stable_id + elif parsed_chunk.get("type") == "response.output_item.done": + item = parsed_chunk.get("item") + if isinstance(item, dict): + parsed_chunk = dict(parsed_chunk) + parsed_chunk["item"] = {**item, "id": stable_id} + + return parsed_chunk + def validate_environment( self, headers: dict, diff --git a/litellm/llms/google_pse/search/transformation.py b/litellm/llms/google_pse/search/transformation.py index a8aa109cbf0..5cd3f2085a8 100644 --- a/litellm/llms/google_pse/search/transformation.py +++ b/litellm/llms/google_pse/search/transformation.py @@ -85,7 +85,13 @@ class GooglePSESearchConfig(BaseSearchConfig): Google PSE uses API key as a query parameter, not in headers. This method is called but headers are not used for authentication. """ - api_key = api_key or get_secret_str("GOOGLE_PSE_API_KEY") + api_key = self.resolve_server_api_key( + caller_api_key=api_key, + caller_api_base=api_base, + key_env_vars=("GOOGLE_PSE_API_KEY",), + base_env_var="GOOGLE_PSE_API_BASE", + default_api_base=self.GOOGLE_PSE_API_BASE, + ) if not api_key: raise ValueError( "GOOGLE_PSE_API_KEY is not set. Set `GOOGLE_PSE_API_KEY` environment variable." @@ -137,6 +143,7 @@ class GooglePSESearchConfig(BaseSearchConfig): query: Union[str, List[str]], optional_params: dict, api_key: Optional[str] = None, + api_base: str | None = None, search_engine_id: Optional[str] = None, **kwargs, ) -> Dict: @@ -165,8 +172,16 @@ class GooglePSESearchConfig(BaseSearchConfig): # Google PSE only supports single string queries query = " ".join(query) - # Get API credentials - api_key = api_key or get_secret_str("GOOGLE_PSE_API_KEY") + # Get API credentials. The key is sent as a query param to api_base, so + # resolve it host-aware to avoid leaking a server-managed key to a + # caller-supplied host. + api_key = self.resolve_server_api_key( + caller_api_key=api_key, + caller_api_base=api_base, + key_env_vars=("GOOGLE_PSE_API_KEY",), + base_env_var="GOOGLE_PSE_API_BASE", + default_api_base=self.GOOGLE_PSE_API_BASE, + ) search_engine_id = search_engine_id or get_secret_str("GOOGLE_PSE_ENGINE_ID") if not api_key: diff --git a/litellm/llms/hosted_vllm/chat/transformation.py b/litellm/llms/hosted_vllm/chat/transformation.py index 1824314865c..7c42e6a9a00 100644 --- a/litellm/llms/hosted_vllm/chat/transformation.py +++ b/litellm/llms/hosted_vllm/chat/transformation.py @@ -2,6 +2,7 @@ Translate from OpenAI's `/v1/chat/completions` to VLLM's `/v1/chat/completions` """ +import json from typing import ( Any, Coroutine, @@ -22,7 +23,9 @@ from litellm.litellm_core_utils.prompt_templates.factory import _parse_mime_type from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import ( AllMessageValues, + ChatCompletionAssistantToolCall, ChatCompletionFileObject, + ChatCompletionToolCallFunctionChunk, ChatCompletionVideoObject, ChatCompletionVideoUrlObject, ) @@ -101,26 +104,18 @@ class HostedVLLMChatConfig(OpenAIGPTConfig): ) -> dict: _tools = non_default_params.pop("tools", None) if _tools is not None: - # remove 'additionalProperties' from tools _tools = _remove_additional_properties(_tools) - # remove 'strict' from tools _tools = _remove_strict_from_schema(_tools) if isinstance(_tools, list): _tools = self._convert_custom_tools_to_function_tools(_tools) if _tools is not None: non_default_params["tools"] = _tools - # Handle thinking parameter - convert Anthropic-style to OpenAI-style reasoning_effort - # vLLM is OpenAI-compatible, so it understands reasoning_effort, not thinking - # Reference: https://github.com/BerriAI/litellm/issues/19761 thinking = non_default_params.pop("thinking", None) if thinking is not None and isinstance(thinking, dict): if thinking.get("type") == "enabled": - # Only convert if reasoning_effort not already set if "reasoning_effort" not in non_default_params: budget_tokens = thinking.get("budget_tokens", 0) - # Map budget_tokens to reasoning_effort level - # Same logic as Anthropic adapter (translate_anthropic_thinking_to_reasoning_effort) if budget_tokens >= 10000: non_default_params["reasoning_effort"] = "high" elif budget_tokens >= 5000: @@ -137,20 +132,13 @@ class HostedVLLMChatConfig(OpenAIGPTConfig): def _get_openai_compatible_provider_info( self, api_base: Optional[str], api_key: Optional[str] ) -> Tuple[Optional[str], Optional[str]]: - api_base = api_base or get_secret_str("HOSTED_VLLM_API_BASE") # type: ignore + api_base = api_base or get_secret_str("HOSTED_VLLM_API_BASE") dynamic_api_key = ( api_key or get_secret_str("HOSTED_VLLM_API_KEY") or "fake-api-key" - ) # vllm does not require an api key + ) return api_base, dynamic_api_key def _is_video_file(self, content_item: ChatCompletionFileObject) -> bool: - """ - Check if the file is a video - - - format: video/ - - file_data: base64 encoded video data - - file_id: infer mp4 from extension - """ file = content_item.get("file", {}) format = file.get("format") file_data = file.get("file_data") @@ -205,29 +193,69 @@ class HostedVLLMChatConfig(OpenAIGPTConfig): """ Support translating: - video files from file_id or file_data to video_url - - thinking_blocks on assistant messages to content blocks + - thinking_blocks on assistant messages are removed, and content lists + are converted to strings for vLLM compatibility """ for message in messages: if message["role"] == "assistant": - thinking_blocks = message.pop("thinking_blocks", None) # type: ignore - if thinking_blocks: - new_content: list = [ - ( - { - "type": block["type"], - "thinking": block.get("thinking", ""), + message.pop("thinking_blocks", None) + existing_content = message.get("content") + if isinstance(existing_content, list): + text_parts = [] + tool_calls: list[ChatCompletionAssistantToolCall] = [] + content_blocks: list[object] = [] + has_structured_content = False + for c in existing_content: + if isinstance(c, dict) and c.get("type") == "text": + text_parts.append(c.get("text", "")) + content_blocks.append(c) + elif isinstance(c, dict) and c.get("type") == "tool_use": + tool_input = c.get("input", {}) + tool_calls.append( + ChatCompletionAssistantToolCall( + id=c.get("id"), + type="function", + function=ChatCompletionToolCallFunctionChunk( + name=c.get("name"), + arguments=( + tool_input + if isinstance( + tool_input, + str, + ) + else json.dumps(tool_input) + ), + ), + ) + ) + else: + content_blocks.append(c) + has_structured_content = True + if tool_calls: + existing_tool_calls = message.get("tool_calls") + if isinstance(existing_tool_calls, list): + existing_tool_call_ids = { + tool_call.get("id") + for tool_call in existing_tool_calls + if isinstance(tool_call, dict) + and tool_call.get("id") is not None } - if block.get("type") == "thinking" - else {"type": block["type"], "data": block.get("data", "")} - ) - for block in thinking_blocks - ] - existing_content = message.get("content") - if isinstance(existing_content, str): - new_content.append({"type": "text", "text": existing_content}) - elif isinstance(existing_content, list): - new_content.extend(existing_content) - message["content"] = new_content # type: ignore + new_tool_calls = [ + tool_call + for tool_call in tool_calls + if tool_call.get("id") not in existing_tool_call_ids + ] + if new_tool_calls: + message["tool_calls"] = ( + existing_tool_calls + new_tool_calls + ) + else: + message["tool_calls"] = tool_calls + content_str = "\n".join(text_parts) + new_content = ( + content_blocks if has_structured_content else content_str + ) + message["content"] = new_content # type: ignore[typeddict-item] elif message["role"] == "user": message_content = message.get("content") if message_content and isinstance(message_content, list): @@ -243,6 +271,7 @@ class HostedVLLMChatConfig(OpenAIGPTConfig): message_content[idx] = self._convert_file_to_video_url( content_item ) + if is_async: return super()._transform_messages( messages, model, is_async=cast(Literal[True], True) diff --git a/litellm/llms/huggingface/embedding/transformation.py b/litellm/llms/huggingface/embedding/transformation.py index 88d42cfcdcc..7cddda617a9 100644 --- a/litellm/llms/huggingface/embedding/transformation.py +++ b/litellm/llms/huggingface/embedding/transformation.py @@ -404,7 +404,7 @@ class HuggingFaceEmbeddingConfig(BaseConfig): ) return completion_response - def convert_to_model_response_object( # noqa: PLR0915 + def convert_to_model_response_object( self, completion_response: Union[List[Dict[str, Any]], Dict[str, Any]], model_response: ModelResponse, diff --git a/litellm/llms/linkup/search/transformation.py b/litellm/llms/linkup/search/transformation.py index 2b17d5642ac..d27ae038f9e 100644 --- a/litellm/llms/linkup/search/transformation.py +++ b/litellm/llms/linkup/search/transformation.py @@ -61,7 +61,13 @@ class LinkupSearchConfig(BaseSearchConfig): """ Validate environment and return headers. """ - api_key = api_key or get_secret_str("LINKUP_API_KEY") + api_key = self.resolve_server_api_key( + caller_api_key=api_key, + caller_api_base=api_base, + key_env_vars=("LINKUP_API_KEY",), + base_env_var="LINKUP_API_BASE", + default_api_base=self.LINKUP_API_BASE, + ) if not api_key: raise ValueError( "LINKUP_API_KEY is not set. Set `LINKUP_API_KEY` environment variable." diff --git a/litellm/llms/litellm_proxy/skills/README.md b/litellm/llms/litellm_proxy/skills/README.md index 1dfeff1a42c..a896aa1166e 100644 --- a/litellm/llms/litellm_proxy/skills/README.md +++ b/litellm/llms/litellm_proxy/skills/README.md @@ -18,7 +18,7 @@ flowchart TB F[Request with container.skills] --> G[SkillsInjectionHook] G --> H{skill_id prefix?} - H -->|"litellm:skill_abc"| I[Fetch from LiteLLM DB] + H -->|"litellm_skill_abc"| I[Fetch from LiteLLM DB] H -->|"skill_xyz" no prefix| J[Pass to Anthropic as native skill] I --> K{Model provider?} @@ -57,7 +57,7 @@ sequenceDiagram Note over LiteLLM,PreHook: PRE-CALL HOOK LiteLLM->>PreHook: Intercept request - PreHook->>PreHook: Fetch skill from DB (litellm:skill_id) + PreHook->>PreHook: Fetch skill from DB (litellm_skill_id) PreHook->>PreHook: Extract SKILL.md from ZIP PreHook->>PreHook: Inject SKILL.md into system prompt PreHook->>PreHook: Add litellm_code_execution tool @@ -105,7 +105,7 @@ response = await litellm.acompletion( model="gpt-4o-mini", messages=[{"role": "user", "content": "Create a bouncing ball GIF"}], container={ - "skills": [{"type": "custom", "skill_id": "litellm:skill_abc123"}] + "skills": [{"type": "custom", "skill_id": "litellm_skill_abc123"}] }, ) @@ -261,7 +261,7 @@ response = litellm.completion( messages=[{"role": "user", "content": "Analyze this data..."}], container={ "skills": [ - {"type": "custom", "skill_id": "litellm:skill_abc123"} # litellm: prefix + {"type": "custom", "skill_id": "litellm_skill_abc123"} # litellm_skill_ prefix ] } ) @@ -277,7 +277,7 @@ response = litellm.completion( "messages": [{"role": "user", "content": "Help me analyze data"}], "container": { "skills": [ - {"type": "custom", "skill_id": "litellm:skill_abc123"} + {"type": "custom", "skill_id": "litellm_skill_abc123"} ] } } @@ -287,7 +287,7 @@ response = litellm.completion( The hook (`litellm/proxy/hooks/litellm_skills/main.py`) intercepts the request: -1. **Detects `litellm:` prefix** → Fetches skill from database +1. **Detects `litellm_skill_` prefix** → Fetches skill from database 2. **Checks model provider** → Bedrock is not Anthropic 3. **Extracts SKILL.md** from stored ZIP file 4. **Converts skill to tool** + **Injects content into system prompt** @@ -361,8 +361,8 @@ model LiteLLM_SkillsTable { | Create skill on Anthropic | `anthropic` | N/A | Forward to Anthropic API | | Create skill in LiteLLM DB | `litellm_proxy` | N/A | Store in database | | Use Anthropic native skill | N/A | `skill_xyz` | Pass to Anthropic container.skills | -| Use LiteLLM skill on Anthropic | N/A | `litellm:skill_abc` | Convert to tools | -| Use LiteLLM skill on Bedrock/OpenAI | N/A | `litellm:skill_abc` | Convert to tools + inject SKILL.md | +| Use LiteLLM skill on Anthropic | N/A | `litellm_skill_abc` | Convert to tools | +| Use LiteLLM skill on Bedrock/OpenAI | N/A | `litellm_skill_abc` | Convert to tools + inject SKILL.md | ## Testing diff --git a/litellm/llms/litellm_proxy/skills/constants.py b/litellm/llms/litellm_proxy/skills/constants.py index a8c2697fcee..0c60a60842a 100644 --- a/litellm/llms/litellm_proxy/skills/constants.py +++ b/litellm/llms/litellm_proxy/skills/constants.py @@ -4,6 +4,10 @@ Constants for LiteLLM Skills Centralized constants for skills processing, code execution, and sandbox configuration. """ +LITELLM_SKILL_ID_PREFIX: str = "litellm_skill_" +"""Prefix for DB-backed skill IDs. The model-facing tool name is the skill ID +with hyphens/spaces replaced by underscores, which leaves this prefix intact.""" + # Code execution loop settings DEFAULT_MAX_ITERATIONS: int = 10 """Maximum number of iterations for the automatic code execution loop.""" diff --git a/litellm/llms/litellm_proxy/skills/handler.py b/litellm/llms/litellm_proxy/skills/handler.py index 7b259c1ed66..9138b9a712f 100644 --- a/litellm/llms/litellm_proxy/skills/handler.py +++ b/litellm/llms/litellm_proxy/skills/handler.py @@ -10,6 +10,7 @@ from typing import Any, Dict, List, Optional from litellm._logging import verbose_logger from litellm.caching.in_memory_cache import InMemoryCache +from litellm.llms.litellm_proxy.skills.constants import LITELLM_SKILL_ID_PREFIX from litellm.proxy._types import LiteLLM_SkillsTable, NewSkillRequest, UserAPIKeyAuth from litellm.proxy.common_utils.resource_ownership import ( get_primary_resource_owner_scope, @@ -68,7 +69,7 @@ class LiteLLMSkillsHandler: ) -> LiteLLM_SkillsTable: prisma_client = await LiteLLMSkillsHandler._get_prisma_client() - skill_id = f"litellm_skill_{uuid.uuid4()}" + skill_id = f"{LITELLM_SKILL_ID_PREFIX}{uuid.uuid4()}" owner = get_primary_resource_owner_scope(user_api_key_dict) or user_id if owner is None: # Identity-less callers (no user_id / team_id / org_id / diff --git a/litellm/llms/mistral/chat/transformation.py b/litellm/llms/mistral/chat/transformation.py index f1ad3708236..8d0cf993814 100644 --- a/litellm/llms/mistral/chat/transformation.py +++ b/litellm/llms/mistral/chat/transformation.py @@ -247,6 +247,8 @@ class MistralConfig(OpenAIGPTConfig): The above statement is not valid now. Need to plan to remove all the #1,2,3 Mistral API supports content as a list. """ + messages = [self._strip_output_only_fields(m) for m in messages] + ## 1. If 'image_url' or 'file' in content, then transform with base class and mistral-specific handling for m in messages: _content_block = m.get("content") @@ -409,6 +411,25 @@ class MistralConfig(OpenAIGPTConfig): return cleaned_tools + @classmethod + def _strip_output_only_fields(cls, message: AllMessageValues) -> AllMessageValues: + """ + ``reasoning_content`` and ``thinking_blocks`` are output-only fields that + LiteLLM attaches to assistant responses. Mistral's input schema forbids + unknown fields, so replaying them verbatim in a follow-up turn triggers a + 422 ``extra_forbidden``. Drop them before the request is sent. + """ + if message["role"] != "assistant": + return message + return cast( + AllMessageValues, + { + k: v + for k, v in message.items() + if k not in ("reasoning_content", "thinking_blocks") + }, + ) + @classmethod def _handle_name_in_message(cls, message: AllMessageValues) -> AllMessageValues: """ diff --git a/litellm/llms/modelscope/chat/transformation.py b/litellm/llms/modelscope/chat/transformation.py new file mode 100644 index 00000000000..162ef1a236c --- /dev/null +++ b/litellm/llms/modelscope/chat/transformation.py @@ -0,0 +1,93 @@ +""" +Translates from OpenAI's `/v1/chat/completions` to ModelScope's `/v1/chat/completions` +""" + +from typing import Any, Coroutine, Literal, Optional, Tuple, Union, cast, overload + +from typing_extensions import override + +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import AllMessageValues + +from ...openai.chat.gpt_transformation import OpenAIGPTConfig + + +def _has_non_text_content(message: AllMessageValues) -> bool: + """Check if a message has non-text content items (e.g. image_url).""" + content = message.get("content") + if not isinstance(content, list): + return False + return any(item.get("type") != "text" for item in content) + + +class ModelScopeChatConfig(OpenAIGPTConfig): + DEFAULT_BASE_URL: str = "https://api-inference.modelscope.cn/v1" + + @overload + def _transform_messages( + self, messages: list[AllMessageValues], model: str, is_async: Literal[True] + ) -> Coroutine[Any, Any, list[AllMessageValues]]: ... + + @overload + def _transform_messages( + self, + messages: list[AllMessageValues], + model: str, + is_async: Literal[False] = False, + ) -> list[AllMessageValues]: ... + + def _transform_messages( + self, messages: list[AllMessageValues], model: str, is_async: bool = False + ) -> Union[list[AllMessageValues], Coroutine[Any, Any, list[AllMessageValues]]]: + """ + Flatten text-only content lists to strings for ModelScope. + + Messages with non-text content (e.g. image_url for vision models) + are kept as lists so the parent class can normalize them properly. + """ + messages = [cast(AllMessageValues, {**m}) for m in messages] + for message in messages: + if _has_non_text_content(message): + continue + content = message.get("content") + if isinstance(content, list): + message["content"] = "".join(item.get("text") or "" for item in content) + + if is_async: + return super()._transform_messages( + messages=messages, model=model, is_async=True + ) + else: + return super()._transform_messages( + messages=messages, model=model, is_async=False + ) + + def _get_openai_compatible_provider_info( + self, api_base: Optional[str], api_key: Optional[str] + ) -> Tuple[Optional[str], Optional[str]]: + api_base = ( + api_base or get_secret_str("MODELSCOPE_API_BASE") or self.DEFAULT_BASE_URL + ) # type: ignore + dynamic_api_key = api_key or get_secret_str("MODELSCOPE_API_KEY") + return api_base, dynamic_api_key + + @override + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + """ + If api_base is not provided, use the default ModelScope /chat/completions endpoint. + """ + if not api_base: + api_base = self.DEFAULT_BASE_URL + + if not api_base.endswith("/chat/completions"): + api_base = f"{api_base}/chat/completions" + + return api_base diff --git a/litellm/llms/modelscope/image_generation/__init__.py b/litellm/llms/modelscope/image_generation/__init__.py new file mode 100644 index 00000000000..8b28ea962ce --- /dev/null +++ b/litellm/llms/modelscope/image_generation/__init__.py @@ -0,0 +1,31 @@ +""" +ModelScope Image Generation Module + +Factory function for getting the appropriate config class. +""" + +from litellm.llms.base_llm.image_generation.transformation import ( + BaseImageGenerationConfig, +) + +from .transformation import ModelScopeImageGenerationConfig + +__all__ = [ + "ModelScopeImageGenerationConfig", + "get_modelscope_image_generation_config", +] + + +def get_modelscope_image_generation_config( + model: str, +) -> BaseImageGenerationConfig: + """ + Get the ModelScope config for image generation. + + Args: + model: The model name (e.g., "modelscope/Qwen/Qwen-Image-Edit") + + Returns: + BaseImageGenerationConfig instance for ModelScope + """ + return ModelScopeImageGenerationConfig() diff --git a/litellm/llms/modelscope/image_generation/transformation.py b/litellm/llms/modelscope/image_generation/transformation.py new file mode 100644 index 00000000000..0d85f7796fb --- /dev/null +++ b/litellm/llms/modelscope/image_generation/transformation.py @@ -0,0 +1,248 @@ +""" +ModelScope Image Generation Config + +Handles transformation between OpenAI-compatible format and ModelScope API format. + +API Reference: https://modelscope.cn/docs/model-service/API-Inference/intro +""" + +from typing import TYPE_CHECKING, Optional, Union + +import httpx +from typing_extensions import override + +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.llms.base_llm.image_generation.transformation import ( + BaseImageGenerationConfig, +) +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import ( + AllMessageValues, + OpenAIImageGenerationOptionalParams, +) +from litellm.types.utils import ImageObject, ImageResponse + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + + LiteLLMLoggingObj = _LiteLLMLoggingObj +else: + LiteLLMLoggingObj = object + + +class ModelScopeImageGenerationConfig(BaseImageGenerationConfig): + """ + Configuration for ModelScope image generation. + + Supports text-to-image models like: + - Qwen/Qwen-Image-Edit + - And other ModelScope-hosted image generation models + """ + + DEFAULT_BASE_URL: str = "https://api-inference.modelscope.cn/v1" + + def get_supported_openai_params( + self, model: str + ) -> list[OpenAIImageGenerationOptionalParams]: + """ + Return list of OpenAI params supported by ModelScope. + + ModelScope supports standard OpenAI image generation parameters. + """ + return [ + "n", # Number of images to generate + "size", # Size of the generated images + "response_format", # url or b64_json + "user", # User identifier + ] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + """ + Map OpenAI parameters to ModelScope parameters. + + ModelScope uses the same parameter names as OpenAI. + """ + supported_params = self.get_supported_openai_params(model) + if drop_params: + non_default_params = { + k: v for k, v in non_default_params.items() if k in supported_params + } + optional_params.update(non_default_params) + return optional_params + + @override + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + """ + Get the complete URL for the ModelScope image generation API request. + """ + base_url: str = ( + api_base or get_secret_str("MODELSCOPE_API_BASE") or self.DEFAULT_BASE_URL + ) + base_url = base_url.rstrip("/") + + # Return the images endpoint + return f"{base_url}/images/generations" + + @override + def validate_environment( + self, + headers: dict, + model: str, + messages: list[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + """ + Validate environment and set up headers for ModelScope. + """ + final_api_key: Optional[str] = api_key or get_secret_str("MODELSCOPE_API_KEY") + + if not final_api_key: + raise ValueError( + "MODELSCOPE_API_KEY is not set. " + "Please set it via environment variable or pass api_key parameter." + ) + + default_headers = { + "Content-Type": "application/json", + "Authorization": f"Bearer {final_api_key}", + } + + headers = {**headers, **default_headers} + return headers + + def transform_image_generation_request( + self, + model: str, + prompt: str, + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + """ + Transform OpenAI-style request to ModelScope request format. + + ModelScope uses the same format as OpenAI for image generation. + """ + # Build the request body (same as OpenAI) + request_data: dict = { + "model": model, + "prompt": prompt, + } + + # Add optional params + for key, value in optional_params.items(): + if key.startswith("_"): + continue + request_data[key] = value + + return request_data + + @override + def transform_image_generation_response( + self, + model: str, + raw_response: httpx.Response, + model_response: ImageResponse, + logging_obj: LiteLLMLoggingObj, + request_data: dict, + optional_params: dict, + litellm_params: dict, + encoding: object, + api_key: Optional[str] = None, + json_mode: Optional[bool] = None, + ) -> ImageResponse: + """ + Transform ModelScope response to OpenAI-compatible ImageResponse. + + ModelScope returns the same format as OpenAI: + {"created": timestamp, "data": [{"url": "..."}]} + """ + try: + response_data = raw_response.json() + except Exception as e: + raise self.get_error_class( + error_message=f"Error parsing ModelScope response: {e}", + status_code=raw_response.status_code, + headers=raw_response.headers, + ) + + # Check for errors in response + if "error" in response_data: + error_msg = response_data["error"].get( + "message", str(response_data["error"]) + ) + raise self.get_error_class( + error_message=f"ModelScope error: {error_msg}", + status_code=raw_response.status_code, + headers=raw_response.headers, + ) + + # Extract images from response + data_list = response_data.get("data", []) + if not model_response.data: + model_response.data = [] + + for item in data_list: + image_obj = ImageObject( + url=item.get("url"), + b64_json=item.get("b64_json"), + revised_prompt=item.get("revised_prompt"), + ) + model_response.data.append(image_obj) + + return model_response + + def get_error_class( + self, + error_message: str, + status_code: int, + headers: Union[dict, httpx.Headers], + ) -> BaseLLMException: + """Return the appropriate error class for ModelScope.""" + from litellm.exceptions import ( + AuthenticationError, + BadRequestError, + InternalServerError, + ) + + if status_code == 400: + return BadRequestError( # type: ignore[return-value] + message=error_message, + model="", + llm_provider="modelscope", + ) + elif status_code == 401: + return AuthenticationError( # type: ignore[return-value] + message=error_message, + model="", + llm_provider="modelscope", + ) + elif status_code >= 500: + return InternalServerError( # type: ignore[return-value] + message=error_message, + model="", + llm_provider="modelscope", + ) + else: + return BadRequestError( # type: ignore[return-value] + message=error_message, + model="", + llm_provider="modelscope", + ) diff --git a/litellm/llms/oci/chat/transformation.py b/litellm/llms/oci/chat/transformation.py index f050f9eea36..d1248b6e518 100644 --- a/litellm/llms/oci/chat/transformation.py +++ b/litellm/llms/oci/chat/transformation.py @@ -25,6 +25,7 @@ from typing import ( import httpx import litellm +from litellm.constants import DEFAULT_OCI_CHAT_MAX_TOKENS from litellm.litellm_core_utils.logging_utils import track_llm_api_timing from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException from litellm.llms.custom_httpx.http_handler import ( @@ -87,15 +88,20 @@ STREAMING_TIMEOUT = 60 * 5 def _model_uses_max_completion_tokens(model: str) -> bool: """Return True for OCI-hosted models that require ``maxCompletionTokens``. - Reasoning models on OCI (e.g. the OpenAI GPT-5 family) reject ``maxTokens`` - with HTTP 400 and require ``maxCompletionTokens`` per OpenAI's reasoning-API - convention. Driven by ``supports_reasoning`` in - ``model_prices_and_context_window.json`` so new model families are picked - up via a catalog update rather than a code change. + OpenAI commercial models proxied through OCI (``openai.*``) reject + ``maxTokens`` with HTTP 400 on the reasoning families (gpt-5.x, o-series) + and accept ``maxCompletionTokens`` everywhere, so route the whole vendor + prefix to it rather than chasing each new release in + ``model_prices_and_context_window.json``. The ``openai.gpt-oss-*`` open + weights are served by OCI's own stack and keep ``maxTokens``. Any other + vendor falls back to the catalog's ``supports_reasoning`` flag. """ if not model: return False name = model[4:] if model.lower().startswith("oci/") else model + lowered = name.lower() + if lowered.startswith("openai."): + return not lowered.startswith("openai.gpt-oss") return supports_reasoning(model=name, custom_llm_provider="oci") @@ -193,19 +199,49 @@ def _normalize_response_format(selected_params: Dict, vendor: OCIVendors) -> Non rf = selected_params.get("responseFormat") if not isinstance(rf, dict) or "type" not in rf: return - rf_payload = dict(rf) - selected_params["responseFormat"] = rf_payload - response_type = rf_payload["type"] - if "json_schema" in rf_payload: - raw_schema = rf_payload.pop("json_schema") - rf_payload["jsonSchema"] = ( - dict(raw_schema) if isinstance(raw_schema, dict) else raw_schema - ) + + rf_type = str(rf["type"]).lower() + raw_schema = rf.get("json_schema") + json_schema = raw_schema if isinstance(raw_schema, dict) else None + + if rf_type == "text": + selected_params["responseFormat"] = {"type": "TEXT"} + return + if vendor == OCIVendors.COHERE: - rf_payload["type"] = response_type - else: - fmt = response_type.upper() - rf_payload["type"] = "JSON_OBJECT" if fmt == "JSON" else fmt + # OCI Cohere has no JSON_SCHEMA type; a schema rides on JSON_OBJECT. + payload: Dict[str, Any] = {"type": "JSON_OBJECT"} + if json_schema is not None and json_schema.get("schema") is not None: + payload["schema"] = json_schema["schema"] + selected_params["responseFormat"] = payload + return + + if rf_type == "json_schema": + if json_schema is None: + raise OCIError( + status_code=400, + message="response_format type 'json_schema' requires a 'json_schema' object", + ) + # OCI's ResponseJsonSchema accepts only name/description/schema/isStrict. + # OpenAI sends `strict` instead of `isStrict`; forwarding it (or any + # other extra key) makes OCI reject the whole request with HTTP 400. + oci_schema: Dict[str, Any] = {"name": json_schema.get("name") or "response"} + if json_schema.get("description") is not None: + oci_schema["description"] = json_schema["description"] + if json_schema.get("schema") is not None: + oci_schema["schema"] = json_schema["schema"] + if json_schema.get("strict") is not None: + oci_schema["isStrict"] = json_schema["strict"] + selected_params["responseFormat"] = { + "type": "JSON_SCHEMA", + "jsonSchema": oci_schema, + } + return + + fmt = rf_type.upper() + selected_params["responseFormat"] = { + "type": "JSON_OBJECT" if fmt == "JSON" else fmt + } def get_vendor_from_model(model: str) -> OCIVendors: @@ -297,6 +333,11 @@ class OCIChatConfig(BaseConfig): if get_vendor_from_model(model) == OCIVendors.COHERE else self.openai_to_oci_generic_param_map ) + # `n` is intentionally not advertised for Cohere even though n=1 is + # tolerated: Cohere has no numGenerations field, so n>1 cannot be + # honoured and advertising it would be misleading. Callers that gate on + # this list strip n=1 (a no-op, matching what map_openai_params does); + # callers that bypass it have n=1 dropped there. Both paths converge. return [key for key, value in param_map.items() if value] def map_openai_params( @@ -317,6 +358,19 @@ class OCIChatConfig(BaseConfig): for key, value in {**non_default_params, **optional_params}.items(): alias = param_map.get(key) if alias is False: + # max_retries is a litellm-level control param (litellm applies + # retries itself); it is never a generation param OCI accepts, so + # drop it silently. The litellm proxy injects it on every request, + # which otherwise 500s OCI calls unless drop_params is set. + if key == "max_retries": + continue + # n=1 (or None) is the OpenAI default: a single generation, which + # every OCI model produces anyway. Drop it silently so standard + # clients that always send n=1 (e.g. the MLflow gateway) are not + # rejected; only n>1 is genuinely unsupported on Cohere, which + # has no numGenerations field. + if key == "n" and (value is None or value == 1): + continue if drop_params or litellm.drop_params: continue raise OCIError( @@ -451,6 +505,13 @@ class OCIChatConfig(BaseConfig): elif oci_alias in optional_params: selected_params[target] = optional_params[oci_alias] # type: ignore[index] + # OCI's server-side default token cap is tiny (~20 tokens), so an + # omitted max_tokens silently truncates the response mid-string. Most + # callers never send a limit (MLflow judges among them), so inject a + # sane default when one is absent, mirroring litellm's Anthropic config. + if max_tokens_key not in selected_params: + selected_params[max_tokens_key] = DEFAULT_OCI_CHAT_MAX_TOKENS + # OCI expects uppercase reasoning levels (LOW/MEDIUM/HIGH/NONE); OpenAI # clients send lowercase. OpenAI's "disable" maps to OCI's "NONE". if "reasoningEffort" in selected_params: diff --git a/litellm/llms/openai/chat/gpt_transformation.py b/litellm/llms/openai/chat/gpt_transformation.py index 5464b5bb7ee..b8b750b8c12 100644 --- a/litellm/llms/openai/chat/gpt_transformation.py +++ b/litellm/llms/openai/chat/gpt_transformation.py @@ -18,6 +18,9 @@ from typing import ( overload, ) +import os +from urllib.parse import urlparse + import httpx import litellm @@ -426,6 +429,32 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): ) return messages, tools + def _should_preserve_cache_control_for_endpoint( + self, + custom_llm_provider: str | None, + api_base: str | None, + ) -> bool: + """ + The generic `openai` provider also reaches OpenAI-compatible endpoints + (a LiteLLM proxy, vLLM, an Anthropic-compatible gateway) via a custom + 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 = urlparse(resolved_api_base).hostname + if hostname is None: + return False + return hostname != "openai.com" and not hostname.endswith(".openai.com") + def transform_request( self, model: str, @@ -441,11 +470,14 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): dict: The transformed request. Sent as the body of the API call. """ messages = self._transform_messages(messages=messages, model=model) - messages, tools = self.remove_cache_control_flag_from_messages_and_tools( - model=model, messages=messages, tools=optional_params.get("tools", []) - ) - if tools is not None and len(tools) > 0: - optional_params["tools"] = tools + if not self._should_preserve_cache_control_for_endpoint( + litellm_params.get("custom_llm_provider"), litellm_params.get("api_base") + ): + messages, tools = self.remove_cache_control_flag_from_messages_and_tools( + model=model, messages=messages, tools=optional_params.get("tools", []) + ) + if tools is not None and len(tools) > 0: + optional_params["tools"] = tools optional_params.pop("max_retries", None) @@ -466,16 +498,19 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): transformed_messages = await self._transform_messages( messages=messages, model=model, is_async=True ) - ( - transformed_messages, - tools, - ) = self.remove_cache_control_flag_from_messages_and_tools( - model=model, - messages=transformed_messages, - tools=optional_params.get("tools", []), - ) - if tools is not None and len(tools) > 0: - optional_params["tools"] = tools + if not self._should_preserve_cache_control_for_endpoint( + litellm_params.get("custom_llm_provider"), litellm_params.get("api_base") + ): + ( + transformed_messages, + tools, + ) = self.remove_cache_control_flag_from_messages_and_tools( + model=model, + messages=transformed_messages, + tools=optional_params.get("tools", []), + ) + if tools is not None and len(tools) > 0: + optional_params["tools"] = tools if self.__class__._is_base_class: return { "model": model, diff --git a/litellm/llms/openai/completion/handler.py b/litellm/llms/openai/completion/handler.py index 1641615126e..63d39151254 100644 --- a/litellm/llms/openai/completion/handler.py +++ b/litellm/llms/openai/completion/handler.py @@ -49,6 +49,8 @@ class OpenAITextCompletion(BaseLLM): headers: Optional[dict] = None, ): try: + if headers: + optional_params = {**optional_params, "extra_headers": headers} if headers is None: headers = self.validate_environment(api_key=api_key) if model is None or messages is None: diff --git a/litellm/llms/openai/openai.py b/litellm/llms/openai/openai.py index 194f29648c4..ea905d8ebca 100644 --- a/litellm/llms/openai/openai.py +++ b/litellm/llms/openai/openai.py @@ -608,7 +608,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): return streaming_response - def completion( # type: ignore # noqa: PLR0915 + def completion( # type: ignore self, model_response: ModelResponse, timeout: Union[float, httpx.Timeout], diff --git a/litellm/llms/openai/realtime/handler.py b/litellm/llms/openai/realtime/handler.py index f34dae2df09..6751004f1b1 100644 --- a/litellm/llms/openai/realtime/handler.py +++ b/litellm/llms/openai/realtime/handler.py @@ -157,8 +157,14 @@ class OpenAIRealtime(OpenAIChatCompletion): websocket, cast(ClientConnection, backend_ws), logging_obj, + model=model, user_api_key_dict=user_api_key_dict, request_data={"litellm_metadata": litellm_metadata or {}}, + force_transcription_model=( + model + if (query_params or {}).get("intent") == "transcription" + else None + ), ) await realtime_streaming.bidirectional_forward() diff --git a/litellm/llms/openai/realtime/http_transformation.py b/litellm/llms/openai/realtime/http_transformation.py index 1663fcd1fcd..7a6af39ba65 100644 --- a/litellm/llms/openai/realtime/http_transformation.py +++ b/litellm/llms/openai/realtime/http_transformation.py @@ -41,6 +41,14 @@ class OpenAIRealtimeHTTPConfig(BaseRealtimeHTTPConfig): base = base[:-3] return f"{base}/v1/realtime/calls" + def get_transcription_session_url( + self, api_base: Optional[str], model: str, api_version: Optional[str] = None + ) -> str: + base = self.get_api_base(api_base).rstrip("/") + if base.endswith("/v1"): + base = base[:-3] + return f"{base}/v1/realtime/transcription_sessions" + def validate_environment( self, headers: dict, diff --git a/litellm/llms/openai/responses/guardrail_translation/handler.py b/litellm/llms/openai/responses/guardrail_translation/handler.py index f7dd68aec55..b5319797cc6 100644 --- a/litellm/llms/openai/responses/guardrail_translation/handler.py +++ b/litellm/llms/openai/responses/guardrail_translation/handler.py @@ -35,7 +35,6 @@ from pydantic import BaseModel from litellm._logging import verbose_proxy_logger from litellm.completion_extras.litellm_responses_transformation.transformation import ( - LiteLLMResponsesTransformationHandler, OpenAiResponsesToChatCompletionStreamIterator, ) from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation @@ -479,90 +478,137 @@ class OpenAIResponsesHandler(BaseTranslation): ) -> List[Any]: """ Process output streaming response by applying guardrails to text content. + + Mirrors the Chat Completions handler pattern: extract text from the final + chunk, apply the guardrail, then write the result back in-place so the + caller sees the modified content (e.g. PII tokens replaced). + + For ``response.completed`` events (the normal end-of-stream signal) we + use the same per-item extraction + task-mapping approach as + ``process_output_response`` so that unmasking / blocking works correctly + for every output item. """ + if not responses_so_far: + return responses_so_far final_chunk = responses_so_far[-1] + # Accept both plain dicts and Pydantic models (BaseLiteLLMOpenAIResponseObject + # exposes a .get() shim, so all the .get() calls below work for both). + if not (isinstance(final_chunk, dict) or hasattr(final_chunk, "get")): + return responses_so_far + # ------------------------------------------------------------------ # + # Case 1: response.completed — full response is available in the # + # final chunk; iterate output items, apply guardrail, write back. # + # ------------------------------------------------------------------ # + if final_chunk.get("type") == "response.completed": + response_obj = final_chunk.get("response") or {} + if not hasattr(response_obj, "get"): + return responses_so_far + outputs: List[Any] = response_obj.get("output") or [] + + texts_to_check: List[str] = [] + tool_calls_to_check: List[ChatCompletionToolCallChunk] = [] + task_mappings: List[Tuple[int, int]] = [] + + for output_idx, output_item in enumerate(outputs): + self._extract_output_text_and_images( + output_item=output_item, + output_idx=output_idx, + texts_to_check=texts_to_check, + images_to_check=[], + task_mappings=task_mappings, + tool_calls_to_check=tool_calls_to_check, + ) + + if texts_to_check or tool_calls_to_check: + if request_data is None: + request_data = {} + if "response" not in request_data: + request_data["response"] = response_obj + if "litellm_metadata" not in request_data: + user_metadata = self.transform_user_api_key_dict_to_metadata( + user_api_key_dict + ) + if user_metadata: + request_data["litellm_metadata"] = user_metadata + + inputs = GenericGuardrailAPIInputs(texts=texts_to_check) + if tool_calls_to_check: + inputs["tool_calls"] = cast( + List[ChatCompletionToolCallChunk], tool_calls_to_check + ) + response_model = response_obj.get("model") + if response_model: + inputs["model"] = response_model + + guardrailed_inputs = await guardrail_to_apply.apply_guardrail( + inputs=inputs, + request_data=request_data, + input_type="response", + logging_obj=litellm_logging_obj, + ) + + guardrailed_texts = guardrailed_inputs.get("texts", []) + + # Write guardrailed texts back into the output items in-place. + # final_chunk is a reference into responses_so_far so this + # mutates the list that the caller holds. + await self._apply_guardrail_responses_to_output( + response=response_obj, + responses=guardrailed_texts, + task_mappings=task_mappings, + ) + + return responses_so_far + + # ------------------------------------------------------------------ # + # Case 2: response.output_item.done — extract tool calls only. # + # ------------------------------------------------------------------ # if final_chunk.get("type") == "response.output_item.done": - # convert openai response to model response model_response_stream = OpenAiResponsesToChatCompletionStreamIterator.translate_responses_chunk_to_openai_stream( final_chunk ) - tool_calls = model_response_stream.choices[0].delta.tool_calls if tool_calls: inputs = GenericGuardrailAPIInputs() inputs["tool_calls"] = cast( List[ChatCompletionToolCallChunk], tool_calls ) - # Include model information if available if ( hasattr(model_response_stream, "model") and model_response_stream.model ): inputs["model"] = model_response_stream.model - _guardrailed_inputs = await guardrail_to_apply.apply_guardrail( + await guardrail_to_apply.apply_guardrail( inputs=inputs, request_data=request_data if request_data is not None else {}, input_type="response", logging_obj=litellm_logging_obj, ) - return responses_so_far - elif final_chunk.get("type") == "response.completed": - # convert openai response to model response - outputs = final_chunk.get("response", {}).get("output", []) + return responses_so_far - model_response_choices = LiteLLMResponsesTransformationHandler._convert_response_output_to_choices( - output_items=outputs, - handle_raw_dict_callback=None, - ) - - if model_response_choices: - tool_calls = model_response_choices[0].message.tool_calls - text = model_response_choices[0].message.content - guardrail_inputs = GenericGuardrailAPIInputs() - if text: - guardrail_inputs["texts"] = [text] - if tool_calls: - guardrail_inputs["tool_calls"] = cast( - List[ChatCompletionToolCallChunk], tool_calls - ) - # Include model information from the response if available - response_model = final_chunk.get("response", {}).get("model") - if response_model: - guardrail_inputs["model"] = response_model - if tool_calls or text: - _guardrailed_inputs = await guardrail_to_apply.apply_guardrail( - inputs=guardrail_inputs, - request_data=request_data if request_data is not None else {}, - input_type="response", - logging_obj=litellm_logging_obj, - ) - return responses_so_far - else: - verbose_proxy_logger.debug( - "Skipping output guardrail - model response has no choices" - ) - # model_response_stream = OpenAiResponsesToChatCompletionStreamIterator.translate_responses_chunk_to_openai_stream(final_chunk) - # tool_calls = model_response_stream.choices[0].tool_calls - # convert openai response to model response + # ------------------------------------------------------------------ # + # Fallback: apply guardrail to the accumulated text string. # + # No structured write-back is possible here; guardrails that only # + # need to block/flag (not rewrite) still work correctly. # + # ------------------------------------------------------------------ # string_so_far = self.get_streaming_string_so_far(responses_so_far) - inputs = GenericGuardrailAPIInputs(texts=[string_so_far]) - # Try to get model from the final chunk if available - if isinstance(final_chunk, dict): + if string_so_far: + fallback_inputs = GenericGuardrailAPIInputs(texts=[string_so_far]) response_model = ( final_chunk.get("response", {}).get("model") if isinstance(final_chunk.get("response"), dict) else None ) if response_model: - inputs["model"] = response_model - _guardrailed_inputs = await guardrail_to_apply.apply_guardrail( - inputs=inputs, - request_data=request_data if request_data is not None else {}, - input_type="response", - logging_obj=litellm_logging_obj, - ) + fallback_inputs["model"] = response_model + await guardrail_to_apply.apply_guardrail( + inputs=fallback_inputs, + request_data=request_data if request_data is not None else {}, + input_type="response", + logging_obj=litellm_logging_obj, + ) return responses_so_far def _check_streaming_has_ended(self, responses_so_far: List[Any]) -> bool: @@ -721,7 +767,7 @@ class OpenAIResponsesHandler(BaseTranslation): async def _apply_guardrail_responses_to_output( self, - response: "ResponsesAPIResponse", + response: Union["ResponsesAPIResponse", Dict[Any, Any]], responses: List[str], task_mappings: List[Tuple[int, int]], ) -> None: diff --git a/litellm/llms/openai/transcriptions/whisper_transformation.py b/litellm/llms/openai/transcriptions/whisper_transformation.py index fa507e1bc26..2c01156fe05 100644 --- a/litellm/llms/openai/transcriptions/whisper_transformation.py +++ b/litellm/llms/openai/transcriptions/whisper_transformation.py @@ -1,3 +1,4 @@ +import json from typing import List, Optional, Union from httpx import Headers, Response @@ -107,9 +108,7 @@ class OpenAIWhisperAudioTranscriptionConfig(BaseAudioTranscriptionConfig): """ data = {"model": model, "file": audio_file, **optional_params} - if "response_format" not in data or ( - data["response_format"] == "text" or data["response_format"] == "json" - ): + if "response_format" not in data: data["response_format"] = ( "verbose_json" # ensures 'duration' is received - used for cost calculation ) @@ -133,10 +132,11 @@ class OpenAIWhisperAudioTranscriptionConfig(BaseAudioTranscriptionConfig): ) -> TranscriptionResponse: try: raw_response_json = raw_response.json() - except Exception as e: - raise ValueError( - f"Error transforming response to json: {str(e)}\nResponse: {raw_response.text}" - ) + except json.JSONDecodeError: + content_type = raw_response.headers.get("content-type", "").lower() + if "application/json" in content_type: + raise + return TranscriptionResponse(text=raw_response.text) if any( key in raw_response_json diff --git a/litellm/llms/openai_like/providers.json b/litellm/llms/openai_like/providers.json index 13d22488838..d87346fea70 100644 --- a/litellm/llms/openai_like/providers.json +++ b/litellm/llms/openai_like/providers.json @@ -115,6 +115,14 @@ "max_completion_tokens": "max_tokens" } }, + "darkbloom": { + "base_url": "https://api.darkbloom.dev/v1", + "api_key_env": "DARKBLOOM_API_KEY", + "api_base_env": "DARKBLOOM_API_BASE", + "param_mappings": { + "max_completion_tokens": "max_tokens" + } + }, "neosantara": { "base_url": "https://api.neosantara.xyz/v1", "api_key_env": "NEOSANTARA_API_KEY", @@ -131,7 +139,8 @@ "base_class": "openai_gpt", "param_mappings": { "max_completion_tokens": "max_tokens" - } + }, + "supported_endpoints": ["/v1/chat/completions", "/v1/responses"] }, "parasail": { "base_url": "https://api.parasail.io/v1", @@ -141,5 +150,31 @@ "special_handling": { "force_store_false": true } + }, + "libertai": { + "base_url": "https://api.libertai.io/v1", + "api_key_env": "LIBERTAI_API_KEY", + "api_base_env": "LIBERTAI_API_BASE", + "param_mappings": { + "max_completion_tokens": "max_tokens" + } + }, + "empiriolabs": { + "base_url": "https://api.empiriolabs.ai/v1", + "api_key_env": "EMPIRIOLABS_API_KEY", + "api_base_env": "EMPIRIOLABS_API_BASE", + "param_mappings": { + "max_completion_tokens": "max_tokens" + }, + "supported_endpoints": ["/v1/chat/completions", "/v1/responses"] + }, + "pinstripes": { + "base_url": "https://pinstripes.io/v1", + "api_key_env": "PINSTRIPES_API_KEY", + "api_base_env": "PINSTRIPES_API_BASE", + "param_mappings": { + "max_completion_tokens": "max_tokens" + }, + "supported_endpoints": ["/v1/chat/completions", "/v1/responses", "/v1/embeddings"] } } diff --git a/litellm/llms/openrouter/chat/transformation.py b/litellm/llms/openrouter/chat/transformation.py index 0d7850e8c74..107d5c25e6d 100644 --- a/litellm/llms/openrouter/chat/transformation.py +++ b/litellm/llms/openrouter/chat/transformation.py @@ -50,11 +50,15 @@ class OpenrouterConfig(OpenAIGPTConfig): def map_openai_params( self, - non_default_params: dict, + non_default_params: dict[str, object], optional_params: dict, model: str, drop_params: bool, ) -> dict: + # OpenRouter expects "xhigh" instead of "max" for reasoning_effort. + if non_default_params.get("reasoning_effort") == "max": + non_default_params = {**non_default_params, "reasoning_effort": "xhigh"} + mapped_openai_params = super().map_openai_params( non_default_params, optional_params, model, drop_params ) diff --git a/litellm/llms/opensandbox/__init__.py b/litellm/llms/opensandbox/__init__.py new file mode 100644 index 00000000000..8b137891791 --- /dev/null +++ b/litellm/llms/opensandbox/__init__.py @@ -0,0 +1 @@ + diff --git a/litellm/llms/opensandbox/sandbox/__init__.py b/litellm/llms/opensandbox/sandbox/__init__.py new file mode 100644 index 00000000000..8b137891791 --- /dev/null +++ b/litellm/llms/opensandbox/sandbox/__init__.py @@ -0,0 +1 @@ + diff --git a/litellm/llms/opensandbox/sandbox/transformation.py b/litellm/llms/opensandbox/sandbox/transformation.py new file mode 100644 index 00000000000..dc9f8440d30 --- /dev/null +++ b/litellm/llms/opensandbox/sandbox/transformation.py @@ -0,0 +1,598 @@ +import asyncio +import json +import time +from typing import Union, cast + +import httpx + +from litellm.constants import ( + OPEN_SANDBOX_API_BASE_ENV_VAR, + OPEN_SANDBOX_API_KEY_ENV_VAR, + OPEN_SANDBOX_DEFAULT_CPU_LIMIT, + OPEN_SANDBOX_DEFAULT_ENTRYPOINT, + OPEN_SANDBOX_DEFAULT_LANGUAGE, + OPEN_SANDBOX_DEFAULT_MEMORY_LIMIT, + OPEN_SANDBOX_DEFAULT_TEMPLATE, + OPEN_SANDBOX_DEFAULT_TIMEOUT, + OPEN_SANDBOX_EXECD_PORT, + OPEN_SANDBOX_POLL_INTERVAL, + OPEN_SANDBOX_READY_TIMEOUT, +) +from litellm.llms.base_llm.sandbox.transformation import ( + BaseSandboxConfig, + CodeExecutionResult, + ContainerHandle, + SANDBOX_MAX_OUTPUT_BYTES, +) +from litellm.llms.custom_httpx.http_handler import ( + AsyncHTTPHandler, + get_async_httpx_client, +) +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.custom_http import httpxSpecialProvider + +DEFAULT_SANDBOX_TIMEOUT = OPEN_SANDBOX_DEFAULT_TIMEOUT +DEFAULT_READY_TIMEOUT = OPEN_SANDBOX_READY_TIMEOUT +DEFAULT_POLL_INTERVAL = OPEN_SANDBOX_POLL_INTERVAL +MAX_OUTPUT_BYTES = SANDBOX_MAX_OUTPUT_BYTES + + +class OpenSandboxSandboxConfig(BaseSandboxConfig): + def _http(self, client: AsyncHTTPHandler | None) -> AsyncHTTPHandler: + if client is not None: + return client + return get_async_httpx_client(llm_provider=httpxSpecialProvider.Sandbox) + + def validate_environment(self, api_key: str | None = None, **kwargs) -> str: + if api_key is not None: + return api_key + return get_secret_str(OPEN_SANDBOX_API_KEY_ENV_VAR) or "" + + async def acreate_sandbox( + self, + *, + template: str | None = None, + timeout: int | None = None, + allow_internet_access: bool | None = None, + api_key: str | None = None, + api_base: str | None = None, + metadata: dict[str, str] | None = None, + env_vars: dict[str, str] | None = None, + resource_limits: dict[str, str] | None = None, + resource_requests: dict[str, str] | None = None, + entrypoint: list[str] | tuple[str, ...] | None = None, + network_policy: dict[str, object] | None = None, + secure_access: bool = False, + use_server_proxy: bool = False, + ready_timeout: float | None = None, + poll_interval: float | None = None, + client: AsyncHTTPHandler | None = None, + **kwargs, + ) -> ContainerHandle: + key = self.validate_environment(api_key=api_key) + base = self._api_base(api_base) + ready_timeout_seconds = ( + float(ready_timeout) if ready_timeout is not None else DEFAULT_READY_TIMEOUT + ) + poll_interval_seconds = ( + float(poll_interval) if poll_interval is not None else DEFAULT_POLL_INTERVAL + ) + body = self._create_body( + template=template, + timeout=timeout, + allow_internet_access=allow_internet_access, + metadata=metadata, + env_vars=env_vars, + resource_limits=resource_limits, + resource_requests=resource_requests, + entrypoint=entrypoint, + network_policy=network_policy, + secure_access=secure_access, + ) + + response = cast( + httpx.Response, + await self._http(client).post( + url=f"{base}/sandboxes", + headers=self._lifecycle_headers(key), + json=body, + ), + ) + data = response.json() + sandbox_id = str(data["id"]) + + if self._sandbox_state(data) != "Running": + await self._wait_until_running( + sandbox_id=sandbox_id, + api_base=base, + headers=self._lifecycle_headers(key), + client=client, + ready_timeout=ready_timeout_seconds, + poll_interval=poll_interval_seconds, + ) + + endpoint, endpoint_headers = await self._wait_for_execd_endpoint( + sandbox_id=sandbox_id, + api_base=base, + headers=self._lifecycle_headers(key), + use_server_proxy=use_server_proxy, + client=client, + ready_timeout=ready_timeout_seconds, + poll_interval=poll_interval_seconds, + ) + + handle = ContainerHandle(id=sandbox_id, provider="opensandbox", domain=base) + handle._hidden_params = { + "api_base": base, + "api_key": key, + "execd_endpoint": endpoint, + "execd_headers": endpoint_headers, + "use_server_proxy": use_server_proxy, + } + return handle + + async def arun_code( + self, + *, + container: Union[ContainerHandle, str], + code: str, + api_key: str | None = None, + api_base: str | None = None, + language: str = OPEN_SANDBOX_DEFAULT_LANGUAGE, + use_server_proxy: bool = False, + ready_timeout: float | None = None, + poll_interval: float | None = None, + client: AsyncHTTPHandler | None = None, + **kwargs, + ) -> CodeExecutionResult: + handle = await self._ensure_handle( + container=container, + api_key=api_key, + api_base=api_base, + use_server_proxy=use_server_proxy, + ready_timeout=( + float(ready_timeout) + if ready_timeout is not None + else DEFAULT_READY_TIMEOUT + ), + poll_interval=( + float(poll_interval) + if poll_interval is not None + else DEFAULT_POLL_INTERVAL + ), + client=client, + ) + endpoint = str(handle._hidden_params["execd_endpoint"]) + endpoint_headers = self._as_str_dict(handle._hidden_params.get("execd_headers")) + base = str( + handle._hidden_params.get("api_base") + or handle.domain + or self._api_base(api_base) + ) + lines = await self._post_code( + url=f"{self._endpoint_base_url(endpoint, base)}/code", + headers={ + "Content-Type": "application/json", + "Accept": "text/event-stream", + "Cache-Control": "no-cache", + **endpoint_headers, + }, + body={ + "code": code, + "context": {"language": language}, + }, + client=client, + ) + return self._parse_lines(lines) + + async def adelete_sandbox( + self, + *, + container: Union[ContainerHandle, str], + api_key: str | None = None, + api_base: str | None = None, + client: AsyncHTTPHandler | None = None, + **kwargs, + ) -> bool: + handle = self._as_handle(container, api_base=api_base) + base = str(handle._hidden_params.get("api_base") or self._api_base(api_base)) + key = self._api_key(api_key=api_key, handle=handle) + try: + response = cast( + httpx.Response, + 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: + return False + raise + return 200 <= response.status_code < 300 + + async def _ensure_handle( + self, + *, + container: Union[ContainerHandle, str], + api_key: str | None, + api_base: str | None, + use_server_proxy: bool, + ready_timeout: float, + poll_interval: float, + client: AsyncHTTPHandler | None, + ) -> ContainerHandle: + handle = self._as_handle(container, api_base=api_base) + if handle._hidden_params.get("execd_endpoint"): + return handle + + base = str(handle._hidden_params.get("api_base") or self._api_base(api_base)) + key = self._api_key(api_key=api_key, handle=handle) + resolved_use_server_proxy = bool( + handle._hidden_params.get("use_server_proxy", use_server_proxy) + ) + endpoint, endpoint_headers = await self._wait_for_execd_endpoint( + sandbox_id=handle.id, + api_base=base, + headers=self._lifecycle_headers(key), + use_server_proxy=resolved_use_server_proxy, + client=client, + ready_timeout=ready_timeout, + poll_interval=poll_interval, + ) + handle.domain = base + handle._hidden_params = { + **handle._hidden_params, + "api_base": base, + "api_key": key, + "execd_endpoint": endpoint, + "execd_headers": endpoint_headers, + "use_server_proxy": resolved_use_server_proxy, + } + return handle + + async def _wait_until_running( + self, + *, + sandbox_id: str, + api_base: str, + headers: dict[str, str], + client: AsyncHTTPHandler | None, + ready_timeout: float, + poll_interval: float, + ) -> None: + deadline = time.monotonic() + ready_timeout + while True: + response = cast( + httpx.Response, + await self._http(client).get( + url=f"{api_base}/sandboxes/{sandbox_id}", + headers=headers, + ), + ) + data = response.json() + state = self._sandbox_state(data) + if state == "Running": + return + if state in {"Failed", "Stopping", "Terminated"}: + raise ValueError(f"OpenSandbox sandbox {sandbox_id} entered {state}") + if time.monotonic() >= deadline: + raise TimeoutError( + f"OpenSandbox sandbox {sandbox_id} was not Running within " + f"{ready_timeout} seconds" + ) + await asyncio.sleep(poll_interval) + + async def _wait_for_execd_endpoint( + self, + *, + sandbox_id: str, + api_base: str, + headers: dict[str, str], + use_server_proxy: bool, + client: AsyncHTTPHandler | None, + ready_timeout: float, + poll_interval: float, + ) -> tuple[str, dict[str, str]]: + deadline = time.monotonic() + ready_timeout + last_error: Exception | None = None + while True: + try: + return await self._get_execd_endpoint( + sandbox_id=sandbox_id, + api_base=api_base, + headers=headers, + use_server_proxy=use_server_proxy, + client=client, + ) + except httpx.HTTPStatusError as e: + if e.response.status_code != 404: + raise + last_error = e + except ValueError as e: + last_error = e + + if time.monotonic() >= deadline: + raise TimeoutError( + f"OpenSandbox execd endpoint for {sandbox_id} was not ready within " + f"{ready_timeout} seconds" + ) from last_error + await asyncio.sleep(poll_interval) + + async def _get_execd_endpoint( + self, + *, + sandbox_id: str, + api_base: str, + headers: dict[str, str], + use_server_proxy: bool, + client: AsyncHTTPHandler | None, + ) -> tuple[str, dict[str, str]]: + response = 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}, + ), + ) + data = response.json() + endpoint = data.get("endpoint") + if not endpoint: + raise ValueError( + f"OpenSandbox did not return an execd endpoint for {sandbox_id}" + ) + return str(endpoint), self._as_str_dict(data.get("headers")) + + async def _post_code( + self, + *, + url: str, + headers: dict[str, str], + body: dict[str, object], + client: AsyncHTTPHandler | None, + ) -> list[str]: + timeout = httpx.Timeout(connect=30.0, read=None, write=30.0, pool=None) + response = cast( + httpx.Response, + await self._http(client).post( + url=url, + headers=headers, + timeout=timeout, + json=body, + stream=True, + ), + ) + return await self._read_capped_lines(response) + + def _api_key(self, *, api_key: str | None, handle: ContainerHandle) -> str: + if api_key is not None: + return api_key + if "api_key" in handle._hidden_params: + return str(handle._hidden_params["api_key"]) + return self.validate_environment() + + @staticmethod + def _create_body( + *, + template: str | None, + timeout: int | None, + allow_internet_access: bool | None, + metadata: dict[str, str] | None, + env_vars: dict[str, str] | None, + resource_limits: dict[str, str] | None, + resource_requests: dict[str, str] | None, + entrypoint: list[str] | tuple[str, ...] | None, + network_policy: dict[str, object] | None, + secure_access: bool, + ) -> dict[str, object]: + body: dict[str, object] = { + "image": {"uri": template or OPEN_SANDBOX_DEFAULT_TEMPLATE}, + "entrypoint": list(entrypoint or OPEN_SANDBOX_DEFAULT_ENTRYPOINT), + "timeout": timeout if timeout is not None else DEFAULT_SANDBOX_TIMEOUT, + "resourceLimits": resource_limits + or OpenSandboxSandboxConfig._default_resource_limits(), + } + if metadata: + body["metadata"] = metadata + if env_vars: + body["env"] = env_vars + if resource_requests: + body["resourceRequests"] = resource_requests + if network_policy is not None: + body["networkPolicy"] = network_policy + elif allow_internet_access is not True: + body["networkPolicy"] = {"defaultAction": "deny", "egress": []} + if secure_access: + body["secureAccess"] = True + return body + + @staticmethod + def _default_resource_limits() -> dict[str, str]: + return { + "cpu": OPEN_SANDBOX_DEFAULT_CPU_LIMIT, + "memory": OPEN_SANDBOX_DEFAULT_MEMORY_LIMIT, + } + + @staticmethod + def _sandbox_state(data: object) -> str | None: + if not isinstance(data, dict): + return None + status = data.get("status") + if not isinstance(status, dict): + return None + state = status.get("state") + return str(state) if state is not None else None + + @staticmethod + def _as_str_dict(value: object) -> dict[str, str]: + if not isinstance(value, dict): + return {} + return {str(k): str(v) for k, v in value.items()} + + @staticmethod + def _api_base(api_base: str | None) -> str: + base = api_base or get_secret_str(OPEN_SANDBOX_API_BASE_ENV_VAR) + if not base: + raise ValueError( + "OpenSandbox api_base is required. Pass api_base or set " + f"{OPEN_SANDBOX_API_BASE_ENV_VAR}." + ) + return str(base).rstrip("/") + + @staticmethod + def _lifecycle_headers(api_key: str) -> dict[str, str]: + headers = {"Content-Type": "application/json"} + if api_key: + headers["OPEN-SANDBOX-API-KEY"] = api_key + return headers + + @staticmethod + def _endpoint_base_url(endpoint: str, api_base: str) -> str: + normalized_endpoint = endpoint.rstrip("/") + if normalized_endpoint.startswith(("http://", "https://")): + return normalized_endpoint + protocol = api_base.split("://", 1)[0] if "://" in api_base else "http" + return f"{protocol}://{normalized_endpoint}" + + @staticmethod + def _as_handle( + container: Union[ContainerHandle, str], *, api_base: str | None + ) -> ContainerHandle: + if isinstance(container, ContainerHandle): + return container + handle = ContainerHandle( + id=str(container), + provider="opensandbox", + domain=OpenSandboxSandboxConfig._api_base(api_base), + ) + handle._hidden_params = {} + return handle + + @staticmethod + def _parse_lines(lines: list[str]) -> CodeExecutionResult: + messages = tuple( + event + for line in lines + if (event := OpenSandboxSandboxConfig._parse_sse_line(line)) is not None + ) + + def of_type(message_type: str): + return (m for m in messages if m.get("type") == message_type) + + error = next( + (OpenSandboxSandboxConfig._normalize_error(m) for m in of_type("error")), + None, + ) + execution_count = next( + ( + OpenSandboxSandboxConfig._as_int(m.get("execution_count")) + for m in of_type("execution_count") + if OpenSandboxSandboxConfig._as_int(m.get("execution_count")) + is not None + ), + None, + ) + + return CodeExecutionResult( + stdout="".join(str(m.get("text", "")) for m in of_type("stdout")), + stderr="".join(str(m.get("text", "")) for m in of_type("stderr")), + results=[ + OpenSandboxSandboxConfig._normalize_result(m) for m in of_type("result") + ], + error=error, + execution_count=execution_count, + ) + + @staticmethod + def _parse_sse_line(line: str) -> dict[str, object] | None: + stripped = line.strip() + if not stripped or stripped.startswith( + ( + ":", + "event:", + "id:", + "retry:", + ) + ): + return None + data = stripped[5:].strip() if stripped.startswith("data:") else stripped + if not data: + return None + try: + parsed = json.loads(data) + except json.JSONDecodeError: + return None + if not isinstance(parsed, dict): + return None + if "type" not in parsed and "code" in parsed and "message" in parsed: + return { + "type": "error", + "error": { + "ename": str(parsed["code"]), + "evalue": str(parsed["message"]), + "traceback": [], + }, + } + return parsed + + @staticmethod + def _normalize_result(message: dict[str, object]) -> dict[str, object]: + results = message.get("results") + if isinstance(results, dict): + return {str(k): v for k, v in results.items()} + return { + str(k): v + for k, v in message.items() + if k not in {"type", "timestamp", "execution_count"} + } + + @staticmethod + def _normalize_error(message: dict[str, object]) -> dict[str, object]: + raw_error = message.get("error") + if isinstance(raw_error, dict): + name = OpenSandboxSandboxConfig._first_non_none_value( + raw_error, "ename", "name", default="" + ) + value = OpenSandboxSandboxConfig._first_non_none_value( + raw_error, "evalue", "value", default="" + ) + traceback = OpenSandboxSandboxConfig._first_non_none_value( + raw_error, "traceback", default=[] + ) + return { + "name": name, + "value": value, + "traceback": traceback, + } + return { + "name": OpenSandboxSandboxConfig._first_non_none_value( + message, "name", default="" + ), + "value": OpenSandboxSandboxConfig._first_non_none_value( + message, "value", "text", default="" + ), + "traceback": OpenSandboxSandboxConfig._first_non_none_value( + message, "traceback", default=[] + ), + } + + @staticmethod + def _as_int(value: object) -> int | None: + if isinstance(value, int): + return value + if isinstance(value, str): + try: + return int(value) + except ValueError: + return None + return None + + @staticmethod + def _first_non_none_value( + values: dict[str, object], *keys: str, default: object + ) -> object: + return next( + (values[key] for key in keys if key in values and values[key] is not None), + default, + ) diff --git a/litellm/llms/parallel_ai/search/transformation.py b/litellm/llms/parallel_ai/search/transformation.py index 12d570f1733..35a0d84df40 100644 --- a/litellm/llms/parallel_ai/search/transformation.py +++ b/litellm/llms/parallel_ai/search/transformation.py @@ -1,7 +1,7 @@ """ -Calls Parallel AI's /search endpoint to search the web. +Calls Parallel AI's /v1/search endpoint to search the web. -Parallel AI API Reference: https://docs.parallel.ai/api-reference/search-and-extract-api-beta/search +Parallel AI API Reference: https://docs.parallel.ai/api-reference/search/search """ from typing import Dict, List, Optional, TypedDict, Union @@ -18,36 +18,43 @@ from litellm.secret_managers.main import get_secret_str class _ParallelAISourcePolicy(TypedDict, total=False): - """Source policy for Parallel AI search results.""" - - allowed_domains: List[str] # Optional - list of allowed domains - disallowed_domains: List[str] # Optional - list of disallowed domains + include_domains: List[str] + exclude_domains: List[str] + after_date: str -class _ParallelAISearchRequestRequired(TypedDict): - """Required fields for Parallel AI Search API request.""" - - # Note: At least one of objective or search_queries must be provided - pass +class _ParallelAIExcerptSettings(TypedDict, total=False): + max_chars_per_result: int -class ParallelAISearchRequest(_ParallelAISearchRequestRequired, total=False): +class _ParallelAIAdvancedSettings(TypedDict, total=False): + source_policy: _ParallelAISourcePolicy + excerpt_settings: _ParallelAIExcerptSettings + fetch_policy: Dict + location: str + max_results: int + + +class ParallelAISearchRequest(TypedDict, total=False): """ - Parallel AI Search API request format. - Based on: https://docs.parallel.ai/api-reference/search-and-extract-api-beta/search + Parallel AI v1 Search API request format. + Based on: https://docs.parallel.ai/api-reference/search/search """ + search_queries: List[str] # Required - at least one keyword search query objective: str # Optional - natural-language description of search goal - search_queries: List[str] # Optional - list of keyword search queries - processor: str # Optional - search processor ('base', 'pro'), default 'base' - max_results: int # Optional - maximum number of results, default 10 - max_chars_per_result: int # Optional - max characters per result excerpt - source_policy: _ParallelAISourcePolicy # Optional - source policy for allowed/disallowed domains + mode: str # Optional - 'turbo', '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 = {"base": "basic", "pro": "advanced"} class ParallelAISearchConfig(BaseSearchConfig): PARALLEL_AI_API_BASE = "https://api.parallel.ai" - PARALLEL_HEADER_SEARCH_EXTRACT_VALUE = "search-extract-2025-10-10" @staticmethod def ui_friendly_name() -> str: @@ -60,13 +67,12 @@ class ParallelAISearchConfig(BaseSearchConfig): api_base: Optional[str] = None, **kwargs, ) -> Dict: - """ - Validate environment and return headers. - """ - api_key = ( - api_key - or get_secret_str("PARALLEL_AI_API_KEY") - or get_secret_str("PARALLEL_API_KEY") + api_key = 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: raise ValueError( @@ -74,7 +80,6 @@ class ParallelAISearchConfig(BaseSearchConfig): ) headers["x-api-key"] = api_key headers["Content-Type"] = "application/json" - headers["parallel-beta"] = self.PARALLEL_HEADER_SEARCH_EXTRACT_VALUE return headers def get_complete_url( @@ -84,32 +89,18 @@ class ParallelAISearchConfig(BaseSearchConfig): data: Optional[Union[Dict, List[Dict]]] = None, **kwargs, ) -> str: - """ - Get complete URL for Search endpoint. - """ api_base = ( api_base or get_secret_str("PARALLEL_AI_API_BASE") or self.PARALLEL_AI_API_BASE ) - # Parallel AI search endpoint is at /v1beta/search - if not api_base.endswith("/v1beta/search"): - if api_base.endswith("/"): - api_base = f"{api_base}v1beta/search" - else: - api_base = f"{api_base}/v1beta/search" + api_base = api_base.rstrip("/") + if not api_base.endswith("/v1/search"): + api_base = f"{api_base.removesuffix('/v1')}/v1/search" return api_base - def _transform_query_to_objective(self, query: Union[str, List[str]]) -> str: - """ - Transform query to objective. - """ - if isinstance(query, list): - return " ".join(query) - return query - def transform_search_request( self, query: Union[str, List[str]], @@ -117,57 +108,78 @@ class ParallelAISearchConfig(BaseSearchConfig): **kwargs, ) -> Dict: """ - Transform Search request to Parallel AI API format. + Transform Search request to Parallel AI v1 API format. Args: query: Search query (string or list of strings) - - If string: maps to `objective` (natural language) + - 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 - - max_results: Maximum number of search results (default 10) - - search_domain_filter: List of domains to include -> maps to `source_policy.allowed_domains` - - exclude_domains: List of domains to exclude -> maps to `source_policy.disallowed_domains` - - processor: Search processor ('base', 'pro') - - max_chars_per_result: Max characters per result excerpt + - mode: Search mode ('turbo', '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` + - exclude_domains: Domains to exclude -> `advanced_settings.source_policy.exclude_domains` + - country: 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 Returns: - Dict with typed request data following ParallelAISearchRequest spec + Dict with request data following the v1 search request spec """ + params = dict(optional_params) + request_data: ParallelAISearchRequest = {} - # Map query to objective (string or list both become objective) if isinstance(query, list): - request_data["objective"] = self._transform_query_to_objective(query) + request_data["search_queries"] = query else: + request_data["search_queries"] = [query] request_data["objective"] = query - # Transform Perplexity unified spec parameters to Parallel AI format - if "max_results" in optional_params: - request_data["max_results"] = optional_params["max_results"] + mode = params.pop("mode", None) + processor = params.pop("processor", None) + if mode is None and processor is not None: + 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 + request_data["mode"] = mode or "basic" + + advanced_settings: _ParallelAIAdvancedSettings = {} + + if "max_results" in params: + advanced_settings["max_results"] = params.pop("max_results") + + if "country" in params: + advanced_settings["location"] = params.pop("country") + + if "max_chars_per_result" in params: + advanced_settings["excerpt_settings"] = { + "max_chars_per_result": params.pop("max_chars_per_result") + } - # Map domain filters to source_policy source_policy: _ParallelAISourcePolicy = {} - if "search_domain_filter" in optional_params: - source_policy["allowed_domains"] = optional_params["search_domain_filter"] + if "search_domain_filter" in params: + source_policy["include_domains"] = params.pop("search_domain_filter") - if "exclude_domains" in optional_params: - source_policy["disallowed_domains"] = optional_params["exclude_domains"] + if "exclude_domains" in params: + source_policy["exclude_domains"] = params.pop("exclude_domains") if source_policy: - request_data["source_policy"] = source_policy + advanced_settings["source_policy"] = source_policy - # Convert to dict before dynamic key assignments - result_data = dict(request_data) + advanced_settings.update(params.pop("advanced_settings", {})) - # pass through all other parameters as-is - for param, value in optional_params.items(): - if ( - param not in self.get_supported_perplexity_optional_params() - and param not in result_data - ): - result_data[param] = value + if advanced_settings: + request_data["advanced_settings"] = advanced_settings + # unified-spec param with no v1 equivalent + params.pop("max_tokens_per_page", None) + + result_data: Dict = dict(request_data) + result_data.update(params) return result_data def transform_search_response( @@ -177,36 +189,27 @@ class ParallelAISearchConfig(BaseSearchConfig): **kwargs, ) -> SearchResponse: """ - Transform Parallel AI API response to LiteLLM unified SearchResponse format. + Transform Parallel AI v1 API response to LiteLLM unified SearchResponse format. - Parallel AI → LiteLLM mappings: - - results[].title → SearchResult.title - - results[].url → SearchResult.url - - results[].excerpts (array) → SearchResult.snippet (joined string) - - No date/last_updated fields in Parallel AI response (set to None) - - Args: - raw_response: Raw httpx response from Parallel AI API - logging_obj: Logging object for tracking - - Returns: - SearchResponse with standardized format + Parallel AI -> LiteLLM mappings: + - results[].title -> SearchResult.title + - results[].url -> SearchResult.url + - results[].excerpts (array) -> SearchResult.snippet (joined string) + - results[].publish_date -> SearchResult.date """ response_json = raw_response.json() - # Transform results to SearchResult objects results = [] for result in response_json.get("results", []): - # Join excerpts array into a single snippet string - excerpts = result.get("excerpts", []) + excerpts = result.get("excerpts") or [] snippet = " ... ".join(excerpts) if excerpts else "" search_result = SearchResult( - title=result.get("title", ""), - url=result.get("url", ""), + title=result.get("title") or "", + url=result.get("url") or "", snippet=snippet, - date=None, # Parallel AI doesn't provide date in response - last_updated=None, # Parallel AI doesn't provide last_updated in response + date=result.get("publish_date"), + last_updated=None, ) results.append(search_result) diff --git a/litellm/llms/pass_through/guardrail_translation/__init__.py b/litellm/llms/pass_through/guardrail_translation/__init__.py index db69c8e378a..46fea242c13 100644 --- a/litellm/llms/pass_through/guardrail_translation/__init__.py +++ b/litellm/llms/pass_through/guardrail_translation/__init__.py @@ -1,15 +1,18 @@ """Pass-Through Endpoint guardrail translation handler.""" from litellm.llms.pass_through.guardrail_translation.handler import ( + LlmPassthroughRouteHandler, PassThroughEndpointHandler, ) from litellm.types.utils import CallTypes guardrail_translation_mappings = { CallTypes.pass_through: PassThroughEndpointHandler, + CallTypes.allm_passthrough_route: LlmPassthroughRouteHandler, } __all__ = [ "guardrail_translation_mappings", + "LlmPassthroughRouteHandler", "PassThroughEndpointHandler", ] diff --git a/litellm/llms/pass_through/guardrail_translation/handler.py b/litellm/llms/pass_through/guardrail_translation/handler.py index a8cc42d7c54..db8d519d9be 100644 --- a/litellm/llms/pass_through/guardrail_translation/handler.py +++ b/litellm/llms/pass_through/guardrail_translation/handler.py @@ -6,7 +6,7 @@ It uses the field targeting configuration from litellm_logging_obj to extract specific fields for guardrail processing. """ -from typing import TYPE_CHECKING, Any, List, Optional +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Type from litellm._logging import verbose_proxy_logger from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation @@ -16,6 +16,8 @@ from litellm.types.utils import GenericGuardrailAPIInputs if TYPE_CHECKING: from litellm.integrations.custom_guardrail import CustomGuardrail from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.proxy._types import UserAPIKeyAuth + from litellm.proxy.utils import ProxyLogging class PassThroughEndpointHandler(BaseTranslation): @@ -208,3 +210,128 @@ class PassThroughEndpointHandler(BaseTranslation): ) return response + + +_PROVIDER_HANDLERS: Dict[str, Type[BaseTranslation]] = {} + + +def _get_provider_handlers() -> Dict[str, Type[BaseTranslation]]: + global _PROVIDER_HANDLERS + if not _PROVIDER_HANDLERS: + from litellm.llms.bedrock.passthrough.guardrail_translation.handler import ( + BedrockPassthroughGuardrailHandler, + ) + + _PROVIDER_HANDLERS = {"bedrock": BedrockPassthroughGuardrailHandler} + return _PROVIDER_HANDLERS + + +class LlmPassthroughRouteHandler(BaseTranslation): + """ + Dispatcher for allm_passthrough_route guardrail translation. + + Routes to a per-provider handler based on data["custom_llm_provider"]. + Unknown providers are skipped with a debug log. + """ + + async def process_input_messages( + self, + data: dict, + guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, + ) -> Any: + provider = data.get("custom_llm_provider") + handler_cls = _get_provider_handlers().get(provider or "") + if handler_cls is None: + verbose_proxy_logger.debug( + "LlmPassthroughRouteHandler: no handler for provider=%s, skipping guardrail", + provider, + ) + return data + return await handler_cls().process_input_messages( + data=data, + guardrail_to_apply=guardrail_to_apply, + litellm_logging_obj=litellm_logging_obj, + ) + + async def process_output_response( + self, + response: Any, + guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, + user_api_key_dict: Optional[Any] = None, + request_data: Optional[dict] = None, + ) -> Any: + provider = (request_data or {}).get("custom_llm_provider") + handler_cls = _get_provider_handlers().get(provider or "") + if handler_cls is None: + verbose_proxy_logger.debug( + "LlmPassthroughRouteHandler: no handler for provider=%s, skipping guardrail", + provider, + ) + return response + return await handler_cls().process_output_response( + response=response, + guardrail_to_apply=guardrail_to_apply, + litellm_logging_obj=litellm_logging_obj, + user_api_key_dict=user_api_key_dict, + request_data=request_data, + ) + + @staticmethod + def is_event_stream_response(provider: Optional[str], content_type: str) -> bool: + handler_cls = _get_provider_handlers().get(provider or "") + detector = getattr(handler_cls, "is_event_stream_content_type", None) + if detector is None: + return False + return detector(content_type) + + @staticmethod + def event_stream_media_type(provider: Optional[str]) -> Optional[str]: + handler_cls = _get_provider_handlers().get(provider or "") + getter = getattr(handler_cls, "event_stream_media_type", None) + if getter is None: + return None + return getter() + + @staticmethod + def _resolve_event_stream_de_anonymizer(provider: Optional[str]): + handler_cls = _get_provider_handlers().get(provider or "") + return getattr(handler_cls, "de_anonymize_event_stream", None) + + @staticmethod + def supports_event_stream_de_anonymization( + provider: Optional[str], endpoint: Optional[str] + ) -> bool: + handler_cls = _get_provider_handlers().get(provider or "") + endpoint_check = getattr( + handler_cls, "event_stream_endpoint_is_de_anonymizable", None + ) + if endpoint_check is None: + return False + return endpoint_check(endpoint or "") + + @staticmethod + async def de_anonymize_event_stream( + body_bytes: bytes, + proxy_logging_obj: "ProxyLogging", + user_api_key_dict: "UserAPIKeyAuth", + data: dict, + ) -> bytes: + provider = data.get("custom_llm_provider") + de_anonymize = LlmPassthroughRouteHandler._resolve_event_stream_de_anonymizer( + provider + ) + if de_anonymize is None: + verbose_proxy_logger.debug( + "LlmPassthroughRouteHandler: no event-stream handler for provider=%s, " + "leaving stream unmodified", + provider, + ) + return body_bytes + return await de_anonymize( + body_bytes=body_bytes, + proxy_logging_obj=proxy_logging_obj, + user_api_key_dict=user_api_key_dict, + data=data, + ) diff --git a/litellm/llms/perplexity/cost_calculator.py b/litellm/llms/perplexity/cost_calculator.py index 0f9c3cad841..ec7ec397ea6 100644 --- a/litellm/llms/perplexity/cost_calculator.py +++ b/litellm/llms/perplexity/cost_calculator.py @@ -58,11 +58,8 @@ def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]: ## CALCULATE OUTPUT COST output_cost_per_token = _safe_float_cast(model_info.get("output_cost_per_token")) - completion_cost: float = (usage.completion_tokens or 0) * output_cost_per_token - ## ADD REASONING TOKENS COST (if present) reasoning_tokens = getattr(usage, "reasoning_tokens", 0) or 0 - # Also check completion_tokens_details if reasoning_tokens is not directly available if ( reasoning_tokens == 0 and hasattr(usage, "completion_tokens_details") @@ -73,9 +70,19 @@ def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]: ) reasoning_cost_value = model_info.get("output_cost_per_reasoning_token") + + # `completion_tokens` includes `reasoning_tokens` per the OpenAI/Perplexity usage + # convention (codified for the central path in PR #18607). When a reasoning rate is + # configured we subtract before the output-rate multiplication so the reasoning + # tokens are not billed twice. if reasoning_tokens > 0 and reasoning_cost_value is not None: - reasoning_cost_per_token = _safe_float_cast(reasoning_cost_value) - completion_cost += reasoning_tokens * reasoning_cost_per_token + non_reasoning_completion_tokens = max( + 0, (usage.completion_tokens or 0) - reasoning_tokens + ) + completion_cost: float = non_reasoning_completion_tokens * output_cost_per_token + completion_cost += reasoning_tokens * _safe_float_cast(reasoning_cost_value) + else: + completion_cost = (usage.completion_tokens or 0) * output_cost_per_token ## ADD SEARCH QUERIES COST (if present) num_search_queries = 0 @@ -91,10 +98,11 @@ def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]: if num_search_queries > 0 and search_cost_value is not None: # Handle both dict and float formats if isinstance(search_cost_value, dict): - # Use the "low" size as default - tests expect 0.005 / 1000 - search_cost_per_query = ( - _safe_float_cast(search_cost_value.get("search_context_size_low", 0)) - / 1000 + # search_context_cost_per_query stores the per-request price in USD + # (e.g. sonar low = $0.005/request). Use it directly, matching the + # gemini cost calculator which reads the same field per request. + search_cost_per_query = _safe_float_cast( + search_cost_value.get("search_context_size_low", 0) ) else: search_cost_per_query = _safe_float_cast(search_cost_value) diff --git a/litellm/llms/perplexity/search/transformation.py b/litellm/llms/perplexity/search/transformation.py index ea96f87957c..55de52c5384 100644 --- a/litellm/llms/perplexity/search/transformation.py +++ b/litellm/llms/perplexity/search/transformation.py @@ -50,7 +50,13 @@ class PerplexitySearchConfig(BaseSearchConfig): """ Validate environment and return headers. """ - api_key = api_key or get_secret_str("PERPLEXITYAI_API_KEY") + api_key = self.resolve_server_api_key( + caller_api_key=api_key, + caller_api_base=api_base, + key_env_vars=("PERPLEXITYAI_API_KEY",), + base_env_var="PERPLEXITY_API_BASE", + default_api_base=self.PERPLEXITY_API_BASE, + ) if not api_key: raise ValueError( "PERPLEXITYAI_API_KEY is not set. Set `PERPLEXITYAI_API_KEY` environment variable." diff --git a/litellm/llms/predibase/chat/transformation.py b/litellm/llms/predibase/chat/transformation.py index 3d251d24b0d..ce004f60bfc 100644 --- a/litellm/llms/predibase/chat/transformation.py +++ b/litellm/llms/predibase/chat/transformation.py @@ -129,7 +129,7 @@ class PredibaseConfig(BaseConfig): optional_params["response_format"] = value return optional_params - def transform_response( # noqa: PLR0915 + def transform_response( self, model: str, raw_response: Response, diff --git a/litellm/llms/sagemaker/completion/handler.py b/litellm/llms/sagemaker/completion/handler.py index de7be18e8ba..aa4663666c2 100644 --- a/litellm/llms/sagemaker/completion/handler.py +++ b/litellm/llms/sagemaker/completion/handler.py @@ -138,7 +138,7 @@ class SagemakerLLM(BaseAWSLLM): return prepped_request - def completion( # noqa: PLR0915 + def completion( self, model: str, messages: list, diff --git a/litellm/llms/searchapi/search/transformation.py b/litellm/llms/searchapi/search/transformation.py index c04e1377f9c..ae8413684cc 100644 --- a/litellm/llms/searchapi/search/transformation.py +++ b/litellm/llms/searchapi/search/transformation.py @@ -74,7 +74,13 @@ class SearchAPIConfig(BaseSearchConfig): """ Validate environment and return headers. """ - api_key = api_key or get_secret_str("SEARCHAPI_API_KEY") + api_key = self.resolve_server_api_key( + caller_api_key=api_key, + caller_api_base=api_base, + key_env_vars=("SEARCHAPI_API_KEY",), + base_env_var="SEARCHAPI_API_BASE", + default_api_base=self.SEARCHAPI_API_BASE, + ) if not api_key: raise ValueError( @@ -114,6 +120,7 @@ class SearchAPIConfig(BaseSearchConfig): query: Union[str, List[str]], optional_params: dict, api_key: Optional[str] = None, + api_base: str | None = None, search_engine_id: Optional[str] = None, **kwargs, ) -> Dict: @@ -137,8 +144,16 @@ class SearchAPIConfig(BaseSearchConfig): if isinstance(query, list): query = " ".join(query) - # Get API key from parameter or environment - api_key = api_key or get_secret_str("SEARCHAPI_API_KEY") + # Get API key from parameter or environment. The key is sent as a query + # param to api_base, so resolve it host-aware to avoid leaking a + # server-managed key to a caller-supplied host. + api_key = self.resolve_server_api_key( + caller_api_key=api_key, + caller_api_base=api_base, + key_env_vars=("SEARCHAPI_API_KEY",), + base_env_var="SEARCHAPI_API_BASE", + default_api_base=self.SEARCHAPI_API_BASE, + ) if not api_key: raise ValueError( "SEARCHAPI_API_KEY is not set. Set `SEARCHAPI_API_KEY` environment variable." diff --git a/litellm/llms/searxng/search/transformation.py b/litellm/llms/searxng/search/transformation.py index ee6f3895721..ff68be5709e 100644 --- a/litellm/llms/searxng/search/transformation.py +++ b/litellm/llms/searxng/search/transformation.py @@ -61,7 +61,13 @@ class SearXNGSearchConfig(BaseSearchConfig): Some instances may require authentication via headers. """ # SearXNG typically doesn't require API keys, but support optional auth - api_key = api_key or get_secret_str("SEARXNG_API_KEY") + api_key = self.resolve_server_api_key( + caller_api_key=api_key, + caller_api_base=api_base, + key_env_vars=("SEARXNG_API_KEY",), + base_env_var="SEARXNG_API_BASE", + default_api_base=None, + ) if api_key: headers["Authorization"] = f"Bearer {api_key}" headers["Content-Type"] = "application/json" diff --git a/litellm/llms/serper/search/transformation.py b/litellm/llms/serper/search/transformation.py index 0daccbe652b..dd43f2d2dc9 100644 --- a/litellm/llms/serper/search/transformation.py +++ b/litellm/llms/serper/search/transformation.py @@ -55,7 +55,13 @@ class SerperSearchConfig(BaseSearchConfig): """ Validate environment and return headers. """ - api_key = api_key or get_secret_str("SERPER_API_KEY") + api_key = self.resolve_server_api_key( + caller_api_key=api_key, + caller_api_base=api_base, + key_env_vars=("SERPER_API_KEY",), + base_env_var="SERPER_API_BASE", + default_api_base=self.SERPER_API_BASE, + ) if not api_key: raise ValueError( "SERPER_API_KEY is not set. Set `SERPER_API_KEY` environment variable." diff --git a/litellm/llms/snowflake/chat/transformation.py b/litellm/llms/snowflake/chat/transformation.py index 23bb6f44757..ed30522876a 100644 --- a/litellm/llms/snowflake/chat/transformation.py +++ b/litellm/llms/snowflake/chat/transformation.py @@ -1,17 +1,32 @@ """ -Support for Snowflake REST API +Snowflake Cortex REST API — Chat Transformation + +Routes to native Cortex REST API endpoints based on model: + - Claude models → POST /api/v2/cortex/v1/messages (Anthropic format) + - All other models → POST /api/v2/cortex/v1/chat/completions (OpenAI format) + +Ref: https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-rest-api """ import json -from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union +from typing import TYPE_CHECKING, Any, Dict, List, Optional import httpx -from litellm.types.llms.openai import AllMessageValues -from litellm.types.utils import ChatCompletionMessageToolCall, Function, ModelResponse +from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolCallChunk +from litellm.types.utils import ( + ChatCompletionMessageToolCall, + ChatCompletionUsageBlock, + Choices, + Function, + GenericStreamingChunk, + Message, + ModelResponse, + Usage, +) +from ...base_llm.base_model_iterator import BaseModelResponseIterator from ...openai_like.chat.transformation import OpenAIGPTConfig - from ..utils import SnowflakeBaseConfig if TYPE_CHECKING: @@ -21,69 +36,343 @@ if TYPE_CHECKING: else: LiteLLMLoggingObj = Any +ANTHROPIC_VERSION = "2023-06-01" + +_CLAUDE_MODEL_PREFIXES = ( + "claude-", + "claude_", +) + + +def _is_claude_model(model: str) -> bool: + """Return True if model name (after stripping snowflake/ prefix) is a Claude model.""" + name = model.lower().removeprefix("snowflake/") + return any(name.startswith(p) for p in _CLAUDE_MODEL_PREFIXES) + class SnowflakeConfig(SnowflakeBaseConfig, OpenAIGPTConfig): """ - Reference: https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-llm-rest-api + Snowflake Cortex REST API — unified provider. - Snowflake Cortex LLM REST API supports function calling with specific models (e.g., Claude 3.5 Sonnet). - This config handles transformation between OpenAI format and Snowflake's tool_spec format. + Auto-routes based on model name: + - Claude models → /api/v2/cortex/v1/messages (Anthropic Messages format) + - All others → /api/v2/cortex/v1/chat/completions (OpenAI format) + + Auth: + PAT: api_key="pat/" → X-Snowflake-Authorization-Token-Type: PROGRAMMATIC_ACCESS_TOKEN + JWT: api_key="" → X-Snowflake-Authorization-Token-Type: KEYPAIR_JWT """ @classmethod def get_config(cls): return super().get_config() - def _transform_tool_calls_from_snowflake_to_openai( - self, content_list: List[Dict[str, Any]] - ) -> Tuple[str, Optional[List[ChatCompletionMessageToolCall]]]: + def get_supported_openai_params(self, model: str) -> List[str]: + params = [ + "temperature", + "max_tokens", + "max_completion_tokens", + "top_p", + "stream", + "tools", + "tool_choice", + ] + if _is_claude_model(model): + params.append("thinking") + return params + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + api_base = self._get_api_base(api_base, optional_params) + if _is_claude_model(model): + return f"{api_base}/cortex/v1/messages" + return f"{api_base}/cortex/v1/chat/completions" + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + headers = super().validate_environment( + headers=headers, + model=model, + messages=messages, + optional_params=optional_params, + litellm_params=litellm_params, + api_key=api_key, + api_base=api_base, + ) + if _is_claude_model(model): + headers["anthropic-version"] = ANTHROPIC_VERSION + return headers + + def _transform_tools_to_anthropic(self, tools: List[Dict]) -> List[Dict]: """ - Transform Snowflake tool calls to OpenAI format. + Convert tools from OpenAI format to Anthropic format. - Args: - content_list: Snowflake's content_list array containing text and tool_use items + OpenAI: {"type": "function", "function": {"name": ..., "parameters": {...}}} + Anthropic: {"name": ..., "description": ..., "input_schema": {...}} + """ + anthropic_tools = [] + for tool in tools: + if tool.get("type") == "function" and "function" in tool: + func = tool["function"] + anthropic_tool: Dict[str, Any] = { + "name": func.get("name", ""), + } + if "description" in func: + anthropic_tool["description"] = func["description"] + if "parameters" in func: + anthropic_tool["input_schema"] = func["parameters"] + else: + anthropic_tool["input_schema"] = { + "type": "object", + "properties": {}, + } + anthropic_tools.append(anthropic_tool) + else: + anthropic_tools.append(tool) + return anthropic_tools - Returns: - Tuple of (text_content, tool_calls) + def _extract_system_and_messages( + self, messages: List[AllMessageValues] + ) -> tuple[Optional[str], List[Dict]]: + """ + Split messages into system prompt and conversation turns for Anthropic format. - Snowflake format in content_list: - { - "type": "tool_use", - "tool_use": { - "tool_use_id": "tooluse_...", - "name": "get_weather", - "input": {"location": "Paris"} - } + - system messages → collected and joined (preserves guardrail prompts) + - assistant messages with tool_calls → tool_use content blocks + - tool role messages → user role with tool_result content blocks + """ + system_parts: List[str] = [] + conversation: List[Dict] = [] + + for msg in messages: + if isinstance(msg, dict): + role = msg.get("role", "") + content: Any = msg.get("content", "") + else: + role = getattr(msg, "role", "") + content = getattr(msg, "content", "") + + if role == "system": + if isinstance(content, str) and content: + system_parts.append(content) + elif isinstance(content, list): + system_parts.append( + "\n".join( + b.get("text", "") + for b in content + if b.get("type") == "text" + ) + ) + elif role == "assistant": + tool_calls = ( + msg.get("tool_calls") + if isinstance(msg, dict) + else getattr(msg, "tool_calls", None) + ) + if tool_calls: # type: ignore[truthy-bool] + content_blocks: List[Dict[str, Any]] = [] + if content: + content_blocks.append({"type": "text", "text": content}) + for tc in tool_calls: # type: ignore[attr-defined] + func = ( + tc.get("function", {}) + if isinstance(tc, dict) + else getattr(tc, "function", {}) + ) + tc_id = ( + tc.get("id", "") + if isinstance(tc, dict) + else getattr(tc, "id", "") + ) + func_name = ( + func.get("name", "") + if isinstance(func, dict) + else getattr(func, "name", "") + ) + func_args = ( + func.get("arguments", "{}") + if isinstance(func, dict) + else getattr(func, "arguments", "{}") + ) + try: + input_data = ( + json.loads(func_args) + if isinstance(func_args, str) + else func_args + ) + except (json.JSONDecodeError, TypeError): + input_data = {} + content_blocks.append( + { + "type": "tool_use", + "id": tc_id, + "name": func_name, + "input": input_data, + } + ) + conversation.append( + {"role": "assistant", "content": content_blocks} + ) + else: + conversation.append({"role": "assistant", "content": content}) + elif role == "tool": + tool_call_id = ( + msg.get("tool_call_id", "") + if isinstance(msg, dict) + else getattr(msg, "tool_call_id", "") + ) + tool_content = ( + content if isinstance(content, str) else json.dumps(content) + ) + tool_result_block = { + "type": "tool_result", + "tool_use_id": tool_call_id, + "content": tool_content, + } + if ( + conversation + and conversation[-1]["role"] == "user" + and isinstance(conversation[-1]["content"], list) + and conversation[-1]["content"] + and conversation[-1]["content"][0].get("type") == "tool_result" + ): + conversation[-1]["content"].append(tool_result_block) + else: + conversation.append( + {"role": "user", "content": [tool_result_block]} + ) + else: + conversation.append({"role": role, "content": content}) + + system: Optional[str] = "\n\n".join(system_parts) if system_parts else None + return system, conversation + + def transform_request( + self, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + stream: bool = optional_params.pop("stream", False) or False + extra_body = optional_params.pop("extra_body", {}) + + if _is_claude_model(model): + return self._transform_request_anthropic( + model, messages, optional_params, stream, extra_body + ) + return self._transform_request_openai( + model, messages, optional_params, stream, extra_body + ) + + def _transform_request_openai( + self, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + stream: bool, + extra_body: dict, + ) -> dict: + """OpenAI format for /chat/completions endpoint.""" + max_tokens = optional_params.pop("max_tokens", None) + max_completion_tokens = optional_params.pop("max_completion_tokens", None) + resolved_max = max_completion_tokens or max_tokens + + body: dict = { + "model": model.removeprefix("snowflake/"), + "messages": messages, + "stream": stream, + **optional_params, + **extra_body, } - OpenAI format (returned tool_calls): - ChatCompletionMessageToolCall( - id="tooluse_...", - type="function", - function=Function(name="get_weather", arguments='{"location": "Paris"}') - ) + if resolved_max is not None: + body["max_completion_tokens"] = resolved_max + + return body + + def _transform_tool_choice_to_anthropic(self, tool_choice: Any) -> Dict[str, Any]: """ - text_content = "" - tool_calls: List[ChatCompletionMessageToolCall] = [] + Convert tool_choice from OpenAI format to Anthropic format. - for idx, content_item in enumerate(content_list): - if content_item.get("type") == "text": - text_content += content_item.get("text", "") + OpenAI string values: "auto", "required", "none" + OpenAI dict: {"type": "function", "function": {"name": "..."}} + Anthropic: {"type": "auto"}, {"type": "any"}, {"type": "tool", "name": "..."} + """ + if isinstance(tool_choice, str): + mapping = { + "auto": {"type": "auto"}, + "required": {"type": "any"}, + "none": {"type": "none"}, + } + return mapping.get(tool_choice, {"type": "auto"}) + elif isinstance(tool_choice, dict): + if tool_choice.get("type") == "function": + func = tool_choice.get("function", {}) + return {"type": "tool", "name": func.get("name", "")} + return tool_choice + return {"type": "auto"} - ## TOOL CALLING - elif content_item.get("type") == "tool_use": - tool_use_data = content_item.get("tool_use", {}) - tool_call = ChatCompletionMessageToolCall( - id=tool_use_data.get("tool_use_id", ""), - type="function", - function=Function( - name=tool_use_data.get("name", ""), - arguments=json.dumps(tool_use_data.get("input", {})), - ), - ) - tool_calls.append(tool_call) + def _transform_request_anthropic( + self, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + stream: bool, + extra_body: dict, + ) -> dict: + """Anthropic Messages format for /messages endpoint.""" + system, conversation = self._extract_system_and_messages(messages) - return text_content, tool_calls if tool_calls else None + if "tools" in optional_params: + optional_params["tools"] = self._transform_tools_to_anthropic( + optional_params["tools"] + ) + + if "tool_choice" in optional_params: + optional_params["tool_choice"] = self._transform_tool_choice_to_anthropic( + optional_params["tool_choice"] + ) + + max_completion_tokens = optional_params.pop("max_completion_tokens", None) + if max_completion_tokens and "max_tokens" not in optional_params: + optional_params["max_tokens"] = max_completion_tokens + + model_name = model.removeprefix("snowflake/") + + body: Dict[str, Any] = { + "model": model_name, + "messages": conversation, + "stream": stream, + **optional_params, + **extra_body, + } + + if system is not None: + body["system"] = system + + if "max_tokens" not in body: + body["max_tokens"] = ( + 4096 # reasonable default; Anthropic API max varies by model + ) + + return body def transform_response( self, @@ -99,6 +388,24 @@ class SnowflakeConfig(SnowflakeBaseConfig, OpenAIGPTConfig): api_key: Optional[str] = None, json_mode: Optional[bool] = None, ) -> ModelResponse: + if _is_claude_model(model): + return self._transform_response_anthropic( + model, raw_response, model_response, logging_obj, request_data, messages + ) + return self._transform_response_openai( + model, raw_response, model_response, logging_obj, request_data, messages + ) + + def _transform_response_openai( + self, + model: str, + raw_response: httpx.Response, + model_response: ModelResponse, + logging_obj: LiteLLMLoggingObj, + request_data: dict, + messages: List[AllMessageValues], + ) -> ModelResponse: + """Parse standard OpenAI chat completions response.""" response_json = raw_response.json() logging_obj.post_call( @@ -108,180 +415,278 @@ class SnowflakeConfig(SnowflakeBaseConfig, OpenAIGPTConfig): additional_args={"complete_input_dict": request_data}, ) - ## RESPONSE TRANSFORMATION - # Snowflake returns content_list (not content) with tool_use objects - # We need to transform this to OpenAI's format with content + tool_calls - if "choices" in response_json and len(response_json["choices"]) > 0: - choice = response_json["choices"][0] - if "message" in choice and "content_list" in choice["message"]: - content_list = choice["message"]["content_list"] - ( - text_content, - tool_calls, - ) = self._transform_tool_calls_from_snowflake_to_openai(content_list) - - # Update the choice message with OpenAI format - choice["message"]["content"] = text_content - if tool_calls: - choice["message"]["tool_calls"] = tool_calls - - # Remove Snowflake-specific content_list - del choice["message"]["content_list"] - returned_response = ModelResponse(**response_json) - returned_response.model = "snowflake/" + (returned_response.model or "") if model is not None: returned_response._hidden_params["model"] = model + return returned_response - def get_complete_url( - self, - api_base: Optional[str], - api_key: Optional[str], - model: str, - optional_params: dict, - litellm_params: dict, - stream: Optional[bool] = None, - ) -> str: - """ - If api_base is not provided, use the default DeepSeek /chat/completions endpoint. - """ - - api_base = self._get_api_base(api_base, optional_params) - - return f"{api_base}/cortex/inference:complete" - - def _transform_tools(self, tools: List[Dict[str, Any]]) -> List[Dict[str, Any]]: - """ - Transform OpenAI tool format to Snowflake tool format. - - Args: - tools: List of tools in OpenAI format - - Returns: - List of tools in Snowflake format - - OpenAI format: - { - "type": "function", - "function": { - "name": "get_weather", - "description": "...", - "parameters": {...} - } - } - - Snowflake format: - { - "tool_spec": { - "type": "generic", - "name": "get_weather", - "description": "...", - "input_schema": {...} - } - } - """ - snowflake_tools: List[Dict[str, Any]] = [] - for tool in tools: - if tool.get("type") == "function": - function = tool.get("function", {}) - snowflake_tool: Dict[str, Any] = { - "tool_spec": { - "type": "generic", - "name": function.get("name"), - "input_schema": function.get( - "parameters", - {"type": "object", "properties": {}}, - ), - } - } - # Add description if present - if "description" in function: - snowflake_tool["tool_spec"]["description"] = function["description"] - - snowflake_tools.append(snowflake_tool) - - return snowflake_tools - - def _transform_tool_choice( - self, tool_choice: Union[str, Dict[str, Any]] - ) -> Dict[str, Any]: - """ - Transform OpenAI tool_choice format to Snowflake format. - - Snowflake requires tool_choice to be an object, not a string. - Ref: https://docs.snowflake.com/en/developer-guide/snowflake-rest-api/reference/cortex-inference#post--api-v2-cortex-inference-complete-req-body-schema - - Args: - tool_choice: Tool choice in OpenAI format (str or dict) - - Returns: - Tool choice in Snowflake format (always an object, never a string) - - OpenAI format (string): - "auto", "required", "none" - - OpenAI format (dict): - {"type": "function", "function": {"name": "get_weather"}} - - Snowflake format: - {"type": "auto"} / {"type": "any"} / {"type": "none"} - {"type": "tool", "name": ["get_weather"]} - - Snowflake's API (like Anthropic) requires tool_choice as an object - with a "type" field, not as a bare string. - """ - if isinstance(tool_choice, str): - # Snowflake requires object format, not string. - # Map OpenAI string values to Snowflake object format. - # "required" maps to "any" (Snowflake/Anthropic convention). - _type_map = { - "auto": "auto", - "required": "any", - "none": "none", - } - mapped_type = _type_map.get(tool_choice, tool_choice) - return {"type": mapped_type} - - if isinstance(tool_choice, dict): - if tool_choice.get("type") == "function": - function_name = tool_choice.get("function", {}).get("name") - if function_name: - return { - "type": "tool", - "name": [function_name], # Snowflake expects array - } - - return tool_choice - - def transform_request( + def _transform_response_anthropic( self, model: str, + raw_response: httpx.Response, + model_response: ModelResponse, + logging_obj: LiteLLMLoggingObj, + request_data: dict, messages: List[AllMessageValues], - optional_params: dict, - litellm_params: dict, - headers: dict, - ) -> dict: - stream: bool = optional_params.pop("stream", None) or False - extra_body = optional_params.pop("extra_body", {}) + ) -> ModelResponse: + """Parse Anthropic Messages response into OpenAI format.""" + response_json = raw_response.json() - ## TOOL CALLING - # Transform tools from OpenAI format to Snowflake's tool_spec format - tools = optional_params.pop("tools", None) - if tools: - optional_params["tools"] = self._transform_tools(tools) + logging_obj.post_call( + input=messages, + api_key="", + original_response=response_json, + additional_args={"complete_input_dict": request_data}, + ) - # Transform tool_choice from OpenAI format to Snowflake's tool name array format - tool_choice = optional_params.pop("tool_choice", None) - if tool_choice: - optional_params["tool_choice"] = self._transform_tool_choice(tool_choice) + text_content = "" + tool_calls = [] - return { - "model": model, - "messages": messages, - "stream": stream, - **optional_params, - **extra_body, + for block in response_json.get("content", []): + if block.get("type") == "text": + text_content += block.get("text", "") + elif block.get("type") == "tool_use": + tool_calls.append( + ChatCompletionMessageToolCall( + id=block.get("id", ""), + type="function", + function=Function( + name=block.get("name", ""), + arguments=json.dumps(block.get("input", {})), + ), + ) + ) + + _stop_reason_map = { + "end_turn": "stop", + "max_tokens": "length", + "tool_use": "tool_calls", + "stop_sequence": "stop", } + finish_reason = _stop_reason_map.get( + response_json.get("stop_reason", "end_turn"), "stop" + ) + + message = Message(content=text_content or None, role="assistant") + if tool_calls: + message.tool_calls = tool_calls + + choice = Choices( + finish_reason=finish_reason, + index=0, + message=message, + ) + + usage_data = response_json.get("usage", {}) + usage = Usage( + prompt_tokens=usage_data.get("input_tokens", 0), + completion_tokens=usage_data.get("output_tokens", 0), + total_tokens=usage_data.get("input_tokens", 0) + + usage_data.get("output_tokens", 0), + ) + + model_response.choices = [choice] + model_response.usage = usage # type: ignore[attr-defined] + model_response.model = "snowflake/" + response_json.get("model", model) + model_response.id = response_json.get("id", "") + + if model is not None: + model_response._hidden_params["model"] = model + + return model_response + + def get_model_response_iterator( + self, + streaming_response: Any, + sync_stream: bool, + json_mode: Optional[bool] = False, + ) -> Any: + return SnowflakeStreamingHandler( + streaming_response=streaming_response, + sync_stream=sync_stream, + json_mode=json_mode, + ) + + +class SnowflakeStreamingHandler(BaseModelResponseIterator): + """ + Parse streaming events from both Snowflake endpoints. + + - /chat/completions: OpenAI SSE format (has "choices" key) + - /messages: Anthropic SSE format (has "type" key like content_block_delta) + """ + + def __init__( + self, + streaming_response: Any, + sync_stream: bool, + json_mode: Optional[bool] = False, + ): + super().__init__(streaming_response=streaming_response, sync_stream=sync_stream) + self._tool_index = 0 + self._tool_id = "" + self._tool_name = "" + self._input_tokens = 0 + + def chunk_parser(self, chunk: dict) -> GenericStreamingChunk: + if "choices" in chunk: + return self._parse_openai_chunk(chunk) + return self._parse_anthropic_chunk(chunk) + + def _parse_openai_chunk(self, chunk: dict) -> GenericStreamingChunk: + choices = chunk.get("choices", []) + if not choices: + return GenericStreamingChunk( + text="", + is_finished=False, + finish_reason="", + usage=None, + index=0, + tool_use=None, + ) + + choice = choices[0] + delta = choice.get("delta", {}) + finish_reason = choice.get("finish_reason") or "" + text = delta.get("content") or "" + + tool_use = None + tool_calls = delta.get("tool_calls") + if tool_calls: + tc = tool_calls[0] + func = tc.get("function", {}) + tool_use = ChatCompletionToolCallChunk( + id=tc.get("id", ""), + type="function", + function={ + "name": func.get("name", ""), + "arguments": func.get("arguments", ""), + }, + index=tc.get("index", 0), + ) + + return GenericStreamingChunk( + text=text, + is_finished=finish_reason != "", + finish_reason=finish_reason, + usage=None, + index=choice.get("index", 0), + tool_use=tool_use, + ) + + def _parse_anthropic_chunk(self, chunk: dict) -> GenericStreamingChunk: + event_type = chunk.get("type", "") + + if event_type == "message_start": + message = chunk.get("message", {}) + usage_data = message.get("usage", {}) + self._input_tokens = usage_data.get("input_tokens", 0) + return GenericStreamingChunk( + text="", + is_finished=False, + finish_reason="", + usage=None, + index=0, + tool_use=None, + ) + + elif event_type == "content_block_delta": + delta = chunk.get("delta", {}) + delta_type = delta.get("type", "") + + if delta_type == "text_delta": + return GenericStreamingChunk( + text=delta.get("text", ""), + is_finished=False, + finish_reason="", + usage=None, + index=chunk.get("index", 0), + tool_use=None, + ) + elif delta_type == "input_json_delta": + return GenericStreamingChunk( + text="", + is_finished=False, + finish_reason="", + usage=None, + index=chunk.get("index", 0), + tool_use=ChatCompletionToolCallChunk( + id=self._tool_id, + type="function", + function={ + "name": self._tool_name, + "arguments": delta.get("partial_json", ""), + }, + index=self._tool_index, + ), + ) + + elif event_type == "content_block_start": + content_block = chunk.get("content_block", {}) + if content_block.get("type") == "tool_use": + self._tool_id = content_block.get("id", "") + self._tool_name = content_block.get("name", "") + self._tool_index = chunk.get("index", 0) + return GenericStreamingChunk( + text="", + is_finished=False, + finish_reason="", + usage=None, + index=chunk.get("index", 0), + tool_use=ChatCompletionToolCallChunk( + id=self._tool_id, + type="function", + function={"name": self._tool_name, "arguments": ""}, + index=self._tool_index, + ), + ) + + elif event_type == "message_delta": + delta = chunk.get("delta", {}) + stop_reason = delta.get("stop_reason", "") + usage_data = chunk.get("usage", {}) + _stop_map = { + "end_turn": "stop", + "max_tokens": "length", + "tool_use": "tool_calls", + "stop_sequence": "stop", + } + usage = None + if usage_data or self._input_tokens: + output_t = usage_data.get("output_tokens", 0) + input_t = self._input_tokens or usage_data.get("input_tokens", 0) + usage = ChatCompletionUsageBlock( + prompt_tokens=input_t, + completion_tokens=output_t, + total_tokens=input_t + output_t, + ) + return GenericStreamingChunk( + text="", + is_finished=True, + finish_reason=_stop_map.get(stop_reason, "stop"), + usage=usage, + index=0, + tool_use=None, + ) + + elif event_type == "message_stop": + return GenericStreamingChunk( + text="", + is_finished=True, + finish_reason="stop", + usage=None, + index=0, + tool_use=None, + ) + + return GenericStreamingChunk( + text="", + is_finished=False, + finish_reason="", + usage=None, + index=0, + tool_use=None, + ) diff --git a/litellm/llms/tavily/search/transformation.py b/litellm/llms/tavily/search/transformation.py index ec96db96f36..647cfb5fa84 100644 --- a/litellm/llms/tavily/search/transformation.py +++ b/litellm/llms/tavily/search/transformation.py @@ -64,7 +64,13 @@ class TavilySearchConfig(BaseSearchConfig): """ Validate environment and return headers. """ - api_key = api_key or get_secret_str("TAVILY_API_KEY") + api_key = self.resolve_server_api_key( + caller_api_key=api_key, + caller_api_base=api_base, + key_env_vars=("TAVILY_API_KEY",), + base_env_var="TAVILY_API_BASE", + default_api_base=self.TAVILY_API_BASE, + ) if not api_key: raise ValueError( "TAVILY_API_KEY is not set. Set `TAVILY_API_KEY` environment variable." diff --git a/litellm/llms/tinyfish/search/__init__.py b/litellm/llms/tinyfish/search/__init__.py new file mode 100644 index 00000000000..9777e735aac --- /dev/null +++ b/litellm/llms/tinyfish/search/__init__.py @@ -0,0 +1,3 @@ +from litellm.llms.tinyfish.search.transformation import TinyfishSearchConfig + +__all__ = ["TinyfishSearchConfig"] diff --git a/litellm/llms/tinyfish/search/transformation.py b/litellm/llms/tinyfish/search/transformation.py new file mode 100644 index 00000000000..b92f7ca1aff --- /dev/null +++ b/litellm/llms/tinyfish/search/transformation.py @@ -0,0 +1,170 @@ +""" +TinyFish Search API. +Endpoint: GET https://api.search.tinyfish.ai +Docs: https://docs.tinyfish.ai/search-api +""" + +from __future__ import annotations + +from typing import Literal, TypedDict +from urllib.parse import urlencode + +import httpx +from pydantic import BaseModel, TypeAdapter, ValidationError + +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.base_llm.search.transformation import ( + BaseSearchConfig, + SearchResponse, + SearchResult, +) +from litellm.secret_managers.main import get_secret_str + + +class _TinyfishSearchRequestRequired(TypedDict): + query: str + + +class TinyfishSearchRequest(_TinyfishSearchRequestRequired, total=False): + location: str + language: str + page: int + include_thumbnail: bool + max_results: int + + +class _TinyfishResultItem(BaseModel, frozen=True): + title: str = "" + url: str = "" + snippet: str = "" + + +class _TinyfishApiResponse(BaseModel, frozen=True): + results: tuple[_TinyfishResultItem, ...] = () + + +_UrlEncodableParams = TypeAdapter(dict[str, str | int | bool]) +_StrList = TypeAdapter(list[str]) +_StrFrozenSet = TypeAdapter(frozenset[str]) + +_TINYFISH_PARAMS_KEY = "_tinyfish_params" + + +class TinyfishSearchConfig(BaseSearchConfig): + TINYFISH_API_BASE = "https://api.search.tinyfish.ai" + + @staticmethod + def ui_friendly_name() -> str: + return "TinyFish" + + def get_http_method(self) -> Literal["GET", "POST"]: + return "GET" + + def validate_environment( + self, + headers: dict[str, str], + api_key: str | None = None, + api_base: str | None = None, + **kwargs: object, + ) -> dict[str, str]: + resolved_key = self.resolve_server_api_key( + caller_api_key=api_key, + caller_api_base=api_base, + key_env_vars=("TINYFISH_API_KEY",), + base_env_var="TINYFISH_API_BASE", + default_api_base=self.TINYFISH_API_BASE, + ) + if not resolved_key: + raise ValueError( + "TINYFISH_API_KEY is not set. Set `TINYFISH_API_KEY` environment variable." + ) + return {**headers, "X-API-Key": resolved_key, "Accept": "application/json"} + + def get_complete_url( + self, + api_base: str | None, + optional_params: dict[str, object], + data: dict[str, object] | list[dict[str, object]] | None = None, + **kwargs: object, + ) -> str: + resolved_base = ( + api_base or get_secret_str("TINYFISH_API_BASE") or self.TINYFISH_API_BASE + ) + if isinstance(data, dict) and _TINYFISH_PARAMS_KEY in data: + validated_params = _UrlEncodableParams.validate_python( + data[_TINYFISH_PARAMS_KEY] + ) + return f"{resolved_base}?{urlencode(validated_params, doseq=True)}" + return resolved_base + + def transform_search_request( + self, + query: str | list[str], + optional_params: dict[str, object], + **kwargs: object, + ) -> dict[str, object]: + resolved_query = " ".join(query) if isinstance(query, list) else query + + request_data: TinyfishSearchRequest = {"query": resolved_query} + + country = optional_params.get("country") + if isinstance(country, str): + request_data["location"] = country + + raw_max = optional_params.get("max_results") + if isinstance(raw_max, (int, float, str)): + request_data["max_results"] = max(1, min(int(raw_max), 20)) + + try: + domains = _StrList.validate_python( + optional_params.get("search_domain_filter") + ) + except (ValidationError, TypeError): + domains = [] + if domains: + request_data["query"] = _append_domain_filters( + request_data["query"], domains + ) + + result_data: dict[str, object] = dict(request_data) + + raw_supported: object = ( + self.get_supported_perplexity_optional_params() # any-ok: base class returns bare set + ) + supported_perplexity = _StrFrozenSet.validate_python(raw_supported) + for param, value in optional_params.items(): + if param not in supported_perplexity and param not in result_data: + result_data[param] = value + + return {_TINYFISH_PARAMS_KEY: result_data} + + def transform_search_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj | None, + **kwargs: object, + ) -> SearchResponse: + raw_json: object = raw_response.json() # any-ok: httpx Response.json() -> Any + parsed = _TinyfishApiResponse.model_validate(raw_json) + + max_results_str: str = "20" + if raw_response.request: + raw_param: object = ( + raw_response.request.url.params.get( # any-ok: httpx QueryParams.get() -> Any + "max_results", "20" + ) + ) + max_results_str = str(raw_param) + max_results: int = min(int(max_results_str), 20) + + results = [ + SearchResult(title=item.title, url=item.url, snippet=item.snippet) + for item in parsed.results[:max_results] + ] + + return SearchResponse(results=results, object="search") + + +def _append_domain_filters(query: str, domains: list[str]) -> str: + domain_clauses = " OR ".join(f"site:{d}" for d in domains) + return f"({query}) ({domain_clauses})" diff --git a/litellm/llms/vertex_ai/common_utils.py b/litellm/llms/vertex_ai/common_utils.py index 85c23d8603c..5028c0cf5c8 100644 --- a/litellm/llms/vertex_ai/common_utils.py +++ b/litellm/llms/vertex_ai/common_utils.py @@ -271,7 +271,7 @@ def supports_response_json_schema(model: str) -> bool: # Gemini 2.0+ and 2.5+ models support responseJsonSchema # Pattern matches: gemini-2.0-*, gemini-2.5-*, gemini-3-*, etc. - gemini_2_plus_pattern = re.compile(r"gemini-([2-9]|[1-9]\d+)\.") + gemini_2_plus_pattern = re.compile(r"gemini-(?:[2-9]|[1-9]\d+)(?:\.|\-)") return bool(gemini_2_plus_pattern.search(model_lower)) diff --git a/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py b/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py index e9f08f403f9..103801a1e8d 100644 --- a/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py +++ b/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py @@ -19,7 +19,7 @@ from litellm.types.llms.vertex_ai import ( VertexAICachedContentResponseObject, ) -from ..common_utils import VertexAIError +from ..common_utils import VertexAIError, get_vertex_base_url from ..vertex_llm_base import VertexBase from .transformation import ( separate_cached_messages, @@ -69,17 +69,13 @@ class ContextCachingEndpoints(VertexBase): elif custom_llm_provider == "vertex_ai": auth_header = vertex_auth_header endpoint = "cachedContents" - if vertex_location == "global": - url = f"https://aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/{endpoint}" - else: - url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/{endpoint}" + base_url = get_vertex_base_url(vertex_location) + url = f"{base_url}/v1/projects/{vertex_project}/locations/{vertex_location}/{endpoint}" else: auth_header = vertex_auth_header endpoint = "cachedContents" - if vertex_location == "global": - url = f"https://aiplatform.googleapis.com/v1beta1/projects/{vertex_project}/locations/{vertex_location}/{endpoint}" - else: - url = f"https://{vertex_location}-aiplatform.googleapis.com/v1beta1/projects/{vertex_project}/locations/{vertex_location}/{endpoint}" + base_url = get_vertex_base_url(vertex_location) + url = f"{base_url}/v1beta1/projects/{vertex_project}/locations/{vertex_location}/{endpoint}" return self._check_custom_proxy( api_base=api_base, diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py index c578d6cd28b..f5a2b268263 100644 --- a/litellm/llms/vertex_ai/gemini/transformation.py +++ b/litellm/llms/vertex_ai/gemini/transformation.py @@ -678,7 +678,7 @@ def check_if_part_exists_in_parts( return False -def _gemini_convert_messages_with_history( # noqa: PLR0915 +def _gemini_convert_messages_with_history( messages: List[AllMessageValues], model: Optional[str] = None, litellm_params: Optional[dict] = None, @@ -1176,7 +1176,7 @@ def _rewrite_google_maps_response_format(data: RequestBody) -> None: _rewrite_mime_type_to_response_format(generation_config) -def _transform_request_body( # noqa: PLR0915 +def _transform_request_body( messages: List[AllMessageValues], model: str, optional_params: dict, diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py index 430a789d2a0..c171538b9c0 100644 --- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py +++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py @@ -614,9 +614,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): return googleSearch, googleSearchRetrieval, enterpriseWebSearch, urlContext - def _map_function( # noqa: PLR0915 - self, value: List[dict], optional_params: dict - ) -> List[Tools]: + def _map_function(self, value: List[dict], optional_params: dict) -> List[Tools]: """ Map OpenAI-style tools/functions to Vertex AI format. @@ -1173,7 +1171,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): optional_params["include_server_side_tool_invocations"] = True return - def map_openai_params( # noqa: PLR0915 + def map_openai_params( self, non_default_params: Dict, optional_params: Dict, @@ -1904,7 +1902,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): return False @staticmethod - def _calculate_usage( # noqa: PLR0915 + def _calculate_usage( completion_response: Union[ GenerateContentResponseBody, BidiGenerateContentServerMessage ], @@ -2380,7 +2378,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): return annotations @staticmethod - def _process_candidates( # noqa: PLR0915 + def _process_candidates( _candidates: List[Candidates], model_response: Union[ModelResponse, "ModelResponseStream"], standard_optional_params: dict, @@ -2846,6 +2844,7 @@ async def make_call( sync_stream=False, logging_obj=logging_obj, response_headers=response.headers, + response=response, ) # LOGGING logging_obj.post_call( @@ -2889,6 +2888,7 @@ def make_sync_call( sync_stream=True, logging_obj=logging_obj, response_headers=response.headers, + response=response, ) # LOGGING @@ -3350,12 +3350,14 @@ class ModelResponseIterator: sync_stream: bool, logging_obj: LoggingClass, response_headers: Optional[Dict[str, str]] = None, + response: httpx.Response | None = None, ): from litellm.litellm_core_utils.prompt_templates.common_utils import ( check_is_function_call, ) self.streaming_response = streaming_response + self.response = response self.chunk_type: Literal["valid_json", "accumulated_json"] = "valid_json" self.accumulated_json = "" self.sent_first_chunk = False @@ -3422,14 +3424,18 @@ class ModelResponseIterator: self.has_seen_tool_calls = True break - # Handle final chunk with finishReason but no content. - # _process_candidates skips candidates without "content", - # so the finish_reason from the final chunk is lost. + # _process_candidates skips candidates without a "content" part, so a + # content-less chunk leaves choices empty and the downstream streaming + # handler hits IndexError on choices[0]. This covers the final chunk + # (finishReason, no content) and mid-stream metadata-only chunks + # (grounding/web-search/thought, no content and no finishReason — seen + # with web_search + reasoning) by emitting an empty-delta choice. if not model_response.choices and _candidates: from litellm.types.utils import Delta, StreamingChoices for candidate in _candidates: finish_reason_str = candidate.get("finishReason") + mapped_finish_reason = None if finish_reason_str is not None: if self.has_seen_tool_calls: mapped_finish_reason = "tool_calls" @@ -3437,14 +3443,14 @@ class ModelResponseIterator: mapped_finish_reason = VertexGeminiConfig._check_finish_reason( None, finish_reason_str ) - choice = StreamingChoices( - finish_reason=mapped_finish_reason, - index=candidate.get("index", 0), - delta=Delta(content=None, role=None), - logprobs=None, - enhancements=None, - ) - model_response.choices.append(choice) + choice = StreamingChoices( + finish_reason=mapped_finish_reason, + index=candidate.get("index", 0), + delta=Delta(content=None, role=None), + logprobs=None, + enhancements=None, + ) + model_response.choices.append(choice) # Also handle the case where the final chunk has empty # content (e.g. text:"") WITH finishReason. In this case @@ -3653,3 +3659,41 @@ class ModelResponseIterator: raise StopAsyncIteration except ValueError as e: raise RuntimeError(f"Error parsing chunk: {e},\nReceived chunk: {chunk}") + + async def aclose(self) -> None: + iterator = getattr( + self, + "async_response_iterator", + self.streaming_response, + ) + if iterator is not None and hasattr(iterator, "aclose"): + try: + await iterator.aclose() + except Exception as e: # noqa: BLE001 + verbose_logger.debug( + "ModelResponseIterator.aclose: error closing iterator: %s", e + ) + if self.response is not None: + try: + await self.response.aclose() + except Exception as e: # noqa: BLE001 + verbose_logger.debug( + "ModelResponseIterator.aclose: error closing response: %s", e + ) + + def close(self) -> None: + iterator = getattr(self, "response_iterator", self.streaming_response) + if iterator is not None and hasattr(iterator, "close"): + try: + iterator.close() + except Exception as e: # noqa: BLE001 + verbose_logger.debug( + "ModelResponseIterator.close: error closing iterator: %s", e + ) + if self.response is not None: + try: + self.response.close() + except Exception as e: # noqa: BLE001 + verbose_logger.debug( + "ModelResponseIterator.close: error closing response: %s", e + ) diff --git a/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_handler.py b/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_handler.py index 99165c37c93..165dac24903 100644 --- a/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_handler.py +++ b/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_handler.py @@ -124,7 +124,7 @@ class GoogleBatchEmbeddings(VertexLLM): return resolved_files - def batch_embeddings( # noqa: PLR0915 + def batch_embeddings( self, model: str, input: GeminiEmbeddingInput, diff --git a/litellm/llms/vertex_ai/image_generation/cost_calculator.py b/litellm/llms/vertex_ai/image_generation/cost_calculator.py index 012de5498cb..5c04ebf79ee 100644 --- a/litellm/llms/vertex_ai/image_generation/cost_calculator.py +++ b/litellm/llms/vertex_ai/image_generation/cost_calculator.py @@ -5,6 +5,7 @@ Vertex AI Image Generation Cost Calculator import litellm from litellm.litellm_core_utils.llm_cost_calc.utils import ( calculate_image_response_cost_from_usage, + calculate_image_response_web_search_cost, ) from litellm.types.utils import ImageResponse @@ -21,16 +22,20 @@ def cost_calculator( custom_llm_provider="vertex_ai", ) + web_search_cost = calculate_image_response_web_search_cost( + image_response=image_response, + custom_llm_provider="vertex_ai", + model_info=_model_info, + ) + token_based_cost = calculate_image_response_cost_from_usage( model=model, image_response=image_response, custom_llm_provider="vertex_ai", ) if token_based_cost is not None: - return token_based_cost + return token_based_cost + web_search_cost output_cost_per_image: float = _model_info.get("output_cost_per_image") or 0.0 - num_images: int = 0 - if image_response.data: - num_images = len(image_response.data) - return output_cost_per_image * num_images + num_images: int = len(image_response.data) if image_response.data else 0 + return output_cost_per_image * num_images + web_search_cost diff --git a/litellm/llms/vertex_ai/image_generation/vertex_gemini_transformation.py b/litellm/llms/vertex_ai/image_generation/vertex_gemini_transformation.py index f4bda8d1bed..103c7b2a28a 100644 --- a/litellm/llms/vertex_ai/image_generation/vertex_gemini_transformation.py +++ b/litellm/llms/vertex_ai/image_generation/vertex_gemini_transformation.py @@ -7,6 +7,10 @@ import litellm from litellm.llms.base_llm.image_generation.transformation import ( BaseImageGenerationConfig, ) +from litellm.llms.gemini.common_utils import ( + get_gemini_image_web_search_requests, + map_gemini_image_tools_params, +) from litellm.llms.vertex_ai.common_utils import get_vertex_base_url from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexLLM from litellm.secret_managers.main import get_secret_str @@ -52,6 +56,8 @@ class VertexAIGeminiImageGenerationConfig(BaseImageGenerationConfig, VertexLLM): "aspect_ratio", "imageSize", "image_size", + "tools", + "web_search_options", ] def map_openai_params( @@ -77,9 +83,10 @@ class VertexAIGeminiImageGenerationConfig(BaseImageGenerationConfig, VertexLLM): mapped_params["aspectRatio"] = v elif k in ("imageSize", "image_size"): mapped_params["imageSize"] = v - else: + elif k not in ("tools", "web_search_options"): mapped_params[k] = v + mapped_params = map_gemini_image_tools_params(non_default_params, mapped_params) return mapped_params def _map_size_to_aspect_ratio(self, size: str) -> str: @@ -247,6 +254,11 @@ class VertexAIGeminiImageGenerationConfig(BaseImageGenerationConfig, VertexLLM): "generationConfig": generation_config, } + if tools := optional_params.get("tools"): + request_body["tools"] = tools + if tool_config := optional_params.get("toolConfig"): + request_body["toolConfig"] = tool_config + return request_body def _transform_image_usage(self, usage: dict) -> ImageUsage: @@ -324,4 +336,8 @@ class VertexAIGeminiImageGenerationConfig(BaseImageGenerationConfig, VertexLLM): if usage_metadata := response_data.get("usageMetadata", None): model_response.usage = self._transform_image_usage(usage_metadata) + web_search_requests = get_gemini_image_web_search_requests(response_data) + if web_search_requests and model_response.usage is not None: + setattr(model_response.usage, "web_search_requests", web_search_requests) + return model_response diff --git a/litellm/llms/vertex_ai/realtime/transformation.py b/litellm/llms/vertex_ai/realtime/transformation.py index ea4dbccc8c8..1fe9f15c9f0 100644 --- a/litellm/llms/vertex_ai/realtime/transformation.py +++ b/litellm/llms/vertex_ai/realtime/transformation.py @@ -32,6 +32,9 @@ class VertexAIRealtimeConfig(GeminiRealtimeConfig): self._project = project self._location = location + def _include_function_response_id(self) -> bool: + return False + # ------------------------------------------------------------------ # URL # ------------------------------------------------------------------ @@ -90,7 +93,8 @@ class VertexAIRealtimeConfig(GeminiRealtimeConfig): def get_audio_mime_type(self, input_audio_format: str = "pcm16") -> str: mime_types = { - "pcm16": "audio/pcm;rate=16000", + # Gemini Live native audio (OpenAI GA realtime default) is 24kHz PCM. + "pcm16": "audio/pcm;rate=24000", "g711_ulaw": "audio/pcmu", "g711_alaw": "audio/pcma", } diff --git a/litellm/llms/vertex_ai/vertex_ai_non_gemini.py b/litellm/llms/vertex_ai/vertex_ai_non_gemini.py index cfbab584f6a..c134dee7ad4 100644 --- a/litellm/llms/vertex_ai/vertex_ai_non_gemini.py +++ b/litellm/llms/vertex_ai/vertex_ai_non_gemini.py @@ -77,7 +77,7 @@ def _set_client_in_cache(client_cache_key: str, vertex_llm_model: Any): ) -def completion( # noqa: PLR0915 +def completion( model: str, messages: list, model_response: ModelResponse, @@ -485,7 +485,7 @@ def completion( # noqa: PLR0915 ) -async def async_completion( # noqa: PLR0915 +async def async_completion( llm_model, mode: str, prompt: str, @@ -650,7 +650,7 @@ async def async_completion( # noqa: PLR0915 raise VertexAIError(status_code=500, message=str(e)) -async def async_streaming( # noqa: PLR0915 +async def async_streaming( llm_model, mode: str, prompt: str, diff --git a/litellm/llms/voyage/embedding/transformation_multimodal.py b/litellm/llms/voyage/embedding/transformation_multimodal.py new file mode 100644 index 00000000000..55e221b065b --- /dev/null +++ b/litellm/llms/voyage/embedding/transformation_multimodal.py @@ -0,0 +1,183 @@ +""" +Transform request/response for Voyage multimodal embeddings. + +Voyage multimodal models use /v1/multimodalembeddings and accept `inputs` +containing content blocks, unlike standard Voyage embeddings which use +/v1/embeddings and a string/list `input` field. +""" + +from typing import Any, Dict, List, Optional, Union + +import httpx + +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.secret_managers.main import get_secret_str +from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues +from litellm.types.utils import EmbeddingResponse, Usage + + +class VoyageMultimodalEmbeddingError(BaseLLMException): + def __init__( + self, + status_code: int, + message: str, + headers: Union[dict, httpx.Headers] = {}, + ): + self.status_code = status_code + self.message = message + self.request = httpx.Request( + method="POST", url="https://api.voyageai.com/v1/multimodalembeddings" + ) + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + status_code=status_code, + message=message, + headers=headers, + ) + + +class VoyageMultimodalEmbeddingConfig(BaseEmbeddingConfig): + """ + Reference: https://docs.voyageai.com/reference/multimodal-embeddings-api + """ + + @staticmethod + def is_multimodal_embeddings(model: str) -> bool: + return "multimodal" in model.lower() + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + if api_base: + if not api_base.endswith("/multimodalembeddings"): + api_base = f"{api_base}/multimodalembeddings" + return api_base + return "https://api.voyageai.com/v1/multimodalembeddings" + + def get_supported_openai_params(self, model: str) -> list: + return ["dimensions"] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + if "dimensions" in non_default_params: + optional_params["output_dimension"] = non_default_params["dimensions"] + return optional_params + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + if api_key is None: + api_key = ( + get_secret_str("VOYAGE_API_KEY") + or get_secret_str("VOYAGE_AI_API_KEY") + or get_secret_str("VOYAGE_AI_TOKEN") + ) + if not api_key: + raise ValueError( + "Voyage API key is required for multimodal embeddings. " + "Set VOYAGE_API_KEY / VOYAGE_AI_API_KEY / VOYAGE_AI_TOKEN " + "or pass `api_key` explicitly." + ) + return {"Authorization": f"Bearer {api_key}"} + + def _normalize_content_item(self, item: Dict[str, Any]) -> Dict[str, Any]: + item_type = item.get("type") + if item_type == "image_url": + image_url = item.get("image_url") + if isinstance(image_url, dict): + image_url = image_url.get("url") + if image_url is None: + raise ValueError( + "Voyage multimodal embeddings require a non-empty `image_url`. " + "Got an image content block without a `url`." + ) + if isinstance(image_url, str) and image_url.startswith("data:image/"): + _, _, encoded = image_url.partition(",") + return {"type": "image_base64", "image_base64": encoded} + return {"type": "image_url", "image_url": image_url} + return item + + def _normalize_input_item(self, item: Any) -> Dict[str, Any]: + if isinstance(item, str): + return {"content": [{"type": "text", "text": item}]} + if isinstance(item, dict) and "content" in item: + content = item.get("content") or [] + return { + **item, + "content": [ + self._normalize_content_item(content_item) + for content_item in content + ], + } + return item + + def transform_embedding_request( + self, + model: str, + input: AllEmbeddingInputValues, + optional_params: dict, + headers: dict, + ) -> dict: + inputs = input if isinstance(input, list) else [input] + return { + "inputs": [self._normalize_input_item(item) for item in inputs], + "model": model, + **optional_params, + } + + def transform_embedding_response( + self, + model: str, + raw_response: httpx.Response, + model_response: EmbeddingResponse, + logging_obj: LiteLLMLoggingObj, + api_key: Optional[str] = None, + request_data: dict = {}, + optional_params: dict = {}, + litellm_params: dict = {}, + ) -> EmbeddingResponse: + try: + raw_response_json = raw_response.json() + except Exception: + raise VoyageMultimodalEmbeddingError( + message=raw_response.text, status_code=raw_response.status_code + ) + + model_response.model = raw_response_json.get("model") + model_response.data = raw_response_json.get("data") + model_response.object = raw_response_json.get("object") + + usage_payload = raw_response_json.get("usage", {}) + total_tokens = usage_payload.get("total_tokens", 0) + model_response.usage = Usage( + prompt_tokens=total_tokens, + total_tokens=total_tokens, + ) + return model_response + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers] + ) -> BaseLLMException: + return VoyageMultimodalEmbeddingError( + message=error_message, status_code=status_code, headers=headers + ) diff --git a/litellm/llms/watsonx/embed/transformation.py b/litellm/llms/watsonx/embed/transformation.py index 930212e3ef3..ae873d63fe9 100644 --- a/litellm/llms/watsonx/embed/transformation.py +++ b/litellm/llms/watsonx/embed/transformation.py @@ -43,8 +43,19 @@ class IBMWatsonXEmbeddingConfig(IBMWatsonXMixin, BaseEmbeddingConfig): api_params=watsonx_api_params, ) + if isinstance(input, str): + inputs: list[str] = [input] + elif isinstance(input, list): + if len(input) > 0 and isinstance(input[0], (list, int)): + raise ValueError( + "WatsonX embeddings require a string or list of strings" + ) + inputs = input + else: + inputs = [input] + return { - "inputs": input, + "inputs": inputs, "parameters": optional_params, **watsonx_auth_payload, } diff --git a/litellm/llms/xai/chat/transformation.py b/litellm/llms/xai/chat/transformation.py index c06928516ef..8019bb67991 100644 --- a/litellm/llms/xai/chat/transformation.py +++ b/litellm/llms/xai/chat/transformation.py @@ -5,6 +5,7 @@ import httpx import litellm from litellm._logging import verbose_logger from litellm.constants import XAI_API_BASE +from litellm.exceptions import AuthenticationError from litellm.litellm_core_utils.prompt_templates.common_utils import ( filter_value_from_dict, strip_name_from_messages, @@ -39,6 +40,72 @@ class XAIChatConfig(OpenAIGPTConfig): dynamic_api_key = XAIModelInfo.get_api_key(api_key) return api_base, dynamic_api_key + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + from litellm.llms.xai.oauth import ( + XAIOAuthAuthenticator, + XAIOAuthError, + should_use_xai_oauth, + ) + + dynamic_api_key = XAIModelInfo.get_api_key(api_key) + if should_use_xai_oauth(litellm_params) and not dynamic_api_key: + try: + headers["Authorization"] = ( + f"Bearer {XAIOAuthAuthenticator().get_access_token()}" + ) + except XAIOAuthError as exc: + raise AuthenticationError( + model=model, + llm_provider=self.custom_llm_provider or "xai", + message=str(exc), + ) from exc + if "content-type" not in headers and "Content-Type" not in headers: + headers["Content-Type"] = "application/json" + return headers + + return super().validate_environment( + headers=headers, + model=model, + messages=messages, + optional_params=optional_params, + litellm_params=litellm_params, + api_key=dynamic_api_key, + api_base=api_base, + ) + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + from litellm.llms.xai.oauth import XAIOAuthAuthenticator, should_use_xai_oauth + + dynamic_api_key = XAIModelInfo.get_api_key(api_key) + if should_use_xai_oauth(litellm_params) and not dynamic_api_key: + api_base = XAIOAuthAuthenticator().get_api_base() + + return super().get_complete_url( + api_base=api_base, + api_key=dynamic_api_key, + model=model, + optional_params=optional_params, + litellm_params=litellm_params, + stream=stream, + ) + def get_supported_openai_params(self, model: str) -> list: base_openai_params = [ "logit_bias", diff --git a/litellm/llms/xai/oauth.py b/litellm/llms/xai/oauth.py new file mode 100644 index 00000000000..30c717b7ca0 --- /dev/null +++ b/litellm/llms/xai/oauth.py @@ -0,0 +1,421 @@ +import base64 +import hashlib +import json +import os +import secrets +import sys +import threading +import time +import uuid +import webbrowser +from http.server import BaseHTTPRequestHandler, HTTPServer +from typing import Any, Dict, Optional, Tuple, Union +from urllib.parse import parse_qs, urlencode, urlparse + +import httpx + +from litellm._logging import verbose_logger +from litellm.constants import XAI_API_BASE +from litellm.llms.custom_httpx.http_handler import HTTPHandler, _get_httpx_client +from litellm.secret_managers.main import get_secret_str + +XAI_OAUTH_ISSUER = "https://auth.x.ai" +XAI_OAUTH_DISCOVERY_URL = f"{XAI_OAUTH_ISSUER}/.well-known/openid-configuration" +XAI_OAUTH_CLIENT_ID = "b1a00492-073a-47ea-816f-4c329264a828" +XAI_OAUTH_SCOPE = "openid profile email offline_access grok-cli:access api:access" +XAI_OAUTH_REDIRECT_HOST = "127.0.0.1" +XAI_OAUTH_REDIRECT_PORT = 56121 +XAI_OAUTH_REDIRECT_PATH = "/callback" +XAI_OAUTH_EXPIRY_SKEW_SECONDS = 120 +XAI_OAUTH_CALLBACK_TIMEOUT_SECONDS = 180 +_XAI_OAUTH_REFRESH_LOCK = threading.Lock() + + +class XAIOAuthError(Exception): + pass + + +class XAIOAuthLoginRequiredError(XAIOAuthError): + pass + + +class _CallbackHandler(BaseHTTPRequestHandler): + server: "_CallbackServer" + + def do_GET(self) -> None: + parsed = urlparse(self.path) + if parsed.path != XAI_OAUTH_REDIRECT_PATH: + self.send_response(404) + self.end_headers() + return + + params = parse_qs(parsed.query) + result = { + "code": params.get("code", [None])[0], + "state": params.get("state", [None])[0], + "error": params.get("error", [None])[0], + "error_description": params.get("error_description", [None])[0], + } + self.server.callback_result = result + + if result["state"] != self.server.expected_state: + self.send_response(400) + self.send_header("Content-Type", "text/html; charset=utf-8") + self.end_headers() + self.wfile.write( + b"

xAI authorization state mismatch.

" + ) + return + + self.send_response(200) + self.send_header("Content-Type", "text/html; charset=utf-8") + self.end_headers() + body = ( + b"

xAI authorization failed.

You can close this tab." + if result["error"] + else b"

xAI authorization received.

You can close this tab." + ) + self.wfile.write(body) + + def log_message(self, format: str, *args: Any) -> None: + return + + +class _CallbackServer(HTTPServer): + expected_state: str + callback_result: Optional[Dict[str, Optional[str]]] + + +class XAIOAuthAuthenticator: + def __init__( + self, http_client: Optional[Union[httpx.Client, HTTPHandler]] = None + ) -> None: + self.token_dir = get_secret_str("XAI_OAUTH_TOKEN_DIR") or os.path.expanduser( + "~/.config/litellm/xai_oauth" + ) + self.auth_file = os.path.join( + self.token_dir, get_secret_str("XAI_OAUTH_AUTH_FILE") or "auth.json" + ) + self.http_client = http_client + + def get_api_base(self) -> str: + return ( + get_secret_str("XAI_OAUTH_API_BASE") + or get_secret_str("XAI_API_BASE") + or XAI_API_BASE + ) + + def get_access_token(self) -> str: + auth_data = self._read_auth_file() + if not auth_data: + raise XAIOAuthLoginRequiredError( + "xAI OAuth login required. Run `litellm xai-oauth login`." + ) + + access_token = auth_data.get("access_token") + if access_token and not self._is_expired(auth_data): + return access_token + + refresh_token = auth_data.get("refresh_token") + if not refresh_token: + raise XAIOAuthLoginRequiredError( + "xAI OAuth refresh token missing. Run `litellm xai-oauth login`." + ) + + with _XAI_OAUTH_REFRESH_LOCK: + locked_auth_data = self._read_auth_file() or auth_data + access_token = locked_auth_data.get("access_token") + if access_token and not self._is_expired(locked_auth_data): + return access_token + + refreshed = self._refresh_tokens(locked_auth_data) + return refreshed["access_token"] + + def login(self, force: bool = False, no_browser: bool = False) -> Dict[str, Any]: + existing = self._read_auth_file() + if existing and not force and existing.get("access_token"): + if not self._is_expired(existing): + return existing + if existing.get("refresh_token"): + try: + return self._refresh_tokens(existing) + except XAIOAuthError: + pass + + discovery = self._discover() + verifier, challenge = self._pkce_pair() + state = uuid.uuid4().hex + nonce = uuid.uuid4().hex + server, redirect_uri = self._start_callback_server(state) + authorize_url = self._build_authorize_url( + authorization_endpoint=discovery["authorization_endpoint"], + redirect_uri=redirect_uri, + challenge=challenge, + state=state, + nonce=nonce, + ) + + if no_browser or not webbrowser.open(authorize_url): + sys.stdout.write( + f"Open this URL to authenticate with xAI:\n{authorize_url}\n" + ) + sys.stdout.flush() + + result = self._wait_for_callback(server) + if result.get("state") != state: + raise XAIOAuthError("xAI OAuth state mismatch") + if result.get("error"): + description = result.get("error_description") or result["error"] + raise XAIOAuthError(f"xAI authorization failed: {description}") + code = result.get("code") + if not code: + raise XAIOAuthError("xAI authorization failed: no code returned") + + token_payload = self._exchange_token( + discovery["token_endpoint"], + { + "grant_type": "authorization_code", + "code": code, + "redirect_uri": redirect_uri, + "client_id": XAI_OAUTH_CLIENT_ID, + "code_verifier": verifier, + }, + ) + auth_data = self._build_auth_record(token_payload, discovery["token_endpoint"]) + self._write_auth_file(auth_data) + return auth_data + + def _client(self) -> Union[httpx.Client, HTTPHandler]: + return self.http_client or _get_httpx_client() + + def _ensure_token_dir(self) -> None: + os.makedirs(self.token_dir, mode=0o700, exist_ok=True) + try: + os.chmod(self.token_dir, 0o700) + except OSError: + verbose_logger.debug("Could not chmod xAI OAuth token directory") + + def _read_auth_file(self) -> Optional[Dict[str, Any]]: + try: + with open(self.auth_file, "r") as f: + data = json.load(f) + return data if isinstance(data, dict) else None + except (IOError, json.JSONDecodeError): + return None + + def _write_auth_file(self, data: Dict[str, Any]) -> None: + self._ensure_token_dir() + tmp_file = os.path.join( + self.token_dir, + f".{os.path.basename(self.auth_file)}.{uuid.uuid4().hex}.tmp", + ) + flags = os.O_WRONLY | os.O_CREAT | os.O_EXCL + if hasattr(os, "O_NOFOLLOW"): + flags |= os.O_NOFOLLOW + fd = os.open(tmp_file, flags, 0o600) + try: + with os.fdopen(fd, "w") as f: + json.dump(data, f) + f.flush() + os.fsync(f.fileno()) + os.replace(tmp_file, self.auth_file) + try: + os.chmod(self.auth_file, 0o600) + except OSError: + verbose_logger.debug("Could not chmod xAI OAuth auth file") + except Exception: + try: + os.close(fd) + except OSError: + pass + try: + os.unlink(tmp_file) + except OSError: + pass + raise + + def _is_expired(self, auth_data: Dict[str, Any]) -> bool: + expires_at = auth_data.get("expires_at") + if expires_at is None: + return True + try: + return time.time() >= float(expires_at) - XAI_OAUTH_EXPIRY_SKEW_SECONDS + except (TypeError, ValueError): + return True + + def _discover(self) -> Dict[str, str]: + try: + response = self._client().get( + XAI_OAUTH_DISCOVERY_URL, headers={"Accept": "application/json"} + ) + response.raise_for_status() + except httpx.HTTPStatusError as exc: + raise XAIOAuthError( + f"xAI OAuth discovery request failed: {exc.response.status_code} {exc.response.text}" + ) from exc + try: + data = response.json() + except ValueError as exc: + raise XAIOAuthError( + "xAI OAuth discovery response was not valid JSON" + ) from exc + authorization_endpoint = data.get("authorization_endpoint") + token_endpoint = data.get("token_endpoint") + if not authorization_endpoint or not token_endpoint: + raise XAIOAuthError("xAI OAuth discovery missing endpoints") + return { + "authorization_endpoint": self._validate_xai_endpoint( + authorization_endpoint + ), + "token_endpoint": self._validate_xai_endpoint(token_endpoint), + } + + def _validate_xai_endpoint(self, url: str) -> str: + parsed = urlparse(url) + host = (parsed.hostname or "").lower() + if parsed.scheme != "https" or (host != "x.ai" and not host.endswith(".x.ai")): + raise XAIOAuthError( + f"xAI OAuth discovery returned unexpected endpoint: {url}" + ) + return url + + def _pkce_pair(self) -> Tuple[str, str]: + verifier = ( + base64.urlsafe_b64encode(secrets.token_bytes(32)).rstrip(b"=").decode() + ) + challenge = ( + base64.urlsafe_b64encode(hashlib.sha256(verifier.encode()).digest()) + .rstrip(b"=") + .decode() + ) + return verifier, challenge + + def _start_callback_server(self, state: str) -> Tuple[_CallbackServer, str]: + last_error: Optional[OSError] = None + for port in (XAI_OAUTH_REDIRECT_PORT, 0): + try: + server = _CallbackServer( + (XAI_OAUTH_REDIRECT_HOST, port), _CallbackHandler + ) + server.expected_state = state + server.callback_result = None + actual_port = server.server_address[1] + redirect_uri = f"http://{XAI_OAUTH_REDIRECT_HOST}:{actual_port}{XAI_OAUTH_REDIRECT_PATH}" + return server, redirect_uri + except OSError as exc: + last_error = exc + raise XAIOAuthError(f"Could not start xAI OAuth callback server: {last_error}") + + def _build_authorize_url( + self, + authorization_endpoint: str, + redirect_uri: str, + challenge: str, + state: str, + nonce: str, + ) -> str: + params = { + "response_type": "code", + "client_id": XAI_OAUTH_CLIENT_ID, + "redirect_uri": redirect_uri, + "scope": XAI_OAUTH_SCOPE, + "code_challenge": challenge, + "code_challenge_method": "S256", + "state": state, + "nonce": nonce, + } + return f"{authorization_endpoint}?{urlencode(params)}" + + def _wait_for_callback(self, server: _CallbackServer) -> Dict[str, Optional[str]]: + server.timeout = 1 + deadline = time.time() + XAI_OAUTH_CALLBACK_TIMEOUT_SECONDS + try: + while time.time() < deadline: + server.handle_request() + if server.callback_result is not None: + return server.callback_result + finally: + server.server_close() + raise XAIOAuthError("Timed out waiting for xAI OAuth callback") + + def _exchange_token( + self, token_endpoint: str, data: Dict[str, str] + ) -> Dict[str, Any]: + try: + response = self._client().post( + token_endpoint, + headers={ + "Accept": "application/json", + "Content-Type": "application/x-www-form-urlencoded", + }, + data=data, + ) + response.raise_for_status() + except httpx.HTTPStatusError as exc: + raise XAIOAuthError( + f"xAI OAuth token request failed: {exc.response.status_code} {exc.response.text}" + ) from exc + try: + body = response.json() + except ValueError as exc: + raise XAIOAuthError("xAI OAuth token response was not valid JSON") from exc + if not isinstance(body, dict): + raise XAIOAuthError("xAI OAuth token response was not an object") + return body + + def _build_auth_record( + self, + token_payload: Dict[str, Any], + token_endpoint: str, + fallback_refresh_token: Optional[str] = None, + ) -> Dict[str, Any]: + access_token = token_payload.get("access_token") + refresh_token = token_payload.get("refresh_token") or fallback_refresh_token + if not access_token: + raise XAIOAuthError("xAI OAuth token response missing access_token") + if not refresh_token: + raise XAIOAuthError("xAI OAuth token response missing refresh_token") + expires_in = token_payload.get("expires_in") or 3600 + try: + expires_at = int(time.time() + int(expires_in)) + except (TypeError, ValueError): + expires_at = int(time.time() + 3600) + return { + "access_token": access_token, + "refresh_token": refresh_token, + "id_token": token_payload.get("id_token"), + "token_type": token_payload.get("token_type") or "Bearer", + "token_endpoint": token_endpoint, + "expires_at": expires_at, + } + + def _refresh_tokens(self, auth_data: Dict[str, Any]) -> Dict[str, Any]: + token_endpoint = auth_data.get("token_endpoint") + if not token_endpoint: + token_endpoint = self._discover()["token_endpoint"] + token_endpoint = self._validate_xai_endpoint(token_endpoint) + refresh_token = auth_data.get("refresh_token") + if not refresh_token: + raise XAIOAuthLoginRequiredError( + "xAI OAuth refresh token missing. Run `litellm xai-oauth login`." + ) + + token_payload = self._exchange_token( + token_endpoint, + { + "grant_type": "refresh_token", + "refresh_token": refresh_token, + "client_id": XAI_OAUTH_CLIENT_ID, + }, + ) + refreshed = self._build_auth_record( + token_payload, + token_endpoint, + fallback_refresh_token=refresh_token, + ) + self._write_auth_file(refreshed) + return refreshed + + +def should_use_xai_oauth(litellm_params: Optional[Dict[str, Any]]) -> bool: + return bool((litellm_params or {}).get("use_xai_oauth")) diff --git a/litellm/llms/xai/responses/transformation.py b/litellm/llms/xai/responses/transformation.py index 55805ddaede..f81e860a8ce 100644 --- a/litellm/llms/xai/responses/transformation.py +++ b/litellm/llms/xai/responses/transformation.py @@ -3,6 +3,7 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union import litellm from litellm._logging import verbose_logger from litellm.constants import XAI_API_BASE +from litellm.exceptions import AuthenticationError from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig from litellm.llms.xai.common_utils import XAIModelInfo from litellm.secret_managers.main import get_secret_str @@ -220,10 +221,27 @@ class XAIResponsesAPIConfig(OpenAIResponsesAPIConfig): litellm_params.api_key, legacy_generic_before_env=True ) + if not api_key: + from litellm.llms.xai.oauth import ( + XAIOAuthAuthenticator, + XAIOAuthError, + should_use_xai_oauth, + ) + + if should_use_xai_oauth(litellm_params.model_dump()): + try: + api_key = XAIOAuthAuthenticator().get_access_token() + except XAIOAuthError as exc: + raise AuthenticationError( + model=model, + llm_provider=self.custom_llm_provider.value, + message=str(exc), + ) from exc + if not api_key: raise ValueError( "XAI API key is required. Set api_key, litellm.xai_key, " - "litellm.api_key, or XAI_API_KEY." + "litellm.api_key, XAI_API_KEY, or use_xai_oauth=True." ) headers.update( @@ -244,12 +262,20 @@ class XAIResponsesAPIConfig(OpenAIResponsesAPIConfig): Returns: str: The full URL for the XAI /responses endpoint """ - api_base = ( - api_base - or litellm.api_base - or get_secret_str("XAI_API_BASE") - or XAI_API_BASE + from litellm.llms.xai.oauth import XAIOAuthAuthenticator, should_use_xai_oauth + + api_key = XAIModelInfo.get_api_key( + litellm_params.get("api_key"), legacy_generic_before_env=True ) + if should_use_xai_oauth(litellm_params) and not api_key: + api_base = XAIOAuthAuthenticator().get_api_base() + else: + api_base = ( + api_base + or litellm.api_base + or get_secret_str("XAI_API_BASE") + or XAI_API_BASE + ) # Remove trailing slashes api_base = api_base.rstrip("/") diff --git a/litellm/llms/you_com/search/transformation.py b/litellm/llms/you_com/search/transformation.py index 3c94b991735..0c7916e4c05 100644 --- a/litellm/llms/you_com/search/transformation.py +++ b/litellm/llms/you_com/search/transformation.py @@ -64,7 +64,13 @@ class YouComSearchConfig(BaseSearchConfig): endpoint with the `X-API-Key` header. Otherwise fall through to the keyless free tier; no auth header is required. """ - api_key = api_key or get_secret_str("YOUCOM_API_KEY") + api_key = self.resolve_server_api_key( + caller_api_key=api_key, + caller_api_base=api_base, + key_env_vars=("YOUCOM_API_KEY",), + base_env_var="YOUCOM_API_BASE", + default_api_base=self.YOU_COM_API_BASE, + ) headers["Content-Type"] = "application/json" # Pin Accept-Encoding to identity: the keyless `api.you.com/v1/agents/search` # endpoint advertises gzip content-encoding but returns body bytes the diff --git a/litellm/main.py b/litellm/main.py index 1a0d0312d73..c3d7ca28c49 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -81,11 +81,18 @@ from litellm.constants import ( from litellm.exceptions import LiteLLMUnknownProvider from litellm.integrations.custom_logger import CustomLogger from litellm.litellm_core_utils.asyncify import run_async_function +from litellm.litellm_core_utils.chat_completion_agentic_loop import ( + maybe_run_chat_completion_agentic_loop, +) from litellm.litellm_core_utils.audio_utils.utils import ( calculate_request_duration, get_audio_file_for_health_check, ) from litellm.litellm_core_utils.completion_timeout import CompletionTimeout +from litellm.litellm_core_utils.request_timeout_resolver import ( + get_configured_request_timeout, +) +from litellm.litellm_core_utils.get_litellm_params import OPTIONAL_KWARGS_KEYS from litellm.litellm_core_utils.dd_tracing import tracer from litellm.litellm_core_utils.get_provider_specific_headers import ( ProviderSpecificHeaderUtils, @@ -117,6 +124,10 @@ from litellm.llms.vertex_ai.common_utils import ( ) from litellm.realtime_api.main import _realtime_health_check from litellm.secret_managers.main import get_secret_bool, get_secret_str +from litellm.types.completion import ( + _CompletionDispatchContext, + _CompletionDispatchResult, +) from litellm.types.router import GenericLiteLLMParams from litellm.types.utils import ( CustomPricingLiteLLMParams, @@ -391,7 +402,7 @@ class AsyncCompletions: @tracer.wrap() @client -async def acompletion( # noqa: PLR0915 +async def acompletion( model: str, # Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create messages: List = [], @@ -649,6 +660,39 @@ async def acompletion( # noqa: PLR0915 response_object=response, model_response_object=litellm.ModelResponse(), ) + # Provider-agnostic dispatch point for the chat-completions agentic loop + # (code-interpreter interception, etc). Chat routing forks per provider + # before this (OpenAI goes through the OpenAI SDK in openai.py, others + # through the shared httpx handler), so a dispatch inside any single + # provider handler would miss the others. Here is where every fork + # reconverges, so the loop runs once for all providers. Responses needs + # no equivalent: every provider already funnels through one shared + # handler where the loop is dispatched. + if isinstance(response, litellm.ModelResponse): + looped = await maybe_run_chat_completion_agentic_loop( + response=response, + model=model, + messages=messages, + optional_params={ + k: v + for k, v in completion_kwargs.items() + if v is not None + and k + not in ( + "model", + "messages", + "stream", + "acompletion", + "deployment_id", + ) + }, + kwargs=kwargs, + logging_obj=kwargs.get("litellm_logging_obj"), + custom_llm_provider=custom_llm_provider, + stream=bool(stream), + ) + if looped is not None: + response = looped if isinstance(response, CustomStreamWrapper): response.set_logging_event_loop( loop=loop @@ -1083,9 +1127,3828 @@ def _build_custom_pricing_entry( return entry +def _complete_azure(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + _azure_detection_model = ctx._azure_detection_model + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + api_version = ctx.api_version + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + extra_headers = ctx.extra_headers + headers = ctx.headers + litellm_params = ctx.litellm_params + logger_fn = ctx.logger_fn + logging = ctx.logging + max_retries = ctx.max_retries + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + timeout = ctx.timeout + + dynamic_params = False + if client is not None and ( + isinstance(client, openai.AzureOpenAI) + or isinstance(client, openai.AsyncAzureOpenAI) + ): + dynamic_params = _check_dynamic_azure_params( + azure_client_params={"api_version": api_version}, + azure_client=client, + ) + + api_type = get_secret("AZURE_API_TYPE") or "azure" + + api_base = api_base or litellm.api_base or get_secret("AZURE_API_BASE") + + api_version = ( + api_version + or litellm.api_version + or get_secret_str("AZURE_API_VERSION") + or litellm.AZURE_DEFAULT_API_VERSION + ) + + api_key = ( + api_key + or litellm.api_key + or litellm.azure_key + or get_secret_str("AZURE_OPENAI_API_KEY") + or get_secret_str("AZURE_API_KEY") + ) + + azure_ad_token = optional_params.get("extra_body", {}).pop( + "azure_ad_token", None + ) or get_secret_str("AZURE_AD_TOKEN") + + azure_ad_token_provider = litellm_params.get("azure_ad_token_provider", None) + + headers = headers or litellm.headers + + if extra_headers is not None: + optional_params["extra_headers"] = extra_headers + if max_retries is not None: + optional_params["max_retries"] = max_retries + + if litellm.AzureOpenAIO1Config().is_o_series_model(model=_azure_detection_model): + ## LOAD CONFIG - if set + config = litellm.AzureOpenAIO1Config.get_config() + for k, v in config.items(): + if ( + k not in optional_params + ): # completion(top_k=3) > azure_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + response = azure_o1_chat_completions.completion( + model=model, + messages=messages, + headers=headers, + api_key=api_key, + api_base=api_base, + api_version=api_version, + dynamic_params=dynamic_params, + azure_ad_token=azure_ad_token, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + logging_obj=logging, + acompletion=acompletion, + timeout=timeout, # type: ignore + client=client, # pass AsyncAzureOpenAI, AzureOpenAI client + custom_llm_provider=custom_llm_provider, + ) + else: + ## LOAD CONFIG - if set + config = litellm.AzureOpenAIConfig.get_config() + for k, v in config.items(): + if ( + k not in optional_params + ): # completion(top_k=3) > azure_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + ## COMPLETION CALL + response = azure_chat_completions.completion( + model=model, + messages=messages, + headers=headers, + api_key=api_key, + api_base=api_base, + api_version=api_version, + api_type=api_type, + dynamic_params=dynamic_params, + azure_ad_token=azure_ad_token, + azure_ad_token_provider=azure_ad_token_provider, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + logging_obj=logging, + acompletion=acompletion, + timeout=timeout, # type: ignore + client=client, # pass AsyncAzureOpenAI, AzureOpenAI client + ) + + if optional_params.get("stream", False): + ## LOGGING + logging.post_call( + input=messages, + api_key=api_key, + original_response=response, + additional_args={ + "headers": headers, + "api_version": api_version, + "api_base": api_base, + }, + ) + + return response # pyright: ignore[reportReturnType] # provider SDK return type is broader than the dispatch contract + + +def _complete_azure_text(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + api_version = ctx.api_version + client = ctx.client + extra_headers = ctx.extra_headers + headers = ctx.headers + litellm_params = ctx.litellm_params + logger_fn = ctx.logger_fn + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + timeout = ctx.timeout + + api_type = get_secret_str("AZURE_API_TYPE") or "azure" + + api_base = api_base or litellm.api_base or get_secret_str("AZURE_API_BASE") + + if api_base is None: + raise ValueError( + "api_base is required for Azure OpenAI LLM provider. Either set it dynamically or set the AZURE_API_BASE environment variable." + ) + + api_version = ( + api_version or litellm.api_version or get_secret_str("AZURE_API_VERSION") + ) + + api_key = ( + api_key + or litellm.api_key + or litellm.azure_key + or get_secret_str("AZURE_OPENAI_API_KEY") + or get_secret_str("AZURE_API_KEY") + ) + + azure_ad_token = optional_params.get("extra_body", {}).pop( + "azure_ad_token", None + ) or get_secret_str("AZURE_AD_TOKEN") + + azure_ad_token_provider = litellm_params.get("azure_ad_token_provider", None) + + headers = headers or litellm.headers + + if extra_headers is not None: + optional_params["extra_headers"] = extra_headers + + ## LOAD CONFIG - if set + config = litellm.AzureOpenAIConfig.get_config() + for k, v in config.items(): + if ( + k not in optional_params + ): # completion(top_k=3) > azure_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + ## COMPLETION CALL + response = azure_text_completions.completion( + model=model, + messages=messages, + headers=headers, + api_key=api_key, + api_base=api_base, + api_version=cast(str, api_version), + api_type=api_type, + azure_ad_token=azure_ad_token, + azure_ad_token_provider=azure_ad_token_provider, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + logging_obj=logging, + acompletion=acompletion, + timeout=timeout, + client=client, # pass AsyncAzureOpenAI, AzureOpenAI client + ) + + if optional_params.get("stream", False) or acompletion is True: + ## LOGGING + logging.post_call( + input=messages, + api_key=api_key, + original_response=response, + additional_args={ + "headers": headers, + "api_version": api_version, + "api_base": api_base, + }, + ) + + return response + + +def _complete_deepseek(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + provider_config = ctx.provider_config + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + try: + response = base_llm_http_handler.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + timeout=timeout, # type: ignore + client=client, + custom_llm_provider=custom_llm_provider, + encoding=_get_encoding(), + stream=stream, + provider_config=provider_config, + ) + except Exception as e: + ## LOGGING - log the original exception returned + logging.post_call( + input=messages, + api_key=api_key, + original_response=str(e), + additional_args={"headers": headers}, + ) + raise e + + return response + + +def _complete_azure_ai(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + extra_headers = ctx.extra_headers + headers = ctx.headers + litellm_params = ctx.litellm_params + logger_fn = ctx.logger_fn + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + from litellm.llms.azure_ai.common_utils import AzureFoundryModelInfo + + azure_ai_route = AzureFoundryModelInfo.get_azure_ai_route(model) + + # Check if this is an agents route - model format: azure_ai/agents/ + if azure_ai_route == "agents": + from litellm.llms.azure_ai.agents import AzureAIAgentsConfig + + api_base = AzureFoundryModelInfo.get_api_base(api_base) + if api_base is None: + raise ValueError( + "Azure AI Agents requests require an api_base. " + "Set `api_base` or the AZURE_AI_API_BASE env var." + ) + api_key = AzureFoundryModelInfo.get_api_key(api_key) + + response = AzureAIAgentsConfig.completion( + model=model, + messages=messages, + api_base=api_base, + api_key=api_key, + model_response=model_response, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + timeout=timeout, + acompletion=acompletion, + stream=stream, + headers=headers or litellm.headers, + ) + + # Check if this is a Claude model - route to Azure Anthropic handler + elif "claude" in model.lower(): + # Use Azure Anthropic handler for Claude models + api_base = AzureFoundryModelInfo.get_api_base(api_base) + if api_base is None: + raise ValueError( + "Azure Anthropic requests require an api_base. " + "Set `api_base` or the AZURE_AI_API_BASE env var." + ) + api_key = AzureFoundryModelInfo.get_api_key(api_key) + + # Ensure the URL ends with /v1/messages for Anthropic + if api_base: + api_base = api_base.rstrip("/") + if not api_base.endswith("/v1/messages"): + if "/anthropic" in api_base: + parts = api_base.split("/anthropic", 1) + api_base = parts[0] + "/anthropic" + else: + api_base = api_base + "/anthropic" + api_base = api_base + "/v1/messages" + + response = azure_anthropic_chat_completions.completion( + model=model, + messages=messages, + api_base=api_base, + acompletion=acompletion, + custom_prompt_dict=litellm.custom_prompt_dict, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=_get_encoding(), + api_key=api_key, + logging_obj=logging, + headers=headers, + timeout=timeout, + client=client, + custom_llm_provider=custom_llm_provider, + ) + if optional_params.get("stream", False) or acompletion is True: + ## LOGGING + logging.post_call( + input=messages, + api_key=api_key, + original_response=response, + ) + response = response + else: + # Non-Claude models use standard Azure AI flow + api_base = AzureFoundryModelInfo.get_api_base(api_base) + # set API KEY + api_key = AzureFoundryModelInfo.get_api_key(api_key) + + headers = headers or litellm.headers + + if extra_headers is not None: + optional_params["extra_headers"] = extra_headers + + ## FOR COHERE + if "command-r" in model: # make sure tool call in messages are str + messages = stringify_json_tool_call_content(messages=messages) + + ## COMPLETION CALL + try: + response = base_llm_http_handler.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + timeout=timeout, # type: ignore + client=client, # pass AsyncOpenAI, OpenAI client + custom_llm_provider=custom_llm_provider, + encoding=_get_encoding(), + stream=stream, + ) + except Exception as e: + ## LOGGING - log the original exception returned + logging.post_call( + input=messages, + api_key=api_key, + original_response=str(e), + additional_args={"headers": headers}, + ) + raise e + + if optional_params.get("stream", False): + ## LOGGING + logging.post_call( + input=messages, + api_key=api_key, + original_response=response, + additional_args={"headers": headers}, + ) + + return response + + +def _complete_text_completion_openai( + ctx: _CompletionDispatchContext, +) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logger_fn = ctx.logger_fn + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + text_completion = ctx.text_completion + timeout = ctx.timeout + + openai.api_type = "openai" + + api_base = ( + api_base + or litellm.api_base + or get_secret("OPENAI_BASE_URL") + or get_secret("OPENAI_API_BASE") + or "https://api.openai.com/v1" + ) + + openai.api_version = None + # set API KEY + + api_key = ( + api_key or litellm.api_key or litellm.openai_key or get_secret("OPENAI_API_KEY") + ) + + headers = headers or litellm.headers + + ## LOAD CONFIG - if set + config = litellm.OpenAITextCompletionConfig.get_config() + for k, v in config.items(): + if ( + k not in optional_params + ): # completion(top_k=3) > openai_text_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + if litellm.organization: + openai.organization = litellm.organization + + ## COMPLETION CALL + _response = openai_text_completions.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + print_verbose=print_verbose, + api_key=api_key, + custom_llm_provider=custom_llm_provider, + api_base=api_base, + acompletion=acompletion, + client=client, # pass AsyncOpenAI, OpenAI client + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + timeout=timeout, # type: ignore + ) + + if ( + optional_params.get("stream", False) is False + and acompletion is False + and text_completion is False + ): + # convert to chat completion response + _response = ( + litellm.OpenAITextCompletionConfig().convert_to_chat_model_response_object( + response_object=_response, model_response_object=model_response + ) + ) + + if optional_params.get("stream", False) or acompletion is True: + ## LOGGING + logging.post_call( + input=messages, + api_key=api_key, + original_response=_response, + additional_args={"headers": headers}, + ) + return _response # pyright: ignore[reportReturnType] # provider SDK return type is broader than the dispatch contract + + +def _complete_fireworks_ai( + ctx: _CompletionDispatchContext, +) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + provider_config = ctx.provider_config + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + try: + response = base_llm_http_handler.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + timeout=timeout, # type: ignore + client=client, + custom_llm_provider=custom_llm_provider, + encoding=_get_encoding(), + stream=stream, + provider_config=provider_config, + ) + except Exception as e: + ## LOGGING - log the original exception returned + logging.post_call( + input=messages, + api_key=api_key, + original_response=str(e), + additional_args={"headers": headers}, + ) + raise e + + return response + + +def _complete_heroku(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + provider_config = ctx.provider_config + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + try: + response = base_llm_http_handler.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + timeout=timeout, + client=client, + custom_llm_provider=custom_llm_provider, + encoding=_get_encoding(), + stream=stream, + provider_config=provider_config, + ) + except Exception as e: + logging.post_call( + input=messages, + api_key=api_key, + original_response=str(e), + additional_args={"headers": headers}, + ) + raise e + + return response + + +def _complete_ragflow(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + provider_config = ctx.provider_config + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + try: + response = base_llm_http_handler.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + timeout=timeout, + client=client, + custom_llm_provider=custom_llm_provider, + encoding=_get_encoding(), + stream=stream, + provider_config=provider_config, + ) + except Exception as e: + logging.post_call( + input=messages, + api_key=api_key, + original_response=str(e), + additional_args={"headers": headers}, + ) + raise e + + return response + + +def _complete_xai(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + provider_config = ctx.provider_config + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + try: + response = base_llm_http_handler.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + timeout=timeout, # type: ignore + client=client, + custom_llm_provider=custom_llm_provider, + encoding=_get_encoding(), + stream=stream, + provider_config=provider_config, + ) + except Exception as e: + ## LOGGING - log the original exception returned + logging.post_call( + input=messages, + api_key=api_key, + original_response=str(e), + additional_args={"headers": headers}, + ) + raise e + + return response + + +def _complete_groq(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + api_base = ( + api_base # for deepinfra/perplexity/anyscale/groq/friendliai we check in get_llm_provider and pass in the api base from there + or litellm.api_base + or get_secret("GROQ_API_BASE") + or "https://api.groq.com/openai/v1" + ) + + # set API KEY + api_key = ( + api_key + or litellm.api_key # for deepinfra/perplexity/anyscale/friendliai we check in get_llm_provider and pass in the api key from there + or litellm.groq_key + or get_secret("GROQ_API_KEY") + ) + + headers = headers or litellm.headers + + ## LOAD CONFIG - if set + config = litellm.GroqChatConfig.get_config() + for k, v in config.items(): + if ( + k not in optional_params + ): # completion(top_k=3) > openai_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + return base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + custom_llm_provider=custom_llm_provider, + timeout=timeout, + headers=headers, + encoding=_get_encoding(), + api_key=api_key, + logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements + client=client, + ) + + +def _complete_bedrock_mantle( + ctx: _CompletionDispatchContext, +) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + api_base = api_base or litellm.api_base or get_secret("BEDROCK_MANTLE_API_BASE") + api_key = api_key or litellm.api_key or get_secret("BEDROCK_MANTLE_API_KEY") + headers = headers or litellm.headers + config = litellm.BedrockMantleChatConfig.get_config() + for k, v in config.items(): + if k not in optional_params: + optional_params[k] = v + return base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + custom_llm_provider=custom_llm_provider, + timeout=timeout, + headers=headers, + encoding=_get_encoding(), + api_key=api_key, + logging_obj=logging, + client=client, + ) + + +def _complete_a2a(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + provider_config = ctx.provider_config + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + ( + api_base, + api_key, + headers, + ) = litellm.A2AConfig.resolve_agent_config_from_registry( + model=model, + api_base=api_base, + api_key=api_key, + headers=headers, + optional_params=optional_params, + ) + + # Fall back to environment variables and defaults + api_base = api_base or litellm.api_base or get_secret_str("A2A_API_BASE") + + if api_base is None: + raise Exception( + "api_base is required for A2A provider. " + "Either provide api_base parameter, set A2A_API_BASE environment variable, " + "or register the agent in the proxy with model='a2a/'." + ) + + headers = headers or litellm.headers + + return base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + custom_llm_provider=custom_llm_provider, + timeout=timeout, + headers=headers, + encoding=_get_encoding(), + api_key=api_key, + logging_obj=logging, + client=client, + provider_config=provider_config, + ) + + +def _complete_gigachat(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + provider_config = ctx.provider_config + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + api_key = ( + api_key + or litellm.api_key + or litellm.gigachat_key + or get_secret("GIGACHAT_API_KEY") + or get_secret("GIGACHAT_CREDENTIALS") + ) + + headers = headers or litellm.headers or {} + + ## COMPLETION CALL + try: + response = base_llm_http_handler.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + timeout=timeout, + client=client, + custom_llm_provider=custom_llm_provider, + encoding=_get_encoding(), + stream=stream, + provider_config=provider_config, + ) + except Exception as e: + ## LOGGING - log the original exception returned + logging.post_call( + input=messages, + api_key=api_key, + original_response=str(e), + additional_args={"headers": headers}, + ) + raise e + + return response + + +def _complete_sap(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + headers = headers or litellm.headers + ## LOAD CONFIG - if set + config = litellm.GenAIHubOrchestrationConfig.get_config() + for k, v in config.items(): + if ( + k not in optional_params + ): # completion(top_k=3) > openai_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + return sap_gen_ai_hub_chat_completions.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + timeout=timeout, # type: ignore + shared_session=shared_session, + client=client, + custom_llm_provider=custom_llm_provider, + encoding=_get_encoding(), + api_key=api_key, + api_base=api_base, + stream=stream, + ) + + +def _complete_aiohttp_openai( + ctx: _CompletionDispatchContext, +) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + extra_headers = ctx.extra_headers + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + stream = ctx.stream + timeout = ctx.timeout + + api_base = ( + api_base # for deepinfra/perplexity/anyscale/groq/friendliai we check in get_llm_provider and pass in the api base from there + or litellm.api_base + or get_secret("OPENAI_BASE_URL") + or get_secret("OPENAI_API_BASE") + or "https://api.openai.com/v1" + ) + # set API KEY + api_key = ( + api_key + or litellm.api_key # for deepinfra/perplexity/anyscale/friendliai we check in get_llm_provider and pass in the api key from there + or litellm.openai_key + or get_secret("OPENAI_API_KEY") + ) + + headers = headers or litellm.headers + + if extra_headers is not None: + optional_params["extra_headers"] = extra_headers + return base_llm_aiohttp_handler.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + timeout=timeout, + client=client, + custom_llm_provider=custom_llm_provider, + encoding=_get_encoding(), + stream=stream, + ) + + +def _complete_cometapi(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + provider_config = ctx.provider_config + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + api_key = ( + api_key + or litellm.cometapi_key + or get_secret_str("COMETAPI_KEY") + or litellm.api_key + ) + + api_base = ( + api_base + or litellm.api_base + or get_secret_str("COMETAPI_API_BASE") + or "https://api.cometapi.com/v1" + ) + + ## COMPLETION CALL + response = base_llm_http_handler.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + timeout=timeout, + client=client, + custom_llm_provider=custom_llm_provider, + encoding=_get_encoding(), + stream=stream, + provider_config=provider_config, + ) + + ## LOGGING + logging.post_call(input=messages, api_key=api_key, original_response=response) + + return response + + +def _complete_minimax(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + provider_config = ctx.provider_config + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + api_key = api_key or get_secret_str("MINIMAX_API_KEY") or litellm.api_key + + api_base = ( + api_base + or litellm.api_base + or get_secret_str("MINIMAX_API_BASE") + or "https://api.minimax.io/v1" + ) + + response = base_llm_http_handler.completion( + model=model, + messages=messages, + api_base=api_base, + custom_llm_provider=custom_llm_provider, + model_response=model_response, + encoding=_get_encoding(), + logging_obj=logging, + optional_params=optional_params, + timeout=timeout, + litellm_params=litellm_params, + shared_session=shared_session, + acompletion=acompletion, + stream=stream, + api_key=api_key, + headers=headers, + client=client, + provider_config=provider_config, + ) + logging.post_call(input=messages, api_key=api_key, original_response=response) + + return response + + +def _complete_hosted_vllm(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + provider_config = ctx.provider_config + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + api_base = api_base or litellm.api_base or get_secret_str("HOSTED_VLLM_API_BASE") + + response = base_llm_http_handler.completion( + model=model, + messages=messages, + api_base=api_base, + custom_llm_provider=custom_llm_provider, + model_response=model_response, + encoding=_get_encoding(), + logging_obj=logging, + optional_params=optional_params, + timeout=timeout, + litellm_params=litellm_params, + shared_session=shared_session, + acompletion=acompletion, + stream=stream, + api_key=api_key, + headers=headers, + client=client, + provider_config=provider_config, + ) + logging.post_call(input=messages, api_key=api_key, original_response=response) + + return response + + +def _complete_custom_openai( + ctx: _CompletionDispatchContext, +) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + custom_prompt_dict = ctx.custom_prompt_dict + extra_headers = ctx.extra_headers + headers = ctx.headers + litellm_params = ctx.litellm_params + logger_fn = ctx.logger_fn + logging = ctx.logging + messages = ctx.messages + metadata = ctx.metadata + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + organization = ctx.organization + provider_config = ctx.provider_config + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + api_base = ( + api_base # for deepinfra/perplexity/anyscale/groq/friendliai we check in get_llm_provider and pass in the api base from there + or litellm.api_base + or get_secret("OPENAI_BASE_URL") + or get_secret("OPENAI_API_BASE") + or "https://api.openai.com/v1" + ) + organization = ( + organization + or litellm.organization + or get_secret("OPENAI_ORGANIZATION") + or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105 + ) + openai.organization = organization + # set API KEY + api_key = ( + api_key + or litellm.api_key # for deepinfra/perplexity/anyscale/friendliai we check in get_llm_provider and pass in the api key from there + or litellm.openai_key + or get_secret("OPENAI_API_KEY") + ) + + headers = headers or litellm.headers + + # Add GitHub Copilot headers (same as /responses endpoint does) + if custom_llm_provider == "github_copilot": + from litellm.llms.github_copilot.authenticator import Authenticator + from litellm.llms.github_copilot.common_utils import ( + get_copilot_default_headers, + ) + + copilot_auth = Authenticator() + copilot_api_key = copilot_auth.get_api_key() + copilot_headers = get_copilot_default_headers(copilot_api_key) + if extra_headers: + copilot_headers.update(extra_headers) + extra_headers = copilot_headers + + if extra_headers is not None: + optional_params["extra_headers"] = extra_headers + + if ( + litellm.enable_preview_features and metadata is not None + ): # [PREVIEW] allow metadata to be passed to OPENAI + openai_metadata = get_requester_metadata(metadata) + if openai_metadata is not None: + optional_params["metadata"] = openai_metadata + + ## LOAD CONFIG - if set + config = litellm.OpenAIConfig.get_config() + for k, v in config.items(): + if ( + k not in optional_params + ): # completion(top_k=3) > openai_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + ## COMPLETION CALL + use_base_llm_http_handler = get_secret_bool( + "EXPERIMENTAL_OPENAI_BASE_LLM_HTTP_HANDLER" + ) + + try: + if use_base_llm_http_handler: + response = base_llm_http_handler.completion( + model=model, + messages=messages, + api_base=api_base, + custom_llm_provider=custom_llm_provider, + model_response=model_response, + encoding=_get_encoding(), + logging_obj=logging, + optional_params=optional_params, + timeout=timeout, + litellm_params=litellm_params, + shared_session=shared_session, + acompletion=acompletion, + stream=stream, + api_key=api_key, + headers=headers, + client=client, + provider_config=provider_config, + ) + else: + response = openai_chat_completions.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + print_verbose=print_verbose, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + timeout=timeout, # type: ignore + custom_prompt_dict=custom_prompt_dict, + client=client, # pass AsyncOpenAI, OpenAI client + organization=organization, + custom_llm_provider=custom_llm_provider, + shared_session=shared_session, + ) + except Exception as e: + ## LOGGING - log the original exception returned + logging.post_call( + input=messages, + api_key=api_key, + original_response=str(e), + additional_args={"headers": headers}, + ) + raise e + + if optional_params.get("stream", False): + ## LOGGING + logging.post_call( + input=messages, + api_key=api_key, + original_response=response, + additional_args={"headers": headers}, + ) + + return response # pyright: ignore[reportReturnType] # provider SDK return type is broader than the dispatch contract + + +def _complete_mistral(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + provider_config = ctx.provider_config + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + api_key = api_key or litellm.api_key or get_secret("MISTRAL_API_KEY") + api_base = ( + api_base + or litellm.api_base + or get_secret("MISTRAL_API_BASE") + or "https://api.mistral.ai/v1" + ) + + return base_llm_http_handler.completion( + model=model, + messages=messages, + api_base=api_base, + custom_llm_provider=custom_llm_provider, + model_response=model_response, + encoding=_get_encoding(), + logging_obj=logging, + optional_params=optional_params, + timeout=timeout, + litellm_params=litellm_params, + shared_session=shared_session, + acompletion=acompletion, + stream=stream, + api_key=api_key, + headers=headers, + client=client, + provider_config=provider_config, + ) + + +def _complete_replicate(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + custom_prompt_dict = ctx.custom_prompt_dict + headers = ctx.headers + litellm_params = ctx.litellm_params + logger_fn = ctx.logger_fn + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + + replicate_key = ( + api_key + or litellm.replicate_key + or litellm.api_key + or get_secret("REPLICATE_API_KEY") + or get_secret("REPLICATE_API_TOKEN") + ) + + api_base = ( + api_base + or litellm.api_base + or get_secret("REPLICATE_API_BASE") + or "https://api.replicate.com/v1" + ) + + custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict + + model_response = replicate_chat_completion( # type: ignore + model=model, + messages=messages, + api_base=api_base, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=_get_encoding(), # for calculating input/output tokens + api_key=replicate_key, + logging_obj=logging, + custom_prompt_dict=custom_prompt_dict, + acompletion=acompletion, + headers=headers, + ) + + if optional_params.get("stream", False) is True: + ## LOGGING + logging.post_call( + input=messages, + api_key=replicate_key, + original_response=model_response, + ) + + return model_response + + +def _complete_anthropic_text( + ctx: _CompletionDispatchContext, +) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + custom_prompt_dict = ctx.custom_prompt_dict + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + api_key = ( + api_key + or litellm.anthropic_key + or litellm.api_key + or os.environ.get("ANTHROPIC_API_KEY") + ) + custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict + api_base = cast( + Optional[str], + api_base + or litellm.api_base + or get_secret("ANTHROPIC_API_BASE") + or get_secret("ANTHROPIC_BASE_URL") + or "https://api.anthropic.com/v1/complete", + ) + + # Check if we should disable automatic URL suffix appending + disable_url_suffix = get_secret_bool("LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX") + if ( + api_base is not None + and not disable_url_suffix + and not api_base.endswith("/v1/complete") + ): + api_base += "/v1/complete" + elif disable_url_suffix: + verbose_logger.debug( + "LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX is set, skipping /v1/complete suffix" + ) + + return base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + custom_llm_provider="anthropic_text", + timeout=timeout, + headers=headers, + encoding=_get_encoding(), + api_key=api_key, + logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements + ) + + +def _complete_anthropic(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + custom_prompt_dict = ctx.custom_prompt_dict + headers = ctx.headers + litellm_params = ctx.litellm_params + logger_fn = ctx.logger_fn + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + timeout = ctx.timeout + + api_key = ( + api_key + or litellm.anthropic_key + or litellm.api_key + or os.environ.get("ANTHROPIC_API_KEY") + ) + custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict + # call /messages + # default route for all anthropic models + api_base = cast( + Optional[str], + api_base + or litellm.api_base + or get_secret("ANTHROPIC_API_BASE") + or get_secret("ANTHROPIC_BASE_URL") + or "https://api.anthropic.com/v1/messages", + ) + + # Check if we should disable automatic URL suffix appending + disable_url_suffix = get_secret_bool("LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX") + if ( + api_base is not None + and not disable_url_suffix + and not api_base.endswith("/v1/messages") + ): + api_base += "/v1/messages" + elif disable_url_suffix: + verbose_logger.debug( + "LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX is set, skipping /v1/messages suffix" + ) + + response = anthropic_chat_completions.completion( + model=model, + messages=messages, + api_base=api_base, + acompletion=acompletion, + custom_prompt_dict=litellm.custom_prompt_dict, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=_get_encoding(), # for calculating input/output tokens + api_key=api_key, + logging_obj=logging, + headers=headers, + timeout=timeout, + client=client, + custom_llm_provider=custom_llm_provider, + ) + if optional_params.get("stream", False) or acompletion is True: + ## LOGGING + logging.post_call( + input=messages, + api_key=api_key, + original_response=response, + ) + return response + + +def _complete_nlp_cloud(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + litellm_params = ctx.litellm_params + logger_fn = ctx.logger_fn + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + + nlp_cloud_key = ( + api_key + or litellm.nlp_cloud_key + or get_secret("NLP_CLOUD_API_KEY") + or litellm.api_key + ) + + api_base = ( + api_base + or litellm.api_base + or get_secret("NLP_CLOUD_API_BASE") + or "https://api.nlpcloud.io/v1/gpu/" + ) + + response = nlp_cloud_chat_completion( + model=model, + messages=messages, + api_base=api_base, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=_get_encoding(), + api_key=nlp_cloud_key, + logging_obj=logging, + ) + + if "stream" in optional_params and optional_params["stream"] is True: + # don't try to access stream object, + response = CustomStreamWrapper( + response, + model, + custom_llm_provider="nlp_cloud", + logging_obj=logging, + ) + + if optional_params.get("stream", False) or acompletion is True: + ## LOGGING + logging.post_call( + input=messages, + api_key=api_key, + original_response=response, + ) + + return response # pyright: ignore[reportReturnType] # provider SDK return type is broader than the dispatch contract + + +def _complete_aleph_alpha(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + api_base = ctx.api_base + api_key = ctx.api_key + litellm_params = ctx.litellm_params + logger_fn = ctx.logger_fn + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + + aleph_alpha_key = ( + api_key + or litellm.aleph_alpha_key + or get_secret("ALEPH_ALPHA_API_KEY") + or get_secret("ALEPHALPHA_API_KEY") + or litellm.api_key + ) + + api_base = ( + api_base + or litellm.api_base + or get_secret("ALEPH_ALPHA_API_BASE") + or "https://api.aleph-alpha.com/complete" + ) + + model_response = aleph_alpha.completion( + model=model, + messages=messages, + api_base=api_base, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=_get_encoding(), + default_max_tokens_to_sample=litellm.max_tokens, + api_key=aleph_alpha_key, + logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements + ) + + if "stream" in optional_params and optional_params["stream"] is True: + # don't try to access stream object, + return CustomStreamWrapper( + model_response, + model, + custom_llm_provider="aleph_alpha", + logging_obj=logging, + ) + return model_response # pyright: ignore[reportReturnType] # provider SDK return type is broader than the dispatch contract + + +def _complete_cohere_chat(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + extra_headers = ctx.extra_headers + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + provider_config = ctx.provider_config + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + cohere_key = ( + api_key + or litellm.cohere_key + or get_secret_str("COHERE_API_KEY") + or get_secret_str("CO_API_KEY") + or litellm.api_key + ) + + cohere_route = CohereModelInfo.get_cohere_route(model) + verbose_logger.debug(f"Cohere route: {cohere_route}") + # Set API base based on route + if cohere_route == "v2": + api_base = ( + api_base + or litellm.api_base + or get_secret_str("COHERE_API_BASE") + or "https://api.cohere.com/v2/chat" + ) + # Remove v2/ prefix from model name for the actual API call + if "v2/" in model: + model = model.replace("v2/", "") + else: + api_base = ( + api_base + or litellm.api_base + or get_secret_str("COHERE_API_BASE") + or "https://api.cohere.ai/v1/chat" + ) + + headers = headers or litellm.headers or {} + if headers is None: + headers = {} + + if extra_headers is not None: + headers.update(extra_headers) + + verbose_logger.debug(f"Model: {model}, API Base: {api_base}") + verbose_logger.debug(f"Provider Config: {provider_config}") + return base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + custom_llm_provider="cohere_chat", + timeout=timeout, + headers=headers, + encoding=_get_encoding(), + api_key=cohere_key, + provider_config=provider_config, + logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements + ) + + +def _complete_maritalk(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + api_base = ctx.api_base + api_key = ctx.api_key + custom_prompt_dict = ctx.custom_prompt_dict + litellm_params = ctx.litellm_params + logger_fn = ctx.logger_fn + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + + maritalk_key = ( + api_key + or litellm.maritalk_key + or get_secret("MARITALK_API_KEY") + or litellm.api_key + ) + + api_base = ( + api_base + or litellm.api_base + or get_secret("MARITALK_API_BASE") + or "https://chat.maritaca.ai/api" + ) + + return openai_like_chat_completion.completion( + model=model, + messages=messages, + api_base=api_base, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=_get_encoding(), + api_key=maritalk_key, + logging_obj=logging, + custom_llm_provider="maritalk", + custom_prompt_dict=custom_prompt_dict, + ) + + +def _complete_amazon_nova(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + api_base = ctx.api_base + api_key = ctx.api_key + custom_llm_provider = ctx.custom_llm_provider + custom_prompt_dict = ctx.custom_prompt_dict + litellm_params = ctx.litellm_params + logger_fn = ctx.logger_fn + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + timeout = ctx.timeout + + api_key = ( + api_key + or litellm.amazon_nova_api_key + or get_secret_str("AMAZON_NOVA_API_KEY") + or litellm.api_key + ) + api_base = ( + api_base + or litellm.api_base + or get_secret_str("AMAZON_NOVA_API_BASE") + or "https://api.nova.amazon.com/v1" + ) + return openai_like_chat_completion.completion( + model=model, + messages=messages, + api_base=api_base, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=_get_encoding(), + api_key=api_key, + logging_obj=logging, + timeout=timeout, + custom_llm_provider=custom_llm_provider, + custom_prompt_dict=custom_prompt_dict, + ) + + +def _complete_huggingface(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + stream = ctx.stream + timeout = ctx.timeout + + huggingface_key = ( + api_key + or litellm.huggingface_key + or os.environ.get("HF_TOKEN") + or os.environ.get("HUGGINGFACE_API_KEY") + or litellm.api_key + ) + hf_headers = headers or litellm.headers + return base_llm_http_handler.completion( + model=model, + messages=messages, + headers=hf_headers, + model_response=model_response, + api_key=huggingface_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + timeout=timeout, # type: ignore + client=client, + custom_llm_provider=custom_llm_provider, + encoding=_get_encoding(), + stream=stream, + ) + + +def _complete_oci(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + stream = ctx.stream + timeout = ctx.timeout + + return base_llm_http_handler.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + timeout=timeout, # type: ignore + client=client, + custom_llm_provider=custom_llm_provider, + encoding=_get_encoding(), + stream=stream, + ) + + +def _complete_compactifai(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + provider_config = ctx.provider_config + stream = ctx.stream + timeout = ctx.timeout + + api_key = api_key or get_secret_str("COMPACTIFAI_API_KEY") or litellm.api_key + + api_base = api_base or "https://api.compactif.ai/v1" + + ## COMPLETION CALL + return base_llm_http_handler.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + timeout=timeout, + client=client, + custom_llm_provider=custom_llm_provider, + encoding=_get_encoding(), + stream=stream, + provider_config=provider_config, + ) + + +def _complete_oobabooga(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + api_base = ctx.api_base + litellm_params = ctx.litellm_params + logger_fn = ctx.logger_fn + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + + model_response = oobabooga.completion( + model=model, + messages=messages, + model_response=model_response, + api_base=api_base, # type: ignore + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + api_key=None, + logger_fn=logger_fn, + encoding=_get_encoding(), + logging_obj=logging, + ) + if "stream" in optional_params and optional_params["stream"] is True: + # don't try to access stream object, + return CustomStreamWrapper( + model_response, + model, + custom_llm_provider="oobabooga", + logging_obj=logging, + ) + return model_response # pyright: ignore[reportReturnType] # provider SDK return type is broader than the dispatch contract + + +def _complete_databricks(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + stream = ctx.stream + timeout = ctx.timeout + + api_base = ( + api_base # for databricks we check in get_llm_provider and pass in the api base from there + or litellm.api_base + or os.getenv("DATABRICKS_API_BASE") + ) + + # set API KEY + api_key = ( + api_key + or litellm.api_key # for databricks we check in get_llm_provider and pass in the api key from there + or litellm.databricks_key + or get_secret("DATABRICKS_API_KEY") + ) + + headers = headers or litellm.headers + + ## COMPLETION CALL + try: + response = base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + custom_llm_provider="databricks", + timeout=timeout, + headers=headers, + encoding=_get_encoding(), + api_key=api_key, + logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements + client=client, + ) + except Exception as e: + ## LOGGING - log the original exception returned + logging.post_call( + input=messages, + api_key=api_key, + original_response=str(e), + additional_args={"headers": headers}, + ) + raise e + + if optional_params.get("stream", False): + ## LOGGING + logging.post_call( + input=messages, + api_key=api_key, + original_response=response, + additional_args={"headers": headers}, + ) + + return response + + +def _complete_datarobot(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + provider_config = ctx.provider_config + stream = ctx.stream + timeout = ctx.timeout + + return base_llm_http_handler.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + timeout=timeout, # type: ignore + client=client, + custom_llm_provider=custom_llm_provider, + encoding=_get_encoding(), + stream=stream, + provider_config=provider_config, + ) + + +def _complete_openrouter(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + api_base = ( + api_base + or litellm.api_base + or get_secret_str("OPENROUTER_API_BASE") + or "https://openrouter.ai/api/v1" + ) + + api_key = ( + api_key + or litellm.api_key + or litellm.openrouter_key + or get_secret_str("OPENROUTER_API_KEY") + or get_secret_str("OR_API_KEY") + ) + + openrouter_site_url = get_secret("OR_SITE_URL") or "https://litellm.ai" + openrouter_app_name = get_secret("OR_APP_NAME") or "liteLLM" + + openrouter_headers = { + "HTTP-Referer": openrouter_site_url, + "X-Title": openrouter_app_name, + } + + _headers = headers or litellm.headers + if _headers: + openrouter_headers.update(_headers) + + headers = openrouter_headers + + ## Load Config + config = litellm.OpenrouterConfig.get_config() + for k, v in config.items(): + if k == "extra_body": + # we use openai 'extra_body' to pass openrouter specific params - transforms, route, models + if "extra_body" in optional_params: + optional_params[k].update(v) + else: + optional_params[k] = v + elif k not in optional_params: + optional_params[k] = v + + ## COMPLETION CALL + response = base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + custom_llm_provider="openrouter", + timeout=timeout, + headers=headers, + encoding=_get_encoding(), + api_key=api_key, + logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements + client=client, + ) + ## LOGGING + logging.post_call( + input=messages, api_key=openai.api_key, original_response=response + ) + + return response + + +def _complete_vercel_ai_gateway( + ctx: _CompletionDispatchContext, +) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + api_base = ( + api_base + or litellm.api_base + or get_secret_str("VERCEL_AI_GATEWAY_API_BASE") + or "https://ai-gateway.vercel.sh/v1" + ) + + api_key = api_key or litellm.api_key or get_secret("VERCEL_AI_GATEWAY_API_KEY") + + vercel_site_url = get_secret("VERCEL_SITE_URL") or "https://litellm.ai" + vercel_app_name = get_secret("VERCEL_APP_NAME") or "liteLLM" + + vercel_headers = { + "http-referer": vercel_site_url, + "x-title": vercel_app_name, + } + + _headers = headers or litellm.headers + if _headers: + vercel_headers.update(_headers) + + headers = vercel_headers + + ## Load Config + config = litellm.VercelAIGatewayConfig.get_config() + for k, v in config.items(): + if k == "extra_body": + # we use openai 'extra_body' to pass vercel specific params - providerOptions + if "extra_body" in optional_params: + optional_params[k].update(v) + else: + optional_params[k] = v + elif k not in optional_params: + optional_params[k] = v + + ## COMPLETION CALL + response = base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + custom_llm_provider="vercel_ai_gateway", + timeout=timeout, + headers=headers, + encoding=_get_encoding(), + api_key=api_key, + logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements + client=client, + ) + ## LOGGING + logging.post_call( + input=messages, api_key=openai.api_key, original_response=response + ) + + return response + + +def _complete_vertex_ai_beta( + ctx: _CompletionDispatchContext, +) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logger_fn = ctx.logger_fn + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + timeout = ctx.timeout + + vertex_ai_project = ( + optional_params.pop("vertex_project", None) + or optional_params.pop("vertex_ai_project", None) + or litellm.vertex_project + or get_secret("VERTEXAI_PROJECT") + ) + vertex_ai_location = ( + optional_params.pop("vertex_location", None) + or optional_params.pop("vertex_ai_location", None) + or litellm.vertex_location + or get_secret("VERTEXAI_LOCATION") + ) + vertex_credentials = ( + optional_params.pop("vertex_credentials", None) + or optional_params.pop("vertex_ai_credentials", None) + or get_secret("VERTEXAI_CREDENTIALS") + ) + + gemini_api_key = ( + api_key + or get_api_key_from_env() + or get_secret("PALM_API_KEY") # older palm api key should also work + or litellm.api_key + ) + + api_base = api_base or litellm.api_base or get_secret("GEMINI_API_BASE") + new_params = safe_deep_copy(optional_params or {}) + return vertex_chat_completion.completion( # type: ignore + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + optional_params=new_params, + litellm_params=litellm_params, # type: ignore + logger_fn=logger_fn, + encoding=_get_encoding(), + vertex_location=vertex_ai_location, + vertex_project=vertex_ai_project, + vertex_credentials=vertex_credentials, + gemini_api_key=gemini_api_key, + logging_obj=logging, + acompletion=acompletion, + timeout=timeout, + custom_llm_provider=custom_llm_provider, # type: ignore + client=client, + api_base=api_base, + extra_headers=headers, + ) + + +def _complete_vertex_ai(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + custom_prompt_dict = ctx.custom_prompt_dict + headers = ctx.headers + litellm_params = ctx.litellm_params + logger_fn = ctx.logger_fn + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + stream = ctx.stream + timeout = ctx.timeout + + vertex_ai_project = ( + optional_params.pop("vertex_project", None) + or optional_params.pop("vertex_ai_project", None) + or litellm.vertex_project + or get_secret("VERTEXAI_PROJECT") + ) + vertex_ai_location = ( + optional_params.pop("vertex_location", None) + or optional_params.pop("vertex_ai_location", None) + or litellm.vertex_location + or get_secret("VERTEXAI_LOCATION") + ) + vertex_credentials = ( + optional_params.pop("vertex_credentials", None) + or optional_params.pop("vertex_ai_credentials", None) + or get_secret("VERTEXAI_CREDENTIALS") + ) + + api_base = api_base or litellm.api_base or get_secret("VERTEXAI_API_BASE") + + new_params = safe_deep_copy(optional_params or {}) + model_route = get_vertex_ai_model_route(model=model, litellm_params=litellm_params) + + if model_route == VertexAIModelRoute.PARTNER_MODELS: + model_response = vertex_partner_models_chat_completion.completion( + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + optional_params=new_params, + litellm_params=litellm_params, # type: ignore + logger_fn=logger_fn, + encoding=_get_encoding(), + api_base=api_base, + vertex_location=vertex_ai_location, + vertex_project=vertex_ai_project, + vertex_credentials=vertex_credentials, + logging_obj=logging, + acompletion=acompletion, + headers=headers, + custom_prompt_dict=custom_prompt_dict, + timeout=timeout, + client=client, + ) + elif model_route == VertexAIModelRoute.GEMINI: + model_response = vertex_chat_completion.completion( # type: ignore + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + optional_params=new_params, + litellm_params=litellm_params, # type: ignore + logger_fn=logger_fn, + encoding=_get_encoding(), + vertex_location=vertex_ai_location, + vertex_project=vertex_ai_project, + vertex_credentials=vertex_credentials, + gemini_api_key=None, + logging_obj=logging, + acompletion=acompletion, + timeout=timeout, + custom_llm_provider=custom_llm_provider, # type: ignore + client=client, + api_base=api_base, + extra_headers=headers, + ) + elif model_route == VertexAIModelRoute.GEMMA: + # Vertex Gemma Models with custom prediction endpoint + model_response = vertex_gemma_chat_completion.completion( + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + optional_params=new_params, + litellm_params=litellm_params, # type: ignore + logger_fn=logger_fn, + encoding=_get_encoding(), + api_base=api_base, + vertex_location=vertex_ai_location, + vertex_project=vertex_ai_project, + vertex_credentials=vertex_credentials, + logging_obj=logging, + acompletion=acompletion, + headers=headers, + custom_prompt_dict=custom_prompt_dict, + timeout=timeout, + client=client, + ) + elif model_route == VertexAIModelRoute.MODEL_GARDEN: + # Vertex Model Garden - OpenAI compatible models + model_response = vertex_model_garden_chat_completion.completion( + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + optional_params=new_params, + litellm_params=litellm_params, # type: ignore + logger_fn=logger_fn, + encoding=_get_encoding(), + api_base=api_base, + vertex_location=vertex_ai_location, + vertex_project=vertex_ai_project, + vertex_credentials=vertex_credentials, + logging_obj=logging, + acompletion=acompletion, + headers=headers, + custom_prompt_dict=custom_prompt_dict, + timeout=timeout, + client=client, + ) + elif model_route == VertexAIModelRoute.AGENT_ENGINE: + # Vertex AI Agent Engine (Reasoning Engines) + from litellm.llms.vertex_ai.agent_engine.transformation import ( + VertexAgentEngineConfig, + ) + + vertex_agent_engine_config = VertexAgentEngineConfig() + + # Update litellm_params with vertex credentials + litellm_params["vertex_project"] = vertex_ai_project + litellm_params["vertex_location"] = vertex_ai_location + litellm_params["vertex_credentials"] = vertex_credentials + + model_response = base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + model_response=model_response, + optional_params=new_params, + litellm_params=litellm_params, # type: ignore + encoding=_get_encoding(), + api_key=None, + api_base=api_base, + logging_obj=logging, + acompletion=acompletion, + timeout=timeout, + client=client, + custom_llm_provider="vertex_ai", + provider_config=vertex_agent_engine_config, + headers=headers or {}, + ) + else: # VertexAIModelRoute.NON_GEMINI + model_response = vertex_ai_non_gemini.completion( + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + optional_params=new_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=_get_encoding(), + vertex_location=vertex_ai_location, + vertex_project=vertex_ai_project, + vertex_credentials=vertex_credentials, + logging_obj=logging, + acompletion=acompletion, + ) + + if ( + "stream" in optional_params + and optional_params["stream"] is True + and acompletion is False + ): + return CustomStreamWrapper( + model_response, + model, + custom_llm_provider="vertex_ai", + logging_obj=logging, + ) + return model_response # pyright: ignore[reportReturnType] # provider SDK return type is broader than the dispatch contract + + +def _complete_predibase(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + custom_prompt_dict = ctx.custom_prompt_dict + litellm_params = ctx.litellm_params + logger_fn = ctx.logger_fn + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + timeout = ctx.timeout + + tenant_id = ( + optional_params.pop("tenant_id", None) + or optional_params.pop("predibase_tenant_id", None) + or litellm.predibase_tenant_id + or get_secret("PREDIBASE_TENANT_ID") + ) + + if tenant_id is None: + raise ValueError( + "Missing Predibase Tenant ID - Required for making the request. Set dynamically (e.g. `completion(..tenant_id=)`) or in env - `PREDIBASE_TENANT_ID`." + ) + + api_base = ( + api_base + or optional_params.pop("api_base", None) + or optional_params.pop("base_url", None) + or litellm.api_base + or get_secret("PREDIBASE_API_BASE") + ) + + api_key = ( + api_key + or litellm.api_key + or litellm.predibase_key + or get_secret("PREDIBASE_API_KEY") + ) + + _model_response = predibase_chat_completions.completion( + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=_get_encoding(), + logging_obj=logging, + acompletion=acompletion, + api_base=api_base, + custom_prompt_dict=custom_prompt_dict, + api_key=api_key, + tenant_id=tenant_id, + timeout=timeout, + ) + + if ( + "stream" in optional_params + and optional_params["stream"] is True + and acompletion is False + ): + return _model_response + return _model_response + + +def _complete_text_completion_codestral( + ctx: _CompletionDispatchContext, +) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + custom_prompt_dict = ctx.custom_prompt_dict + litellm_params = ctx.litellm_params + logger_fn = ctx.logger_fn + logging = ctx.logging + messages = ctx.messages + model = ctx.model + optional_params = ctx.optional_params + stream = ctx.stream + timeout = ctx.timeout + + api_base = ( + api_base + or optional_params.pop("api_base", None) + or optional_params.pop("base_url", None) + or litellm.api_base + or "https://codestral.mistral.ai/v1/fim/completions" + ) + + api_key = api_key or litellm.api_key or get_secret("CODESTRAL_API_KEY") + + text_completion_model_response = litellm.TextCompletionResponse(stream=stream) + + _model_response = codestral_text_completions.completion( # type: ignore + model=model, + messages=messages, + model_response=text_completion_model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=_get_encoding(), + logging_obj=logging, + acompletion=acompletion, + api_base=api_base, + custom_prompt_dict=custom_prompt_dict, + api_key=api_key, + timeout=timeout, + ) + + if ( + "stream" in optional_params + and optional_params["stream"] is True + and acompletion is False + ): + return _model_response # pyright: ignore[reportReturnType] # provider SDK return type is broader than the dispatch contract + return _model_response # pyright: ignore[reportReturnType] # provider SDK return type is broader than the dispatch contract + + +def _complete_text_completion_inception( + ctx: _CompletionDispatchContext, +) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + headers = ctx.headers + litellm_params = ctx.litellm_params + logger_fn = ctx.logger_fn + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + text_completion = ctx.text_completion + timeout = ctx.timeout + + passed_api_base = ( + api_base + or optional_params.pop("api_base", None) + or optional_params.pop("base_url", None) + ) + api_base = ( + passed_api_base + or get_secret_str("INCEPTION_API_BASE") + or "https://api.inceptionlabs.ai/v1" + ) + # FIM is served at `/v1/fim/completions`; the OpenAI client appends + # `/completions`, so point it at the `/v1/fim` base. + api_base = api_base.rstrip("/") + if not api_base.endswith("/fim"): + api_base += "/fim" + + # Don't forward the server-managed Inception key to a caller-supplied + # api_base; only resolve it for the default/server base, or when the + # caller passes their own key. + if passed_api_base is None or api_key: + api_key = ( + api_key or litellm.inception_key or get_secret_str("INCEPTION_API_KEY") + ) + + _response = openai_text_completions.completion( + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + api_key=api_key, # type: ignore[arg-type] + custom_llm_provider="text-completion-inception", + api_base=api_base, + acompletion=acompletion, + client=client, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + timeout=timeout, # type: ignore + ) + + if ( + optional_params.get("stream", False) is False + and acompletion is False + and text_completion is False + ): + _response = ( + litellm.OpenAITextCompletionConfig().convert_to_chat_model_response_object( + response_object=_response, model_response_object=model_response + ) + ) + + if optional_params.get("stream", False) or acompletion is True: + logging.post_call( + input=messages, + api_key=api_key, + original_response=_response, + additional_args={"headers": headers}, + ) + return _response # pyright: ignore[reportReturnType] # provider SDK return type is broader than the dispatch contract + + +def _complete_sagemaker_chat( + ctx: _CompletionDispatchContext, +) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + stream = ctx.stream + timeout = ctx.timeout + + return base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + custom_llm_provider=custom_llm_provider, + timeout=timeout, + headers=headers, + encoding=_get_encoding(), + api_key=api_key, + logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements + client=client, + ) + + +def _complete_sagemaker(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + custom_prompt_dict = ctx.custom_prompt_dict + hf_model_name = ctx.hf_model_name + litellm_params = ctx.litellm_params + logger_fn = ctx.logger_fn + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + + return sagemaker_llm.completion( + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + custom_prompt_dict=custom_prompt_dict, + hf_model_name=hf_model_name, + logger_fn=logger_fn, + encoding=_get_encoding(), + logging_obj=logging, + acompletion=acompletion, + ) + + +def _complete_bedrock(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_prompt_dict = ctx.custom_prompt_dict + headers = ctx.headers + litellm_params = ctx.litellm_params + logger_fn = ctx.logger_fn + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + provider_config = ctx.provider_config + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict + + if "aws_bedrock_client" in optional_params: + verbose_logger.warning( + "'aws_bedrock_client' is a deprecated param. Please move to another auth method - https://docs.litellm.ai/docs/providers/bedrock#boto3---authentication." + ) + # Extract credentials for legacy boto3 client and pass thru to httpx + aws_bedrock_client = optional_params.pop("aws_bedrock_client") + creds = aws_bedrock_client._get_credentials().get_frozen_credentials() + + if creds.access_key: + optional_params["aws_access_key_id"] = creds.access_key + if creds.secret_key: + optional_params["aws_secret_access_key"] = creds.secret_key + if creds.token: + optional_params["aws_session_token"] = creds.token + if ( + "aws_region_name" not in optional_params + or optional_params["aws_region_name"] is None + ): + optional_params["aws_region_name"] = aws_bedrock_client.meta.region_name + + bedrock_route = BedrockModelInfo.get_bedrock_route(model) + if bedrock_route == "claude_platform": + provider_config = ProviderConfigManager.get_provider_chat_config( + model=model, + provider=LlmProviders.BEDROCK, + ) + model = BedrockModelInfo.get_claude_platform_model(model) + return base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + custom_llm_provider="bedrock", + timeout=timeout, + headers=headers, + encoding=_get_encoding(), + api_key=api_key, + logging_obj=logging, + client=client, + provider_config=provider_config, + ) + elif bedrock_route == "converse": + model = model.replace("converse/", "") + response = bedrock_converse_chat_completion.completion( + model=model, + messages=messages, + custom_prompt_dict=custom_prompt_dict, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, # type: ignore + logger_fn=logger_fn, + encoding=_get_encoding(), + logging_obj=logging, + extra_headers=headers, # Use merged headers instead of original extra_headers + timeout=timeout, + acompletion=acompletion, + client=client, + api_base=api_base, + api_key=api_key, + ) + elif bedrock_route == "converse_like": + model = model.replace("converse_like/", "") + response = base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + custom_llm_provider="bedrock", + timeout=timeout, + headers=headers, + encoding=_get_encoding(), + api_key=api_key, + logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements + client=client, + ) + else: + response = base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + custom_llm_provider="bedrock", + timeout=timeout, + headers=headers, + encoding=_get_encoding(), + api_key=api_key, + logging_obj=logging, + client=client, + ) + + return response + + +def _complete_watsonx(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_prompt_dict = ctx.custom_prompt_dict + headers = ctx.headers + litellm_params = ctx.litellm_params + logger_fn = ctx.logger_fn + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + timeout = ctx.timeout + + return watsonx_chat_completion.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + print_verbose=print_verbose, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + timeout=timeout, # type: ignore + custom_prompt_dict=custom_prompt_dict, + client=client, # pass AsyncOpenAI, OpenAI client + encoding=_get_encoding(), + custom_llm_provider="watsonx", + ) + + +def _complete_watsonx_text( + ctx: _CompletionDispatchContext, +) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + api_key = ( + api_key + or optional_params.pop("apikey", None) + or get_secret_str("WATSONX_APIKEY") + or get_secret_str("WATSONX_API_KEY") + or get_secret_str("WX_API_KEY") + ) + + api_base = ( + api_base + or optional_params.pop( + "url", + optional_params.pop("api_base", optional_params.pop("base_url", None)), + ) + or get_secret_str("WATSONX_API_BASE") + or get_secret_str("WATSONX_URL") + or get_secret_str("WX_URL") + or get_secret_str("WML_URL") + ) + + wx_credentials = optional_params.pop( + "wx_credentials", + optional_params.pop( + "watsonx_credentials", None + ), # follow {provider}_credentials, same as vertex ai + ) + + token: Optional[str] = None + if wx_credentials is not None: + api_base = wx_credentials.get("url", api_base) + api_key = wx_credentials.get("apikey", wx_credentials.get("api_key", api_key)) + token = wx_credentials.get( + "token", + wx_credentials.get( + "watsonx_token", None + ), # follow format of {provider}_token, same as azure - e.g. 'azure_ad_token=..' + ) + + if token is not None: + optional_params["token"] = token + + return base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + custom_llm_provider="watsonx_text", + timeout=timeout, + headers=headers, + encoding=_get_encoding(), + api_key=api_key, + logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements + client=client, + ) + + +def _complete_vllm(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + custom_prompt_dict = ctx.custom_prompt_dict + litellm_params = ctx.litellm_params + logger_fn = ctx.logger_fn + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + + custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict + model_response = vllm_handler.completion( + model=model, + messages=messages, + custom_prompt_dict=custom_prompt_dict, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=_get_encoding(), + logging_obj=logging, + ) + + if "stream" in optional_params and optional_params["stream"] is True: ## [BETA] + # don't try to access stream object, + return CustomStreamWrapper( + model_response, + model, + custom_llm_provider="vllm", + logging_obj=logging, + ) + + ## RESPONSE OBJECT + return model_response + + +def _complete_ollama(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + api_base = ( + litellm.api_base + or api_base + or get_secret("OLLAMA_API_BASE") + or "http://localhost:11434" + ) + if api_key is not None and "Authorization" not in headers: + headers["Authorization"] = f"Bearer {api_key}" + + return base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + custom_llm_provider="ollama", + timeout=timeout, + headers=headers, + encoding=_get_encoding(), + api_key=api_key, + logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements + client=client, + ) + + +def _complete_ollama_chat(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + api_base = ( + litellm.api_base + or api_base + or get_secret("OLLAMA_API_BASE") + or "http://localhost:11434" + ) + + api_key = ( + api_key + or litellm.ollama_key + or os.environ.get("OLLAMA_API_KEY") + or litellm.api_key + ) + if api_key is not None and "Authorization" not in headers: + headers["Authorization"] = f"Bearer {api_key}" + + return base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + custom_llm_provider="ollama_chat", + timeout=timeout, + headers=headers, + encoding=_get_encoding(), + api_key=api_key, + logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements + client=client, + ) + + +def _complete_triton(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + api_base = litellm.api_base or api_base + return base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + custom_llm_provider=custom_llm_provider, + timeout=timeout, + headers=headers, + encoding=_get_encoding(), + api_key=api_key, + logging_obj=logging, + ) + + +def _complete_cloudflare(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + custom_prompt_dict = ctx.custom_prompt_dict + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + api_key = ( + api_key + or litellm.cloudflare_api_key + or litellm.api_key + or get_secret("CLOUDFLARE_API_KEY") + ) + api_base = api_base or litellm.api_base or get_secret("CLOUDFLARE_API_BASE") + + custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict + return base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + custom_llm_provider="cloudflare", + timeout=timeout, + headers=headers, + encoding=_get_encoding(), + api_key=api_key, + logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements + ) + + +def _complete_petals(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + api_base = ctx.api_base + client = ctx.client + litellm_params = ctx.litellm_params + logger_fn = ctx.logger_fn + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + stream = ctx.stream + + api_base = api_base or litellm.api_base + + stream = optional_params.pop("stream", False) + model_response = petals_handler.completion( + model=model, + messages=messages, + api_base=api_base, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=_get_encoding(), + logging_obj=logging, + client=client, + ) + if stream is True: ## [BETA] + # Fake streaming for petals + resp_string = model_response["choices"][0]["message"]["content"] + return CustomStreamWrapper( + resp_string, + model, + custom_llm_provider="petals", + logging_obj=logging, + ) + return model_response + + +def _complete_snowflake(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + try: + client = ( + HTTPHandler(timeout=timeout) if stream is False else None + ) # Keep this here, otherwise, the httpx.client closes and streaming is impossible + response = base_llm_http_handler.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + timeout=timeout, # type: ignore + client=client, + custom_llm_provider=custom_llm_provider, + encoding=_get_encoding(), + stream=stream, + ) + + except Exception as e: + ## LOGGING - log the original exception returned + logging.post_call( + input=messages, + api_key=api_key, + original_response=str(e), + additional_args={"headers": headers}, + ) + raise e + + return response + + +def _complete_gradient_ai(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + api_base = litellm.api_base or api_base + return base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + custom_llm_provider="gradient_ai", + timeout=timeout, + headers=headers, + encoding=_get_encoding(), + api_key=api_key, + logging_obj=logging, + ) + + +def _complete_bytez(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + stream = ctx.stream + timeout = ctx.timeout + + api_key = ( + api_key + or litellm.bytez_key + or get_secret_str("BYTEZ_API_KEY") + or litellm.api_key + ) + + response = base_llm_http_handler.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + timeout=timeout, # type: ignore + client=client, + custom_llm_provider=custom_llm_provider, + encoding=_get_encoding(), + stream=stream, + provider_config=bytez_transformation, + ) + + pass + + return response + + +def _complete_lemonade(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + stream = ctx.stream + timeout = ctx.timeout + + api_key = ( + api_key + or litellm.lemonade_key + or get_secret_str("LEMONADE_API_KEY") + or litellm.api_key + ) + + response = base_llm_http_handler.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + timeout=timeout, # type: ignore + client=client, + custom_llm_provider=custom_llm_provider, + encoding=_get_encoding(), + stream=stream, + provider_config=lemonade_transformation, + ) + + pass + + return response + + +def _complete_ovhcloud(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + stream = ctx.stream + timeout = ctx.timeout + + api_key = ( + api_key + or litellm.ovhcloud_key + or get_secret_str("OVHCLOUD_API_KEY") + or litellm.api_key + ) + + api_base = ( + api_base + or litellm.api_base + or get_secret_str("OVHCLOUD_API_BASE") + or "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1" + ) + + response = base_llm_http_handler.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + timeout=timeout, # type: ignore + client=client, + custom_llm_provider=custom_llm_provider, + encoding=_get_encoding(), + stream=stream, + provider_config=ovhcloud_transformation, + ) + + pass + + return response + + +def _complete_custom(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + api_base = ctx.api_base + headers = ctx.headers + kwargs = ctx.kwargs + max_tokens = ctx.max_tokens + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + temperature = ctx.temperature + top_p = ctx.top_p + + url = litellm.api_base or api_base or "" + if url is None or url == "": + raise ValueError( + "api_base not set. Set api_base or litellm.api_base for custom endpoints" + ) + + """ + assume input to custom LLM api bases follow this format: + resp = litellm.module_level_client.post( + api_base, + json={ + 'model': 'meta-llama/Llama-2-13b-hf', # model name + 'params': { + 'prompt': ["The capital of France is P"], + 'max_tokens': 32, + 'temperature': 0.7, + 'top_p': 1.0, + 'top_k': 40, + } + } + ) + + """ + prompt = " ".join([message["content"] for message in messages]) # type: ignore + resp = litellm.module_level_client.post( + url, + headers=headers, + json={ + "model": model, + "params": { + "prompt": [prompt], + "max_tokens": max_tokens, + "temperature": temperature, + "top_p": top_p, + "top_k": kwargs.get("top_k"), + }, + **kwargs.get("extra_body", {}), + }, + ) + response_json = resp.json() + """ + assume all responses from custom api_bases of this format: + { + 'data': [ + { + 'prompt': 'The capital of France is P', + 'output': ['The capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France'], + 'params': {'temperature': 0.7, 'top_k': 40, 'top_p': 1}}], + 'message': 'ok' + } + ] + } + """ + string_response = response_json["data"][0]["output"][0] + ## RESPONSE OBJECT + model_response.choices[0].message.content = string_response # type: ignore + model_response.created = int(time.time()) + model_response.model = model + return model_response + + +def _complete_custom_providers( + ctx: _CompletionDispatchContext, +) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + custom_prompt_dict = ctx.custom_prompt_dict + headers = ctx.headers + litellm_params = ctx.litellm_params + logger_fn = ctx.logger_fn + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + stream = ctx.stream + timeout = ctx.timeout + + custom_handler: Optional[CustomLLM] = None + for item in litellm.custom_provider_map: + if item["provider"] == custom_llm_provider: + custom_handler = item["custom_handler"] + + if custom_handler is None: + raise LiteLLMUnknownProvider( + model=model, custom_llm_provider=custom_llm_provider + ) + + ## ROUTE LLM CALL ## + handler_fn = custom_chat_llm_router( + async_fn=acompletion, stream=stream, custom_llm=custom_handler + ) + + headers = headers or litellm.headers or {} + + ## CALL FUNCTION + response = handler_fn( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + print_verbose=print_verbose, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + timeout=timeout, # type: ignore + custom_prompt_dict=custom_prompt_dict, + client=client, # pass AsyncOpenAI, OpenAI client + encoding=_get_encoding(), + ) + if stream is True: + return CustomStreamWrapper( + completion_stream=response, + model=model, + custom_llm_provider=custom_llm_provider, + logging_obj=logging, + ) + + return response # pyright: ignore[reportReturnType] # provider SDK return type is broader than the dispatch contract + + +def _complete_langgraph(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + from litellm.llms.langgraph.chat.transformation import LangGraphConfig + + ( + api_base, + api_key, + ) = LangGraphConfig()._get_openai_compatible_provider_info( + api_base=api_base or litellm.api_base, + api_key=api_key or litellm.api_key, + ) + + headers = headers or litellm.headers + + return base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + custom_llm_provider=custom_llm_provider, + timeout=timeout, + headers=headers, + encoding=_get_encoding(), + api_key=api_key, + logging_obj=logging, + client=client, + ) + + +def _complete_langflow(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion = ctx.acompletion + api_base = ctx.api_base + api_key = ctx.api_key + client = ctx.client + custom_llm_provider = ctx.custom_llm_provider + headers = ctx.headers + litellm_params = ctx.litellm_params + logging = ctx.logging + messages = ctx.messages + model = ctx.model + model_response = ctx.model_response + optional_params = ctx.optional_params + shared_session = ctx.shared_session + stream = ctx.stream + timeout = ctx.timeout + + from litellm.llms.langflow.chat.transformation import LangFlowConfig + + ( + api_base, + api_key, + ) = LangFlowConfig()._get_openai_compatible_provider_info( + api_base=api_base or litellm.api_base, + api_key=api_key or litellm.api_key, + ) + + headers = headers or litellm.headers + + return base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + custom_llm_provider=custom_llm_provider, + timeout=timeout, + headers=headers, + encoding=_get_encoding(), + api_key=api_key, + logging_obj=logging, + client=client, + ) + + @tracer.wrap() @client -def completion( # type: ignore # noqa: PLR0915 +def completion( # type: ignore model: str, # Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create messages: List = [], @@ -1214,9 +5077,7 @@ def completion( # type: ignore # noqa: PLR0915 if LiteLLM_Proxy_MCP_Handler._should_use_litellm_mcp_gateway( tools=tools_for_mcp ): - # Return coroutine - acompletion will await it - # completion() can return a coroutine when MCP tools are present, which acompletion() awaits - return acompletion_with_mcp( # type: ignore[return-value] + return acompletion_with_mcp( # pyright: ignore[reportReturnType] # MCP path returns a coroutine that acompletion() awaits; completion()'s sync return type omits it model=model, messages=messages, functions=functions, @@ -1388,12 +5249,16 @@ def completion( # type: ignore # noqa: PLR0915 logging: LiteLLMLoggingObj = cast(LiteLLMLoggingObj, litellm_logging_obj) fallbacks = fallbacks or litellm.model_fallbacks if fallbacks is not None: - return completion_with_fallbacks(**args) + return completion_with_fallbacks( # pyright: ignore[reportReturnType] # fallback runner is untyped; resolves to ModelResponse|CustomStreamWrapper at runtime + **args + ) if model_list is not None: deployments = [ m["litellm_params"] for m in model_list if m["model_name"] == model ] - return litellm.batch_completion_models(deployments=deployments, **args) + return litellm.batch_completion_models( # pyright: ignore[reportReturnType] # batch path returns a list of responses, outside completion()'s single-response return type + deployments=deployments, **args + ) if litellm.model_alias_map and model in litellm.model_alias_map: model = litellm.model_alias_map[ model @@ -1407,11 +5272,19 @@ def completion( # type: ignore # noqa: PLR0915 if deployment_id is not None: # azure llms model = deployment_id custom_llm_provider = "azure" + _supplemental_provider_params = { + k: kwargs[k] for k in OPTIONAL_KWARGS_KEYS if k in kwargs + } model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider( model=model, custom_llm_provider=custom_llm_provider, api_base=api_base, api_key=api_key, + litellm_params=( + GenericLiteLLMParams(**_supplemental_provider_params) + if _supplemental_provider_params + else None + ), ) ## RESPONSES API BRIDGE LOGIC ## - check early and normalize model name @@ -1445,7 +5318,7 @@ def completion( # type: ignore # noqa: PLR0915 timeout, kwargs, custom_llm_provider, - global_timeout=getattr(litellm, "request_timeout", None), + global_timeout=get_configured_request_timeout(), supports_httpx_timeout=supports_httpx_timeout, ) @@ -1638,6 +5511,8 @@ def completion( # type: ignore # noqa: PLR0915 litellm_request_debug=kwargs.get("litellm_request_debug", False), tpm=kwargs.get("tpm"), rpm=kwargs.get("rpm"), + use_xai_oauth=kwargs.get("use_xai_oauth", False), + aws_bedrock_project_id=kwargs.get("aws_bedrock_project_id"), ) cast(LiteLLMLoggingObj, logging).update_environment_variables( model=model, @@ -1705,7 +5580,7 @@ def completion( # type: ignore # noqa: PLR0915 else: optional_params["reasoning_effort"] = {"summary": rs_val} - return responses_api_bridge.completion( + return responses_api_bridge.completion( # pyright: ignore[reportReturnType] # bridge returns a coroutine on the acompletion path; awaited by the async caller model=model, messages=messages, headers=headers, @@ -1735,375 +5610,52 @@ def completion( # type: ignore # noqa: PLR0915 optional_params ) + _dispatch_ctx = _CompletionDispatchContext( + _azure_detection_model=_azure_detection_model, + acompletion=acompletion, + api_base=api_base, + api_key=api_key, + api_version=api_version, + client=client, + custom_llm_provider=custom_llm_provider, + custom_prompt_dict=custom_prompt_dict, + extra_headers=extra_headers, + headers=headers, + hf_model_name=hf_model_name, + kwargs=kwargs, + litellm_params=litellm_params, + logger_fn=logger_fn, + logging=logging, + max_retries=max_retries, + max_tokens=max_tokens, + messages=messages, + metadata=metadata, + model=model, + model_response=model_response, + optional_params=optional_params, + organization=organization, + provider_config=provider_config, + shared_session=shared_session, + stream=stream, + temperature=temperature, + text_completion=text_completion, + timeout=timeout, + top_p=top_p, + ) if custom_llm_provider == "azure": # azure configs ## check dynamic params ## - dynamic_params = False - if client is not None and ( - isinstance(client, openai.AzureOpenAI) - or isinstance(client, openai.AsyncAzureOpenAI) - ): - dynamic_params = _check_dynamic_azure_params( - azure_client_params={"api_version": api_version}, - azure_client=client, - ) - - api_type = get_secret("AZURE_API_TYPE") or "azure" - - api_base = api_base or litellm.api_base or get_secret("AZURE_API_BASE") - - api_version = ( - api_version - or litellm.api_version - or get_secret_str("AZURE_API_VERSION") - or litellm.AZURE_DEFAULT_API_VERSION - ) - - api_key = ( - api_key - or litellm.api_key - or litellm.azure_key - or get_secret_str("AZURE_OPENAI_API_KEY") - or get_secret_str("AZURE_API_KEY") - ) - - azure_ad_token = optional_params.get("extra_body", {}).pop( - "azure_ad_token", None - ) or get_secret_str("AZURE_AD_TOKEN") - - azure_ad_token_provider = litellm_params.get( - "azure_ad_token_provider", None - ) - - headers = headers or litellm.headers - - if extra_headers is not None: - optional_params["extra_headers"] = extra_headers - if max_retries is not None: - optional_params["max_retries"] = max_retries - - if litellm.AzureOpenAIO1Config().is_o_series_model( - model=_azure_detection_model - ): - ## LOAD CONFIG - if set - config = litellm.AzureOpenAIO1Config.get_config() - for k, v in config.items(): - if ( - k not in optional_params - ): # completion(top_k=3) > azure_config(top_k=3) <- allows for dynamic variables to be passed in - optional_params[k] = v - - response = azure_o1_chat_completions.completion( - model=model, - messages=messages, - headers=headers, - api_key=api_key, - api_base=api_base, - api_version=api_version, - dynamic_params=dynamic_params, - azure_ad_token=azure_ad_token, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - logging_obj=logging, - acompletion=acompletion, - timeout=timeout, # type: ignore - client=client, # pass AsyncAzureOpenAI, AzureOpenAI client - custom_llm_provider=custom_llm_provider, - ) - else: - ## LOAD CONFIG - if set - config = litellm.AzureOpenAIConfig.get_config() - for k, v in config.items(): - if ( - k not in optional_params - ): # completion(top_k=3) > azure_config(top_k=3) <- allows for dynamic variables to be passed in - optional_params[k] = v - - ## COMPLETION CALL - response = azure_chat_completions.completion( - model=model, - messages=messages, - headers=headers, - api_key=api_key, - api_base=api_base, - api_version=api_version, - api_type=api_type, - dynamic_params=dynamic_params, - azure_ad_token=azure_ad_token, - azure_ad_token_provider=azure_ad_token_provider, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - logging_obj=logging, - acompletion=acompletion, - timeout=timeout, # type: ignore - client=client, # pass AsyncAzureOpenAI, AzureOpenAI client - ) - - if optional_params.get("stream", False): - ## LOGGING - logging.post_call( - input=messages, - api_key=api_key, - original_response=response, - additional_args={ - "headers": headers, - "api_version": api_version, - "api_base": api_base, - }, - ) + response = _complete_azure(_dispatch_ctx) elif custom_llm_provider == "azure_text": # azure configs - api_type = get_secret_str("AZURE_API_TYPE") or "azure" - - api_base = api_base or litellm.api_base or get_secret_str("AZURE_API_BASE") - - if api_base is None: - raise ValueError( - "api_base is required for Azure OpenAI LLM provider. Either set it dynamically or set the AZURE_API_BASE environment variable." - ) - - api_version = ( - api_version - or litellm.api_version - or get_secret_str("AZURE_API_VERSION") - ) - - api_key = ( - api_key - or litellm.api_key - or litellm.azure_key - or get_secret_str("AZURE_OPENAI_API_KEY") - or get_secret_str("AZURE_API_KEY") - ) - - azure_ad_token = optional_params.get("extra_body", {}).pop( - "azure_ad_token", None - ) or get_secret_str("AZURE_AD_TOKEN") - - azure_ad_token_provider = litellm_params.get( - "azure_ad_token_provider", None - ) - - headers = headers or litellm.headers - - if extra_headers is not None: - optional_params["extra_headers"] = extra_headers - - ## LOAD CONFIG - if set - config = litellm.AzureOpenAIConfig.get_config() - for k, v in config.items(): - if ( - k not in optional_params - ): # completion(top_k=3) > azure_config(top_k=3) <- allows for dynamic variables to be passed in - optional_params[k] = v - - ## COMPLETION CALL - response = azure_text_completions.completion( - model=model, - messages=messages, - headers=headers, - api_key=api_key, - api_base=api_base, - api_version=cast(str, api_version), - api_type=api_type, - azure_ad_token=azure_ad_token, - azure_ad_token_provider=azure_ad_token_provider, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - logging_obj=logging, - acompletion=acompletion, - timeout=timeout, - client=client, # pass AsyncAzureOpenAI, AzureOpenAI client - ) - - if optional_params.get("stream", False) or acompletion is True: - ## LOGGING - logging.post_call( - input=messages, - api_key=api_key, - original_response=response, - additional_args={ - "headers": headers, - "api_version": api_version, - "api_base": api_base, - }, - ) + response = _complete_azure_text(_dispatch_ctx) elif custom_llm_provider == "deepseek": ## COMPLETION CALL - try: - response = base_llm_http_handler.completion( - model=model, - messages=messages, - headers=headers, - model_response=model_response, - api_key=api_key, - api_base=api_base, - acompletion=acompletion, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - timeout=timeout, # type: ignore - client=client, - custom_llm_provider=custom_llm_provider, - encoding=_get_encoding(), - stream=stream, - provider_config=provider_config, - ) - except Exception as e: - ## LOGGING - log the original exception returned - logging.post_call( - input=messages, - api_key=api_key, - original_response=str(e), - additional_args={"headers": headers}, - ) - raise e + response = _complete_deepseek(_dispatch_ctx) elif custom_llm_provider == "azure_ai": - from litellm.llms.azure_ai.common_utils import AzureFoundryModelInfo - - azure_ai_route = AzureFoundryModelInfo.get_azure_ai_route(model) - - # Check if this is an agents route - model format: azure_ai/agents/ - if azure_ai_route == "agents": - from litellm.llms.azure_ai.agents import AzureAIAgentsConfig - - api_base = AzureFoundryModelInfo.get_api_base(api_base) - if api_base is None: - raise ValueError( - "Azure AI Agents requests require an api_base. " - "Set `api_base` or the AZURE_AI_API_BASE env var." - ) - api_key = AzureFoundryModelInfo.get_api_key(api_key) - - response = AzureAIAgentsConfig.completion( - model=model, - messages=messages, - api_base=api_base, - api_key=api_key, - model_response=model_response, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - timeout=timeout, - acompletion=acompletion, - stream=stream, - headers=headers or litellm.headers, - ) - - # Check if this is a Claude model - route to Azure Anthropic handler - elif "claude" in model.lower(): - # Use Azure Anthropic handler for Claude models - api_base = AzureFoundryModelInfo.get_api_base(api_base) - if api_base is None: - raise ValueError( - "Azure Anthropic requests require an api_base. " - "Set `api_base` or the AZURE_AI_API_BASE env var." - ) - api_key = AzureFoundryModelInfo.get_api_key(api_key) - - # Ensure the URL ends with /v1/messages for Anthropic - if api_base: - api_base = api_base.rstrip("/") - if not api_base.endswith("/v1/messages"): - if "/anthropic" in api_base: - parts = api_base.split("/anthropic", 1) - api_base = parts[0] + "/anthropic" - else: - api_base = api_base + "/anthropic" - api_base = api_base + "/v1/messages" - - response = azure_anthropic_chat_completions.completion( - model=model, - messages=messages, - api_base=api_base, - acompletion=acompletion, - custom_prompt_dict=litellm.custom_prompt_dict, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=_get_encoding(), - api_key=api_key, - logging_obj=logging, - headers=headers, - timeout=timeout, - client=client, - custom_llm_provider=custom_llm_provider, - ) - if optional_params.get("stream", False) or acompletion is True: - ## LOGGING - logging.post_call( - input=messages, - api_key=api_key, - original_response=response, - ) - response = response - else: - # Non-Claude models use standard Azure AI flow - api_base = AzureFoundryModelInfo.get_api_base(api_base) - # set API KEY - api_key = AzureFoundryModelInfo.get_api_key(api_key) - - headers = headers or litellm.headers - - if extra_headers is not None: - optional_params["extra_headers"] = extra_headers - - ## FOR COHERE - if "command-r" in model: # make sure tool call in messages are str - messages = stringify_json_tool_call_content(messages=messages) - - ## COMPLETION CALL - try: - response = base_llm_http_handler.completion( - model=model, - messages=messages, - headers=headers, - model_response=model_response, - api_key=api_key, - api_base=api_base, - acompletion=acompletion, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - timeout=timeout, # type: ignore - client=client, # pass AsyncOpenAI, OpenAI client - custom_llm_provider=custom_llm_provider, - encoding=_get_encoding(), - stream=stream, - ) - except Exception as e: - ## LOGGING - log the original exception returned - logging.post_call( - input=messages, - api_key=api_key, - original_response=str(e), - additional_args={"headers": headers}, - ) - raise e - - if optional_params.get("stream", False): - ## LOGGING - logging.post_call( - input=messages, - api_key=api_key, - original_response=response, - additional_args={"headers": headers}, - ) + response = _complete_azure_ai(_dispatch_ctx) elif ( custom_llm_provider == "text-completion-openai" or "ft:babbage-002" in model @@ -2112,537 +5664,42 @@ def completion( # type: ignore # noqa: PLR0915 in litellm.openai_text_completion_compatible_providers and kwargs.get("text_completion") is True ): - openai.api_type = "openai" - - api_base = ( - api_base - or litellm.api_base - or get_secret("OPENAI_BASE_URL") - or get_secret("OPENAI_API_BASE") - or "https://api.openai.com/v1" - ) - - openai.api_version = None - # set API KEY - - api_key = ( - api_key - or litellm.api_key - or litellm.openai_key - or get_secret("OPENAI_API_KEY") - ) - - headers = headers or litellm.headers - - if extra_headers is not None: - optional_params["extra_headers"] = extra_headers - - ## LOAD CONFIG - if set - config = litellm.OpenAITextCompletionConfig.get_config() - for k, v in config.items(): - if ( - k not in optional_params - ): # completion(top_k=3) > openai_text_config(top_k=3) <- allows for dynamic variables to be passed in - optional_params[k] = v - if litellm.organization: - openai.organization = litellm.organization - - if ( - len(messages) > 0 - and "content" in messages[0] - and isinstance(messages[0]["content"], list) - ): - # text-davinci-003 can accept a string or array, if it's an array, assume the array is set in messages[0]['content'] - # https://platform.openai.com/docs/api-reference/completions/create - prompt = messages[0]["content"] - else: - prompt = " ".join([message["content"] for message in messages]) # type: ignore - - ## COMPLETION CALL - _response = openai_text_completions.completion( - model=model, - messages=messages, - model_response=model_response, - print_verbose=print_verbose, - api_key=api_key, - custom_llm_provider=custom_llm_provider, - api_base=api_base, - acompletion=acompletion, - client=client, # pass AsyncOpenAI, OpenAI client - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - timeout=timeout, # type: ignore - ) - - if ( - optional_params.get("stream", False) is False - and acompletion is False - and text_completion is False - ): - # convert to chat completion response - _response = litellm.OpenAITextCompletionConfig().convert_to_chat_model_response_object( - response_object=_response, model_response_object=model_response - ) - - if optional_params.get("stream", False) or acompletion is True: - ## LOGGING - logging.post_call( - input=messages, - api_key=api_key, - original_response=_response, - additional_args={"headers": headers}, - ) - response = _response + response = _complete_text_completion_openai(_dispatch_ctx) elif custom_llm_provider == "fireworks_ai": ## COMPLETION CALL - try: - response = base_llm_http_handler.completion( - model=model, - messages=messages, - headers=headers, - model_response=model_response, - api_key=api_key, - api_base=api_base, - acompletion=acompletion, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - timeout=timeout, # type: ignore - client=client, - custom_llm_provider=custom_llm_provider, - encoding=_get_encoding(), - stream=stream, - provider_config=provider_config, - ) - except Exception as e: - ## LOGGING - log the original exception returned - logging.post_call( - input=messages, - api_key=api_key, - original_response=str(e), - additional_args={"headers": headers}, - ) - raise e + response = _complete_fireworks_ai(_dispatch_ctx) elif custom_llm_provider == "heroku": - try: - response = base_llm_http_handler.completion( - model=model, - messages=messages, - headers=headers, - model_response=model_response, - api_key=api_key, - api_base=api_base, - acompletion=acompletion, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - timeout=timeout, - client=client, - custom_llm_provider=custom_llm_provider, - encoding=_get_encoding(), - stream=stream, - provider_config=provider_config, - ) - except Exception as e: - logging.post_call( - input=messages, - api_key=api_key, - original_response=str(e), - additional_args={"headers": headers}, - ) - raise e + response = _complete_heroku(_dispatch_ctx) elif custom_llm_provider == "ragflow": ## COMPLETION CALL - RAGFlow uses HTTP handler to support custom URL paths - try: - response = base_llm_http_handler.completion( - model=model, - messages=messages, - headers=headers, - model_response=model_response, - api_key=api_key, - api_base=api_base, - acompletion=acompletion, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - timeout=timeout, - client=client, - custom_llm_provider=custom_llm_provider, - encoding=_get_encoding(), - stream=stream, - provider_config=provider_config, - ) - except Exception as e: - logging.post_call( - input=messages, - api_key=api_key, - original_response=str(e), - additional_args={"headers": headers}, - ) - raise e + response = _complete_ragflow(_dispatch_ctx) elif custom_llm_provider == "xai": ## COMPLETION CALL - try: - response = base_llm_http_handler.completion( - model=model, - messages=messages, - headers=headers, - model_response=model_response, - api_key=api_key, - api_base=api_base, - acompletion=acompletion, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - timeout=timeout, # type: ignore - client=client, - custom_llm_provider=custom_llm_provider, - encoding=_get_encoding(), - stream=stream, - provider_config=provider_config, - ) - except Exception as e: - ## LOGGING - log the original exception returned - logging.post_call( - input=messages, - api_key=api_key, - original_response=str(e), - additional_args={"headers": headers}, - ) - raise e + response = _complete_xai(_dispatch_ctx) elif custom_llm_provider == "groq": - api_base = ( - api_base # for deepinfra/perplexity/anyscale/groq/friendliai we check in get_llm_provider and pass in the api base from there - or litellm.api_base - or get_secret("GROQ_API_BASE") - or "https://api.groq.com/openai/v1" - ) - - # set API KEY - api_key = ( - api_key - or litellm.api_key # for deepinfra/perplexity/anyscale/friendliai we check in get_llm_provider and pass in the api key from there - or litellm.groq_key - or get_secret("GROQ_API_KEY") - ) - - headers = headers or litellm.headers - - ## LOAD CONFIG - if set - config = litellm.GroqChatConfig.get_config() - for k, v in config.items(): - if ( - k not in optional_params - ): # completion(top_k=3) > openai_config(top_k=3) <- allows for dynamic variables to be passed in - optional_params[k] = v - - response = base_llm_http_handler.completion( - model=model, - stream=stream, - messages=messages, - acompletion=acompletion, - api_base=api_base, - model_response=model_response, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - custom_llm_provider=custom_llm_provider, - timeout=timeout, - headers=headers, - encoding=_get_encoding(), - api_key=api_key, - logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements - client=client, - ) + response = _complete_groq(_dispatch_ctx) elif custom_llm_provider == "bedrock_mantle": - api_base = ( - api_base or litellm.api_base or get_secret("BEDROCK_MANTLE_API_BASE") - ) - api_key = api_key or litellm.api_key or get_secret("BEDROCK_MANTLE_API_KEY") - headers = headers or litellm.headers - config = litellm.BedrockMantleChatConfig.get_config() - for k, v in config.items(): - if k not in optional_params: - optional_params[k] = v - response = base_llm_http_handler.completion( - model=model, - stream=stream, - messages=messages, - acompletion=acompletion, - api_base=api_base, - model_response=model_response, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - custom_llm_provider=custom_llm_provider, - timeout=timeout, - headers=headers, - encoding=_get_encoding(), - api_key=api_key, - logging_obj=logging, - client=client, - ) + response = _complete_bedrock_mantle(_dispatch_ctx) elif custom_llm_provider == "a2a": # A2A (Agent-to-Agent) Protocol # Resolve agent configuration from registry if model format is "a2a/" - ( - api_base, - api_key, - headers, - ) = litellm.A2AConfig.resolve_agent_config_from_registry( - model=model, - api_base=api_base, - api_key=api_key, - headers=headers, - optional_params=optional_params, - ) - - # Fall back to environment variables and defaults - api_base = api_base or litellm.api_base or get_secret_str("A2A_API_BASE") - - if api_base is None: - raise Exception( - "api_base is required for A2A provider. " - "Either provide api_base parameter, set A2A_API_BASE environment variable, " - "or register the agent in the proxy with model='a2a/'." - ) - - headers = headers or litellm.headers - - response = base_llm_http_handler.completion( - model=model, - stream=stream, - messages=messages, - acompletion=acompletion, - api_base=api_base, - model_response=model_response, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - custom_llm_provider=custom_llm_provider, - timeout=timeout, - headers=headers, - encoding=_get_encoding(), - api_key=api_key, - logging_obj=logging, - client=client, - provider_config=provider_config, - ) + response = _complete_a2a(_dispatch_ctx) elif custom_llm_provider == "gigachat": # GigaChat - Sber AI's LLM (Russia) - api_key = ( - api_key - or litellm.api_key - or litellm.gigachat_key - or get_secret("GIGACHAT_API_KEY") - or get_secret("GIGACHAT_CREDENTIALS") - ) - - headers = headers or litellm.headers or {} - - ## COMPLETION CALL - try: - response = base_llm_http_handler.completion( - model=model, - messages=messages, - headers=headers, - model_response=model_response, - api_key=api_key, - api_base=api_base, - acompletion=acompletion, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - timeout=timeout, - client=client, - custom_llm_provider=custom_llm_provider, - encoding=_get_encoding(), - stream=stream, - provider_config=provider_config, - ) - except Exception as e: - ## LOGGING - log the original exception returned - logging.post_call( - input=messages, - api_key=api_key, - original_response=str(e), - additional_args={"headers": headers}, - ) - raise e + response = _complete_gigachat(_dispatch_ctx) elif custom_llm_provider == "sap": - headers = headers or litellm.headers - ## LOAD CONFIG - if set - config = litellm.GenAIHubOrchestrationConfig.get_config() - for k, v in config.items(): - if ( - k not in optional_params - ): # completion(top_k=3) > openai_config(top_k=3) <- allows for dynamic variables to be passed in - optional_params[k] = v - - response = sap_gen_ai_hub_chat_completions.completion( - model=model, - messages=messages, - headers=headers, - model_response=model_response, - acompletion=acompletion, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - timeout=timeout, # type: ignore - shared_session=shared_session, - client=client, - custom_llm_provider=custom_llm_provider, - encoding=_get_encoding(), - api_key=api_key, - api_base=api_base, - stream=stream, - ) + response = _complete_sap(_dispatch_ctx) elif custom_llm_provider == "aiohttp_openai": # NEW aiohttp provider for 10-100x higher RPS - api_base = ( - api_base # for deepinfra/perplexity/anyscale/groq/friendliai we check in get_llm_provider and pass in the api base from there - or litellm.api_base - or get_secret("OPENAI_BASE_URL") - or get_secret("OPENAI_API_BASE") - or "https://api.openai.com/v1" - ) - # set API KEY - api_key = ( - api_key - or litellm.api_key # for deepinfra/perplexity/anyscale/friendliai we check in get_llm_provider and pass in the api key from there - or litellm.openai_key - or get_secret("OPENAI_API_KEY") - ) - - headers = headers or litellm.headers - - if extra_headers is not None: - optional_params["extra_headers"] = extra_headers - response = base_llm_aiohttp_handler.completion( - model=model, - messages=messages, - headers=headers, - model_response=model_response, - api_key=api_key, - api_base=api_base, - acompletion=acompletion, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - timeout=timeout, - client=client, - custom_llm_provider=custom_llm_provider, - encoding=_get_encoding(), - stream=stream, - ) + response = _complete_aiohttp_openai(_dispatch_ctx) elif custom_llm_provider == "cometapi": - api_key = ( - api_key - or litellm.cometapi_key - or get_secret_str("COMETAPI_KEY") - or litellm.api_key - ) - - api_base = ( - api_base - or litellm.api_base - or get_secret_str("COMETAPI_API_BASE") - or "https://api.cometapi.com/v1" - ) - - ## COMPLETION CALL - response = base_llm_http_handler.completion( - model=model, - messages=messages, - headers=headers, - model_response=model_response, - api_key=api_key, - api_base=api_base, - acompletion=acompletion, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - timeout=timeout, - client=client, - custom_llm_provider=custom_llm_provider, - encoding=_get_encoding(), - stream=stream, - provider_config=provider_config, - ) - - ## LOGGING - logging.post_call( - input=messages, api_key=api_key, original_response=response - ) + response = _complete_cometapi(_dispatch_ctx) elif custom_llm_provider == "minimax": - api_key = api_key or get_secret_str("MINIMAX_API_KEY") or litellm.api_key - - api_base = ( - api_base - or litellm.api_base - or get_secret_str("MINIMAX_API_BASE") - or "https://api.minimax.io/v1" - ) - - response = base_llm_http_handler.completion( - model=model, - messages=messages, - api_base=api_base, - custom_llm_provider=custom_llm_provider, - model_response=model_response, - encoding=_get_encoding(), - logging_obj=logging, - optional_params=optional_params, - timeout=timeout, - litellm_params=litellm_params, - shared_session=shared_session, - acompletion=acompletion, - stream=stream, - api_key=api_key, - headers=headers, - client=client, - provider_config=provider_config, - ) - logging.post_call( - input=messages, api_key=api_key, original_response=response - ) + response = _complete_minimax(_dispatch_ctx) elif custom_llm_provider == "hosted_vllm": - api_base = ( - api_base or litellm.api_base or get_secret_str("HOSTED_VLLM_API_BASE") - ) - - response = base_llm_http_handler.completion( - model=model, - messages=messages, - api_base=api_base, - custom_llm_provider=custom_llm_provider, - model_response=model_response, - encoding=_get_encoding(), - logging_obj=logging, - optional_params=optional_params, - timeout=timeout, - litellm_params=litellm_params, - shared_session=shared_session, - acompletion=acompletion, - stream=stream, - api_key=api_key, - headers=headers, - client=client, - provider_config=provider_config, - ) - logging.post_call( - input=messages, api_key=api_key, original_response=response - ) + response = _complete_hosted_vllm(_dispatch_ctx) elif ( model in litellm.open_ai_chat_completion_models or custom_llm_provider == "custom_openai" @@ -2667,205 +5724,17 @@ def completion( # type: ignore # noqa: PLR0915 ): # allow user to make an openai call with a custom base # note: if a user sets a custom base - we should ensure this works # allow for the setting of dynamic and stateful api-bases - api_base = ( - api_base # for deepinfra/perplexity/anyscale/groq/friendliai we check in get_llm_provider and pass in the api base from there - or litellm.api_base - or get_secret("OPENAI_BASE_URL") - or get_secret("OPENAI_API_BASE") - or "https://api.openai.com/v1" - ) - organization = ( - organization - or litellm.organization - or get_secret("OPENAI_ORGANIZATION") - or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105 - ) - openai.organization = organization - # set API KEY - api_key = ( - api_key - or litellm.api_key # for deepinfra/perplexity/anyscale/friendliai we check in get_llm_provider and pass in the api key from there - or litellm.openai_key - or get_secret("OPENAI_API_KEY") - ) - - headers = headers or litellm.headers - - # Add GitHub Copilot headers (same as /responses endpoint does) - if custom_llm_provider == "github_copilot": - from litellm.llms.github_copilot.authenticator import Authenticator - from litellm.llms.github_copilot.common_utils import ( - get_copilot_default_headers, - ) - - copilot_auth = Authenticator() - copilot_api_key = copilot_auth.get_api_key() - copilot_headers = get_copilot_default_headers(copilot_api_key) - if extra_headers: - copilot_headers.update(extra_headers) - extra_headers = copilot_headers - - if extra_headers is not None: - optional_params["extra_headers"] = extra_headers - - if ( - litellm.enable_preview_features and metadata is not None - ): # [PREVIEW] allow metadata to be passed to OPENAI - openai_metadata = get_requester_metadata(metadata) - if openai_metadata is not None: - optional_params["metadata"] = openai_metadata - - ## LOAD CONFIG - if set - config = litellm.OpenAIConfig.get_config() - for k, v in config.items(): - if ( - k not in optional_params - ): # completion(top_k=3) > openai_config(top_k=3) <- allows for dynamic variables to be passed in - optional_params[k] = v - - ## COMPLETION CALL - use_base_llm_http_handler = get_secret_bool( - "EXPERIMENTAL_OPENAI_BASE_LLM_HTTP_HANDLER" - ) - - try: - if use_base_llm_http_handler: - response = base_llm_http_handler.completion( - model=model, - messages=messages, - api_base=api_base, - custom_llm_provider=custom_llm_provider, - model_response=model_response, - encoding=_get_encoding(), - logging_obj=logging, - optional_params=optional_params, - timeout=timeout, - litellm_params=litellm_params, - shared_session=shared_session, - acompletion=acompletion, - stream=stream, - api_key=api_key, - headers=headers, - client=client, - provider_config=provider_config, - ) - else: - response = openai_chat_completions.completion( - model=model, - messages=messages, - headers=headers, - model_response=model_response, - print_verbose=print_verbose, - api_key=api_key, - api_base=api_base, - acompletion=acompletion, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - timeout=timeout, # type: ignore - custom_prompt_dict=custom_prompt_dict, - client=client, # pass AsyncOpenAI, OpenAI client - organization=organization, - custom_llm_provider=custom_llm_provider, - shared_session=shared_session, - ) - except Exception as e: - ## LOGGING - log the original exception returned - logging.post_call( - input=messages, - api_key=api_key, - original_response=str(e), - additional_args={"headers": headers}, - ) - raise e - - if optional_params.get("stream", False): - ## LOGGING - logging.post_call( - input=messages, - api_key=api_key, - original_response=response, - additional_args={"headers": headers}, - ) + response = _complete_custom_openai(_dispatch_ctx) elif custom_llm_provider == "mistral": - api_key = api_key or litellm.api_key or get_secret("MISTRAL_API_KEY") - api_base = ( - api_base - or litellm.api_base - or get_secret("MISTRAL_API_BASE") - or "https://api.mistral.ai/v1" - ) - - response = base_llm_http_handler.completion( - model=model, - messages=messages, - api_base=api_base, - custom_llm_provider=custom_llm_provider, - model_response=model_response, - encoding=_get_encoding(), - logging_obj=logging, - optional_params=optional_params, - timeout=timeout, - litellm_params=litellm_params, - shared_session=shared_session, - acompletion=acompletion, - stream=stream, - api_key=api_key, - headers=headers, - client=client, - provider_config=provider_config, - ) + response = _complete_mistral(_dispatch_ctx) elif ( "replicate" in model or custom_llm_provider == "replicate" or model in litellm.replicate_models ): # Setting the relevant API KEY for replicate, replicate defaults to using os.environ.get("REPLICATE_API_TOKEN") - replicate_key = ( - api_key - or litellm.replicate_key - or litellm.api_key - or get_secret("REPLICATE_API_KEY") - or get_secret("REPLICATE_API_TOKEN") - ) - - api_base = ( - api_base - or litellm.api_base - or get_secret("REPLICATE_API_BASE") - or "https://api.replicate.com/v1" - ) - - custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict - - model_response = replicate_chat_completion( # type: ignore - model=model, - messages=messages, - api_base=api_base, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=_get_encoding(), # for calculating input/output tokens - api_key=replicate_key, - logging_obj=logging, - custom_prompt_dict=custom_prompt_dict, - acompletion=acompletion, - headers=headers, - ) - - if optional_params.get("stream", False) is True: - ## LOGGING - logging.post_call( - input=messages, - api_key=replicate_key, - original_response=model_response, - ) - - response = model_response + response = _complete_replicate(_dispatch_ctx) elif ( "clarifai" in model or custom_llm_provider == "clarifai" @@ -2873,614 +5742,36 @@ def completion( # type: ignore # noqa: PLR0915 ): pass # Deprecated - handled in the openai compatible provider section above elif custom_llm_provider == "anthropic_text": - api_key = ( - api_key - or litellm.anthropic_key - or litellm.api_key - or os.environ.get("ANTHROPIC_API_KEY") - ) - custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict - api_base = ( - api_base - or litellm.api_base - or get_secret("ANTHROPIC_API_BASE") - or get_secret("ANTHROPIC_BASE_URL") - or "https://api.anthropic.com/v1/complete" - ) - - # Check if we should disable automatic URL suffix appending - disable_url_suffix = get_secret_bool("LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX") - if ( - api_base is not None - and not disable_url_suffix - and not api_base.endswith("/v1/complete") - ): - api_base += "/v1/complete" - elif disable_url_suffix: - verbose_logger.debug( - "LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX is set, skipping /v1/complete suffix" - ) - - response = base_llm_http_handler.completion( - model=model, - stream=stream, - messages=messages, - acompletion=acompletion, - api_base=api_base, - model_response=model_response, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - custom_llm_provider="anthropic_text", - timeout=timeout, - headers=headers, - encoding=_get_encoding(), - api_key=api_key, - logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements - ) + response = _complete_anthropic_text(_dispatch_ctx) elif custom_llm_provider == "anthropic": - api_key = ( - api_key - or litellm.anthropic_key - or litellm.api_key - or os.environ.get("ANTHROPIC_API_KEY") - ) - custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict - # call /messages - # default route for all anthropic models - api_base = ( - api_base - or litellm.api_base - or get_secret("ANTHROPIC_API_BASE") - or get_secret("ANTHROPIC_BASE_URL") - or "https://api.anthropic.com/v1/messages" - ) - - # Check if we should disable automatic URL suffix appending - disable_url_suffix = get_secret_bool("LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX") - if ( - api_base is not None - and not disable_url_suffix - and not api_base.endswith("/v1/messages") - ): - api_base += "/v1/messages" - elif disable_url_suffix: - verbose_logger.debug( - "LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX is set, skipping /v1/messages suffix" - ) - - response = anthropic_chat_completions.completion( - model=model, - messages=messages, - api_base=api_base, - acompletion=acompletion, - custom_prompt_dict=litellm.custom_prompt_dict, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=_get_encoding(), # for calculating input/output tokens - api_key=api_key, - logging_obj=logging, - headers=headers, - timeout=timeout, - client=client, - custom_llm_provider=custom_llm_provider, - ) - if optional_params.get("stream", False) or acompletion is True: - ## LOGGING - logging.post_call( - input=messages, - api_key=api_key, - original_response=response, - ) - response = response + response = _complete_anthropic(_dispatch_ctx) elif custom_llm_provider == "nlp_cloud": - nlp_cloud_key = ( - api_key - or litellm.nlp_cloud_key - or get_secret("NLP_CLOUD_API_KEY") - or litellm.api_key - ) - - api_base = ( - api_base - or litellm.api_base - or get_secret("NLP_CLOUD_API_BASE") - or "https://api.nlpcloud.io/v1/gpu/" - ) - - response = nlp_cloud_chat_completion( - model=model, - messages=messages, - api_base=api_base, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=_get_encoding(), - api_key=nlp_cloud_key, - logging_obj=logging, - ) - - if "stream" in optional_params and optional_params["stream"] is True: - # don't try to access stream object, - response = CustomStreamWrapper( - response, - model, - custom_llm_provider="nlp_cloud", - logging_obj=logging, - ) - - if optional_params.get("stream", False) or acompletion is True: - ## LOGGING - logging.post_call( - input=messages, - api_key=api_key, - original_response=response, - ) - - response = response + response = _complete_nlp_cloud(_dispatch_ctx) elif custom_llm_provider == "aleph_alpha": - aleph_alpha_key = ( - api_key - or litellm.aleph_alpha_key - or get_secret("ALEPH_ALPHA_API_KEY") - or get_secret("ALEPHALPHA_API_KEY") - or litellm.api_key - ) - - api_base = ( - api_base - or litellm.api_base - or get_secret("ALEPH_ALPHA_API_BASE") - or "https://api.aleph-alpha.com/complete" - ) - - model_response = aleph_alpha.completion( - model=model, - messages=messages, - api_base=api_base, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=_get_encoding(), - default_max_tokens_to_sample=litellm.max_tokens, - api_key=aleph_alpha_key, - logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements - ) - - if "stream" in optional_params and optional_params["stream"] is True: - # don't try to access stream object, - response = CustomStreamWrapper( - model_response, - model, - custom_llm_provider="aleph_alpha", - logging_obj=logging, - ) - return response - response = model_response + response = _complete_aleph_alpha(_dispatch_ctx) elif custom_llm_provider == "cohere_chat" or custom_llm_provider == "cohere": - cohere_key = ( - api_key - or litellm.cohere_key - or get_secret_str("COHERE_API_KEY") - or get_secret_str("CO_API_KEY") - or litellm.api_key - ) - - cohere_route = CohereModelInfo.get_cohere_route(model) - verbose_logger.debug(f"Cohere route: {cohere_route}") - # Set API base based on route - if cohere_route == "v2": - api_base = ( - api_base - or litellm.api_base - or get_secret_str("COHERE_API_BASE") - or "https://api.cohere.com/v2/chat" - ) - # Remove v2/ prefix from model name for the actual API call - if "v2/" in model: - model = model.replace("v2/", "") - else: - api_base = ( - api_base - or litellm.api_base - or get_secret_str("COHERE_API_BASE") - or "https://api.cohere.ai/v1/chat" - ) - - headers = headers or litellm.headers or {} - if headers is None: - headers = {} - - if extra_headers is not None: - headers.update(extra_headers) - - verbose_logger.debug(f"Model: {model}, API Base: {api_base}") - verbose_logger.debug(f"Provider Config: {provider_config}") - response = base_llm_http_handler.completion( - model=model, - stream=stream, - messages=messages, - acompletion=acompletion, - api_base=api_base, - model_response=model_response, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - custom_llm_provider="cohere_chat", - timeout=timeout, - headers=headers, - encoding=_get_encoding(), - api_key=cohere_key, - provider_config=provider_config, - logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements - ) + response = _complete_cohere_chat(_dispatch_ctx) elif custom_llm_provider == "maritalk": - maritalk_key = ( - api_key - or litellm.maritalk_key - or get_secret("MARITALK_API_KEY") - or litellm.api_key - ) - - api_base = ( - api_base - or litellm.api_base - or get_secret("MARITALK_API_BASE") - or "https://chat.maritaca.ai/api" - ) - - model_response = openai_like_chat_completion.completion( - model=model, - messages=messages, - api_base=api_base, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=_get_encoding(), - api_key=maritalk_key, - logging_obj=logging, - custom_llm_provider="maritalk", - custom_prompt_dict=custom_prompt_dict, - ) - - response = model_response + response = _complete_maritalk(_dispatch_ctx) elif custom_llm_provider == "amazon_nova": - api_key = ( - api_key - or litellm.amazon_nova_api_key - or get_secret_str("AMAZON_NOVA_API_KEY") - or litellm.api_key - ) - api_base = ( - api_base - or litellm.api_base - or get_secret_str("AMAZON_NOVA_API_BASE") - or "https://api.nova.amazon.com/v1" - ) - response = openai_like_chat_completion.completion( - model=model, - messages=messages, - api_base=api_base, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=_get_encoding(), - api_key=api_key, - logging_obj=logging, - timeout=timeout, - custom_llm_provider=custom_llm_provider, - custom_prompt_dict=custom_prompt_dict, - ) + response = _complete_amazon_nova(_dispatch_ctx) elif custom_llm_provider == "huggingface": - huggingface_key = ( - api_key - or litellm.huggingface_key - or os.environ.get("HF_TOKEN") - or os.environ.get("HUGGINGFACE_API_KEY") - or litellm.api_key - ) - hf_headers = headers or litellm.headers - response = base_llm_http_handler.completion( - model=model, - messages=messages, - headers=hf_headers, - model_response=model_response, - api_key=huggingface_key, - api_base=api_base, - acompletion=acompletion, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - timeout=timeout, # type: ignore - client=client, - custom_llm_provider=custom_llm_provider, - encoding=_get_encoding(), - stream=stream, - ) + response = _complete_huggingface(_dispatch_ctx) elif custom_llm_provider == "oci": - response = base_llm_http_handler.completion( - model=model, - messages=messages, - headers=headers, - model_response=model_response, - api_key=api_key, - api_base=api_base, - acompletion=acompletion, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - timeout=timeout, # type: ignore - client=client, - custom_llm_provider=custom_llm_provider, - encoding=_get_encoding(), - stream=stream, - ) + response = _complete_oci(_dispatch_ctx) elif custom_llm_provider == "compactifai": - api_key = ( - api_key or get_secret_str("COMPACTIFAI_API_KEY") or litellm.api_key - ) - - api_base = api_base or "https://api.compactif.ai/v1" - - ## COMPLETION CALL - response = base_llm_http_handler.completion( - model=model, - messages=messages, - headers=headers, - model_response=model_response, - api_key=api_key, - api_base=api_base, - acompletion=acompletion, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - timeout=timeout, - client=client, - custom_llm_provider=custom_llm_provider, - encoding=_get_encoding(), - stream=stream, - provider_config=provider_config, - ) + response = _complete_compactifai(_dispatch_ctx) elif custom_llm_provider == "oobabooga": - custom_llm_provider = "oobabooga" - model_response = oobabooga.completion( - model=model, - messages=messages, - model_response=model_response, - api_base=api_base, # type: ignore - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - api_key=None, - logger_fn=logger_fn, - encoding=_get_encoding(), - logging_obj=logging, - ) - if "stream" in optional_params and optional_params["stream"] is True: - # don't try to access stream object, - response = CustomStreamWrapper( - model_response, - model, - custom_llm_provider="oobabooga", - logging_obj=logging, - ) - return response - response = model_response + response = _complete_oobabooga(_dispatch_ctx) elif custom_llm_provider == "databricks": - api_base = ( - api_base # for databricks we check in get_llm_provider and pass in the api base from there - or litellm.api_base - or os.getenv("DATABRICKS_API_BASE") - ) - - # set API KEY - api_key = ( - api_key - or litellm.api_key # for databricks we check in get_llm_provider and pass in the api key from there - or litellm.databricks_key - or get_secret("DATABRICKS_API_KEY") - ) - - headers = headers or litellm.headers - - ## COMPLETION CALL - try: - response = base_llm_http_handler.completion( - model=model, - stream=stream, - messages=messages, - acompletion=acompletion, - api_base=api_base, - model_response=model_response, - optional_params=optional_params, - litellm_params=litellm_params, - custom_llm_provider="databricks", - timeout=timeout, - headers=headers, - encoding=_get_encoding(), - api_key=api_key, - logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements - client=client, - ) - except Exception as e: - ## LOGGING - log the original exception returned - logging.post_call( - input=messages, - api_key=api_key, - original_response=str(e), - additional_args={"headers": headers}, - ) - raise e - - if optional_params.get("stream", False): - ## LOGGING - logging.post_call( - input=messages, - api_key=api_key, - original_response=response, - additional_args={"headers": headers}, - ) + response = _complete_databricks(_dispatch_ctx) elif custom_llm_provider == "datarobot": - response = base_llm_http_handler.completion( - model=model, - messages=messages, - headers=headers, - model_response=model_response, - api_key=api_key, - api_base=api_base, - acompletion=acompletion, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - timeout=timeout, # type: ignore - client=client, - custom_llm_provider=custom_llm_provider, - encoding=_get_encoding(), - stream=stream, - provider_config=provider_config, - ) + response = _complete_datarobot(_dispatch_ctx) elif custom_llm_provider == "openrouter": - api_base = ( - api_base - or litellm.api_base - or get_secret_str("OPENROUTER_API_BASE") - or "https://openrouter.ai/api/v1" - ) - - api_key = ( - api_key - or litellm.api_key - or litellm.openrouter_key - or get_secret_str("OPENROUTER_API_KEY") - or get_secret_str("OR_API_KEY") - ) - - openrouter_site_url = get_secret("OR_SITE_URL") or "https://litellm.ai" - openrouter_app_name = get_secret("OR_APP_NAME") or "liteLLM" - - openrouter_headers = { - "HTTP-Referer": openrouter_site_url, - "X-Title": openrouter_app_name, - } - - _headers = headers or litellm.headers - if _headers: - openrouter_headers.update(_headers) - - headers = openrouter_headers - - ## Load Config - config = litellm.OpenrouterConfig.get_config() - for k, v in config.items(): - if k == "extra_body": - # we use openai 'extra_body' to pass openrouter specific params - transforms, route, models - if "extra_body" in optional_params: - optional_params[k].update(v) - else: - optional_params[k] = v - elif k not in optional_params: - optional_params[k] = v - - data = {"model": model, "messages": messages, **optional_params} - - ## COMPLETION CALL - response = base_llm_http_handler.completion( - model=model, - stream=stream, - messages=messages, - acompletion=acompletion, - api_base=api_base, - model_response=model_response, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - custom_llm_provider="openrouter", - timeout=timeout, - headers=headers, - encoding=_get_encoding(), - api_key=api_key, - logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements - client=client, - ) - ## LOGGING - logging.post_call( - input=messages, api_key=openai.api_key, original_response=response - ) + response = _complete_openrouter(_dispatch_ctx) elif custom_llm_provider == "vercel_ai_gateway": - api_base = ( - api_base - or litellm.api_base - or get_secret_str("VERCEL_AI_GATEWAY_API_BASE") - or "https://ai-gateway.vercel.sh/v1" - ) - - api_key = ( - api_key or litellm.api_key or get_secret("VERCEL_AI_GATEWAY_API_KEY") - ) - - vercel_site_url = get_secret("VERCEL_SITE_URL") or "https://litellm.ai" - vercel_app_name = get_secret("VERCEL_APP_NAME") or "liteLLM" - - vercel_headers = { - "http-referer": vercel_site_url, - "x-title": vercel_app_name, - } - - _headers = headers or litellm.headers - if _headers: - vercel_headers.update(_headers) - - headers = vercel_headers - - ## Load Config - config = litellm.VercelAIGatewayConfig.get_config() - for k, v in config.items(): - if k == "extra_body": - # we use openai 'extra_body' to pass vercel specific params - providerOptions - if "extra_body" in optional_params: - optional_params[k].update(v) - else: - optional_params[k] = v - elif k not in optional_params: - optional_params[k] = v - - data = {"model": model, "messages": messages, **optional_params} - - ## COMPLETION CALL - response = base_llm_http_handler.completion( - model=model, - stream=stream, - messages=messages, - acompletion=acompletion, - api_base=api_base, - model_response=model_response, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - custom_llm_provider="vercel_ai_gateway", - timeout=timeout, - headers=headers, - encoding=_get_encoding(), - api_key=api_key, - logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements - client=client, - ) - ## LOGGING - logging.post_call( - input=messages, api_key=openai.api_key, original_response=response - ) + response = _complete_vercel_ai_gateway(_dispatch_ctx) elif ( custom_llm_provider == "together_ai" or ("togethercomputer" in model) @@ -3495,1114 +5786,75 @@ def completion( # type: ignore # noqa: PLR0915 "Palm was decommisioned on October 2024. Please use the `gemini/` route for Gemini Google AI Studio Models. Announcement: https://ai.google.dev/palm_docs/palm?hl=en" ) elif custom_llm_provider == "vertex_ai_beta" or custom_llm_provider == "gemini": - vertex_ai_project = ( - optional_params.pop("vertex_project", None) - or optional_params.pop("vertex_ai_project", None) - or litellm.vertex_project - or get_secret("VERTEXAI_PROJECT") - ) - vertex_ai_location = ( - optional_params.pop("vertex_location", None) - or optional_params.pop("vertex_ai_location", None) - or litellm.vertex_location - or get_secret("VERTEXAI_LOCATION") - ) - vertex_credentials = ( - optional_params.pop("vertex_credentials", None) - or optional_params.pop("vertex_ai_credentials", None) - or get_secret("VERTEXAI_CREDENTIALS") - ) - - gemini_api_key = ( - api_key - or get_api_key_from_env() - or get_secret("PALM_API_KEY") # older palm api key should also work - or litellm.api_key - ) - - api_base = api_base or litellm.api_base or get_secret("GEMINI_API_BASE") - new_params = safe_deep_copy(optional_params or {}) - response = vertex_chat_completion.completion( # type: ignore - model=model, - messages=messages, - model_response=model_response, - print_verbose=print_verbose, - optional_params=new_params, - litellm_params=litellm_params, # type: ignore - logger_fn=logger_fn, - encoding=_get_encoding(), - vertex_location=vertex_ai_location, - vertex_project=vertex_ai_project, - vertex_credentials=vertex_credentials, - gemini_api_key=gemini_api_key, - logging_obj=logging, - acompletion=acompletion, - timeout=timeout, - custom_llm_provider=custom_llm_provider, # type: ignore - client=client, - api_base=api_base, - extra_headers=headers, - ) + response = _complete_vertex_ai_beta(_dispatch_ctx) elif custom_llm_provider == "vertex_ai": - vertex_ai_project = ( - optional_params.pop("vertex_project", None) - or optional_params.pop("vertex_ai_project", None) - or litellm.vertex_project - or get_secret("VERTEXAI_PROJECT") - ) - vertex_ai_location = ( - optional_params.pop("vertex_location", None) - or optional_params.pop("vertex_ai_location", None) - or litellm.vertex_location - or get_secret("VERTEXAI_LOCATION") - ) - vertex_credentials = ( - optional_params.pop("vertex_credentials", None) - or optional_params.pop("vertex_ai_credentials", None) - or get_secret("VERTEXAI_CREDENTIALS") - ) - - api_base = api_base or litellm.api_base or get_secret("VERTEXAI_API_BASE") - - new_params = safe_deep_copy(optional_params or {}) - model_route = get_vertex_ai_model_route( - model=model, litellm_params=litellm_params - ) - - if model_route == VertexAIModelRoute.PARTNER_MODELS: - model_response = vertex_partner_models_chat_completion.completion( - model=model, - messages=messages, - model_response=model_response, - print_verbose=print_verbose, - optional_params=new_params, - litellm_params=litellm_params, # type: ignore - logger_fn=logger_fn, - encoding=_get_encoding(), - api_base=api_base, - vertex_location=vertex_ai_location, - vertex_project=vertex_ai_project, - vertex_credentials=vertex_credentials, - logging_obj=logging, - acompletion=acompletion, - headers=headers, - custom_prompt_dict=custom_prompt_dict, - timeout=timeout, - client=client, - ) - elif model_route == VertexAIModelRoute.GEMINI: - model_response = vertex_chat_completion.completion( # type: ignore - model=model, - messages=messages, - model_response=model_response, - print_verbose=print_verbose, - optional_params=new_params, - litellm_params=litellm_params, # type: ignore - logger_fn=logger_fn, - encoding=_get_encoding(), - vertex_location=vertex_ai_location, - vertex_project=vertex_ai_project, - vertex_credentials=vertex_credentials, - gemini_api_key=None, - logging_obj=logging, - acompletion=acompletion, - timeout=timeout, - custom_llm_provider=custom_llm_provider, # type: ignore - client=client, - api_base=api_base, - extra_headers=headers, - ) - elif model_route == VertexAIModelRoute.GEMMA: - # Vertex Gemma Models with custom prediction endpoint - model_response = vertex_gemma_chat_completion.completion( - model=model, - messages=messages, - model_response=model_response, - print_verbose=print_verbose, - optional_params=new_params, - litellm_params=litellm_params, # type: ignore - logger_fn=logger_fn, - encoding=_get_encoding(), - api_base=api_base, - vertex_location=vertex_ai_location, - vertex_project=vertex_ai_project, - vertex_credentials=vertex_credentials, - logging_obj=logging, - acompletion=acompletion, - headers=headers, - custom_prompt_dict=custom_prompt_dict, - timeout=timeout, - client=client, - ) - elif model_route == VertexAIModelRoute.MODEL_GARDEN: - # Vertex Model Garden - OpenAI compatible models - model_response = vertex_model_garden_chat_completion.completion( - model=model, - messages=messages, - model_response=model_response, - print_verbose=print_verbose, - optional_params=new_params, - litellm_params=litellm_params, # type: ignore - logger_fn=logger_fn, - encoding=_get_encoding(), - api_base=api_base, - vertex_location=vertex_ai_location, - vertex_project=vertex_ai_project, - vertex_credentials=vertex_credentials, - logging_obj=logging, - acompletion=acompletion, - headers=headers, - custom_prompt_dict=custom_prompt_dict, - timeout=timeout, - client=client, - ) - elif model_route == VertexAIModelRoute.AGENT_ENGINE: - # Vertex AI Agent Engine (Reasoning Engines) - from litellm.llms.vertex_ai.agent_engine.transformation import ( - VertexAgentEngineConfig, - ) - - vertex_agent_engine_config = VertexAgentEngineConfig() - - # Update litellm_params with vertex credentials - litellm_params["vertex_project"] = vertex_ai_project - litellm_params["vertex_location"] = vertex_ai_location - litellm_params["vertex_credentials"] = vertex_credentials - - model_response = base_llm_http_handler.completion( - model=model, - stream=stream, - messages=messages, - model_response=model_response, - optional_params=new_params, - litellm_params=litellm_params, # type: ignore - encoding=_get_encoding(), - api_key=None, - api_base=api_base, - logging_obj=logging, - acompletion=acompletion, - timeout=timeout, - client=client, - custom_llm_provider="vertex_ai", - provider_config=vertex_agent_engine_config, - headers=headers or {}, - ) - else: # VertexAIModelRoute.NON_GEMINI - model_response = vertex_ai_non_gemini.completion( - model=model, - messages=messages, - model_response=model_response, - print_verbose=print_verbose, - optional_params=new_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=_get_encoding(), - vertex_location=vertex_ai_location, - vertex_project=vertex_ai_project, - vertex_credentials=vertex_credentials, - logging_obj=logging, - acompletion=acompletion, - ) - - if ( - "stream" in optional_params - and optional_params["stream"] is True - and acompletion is False - ): - response = CustomStreamWrapper( - model_response, - model, - custom_llm_provider="vertex_ai", - logging_obj=logging, - ) - return response - response = model_response + response = _complete_vertex_ai(_dispatch_ctx) elif custom_llm_provider == "predibase": - tenant_id = ( - optional_params.pop("tenant_id", None) - or optional_params.pop("predibase_tenant_id", None) - or litellm.predibase_tenant_id - or get_secret("PREDIBASE_TENANT_ID") - ) - - if tenant_id is None: - raise ValueError( - "Missing Predibase Tenant ID - Required for making the request. Set dynamically (e.g. `completion(..tenant_id=)`) or in env - `PREDIBASE_TENANT_ID`." - ) - - api_base = ( - api_base - or optional_params.pop("api_base", None) - or optional_params.pop("base_url", None) - or litellm.api_base - or get_secret("PREDIBASE_API_BASE") - ) - - api_key = ( - api_key - or litellm.api_key - or litellm.predibase_key - or get_secret("PREDIBASE_API_KEY") - ) - - _model_response = predibase_chat_completions.completion( - model=model, - messages=messages, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=_get_encoding(), - logging_obj=logging, - acompletion=acompletion, - api_base=api_base, - custom_prompt_dict=custom_prompt_dict, - api_key=api_key, - tenant_id=tenant_id, - timeout=timeout, - ) - - if ( - "stream" in optional_params - and optional_params["stream"] is True - and acompletion is False - ): - return _model_response - response = _model_response + response = _complete_predibase(_dispatch_ctx) elif custom_llm_provider == "text-completion-codestral": - api_base = ( - api_base - or optional_params.pop("api_base", None) - or optional_params.pop("base_url", None) - or litellm.api_base - or "https://codestral.mistral.ai/v1/fim/completions" - ) - - api_key = api_key or litellm.api_key or get_secret("CODESTRAL_API_KEY") - - text_completion_model_response = litellm.TextCompletionResponse( - stream=stream - ) - - _model_response = codestral_text_completions.completion( # type: ignore - model=model, - messages=messages, - model_response=text_completion_model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=_get_encoding(), - logging_obj=logging, - acompletion=acompletion, - api_base=api_base, - custom_prompt_dict=custom_prompt_dict, - api_key=api_key, - timeout=timeout, - ) - - if ( - "stream" in optional_params - and optional_params["stream"] is True - and acompletion is False - ): - return _model_response - response = _model_response + response = _complete_text_completion_codestral(_dispatch_ctx) elif custom_llm_provider == "text-completion-inception": - passed_api_base = ( - api_base - or optional_params.pop("api_base", None) - or optional_params.pop("base_url", None) - ) - api_base = ( - passed_api_base - or get_secret_str("INCEPTION_API_BASE") - or "https://api.inceptionlabs.ai/v1" - ) - # FIM is served at `/v1/fim/completions`; the OpenAI client appends - # `/completions`, so point it at the `/v1/fim` base. - api_base = api_base.rstrip("/") - if not api_base.endswith("/fim"): - api_base += "/fim" - - # Don't forward the server-managed Inception key to a caller-supplied - # api_base; only resolve it for the default/server base, or when the - # caller passes their own key. - if passed_api_base is None or api_key: - api_key = ( - api_key - or litellm.inception_key - or get_secret_str("INCEPTION_API_KEY") - ) - - _response = openai_text_completions.completion( - model=model, - messages=messages, - model_response=model_response, - print_verbose=print_verbose, - api_key=api_key, # type: ignore[arg-type] - custom_llm_provider="text-completion-inception", - api_base=api_base, - acompletion=acompletion, - client=client, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - timeout=timeout, # type: ignore - ) - - if ( - optional_params.get("stream", False) is False - and acompletion is False - and text_completion is False - ): - _response = litellm.OpenAITextCompletionConfig().convert_to_chat_model_response_object( - response_object=_response, model_response_object=model_response - ) - - if optional_params.get("stream", False) or acompletion is True: - logging.post_call( - input=messages, - api_key=api_key, - original_response=_response, - additional_args={"headers": headers}, - ) - response = _response + response = _complete_text_completion_inception(_dispatch_ctx) elif custom_llm_provider in ("sagemaker_chat", "sagemaker_nova"): # boto3 reads keys from .env # sagemaker_chat: HF Messages API endpoints # sagemaker_nova: Nova models on SageMaker (OpenAI-compatible) - model_response = base_llm_http_handler.completion( - model=model, - stream=stream, - messages=messages, - acompletion=acompletion, - api_base=api_base, - model_response=model_response, - optional_params=optional_params, - litellm_params=litellm_params, - custom_llm_provider=custom_llm_provider, - timeout=timeout, - headers=headers, - encoding=_get_encoding(), - api_key=api_key, - logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements - client=client, - ) - - ## RESPONSE OBJECT - response = model_response + response = _complete_sagemaker_chat(_dispatch_ctx) elif custom_llm_provider == "sagemaker": # boto3 reads keys from .env - model_response = sagemaker_llm.completion( - model=model, - messages=messages, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - custom_prompt_dict=custom_prompt_dict, - hf_model_name=hf_model_name, - logger_fn=logger_fn, - encoding=_get_encoding(), - logging_obj=logging, - acompletion=acompletion, - ) - - ## RESPONSE OBJECT - response = model_response + response = _complete_sagemaker(_dispatch_ctx) elif custom_llm_provider == "bedrock": # boto3 reads keys from .env - custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict - - if "aws_bedrock_client" in optional_params: - verbose_logger.warning( - "'aws_bedrock_client' is a deprecated param. Please move to another auth method - https://docs.litellm.ai/docs/providers/bedrock#boto3---authentication." - ) - # Extract credentials for legacy boto3 client and pass thru to httpx - aws_bedrock_client = optional_params.pop("aws_bedrock_client") - creds = aws_bedrock_client._get_credentials().get_frozen_credentials() - - if creds.access_key: - optional_params["aws_access_key_id"] = creds.access_key - if creds.secret_key: - optional_params["aws_secret_access_key"] = creds.secret_key - if creds.token: - optional_params["aws_session_token"] = creds.token - if ( - "aws_region_name" not in optional_params - or optional_params["aws_region_name"] is None - ): - optional_params["aws_region_name"] = ( - aws_bedrock_client.meta.region_name - ) - - bedrock_route = BedrockModelInfo.get_bedrock_route(model) - if bedrock_route == "claude_platform": - provider_config = ProviderConfigManager.get_provider_chat_config( - model=model, - provider=LlmProviders.BEDROCK, - ) - model = BedrockModelInfo.get_claude_platform_model(model) - response = base_llm_http_handler.completion( - model=model, - stream=stream, - messages=messages, - acompletion=acompletion, - api_base=api_base, - model_response=model_response, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - custom_llm_provider="bedrock", - timeout=timeout, - headers=headers, - encoding=_get_encoding(), - api_key=api_key, - logging_obj=logging, - client=client, - provider_config=provider_config, - ) - return response - elif bedrock_route == "converse": - model = model.replace("converse/", "") - response = bedrock_converse_chat_completion.completion( - model=model, - messages=messages, - custom_prompt_dict=custom_prompt_dict, - model_response=model_response, - optional_params=optional_params, - litellm_params=litellm_params, # type: ignore - logger_fn=logger_fn, - encoding=_get_encoding(), - logging_obj=logging, - extra_headers=headers, # Use merged headers instead of original extra_headers - timeout=timeout, - acompletion=acompletion, - client=client, - api_base=api_base, - api_key=api_key, - ) - elif bedrock_route == "converse_like": - model = model.replace("converse_like/", "") - response = base_llm_http_handler.completion( - model=model, - stream=stream, - messages=messages, - acompletion=acompletion, - api_base=api_base, - model_response=model_response, - optional_params=optional_params, - litellm_params=litellm_params, - custom_llm_provider="bedrock", - timeout=timeout, - headers=headers, - encoding=_get_encoding(), - api_key=api_key, - logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements - client=client, - ) - else: - response = base_llm_http_handler.completion( - model=model, - stream=stream, - messages=messages, - acompletion=acompletion, - api_base=api_base, - model_response=model_response, - optional_params=optional_params, - litellm_params=litellm_params, - custom_llm_provider="bedrock", - timeout=timeout, - headers=headers, - encoding=_get_encoding(), - api_key=api_key, - logging_obj=logging, - client=client, - ) + response = _complete_bedrock(_dispatch_ctx) elif custom_llm_provider == "watsonx": - response = watsonx_chat_completion.completion( - model=model, - messages=messages, - headers=headers, - model_response=model_response, - print_verbose=print_verbose, - api_key=api_key, - api_base=api_base, - acompletion=acompletion, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - timeout=timeout, # type: ignore - custom_prompt_dict=custom_prompt_dict, - client=client, # pass AsyncOpenAI, OpenAI client - encoding=_get_encoding(), - custom_llm_provider="watsonx", - ) + response = _complete_watsonx(_dispatch_ctx) elif custom_llm_provider == "watsonx_text": - api_key = ( - api_key - or optional_params.pop("apikey", None) - or get_secret_str("WATSONX_APIKEY") - or get_secret_str("WATSONX_API_KEY") - or get_secret_str("WX_API_KEY") - ) - - api_base = ( - api_base - or optional_params.pop( - "url", - optional_params.pop( - "api_base", optional_params.pop("base_url", None) - ), - ) - or get_secret_str("WATSONX_API_BASE") - or get_secret_str("WATSONX_URL") - or get_secret_str("WX_URL") - or get_secret_str("WML_URL") - ) - - wx_credentials = optional_params.pop( - "wx_credentials", - optional_params.pop( - "watsonx_credentials", None - ), # follow {provider}_credentials, same as vertex ai - ) - - token: Optional[str] = None - if wx_credentials is not None: - api_base = wx_credentials.get("url", api_base) - api_key = wx_credentials.get( - "apikey", wx_credentials.get("api_key", api_key) - ) - token = wx_credentials.get( - "token", - wx_credentials.get( - "watsonx_token", None - ), # follow format of {provider}_token, same as azure - e.g. 'azure_ad_token=..' - ) - - if token is not None: - optional_params["token"] = token - - response = base_llm_http_handler.completion( - model=model, - stream=stream, - messages=messages, - acompletion=acompletion, - api_base=api_base, - model_response=model_response, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - custom_llm_provider="watsonx_text", - timeout=timeout, - headers=headers, - encoding=_get_encoding(), - api_key=api_key, - logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements - client=client, - ) + response = _complete_watsonx_text(_dispatch_ctx) elif custom_llm_provider == "vllm": - custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict - model_response = vllm_handler.completion( - model=model, - messages=messages, - custom_prompt_dict=custom_prompt_dict, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=_get_encoding(), - logging_obj=logging, - ) - - if ( - "stream" in optional_params and optional_params["stream"] is True - ): ## [BETA] - # don't try to access stream object, - response = CustomStreamWrapper( - model_response, - model, - custom_llm_provider="vllm", - logging_obj=logging, - ) - return response - - ## RESPONSE OBJECT - response = model_response + response = _complete_vllm(_dispatch_ctx) elif custom_llm_provider == "ollama": - api_base = ( - litellm.api_base - or api_base - or get_secret("OLLAMA_API_BASE") - or "http://localhost:11434" - ) - if api_key is not None and "Authorization" not in headers: - headers["Authorization"] = f"Bearer {api_key}" - - response = base_llm_http_handler.completion( - model=model, - stream=stream, - messages=messages, - acompletion=acompletion, - api_base=api_base, - model_response=model_response, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - custom_llm_provider="ollama", - timeout=timeout, - headers=headers, - encoding=_get_encoding(), - api_key=api_key, - logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements - client=client, - ) + response = _complete_ollama(_dispatch_ctx) elif custom_llm_provider == "ollama_chat": - api_base = ( - litellm.api_base - or api_base - or get_secret("OLLAMA_API_BASE") - or "http://localhost:11434" - ) - - api_key = ( - api_key - or litellm.ollama_key - or os.environ.get("OLLAMA_API_KEY") - or litellm.api_key - ) - if api_key is not None and "Authorization" not in headers: - headers["Authorization"] = f"Bearer {api_key}" - - response = base_llm_http_handler.completion( - model=model, - stream=stream, - messages=messages, - acompletion=acompletion, - api_base=api_base, - model_response=model_response, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - custom_llm_provider="ollama_chat", - timeout=timeout, - headers=headers, - encoding=_get_encoding(), - api_key=api_key, - logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements - client=client, - ) + response = _complete_ollama_chat(_dispatch_ctx) elif custom_llm_provider == "triton": - api_base = litellm.api_base or api_base - response = base_llm_http_handler.completion( - model=model, - stream=stream, - messages=messages, - acompletion=acompletion, - api_base=api_base, - model_response=model_response, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - custom_llm_provider=custom_llm_provider, - timeout=timeout, - headers=headers, - encoding=_get_encoding(), - api_key=api_key, - logging_obj=logging, - ) + response = _complete_triton(_dispatch_ctx) elif custom_llm_provider == "cloudflare": - api_key = ( - api_key - or litellm.cloudflare_api_key - or litellm.api_key - or get_secret("CLOUDFLARE_API_KEY") - ) - account_id = get_secret("CLOUDFLARE_ACCOUNT_ID") - api_base = ( - api_base - or litellm.api_base - or get_secret("CLOUDFLARE_API_BASE") - or f"https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/run/" - ) - - custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict - response = base_llm_http_handler.completion( - model=model, - stream=stream, - messages=messages, - acompletion=acompletion, - api_base=api_base, - model_response=model_response, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - custom_llm_provider="cloudflare", - timeout=timeout, - headers=headers, - encoding=_get_encoding(), - api_key=api_key, - logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements - ) + response = _complete_cloudflare(_dispatch_ctx) elif custom_llm_provider == "petals" or model in litellm.petals_models: - api_base = api_base or litellm.api_base - - custom_llm_provider = "petals" - stream = optional_params.pop("stream", False) - model_response = petals_handler.completion( - model=model, - messages=messages, - api_base=api_base, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=_get_encoding(), - logging_obj=logging, - client=client, - ) - if stream is True: ## [BETA] - # Fake streaming for petals - resp_string = model_response["choices"][0]["message"]["content"] - response = CustomStreamWrapper( - resp_string, - model, - custom_llm_provider="petals", - logging_obj=logging, - ) - return response - response = model_response + response = _complete_petals(_dispatch_ctx) elif custom_llm_provider == "snowflake" or model in litellm.snowflake_models: - try: - client = ( - HTTPHandler(timeout=timeout) if stream is False else None - ) # Keep this here, otherwise, the httpx.client closes and streaming is impossible - response = base_llm_http_handler.completion( - model=model, - messages=messages, - headers=headers, - model_response=model_response, - api_key=api_key, - api_base=api_base, - acompletion=acompletion, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - timeout=timeout, # type: ignore - client=client, - custom_llm_provider=custom_llm_provider, - encoding=_get_encoding(), - stream=stream, - ) - - except Exception as e: - ## LOGGING - log the original exception returned - logging.post_call( - input=messages, - api_key=api_key, - original_response=str(e), - additional_args={"headers": headers}, - ) - raise e + response = _complete_snowflake(_dispatch_ctx) elif custom_llm_provider == "gradient_ai": - api_base = litellm.api_base or api_base - response = base_llm_http_handler.completion( - model=model, - stream=stream, - messages=messages, - acompletion=acompletion, - api_base=api_base, - model_response=model_response, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - custom_llm_provider="gradient_ai", - timeout=timeout, - headers=headers, - encoding=_get_encoding(), - api_key=api_key, - logging_obj=logging, - ) + response = _complete_gradient_ai(_dispatch_ctx) elif custom_llm_provider == "bytez": - api_key = ( - api_key - or litellm.bytez_key - or get_secret_str("BYTEZ_API_KEY") - or litellm.api_key - ) - - response = base_llm_http_handler.completion( - model=model, - messages=messages, - headers=headers, - model_response=model_response, - api_key=api_key, - api_base=api_base, - acompletion=acompletion, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - timeout=timeout, # type: ignore - client=client, - custom_llm_provider=custom_llm_provider, - encoding=_get_encoding(), - stream=stream, - provider_config=bytez_transformation, - ) - - pass + response = _complete_bytez(_dispatch_ctx) elif custom_llm_provider == "lemonade": - api_key = ( - api_key - or litellm.lemonade_key - or get_secret_str("LEMONADE_API_KEY") - or litellm.api_key - ) - - response = base_llm_http_handler.completion( - model=model, - messages=messages, - headers=headers, - model_response=model_response, - api_key=api_key, - api_base=api_base, - acompletion=acompletion, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - timeout=timeout, # type: ignore - client=client, - custom_llm_provider=custom_llm_provider, - encoding=_get_encoding(), - stream=stream, - provider_config=lemonade_transformation, - ) - - pass + response = _complete_lemonade(_dispatch_ctx) elif custom_llm_provider == "ovhcloud" or model in litellm.ovhcloud_models: - api_key = ( - api_key - or litellm.ovhcloud_key - or get_secret_str("OVHCLOUD_API_KEY") - or litellm.api_key - ) - - api_base = ( - api_base - or litellm.api_base - or get_secret_str("OVHCLOUD_API_BASE") - or "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1" - ) - - response = base_llm_http_handler.completion( - model=model, - messages=messages, - headers=headers, - model_response=model_response, - api_key=api_key, - api_base=api_base, - acompletion=acompletion, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - timeout=timeout, # type: ignore - client=client, - custom_llm_provider=custom_llm_provider, - encoding=_get_encoding(), - stream=stream, - provider_config=ovhcloud_transformation, - ) - - pass + response = _complete_ovhcloud(_dispatch_ctx) elif custom_llm_provider == "custom": - url = litellm.api_base or api_base or "" - if url is None or url == "": - raise ValueError( - "api_base not set. Set api_base or litellm.api_base for custom endpoints" - ) - - """ - assume input to custom LLM api bases follow this format: - resp = litellm.module_level_client.post( - api_base, - json={ - 'model': 'meta-llama/Llama-2-13b-hf', # model name - 'params': { - 'prompt': ["The capital of France is P"], - 'max_tokens': 32, - 'temperature': 0.7, - 'top_p': 1.0, - 'top_k': 40, - } - } - ) - - """ - prompt = " ".join([message["content"] for message in messages]) # type: ignore - resp = litellm.module_level_client.post( - url, - headers=headers, - json={ - "model": model, - "params": { - "prompt": [prompt], - "max_tokens": max_tokens, - "temperature": temperature, - "top_p": top_p, - "top_k": kwargs.get("top_k"), - }, - **kwargs.get("extra_body", {}), - }, - ) - response_json = resp.json() - """ - assume all responses from custom api_bases of this format: - { - 'data': [ - { - 'prompt': 'The capital of France is P', - 'output': ['The capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France'], - 'params': {'temperature': 0.7, 'top_k': 40, 'top_p': 1}}], - 'message': 'ok' - } - ] - } - """ - string_response = response_json["data"][0]["output"][0] - ## RESPONSE OBJECT - model_response.choices[0].message.content = string_response # type: ignore - model_response.created = int(time.time()) - model_response.model = model - response = model_response + response = _complete_custom(_dispatch_ctx) elif ( custom_llm_provider in litellm._custom_providers ): # Assume custom LLM provider # Get the Custom Handler - custom_handler: Optional[CustomLLM] = None - for item in litellm.custom_provider_map: - if item["provider"] == custom_llm_provider: - custom_handler = item["custom_handler"] - - if custom_handler is None: - raise LiteLLMUnknownProvider( - model=model, custom_llm_provider=custom_llm_provider - ) - - ## ROUTE LLM CALL ## - handler_fn = custom_chat_llm_router( - async_fn=acompletion, stream=stream, custom_llm=custom_handler - ) - - headers = headers or litellm.headers or {} - - ## CALL FUNCTION - response = handler_fn( - model=model, - messages=messages, - headers=headers, - model_response=model_response, - print_verbose=print_verbose, - api_key=api_key, - api_base=api_base, - acompletion=acompletion, - logging_obj=logging, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - timeout=timeout, # type: ignore - custom_prompt_dict=custom_prompt_dict, - client=client, # pass AsyncOpenAI, OpenAI client - encoding=_get_encoding(), - ) - if stream is True: - return CustomStreamWrapper( - completion_stream=response, - model=model, - custom_llm_provider=custom_llm_provider, - logging_obj=logging, - ) + response = _complete_custom_providers(_dispatch_ctx) elif custom_llm_provider == "langgraph": # LangGraph - Agent Runtime Provider - from litellm.llms.langgraph.chat.transformation import LangGraphConfig - - ( - api_base, - api_key, - ) = LangGraphConfig()._get_openai_compatible_provider_info( - api_base=api_base or litellm.api_base, - api_key=api_key or litellm.api_key, - ) - - headers = headers or litellm.headers - - response = base_llm_http_handler.completion( - model=model, - stream=stream, - messages=messages, - acompletion=acompletion, - api_base=api_base, - model_response=model_response, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - custom_llm_provider=custom_llm_provider, - timeout=timeout, - headers=headers, - encoding=_get_encoding(), - api_key=api_key, - logging_obj=logging, - client=client, - ) + response = _complete_langgraph(_dispatch_ctx) elif custom_llm_provider == "langflow": # LangFlow - Visual AI Agent Platform - from litellm.llms.langflow.chat.transformation import LangFlowConfig - - ( - api_base, - api_key, - ) = LangFlowConfig()._get_openai_compatible_provider_info( - api_base=api_base or litellm.api_base, - api_key=api_key or litellm.api_key, - ) - - headers = headers or litellm.headers - - response = base_llm_http_handler.completion( - model=model, - stream=stream, - messages=messages, - acompletion=acompletion, - api_base=api_base, - model_response=model_response, - optional_params=optional_params, - litellm_params=litellm_params, - shared_session=shared_session, - custom_llm_provider=custom_llm_provider, - timeout=timeout, - headers=headers, - encoding=_get_encoding(), - api_key=api_key, - logging_obj=logging, - client=client, - ) + response = _complete_langflow(_dispatch_ctx) else: raise LiteLLMUnknownProvider( @@ -4869,7 +6121,7 @@ def embedding( @client -def embedding( # noqa: PLR0915 +def embedding( model, input=[], # Optional params @@ -6116,7 +7368,7 @@ async def atext_completion( @client -def text_completion( # noqa: PLR0915 +def text_completion( prompt: Union[ str, List[Union[str, List[Union[str, List[int]]]]] ], # Required: The prompt(s) to generate completions for. @@ -6655,7 +7907,7 @@ async def atranscription(*args, **kwargs) -> TranscriptionResponse: @client -def transcription( # noqa: PLR0915 +def transcription( model: str, file: FileTypes, ## OPTIONAL OPENAI PARAMS ## @@ -6962,7 +8214,7 @@ async def aspeech(*args, **kwargs) -> HttpxBinaryResponseContent: @client -def speech( # noqa: PLR0915 +def speech( model: str, input: str, voice: Optional[Union[str, dict]] = None, @@ -7425,22 +8677,7 @@ def speech( # noqa: PLR0915 async def ahealth_check( model_params: dict, - mode: Optional[ - Literal[ - "chat", - "completion", - "embedding", - "audio_speech", - "audio_transcription", - "image_generation", - "video_generation", - "batch", - "rerank", - "realtime", - "responses", - "ocr", - ] - ] = "chat", + mode: str | None = "chat", prompt: Optional[str] = None, input: Optional[List] = None, ): @@ -7563,7 +8800,7 @@ def print_verbose(print_statement): try: verbose_logger.debug(print_statement) if litellm.set_verbose: - print(print_statement) # noqa + print(print_statement) # noqa: T201 except Exception: pass @@ -7653,7 +8890,7 @@ def stream_chunk_builder_text_completion( return TextCompletionResponse(**response) -def stream_chunk_builder( # noqa: PLR0915 +def stream_chunk_builder( chunks: list, messages: Optional[list] = None, start_time=None, diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 282a292ab17..6ebac7efc8d 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -571,7 +571,7 @@ "output_vector_size": 1536 }, "amazon.titan-embed-text-v2:0": { - "input_cost_per_token": 2e-07, + "input_cost_per_token": 2e-08, "litellm_provider": "bedrock", "max_input_tokens": 8192, "max_tokens": 8192, @@ -1156,6 +1156,7 @@ "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, @@ -1202,6 +1203,7 @@ "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, @@ -1233,6 +1235,7 @@ "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, @@ -1264,6 +1267,7 @@ "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, @@ -1295,6 +1299,139 @@ "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": true, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "bedrock_output_config_effort_ceiling": "xhigh" + }, + "anthropic.claude-fable-5": { + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 1e-06, + "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, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "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": true, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "bedrock_output_config_effort_ceiling": "xhigh" + }, + "global.anthropic.claude-fable-5": { + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 1e-06, + "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, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "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": true, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "bedrock_output_config_effort_ceiling": "xhigh" + }, + "us.anthropic.claude-fable-5": { + "cache_creation_input_token_cost": 1.375e-05, + "cache_creation_input_token_cost_above_1hr": 2.2e-05, + "cache_read_input_token_cost": 1.1e-06, + "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, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "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": true, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "bedrock_output_config_effort_ceiling": "xhigh" + }, + "eu.anthropic.claude-fable-5": { + "cache_creation_input_token_cost": 1.375e-05, + "cache_creation_input_token_cost_above_1hr": 2.2e-05, + "cache_read_input_token_cost": 1.1e-06, + "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, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "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, @@ -1327,6 +1464,7 @@ "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, @@ -1359,6 +1497,7 @@ "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, @@ -1391,6 +1530,7 @@ "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, @@ -1423,6 +1563,7 @@ "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, @@ -1455,6 +1596,7 @@ "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, @@ -1485,6 +1627,7 @@ "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, @@ -2208,6 +2351,37 @@ "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 + }, + "azure_ai/claude-fable-5": { + "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": { + 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}, + "azure_ai/gpt-5.5": { + "cache_read_input_token_cost": 5e-07, + "cache_read_input_token_cost_above_272k_tokens": 1e-06, + "cache_read_input_token_cost_priority": 1e-06, + "cache_read_input_token_cost_above_272k_tokens_priority": 2e-06, + "input_cost_per_token": 5e-06, + "input_cost_per_token_above_272k_tokens": 1e-05, + "input_cost_per_token_priority": 1e-05, + "input_cost_per_token_above_272k_tokens_priority": 2e-05, + "litellm_provider": "azure_ai", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 3e-05, + "output_cost_per_token_above_272k_tokens": 4.5e-05, + "output_cost_per_token_priority": 6e-05, + "output_cost_per_token_above_272k_tokens_priority": 9e-05, + "source": "https://ai.azure.com/catalog/models/gpt-5.5", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": false + }, + "azure_ai/gpt-5.5-2026-04-23": { + "cache_read_input_token_cost": 5e-07, + "cache_read_input_token_cost_above_272k_tokens": 1e-06, + "cache_read_input_token_cost_priority": 1e-06, + "cache_read_input_token_cost_above_272k_tokens_priority": 2e-06, + "input_cost_per_token": 5e-06, + "input_cost_per_token_above_272k_tokens": 1e-05, + "input_cost_per_token_priority": 1e-05, + "input_cost_per_token_above_272k_tokens_priority": 2e-05, + 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"supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": false + }, "azure_ai/gpt-5.4": { "cache_read_input_token_cost": 2.5e-07, "cache_read_input_token_cost_above_272k_tokens": 5e-07, @@ -4234,6 +4503,23 @@ "/v1/audio/transcriptions" ] }, + "azure/gpt-realtime-whisper": { + "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", + "supported_endpoints": [ + "/v1/realtime", + "/v1/realtime/transcription_sessions" + ], + "supported_modalities": [ + "audio" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true + }, "azure/gpt-5.1-2025-11-13": { "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_priority": 2.5e-07, @@ -7382,6 +7668,45 @@ "supports_function_calling": true, "supports_tool_choice": true }, + "azure_ai/deepseek-v3.1": { + "input_cost_per_token": 1.23e-06, + "litellm_provider": "azure_ai", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 4.94e-06, + "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "azure_ai/deepseek-v4-pro": { + "input_cost_per_token": 1.74e-06, + "litellm_provider": "azure_ai", + "max_input_tokens": 1000000, + "max_output_tokens": 384000, + "max_tokens": 384000, + "mode": "chat", + "output_cost_per_token": 3.48e-06, + "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "azure_ai/deepseek-v4-flash": { + "input_cost_per_token": 1.9e-07, + "litellm_provider": "azure_ai", + "max_input_tokens": 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"cache_read_input_token_cost": 3e-07, "input_cost_per_token": 3e-06, "input_cost_per_token_above_200k_tokens": 6e-06, @@ -9896,6 +10225,7 @@ }, "claude-sonnet-4-6": { "cache_creation_input_token_cost": 3.75e-06, + "cache_creation_input_token_cost_above_1hr": 6e-06, "cache_read_input_token_cost": 3e-07, "input_cost_per_token": 3e-06, "litellm_provider": "anthropic", @@ -9924,6 +10254,8 @@ }, "claude-sonnet-4-5-20250929-v1:0": { "cache_creation_input_token_cost": 3.75e-06, + "cache_creation_input_token_cost_above_1hr": 6e-06, + "cache_creation_input_token_cost_above_1hr_above_200k_tokens": 1.2e-05, "cache_read_input_token_cost": 3e-07, "input_cost_per_token": 3e-06, "input_cost_per_token_above_200k_tokens": 6e-06, @@ -10111,7 +10443,8 @@ "fast": 6.0 }, "supports_output_config": true, - "supports_max_reasoning_effort": true + "supports_max_reasoning_effort": true, + "supports_speed": true }, "claude-opus-4-6-20260205": { "cache_creation_input_token_cost": 6.25e-06, @@ -10144,7 +10477,8 @@ "fast": 6.0 }, "supports_max_reasoning_effort": true, - "supports_output_config": true + "supports_output_config": true, + "supports_speed": true }, "claude-opus-4-7": { "cache_creation_input_token_cost": 6.25e-06, @@ -10170,6 +10504,7 @@ "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, @@ -10178,7 +10513,8 @@ "us": 1.1, "fast": 6.0 }, - "supports_output_config": true + "supports_output_config": true, + "supports_speed": true }, "claude-opus-4-7-20260416": { "cache_creation_input_token_cost": 6.25e-06, @@ -10204,6 +10540,7 @@ "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, @@ -10212,6 +10549,41 @@ "us": 1.1, "fast": 6.0 }, + 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"fireworks_ai/accounts/fireworks/models/kimi-k2-instruct": { "input_cost_per_token": 6e-07, @@ -14410,6 +15099,38 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "fireworks_ai/accounts/fireworks/models/kimi-k2p6": { + "cache_read_input_token_cost": 1.6e-07, + "input_cost_per_token": 9.5e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 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": true + }, + "fireworks_ai/accounts/fireworks/models/kimi-k2p7-code": { + "cache_read_input_token_cost": 1.9e-07, + "input_cost_per_token": 9.5e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + 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"supports_vision": false + }, + "fireworks_ai/accounts/fireworks/models/minimax-m3": { + "cache_read_input_token_cost": 6e-08, + "input_cost_per_token": 3e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 512000, + "max_output_tokens": 512000, + "max_tokens": 512000, + "mode": "chat", + "output_cost_per_token": 1.2e-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": true + }, "fireworks_ai/accounts/fireworks/models/mixtral-8x22b-instruct-hf": { "input_cost_per_token": 1.2e-06, "litellm_provider": "fireworks_ai", @@ -14579,6 +15332,38 @@ "supports_response_schema": true, "supports_tool_choice": false }, + "fireworks_ai/deepseek-v4-flash": { + "cache_read_input_token_cost": 2.8e-08, + "input_cost_per_token": 1.4e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 1048576, + "max_output_tokens": 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"supports_tool_choice": true, + "supports_vision": false + }, + "fireworks_ai/glm-5p2": { + "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": 131072, + "max_tokens": 131072, + "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/gpt-oss-120b": { + "cache_read_input_token_cost": 1.5e-08, + "input_cost_per_token": 1.5e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 131072, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 6e-07, + "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/gpt-oss-20b": { + "cache_read_input_token_cost": 3.5e-08, + "input_cost_per_token": 7e-08, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 131072, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 3e-07, + "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/kimi-k2p5": { "cache_read_input_token_cost": 1e-07, @@ -14623,6 +15473,70 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "fireworks_ai/kimi-k2p6": { + "cache_read_input_token_cost": 1.6e-07, + "input_cost_per_token": 9.5e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": 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"max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 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": true + }, + "fireworks_ai/kimi-k2p7-code-fast": { + "cache_read_input_token_cost": 3.8e-07, + "input_cost_per_token": 1.9e-06, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 8e-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": true + }, "fireworks_ai/minimax-m2p1": { "cache_read_input_token_cost": 3e-08, "input_cost_per_token": 3e-07, @@ -14637,6 +15551,54 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "fireworks_ai/minimax-m2p7": { + "cache_read_input_token_cost": 6e-08, + "input_cost_per_token": 3e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 196608, + "max_output_tokens": 196608, + "max_tokens": 196608, + "mode": "chat", + "output_cost_per_token": 1.2e-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/minimax-m3": { + "cache_read_input_token_cost": 6e-08, + "input_cost_per_token": 3e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 512000, + "max_output_tokens": 512000, + "max_tokens": 512000, + "mode": "chat", + "output_cost_per_token": 1.2e-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": true + }, + "fireworks_ai/qwen3p7-plus": { + "cache_read_input_token_cost": 8e-08, + "input_cost_per_token": 4e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 262144, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "output_cost_per_token": 1.6e-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": true + }, "fireworks_ai/nomic-ai/nomic-embed-text-v1": { "input_cost_per_token": 8e-09, "litellm_provider": "fireworks_ai-embedding-models", @@ -18554,6 +19516,38 @@ "supports_response_schema": true, "supports_vision": true }, + "github_copilot/mai-code-1-flash": { + "cache_read_input_token_cost": 7.5e-08, + "input_cost_per_token": 7.5e-07, + "litellm_provider": "github_copilot", + "max_input_tokens": 128000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 4.5e-06, + "supported_endpoints": [ + "/v1/chat/completions" + ], + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_response_schema": true + }, + "github_copilot/mai-code-1-flash-internal": { + "cache_read_input_token_cost": 7.5e-08, + "input_cost_per_token": 7.5e-07, + "litellm_provider": "github_copilot", + "max_input_tokens": 128000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 4.5e-06, + "supported_endpoints": [ + "/v1/chat/completions" + ], + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_response_schema": true + }, "github_copilot/text-embedding-3-small": { "litellm_provider": "github_copilot", "max_input_tokens": 8191, @@ -19361,8 +20355,6 @@ "output_cost_per_token": 8e-06, "output_cost_per_token_batches": 4e-06, "output_cost_per_token_priority": 1.4e-05, - "regional_processing_uplift_multiplier_eu": 1.10, - "regional_processing_uplift_multiplier_us": 1.10, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -19436,8 +20428,6 @@ "output_cost_per_token": 1.6e-06, "output_cost_per_token_batches": 8e-07, "output_cost_per_token_priority": 2.8e-06, - "regional_processing_uplift_multiplier_eu": 1.10, - "regional_processing_uplift_multiplier_us": 1.10, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -19511,8 +20501,6 @@ "output_cost_per_token": 4e-07, "output_cost_per_token_batches": 2e-07, "output_cost_per_token_priority": 8e-07, - "regional_processing_uplift_multiplier_eu": 1.10, - "regional_processing_uplift_multiplier_us": 1.10, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -19584,8 +20572,6 @@ "output_cost_per_token": 1e-05, "output_cost_per_token_batches": 5e-06, "output_cost_per_token_priority": 1.7e-05, - "regional_processing_uplift_multiplier_eu": 1.10, - "regional_processing_uplift_multiplier_us": 1.10, "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, @@ -19627,8 +20613,6 @@ "mode": "chat", "output_cost_per_token": 1e-05, "output_cost_per_token_batches": 5e-06, - "regional_processing_uplift_multiplier_eu": 1.10, - "regional_processing_uplift_multiplier_us": 1.10, "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, @@ -19650,8 +20634,6 @@ "mode": "chat", "output_cost_per_token": 1e-05, "output_cost_per_token_batches": 5e-06, - "regional_processing_uplift_multiplier_eu": 1.10, - "regional_processing_uplift_multiplier_us": 1.10, "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, @@ -19940,8 +20922,6 @@ "output_cost_per_token": 6e-07, "output_cost_per_token_batches": 3e-07, "output_cost_per_token_priority": 1e-06, - "regional_processing_uplift_multiplier_eu": 1.10, - "regional_processing_uplift_multiplier_us": 1.10, "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, @@ -20645,8 +21625,6 @@ "output_cost_per_token": 1e-05, "output_cost_per_token_flex": 5e-06, "output_cost_per_token_priority": 2e-05, - "regional_processing_uplift_multiplier_eu": 1.10, - "regional_processing_uplift_multiplier_us": 1.10, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -21040,6 +22018,8 @@ "output_cost_per_token_flex": 1.5e-05, "output_cost_per_token_batches": 1.5e-05, "output_cost_per_token_priority": 6e-05, + "regional_processing_uplift_multiplier_eu": 1.10, + "regional_processing_uplift_multiplier_us": 1.10, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -21088,6 +22068,8 @@ "output_cost_per_token_flex": 1.5e-05, "output_cost_per_token_batches": 1.5e-05, "output_cost_per_token_priority": 6e-05, + "regional_processing_uplift_multiplier_eu": 1.10, + "regional_processing_uplift_multiplier_us": 1.10, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -21132,6 +22114,8 @@ "output_cost_per_token_above_272k_tokens": 0.00027, "output_cost_per_token_flex": 9e-05, "output_cost_per_token_batches": 9e-05, + "regional_processing_uplift_multiplier_eu": 1.10, + "regional_processing_uplift_multiplier_us": 1.10, "supported_endpoints": [ "/v1/responses", "/v1/batch" @@ -21176,6 +22160,8 @@ "output_cost_per_token_above_272k_tokens": 0.00027, "output_cost_per_token_flex": 9e-05, "output_cost_per_token_batches": 9e-05, + "regional_processing_uplift_multiplier_eu": 1.10, + "regional_processing_uplift_multiplier_us": 1.10, "supported_endpoints": [ "/v1/responses", "/v1/batch" @@ -21224,6 +22210,8 @@ "output_cost_per_token_flex": 7.5e-06, "output_cost_per_token_batches": 7.5e-06, "output_cost_per_token_priority": 3e-05, + "regional_processing_uplift_multiplier_eu": 1.10, + "regional_processing_uplift_multiplier_us": 1.10, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -21271,6 +22259,8 @@ "output_cost_per_token_flex": 7.5e-06, "output_cost_per_token_batches": 7.5e-06, "output_cost_per_token_priority": 3e-05, + "regional_processing_uplift_multiplier_eu": 1.10, + "regional_processing_uplift_multiplier_us": 1.10, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -21311,6 +22301,8 @@ "output_cost_per_token_above_272k_tokens": 0.00027, "output_cost_per_token_flex": 9e-05, "output_cost_per_token_batches": 9e-05, + "regional_processing_uplift_multiplier_eu": 1.10, + "regional_processing_uplift_multiplier_us": 1.10, "supported_endpoints": [ "/v1/responses", "/v1/batch" @@ -21354,6 +22346,8 @@ "output_cost_per_token_above_272k_tokens": 0.00027, "output_cost_per_token_flex": 9e-05, "output_cost_per_token_batches": 9e-05, + "regional_processing_uplift_multiplier_eu": 1.10, + "regional_processing_uplift_multiplier_us": 1.10, "supported_endpoints": [ "/v1/responses", "/v1/batch" @@ -21399,6 +22393,8 @@ "output_cost_per_token_flex": 2.25e-06, "output_cost_per_token_batches": 2.25e-06, "output_cost_per_token_priority": 9e-06, + "regional_processing_uplift_multiplier_eu": 1.10, + "regional_processing_uplift_multiplier_us": 1.10, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -21445,6 +22441,8 @@ "output_cost_per_token_flex": 2.25e-06, "output_cost_per_token_batches": 2.25e-06, "output_cost_per_token_priority": 9e-06, + "regional_processing_uplift_multiplier_eu": 1.10, + "regional_processing_uplift_multiplier_us": 1.10, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -21488,6 +22486,8 @@ "output_cost_per_token": 1.25e-06, "output_cost_per_token_flex": 6.25e-07, "output_cost_per_token_batches": 6.25e-07, + "regional_processing_uplift_multiplier_eu": 1.10, + "regional_processing_uplift_multiplier_us": 1.10, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -21531,6 +22531,8 @@ "output_cost_per_token": 1.25e-06, "output_cost_per_token_flex": 6.25e-07, "output_cost_per_token_batches": 6.25e-07, + "regional_processing_uplift_multiplier_eu": 1.10, + "regional_processing_uplift_multiplier_us": 1.10, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -21569,8 +22571,6 @@ "mode": "responses", "output_cost_per_token": 0.00012, "output_cost_per_token_batches": 6e-05, - "regional_processing_uplift_multiplier_eu": 1.10, - "regional_processing_uplift_multiplier_us": 1.10, "supported_endpoints": [ "/v1/batch", "/v1/responses" @@ -21977,8 +22977,6 @@ "output_cost_per_token": 2e-06, "output_cost_per_token_flex": 1e-06, "output_cost_per_token_priority": 3.6e-06, - "regional_processing_uplift_multiplier_eu": 1.10, - "regional_processing_uplift_multiplier_us": 1.10, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -22060,8 +23058,6 @@ "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, - "regional_processing_uplift_multiplier_eu": 1.10, - "regional_processing_uplift_multiplier_us": 1.10, "mode": "chat", "output_cost_per_token": 4e-07, "output_cost_per_token_flex": 2e-07, @@ -24180,9 +25176,12 @@ "max_output_tokens": 8192 }, "minimax/MiniMax-M3": { - "input_cost_per_token": 6e-07, - "output_cost_per_token": 2.4e-06, - "cache_read_input_token_cost": 1.2e-07, + "input_cost_per_token": 3e-07, + "input_cost_per_token_above_512k_tokens": 6e-07, + "output_cost_per_token": 1.2e-06, + "output_cost_per_token_above_512k_tokens": 2.4e-06, + "cache_read_input_token_cost": 6e-08, + "cache_read_input_token_cost_above_512k_tokens": 1.2e-07, "litellm_provider": "minimax", "mode": "chat", "supports_function_calling": true, @@ -24191,7 +25190,7 @@ "supports_reasoning": true, "supports_system_messages": true, "supports_vision": true, - "max_input_tokens": 512000, + "max_input_tokens": 1000000, "max_output_tokens": 128000 }, "mistral.devstral-2-123b": { @@ -24800,6 +25799,21 @@ "supports_tool_choice": true, "supports_vision": true }, + "mistral/mistral-medium-3-5": { + "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_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, "mistral/mistral-small": { "input_cost_per_token": 1e-07, "litellm_provider": "mistral", @@ -34004,6 +35018,7 @@ "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, @@ -34032,6 +35047,67 @@ "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 + }, + "vertex_ai/claude-fable-5": { + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 1e-06, + "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, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "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 + }, + "vertex_ai/claude-fable-5@default": { + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 1e-06, + "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, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "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, @@ -34061,6 +35137,7 @@ "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, @@ -34090,6 +35167,7 @@ "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, @@ -35517,7 +36595,17 @@ "max_input_tokens": 32000, "max_tokens": 32000, "mode": "embedding", - "output_cost_per_token": 0.0 + "output_cost_per_token": 0.0, + "supports_vision": true + }, + "voyage/voyage-multimodal-3.5": { + "input_cost_per_token": 1.2e-07, + "litellm_provider": "voyage", + "max_input_tokens": 32000, + "max_tokens": 32000, + "mode": "embedding", + "output_cost_per_token": 0.0, + "supports_vision": true }, "wandb/openai/gpt-oss-120b": { "max_tokens": 131072, @@ -38974,6 +40062,22 @@ "litellm_provider": "fireworks_ai", "mode": "chat" }, + "fireworks_ai/accounts/fireworks/models/qwen3p7-plus": { + "cache_read_input_token_cost": 8e-08, + "input_cost_per_token": 4e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 262144, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "output_cost_per_token": 1.6e-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": true + }, "fireworks_ai/accounts/fireworks/models/qwq-32b": { "max_tokens": 131072, "max_input_tokens": 131072, @@ -39073,24 +40177,6 @@ "litellm_provider": "fireworks_ai", "mode": "chat" }, - "fireworks_ai/accounts/fireworks/models/whisper-v3": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 0.0, - "output_cost_per_token": 0.0, - "litellm_provider": "fireworks_ai", - "mode": "audio_transcription" - }, - "fireworks_ai/accounts/fireworks/models/whisper-v3-turbo": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 0.0, - "output_cost_per_token": 0.0, - "litellm_provider": "fireworks_ai", - "mode": "audio_transcription" - }, "fireworks_ai/accounts/fireworks/models/yi-34b": { "max_tokens": 4096, "max_input_tokens": 4096, @@ -39136,6 +40222,54 @@ "litellm_provider": "fireworks_ai", "mode": "chat" }, + "fireworks_ai/accounts/fireworks/routers/glm-5p1-fast": { + "cache_read_input_token_cost": 5.2e-07, + "input_cost_per_token": 2.8e-06, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 202800, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 8.8e-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/routers/kimi-k2p6-fast": { + "cache_read_input_token_cost": 3e-07, + "input_cost_per_token": 2e-06, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 8e-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": true + }, + "fireworks_ai/accounts/fireworks/routers/kimi-k2p7-code-fast": { + "cache_read_input_token_cost": 3.8e-07, + "input_cost_per_token": 1.9e-06, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 8e-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": true + }, "novita/deepseek/deepseek-v3.2": { "litellm_provider": "novita", "mode": "chat", @@ -40439,6 +41573,174 @@ "litellm_provider": "llamagate", "mode": "embedding" }, + "libertai/hermes-3-8b-tee": { + "max_tokens": 16000, + "max_input_tokens": 16000, + "max_output_tokens": 16000, + "input_cost_per_token": 1.5e-07, + "output_cost_per_token": 6e-07, + "litellm_provider": "libertai", + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_system_messages": true, + "supports_vision": false, + "source": "https://docs.libertai.io/apis/text/" + }, + "libertai/gemma-4-31b-it": { + "max_tokens": 262144, + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "input_cost_per_token": 1.5e-07, + "output_cost_per_token": 4e-07, + "litellm_provider": "libertai", + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_system_messages": true, + "supports_vision": true, + "source": "https://docs.libertai.io/apis/text/" + }, + "libertai/gemma-4-31b-it-thinking": { + "max_tokens": 262144, + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "input_cost_per_token": 1.5e-07, + "output_cost_per_token": 4e-07, + "litellm_provider": "libertai", + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_system_messages": true, + "supports_vision": true, + "supports_reasoning": true, + "source": "https://docs.libertai.io/apis/text/" + }, + "libertai/qwen3.6-27b": { + "max_tokens": 262144, + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "input_cost_per_token": 1.5e-07, + "output_cost_per_token": 5e-07, + "litellm_provider": "libertai", + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_system_messages": true, + "supports_vision": true, + "source": "https://docs.libertai.io/apis/text/" + }, + "libertai/qwen3.6-27b-thinking": { + "max_tokens": 262144, + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "input_cost_per_token": 1.5e-07, + "output_cost_per_token": 5e-07, + "litellm_provider": "libertai", + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_system_messages": true, + "supports_vision": true, + "supports_reasoning": true, + "source": "https://docs.libertai.io/apis/text/" + }, + "libertai/qwen3.6-35b-a3b": { + "max_tokens": 262144, + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "input_cost_per_token": 1.5e-07, + "output_cost_per_token": 5e-07, + "litellm_provider": "libertai", + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_system_messages": true, + "supports_vision": true, + "source": "https://docs.libertai.io/apis/text/" + }, + "libertai/qwen3.6-35b-a3b-thinking": { + "max_tokens": 262144, + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "input_cost_per_token": 1.5e-07, + "output_cost_per_token": 5e-07, + "litellm_provider": "libertai", + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_system_messages": true, + "supports_vision": true, + "supports_reasoning": true, + "source": "https://docs.libertai.io/apis/text/" + }, + "libertai/qwen3.5-122b-a10b": { + "max_tokens": 262144, + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "input_cost_per_token": 2.5e-07, + "output_cost_per_token": 1.75e-06, + "litellm_provider": "libertai", + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_system_messages": true, + "supports_vision": true, + "source": "https://docs.libertai.io/apis/text/" + }, + "libertai/qwen3.5-122b-a10b-thinking": { + "max_tokens": 262144, + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "input_cost_per_token": 2.5e-07, + "output_cost_per_token": 1.75e-06, + "litellm_provider": "libertai", + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_system_messages": true, + "supports_vision": true, + "supports_reasoning": true, + "source": "https://docs.libertai.io/apis/text/" + }, + "libertai/deepseek-v4-flash": { + "max_tokens": 200000, + "max_input_tokens": 200000, + "max_output_tokens": 200000, + "input_cost_per_token": 2.5e-07, + "output_cost_per_token": 1.75e-06, + "litellm_provider": "libertai", + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_system_messages": true, + "supports_vision": false, + "source": "https://docs.libertai.io/apis/text/" + }, + "libertai/deepseek-v4-flash-thinking": { + "max_tokens": 200000, + "max_input_tokens": 200000, + "max_output_tokens": 200000, + "input_cost_per_token": 2.5e-07, + "output_cost_per_token": 1.75e-06, + "litellm_provider": "libertai", + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_system_messages": true, + "supports_vision": false, + "supports_reasoning": true, + "source": "https://docs.libertai.io/apis/text/" + }, + "libertai/bge-m3": { + "max_tokens": 8192, + "max_input_tokens": 8192, + "input_cost_per_token": 1e-08, + "output_cost_per_token": 0.0, + "litellm_provider": "libertai", + "mode": "embedding", + "source": "https://docs.libertai.io/apis/text/" + }, "sarvam/sarvam-m": { "cache_creation_input_token_cost": 0, "cache_creation_input_token_cost_above_1hr": 0, @@ -40637,6 +41939,23 @@ "supports_system_messages": true, "supports_tool_choice": true }, + "gpt-realtime-whisper": { + "input_cost_per_second": 0.0002833333333333333, + "litellm_provider": "openai", + "mode": "audio_transcription", + "source": "https://platform.openai.com/docs/models/gpt-realtime-whisper", + "supported_endpoints": [ + "/v1/realtime", + "/v1/realtime/transcription_sessions" + ], + "supported_modalities": [ + "audio" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true + }, "sora-2": { "litellm_provider": "openai", "mode": "video_generation", @@ -41315,6 +42634,7 @@ "max_output_tokens": 32768, "max_tokens": 32768, "mode": "chat", + "supported_endpoints": ["/v1/chat/completions", "/v1/responses"], "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_reasoning": true, @@ -41329,6 +42649,7 @@ "max_output_tokens": 32768, "max_tokens": 32768, "mode": "chat", + "supported_endpoints": ["/v1/chat/completions", "/v1/responses"], "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_reasoning": true, @@ -41343,6 +42664,7 @@ "max_output_tokens": 65536, "max_tokens": 65536, "mode": "chat", + "supported_endpoints": ["/v1/chat/completions"], "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, @@ -41356,6 +42678,7 @@ "max_output_tokens": 65536, "max_tokens": 65536, "mode": "chat", + "supported_endpoints": ["/v1/chat/completions"], "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, @@ -41370,6 +42693,7 @@ "max_output_tokens": 128000, "max_tokens": 128000, "mode": "responses", + "use_openai_responses_path": true, "supported_endpoints": ["/v1/responses"], "supported_modalities": ["text", "image"], "supported_output_modalities": ["text"], @@ -41389,6 +42713,7 @@ "max_output_tokens": 128000, "max_tokens": 128000, "mode": "responses", + "use_openai_responses_path": true, "supported_endpoints": ["/v1/responses"], "supported_modalities": ["text", "image"], "supported_output_modalities": ["text"], @@ -41399,6 +42724,54 @@ "supports_tool_choice": true, "supports_vision": true }, + "bedrock_mantle/google.gemma-4-31b": { + "input_cost_per_token": 1.4e-07, + "output_cost_per_token": 4e-07, + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "max_tokens": 256000, + "mode": "chat", + "use_openai_responses_path": true, + "supported_endpoints": ["/v1/chat/completions", "/v1/responses"], + "supports_function_calling": true, + "supports_parallel_function_calling": false, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "bedrock_mantle/google.gemma-4-26b-a4b": { + "input_cost_per_token": 1.3e-07, + "output_cost_per_token": 4e-07, + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "max_tokens": 256000, + "mode": "chat", + "use_openai_responses_path": true, + "supported_endpoints": ["/v1/chat/completions", "/v1/responses"], + "supports_function_calling": true, + "supports_parallel_function_calling": false, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "bedrock_mantle/google.gemma-4-e2b": { + "input_cost_per_token": 4e-08, + "output_cost_per_token": 8e-08, + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "use_openai_responses_path": true, + "supported_endpoints": ["/v1/chat/completions", "/v1/responses"], + "supports_function_calling": true, + "supports_parallel_function_calling": false, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": true + }, "volcengine/doubao-seed-2-0-pro-260215": { "litellm_provider": "volcengine", "max_input_tokens": 256000, @@ -41690,5 +43063,312 @@ "/v1/audio/transcriptions" ], "supports_audio_input": true + }, + "soniox/stt-async-v5": { + "litellm_provider": "soniox", + "max_output_tokens": 8000, + "max_tokens": 8000, + "input_cost_per_second": 0.0, + "output_cost_per_second": 0.0000277778, + "mode": "audio_transcription", + "source": "https://soniox.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ], + "supports_audio_input": true + }, + "tensormesh/Qwen/Qwen3.5-397B-A17B-FP8": { + "litellm_provider": "tensormesh", + "mode": "chat", + "input_cost_per_token": 6e-07, + "output_cost_per_token": 3.6e-06, + "cache_read_input_token_cost": 0, + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true, + "supports_prompt_caching": true, + "supports_system_messages": true, + "supports_reasoning": true, + "source": "https://serverless.tensormesh.ai/v1/models/openrouter" + }, + "tensormesh/Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8": { + "litellm_provider": "tensormesh", + "mode": "chat", + "input_cost_per_token": 4.5e-07, + "output_cost_per_token": 1.8e-06, + "cache_read_input_token_cost": 0, + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true, + "supports_prompt_caching": true, + "supports_system_messages": true, + "source": "https://serverless.tensormesh.ai/v1/models/openrouter" + }, + "tensormesh/Qwen/Qwen3.6-27B-FP8": { + "litellm_provider": "tensormesh", + "mode": "chat", + "input_cost_per_token": 3.2e-07, + "output_cost_per_token": 3.2e-06, + "cache_read_input_token_cost": 0, + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true, + "supports_prompt_caching": true, + "supports_system_messages": true, + "supports_reasoning": true, + "source": "https://serverless.tensormesh.ai/v1/models/openrouter" + }, + "tensormesh/lukealonso/GLM-5.1-NVFP4-MTP": { + "litellm_provider": "tensormesh", + "mode": "chat", + "input_cost_per_token": 1.4e-06, + "output_cost_per_token": 4.4e-06, + "cache_read_input_token_cost": 0, + "max_input_tokens": 202752, + "max_output_tokens": 202752, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true, + "supports_prompt_caching": true, + "supports_system_messages": true, + "supports_reasoning": true, + "source": "https://serverless.tensormesh.ai/v1/models/openrouter" + }, + "tensormesh/deepseek-ai/DeepSeek-V4-Flash": { + "litellm_provider": "tensormesh", + "mode": "chat", + "input_cost_per_token": 1.4e-07, + "output_cost_per_token": 2.8e-07, + "cache_read_input_token_cost": 0, + "max_input_tokens": 32768, + "max_output_tokens": 32768, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true, + "supports_prompt_caching": true, + "supports_system_messages": true, + "supports_reasoning": true, + "source": "https://serverless.tensormesh.ai/v1/models/openrouter" + }, + "tensormesh/moonshotai/Kimi-K2.6": { + "litellm_provider": "tensormesh", + "mode": "chat", + "input_cost_per_token": 9.6e-07, + "output_cost_per_token": 4e-06, + "cache_read_input_token_cost": 0, + "max_input_tokens": 32768, + "max_output_tokens": 32768, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true, + "supports_prompt_caching": true, + "supports_system_messages": true, + "supports_reasoning": true, + "source": "https://serverless.tensormesh.ai/v1/models/openrouter" + }, + "tensormesh/MiniMaxAI/MiniMax-M2.5": { + "litellm_provider": "tensormesh", + "mode": "chat", + "input_cost_per_token": 3e-07, + "output_cost_per_token": 1.2e-06, + "cache_read_input_token_cost": 0, + "max_input_tokens": 196608, + "max_output_tokens": 196608, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true, + "supports_prompt_caching": true, + "supports_system_messages": true, + "supports_reasoning": true, + "source": "https://serverless.tensormesh.ai/v1/models/openrouter" + }, + "tensormesh/google/gemma-4-31B-it": { + "litellm_provider": "tensormesh", + "mode": "chat", + "input_cost_per_token": 1.4e-07, + "output_cost_per_token": 5.6e-07, + "cache_read_input_token_cost": 0, + "max_input_tokens": 32768, + "max_output_tokens": 32768, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true, + "supports_prompt_caching": true, + "supports_system_messages": true, + "supports_reasoning": true, + "source": "https://serverless.tensormesh.ai/v1/models/openrouter" + }, + "tensormesh/openai/gpt-oss-120b": { + "litellm_provider": "tensormesh", + "mode": "chat", + "input_cost_per_token": 1.5e-07, + "output_cost_per_token": 6e-07, + "cache_read_input_token_cost": 0, + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true, + "supports_prompt_caching": true, + "supports_system_messages": true, + "supports_reasoning": true, + "source": "https://serverless.tensormesh.ai/v1/models/openrouter" + }, + "tensormesh/openai/gpt-oss-20b": { + "litellm_provider": "tensormesh", + "mode": "chat", + "input_cost_per_token": 7e-08, + "output_cost_per_token": 2.8e-07, + "cache_read_input_token_cost": 0, + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true, + "supports_prompt_caching": true, + "supports_system_messages": true, + "supports_reasoning": true, + "source": "https://serverless.tensormesh.ai/v1/models/openrouter" } -} \ No newline at end of file +, + "deepseek-v4-flash": { + "cache_creation_input_token_cost": 0.0, + "cache_read_input_token_cost": 2.8e-09, + "input_cost_per_token": 1.4e-07, + "input_cost_per_token_cache_hit": 2.8e-09, + "litellm_provider": "deepseek", + "max_input_tokens": 1000000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 2.8e-07, + "source": "https://api-docs.deepseek.com/quick_start/pricing", + "supported_endpoints": [ + "/v1/chat/completions" + ], + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": false + }, + "deepseek-v4-pro": { + "cache_creation_input_token_cost": 0.0, + "cache_read_input_token_cost": 3.625e-09, + "input_cost_per_token": 4.35e-07, + "input_cost_per_token_cache_hit": 3.625e-09, + "litellm_provider": "deepseek", + "max_input_tokens": 1000000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 8.7e-07, + "source": "https://api-docs.deepseek.com/quick_start/pricing", + "supported_endpoints": [ + "/v1/chat/completions" + ], + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": false + }, + "deepseek/deepseek-v4-flash": { + "cache_creation_input_token_cost": 0.0, + "cache_read_input_token_cost": 2.8e-09, + "input_cost_per_token": 1.4e-07, + "input_cost_per_token_cache_hit": 2.8e-09, + "litellm_provider": "deepseek", + "max_input_tokens": 1000000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 2.8e-07, + "source": "https://api-docs.deepseek.com/quick_start/pricing", + "supported_endpoints": [ + "/v1/chat/completions" + ], + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": false + }, + "darkbloom/gemma-4-26b": { + "input_cost_per_token": 3e-08, + "litellm_provider": "darkbloom", + "max_input_tokens": 131072, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 1.65e-07, + "source": "https://www.darkbloom.dev/", + "supported_endpoints": [ + "/v1/chat/completions" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "darkbloom/gpt-oss-20b": { + "input_cost_per_token": 1.45e-08, + "litellm_provider": "darkbloom", + "max_input_tokens": 131072, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 7e-08, + "source": "https://www.darkbloom.dev/", + "supported_endpoints": [ + "/v1/chat/completions" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "deepseek/deepseek-v4-pro": { + "cache_creation_input_token_cost": 0.0, + "cache_read_input_token_cost": 3.625e-09, + "input_cost_per_token": 4.35e-07, + "input_cost_per_token_cache_hit": 3.625e-09, + "litellm_provider": "deepseek", + "max_input_tokens": 1000000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 8.7e-07, + "source": "https://api-docs.deepseek.com/quick_start/pricing", + "supported_endpoints": [ + "/v1/chat/completions" + ], + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": false + } +} diff --git a/litellm/mypy.ini b/litellm/mypy.ini deleted file mode 100644 index 4702b591124..00000000000 --- a/litellm/mypy.ini +++ /dev/null @@ -1,19 +0,0 @@ -[mypy] -warn_return_any = False -ignore_missing_imports = True -mypy_path = litellm/stubs -namespace_packages = True -disable_error_code = - valid-type, - annotation-unchecked, - import-untyped - -[mypy-google.*] -ignore_missing_imports = True - -[mypy-cryptography.hazmat.bindings._rust.x509] -ignore_errors = True - -[mypy-fastuuid.*] -ignore_missing_imports = True -ignore_errors = True \ No newline at end of file diff --git a/litellm/ocr/main.py b/litellm/ocr/main.py index b27082c361a..3a9ef8db804 100644 --- a/litellm/ocr/main.py +++ b/litellm/ocr/main.py @@ -10,7 +10,7 @@ import os import re from functools import partial from io import IOBase -from typing import Any, Coroutine, Dict, Optional, Union +from typing import Any, Callable, Coroutine, Dict, Optional, Union, cast import httpx @@ -20,6 +20,7 @@ from litellm.constants import request_timeout from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.llms.base_llm.ocr.transformation import BaseOCRConfig, OCRResponse from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler +from litellm.ocr.rust_bridge import RustOcr, load_rust_ocr, rust_ocr_enabled from litellm.types.router import GenericLiteLLMParams from litellm.utils import ProviderConfigManager, client @@ -28,6 +29,82 @@ base_llm_http_handler = BaseLLMHTTPHandler() ################################################# +def _timeout_to_seconds( + timeout: Optional[Union[float, httpx.Timeout]], +) -> Optional[float]: + """Convert the Python OCR timeout to a single seconds value for the Rust bridge. + + The Rust HTTP client takes one duration; ``httpx.Timeout`` carries separate + connect/read/write/pool values, so pick the read deadline as the closest + analog to a total-request timeout. + """ + if timeout is None: + return None + if isinstance(timeout, httpx.Timeout): + return timeout.read + return float(timeout) + + +def _run_rust_ocr( + rust_ocr: RustOcr, + logging_obj: LiteLLMLoggingObj, + provider_config: BaseOCRConfig, + resolve_api_key: Callable[[str], Optional[str]], + model: str, + document: dict[str, object], + api_key: Optional[str], + api_base: Optional[str], + optional_params: dict[str, object], + litellm_params: dict[str, object], + timeout_seconds: Optional[float], +) -> OCRResponse: + """Run the Mistral OCR call through the Rust bridge and wrap the result. + + Resolves the key the same way the Python path does so secret-manager backends + (AWS/Azure/GCP/Vault) work; the Rust bridge's own fallback only reads the + process environment. The request that Rust actually sends (resolved URL and + headers) is mirrored into pre_call so logs match the wire. Dependencies are + injected so this stays unit-testable without patching module globals. + """ + resolved_api_key = api_key or resolve_api_key("MISTRAL_API_KEY") + resolved_headers = provider_config.validate_environment( + headers={}, + model=model, + api_key=resolved_api_key, + api_base=api_base, + litellm_params=litellm_params, + ) + resolved_complete_url = provider_config.get_complete_url( + api_base=api_base, + model=model, + optional_params=optional_params, + litellm_params=litellm_params, + ) + logging_obj.pre_call( + input="OCR document processing", + api_key=resolved_api_key, + additional_args={ + "complete_input_dict": { + "model": model, + "document": document, + **optional_params, + }, + "api_base": resolved_complete_url, + "headers": resolved_headers, + }, + ) + return OCRResponse.model_validate( + rust_ocr( + model=model, + document=document, + api_key=resolved_api_key, + api_base=api_base, + optional_params=optional_params, + timeout_seconds=timeout_seconds, + ) + ) + + @client async def aocr( model: str, @@ -220,7 +297,7 @@ def ocr( """ local_vars = locals() try: - litellm_logging_obj: LiteLLMLoggingObj = kwargs.pop("litellm_logging_obj") # type: ignore + litellm_logging_obj = cast(LiteLLMLoggingObj, kwargs.pop("litellm_logging_obj")) litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None) _is_async = kwargs.pop("aocr", False) is True @@ -261,7 +338,6 @@ def ocr( if dynamic_api_base: api_base = dynamic_api_base - # Get provider config ocr_provider_config: Optional[BaseOCRConfig] = ( ProviderConfigManager.get_provider_ocr_config( model=model, @@ -278,17 +354,14 @@ def ocr( f"OCR call - model: {model}, provider: {custom_llm_provider}" ) - # Get litellm params using GenericLiteLLMParams (same as responses API) litellm_params = GenericLiteLLMParams(**kwargs) - # Extract OCR-specific parameters from kwargs supported_params = ocr_provider_config.get_supported_ocr_params(model=model) non_default_params = {} for param in supported_params: if param in kwargs: non_default_params[param] = kwargs.pop(param) - # Map parameters to provider-specific format optional_params = ocr_provider_config.map_ocr_params( non_default_params=non_default_params, optional_params={}, @@ -297,7 +370,8 @@ def ocr( verbose_logger.debug(f"OCR optional_params after mapping: {optional_params}") - # Pre Call logging + effective_timeout = timeout or request_timeout + litellm_logging_obj.update_from_kwargs( kwargs=kwargs, model=model, @@ -309,12 +383,35 @@ def ocr( custom_llm_provider=custom_llm_provider, ) - # Call the handler - pass document dict directly + # Optional Rust path: hand the whole Mistral OCR call to the Rust bridge. + if custom_llm_provider == "mistral" and rust_ocr_enabled(): + rust_ocr = load_rust_ocr() + if rust_ocr is None: + verbose_logger.debug( + "Rust OCR bridge unavailable; falling back to Python path" + ) + else: + from litellm.secret_managers.main import get_secret_str + + return _run_rust_ocr( + rust_ocr=rust_ocr, + logging_obj=litellm_logging_obj, + provider_config=ocr_provider_config, + resolve_api_key=get_secret_str, + model=model, + document=document, + api_key=api_key, + api_base=api_base, + optional_params=optional_params, + litellm_params=dict(litellm_params), + timeout_seconds=_timeout_to_seconds(effective_timeout), + ) + response = base_llm_http_handler.ocr( model=model, - document=document, # Pass the entire document dict + document=document, optional_params=optional_params, - timeout=timeout or request_timeout, + timeout=effective_timeout, logging_obj=litellm_logging_obj, api_key=api_key, api_base=api_base, diff --git a/litellm/ocr/rust_bridge.py b/litellm/ocr/rust_bridge.py new file mode 100644 index 00000000000..61f9e8ca69a --- /dev/null +++ b/litellm/ocr/rust_bridge.py @@ -0,0 +1,74 @@ +""" +Optional Rust-backed OCR path. + +Enable with ``litellm.use_litellm_rust()``; the sync ``litellm.ocr()`` entrypoint +then routes supported Mistral calls through the compiled ``litellm_python_bridge`` +extension, which performs the whole OCR call (URL, headers, HTTP, parse) in Rust. + +No module-level ``litellm`` imports keep this a leaf so ``litellm/ocr/main.py`` +can import it statically without forming an import cycle. +""" + +from __future__ import annotations + +from typing import Final, Protocol, cast + + +class RustOcr(Protocol): + """Signature of the compiled ``litellm_python_bridge.ocr`` entrypoint.""" + + def __call__( + self, + model: str, + document: dict[str, object], + api_key: str | None, + api_base: str | None, + optional_params: dict[str, object], + timeout_seconds: float | None, + ) -> dict[str, object]: ... + + +class _Unset: + """Sentinel type so ``ocr=None`` can clear a prior injection while omission preserves it.""" + + +_UNSET: Final[_Unset] = _Unset() + +_rust_ocr_enabled = False +_rust_ocr_impl: RustOcr | None = None + + +def use_litellm_rust( + enabled: bool = True, *, ocr: RustOcr | None | _Unset = _UNSET +) -> None: + """Route supported OCR calls through the Rust ``litellm_python_bridge`` extension. + + ``ocr`` injects the bridge callable; when omitted the compiled extension is + loaded on demand and any previously injected bridge is preserved. Pass + ``ocr=None`` explicitly to clear a prior injection. + """ + global _rust_ocr_enabled, _rust_ocr_impl + _rust_ocr_enabled = enabled + if not isinstance(ocr, _Unset): + _rust_ocr_impl = ocr + + +def rust_ocr_enabled() -> bool: + """Whether the Rust OCR path has been turned on via ``use_litellm_rust()``.""" + return _rust_ocr_enabled + + +def load_rust_ocr() -> RustOcr | None: + """Return the Rust OCR callable, or ``None`` when no bridge is available. + + Prefers an injected implementation, otherwise loads the compiled + ``litellm_python_bridge`` extension; a missing extension yields ``None`` so + the caller can fall back to the Python path instead of hard-failing. + """ + if _rust_ocr_impl is not None: + return _rust_ocr_impl + try: + import litellm_python_bridge + except ImportError: + return None + return cast(RustOcr, litellm_python_bridge.ocr) diff --git a/litellm/passthrough/main.py b/litellm/passthrough/main.py index c4c9aea6f64..3e60988b9e7 100644 --- a/litellm/passthrough/main.py +++ b/litellm/passthrough/main.py @@ -20,7 +20,6 @@ from typing import ( import httpx from httpx._types import CookieTypes, QueryParamTypes, RequestFiles -import litellm from litellm._logging import verbose_logger from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler @@ -201,12 +200,6 @@ def llm_passthrough_route( _is_async = allm_passthrough_route - if client is None: - if _is_async: - client = litellm.module_level_aclient - else: - client = litellm.module_level_client - litellm_logging_obj = cast("LiteLLMLoggingObj", kwargs.get("litellm_logging_obj")) model, custom_llm_provider, api_key, api_base = get_llm_provider( @@ -218,6 +211,26 @@ def llm_passthrough_route( litellm_params_dict = get_litellm_params(**kwargs) + if client is None: + from litellm.llms.custom_httpx.http_handler import ( + _get_httpx_client, + get_async_httpx_client, + ) + from litellm.passthrough.timeout_utils import resolve_llm_passthrough_timeout + from litellm.types.llms.custom_http import httpxSpecialProvider + + resolved_timeout = resolve_llm_passthrough_timeout( + kwargs=kwargs, + litellm_params=litellm_params_dict, + ) + if _is_async: + client = get_async_httpx_client( + llm_provider=httpxSpecialProvider.PassThroughEndpoint, + params={"timeout": resolved_timeout}, + ) + else: + client = _get_httpx_client(params={"timeout": resolved_timeout}) + # Add model_id to litellm_params if present in kwargs (for Bedrock Application Inference Profiles) if "model_id" in kwargs: litellm_params_dict["model_id"] = kwargs["model_id"] diff --git a/litellm/passthrough/timeout_utils.py b/litellm/passthrough/timeout_utils.py new file mode 100644 index 00000000000..a423db2aa91 --- /dev/null +++ b/litellm/passthrough/timeout_utils.py @@ -0,0 +1,58 @@ +import sys +from typing import Optional + +DEFAULT_PASS_THROUGH_REQUEST_TIMEOUT_SECONDS = 600.0 + + +def resolve_pass_through_request_timeout( + endpoint_timeout: Optional[float] = None, +) -> float: + """ + Resolve the upstream httpx timeout for pass_through_request. + + Precedence: per-endpoint timeout -> general_settings.pass_through_request_timeout -> 600s default. + + Uses sys.modules to read general_settings only when the proxy module is already + loaded, avoiding a fastapi transitive import in pure SDK contexts. + """ + if endpoint_timeout is not None: + return float(endpoint_timeout) + + try: + proxy_server = sys.modules.get("litellm.proxy.proxy_server") + if proxy_server is not None: + global_timeout = getattr(proxy_server, "general_settings", {}).get( + "pass_through_request_timeout" + ) + if global_timeout is not None: + return float(global_timeout) + except Exception: + pass + + return DEFAULT_PASS_THROUGH_REQUEST_TIMEOUT_SECONDS + + +def resolve_llm_passthrough_timeout( + kwargs: Optional[dict] = None, + litellm_params: Optional[dict] = None, + router_timeout: Optional[float] = None, +) -> float: + """ + Resolve upstream httpx timeout for SDK native passthrough (e.g. Bedrock /converse). + + Precedence: kwargs timeout/request_timeout -> litellm_params timeout/request_timeout + -> router_timeout -> general_settings.pass_through_request_timeout -> 600s default. + """ + kwargs = kwargs or {} + litellm_params = litellm_params or {} + + for source in (kwargs, litellm_params): + for key in ("timeout", "request_timeout"): + val = source.get(key) + if val is not None: + return float(val) + + if router_timeout is not None: + return float(router_timeout) + + return resolve_pass_through_request_timeout() diff --git a/litellm/provider_endpoints_support_backup.json b/litellm/provider_endpoints_support_backup.json index e0eeb014c51..dd7712aabca 100644 --- a/litellm/provider_endpoints_support_backup.json +++ b/litellm/provider_endpoints_support_backup.json @@ -1288,6 +1288,23 @@ "interactions": true } }, + "libertai": { + "display_name": "LibertAI (`libertai`)", + "url": "https://docs.litellm.ai/docs/providers/libertai", + "endpoints": { + "chat_completions": true, + "messages": true, + "responses": false, + "embeddings": false, + "image_generations": false, + "audio_transcriptions": false, + "audio_speech": false, + "moderations": false, + "batches": false, + "rerank": false, + "a2a": false + } + }, "litellm_proxy": { "display_name": "LiteLLM Proxy (`litellm_proxy`)", "url": "https://docs.litellm.ai/docs/providers/litellm_proxy", @@ -1818,6 +1835,23 @@ "interactions": true } }, + "darkbloom": { + "display_name": "Darkbloom (`darkbloom`)", + "url": "https://docs.litellm.ai/docs/providers/darkbloom", + "endpoints": { + "chat_completions": true, + "messages": false, + "responses": false, + "embeddings": false, + "image_generations": false, + "audio_transcriptions": false, + "audio_speech": false, + "moderations": false, + "batches": false, + "rerank": false, + "a2a": false + } + }, "predibase": { "display_name": "Predibase (`predibase`)", "url": "https://docs.litellm.ai/docs/providers/predibase", diff --git a/litellm/proxy/_experimental/mcp_server/AGENTS.md b/litellm/proxy/_experimental/mcp_server/AGENTS.md new file mode 100644 index 00000000000..8eebc3ea3b3 --- /dev/null +++ b/litellm/proxy/_experimental/mcp_server/AGENTS.md @@ -0,0 +1,95 @@ +# Experimental MCP Server Change Guidelines + +Read @../../../../CLAUDE.md and @CLAUDE.md before changing this package. + +This directory owns the proxy-hosted MCP server implementation. Keep changes +inside the module that owns the behavior, and only reach outside this package +when the public type contract, database schema, dashboard, or cross-proxy route +wiring must change with it. + +## File Structure + +Respect the current package boundaries: + +```text +litellm/proxy/_experimental/mcp_server/ + AGENTS.md + CLAUDE.md + server.py # ASGI/MCP route handling, sessions, tool calls [PR7: 7-arm only — move BYOK/OAuth pre-fetch into resolver] + mcp_server_manager.py # upstream server registry, clients, tool routing [PR7: _create_mcp_client swaps resolve_mcp_auth -> resolve_credentials] + auth/ + user_api_key_auth_mcp.py # LiteLLM admission auth and MCP request headers + token_exchange.py # OAuth token exchange handling [unchanged; V1TokenExchangeAdapter delegates here] + litellm_auth_handler.py # authenticated-user adapter for MCP sessions + outbound_credentials/ # NEW — typed upstream-credential resolution (resolve_credentials + arms) + __init__.py # public surface: resolve_credentials, the configs, CredError + result.py # Ok | Error union (pure stdlib) + types.py # AuthConfig union, CredError, Subject, ServerSpec + httpx_auth.py # NoOpAuth, StaticHeaderAuth (every mode -> one httpx.Auth) + resolver.py # resolve_credentials(): exhaustive per-mode match + assert_never + seams.py # injected Protocols (one per cache-touching mode) + v1_adapters.py # v1-backed seam bodies; delegate to auth/oauth2/db owners + adapter.py # to_subject / to_server_spec / raise_public (v1 <-> v2 boundary) + discoverable_endpoints.py # MCP OAuth metadata, authorize, token, callback + byok_oauth_endpoints.py # BYOK OAuth UI/API flow + oauth_utils.py # redirect URI and proxy base URL validation + oauth2_token_cache.py # OAuth2 and per-user token resolution/cache [PR7: resolve_mcp_auth removed; cache class stays, V1OAuth2CacheAdapter delegates to async_get_token] + db.py # MCP server, credential, env var, submission DB access [unchanged; V1ByokStore delegates to _get_byok_credential / get_user_credential] + toolset_db.py # MCP toolset DB access + rest_endpoints.py # proxy REST facade for listing/calling MCP tools [PR7: 7-arm only — pass identity + inbound token down instead of mcp_auth_header] + openapi_to_mcp_generator.py# OpenAPI spec to MCP tool generation + sampling_handler.py # MCP sampling to LiteLLM completion flow + elicitation_handler.py # MCP elicitation relay flow + semantic_tool_filter.py # semantic filtering of available MCP tools + guardrail_translation/ + handler.py # MCP guardrail result translation + sse_transport.py # SSE transport implementation + mcp_context.py # contextvars for MCP request/session metadata + mcp_debug.py # debug helpers + tool_registry.py # in-memory MCP tool registry helpers + cost_calculator.py # MCP tool cost calculation + ui_session_utils.py # dashboard session auth context helpers + utils.py # shared primitives used by several modules +``` + +Do not add broad catch-all modules. Prefer the existing owner above, and add a +new file only for a distinct capability that would otherwise make an existing +module materially harder to understand. + +## Implementation Rules + +- Preserve the boundary between LiteLLM admission auth and upstream MCP auth. + Admission belongs in `auth/user_api_key_auth_mcp.py`; upstream token exchange, + delegated auth, per-user OAuth, BYOK, and raw header forwarding belong in the + dedicated OAuth/header modules. +- Treat `none`, bearer/API key, OAuth, OAuth token exchange, delegated upstream + auth, SSE, streamable HTTP, and stdio as separate flows. Do not collapse them + behind a single generic branch unless tests prove every mode still behaves + correctly. +- Be especially careful with `available_on_public_internet: false` combined with + `delegate_auth_to_upstream: true`. The local `CLAUDE.md` explains the anonymous + upstream PKCE path that must remain intentional. +- Keep database-backed fields in sync across migrations, typed models under + `litellm/types/mcp.py` or `litellm/types/mcp_server/`, config loading, this + package, and dashboard state when the field is user-visible. +- Use the official MCP SDK types and established LiteLLM Pydantic models where + they exist. Avoid untyped protocol dictionaries at package boundaries. +- Keep security-sensitive logic easy to audit. Header forwarding, IP filtering, + public internet checks, token storage, env var interpolation, and credential + encryption need focused tests for both allowed and rejected paths. +- Avoid adding comments to new code unless they explain non-obvious security or + protocol behavior. Prefer clear names and small functions. + +## Tests + +Mirror this package under `tests/test_litellm/proxy/_experimental/mcp_server/`. +For regressions, extend the existing mapped test file instead of creating a new +one. Use subdirectories that match the implementation path, such as +`auth/test_token_exchange.py` for `auth/token_exchange.py` and +`guardrail_translation/test_mcp_guardrail_handler.py` for +`guardrail_translation/handler.py`. + +Use `tests/mcp_tests/` only when extending an existing broader MCP integration +scenario that already lives there. Route, auth, tool listing, tool execution, +OAuth, sampling, elicitation, DB, and dashboard-session changes should have +focused coverage in the mirrored `tests/test_litellm/...` path first. diff --git a/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py b/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py index dcf7660d002..90108de25c3 100644 --- a/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py +++ b/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py @@ -12,6 +12,7 @@ from litellm.proxy._types import ( LiteLLM_TeamTable, ProxyException, SpecialHeaders, + SpecialMCPServerNames, UserAPIKeyAuth, ) from litellm.proxy.auth.ip_address_utils import IPAddressUtils @@ -67,9 +68,10 @@ def _is_mcp_passthrough_cold_start( spec-compliant WWW-Authenticate challenge instead of surfacing a generic admission error. - Uses "all" semantics (mirrors :meth:`MCPRequestHandler._target_servers_use_oauth2`): - one non-passthrough target in a co-targeted set must not flip the bypass - open for the others. Fails closed when any target cannot be resolved.""" + Uses "all" semantics (mirrors + :meth:`MCPRequestHandler._target_servers_delegate_auth_to_upstream`): one + non-passthrough target in a co-targeted set must not flip the bypass open + for the others. Fails closed when any target cannot be resolved.""" if not mcp_servers: return False from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( @@ -124,7 +126,7 @@ class MCPRequestHandler: LITELLM_MCP_ACCESS_GROUPS_HEADER_NAME = SpecialHeaders.mcp_access_groups.value @staticmethod - async def process_mcp_request( # noqa: PLR0915 + async def process_mcp_request( scope: Scope, ) -> Tuple[ UserAPIKeyAuth, @@ -214,101 +216,64 @@ class MCPRequestHandler: # Only OAuth metadata routes registered under /.well-known/ are public. if request_route.startswith("/.well-known/"): validated_user_api_key_auth = UserAPIKeyAuth() - elif ( - not litellm_api_key - and MCPRequestHandler._target_servers_delegate_auth_to_upstream( # noqa: E501 - path=request_route, - mcp_servers=mcp_servers, - client_ip=IPAddressUtils.get_mcp_client_ip(request), - ) - ): - # Operator opted this oauth2 server into upstream-delegated auth - # (PKCE passthrough): skip LiteLLM API-key/SSO entirely so the - # client authenticates directly with the upstream MCP server. - # Fires ONLY when neither x-litellm-api-key nor Authorization is - # present. If any LiteLLM key is supplied (primary or secondary - # header), we fall through so user_id is resolved, spend/rate - # limiting apply, and any stored OAuth token can be retrieved - # and forwarded upstream. Gated by - # _target_servers_delegate_auth_to_upstream, which only returns - # True when EVERY target is auth_type=oauth2 AND has the - # delegate_auth_to_upstream flag set — fails closed otherwise. - validated_user_api_key_auth = UserAPIKeyAuth() elif has_explicit_litellm_key: - # Explicit x-litellm-api-key provided - always validate normally + # An explicit x-litellm-api-key is always a LiteLLM credential, even + # for a delegated server, so validate it: identity / spend / rate + # limits resolve and any stored upstream token can be forwarded. validated_user_api_key_auth = await user_api_key_auth( api_key=litellm_api_key, request=request ) + elif MCPRequestHandler._target_servers_delegate_auth_to_upstream( + path=request_route, + mcp_servers=mcp_servers, + client_ip=IPAddressUtils.get_mcp_client_ip(request), + ): + # Operator opted this oauth2 server into upstream-delegated auth: the + # client authenticates directly with the upstream MCP server, so any + # Authorization bearer is an upstream token, never a LiteLLM key. Skip + # LiteLLM validation entirely — covering both the no-credential + # discovery request and the authenticated call carrying the upstream + # bearer — so a tool call that succeeds never carries a phantom 401 + # auth span; the bearer is forwarded upstream unchanged. Gated by + # _target_servers_delegate_auth_to_upstream, which returns True only + # when EVERY target is auth_type=oauth2 with delegate_auth_to_upstream + # set; fails closed otherwise. + validated_user_api_key_auth = UserAPIKeyAuth() elif oauth2_headers: - # No x-litellm-api-key, but Authorization header present. - # Could be a LiteLLM key (backward compat) OR an opaque OAuth2 token - # the operator wants forwarded to an upstream OAuth2-mode MCP server. - # Try LiteLLM auth first; on auth failure, only fall back to anonymous - # passthrough when the request actually targets a server whose operator - # configured ``auth_type=oauth2``. For any other server (api_key, - # bearer_token, basic, etc.), a failed LiteLLM auth is a real failure - # and must propagate — otherwise an attacker can exchange any garbage - # bearer for an anonymous session. + # Authorization on a non-delegated server: the bearer must be a real + # LiteLLM credential, so a failed validation is a genuine 401/403 and + # propagates. The sole anonymous fallback is the auth_type=none + # pass-through cold-start (RFC 9728 discovery return), gated on a 401 + # so a recognized-but-forbidden key still fails closed. + client_ip = IPAddressUtils.get_mcp_client_ip(request) try: validated_user_api_key_auth = await user_api_key_auth( api_key=litellm_api_key, request=request ) except (HTTPException, ProxyException) as e: - # HTTPException.status_code is int; ProxyException.code is - # normalized to str in its __init__ but can be ``"None"`` or any - # non-numeric string when the caller didn't supply a numeric - # code, so we compare against both int and str forms rather - # than coercing (``int("None")`` would raise ValueError and - # rewrite the auth error as a 500). + # ProxyException.code is normalized to str (possibly "None"), so + # compare both int and str forms rather than coercing. status = e.status_code if isinstance(e, HTTPException) else e.code - is_auth_error = status in (401, 403, "401", "403") is_unauthenticated = status in (401, "401") - client_ip = IPAddressUtils.get_mcp_client_ip(request) - if is_auth_error and MCPRequestHandler._target_servers_use_oauth2( - path=request_route, - mcp_servers=mcp_servers, - client_ip=client_ip, + mcp_servers_from_path = _parse_mcp_server_names_from_path( + request_route, mcp_servers + ) + if ( + is_unauthenticated + and mcp_servers_from_path is not None + and not _has_client_supplied_mcp_auth( + mcp_auth_header, + mcp_server_auth_headers, + ) + and _is_mcp_passthrough_cold_start( + mcp_servers_from_path, client_ip=client_ip + ) ): verbose_logger.debug( - "MCP OAuth2: target server is OAuth2-mode, treating " - "Authorization as upstream OAuth2 token passthrough" + "MCP pass-through return: forwarding Authorization as " + "upstream OAuth token for delegated auth" ) validated_user_api_key_auth = UserAPIKeyAuth() - elif is_unauthenticated: - # Pass-through cold-start return: per RFC 9728 / MCP - # Authorization spec the client completes upstream OAuth - # discovery and returns with ``Authorization: Bearer - # ``. For ``auth_type=none`` passthrough - # servers that bearer is not a LiteLLM key (auth above - # failed) but is meant to be forwarded upstream - # unchanged. Fall back to anonymous admission so the - # caller is not rejected for following the discovery - # flow without also setting ``x-litellm-api-key``. - # Only trigger on 401 (token unrecognized); a 403 means - # the key WAS recognized but is forbidden (e.g. over - # budget / rate limited) and must propagate so those - # controls are not bypassed via anonymous admission. - mcp_servers_from_path = _parse_mcp_server_names_from_path( - request_route, mcp_servers - ) - if ( - mcp_servers_from_path is not None - and not _has_client_supplied_mcp_auth( - mcp_auth_header, - mcp_server_auth_headers, - ) - and _is_mcp_passthrough_cold_start( - mcp_servers_from_path, client_ip=client_ip - ) - ): - verbose_logger.debug( - "MCP pass-through return: target server is " - "passthrough, treating Authorization as " - "upstream OAuth token for delegated auth" - ) - validated_user_api_key_auth = UserAPIKeyAuth() - else: - raise else: raise else: @@ -412,45 +377,6 @@ class MCPRequestHandler: return [single_server_match.group(1)] return [servers_and_path] - @staticmethod - def _target_servers_use_oauth2( - path: str, mcp_servers: Optional[List[str]], client_ip: Optional[str] - ) -> bool: - """ - True only when EVERY MCP server the request targets is configured for - ``auth_type == oauth2``. If any target is non-OAuth2 — or if the target - cannot be resolved at all — return False so the caller fails closed. - - Used to gate the "treat Authorization as opaque OAuth2 token" fallback - in :meth:`process_mcp_request` so a failed LiteLLM-auth cannot be - exchanged for an anonymous session against a non-OAuth2 server. - """ - # Inline imports avoid a circular dependency: mcp_server_manager imports - # from this module. - from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( - global_mcp_server_manager, - ) - from litellm.types.mcp import MCPAuth - - # Resolve the same target list downstream routing will use. For - # ``/mcp/...`` routes, ``extract_mcp_auth_context`` overrides the - # ``x-mcp-servers`` header with path-derived names, so we must mirror - # that here — otherwise a caller could set the header to a permissive - # server while the path targets a stricter one (header/path TOCTOU). - target_names = MCPRequestHandler._resolve_target_server_names( - path=path, mcp_servers_header=mcp_servers - ) - if not target_names: - return False - - for name in target_names: - server = global_mcp_server_manager.get_mcp_server_by_name( - name, client_ip=client_ip - ) - if server is None or server.auth_type != MCPAuth.oauth2: - return False - return True - @staticmethod def _target_servers_delegate_auth_to_upstream( path: str, mcp_servers: Optional[List[str]], client_ip: Optional[str] @@ -472,8 +398,8 @@ class MCPRequestHandler: ) from litellm.types.mcp import MCPAuth - # See _target_servers_use_oauth2: must mirror the downstream - # header-vs-path override or an attacker could set + # Must mirror the downstream header-vs-path override + # (``extract_mcp_auth_context``) or an attacker could set # ``x-mcp-servers`` to a delegate-enabled server while the URL path # targets a non-delegate server, skipping LiteLLM auth for it. target_names = MCPRequestHandler._resolve_target_server_names( @@ -717,6 +643,15 @@ class MCPRequestHandler: user_api_key_auth ) ) + + # The key explicitly opted out of every MCP server. This overrides + # team inheritance and additive grants (mirrors no-default-models). + if ( + SpecialMCPServerNames.no_mcp_servers.value + in allowed_mcp_servers_for_key + ): + return [] + allowed_mcp_servers_for_team = ( await MCPRequestHandler._get_allowed_mcp_servers_for_team( user_api_key_auth @@ -1133,6 +1068,13 @@ class MCPRequestHandler: if key_object_permission is None: return [] + # Sentinel opt-out: surface it unexpanded so the caller can short-circuit + # to zero servers instead of inheriting the team. + if SpecialMCPServerNames.no_mcp_servers.value in ( + key_object_permission.mcp_servers or [] + ): + return [SpecialMCPServerNames.no_mcp_servers.value] + # Permission entries may be server_ids OR names/aliases — expand to ids. direct_mcp_servers = global_mcp_server_manager.expand_permission_list( key_object_permission.mcp_servers or [] diff --git a/litellm/proxy/_experimental/mcp_server/db.py b/litellm/proxy/_experimental/mcp_server/db.py index c52752940c3..8edb831a9df 100644 --- a/litellm/proxy/_experimental/mcp_server/db.py +++ b/litellm/proxy/_experimental/mcp_server/db.py @@ -568,10 +568,12 @@ async def delete_mcp_server( """ Delete the mcp server from the db by server_id - The server-row delete is the commit point. Per-user env var rows have no FK - cascade, so they are cleaned up afterwards on a best-effort basis: a transient - failure there leaves only orphaned rows pointing at a now-missing server and - must not turn a successful delete into a caller-visible error. + The server-row delete is the commit point. Per-user credential and env var + rows have no FK cascade, so they are cleaned up afterwards on a best-effort + basis: a transient failure there leaves only orphaned rows pointing at a + now-missing server and must not turn a successful delete into a + caller-visible error. Each table is cleaned independently so a failure on one + still attempts the other. Returns the deleted mcp server record if it exists, otherwise None """ @@ -581,17 +583,20 @@ async def delete_mcp_server( }, ) if deleted_server is not None: - try: - await prisma_client.db.litellm_mcpuserenvvars.delete_many( - where={"server_id": server_id} - ) - except Exception as e: - verbose_proxy_logger.warning( - "MCP server %s deleted but per-user env var cleanup failed; " - "orphaned rows can be removed on a later delete: %s", - server_id, - e, - ) + for model, label in ( + (prisma_client.db.litellm_mcpusercredentials, "credential"), + (prisma_client.db.litellm_mcpuserenvvars, "env var"), + ): + try: + await model.delete_many(where={"server_id": server_id}) + except Exception as e: + verbose_proxy_logger.warning( + "MCP server %s deleted but per-user %s cleanup failed; " + "orphaned rows can be removed on a later delete: %s", + server_id, + label, + e, + ) return deleted_server diff --git a/litellm/proxy/_experimental/mcp_server/exceptions.py b/litellm/proxy/_experimental/mcp_server/exceptions.py index fd8fc3d5e58..a00e797a6bd 100644 --- a/litellm/proxy/_experimental/mcp_server/exceptions.py +++ b/litellm/proxy/_experimental/mcp_server/exceptions.py @@ -10,8 +10,9 @@ class MCPUpstreamAuthError(Exception): (typically HTTP 401) and the gateway should surface it transparently to the client instead of swallowing it. - Only relevant for pass-through MCP servers (see - ``MCPServer.is_oauth_passthrough``). The gateway converts this exception + Relevant for MCP servers that delegate OAuth to the upstream server, + including pass-through servers and OAuth2 servers with + ``delegate_auth_to_upstream`` enabled. The gateway converts this exception into an HTTP 401 response on single-server routes, preserving any ``WWW-Authenticate`` challenge emitted by the upstream so standards- compliant MCP clients can trigger the upstream OAuth flow. diff --git a/litellm/proxy/_experimental/mcp_server/mcp_context.py b/litellm/proxy/_experimental/mcp_server/mcp_context.py index a60138dd340..51918509441 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_context.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_context.py @@ -19,3 +19,9 @@ _mcp_active_toolset_id: ContextVar[Optional[str]] = ContextVar( _mcp_gateway_initialize_instructions: ContextVar[Optional[str]] = ContextVar( "_mcp_gateway_initialize_instructions", default=None ) + +# Per-request scoped server name; set in MCP HTTP/SSE handlers when the path +# identifies exactly one upstream server. Never populated from client-supplied headers. +_mcp_gateway_server_name: ContextVar[Optional[str]] = ContextVar( + "_mcp_gateway_server_name", default=None +) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 85ac6b399f4..5e704b889ae 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -80,6 +80,7 @@ from litellm.proxy._types import ( MCPEnvVar, MCPTransport, MCPTransportType, + SpecialMCPServerNames, UserAPIKeyAuth, ) from litellm.proxy.auth.ip_address_utils import IPAddressUtils @@ -354,6 +355,52 @@ def _deserialize_json_list(data: Any) -> Optional[List[Dict[str, Any]]]: ] +def _normalize_mcp_server_cost_info(mcp_info: MCPInfo) -> None: + """Coerce ``mcp_server_cost_info`` numeric fields to ``float`` at ingest. + + YAML 1.1 parses scientific notation without a decimal point (e.g. + ``7e-05``) as a string, and ``MCPServerCostInfo`` is a TypedDict with no + runtime validation, so string-typed costs flow through to the UI and + crash its ``.toFixed`` formatting. Values that cannot be coerced are + dropped with a warning instead of failing the server load. + """ + cost_info = mcp_info.get("mcp_server_cost_info") + if not isinstance(cost_info, dict): + return + + server_name = mcp_info.get("server_name") + normalized = dict(cost_info) + + default_cost = normalized.get("default_cost_per_query") + if default_cost is not None: + try: + normalized["default_cost_per_query"] = float(default_cost) + except (TypeError, ValueError): + verbose_logger.warning( + "MCP server '%s' has non-numeric default_cost_per_query %r; ignoring it", + server_name, + default_cost, + ) + del normalized["default_cost_per_query"] + + tool_costs = normalized.get("tool_name_to_cost_per_query") + if isinstance(tool_costs, dict): + normalized_tool_costs = {} + for tool_name, cost in tool_costs.items(): + try: + normalized_tool_costs[tool_name] = float(cost) + except (TypeError, ValueError): + verbose_logger.warning( + "MCP server '%s' has non-numeric cost %r for tool '%s'; ignoring it", + server_name, + cost, + tool_name, + ) + normalized["tool_name_to_cost_per_query"] = normalized_tool_costs + + mcp_info["mcp_server_cost_info"] = normalized + + def _create_sampling_callback(user_api_key_auth: Optional[Any] = None): """ Create a sampling callback for MCP ClientSession. @@ -621,6 +668,7 @@ class MCPServerManager: mcp_info["server_name"] = server_name if "description" not in mcp_info and server_config.get("description"): mcp_info["description"] = server_config.get("description") + _normalize_mcp_server_cost_info(mcp_info) # Use alias for name if present, else server_name alias = server_config.get("alias", None) @@ -1091,6 +1139,7 @@ class MCPServerManager: mcp_info["server_name"] = mcp_server.server_name or mcp_server.server_id if "description" not in mcp_info and mcp_server.description: mcp_info["description"] = mcp_server.description + _normalize_mcp_server_cost_info(mcp_info) auth_type = cast(MCPAuthType, mcp_server.auth_type) server_url = mcp_server.url @@ -1301,6 +1350,17 @@ class MCPServerManager: allow_all_server_ids = self.get_allow_all_keys_server_ids() try: + # The key explicitly opted out of every MCP server. Return zero before + # layering on allow_all_keys servers so the opt-out is absolute. + key_object_permission = ( + user_api_key_auth.object_permission if user_api_key_auth else None + ) + if key_object_permission is not None and ( + SpecialMCPServerNames.no_mcp_servers.value + in (key_object_permission.mcp_servers or []) + ): + return [] + # Check if object_permission.mcp_servers is explicitly set has_explicit_object_permission = False if user_api_key_auth and user_api_key_auth.object_permission: @@ -1373,8 +1433,11 @@ class MCPServerManager: "No allowed MCP Servers found for user api key auth." ) return list(combined_servers) - except Exception as e: - verbose_logger.warning(f"Failed to get allowed MCP servers: {str(e)}.") + except Exception: # noqa: BLE001 + verbose_logger.exception( + "Failed to get allowed MCP servers; team-level object_permission " + "grants may be dropped. Falling back to global servers only." + ) return allow_all_server_ids async def resolve_toolset_tool_permissions( @@ -2729,28 +2792,40 @@ class MCPServerManager: Uses anyio.fail_after() instead of asyncio.wait_for() to avoid conflicts with the MCP SDK's anyio TaskGroup. See GitHub issue #20715 for details. - For pass-through MCP servers (``MCPServer.is_oauth_passthrough``) an + For OAuth pass-through and upstream-delegated OAuth2 MCP servers, an upstream HTTP 401 is converted into :class:`MCPUpstreamAuthError` instead of being swallowed to an empty tool list. That lets the single-server HTTP routes surface a proper 401 + ``WWW-Authenticate`` challenge so standards-compliant MCP clients trigger the upstream - OAuth flow. Non-pass-through servers keep today's swallow-and-log - behaviour so the multi-server ``/mcp`` aggregator doesn't get - tainted by a single bad server. + OAuth flow. Other servers keep today's swallow-and-log behaviour so + the multi-server ``/mcp`` aggregator doesn't get tainted by a single + bad server. Args: client: MCP client instance server_name: Name of the server for logging - server: Optional MCPServer; when pass-through, auth errors are - re-raised as :class:`MCPUpstreamAuthError`. + server: Optional MCPServer; when upstream auth is delegated, auth + errors are re-raised as :class:`MCPUpstreamAuthError`. Returns: List of tools from the server """ - is_passthrough = bool(server is not None and server.is_oauth_passthrough) + should_surface_upstream_auth = bool( + server is not None + and ( + server.is_oauth_passthrough + or ( + server.auth_type == MCPAuth.oauth2 + and getattr(server, "delegate_auth_to_upstream", False) is True + and not server.has_client_credentials + ) + ) + ) try: with anyio.fail_after(MCP_TOOL_LISTING_TIMEOUT): - tools = await client.list_tools(raise_on_error=is_passthrough) + tools = await client.list_tools( + raise_on_error=should_surface_upstream_auth + ) verbose_logger.debug(f"Tools from {server_name}: {tools}") return tools except TimeoutError: @@ -2767,12 +2842,12 @@ class MCPServerManager: ) return [] except Exception as e: - if is_passthrough: + if should_surface_upstream_auth: auth_info = _extract_upstream_auth_failure(e) if auth_info is not None: status_code, www_authenticate = auth_info verbose_logger.info( - f"Upstream auth failure from pass-through MCP server " + f"Upstream auth failure from MCP server " f"{server_name}: HTTP {status_code}" ) raise MCPUpstreamAuthError( @@ -3295,7 +3370,7 @@ class MCPServerManager: ) ) - async def _call_regular_mcp_tool( # noqa: PLR0915 + async def _call_regular_mcp_tool( self, mcp_server: MCPServer, original_tool_name: str, diff --git a/litellm/proxy/_experimental/mcp_server/outbound_credentials/__init__.py b/litellm/proxy/_experimental/mcp_server/outbound_credentials/__init__.py new file mode 100644 index 00000000000..73166a45d6e --- /dev/null +++ b/litellm/proxy/_experimental/mcp_server/outbound_credentials/__init__.py @@ -0,0 +1,73 @@ +"""Typed upstream-credential resolution for MCP servers. + +This subpackage houses the typed credential vocabulary and the ``resolve_credentials`` +dispatch. A server declares one per-mode config from the ``AuthConfig`` discriminated union; +``UpstreamCredentialProvider.resolve_credentials`` selects one arm and returns an ``httpx.Auth`` +or a typed ``CredError``. Failures are modeled as values via :mod:`.result` (``Result[T, +CredError]``) rather than raised, so every seam is total. Nothing here is wired onto a live +request path yet. +""" + +from litellm.proxy._experimental.mcp_server.outbound_credentials.httpx_auth import ( + NoOpAuth, + StaticHeaderAuth, +) +from litellm.proxy._experimental.mcp_server.outbound_credentials.resolver import ( + UpstreamCredentialProvider, +) +from litellm.proxy._experimental.mcp_server.outbound_credentials.result import ( + Error, + Ok, + Result, +) +from litellm.proxy._experimental.mcp_server.outbound_credentials.types import ( + Ambient, + ApiKeyConfig, + ApiKeySource, + AssumeRole, + AuthConfig, + AuthorizationCodeConfig, + AuthSpecKind, + AwsCredentialSource, + AwsSigV4Config, + Byok, + ClientCredentialsConfig, + CredError, + NoneConfig, + PassthroughConfig, + ServerSpec, + SharedKey, + StaticKeys, + Subject, + TokenExchangeConfig, + parse_auth_spec_kind, +) + +__all__ = [ + "Ok", + "Error", + "Result", + "NoOpAuth", + "StaticHeaderAuth", + "UpstreamCredentialProvider", + "AuthSpecKind", + "CredError", + "Subject", + "ServerSpec", + "AuthConfig", + "parse_auth_spec_kind", + "AuthorizationCodeConfig", + "ClientCredentialsConfig", + "TokenExchangeConfig", + "ApiKeyConfig", + "ApiKeySource", + "SharedKey", + "Byok", + "PassthroughConfig", + "NoneConfig", + "AwsSigV4Config", + "AwsCredentialSource", + "StaticKeys", + "AssumeRole", + "Ambient", +] diff --git a/litellm/proxy/_experimental/mcp_server/outbound_credentials/httpx_auth.py b/litellm/proxy/_experimental/mcp_server/outbound_credentials/httpx_auth.py new file mode 100644 index 00000000000..2345fa98123 --- /dev/null +++ b/litellm/proxy/_experimental/mcp_server/outbound_credentials/httpx_auth.py @@ -0,0 +1,45 @@ +"""Concrete `httpx.Auth` objects the resolver returns for the self-contained modes. + +These are the egress credential as the SDK consumes it: an `httpx.Auth` attached to the +upstream `AsyncClient`. The OAuth-flow modes (`authorization_code`, `client_credentials`, +`token_exchange`) return SDK-provided auth objects instead and land later. + +`auth_flow` mutating the outbound request is the `httpx.Auth` contract, not a house-style +violation: the request is httpx's object, and these carry no state of their own. +""" + +from __future__ import annotations + +from collections.abc import Generator + +import httpx +from pydantic import SecretStr + + +class NoOpAuth(httpx.Auth): + """Attaches nothing — the `none` mode (and the seam-level default).""" + + def auth_flow( + self, request: httpx.Request + ) -> Generator[httpx.Request, httpx.Response, None]: + yield request + + +class StaticHeaderAuth(httpx.Auth): + """Sets one fixed header on every request — the `api_key` family and `passthrough`. + + The header value is a live credential (a bearer token, an API key, a forwarded user + token), so it is held as a `SecretStr` and unwrapped only when written onto the request. + That keeps it masked in reprs, `vars()`, tracebacks, and structured logs, matching the + `SecretStr` discipline the config models use. + """ + + def __init__(self, header_value: str, header_name: str = "Authorization") -> None: + self.header_name = header_name + self._header_value = SecretStr(header_value) + + def auth_flow( + self, request: httpx.Request + ) -> Generator[httpx.Request, httpx.Response, None]: + request.headers[self.header_name] = self._header_value.get_secret_value() + yield request diff --git a/litellm/proxy/_experimental/mcp_server/outbound_credentials/resolver.py b/litellm/proxy/_experimental/mcp_server/outbound_credentials/resolver.py new file mode 100644 index 00000000000..7bcdb3e6529 --- /dev/null +++ b/litellm/proxy/_experimental/mcp_server/outbound_credentials/resolver.py @@ -0,0 +1,70 @@ +"""The one credential resolver: dispatch on the declared mode, fail closed. + +`resolve_credentials` selects exactly one arm off the server's typed `config` and either +produces an `httpx.Auth` or returns a typed `CredError`. The `match` is over the `AuthConfig` +variant, so each arm receives its own fully-typed config with no field-presence inference and +no precedence cascade. It is wildcard-free with an `assert_never` tail, so adding a mode without +an arm fails the type gate (basedpyright `reportMatchNotExhaustive`); a bypassed gate fails loudly +at runtime instead of returning `None`. + +This skeleton ships every arm as a `not_implemented` stub. Each mode's real body, with its +injected seam, lands in its own follow-up PR; until then the arm returns a typed error rather +than silently producing no credential. Pure v2: no imports from v1. +""" + +from __future__ import annotations + +import httpx +from typing_extensions import assert_never + +from litellm.proxy._experimental.mcp_server.outbound_credentials.result import ( + Error, + Result, +) +from litellm.proxy._experimental.mcp_server.outbound_credentials.types import ( + ApiKeyConfig, + AuthorizationCodeConfig, + AuthSpecKind, + AwsSigV4Config, + ClientCredentialsConfig, + CredError, + NoneConfig, + PassthroughConfig, + ServerSpec, + Subject, + TokenExchangeConfig, +) + + +class UpstreamCredentialProvider: + """Produces the one `httpx.Auth` for a `(subject, upstream)` pair, per declared mode. + + Collaborators (the per-mode credential stores and token fetchers) are injected as each arm + is built; the skeleton needs none, since every arm is a stub. + """ + + async def resolve_credentials( + self, subject: Subject, server: ServerSpec + ) -> Result[httpx.Auth, CredError]: + match server.config: + case NoneConfig(): + return _not_implemented(AuthSpecKind.none) + case ApiKeyConfig(): + return _not_implemented(AuthSpecKind.api_key) + case PassthroughConfig(): + return _not_implemented(AuthSpecKind.passthrough) + case ClientCredentialsConfig(): + return _not_implemented(AuthSpecKind.client_credentials) + case TokenExchangeConfig(): + return _not_implemented(AuthSpecKind.token_exchange) + case AuthorizationCodeConfig(): + return _not_implemented(AuthSpecKind.authorization_code) + case AwsSigV4Config(): + return _not_implemented(AuthSpecKind.aws_sigv4) + assert_never(server.config) + + +def _not_implemented(kind: AuthSpecKind) -> Result[httpx.Auth, CredError]: + return Error( + CredError.of_not_implemented(f"{kind.value}: resolver arm not implemented yet") + ) diff --git a/litellm/proxy/_experimental/mcp_server/outbound_credentials/result.py b/litellm/proxy/_experimental/mcp_server/outbound_credentials/result.py new file mode 100644 index 00000000000..a612e8510f5 --- /dev/null +++ b/litellm/proxy/_experimental/mcp_server/outbound_credentials/result.py @@ -0,0 +1,54 @@ +"""A tagged-union ``Result`` the type checker can actually narrow. + +``Ok`` and ``Error`` are separate frozen classes joined by a ``Union`` alias, so +reaching for ``result.ok`` before eliminating the ``Error`` arm (via ``isinstance`` +or a ``match`` pattern) is a type error rather than a runtime ``AttributeError``. A +single class carrying both payload fields would make that unguarded access invisible +to the type checker. + +Both variants are covariant and frozen; the absent side defaults to ``Never`` so a +bare ``Ok(value)`` or ``Error(err)`` infers fully and is assignable to any ``Result`` +whose matching side fits. + +``is_ok`` / ``is_error`` are runtime predicates that also narrow via their ``Literal`` +returns; inside strictly typed code, discriminate with ``match`` or ``isinstance``. + +This is the shared ``Result`` shape for the ``outbound_credentials`` resolver: every +seam returns ``Result[T, CredError]`` instead of raising, so each failure is a value +the caller must handle rather than an exception that can slip past the type checker. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Generic, Literal, TypeAlias + +from typing_extensions import Never, TypeVar + +_TOk_co = TypeVar("_TOk_co", covariant=True, default=Never) +_TError_co = TypeVar("_TError_co", covariant=True, default=Never) + + +@dataclass(frozen=True) +class Ok(Generic[_TOk_co, _TError_co]): + ok: _TOk_co + + def is_ok(self) -> Literal[True]: + return True + + def is_error(self) -> Literal[False]: + return False + + +@dataclass(frozen=True) +class Error(Generic[_TOk_co, _TError_co]): + error: _TError_co + + def is_ok(self) -> Literal[False]: + return False + + def is_error(self) -> Literal[True]: + return True + + +Result: TypeAlias = Ok[_TOk_co, _TError_co] | Error[_TOk_co, _TError_co] diff --git a/litellm/proxy/_experimental/mcp_server/outbound_credentials/types.py b/litellm/proxy/_experimental/mcp_server/outbound_credentials/types.py new file mode 100644 index 00000000000..2088dc77252 --- /dev/null +++ b/litellm/proxy/_experimental/mcp_server/outbound_credentials/types.py @@ -0,0 +1,334 @@ +"""The upstream-credential vocabulary — the typed seam the resolver dispatches on. + +This module ships the data types only; the resolver lands in a later PR. It is the contract +the credential build implements and the spec tests assert against. + +Design invariants encoded here: + +- **Mode is the single source of truth.** A server declares exactly one per-mode `config` + (the `AuthConfig` discriminated union); `auth_spec_kind` is *derived* from it, never a + second field that can drift. The resolver dispatches on the config variant, one arm per + mode. No field-presence inference, no precedence cascade. +- **Illegal states unrepresentable.** Each mode's config is its own frozen model holding + only that mode's fields — an `aws_sigv4` server cannot hold OAuth fields, and a config + missing a required field is rejected at construction, not at call time. +- **Fail-closed at the boundary.** A raw mode string can only enter through + `parse_auth_spec_kind()`, which returns a typed `CredError`. +- **Errors as values.** Every seam returns `Result[_, CredError]`; only edge adapters raise. +- **No v1 imports.** This vocabulary stays free of `MCPServer` and the rest of v1; the + v1 -> v2 adapter maps onto these types in a later PR. + +Sum types are Expression `@tagged_union`s discriminated on a `Literal` `tag`, matched via +`self.tag` with an `assert_never` tail; `Result` is this package's vendored `Ok | Error` +union (see `result.py`), not `expression.Result`. +""" + +from __future__ import annotations + +from enum import Enum +from typing import Annotated, Literal + +from expression import case, tag, tagged_union +from pydantic import BaseModel, ConfigDict, Field, SecretStr +from typing_extensions import assert_never + +from litellm.proxy._experimental.mcp_server.outbound_credentials.result import ( + Error, + Ok, + Result, +) + + +class AuthSpecKind(str, Enum): + """The server's statically-declared upstream-auth mode — derived from its `config`. + + Covers v1's full `MCPAuth` surface, not only OAuth grants: the three grant modes, the + collapsed static-header family, client passthrough, no-auth, and AWS request signing. + BYOK is *not* a member: it is the `api_key` mode seeded per-user, a source selector + inside that arm. The static-header schemes v1 splits into separate `MCPAuth` values + (`bearer_token`/`api_key`/`basic`/`token`/`authorization`) collapse into `api_key`; the + scheme is a parameter the arm carries, not its own mode. + """ + + authorization_code = "authorization_code" # per-user 3LO; gateway-stored token + client_credentials = "client_credentials" # gateway service account (M2M) + token_exchange = "token_exchange" # RFC 8693: token endpoint + subject_token (OBO) + api_key = "api_key" # static header, any scheme (BYOK = per-user-seeded source) + passthrough = "passthrough" # client forwards an upstream-audience token + none = "none" # no upstream credential; resolve yields a no-op auth, never an error + aws_sigv4 = "aws_sigv4" # AWS SigV4 per-request signing (e.g. Bedrock AgentCore) + + +@tagged_union(frozen=True) +class CredError: + """Why a credential could not be produced. Fail-closed: an arm yields this or an `httpx.Auth`. + + Discriminated on the `Literal` `tag`; consumers `match self.tag` (see `summary`) so the + type checker can prove exhaustiveness. Construct via the `of_*` factories. + """ + + tag: Literal[ + "unauthorized", + "misconfigured", + "upstream_unavailable", + "unsupported_mode", + "precondition_required", + "not_implemented", + ] = tag() + + unauthorized: str = ( + case() + ) # no usable credential for this (subject, server) -> 401 challenge + misconfigured: str = ( + case() + ) # the declared mode is missing required config -> 5xx (operator) + upstream_unavailable: str = ( + case() + ) # the IdP / token endpoint could not be reached -> 503 + unsupported_mode: str = ( + case() + ) # a raw mode string did not parse into AuthSpecKind (boundary) + precondition_required: str = ( + case() + ) # a required per-user value (e.g. an env var) has not been provided -> 412 + not_implemented: str = ( + case() + ) # the declared mode's resolver arm is not built yet -> 501 (not operator error) + + @staticmethod + def of_unauthorized(detail: str) -> CredError: + return CredError(unauthorized=detail) + + @staticmethod + def of_misconfigured(detail: str) -> CredError: + return CredError(misconfigured=detail) + + @staticmethod + def of_upstream_unavailable(detail: str) -> CredError: + return CredError(upstream_unavailable=detail) + + @staticmethod + def of_unsupported_mode(detail: str) -> CredError: + return CredError(unsupported_mode=detail) + + @staticmethod + def of_precondition_required(detail: str) -> CredError: + return CredError(precondition_required=detail) + + @staticmethod + def of_not_implemented(detail: str) -> CredError: + return CredError(not_implemented=detail) + + @property + def summary(self) -> str: + # Exhaustiveness: every Literal tag has an arm; the trailing assert_never typechecks + # only while that stays true (a `case _` would defeat reportMatchNotExhaustive). + match self.tag: + case "unauthorized": + return f"unauthorized: {self.unauthorized}" + case "misconfigured": + return f"misconfigured: {self.misconfigured}" + case "upstream_unavailable": + return f"upstream unavailable: {self.upstream_unavailable}" + case "unsupported_mode": + return self.unsupported_mode + case "precondition_required": + return f"precondition required: {self.precondition_required}" + case "not_implemented": + return f"not implemented: {self.not_implemented}" + assert_never(self.tag) + + +class AuthorizationCodeConfig(BaseModel): + """Per-user 3LO; the gateway is the OAuth client and stores the user's token. + + Endpoints are discovered (RFC 9728 -> RFC 8414) and the client is registered via DCR + (RFC 7591), so the common case carries none of the fields below; they are optional manual + overrides for IdPs without discovery / DCR. The per-user token is read from the token store + at resolve time, not held here. + """ + + model_config = ConfigDict(frozen=True) + kind: Literal[AuthSpecKind.authorization_code] = AuthSpecKind.authorization_code + scopes: tuple[str, ...] = () + client_id: str | None = None + client_secret: SecretStr | None = None + authorization_url: str | None = None + token_url: str | None = None + + +class ClientCredentialsConfig(BaseModel): + """M2M service account; one upstream identity for every user. + + Fields are optional so the config can be built incomplete: a value may be supplied at + runtime (`token_url` via RFC 8414 discovery, `client_id`/`secret` via DCR), and the + resolver arm raises `CredError.misconfigured` when a needed field is still absent. + """ + + model_config = ConfigDict(frozen=True) + kind: Literal[AuthSpecKind.client_credentials] = AuthSpecKind.client_credentials + client_id: str | None = None + client_secret: SecretStr | None = None + token_url: str | None = None + scopes: tuple[str, ...] = () + + +class TokenExchangeConfig(BaseModel): + """RFC 8693 OBO; swap the caller's live subject_token for a token bound to the upstream's + audience (`server.resource`, RFC 8707). The gateway authenticates to the exchange endpoint + as an OAuth client (`client_id`/`client_secret`); the inbound token is sent only to that + endpoint, never to the upstream. + """ + + model_config = ConfigDict(frozen=True) + kind: Literal[AuthSpecKind.token_exchange] = AuthSpecKind.token_exchange + subject_token_type: str = "urn:ietf:params:oauth:token-type:access_token" + token_exchange_endpoint: str | None = None + client_id: str | None = None + client_secret: SecretStr | None = None + scopes: tuple[str, ...] = () + + +class SharedKey(BaseModel): + """A fixed key configured on the server, identical for every caller.""" + + model_config = ConfigDict(frozen=True) + source: Literal["shared"] = "shared" + value: SecretStr + + +class Byok(BaseModel): + """A key the user brings via the entry flow, stored per-user and pulled from the credential + store at resolve time. Missing means the user must provide it, a 401 + WWW-Authenticate + challenge.""" + + model_config = ConfigDict(frozen=True) + source: Literal["byok"] = "byok" + + +ApiKeySource = Annotated[SharedKey | Byok, Field(discriminator="source")] + + +class ApiKeyConfig(BaseModel): + """A fixed credential injected as a header. The value is shared (in config) or seeded + per-user (pulled from the store); `header_name` and `value_prefix` say where and how it is + written, modeled like OpenAPI's apiKey scheme so any upstream convention is expressible + (Authorization + Bearer, a raw value on X-API-Key, Ocp-Apim-Subscription-Key, etc.). + """ + + model_config = ConfigDict(frozen=True) + kind: Literal[AuthSpecKind.api_key] = AuthSpecKind.api_key + header_name: str = "Authorization" + value_prefix: str = "Bearer" + key_source: ApiKeySource + + def header(self, value: str) -> tuple[str, str]: + formatted = f"{self.value_prefix} {value}" if self.value_prefix else value + return self.header_name, formatted + + +class PassthroughConfig(BaseModel): + """Client-driven upstream OAuth; the gateway forwards the client's upstream token.""" + + model_config = ConfigDict(frozen=True) + kind: Literal[AuthSpecKind.passthrough] = AuthSpecKind.passthrough + + +class NoneConfig(BaseModel): + """No upstream credential; the request is sent unauthenticated.""" + + model_config = ConfigDict(frozen=True) + kind: Literal[AuthSpecKind.none] = AuthSpecKind.none + + +class StaticKeys(BaseModel): + """Long-lived AWS access keys configured on the server.""" + + model_config = ConfigDict(frozen=True) + source: Literal["static_keys"] = "static_keys" + access_key_id: str + secret_access_key: SecretStr + session_token: SecretStr | None = None + + +class AssumeRole(BaseModel): + """An IAM role the gateway assumes via STS for short-lived, auto-refreshed credentials.""" + + model_config = ConfigDict(frozen=True) + source: Literal["assume_role"] = "assume_role" + role_arn: str + session_name: str | None = None + external_id: str | None = None + + +class Ambient(BaseModel): + """The environment's default AWS credential chain (instance profile, IRSA, env vars).""" + + model_config = ConfigDict(frozen=True) + source: Literal["ambient"] = "ambient" + + +AwsCredentialSource = Annotated[ + StaticKeys | AssumeRole | Ambient, Field(discriminator="source") +] + + +class AwsSigV4Config(BaseModel): + """AWS SigV4 per-request signing for an AWS-hosted upstream (e.g. Bedrock AgentCore). The + gateway signs with its own AWS identity, never the caller's; `credentials` selects how that + identity is obtained, defaulting to the ambient credential chain.""" + + model_config = ConfigDict(frozen=True) + kind: Literal[AuthSpecKind.aws_sigv4] = AuthSpecKind.aws_sigv4 + region: str + service: str = "bedrock-agentcore" + credentials: AwsCredentialSource = Ambient() + + +AuthConfig = Annotated[ + AuthorizationCodeConfig + | ClientCredentialsConfig + | TokenExchangeConfig + | ApiKeyConfig + | PassthroughConfig + | NoneConfig + | AwsSigV4Config, + Field(discriminator="kind"), +] + + +class Subject(BaseModel): + """The validated inbound principal. NOT the v1 request object and NOT the LiteLLM key.""" + + model_config = ConfigDict(frozen=True) + + tenant_id: str + subject_id: str + # Opaque, already-validated inbound identity. Only `token_exchange` / `passthrough` read it. + inbound_token: SecretStr | None = None + + +class ServerSpec(BaseModel): + """The declared upstream. A v2-native type; the v1 -> v2 adapter maps onto this.""" + + model_config = ConfigDict(frozen=True) + + server_id: str + resource: str # RFC 8707 audience URI this upstream's tokens are bound to + config: AuthConfig + + @property + def auth_spec_kind(self) -> AuthSpecKind: + return self.config.kind + + +def parse_auth_spec_kind(raw: str) -> Result[AuthSpecKind, CredError]: + """Boundary parser — the *only* place an unknown mode is handled, and it fails closed. + + Inside the core the mode is always a valid `AuthSpecKind`, so the resolver never needs a + wildcard arm and basedpyright can prove its `match` exhaustive. + """ + try: + return Ok(AuthSpecKind(raw)) + except ValueError: + return Error(CredError.of_unsupported_mode(f"unknown auth_spec_kind: {raw!r}")) diff --git a/litellm/proxy/_experimental/mcp_server/sampling_handler.py b/litellm/proxy/_experimental/mcp_server/sampling_handler.py index 1637c9eb0b9..b659ba6f813 100644 --- a/litellm/proxy/_experimental/mcp_server/sampling_handler.py +++ b/litellm/proxy/_experimental/mcp_server/sampling_handler.py @@ -661,7 +661,7 @@ def _convert_openai_response_to_mcp_result( ) -async def _check_model_access( # noqa: PLR0915 +async def _check_model_access( model: str, user_api_key_auth: Any ) -> Optional["ErrorData"]: """Enforce model-permission checks for MCP sampling requests. diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index 0477a5d3244..e891425274f 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -47,6 +47,7 @@ from litellm.proxy._experimental.mcp_server.discoverable_endpoints import ( from litellm.proxy._experimental.mcp_server.mcp_context import ( _mcp_active_toolset_id, _mcp_gateway_initialize_instructions, + _mcp_gateway_server_name, ) from litellm.proxy._experimental.mcp_server.mcp_debug import MCPDebug from litellm.proxy._experimental.mcp_server.utils import ( @@ -62,7 +63,11 @@ from litellm.llms.custom_httpx.http_handler import ( get_async_httpx_client, httpxSpecialProvider, ) -from litellm.proxy._types import UserAPIKeyAuth +from litellm.proxy._types import ( + ProxyException, + SpecialMCPServerNames, + UserAPIKeyAuth, +) from litellm.proxy.auth.ip_address_utils import IPAddressUtils from litellm.proxy.litellm_pre_call_utils import ( LiteLLMProxyRequestSetup, @@ -228,6 +233,28 @@ def _jsonrpc_text_has_top_level_method(text: str) -> bool: return False +def _proxy_exception_to_http_exception(exc: ProxyException) -> HTTPException: + """Map a ``ProxyException`` to an ``HTTPException`` that preserves its real + status code and headers. + + ``user_api_key_auth`` raises ``ProxyException`` (not ``HTTPException``) on + auth failures. The MCP ASGI handlers re-raise ``HTTPException`` to keep the + status and any ``WWW-Authenticate`` challenge, but a ``ProxyException`` would + otherwise fall through to their generic handler and be flattened to a 500 — + dropping the 401 + challenge an OAuth client needs to re-authenticate, so the + tool call surfaces as a cancelled/terminated session instead. + """ + try: + status_code = int(exc.code) + except (TypeError, ValueError): + status_code = 500 + return HTTPException( + status_code=status_code, + detail=exc.message, + headers=exc.headers or None, + ) + + if MCP_AVAILABLE: from mcp.server import Server from mcp.server.lowlevel.server import NotificationOptions @@ -269,6 +296,9 @@ if MCP_AVAILABLE: global_mcp_tool_registry, ) from litellm.proxy._experimental.mcp_server.utils import ( + MCP_TOOL_PREFIX_SEPARATOR, + is_tool_name_prefixed, + normalize_server_name, split_server_prefix_from_name, ) @@ -323,10 +353,14 @@ if MCP_AVAILABLE: notification_options=notification_options, experimental_capabilities=experimental_capabilities or {}, ) + updates: Dict[str, Any] = {} merged = _mcp_gateway_initialize_instructions.get() if merged is not None: - return opts.model_copy(update={"instructions": merged}) - return opts + updates["instructions"] = merged + scoped_server_name = _mcp_gateway_server_name.get() + if scoped_server_name is not None: + updates["server_name"] = scoped_server_name + return opts.model_copy(update=updates) if updates else opts ######################################################## ############ Initialize the MCP Server ################# @@ -609,7 +643,7 @@ if MCP_AVAILABLE: active_mcp_session_var.reset(_session_reset_token) @server.call_tool() - async def mcp_server_tool_call( # noqa: PLR0915 + async def mcp_server_tool_call( name: str, arguments: Dict[str, Any] | None ) -> CallToolResult: """ @@ -1028,7 +1062,14 @@ if MCP_AVAILABLE: allowed_mcp_servers: List[MCPServer], ) -> List[MCPServer]: """ - Get the filtered MCP servers from the MCP server names + Get the filtered MCP servers from the MCP server names. + + Fails closed when ``mcp_servers`` is explicitly provided (path- or + header-derived) but none of the names resolve to a server alias or + access group the caller can access. The previous behavior returned + the full ``allowed_mcp_servers`` set, which silently widened scope + when a client targeted ``/mcp//`` and made URL/header + namespacing appear to work when it did not. """ filtered_server: dict[str, MCPServer] = {} @@ -1068,6 +1109,17 @@ if MCP_AVAILABLE: if filtered_server: return list(filtered_server.values()) + if mcp_servers is not None: + # Caller asked for a specific scope but nothing resolved. Fail + # closed so URL/header namespacing cannot silently fall back to + # the caller's full allowed-server set. + verbose_logger.debug( + "MCP scope filter resolved to no servers for requested names %s; " + "returning empty list (fail-closed).", + mcp_servers, + ) + return [] + return allowed_mcp_servers def _tool_name_matches(tool_name: str, filter_list: List[str]) -> bool: @@ -1544,6 +1596,7 @@ if MCP_AVAILABLE: user_api_key_auth: Optional[UserAPIKeyAuth], mcp_servers: Optional[List[str]], client_ip: Optional[str], + scoped_server_endpoint: bool = False, ) -> AsyncIterator[None]: allowed = await _get_allowed_mcp_servers( user_api_key_auth=user_api_key_auth, @@ -1565,13 +1618,24 @@ if MCP_AVAILABLE: return_exceptions=True, ) merged = _merge_gateway_initialize_instructions(allowed_mcp_servers=allowed) - tok = _mcp_gateway_initialize_instructions.set(merged) + scoped_server_name = None + if scoped_server_endpoint and len(allowed) == 1: + scoped_server = allowed[0] + scoped_server_name = ( + scoped_server.alias + or scoped_server.server_name + or scoped_server.name + or scoped_server.server_id + ) + instructions_token = _mcp_gateway_initialize_instructions.set(merged) + server_name_token = _mcp_gateway_server_name.set(scoped_server_name) try: yield finally: - _mcp_gateway_initialize_instructions.reset(tok) + _mcp_gateway_initialize_instructions.reset(instructions_token) + _mcp_gateway_server_name.reset(server_name_token) - async def _get_tools_from_mcp_servers( # noqa: PLR0915 + async def _get_tools_from_mcp_servers( user_api_key_auth: Optional[UserAPIKeyAuth], mcp_auth_header: Optional[str], mcp_servers: Optional[List[str]], @@ -2415,7 +2479,7 @@ if MCP_AVAILABLE: }, ) - async def execute_mcp_tool( # noqa: PLR0915 + async def execute_mcp_tool( name: str, arguments: Dict[str, Any], allowed_mcp_servers: List[MCPServer], @@ -2466,47 +2530,60 @@ if MCP_AVAILABLE: None, ) - # Resolve the actual MCP server up-front so the permission check uses - # the canonical server.name even when the tool name is prefixed with a - # short ID (LITELLM_USE_SHORT_MCP_TOOL_PREFIX) that doesn't match the - # server's display name directly. - mcp_server = global_mcp_server_manager._get_mcp_server_from_tool_name(name) - if mcp_server is None and requested_server is not None: - # REST callers may pass the raw tool name (no prefix) plus a - # ``requested_server_id``. The mapping might only contain the - # prefixed form, so retry the lookup with every known prefix of - # the requested server before treating the tool as unresolved — - # otherwise the tool_server_mismatch guard below is silently - # bypassed. - for known_prefix in iter_known_server_prefixes(requested_server): - candidate = global_mcp_server_manager._get_mcp_server_from_tool_name( - add_server_prefix_to_name(name, known_prefix) - ) - if candidate is not None: - mcp_server = candidate - break - if mcp_server is not None: - server_name = mcp_server.name + name_is_prefixed = False + if requested_server is not None and MCP_TOOL_PREFIX_SEPARATOR in name: + all_registry_prefixes: Set[str] = set() + for registry_server in global_mcp_server_manager.get_registry().values(): + for known_prefix in iter_known_server_prefixes(registry_server): + all_registry_prefixes.add(normalize_server_name(known_prefix)) + name_is_prefixed = is_tool_name_prefixed( + name, known_server_prefixes=all_registry_prefixes + ) - # REST /mcp-rest/tools/call passes server_id — tool must belong to that server - if requested_server is not None: - if ( - mcp_server is not None - and mcp_server.server_id != requested_server.server_id - ): - raise HTTPException( - status_code=403, - detail={ - "error": "tool_server_mismatch", - "message": ( - f"Tool '{name}' belongs to MCP server '{mcp_server.name}' " - f"but request specified server_id for '{requested_server.name}'." - ), - }, - ) - if mcp_server is None: - mcp_server = requested_server - server_name = requested_server.name + if requested_server is not None and not name_is_prefixed: + # REST callers may pass server_id with the upstream tool name (no + # LiteLLM prefix). The first segment is not a registered server + # prefix, so the whole string is the upstream tool name and may + # legitimately contain the separator (e.g. "text-to-speech"). + # server_id is authoritative for routing and auth. + mcp_server = requested_server + server_name = requested_server.name + original_tool_name = name + else: + # Resolve from tool name (MCP JSON-RPC or prefixed REST tool names). + mcp_server = global_mcp_server_manager._get_mcp_server_from_tool_name(name) + if mcp_server is None and requested_server is not None: + for known_prefix in iter_known_server_prefixes(requested_server): + candidate = ( + global_mcp_server_manager._get_mcp_server_from_tool_name( + add_server_prefix_to_name(name, known_prefix) + ) + ) + if candidate is not None: + mcp_server = candidate + break + if mcp_server is not None: + server_name = mcp_server.name + + if requested_server is not None: + if ( + mcp_server is not None + and mcp_server.server_id != requested_server.server_id + ): + raise HTTPException( + status_code=403, + detail={ + "error": "tool_server_mismatch", + "message": ( + f"Tool '{name}' belongs to MCP server " + f"'{mcp_server.name}' but request specified " + f"server_id for '{requested_server.name}'." + ), + }, + ) + if mcp_server is None: + mcp_server = requested_server + server_name = requested_server.name # Only enforce server-level permissions when we can resolve a server if server_name: @@ -2535,6 +2612,7 @@ if MCP_AVAILABLE: standard_logging_mcp_tool_call ) litellm_logging_obj.model = f"MCP: {name}" + litellm_logging_obj.model_call_details["model"] = f"MCP: {name}" # Resolve the MCP server early so BYOK checks and credential injection # apply to ALL dispatch paths (local tool registry AND managed MCP server). if mcp_server is None: @@ -3300,6 +3378,19 @@ if MCP_AVAILABLE: from litellm.proxy._types import LiteLLM_ObjectPermissionTable from litellm.proxy.management_endpoints.common_utils import _user_has_admin_view + # A key scoped to no MCP servers opts out of every MCP path. Enforce it + # here too, since toolset scoping replaces mcp_servers and would otherwise + # drop the sentinel. Checked before the admin branch, mirroring + # get_allowed_mcp_servers. + original_op = user_api_key_auth.object_permission + if original_op is not None and SpecialMCPServerNames.no_mcp_servers.value in ( + original_op.mcp_servers or [] + ): + raise HTTPException( + status_code=403, + detail="API key is scoped to no MCP servers; toolset access is denied.", + ) + # Access control: non-admin keys must have this toolset in their grant list. # Use _user_has_admin_view so that PROXY_ADMIN_VIEW_ONLY is also treated as admin. is_admin = _user_has_admin_view(user_api_key_auth) @@ -3409,6 +3500,8 @@ if MCP_AVAILABLE: ) if stored_oauth_headers: continue + if getattr(server, "delegate_auth_to_upstream", False) is True: + continue request = StarletteRequest(scope) base_url = get_request_base_url(request) @@ -3606,7 +3699,7 @@ if MCP_AVAILABLE: detail="Forbidden", ) - async def handle_streamable_http_mcp( # noqa: PLR0915 + async def handle_streamable_http_mcp( scope: Scope, receive: Receive, send: Send ) -> None: """Handle MCP requests through StreamableHTTP.""" @@ -3620,6 +3713,7 @@ if MCP_AVAILABLE: oauth2_headers, raw_headers, ) = await extract_mcp_auth_context(scope, path) + scoped_server_endpoint = len(_get_mcp_servers_in_path(path) or []) == 1 # Extract client IP for MCP access control _client_ip = IPAddressUtils.get_mcp_client_ip(StarletteRequest(scope)) @@ -3896,6 +3990,7 @@ if MCP_AVAILABLE: user_api_key_auth, mcp_servers, _client_ip, + scoped_server_endpoint=scoped_server_endpoint, ): await target_manager.handle_request(scope, receive, local_send) if use_stateful and session_id and scope.get("method") == "DELETE": @@ -3941,7 +4036,7 @@ if MCP_AVAILABLE: ): _stateful_session_locks.pop(active_request_session_id, None) except MCPUpstreamAuthError as e: - # Pass-through server returned 401 — surface it to the client so + # Upstream delegated auth returned 401; surface it to the client so # standards-compliant MCP clients trigger the upstream OAuth flow. raise e.to_http_exception( base_url=get_request_base_url(StarletteRequest(scope)), @@ -3950,6 +4045,12 @@ if MCP_AVAILABLE: except HTTPException: # Re-raise HTTP exceptions to preserve status codes and details raise + except ProxyException as e: + # Auth failures from user_api_key_auth arrive as ProxyException, not + # HTTPException. Preserve the real status (e.g. 401 + WWW-Authenticate) + # so OAuth clients can re-authenticate instead of receiving a generic + # 500 that surfaces as a cancelled tool call. + raise _proxy_exception_to_http_exception(e) except Exception as e: verbose_logger.exception(f"Error handling MCP request: {e}") # Try to send a graceful error response for non-HTTP exceptions @@ -3980,6 +4081,7 @@ if MCP_AVAILABLE: oauth2_headers, raw_headers, ) = await extract_mcp_auth_context(scope, path) + scoped_server_endpoint = len(_get_mcp_servers_in_path(path) or []) == 1 # Extract client IP for MCP access control _sse_client_ip = IPAddressUtils.get_mcp_client_ip(StarletteRequest(scope)) @@ -4052,10 +4154,11 @@ if MCP_AVAILABLE: user_api_key_auth, mcp_servers, _sse_client_ip, + scoped_server_endpoint=scoped_server_endpoint, ): await sse_session_manager.handle_request(scope, receive, send) except MCPUpstreamAuthError as e: - # Pass-through server returned 401 — surface it to the client so + # Upstream delegated auth returned 401; surface it to the client so # standards-compliant MCP clients trigger the upstream OAuth flow. raise e.to_http_exception( base_url=get_request_base_url(StarletteRequest(scope)), @@ -4065,6 +4168,12 @@ if MCP_AVAILABLE: # Re-raise HTTP exceptions to preserve status codes and details # (e.g. 401 + WWW-Authenticate challenges from OAuth pass-through). raise + except ProxyException as e: + # Auth failures from user_api_key_auth arrive as ProxyException, not + # HTTPException. Preserve the real status (e.g. 401 + WWW-Authenticate) + # so OAuth clients can re-authenticate instead of receiving a generic + # 500 that surfaces as a cancelled tool call. + raise _proxy_exception_to_http_exception(e) except Exception as e: verbose_logger.exception(f"Error handling MCP request: {e}") # Try to send a graceful error response for non-HTTP exceptions diff --git a/litellm/proxy/_experimental/mcp_server/utils.py b/litellm/proxy/_experimental/mcp_server/utils.py index 97cfa74ea45..b0141d3207c 100644 --- a/litellm/proxy/_experimental/mcp_server/utils.py +++ b/litellm/proxy/_experimental/mcp_server/utils.py @@ -23,9 +23,20 @@ import os from urllib.parse import quote # Constants -LITELLM_MCP_SERVER_NAME = "litellm-mcp-server" +# +# NOTE: The environment-backed values below are read once, when this module is +# first imported, and cached for the lifetime of the process. Changing the +# corresponding environment variables after import has no effect unless the +# module is reloaded (e.g. ``importlib.reload``). Tests that override these +# variables must reload this module — see +# ``tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_identity_env.py``. +LITELLM_MCP_SERVER_NAME = os.environ.get( + "LITELLM_MCP_SERVER_NAME", "litellm-mcp-server" +) LITELLM_MCP_SERVER_VERSION = "1.0.0" -LITELLM_MCP_SERVER_DESCRIPTION = "MCP Server for LiteLLM" +LITELLM_MCP_SERVER_DESCRIPTION = os.environ.get( + "LITELLM_MCP_SERVER_DESCRIPTION", "MCP Server for LiteLLM" +) MCP_TOOL_PREFIX_SEPARATOR = os.environ.get("MCP_TOOL_PREFIX_SEPARATOR", "-") MCP_TOOL_PREFIX_FORMAT = "{server_name}{separator}{tool_name}" diff --git a/litellm/proxy/_experimental/out/404.html b/litellm/proxy/_experimental/out/404.html index 45de348c4d5..6d246c81652 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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404

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\ No newline at end of file +404: This page could not be found.LiteLLM Dashboard

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y=["top","topLeft","topRight","bottom","bottomLeft","bottomRight"],b={topLeft:"left",topRight:"right",bottomLeft:"left",bottomRight:"right",top:"left",bottom:"left"},w=e=>{let{iconCls:t,componentCls:r,boxShadow:n,fontSizeLG:o,notificationMarginBottom:a,borderRadiusLG:i,colorSuccess:l,colorInfo:s,colorWarning:c,colorError:u,colorTextHeading:d,notificationBg:f,notificationPadding:m,notificationMarginEdge:h,notificationProgressBg:v,notificationProgressHeight:y,fontSize:b,lineHeight:w,width:C,notificationIconSize:x,colorText:S,colorSuccessBg:$,colorErrorBg:E,colorInfoBg:k,colorWarningBg:O}=e,j=`${r}-notice`;return{position:"relative",marginBottom:a,marginInlineStart:"auto",background:f,borderRadius:i,boxShadow:n,[j]:{padding:m,width:C,maxWidth:`calc(100vw - 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Object.assign({[`${t}-stack`]:{[`& > ${t}-notice-wrapper`]:Object.assign({transition:`transform ${e.motionDurationSlow}, backdrop-filter 0s`,willChange:"transform, opacity",position:"absolute"},(e=>{let t={};for(let r=1;r ${e.componentCls}-notice`]:{opacity:0,transition:`opacity ${e.motionDurationMid}`}};return Object.assign({[`&:not(:nth-last-child(-n+${e.notificationStackLayer}))`]:{opacity:0,overflow:"hidden",color:"transparent",pointerEvents:"none"}},t)})(e))},[`${t}-stack:not(${t}-stack-expanded)`]:{[`& > ${t}-notice-wrapper`]:Object.assign({},(e=>{let t={};for(let r=1;r ${t}-notice-wrapper`]:{"&:not(:nth-last-child(-n + 1))":{opacity:1,overflow:"unset",color:"inherit",pointerEvents:"auto",[`& > 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h=(0,m.genStyleHooks)("Message",e=>(e=>{let{componentCls:t,iconCls:r,boxShadow:n,colorText:o,colorSuccess:a,colorError:i,colorWarning:l,colorInfo:s,fontSizeLG:c,motionEaseInOutCirc:u,motionDurationSlow:f,marginXS:m,paddingXS:g,borderRadiusLG:h,zIndexPopup:v,contentPadding:y,contentBg:b}=e,w=`${t}-notice`,C=new d.Keyframes("MessageMoveIn",{"0%":{padding:0,transform:"translateY(-100%)",opacity:0},"100%":{padding:g,transform:"translateY(0)",opacity:1}}),x=new d.Keyframes("MessageMoveOut",{"0%":{maxHeight:e.height,padding:g,opacity:1},"100%":{maxHeight:0,padding:0,opacity:0}}),S={padding:g,textAlign:"center",[`${t}-custom-content`]:{display:"flex",alignItems:"center"},[`${t}-custom-content > ${r}`]:{marginInlineEnd:m,fontSize:c},[`${w}-content`]:{display:"inline-block",padding:y,background:b,borderRadius:h,boxShadow:n,pointerEvents:"all"},[`${t}-success > ${r}`]:{color:a},[`${t}-error > ${r}`]:{color:i},[`${t}-warning > ${r}`]:{color:l},[`${t}-info > ${r}, + ${t}-loading > 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`]:{animationPlayState:"running"},[`${t}-move-up-leave`]:{animationName:x,animationDuration:f,animationPlayState:"paused",animationTimingFunction:u},[`${t}-move-up-leave${t}-move-up-leave-active`]:{animationPlayState:"running"},"&-rtl":{direction:"rtl",span:{direction:"rtl"}}})},{[t]:{[`${w}-wrapper`]:Object.assign({},S)}},{[`${t}-notice-pure-panel`]:Object.assign(Object.assign({},S),{padding:0,textAlign:"start"})}]})((0,g.mergeToken)(e,{height:150})),e=>({zIndexPopup:e.zIndexPopupBase+f.CONTAINER_MAX_OFFSET+10,contentBg:e.colorBgElevated,contentPadding:`${(e.controlHeightLG-e.fontSize*e.lineHeight)/2}px ${e.paddingSM}px`}));e.s(["default",0,h],208224);var v=function(e,t){var r={};for(var n in e)Object.prototype.hasOwnProperty.call(e,n)&&0>t.indexOf(n)&&(r[n]=e[n]);if(null!=e&&"function"==typeof Object.getOwnPropertySymbols)for(var o=0,n=Object.getOwnPropertySymbols(e);ot.indexOf(n[o])&&Object.prototype.propertyIsEnumerable.call(e,n[o])&&(r[n[o]]=e[n[o]]);return r};let y={info:t.createElement(a.default,null),success:t.createElement(r.default,null),error:t.createElement(n.default,null),warning:t.createElement(o.default,null),loading:t.createElement(i.default,null)},b=({prefixCls:e,type:r,icon:n,children:o})=>t.createElement("div",{className:(0,l.default)(`${e}-custom-content`,`${e}-${r}`)},n||y[r],t.createElement("span",null,o));e.s(["PureContent",0,b,"default",0,e=>{let{prefixCls:r,className:n,type:o,icon:a,content:i}=e,d=v(e,["prefixCls","className","type","icon","content"]),{getPrefixCls:f}=t.useContext(c.ConfigContext),p=r||f("message"),m=(0,u.default)(p),[g,y,w]=h(p,m);return g(t.createElement(s.Notice,Object.assign({},d,{prefixCls:p,className:(0,l.default)(n,y,`${p}-notice-pure-panel`,w,m),eventKey:"pure",duration:null,content:t.createElement(b,{prefixCls:p,type:o,icon:a},i)})))}],983320)},727749,698173,190702,e=>{"use strict";var t=e.i(271645);e.i(247167);var r=e.i(738275),n=e.i(609587),o=e.i(242064),a=e.i(783164),i=e.i(645384),l=e.i(343794);e.i(792131);var s=e.i(194732),c=e.i(513139),u=e.i(747656),d=e.i(321883),f=e.i(104458),p=e.i(628918),m=function(e,t){var r={};for(var n in e)Object.prototype.hasOwnProperty.call(e,n)&&0>t.indexOf(n)&&(r[n]=e[n]);if(null!=e&&"function"==typeof Object.getOwnPropertySymbols)for(var o=0,n=Object.getOwnPropertySymbols(e);ot.indexOf(n[o])&&Object.prototype.propertyIsEnumerable.call(e,n[o])&&(r[n[o]]=e[n[o]]);return r};let g=({children:e,prefixCls:r})=>{let n=(0,d.default)(r),[o,a,i]=(0,p.default)(r,n);return o(t.default.createElement(s.NotificationProvider,{classNames:{list:(0,l.default)(a,i,n)}},e))},h=(e,{prefixCls:r,key:n})=>t.default.createElement(g,{prefixCls:r,key:n},e),v=t.default.forwardRef((e,r)=>{let{top:n,bottom:a,prefixCls:s,getContainer:u,maxCount:d,rtl:p,onAllRemoved:m,stack:g,duration:v,pauseOnHover:y=!0,showProgress:b}=e,{getPrefixCls:w,getPopupContainer:C,notification:x,direction:S}=(0,t.useContext)(o.ConfigContext),[,$]=(0,f.useToken)(),E=s||w("notification"),[k,O]=(0,c.useNotification)({prefixCls:E,style:e=>(function(e,t,r){let n;switch(e){case"top":n={left:"50%",transform:"translateX(-50%)",right:"auto",top:t,bottom:"auto"};break;case"topLeft":n={left:0,top:t,bottom:"auto"};break;case"topRight":n={right:0,top:t,bottom:"auto"};break;case"bottom":n={left:"50%",transform:"translateX(-50%)",right:"auto",top:"auto",bottom:r};break;case"bottomLeft":n={left:0,top:"auto",bottom:r};break;default:n={right:0,top:"auto",bottom:r}}return n})(e,null!=n?n:24,null!=a?a:24),className:()=>(0,l.default)({[`${E}-rtl`]:null!=p?p:"rtl"===S}),motion:()=>({motionName:`${E}-fade`}),closable:!0,closeIcon:(0,i.getCloseIcon)(E),duration:null!=v?v:4.5,getContainer:()=>(null==u?void 0:u())||(null==C?void 0:C())||document.body,maxCount:d,pauseOnHover:y,showProgress:b,onAllRemoved:m,renderNotifications:h,stack:!1!==g&&{threshold:"object"==typeof g?null==g?void 0:g.threshold:void 0,offset:8,gap:$.margin}});return t.default.useImperativeHandle(r,()=>Object.assign(Object.assign({},k),{prefixCls:E,notification:x})),O});function y(e){let r=t.default.useRef(null);return(0,u.devUseWarning)("Notification"),[t.default.useMemo(()=>{let n=n=>{var 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t?(0,l.jsx)(et,{prebuiltPatterns:t.prebuilt_patterns||[],categories:t.pattern_categories||[],selectedPatterns:$,blockedWords:M,onPatternAdd:e=>E([...$,e]),onPatternRemove:e=>E($.filter(t=>t.id!==e)),onPatternActionChange:(e,t)=>{E($.map(a=>a.id===e?{...a,action:t}:a))},onBlockedWordAdd:e=>R([...M,e]),onBlockedWordRemove:e=>R(M.filter(t=>t.id!==e)),onBlockedWordUpdate:(e,t,a)=>{R(M.map(l=>l.id===e?{...l,[t]:a}:l))},contentCategories:t.content_categories||[],selectedContentCategories:G,onContentCategoryAdd:e=>z([...G,e]),onContentCategoryRemove:e=>z(G.filter(t=>t.id!==e)),onContentCategoryUpdate:(e,t,a)=>{z(G.map(l=>l.id===e?{...l,[t]:a}:l))},pendingCategorySelection:D,onPendingCategorySelectionChange:K,accessToken:a,showStep:e,competitorIntentEnabled:q,competitorIntentConfig:J,onCompetitorIntentChange:(e,t)=>{H(e),U(t)}}):null},eA=ed(f)?[{title:"Basic Info",optional:!1},{title:"Topics",optional:!1},{title:"Patterns",optional:!1},{title:"Keywords",optional:!1},{title:"Endpoint Settings 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openai + +client = openai.OpenAI( + api_key="${x||"YOUR_LITELLM_API_KEY"}", + base_url="${v}" +)`;switch(h){case n.CHAT:{let e=Object.keys($).length>0,i="";if(e){let e=JSON.stringify({metadata:$},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`, + extra_body=${e}`}let o=C.length>0?C:[{role:"user",content:S}];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="${k}", + messages=${JSON.stringify(o,null,4)}${i} +) + +print(response) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.chat.completions.create( +# model="${k}", +# 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 n.RESPONSES:{let e=Object.keys($).length>0,i="";if(e){let e=JSON.stringify({metadata:$},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`, + extra_body=${e}`}let o=C.length>0?C:[{role:"user",content:S}];t=` +import base64 + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.responses.create( + model="${k}", + 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="${k}", +# input=[ +# { +# "role": "user", +# "content": [ +# {"type": "input_text", "text": "${j}"}, +# { +# "type": "input_image", +# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} +# }, +# ], +# } +# ]${i} +# ) +# print(response_with_file.output_text) +`;break}case n.IMAGE:t="azure"===_?` +# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. +# This snippet uses 'client.images.generate' and will create a new image based on your prompt. +# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. +import os +import requests +import json +import time +from PIL import Image + +result = client.images.generate( + model="${k}", + 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="${k}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case n.IMAGE_EDITS:t="azure"===_?` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# The prompt entered by the user +prompt = "${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="${k}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`:` +import base64 +import os +import time + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${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="${k}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. 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openai + +client = openai.OpenAI( + api_key="${b||"YOUR_LITELLM_API_KEY"}", + base_url="${v}" +)`;switch(_){case r.CHAT:{let e=Object.keys(k).length>0,i="";if(e){let e=JSON.stringify({metadata:k},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`, + extra_body=${e}`}let a=E.length>0?E:[{role:"user",content:w}];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="${I}", + messages=${JSON.stringify(a,null,4)}${i} +) + +print(response) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.chat.completions.create( +# model="${I}", +# 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 r.RESPONSES:{let e=Object.keys(k).length>0,i="";if(e){let e=JSON.stringify({metadata:k},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`, + extra_body=${e}`}let a=E.length>0?E:[{role:"user",content:w}];t=` +import base64 + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.responses.create( + model="${I}", + 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="${I}", +# input=[ +# { +# "role": "user", +# "content": [ +# {"type": "input_text", "text": "${j}"}, +# { +# "type": "input_image", +# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} +# }, +# ], +# } +# ]${i} +# ) +# print(response_with_file.output_text) +`;break}case r.IMAGE:t="azure"===y?` +# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. +# This snippet uses 'client.images.generate' and will create a new image based on your prompt. +# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. +import os +import requests +import json +import time +from PIL import Image + +result = client.images.generate( + model="${I}", + prompt="${n}", + n=1 +) + +json_response = json.loads(result.model_dump_json()) + +# Set the directory for the stored image +image_dir = os.path.join(os.curdir, 'images') + +# If the directory doesn't exist, create it +if not os.path.isdir(image_dir): + os.mkdir(image_dir) + +# Initialize the image path +image_filename = f"generated_image_{int(time.time())}.png" +image_path = os.path.join(image_dir, image_filename) + +try: + # Retrieve the generated image + if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): + image_url = json_response["data"][0]["url"] + generated_image = requests.get(image_url).content + with open(image_path, "wb") as image_file: + image_file.write(generated_image) + + print(f"Image saved to {image_path}") + # Display the image + image = Image.open(image_path) + image.show() + else: + print("Could not find image URL in response.") + print("Full response:", json_response) +except Exception as e: + print(f"An error occurred: {e}") + print("Full response:", json_response) +`:` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${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="${I}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case r.IMAGE_EDITS:t="azure"===y?` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# The prompt entered by the user +prompt = "${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="${I}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`:` +import base64 +import os +import time + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${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="${I}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case r.EMBEDDINGS:t=` +response = client.embeddings.create( + input="${n||"Your string here"}", + model="${I}", + encoding_format="base64" # or "float" +) + +print(response.data[0].embedding) +`;break;case r.TRANSCRIPTION:t=` +# Open the audio file +audio_file = open("path/to/your/audio/file.mp3", "rb") + +# Make the transcription request +response = client.audio.transcriptions.create( + model="${I}", + file=audio_file${n?`, + prompt="${n.replace(/\\/g,"\\\\").replace(/"/g,'\\"')}"`:""} +) + +print(response.text) +`;break;case r.SPEECH:t=` +# Make the text-to-speech request +response = client.audio.speech.create( + model="${I}", + input="${n||"Your text to convert to speech here"}", + voice="${f}" # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer +) + +# Save the audio to a file +output_filename = "output_speech.mp3" +response.stream_to_file(output_filename) +print(f"Audio saved to {output_filename}") + +# Optional: Customize response format and speed +# response = client.audio.speech.create( +# model="${I}", +# input="${n||"Your text to convert to speech here"}", +# voice="alloy", +# response_format="mp3", # Options: mp3, opus, aac, flac, wav, pcm +# speed=1.0 # Range: 0.25 to 4.0 +# ) +# response.stream_to_file("output_speech.mp3") +`;break;default:t="\n# Code generation for this endpoint is not implemented yet."}return`${C} +${t}`}],339019)}]); \ No newline at end of file diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/101fb167bf3e83b1.js b/litellm/proxy/_experimental/out/_next/static/chunks/101fb167bf3e83b1.js new file mode 100644 index 00000000000..a820ce54256 --- /dev/null +++ b/litellm/proxy/_experimental/out/_next/static/chunks/101fb167bf3e83b1.js @@ -0,0 +1 @@ +(globalThis.TURBOPACK||(globalThis.TURBOPACK=[])).push(["object"==typeof 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